fix(api): WS index frames accept both camelCase and snake_case keys (Electron toSnakeCase compat)

Add build_brief_multi_project_manifest() to deep_agent.py that fetches
all project folder manifests via execute_on_client and keeps the top 5
most-recently-modified files per project. Wire into run_home_brief in
brief_agent.py, injecting the <linked_folders> block into the system
prompt alongside FOLDER_TOOLS.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

.env.example

@@ -2,7 +2,7 @@
 ENV=dev
 
 # ── Database ──────────────────────────────────────────────────────────────────
-DATABASE_URL=postgresql+asyncpg://postgres:postgres@localhost:5432/adiuva
+DATABASE_URL=postgresql+asyncpg://postgres:postgres@localhost:5432/adiuvai
 
 # ── Auth ──────────────────────────────────────────────────────────────────────
 JWT_SECRET=replace-with-a-long-random-secret
@@ -13,31 +13,82 @@ JWT_REFRESH_TOKEN_EXPIRE_DAYS=30
 # ── LLM ───────────────────────────────────────────────────────────────────────
 # LiteLLM model identifiers — change to swap providers without code changes.
 # Examples: gpt-4o, anthropic/claude-sonnet-4-20250514, gemini/gemini-pro, ollama/llama3
+#
+# API keys — only the key(s) matching your chosen provider(s) are required.
+# The correct key is picked automatically from the model prefix (e.g.
+# "anthropic/..." → ANTHROPIC_API_KEY, "gemini/..." → GOOGLE_API_KEY).
 OPENAI_API_KEY=
 ANTHROPIC_API_KEY=
 GOOGLE_API_KEY=
-LLM_MODEL=gpt-4o
-LLM_ROUTER_MODEL=gpt-4o-mini
+CEREBRAS_API_KEY=
+GROQ_API_KEY=
+DEEPSEEK_API_KEY=
+
+# Default model used by any agent that does not have a specific override below.
+LLM_MODEL=gpt-5-mini
+LLM_EMBED_MODEL=text-embedding-3-small
+
+# GitHub Copilot — leave empty to use the LiteLLM default token directory.
+# In Docker, point this to a named-volume path so tokens survive restarts.
+# GITHUB_COPILOT_TOKEN_DIR=
+
+# ── Per-agent model overrides ─────────────────────────────────────────────────
+# Leave a value empty to fall back to LLM_MODEL.
+# Each agent resolves its API key from the model prefix automatically.
+#
+# Intent classifier — routes user messages to the right domain agent.
+# A small/fast model (e.g. gpt-4o-mini) is usually sufficient here.
+LLM_MODEL_CLASSIFIER=
+
+# Home-agent — handles chat from the home screen (all tools available).
+LLM_MODEL_HOME_AGENT=
+
+# Floating-agent — handles contextual chat triggered from a task/project/note.
+LLM_MODEL_FLOATING_AGENT=
+
+# Unified-processor — processes local directory files (local agent runner).
+LLM_MODEL_UNIFIED_PROCESSOR=
+
+# Cloud-processor — fetches and processes data from cloud connectors.
+LLM_MODEL_CLOUD_PROCESSOR=
+
+# Brief-agent — produces home and project text briefs.
+# A small model (e.g. gpt-4o-mini) is sufficient.
+# LLM_MODEL_BRIEF_AGENT=
+
+# Task-brief-agent — per-task deep research (Stage 1 executive assistant).
+# Needs tool-use + reasoning; a capable model recommended (e.g. gpt-4o, gemini-2.5-flash).
+# LLM_MODEL_TASK_BRIEF_AGENT=
+
+# Setup-agent — guided journey to build an AgentConfig via WebSocket chat.
+LLM_MODEL_SETUP_AGENT=
+
+# Memory-extractor — Mem0-style extract/decide pipeline (Phase 2).
+# Defaults to gpt-4o-mini when empty (fast + cheap, temperature=0).
+LLM_MODEL_MEMORY_EXTRACTOR=
+
+# Memory-miner — proactive pattern mining from episodic history (Phase 5, Power+ only).
+# Defaults to gpt-4o-mini when empty.
+LLM_MODEL_MEMORY_MINER=
+
+# Memory-auditor — weekly contradiction scan + relation label canonicalization (Phase 7).
+# Defaults to LLM_MODEL when empty (a reasoning-capable model is recommended).
+LLM_MODEL_MEMORY_AUDITOR=
+
+# Scheduler — set to false to disable memory cron jobs (automatically false in tests).
+SCHEDULER_ENABLED=true
 
 # ── Stripe (leave empty to stub billing) ──────────────────────────────────────
 STRIPE_SECRET_KEY=
 STRIPE_WEBHOOK_SECRET=
 
-# ── AWS / S3 ──────────────────────────────────────────────────────────────────
-S3_BUCKET=adiuva
-S3_REGION=us-east-1
-S3_ENDPOINT_URL=
-AWS_ACCESS_KEY_ID=
-AWS_SECRET_ACCESS_KEY=
-# For MinIO (homelab): S3_ENDPOINT_URL=http://minio:9000
 
-# ── Vector Store ──────────────────────────────────────────────────────────────
-# Pinecone is used when PINECONE_API_KEY is set; otherwise falls back to Qdrant.
-PINECONE_API_KEY=
-PINECONE_INDEX=adiuva
-QDRANT_URL=
-QDRANT_API_KEY=
-# For local Qdrant (homelab): QDRANT_URL=http://qdrant:6333
+# ── Langfuse (leave empty to disable observability) ───────────────────────────
+LANGFUSE_SECRET_KEY=
+LANGFUSE_PUBLIC_KEY=
+# LANGFUSE_BASE_URL=https://cloud.langfuse.com        # EU (default)
+# LANGFUSE_BASE_URL=https://us.cloud.langfuse.com     # US
+# LANGFUSE_BASE_URL=http://localhost:3000             # Self-hosted
 
 # ── CORS ──────────────────────────────────────────────────────────────────────
 # Comma-separated list parsed by Settings (override default if needed)

.gitea/workflows/deploy.yaml

@@ -48,23 +48,23 @@ jobs:
           key: ${{ secrets.SSH_KEY }}
           script: |
             set -e
-            DEPLOY_DIR="/opt/adiuva-api"
+            DEPLOY_DIR="/opt/adiuvai-api"
             REPO_URL="http://10.0.0.119:3000/${{ gitea.repository }}.git"
             TAG="${{ gitea.ref_name }}"
 
             # ── Pull latest code ──
-            cd /tmp && rm -rf adiuva-api-deploy
-            git clone --depth 1 --branch "${TAG}" "${REPO_URL}" adiuva-api-deploy
+            cd /tmp && rm -rf adiuvai-api-deploy
+            git clone --depth 1 --branch "${TAG}" "${REPO_URL}" adiuvai-api-deploy
 
             # ── Sync source (preserve .env) ──
-            cp -rf /tmp/adiuva-api-deploy/app/ \
-                   /tmp/adiuva-api-deploy/alembic/ \
-                   /tmp/adiuva-api-deploy/alembic.ini \
-                   /tmp/adiuva-api-deploy/Dockerfile \
-                   /tmp/adiuva-api-deploy/docker-compose.yml \
-                   /tmp/adiuva-api-deploy/requirements.txt \
+            cp -rf /tmp/adiuvai-api-deploy/app/ \
+                   /tmp/adiuvai-api-deploy/alembic/ \
+                   /tmp/adiuvai-api-deploy/alembic.ini \
+                   /tmp/adiuvai-api-deploy/Dockerfile \
+                   /tmp/adiuvai-api-deploy/docker-compose.yml \
+                   /tmp/adiuvai-api-deploy/requirements.txt \
                    "$DEPLOY_DIR/"
-            rm -rf /tmp/adiuva-api-deploy
+            rm -rf /tmp/adiuvai-api-deploy
 
             # ── Verify .env ──
             if [ ! -f "$DEPLOY_DIR/.env" ]; then

.github/workflows/ci.yml

@@ -58,7 +58,7 @@ jobs:
       - uses: actions/checkout@v4
 
       - name: Build image
-        run: docker build -t adiuva-api:ci .
+        run: docker build -t adiuvai-api:ci .
 
       - name: Verify gunicorn installed
-        run: docker run --rm adiuva-api:ci gunicorn --version
+        run: docker run --rm adiuvai-api:ci gunicorn --version

.gitignore

@@ -21,12 +21,16 @@ env/
 .pytest_cache/
 htmlcov/
 .coverage
+tests/fixtures/private*/
 
 # Docker
 *.log
 
 # OS
 .DS_Store
+
+# Smoke scripts (dev-only, not for CI)
+scripts/smoke_*.py
 Thumbs.db
 
 # Claude Code

README.md

@@ -1,793 +1,5 @@
-# Adiuva Cloud API
-
-**AI-powered project management backend with E2E encrypted cloud storage, LLM orchestration, and a plugin marketplace.**
-
-Built with FastAPI · Python 3.12 · PostgreSQL · LangChain · Stripe · AWS S3
-
----
-
-## Table of Contents
-
-- [Overview](#overview)
-- [Architecture](#architecture)
-- [Key Features](#key-features)
-- [Tech Stack](#tech-stack)
-- [Getting Started](#getting-started)
-- [Docker Deployment](#docker-deployment)
-- [Environment Variables](#environment-variables)
-- [API Reference](#api-reference)
-- [Data Model](#data-model)
-- [AI Agent System](#ai-agent-system)
-- [Orchestration & Execution Plans](#orchestration--execution-plans)
-- [Middleware](#middleware)
-- [Storage Layer](#storage-layer)
-- [Billing & Tiers](#billing--tiers)
-- [Plugin Marketplace](#plugin-marketplace)
-- [Testing](#testing)
-- [Project Structure](#project-structure)
-- [License](#license)
-
----
-
-## Overview
-
-Adiuva Cloud API is the FastAPI backend that powers the **Adiuva Electron desktop app**. It provides LLM-powered chat orchestration, end-to-end encrypted cloud storage, a vector search engine, an encrypted backup system, a plugin marketplace with revenue sharing, and Stripe-based subscription billing across four tiers.
-
-### Design Principles
-
-1. **Never persist user data in plaintext** — the database stores only auth, billing, storage metadata, and marketplace data. All user content is E2E encrypted by the client before reaching the server.
-2. **Never expose prompts** — system prompts stay server-side; responses are sanitized to strip any leaked prompt fragments.
-3. **Never decrypt user blobs** — the backend performs only checksum verification; no decryption keys ever reach the server.
-4. **Stateless request handling** — all context comes from the client and JWT; no server-side session state.
-5. **Tier gates enforced server-side** — the server always reads the current tier from the database, never trusting client-reported values.
-
----
-
-## Architecture
-
+## DEV
+Run in DEV with command:
 ```
-┌──────────────┐      ┌────────────────────────────────────────────────────────┐
-│  Electron    │      │  FastAPI  (Uvicorn / Gunicorn)                         │
-│  Desktop App │────▶│                                                        │
-│  (Client)    │◀────│  Middleware: RateLimit → Sanitizer → CORS → Router     │
-└──────────────┘      │                                                        │
-                      │  ┌──────────────────┐  ┌────────────────────────────┐  │
-                      │  │  Auth Routes     │  │  Chat Routes               │  │
-                      │  │  Billing Routes  │  │    ↓                       │  │
-                      │  │  Storage Routes  │  │  Orchestrator (GPT-4o-mini)│  │
-                      │  │  Backup Routes   │  │    ↓ classify intent       │  │
-                      │  │  Plugin Routes   │  │  Agent Registry            │  │
-                      │  │  Vector Routes   │  │    ↓                       │  │
-                      │  │  Plans Routes    │  │  TaskAgent  | ProjectAgent │  │
-                      │  └──────────────────┘  │  NoteAgent  | CheckptAgent │  │
-                      │                        │  (GPT-4o + LangChain)      │  │
-                      │                        └────────────────────────────┘  │
-                      └────────────────────────────────────────────────────────┘
-                               │              │              │
-                      ┌────────▼───┐  ┌───────▼───────┐  ┌──▼─────────────┐
-                      │ PostgreSQL │  │  AWS S3       │  │ Pinecone /     │
-                      │ (Auth,     │  │  (E2E blobs,  │  │ Qdrant         │
-                      │  Billing,  │  │   backups)    │  │ (Vectors)      │
-                      │  Metadata) │  └───────────────┘  └────────────────┘
-                      └────────────┘
-                               │
-                      ┌────────▼───┐
-                      │  Stripe    │
-                      │  (Billing, │
-                      │   Connect) │
-                      └────────────┘
-```
-
----
-
-## Key Features
-
-1. **LLM-powered orchestration** — GPT-4o-mini classifies user intent and routes to the appropriate domain agent.
-2. **4 specialized AI agents** — Tasks (8 tools), Projects (6 tools), Timelines (4 tools), Notes (5 tools), all powered by GPT-4o via LangChain.
-3. **Execution plans & playbooks** — Server-side prompt template registry; clients receive only opaque template IDs, never raw prompts.
-4. **E2E encrypted cloud storage** — The backend never decrypts user data; SHA-256 checksum verification uses constant-time comparison to prevent timing attacks.
-5. **Cloud vector store** — Pinecone or Qdrant with user-isolated namespaces and encrypted blob payloads.
-6. **Encrypted backup system** — Tiered storage limits with `If-Modified-Since` support for efficient syncing.
-7. **Plugin marketplace** — Catalog, admin review/approval workflow, security checklist, and 70/30 revenue sharing via Stripe Connect.
-8. **Stripe billing** — Four-tier subscription model (Free / Pro / Power / Team) with checkout sessions and full webhook lifecycle handling.
-9. **JWT authentication** — Access + refresh tokens with bcrypt password hashing, SHA-256 token hashing, and automatic rotation.
-10. **Prompt IP protection** — Sanitizer middleware strips system prompts, reasoning markers, tool schemas, and agent routing metadata from all chat responses.
-11. **Tier-based rate limiting** — Sliding-window per-user limiter scaling from 20 to 200 requests/min by subscription tier.
-12. **Zero-trust data model** — User content is never stored in plaintext; the database holds only authentication, billing, and metadata records.
-13. **WebSocket streaming** — Real-time chat with 30-second heartbeat keep-alive and chunked text delivery.
-14. **Alembic migrations** — Versioned schema management with seed data for the plugin marketplace.
-15. **Comprehensive test suite** — In-memory SQLite + moto S3 mocks, per-tier test fixtures, and full API coverage without external dependencies.
-
----
-
-## Tech Stack
-
-| Package | Version | Purpose |
-|---|---|---|
-| `fastapi` | ≥ 0.115.0 | Web framework |
-| `uvicorn[standard]` | ≥ 0.34.0 | ASGI development server |
-| `gunicorn` | ≥ 22.0.0 | Production process manager |
-| `langchain` | ≥ 0.3.0 | LLM orchestration framework |
-| `langchain-openai` | ≥ 0.3.0 | OpenAI LLM provider integration |
-| `litellm` | ≥ 1.50.0 | Universal LLM gateway (100+ providers) |
-| `pydantic` | ≥ 2.10.0 | Data validation and serialization |
-| `pydantic-settings` | ≥ 2.7.0 | Environment-based configuration |
-| `python-jose[cryptography]` | ≥ 3.3.0 | JWT encoding and decoding |
-| `stripe` | ≥ 11.0.0 | Billing and payment integration |
-| `boto3` | ≥ 1.35.0 | AWS S3 client |
-| `slowapi` | ≥ 0.1.9 | Rate limiting utilities |
-| `sqlalchemy` | ≥ 2.0.0 | Async ORM and query builder |
-| `asyncpg` | ≥ 0.30.0 | PostgreSQL async driver |
-| `alembic` | ≥ 1.14.0 | Database migration management |
-| `bcrypt` | ≥ 4.2.0 | Password hashing |
-| `python-dotenv` | ≥ 1.0.0 | `.env` file loading |
-| `httpx` | ≥ 0.28.0 | Async HTTP client (used in tests) |
-| `websockets` | ≥ 14.0 | WebSocket protocol support |
-| `psycopg2-binary` | ≥ 2.9.0 | Synchronous PostgreSQL driver (Alembic) |
-| `pinecone` | ≥ 5.0.0 | Pinecone vector store client |
-| `qdrant-client` | ≥ 1.7.0 | Qdrant vector store client |
-| `pytest` | ≥ 8.0.0 | Test framework |
-| `pytest-asyncio` | ≥ 0.24.0 | Async test support |
-| `aiosqlite` | ≥ 0.20.0 | In-memory SQLite for tests |
-| `moto[s3]` | ≥ 5.0.0 | AWS S3 mock for tests |
-| `ruff` | ≥ 0.8.0 | Linter and formatter |
-
----
-
-## Getting Started
-
-### Prerequisites
-
-- Python 3.12+
-- PostgreSQL 16+
-- An OpenAI API key (for LLM features)
-- Stripe API keys (optional — billing stubs gracefully when unconfigured)
-- AWS credentials (optional — needed for S3 storage in production)
-
-### Installation
-
-```bash
-# Clone the repository
-git clone <repo-url> && cd adiuva-api
-
-# Create a virtual environment
-python -m venv .venv && source .venv/bin/activate
-
-# Install dependencies
-pip install -r requirements.txt
-
-# Configure environment
-cp .env.example .env
-# Edit .env with your DATABASE_URL, OPENAI_API_KEY, etc.
-```
-
-### Database Setup
-
-```bash
-# Start PostgreSQL (or use the Docker Compose database)
-docker compose up db -d
-
-# Run migrations
-alembic upgrade head
-```
-
-### Run the Development Server
-
-```bash
-uvicorn app.main:app --reload --host 0.0.0.0 --port 8000
-```
-
-Interactive API docs are available at [http://localhost:8000/docs](http://localhost:8000/docs) in development mode (`ENV=dev`). The `/docs` endpoint is disabled in production.
-
----
-
-## Docker Deployment
-
-### Quick Start
-
-```bash
-docker compose up --build
-```
-
-This starts two services:
-
-- **app** — FastAPI server on port `8000`
-- **db** — PostgreSQL 16 (Alpine) on port `5432` with a persistent volume and health checks
-
-The compose file also includes optional services for fully local deployments:
-
-- **minio** — S3-compatible object storage on ports `9000` (API) and `9001` (console)
-- **qdrant** — Vector search engine on ports `6333` (HTTP) and `6334` (gRPC)
-
-### Dockerfile Details
-
-The Dockerfile uses a multi-stage build:
-
-1. **Builder stage** — Installs Python dependencies into a virtual environment.
-2. **Runtime stage** — Copies only the venv, app source, and Alembic migrations. Runs as a non-root user (`appuser`).
-3. **Production server** — Gunicorn with 4 Uvicorn workers, 120-second timeout, listening on port 8000.
-
-```bash
-# Production command (run by the container)
-gunicorn app.main:app -k uvicorn.workers.UvicornWorker -w 4 --timeout 120 -b 0.0.0.0:8000
-```
-
----
-
-## Homelab / Self-Hosted Deployment
-
-You can run the entire stack locally on a homelab with **no cloud dependencies except the LLM provider**. The compose file includes MinIO (S3 replacement) and Qdrant (vector store) out of the box.
-
-### 1. Start all services
-
-```bash
-docker compose up -d
-```
-
-This starts PostgreSQL, MinIO, and Qdrant alongside the app.
-
-### 2. Create the MinIO bucket
-
-Open the MinIO console at [http://localhost:9001](http://localhost:9001) (login: `minioadmin` / `minioadmin`) and create a bucket named `adiuva`, or use the CLI:
-
-```bash
-docker compose exec minio mc alias set local http://localhost:9000 minioadmin minioadmin
-docker compose exec minio mc mb local/adiuva
-```
-
-### 3. Configure your `.env`
-
-```bash
-# Database (uses the compose PostgreSQL)
-DATABASE_URL=postgresql+asyncpg://postgres:postgres@db:5432/adiuva
-
-# S3 → MinIO
-S3_BUCKET=adiuva
-S3_REGION=us-east-1
-S3_ENDPOINT_URL=http://minio:9000
-AWS_ACCESS_KEY_ID=minioadmin
-AWS_SECRET_ACCESS_KEY=minioadmin
-
-# Vector store → local Qdrant (leave PINECONE_API_KEY empty)
-QDRANT_URL=http://qdrant:6333
-QDRANT_API_KEY=
-PINECONE_API_KEY=
-
-# Billing — leave empty to stub (no Stripe needed)
-STRIPE_SECRET_KEY=
-STRIPE_WEBHOOK_SECRET=
-
-# LLM — the only external service
-OPENAI_API_KEY=sk-...
-LLM_MODEL=gpt-4o
-LLM_ROUTER_MODEL=gpt-4o-mini
-
-# Auth
-JWT_SECRET=your-secret-here
-ENV=dev
-```
-
-### 4. Run migrations
-
-```bash
-docker compose exec app alembic upgrade head
-```
-
-### What runs where
-
-| Service | Runs on | Port | Notes |
-|---|---|---|---|
-| FastAPI app | Docker | 8000 | API server |
-| PostgreSQL | Docker | 5432 | Auth, billing, metadata |
-| MinIO | Docker | 9000 / 9001 | S3-compatible blob & backup storage |
-| Qdrant | Docker | 6333 / 6334 | Vector search (replaces Pinecone) |
-| Stripe | — | — | Stubbed when keys are empty |
-| OpenAI / LLM | Cloud | — | Only external dependency |
-
-> **Want fully offline AI too?** Set `LLM_MODEL=ollama/llama3` and `LLM_ROUTER_MODEL=ollama/llama3`, then add an Ollama container or point at a local Ollama instance. See the [LLM provider switching](#switching-llm-providers) section.
-
----
-
-## Environment Variables
-
-All variables are loaded from a `.env` file via Pydantic Settings. Source: `app/config/settings.py`
-
-| Variable | Type | Default | Description |
-|---|---|---|---|
-| `DATABASE_URL` | `str` | `postgresql+asyncpg://postgres:postgres@localhost:5432/adiuva` | Async SQLAlchemy connection string |
-| `JWT_SECRET` | `str` | `change-me-in-production` | HMAC secret for JWT signing |
-| `JWT_ALGORITHM` | `str` | `HS256` | JWT signing algorithm |
-| `JWT_ACCESS_TOKEN_EXPIRE_MINUTES` | `int` | `30` | Access token time-to-live |
-| `JWT_REFRESH_TOKEN_EXPIRE_DAYS` | `int` | `30` | Refresh token time-to-live |
-| `STRIPE_SECRET_KEY` | `str` | `""` | Stripe API key (empty = stub mode) |
-| `STRIPE_WEBHOOK_SECRET` | `str` | `""` | Stripe webhook signature secret |
-| `S3_BUCKET` | `str` | `""` | S3 bucket for encrypted blobs and backups |
-| `S3_REGION` | `str` | `us-east-1` | AWS region |
-| `S3_ENDPOINT_URL` | `str` | `""` | Custom S3 endpoint (e.g. `http://minio:9000` for MinIO). Leave empty for AWS. |
-| `AWS_ACCESS_KEY_ID` | `str` | `""` | AWS credentials |
-| `AWS_SECRET_ACCESS_KEY` | `str` | `""` | AWS credentials |
-| `PINECONE_API_KEY` | `str` | `""` | Pinecone API key (if set, Pinecone is used for vectors) |
-| `PINECONE_INDEX` | `str` | `adiuva` | Pinecone index name |
-| `QDRANT_URL` | `str` | `""` | Qdrant URL (used when Pinecone is not configured) |
-| `QDRANT_API_KEY` | `str` | `""` | Qdrant API key |
-| `OPENAI_API_KEY` | `str` | `""` | OpenAI key for LLM agent calls |
-| `LLM_MODEL` | `str` | `gpt-4o` | LiteLLM model identifier for agents (e.g. `anthropic/claude-3.5-sonnet`, `gemini/gemini-pro`, `ollama/llama3`) |
-| `LLM_ROUTER_MODEL` | `str` | `gpt-4o-mini` | Lighter model used for intent classification / routing |
-| `CORS_ORIGINS` | `list[str]` | `["app://.", "http://localhost:3000", "http://localhost:5173"]` | Allowed CORS origins |
-| `ENV` | `Literal` | `dev` | `dev` or `prod` — controls `/docs` visibility and SQL echo |
-
----
-
-## API Reference
-
-All routes are prefixed with `/api/v1`. **27 endpoints** total (25 REST + 1 WebSocket + 1 health check).
-
-### Health
-
-| Method | Path | Auth | Description |
-|---|---|---|---|
-| `GET` | `/api/v1/health` | No | Returns `{"status": "ok", "version": "0.1.0"}` |
-
-### Auth
-
-| Method | Path | Auth | Description |
-|---|---|---|---|
-| `POST` | `/api/v1/auth/register` | No | Create account with bcrypt-hashed password, returns `AuthTokens` |
-| `POST` | `/api/v1/auth/login` | No | Validate credentials, returns `AuthTokens` |
-| `POST` | `/api/v1/auth/refresh` | No | Rotate refresh token, returns new `AuthTokens` |
-| `GET` | `/api/v1/auth/me` | JWT | Returns `UserProfile` for the authenticated user |
-
-### Chat
-
-| Method | Path | Auth | Description |
-|---|---|---|---|
-| `POST` | `/api/v1/chat` | JWT | Route message through the orchestrator; returns `ChatResponse` or `ExecutionPlan` depending on execution mode |
-| `WS` | `/api/v1/chat/stream` | JWT (query param `?token=`) | Streaming chat — first frame is a `ChatRequest`, server yields text chunks, final frame is `{"done": true, "response": "...", "actions": [...]}`. 30-second heartbeat ping. |
-
-### Plans
-
-| Method | Path | Auth | Description |
-|---|---|---|---|
-| `GET` | `/api/v1/plans/playbook` | JWT | List all cached execution plan playbooks |
-| `GET` | `/api/v1/plans/playbook/{plan_id}` | JWT | Retrieve a specific playbook by ID |
-
-### Storage (Cloud Records)
-
-| Method | Path | Auth | Description |
-|---|---|---|---|
-| `POST` | `/api/v1/storage/records` | JWT | Upload an E2E encrypted record (verifies checksum, enforces storage quota) |
-| `GET` | `/api/v1/storage/records` | JWT | List record metadata with pagination (`?table`, `?page`, `?limit`); no blob bytes returned |
-| `GET` | `/api/v1/storage/records/{id}` | JWT | Download encrypted blob with `X-Checksum` response header |
-| `PUT` | `/api/v1/storage/records/{id}` | JWT | Replace an existing blob (verifies checksum, enforces quota) |
-| `DELETE` | `/api/v1/storage/records/{id}` | JWT | Delete a record and its S3 blob |
-
-### Vectors (Cloud Vector Store)
-
-| Method | Path | Auth | Description |
-|---|---|---|---|
-| `POST` | `/api/v1/storage/vectors/upsert` | JWT | Verify checksums and upsert encrypted vectors |
-| `POST` | `/api/v1/storage/vectors/search` | JWT | Search user-scoped vector namespace |
-| `DELETE` | `/api/v1/storage/vectors` | JWT | Delete vectors by ID list |
-
-### Backup
-
-| Method | Path | Auth | Description |
-|---|---|---|---|
-| `PUT` | `/api/v1/backup` | JWT | Upload encrypted backup blob with custom headers (`X-Backup-Version`, `X-Backup-Timestamp`, `X-Backup-Checksum`). Tier quota enforced. |
-| `GET` | `/api/v1/backup` | JWT | Download latest backup blob. Supports `If-Modified-Since`. |
-| `GET` | `/api/v1/backup/history` | JWT | List backup metadata (no blob content) |
-| `DELETE` | `/api/v1/backup/{backup_id}` | JWT | Delete a specific backup |
-
-### Plugins (Marketplace)
-
-| Method | Path | Auth | Description |
-|---|---|---|---|
-| `GET` | `/api/v1/plugins` | JWT (Power+) | Browse the marketplace (`?category`, `?q`, `?page`, `?sort=rating\|installs\|newest`) |
-| `GET` | `/api/v1/plugins/{id}` | JWT (Power+) | Plugin detail with install count and ratings |
-| `POST` | `/api/v1/plugins/{id}/install` | JWT (Power+) | Install plugin; triggers Stripe Connect revenue split for paid plugins |
-| `DELETE` | `/api/v1/plugins/{id}/install` | JWT | Uninstall plugin |
-
-### Billing
-
-| Method | Path | Auth | Description |
-|---|---|---|---|
-| `POST` | `/api/v1/billing/checkout` | JWT | Create a Stripe checkout session, returns `{"checkout_url": "..."}` |
-| `POST` | `/api/v1/billing/webhook` | Stripe signature | Handle Stripe events: `checkout.session.completed`, `customer.subscription.updated`, `customer.subscription.deleted`, `invoice.payment_failed` |
-| `GET` | `/api/v1/billing/subscription` | JWT | Get current subscription information |
-| `DELETE` | `/api/v1/billing/subscription` | JWT | Cancel subscription and revert to free tier |
-
----
-
-## Data Model
-
-9 tables managed by Alembic migrations. Source: `app/models.py`
-
-### Tables
-
-| Table | Primary Key | Key Columns | Purpose |
-|---|---|---|---|
-| `users` | `id` (UUID) | `email` (unique), `password_hash`, `tier`, `stripe_customer_id`, timestamps | User accounts |
-| `refresh_tokens` | `id` (UUID) | `user_id` (FK), `token_hash` (SHA-256, unique), `expires_at` | Hashed refresh tokens for rotation |
-| `subscriptions` | `id` (UUID) | `user_id` (FK, unique), `stripe_subscription_id`, `tier`, `status`, `current_period_end` | Stripe subscription records |
-| `storage_records` | `id` (UUID) | `user_id` (FK), `table_name`, `s3_key`, `checksum`, `size_bytes`, timestamps | S3 blob metadata (no plaintext content) |
-| `backup_metadata` | `id` (UUID) | `user_id` (FK), `s3_key`, `version`, `timestamp`, `checksum`, `size_bytes` | Backup manifests |
-| `plugins` | `id` (String) | `name`, `description`, `version`, `author_id` (FK), `category`, `price_cents`, `permissions` (JSON), `status`, `s3_package_key`, `install_count`, `avg_rating` | Marketplace plugin catalog |
-| `plugin_installations` | `id` (UUID) | `plugin_id` (FK), `user_id` (FK), unique constraint on (`plugin_id`, `user_id`) | Per-user install tracking |
-| `plugin_reviews` | `id` (UUID) | `plugin_id` (FK), `reviewer_id` (FK), `decision`, `notes`, `reviewed_at` | Admin review decisions |
-| `revenue_events` | `id` (UUID) | `plugin_id` (FK), `user_id` (FK), `amount_cents`, `developer_share_cents`, `stripe_transfer_id` | 70/30 revenue split ledger |
-
-### Enum Types
-
-| Enum | Values |
-|---|---|
-| `billing_tier` | `free`, `pro`, `power`, `team` |
-| `plugin_status` | `pending_review`, `approved`, `rejected` |
-| `review_decision` | `approved`, `rejected` |
-
-### Migrations
-
-| Version | Description |
-|---|---|
-| `001_initial_schema` | Creates all 9 tables with indexes and foreign key constraints |
-| `002_seed_plugins` | Seeds 3 approved plugins: GitHub Sync (free), Slack Notifier (€4.99), Time Tracker (€9.99) |
-
----
-
-## AI Agent System
-
-The agent system uses a registry pattern with LangChain tool-calling agents powered by GPT-4o. Source: `app/agents/`, `app/core/agent_registry.py`
-
-### Architecture
-
-- **`BaseAgent`** — Abstract base with `user_id`, `shared_memory`, and `vector_store_context`.
-- **`ChatAgent(BaseAgent)`** — Abstract `handle(query, context)` and `get_tools()` methods, plus a shared `_tool_loop(llm, messages, tools, max_iter=5)` for iterative tool calling.
-- **`AgentRegistry`** — Singleton registry with `@register` decorator, `get(name)`, `list_agents()`, and `call_agent(name, query, context)`.
-
-### Registered Agents
-
-| Agent | Registry Name | Tools | Description |
-|---|---|---|---|
-| **TaskAgent** | `task_agent` | 8 | Full task and comment CRUD. Status: `todo` / `in_progress` / `done`. Priority: `high` / `medium` / `low`. Tools: `list_tasks`, `create_task`, `update_task`, `delete_task`, `list_tasks_due_today`, `list_task_comments`, `add_task_comment`, `delete_task_comment` |
-| **ProjectAgent** | `project_agent` | 6 | Project lifecycle management. Status: `active` / `archived`. Prefers archiving over deletion. Tools: `list_projects`, `list_all_projects`, `get_project`, `create_project`, `update_project`, `delete_project` |
-| **TimelineAgent** | `timeline_agent` | 4 | Project milestones. Requires `project_id` for creation. Supports AI-suggestion and approval workflows. Tools: `list_timelines`, `create_timeline`, `update_timeline`, `delete_timeline` |
-| **NoteAgent** | `note_agent` | 5 | Markdown note management. Optionally linked to projects. Tools: `list_notes`, `get_note`, `create_note`, `update_note`, `delete_note` |
-
-All agents use the model configured by `LLM_MODEL` (default: GPT-4o) with `temperature=0` via LiteLLM. Tools return JSON action descriptors that the Electron client interprets and applies locally.
-
-### Switching LLM Providers
-
-The backend uses **LiteLLM** as a universal LLM gateway. All agents and the orchestrator instantiate models through a centralized factory in `app/core/llm.py`. To switch providers, change environment variables — no code changes required:
-
-```bash
-# OpenAI (default)
-LLM_MODEL=gpt-4o
-LLM_ROUTER_MODEL=gpt-4o-mini
-
-# Anthropic
-LLM_MODEL=anthropic/claude-3.5-sonnet
-LLM_ROUTER_MODEL=anthropic/claude-3-haiku
-
-# Google Gemini
-LLM_MODEL=gemini/gemini-pro
-LLM_ROUTER_MODEL=gemini/gemini-flash
-
-# Local Ollama
-LLM_MODEL=ollama/llama3
-LLM_ROUTER_MODEL=ollama/llama3
-
-# AWS Bedrock
-LLM_MODEL=bedrock/anthropic.claude-v2
-LLM_ROUTER_MODEL=bedrock/anthropic.claude-instant-v1
-```
-
-See the [LiteLLM provider docs](https://docs.litellm.ai/docs/providers) for the full list of 100+ supported providers and model naming conventions.
-
----
-
-## Orchestration & Execution Plans
-
-Source: `app/core/orchestrator.py`, `app/core/execution_plan.py`
-
-### Orchestrator
-
-1. **`classify_intent(message, context, registry)`** — Uses the router model (`LLM_ROUTER_MODEL`, default: GPT-4o-mini) to determine which agent should handle a message. Falls back to `task_agent` when classification is ambiguous.
-2. **`route_single(agent_name, message, context)`** — Routes to a single agent and returns a `ChatResponse`.
-3. **`route_pipeline(agent_names, message, context)`** — Executes agents sequentially; each receives `previous_results` from earlier agents. A final LLM synthesis step merges all results.
-4. **`orchestrate(request)`** — Main entry point. In `direct` mode, returns a `ChatResponse`. In `plan` mode, returns an `ExecutionPlan`.
-5. **`orchestrate_stream(request)`** — Streaming variant that yields 50-character text chunks with a final JSON frame.
-
-### Execution Plans
-
-- **`PromptTemplateRegistry`** — Maps template IDs to server-side prompt text. Clients only ever see opaque IDs, never raw prompts.
-- **`ExecutionPlanBuilder`** — Fluent builder API: `add_step()`, `add_llm_step(template_id, vars)`, `add_data_step(action, data_from_step)`. Validates step references on `build()`.
-- **`PlanCache`** — LRU cache (maxsize 1000) for storing plans as reusable playbooks.
-
-### Built-in Templates (6)
-
-`tpl_task_agent_default`, `tpl_timeline_agent_default`, `tpl_project_agent_default`, `tpl_note_agent_default`, `tpl_task_extract_from_project`, `tpl_note_weekly_summary`
-
-### Built-in Playbooks (2)
-
-| Playbook | Description |
-|---|---|
-| `create_tasks_from_project` | LLM extracts actionable tasks from project context, then creates task records |
-| `generate_weekly_note` | LLM generates a weekly summary, then creates a note record |
-
----
-
-## Middleware
-
-Middleware executes in this order on each request: **TierRateLimit → Sanitizer → CORS → Router**
-
-### JWT Authentication
-
-Source: `app/api/middleware/auth.py`
-
-- FastAPI dependency `get_current_user` validates the `Bearer` JWT and extracts `user_id` and `email`.
-- **Live tier lookup** — The current tier is fetched from the `subscriptions` table on every request (not cached in the JWT), so upgrades and downgrades take immediate effect.
-- Falls back to `free` when no subscription row exists.
-- Raises `401 Unauthorized` on invalid or expired tokens.
-- **Exempt paths:** `/api/v1/auth/register`, `/api/v1/auth/login`, `/api/v1/billing/webhook`
-
-### Tier-Based Rate Limiter
-
-Source: `app/api/middleware/rate_limit.py`
-
-- `TierRateLimitMiddleware` — Sliding-window in-process rate limiter (no Redis dependency).
-- Per-user 60-second window sized by subscription tier:
-
-| Tier | Requests / Minute |
-|---|---|
-| Free | 20 |
-| Pro | 60 |
-| Power | 120 |
-| Team | 200 |
-
-- Returns `429 Too Many Requests` with a `Retry-After` header when the limit is exceeded.
-- **Exempt paths:** register, login, webhook, health
-
-### Response Sanitizer
-
-Source: `app/api/middleware/sanitizer.py`
-
-- Runs only on `/api/v1/chat` endpoints.
-- Scans JSON response bodies and replaces leaked prompt IP fragments with `[REDACTED]`.
-- Detects: system prompt openers, agent routing metadata, LangChain tool schemas, internal reasoning markers (`<thinking>`, `[INST]`), and known prompt fingerprints.
-- Logs sanitization events as `WARNING`.
-- Binary responses (storage, backup) are never touched.
-
----
-
-## Storage Layer
-
-### Blob Store
-
-Source: `app/storage/blob_store.py`
-
-- S3-backed storage for E2E encrypted blobs.
-- Object keys follow the pattern: `{user_id}/{table}/{record_id}`
-- Server-side SSE-S3 encryption at rest (additional layer on top of client-side E2E encryption).
-- Methods: `upload()`, `download()`, `delete()` (idempotent), `list_keys()`
-- The backend **never inspects or decrypts blob content**.
-
-### Vector Store
-
-Source: `app/storage/vector_store.py`
-
-- Runtime-configurable: **Pinecone** (when `PINECONE_API_KEY` is set) or **Qdrant** (fallback).
-- User isolation: Pinecone uses `namespace=user_id`; Qdrant filters by `user_id` payload field.
-- 32-dimensional SHA-256-derived float vectors (deterministic, not semantically meaningful on encrypted data — a documented trade-off for privacy).
-- Encrypted blobs are stored as base64 in metadata/payload for verbatim retrieval.
-- Methods: `upsert()`, `search()`, `delete()`
-
-### Encryption Utilities
-
-Source: `app/storage/encryption.py`
-
-- `verify_checksum(blob, checksum)` — SHA-256 hash comparison using `hmac.compare_digest` (constant-time to prevent timing attacks).
-- `reject_if_tampered(blob, checksum)` — Raises HTTP 400 on checksum mismatch.
-- **No decryption key ever reaches the backend.**
-
----
-
-## Billing & Tiers
-
-Source: `app/billing/stripe_service.py`, `app/billing/tier_manager.py`
-
-### Feature Matrix
-
-| Feature | Free | Pro | Power | Team |
-|---|---|---|---|---|
-| AI Agents | 3 | Unlimited | Unlimited | Unlimited |
-| Batch Active | 2 | 10 | Unlimited | Unlimited |
-| Cloud Storage | 0 GB | 5 GB | 25 GB | Unlimited |
-| Backup Storage | 0 GB | 5 GB | 25 GB | Unlimited |
-| LLM Providers | 1 | Unlimited | Unlimited | Unlimited |
-| Batch Builder | — | — | ✓ | ✓ |
-| Plugin Marketplace | — | — | ✓ | ✓ |
-| SSO | — | — | — | ✓ |
-| Rate Limit | 20 req/min | 60 req/min | 120 req/min | 200 req/min |
-
-### Stripe Integration
-
-- **Checkout** — `create_checkout_session(user_id, tier)` creates a Stripe Checkout session. Returns a stub URL when Stripe is not configured.
-- **Webhooks** — Handles `checkout.session.completed`, `customer.subscription.updated`, `customer.subscription.deleted`, and `invoice.payment_failed`.
-- **Subscription management** — `get_subscription()` returns the current subscription record; `cancel_subscription()` cancels via the Stripe API and reverts the user to the free tier.
-- **Price IDs:** `price_pro_monthly`, `price_power_monthly`, `price_team_monthly`
-
-### Tier Manager
-
-- `get_tier(user_id)` — Returns the user's current billing tier.
-- `check_feature(tier, feature)` — Boolean feature gate check.
-- `require_feature(tier, feature)` — Raises HTTP 403 if the feature is not available.
-- `enforce_quota(user_id, tier)` / `enforce_backup_quota(user_id, tier)` — Raises HTTP 402 if storage limits are exceeded.
-
----
-
-## Plugin Marketplace
-
-Source: `app/marketplace/`
-
-### Plugin Registry
-
-- PostgreSQL-backed catalog of submitted and approved plugins.
-- `list_plugins(db, category, query, page, sort)` — Paginated listing (page size: 20) with optional filtering by category, text search, and sorting by `rating`, `installs`, or `newest`.
-- `get_plugin(db, plugin_id)` — Full manifest with install count and ratings.
-- `submit_plugin(db, manifest, s3_key)` — Submits a plugin with `pending_review` status.
-- `approve_plugin()` / `reject_plugin(reason)` — Admin workflow for plugin approval.
-- `record_install()` / `record_uninstall()` — Tracks per-user installations and updates install counts.
-
-### Review Queue
-
-- Automated security checklist before human review:
-  - Plugin ID must match `^[a-z0-9-]+$`
-  - Permissions must be from the allowed set only
-  - No binary blobs in the manifest
-- **Allowed permissions:** `read:tasks`, `write:tasks`, `read:projects`, `write:projects`, `read:notes`, `write:notes`, `read:timelines`, `write:timelines`, `read:calendar`, `write:calendar`
-- `get_pending(db)` — Lists plugins awaiting review.
-- `submit_review(db, plugin_id, reviewer_id, decision, notes)` — Records the review decision.
-
-### Revenue Sharing
-
-- **70% developer / 30% platform** split on all paid plugin sales.
-- `record_install(db, plugin_id, user_id, amount_cents)` — Records the revenue event and triggers a Stripe Connect transfer for the developer share.
-- `get_earnings(db, developer_id, period)` — Aggregated earnings report for plugin developers.
-- Gracefully stubs transfers when Stripe is not configured.
-
-### Seed Plugins
-
-| Plugin | Category | Price |
-|---|---|---|
-| GitHub Sync | Productivity | Free |
-| Slack Notifier | Communication | €4.99 |
-| Time Tracker | Productivity | €9.99 |
-
----
-
-## Testing
-
-### Running Tests
-
-```bash
-# Run all tests
-pytest
-
-# Run a specific test file
-pytest tests/test_auth.py
-
-# Run with verbose output
-pytest -v
-```
-
-### Test Infrastructure
-
-- **Database:** Async SQLite in-memory via `aiosqlite` + `StaticPool` — fast, no PostgreSQL needed.
-- **S3 mock:** `moto[s3]` with a fixture that patches `BlobStore` settings.
-- **Auth helpers:** `make_jwt(tier)` and `auth_header(tier)` generate per-tier test tokens.
-- **Seed data:** Auto-creates one `User` + `Subscription` per tier (free/pro/power/team) before each test.
-- **Plugin seeds:** Fixture adds 3 approved plugins for marketplace tests.
-- **FK enforcement:** SQLite `PRAGMA foreign_keys=ON`.
-- **No external dependencies** — all tests run fully offline.
-
-### Test Coverage
-
-| File | Coverage |
-|---|---|
-| `test_auth.py` | Register, login, token access, refresh, expiration |
-| `test_orchestrator.py` | Intent classification, single agent routing, pipeline, plan mode |
-| `test_agents.py` | Each agent with mocked LLM: registration, tools, handle method |
-| `test_storage.py` | Create, list, download, update, delete records; checksum rejection; quota enforcement |
-| `test_backup.py` | Upload, download, history, delete; tier-based storage limits |
-| `test_plugins.py` | List, install, uninstall, revenue events, tier gate enforcement |
-| `test_agent_registry.py` | Registry singleton, registration, lookup, listing |
-| `test_execution_plan.py` | Plan builder, template registry, plan cache |
-| `test_middleware.py` | Rate limiting by tier, sanitizer prompt leak detection |
-
----
-
-## Project Structure
-
-```
-adiuva-api/
-├── alembic.ini                  # Alembic configuration
-├── BACKEND_PLAN.md              # Architecture & design decisions
-├── docker-compose.yml           # Docker Compose (app + PostgreSQL)
-├── Dockerfile                   # Multi-stage production build
-├── requirements.txt             # Python dependencies
-│
-├── alembic/                     # Database migrations
-│   ├── env.py                   # Alembic environment config
-│   ├── script.py.mako           # Migration template
-│   └── versions/
-│       ├── 001_initial_schema.py    # Tables, indexes, FKs
-│       └── 002_seed_plugins.py      # Seed marketplace plugins
-│
-├── app/                         # Application source
-│   ├── main.py                  # FastAPI app factory, middleware, routes
-│   ├── db.py                    # Async SQLAlchemy engine & session
-│   ├── models.py                # SQLAlchemy ORM models (9 tables)
-│   ├── schemas.py               # Pydantic request/response schemas
-│   │
-│   ├── config/
-│   │   └── settings.py          # Pydantic Settings (env vars)
-│   │
-│   ├── agents/                  # LLM-powered domain agents
-│   │   ├── task_agent.py        # Task & comment CRUD (8 tools)
-│   │   ├── project_agent.py     # Project lifecycle (6 tools)
-│   │   ├── timeline_agent.py  # Milestones (4 tools)
-│   │   └── note_agent.py        # Markdown notes (5 tools)
-│   │
-│   ├── core/                    # Orchestration engine
-│   │   ├── agent_registry.py    # BaseAgent, ChatAgent, AgentRegistry
-│   │   ├── llm.py               # LiteLLM factory (get_llm, get_router_llm)
-│   │   ├── orchestrator.py      # Intent classification & routing
-│   │   └── execution_plan.py    # Plan builder, templates, cache
-│   │
-│   ├── api/                     # HTTP layer
-│   │   ├── deps.py              # Shared FastAPI dependencies
-│   │   ├── middleware/
-│   │   │   ├── auth.py          # JWT validation, live tier lookup
-│   │   │   ├── rate_limit.py    # Sliding-window tier rate limiter
-│   │   │   └── sanitizer.py     # Prompt IP leak protection
-│   │   └── routes/
-│   │       ├── auth.py          # Register, login, refresh, me
-│   │       ├── chat.py          # Chat + WebSocket streaming
-│   │       ├── plans.py         # Execution plan playbooks
-│   │       ├── storage.py       # E2E encrypted record CRUD
-│   │       ├── vectors.py       # Vector upsert, search, delete
-│   │       ├── backup.py        # Encrypted backup management
-│   │       ├── plugins.py       # Marketplace browse & install
-│   │       └── billing.py       # Stripe checkout & webhooks
-│   │
-│   ├── storage/                 # Storage backends
-│   │   ├── blob_store.py        # S3 blob storage
-│   │   ├── vector_store.py      # Pinecone / Qdrant vector store
-│   │   └── encryption.py        # Checksum verification utilities
-│   │
-│   ├── billing/                 # Subscription management
-│   │   ├── stripe_service.py    # Stripe API integration
-│   │   └── tier_manager.py      # Feature matrix & quota enforcement
-│   │
-│   └── marketplace/             # Plugin ecosystem
-│       ├── plugin_registry.py   # Catalog CRUD & search
-│       ├── plugin_review.py     # Security checklist & review queue
-│       └── revenue_share.py     # 70/30 split & Stripe Connect
-│
-└── tests/                       # Test suite
-    ├── conftest.py              # Fixtures: DB, S3, auth, seeds
-    ├── test_auth.py
-    ├── test_orchestrator.py
-    ├── test_agents.py
-    ├── test_storage.py
-    ├── test_backup.py
-    ├── test_plugins.py
-    ├── test_agent_registry.py
-    ├── test_execution_plan.py
-    └── test_middleware.py
-```
-
----
-
-## License
-
-*To be determined.*
+uvicorn app.main:app --host 0.0.0.0 --port 8000 --reload --log-config logging.conf
+```

alembic/env.py

@@ -16,7 +16,7 @@ import re
 from logging.config import fileConfig
 
 from alembic import context
-from sqlalchemy import engine_from_config, pool
+from sqlalchemy import pool
 from sqlalchemy.ext.asyncio import create_async_engine
 
 # Alembic Config object (gives access to alembic.ini values).

alembic/versions/001_initial_schema.py

@@ -1,5 +1,4 @@
-"""Initial schema: users, refresh_tokens, subscriptions, storage_records,
-backup_metadata, plugins, plugin_installations, plugin_reviews, revenue_events.
+"""Initial schema: users, refresh_tokens, subscriptions.
 
 Revision ID: 001
 Revises:
@@ -28,18 +27,6 @@ def upgrade() -> None:
         EXCEPTION WHEN duplicate_object THEN NULL;
         END $$;
     """)
-    op.execute("""
-        DO $$ BEGIN
-            CREATE TYPE plugin_status AS ENUM ('pending_review', 'approved', 'rejected');
-        EXCEPTION WHEN duplicate_object THEN NULL;
-        END $$;
-    """)
-    op.execute("""
-        DO $$ BEGIN
-            CREATE TYPE review_decision AS ENUM ('approved', 'rejected');
-        EXCEPTION WHEN duplicate_object THEN NULL;
-        END $$;
-    """)
 
     # ── users ─────────────────────────────────────────────────────────────
     op.create_table(
@@ -88,122 +75,10 @@ def upgrade() -> None:
     op.create_index("ix_subscriptions_user_id", "subscriptions", ["user_id"])
     op.create_index("ix_subscriptions_stripe_id", "subscriptions", ["stripe_subscription_id"])
 
-    # ── storage_records ───────────────────────────────────────────────────
-    op.create_table(
-        "storage_records",
-        sa.Column("id", postgresql.UUID(as_uuid=False), nullable=False),
-        sa.Column("user_id", postgresql.UUID(as_uuid=False), nullable=False),
-        sa.Column("table_name", sa.String(100), nullable=False),
-        sa.Column("s3_key", sa.String(500), nullable=False),
-        sa.Column("checksum", sa.String(64), nullable=False),
-        sa.Column("size_bytes", sa.Integer, nullable=False),
-        sa.Column("created_at", sa.DateTime(timezone=True), nullable=False, server_default=sa.text("now()")),
-        sa.Column("updated_at", sa.DateTime(timezone=True), nullable=False, server_default=sa.text("now()")),
-        sa.PrimaryKeyConstraint("id"),
-        sa.ForeignKeyConstraint(["user_id"], ["users.id"], ondelete="CASCADE"),
-    )
-    op.create_index("ix_storage_records_user_id", "storage_records", ["user_id"])
-
-    # ── backup_metadata ───────────────────────────────────────────────────
-    op.create_table(
-        "backup_metadata",
-        sa.Column("id", postgresql.UUID(as_uuid=False), nullable=False),
-        sa.Column("user_id", postgresql.UUID(as_uuid=False), nullable=False),
-        sa.Column("s3_key", sa.String(500), nullable=False),
-        sa.Column("version", sa.Integer, nullable=False),
-        sa.Column("timestamp", sa.BigInteger, nullable=False),
-        sa.Column("checksum", sa.String(64), nullable=False),
-        sa.Column("size_bytes", sa.Integer, nullable=False),
-        sa.Column("created_at", sa.DateTime(timezone=True), nullable=False, server_default=sa.text("now()")),
-        sa.PrimaryKeyConstraint("id"),
-        sa.ForeignKeyConstraint(["user_id"], ["users.id"], ondelete="CASCADE"),
-    )
-    op.create_index("ix_backup_metadata_user_id", "backup_metadata", ["user_id"])
-
-    # ── plugins ───────────────────────────────────────────────────────────
-    op.create_table(
-        "plugins",
-        sa.Column("id", sa.String(255), nullable=False),
-        sa.Column("name", sa.String(255), nullable=False),
-        sa.Column("description", sa.Text, nullable=False, server_default=""),
-        sa.Column("version", sa.String(50), nullable=False, server_default="1.0.0"),
-        sa.Column("author_id", postgresql.UUID(as_uuid=False), nullable=True),
-        sa.Column("author_name", sa.String(255), nullable=False, server_default=""),
-        sa.Column("category", sa.String(100), nullable=False, server_default=""),
-        sa.Column("price_cents", sa.Integer, nullable=False, server_default="0"),
-        sa.Column("permissions", sa.Text, nullable=False, server_default="[]"),
-        sa.Column("status", postgresql.ENUM("pending_review", "approved", "rejected", name="plugin_status", create_type=False), nullable=False, server_default="pending_review"),
-        sa.Column("s3_package_key", sa.String(500), nullable=True),
-        sa.Column("install_count", sa.Integer, nullable=False, server_default="0"),
-        sa.Column("avg_rating", sa.Float, nullable=False, server_default="0.0"),
-        sa.Column("rejection_reason", sa.Text, nullable=True),
-        sa.Column("submitted_at", sa.DateTime(timezone=True), nullable=False, server_default=sa.text("now()")),
-        sa.Column("created_at", sa.DateTime(timezone=True), nullable=False, server_default=sa.text("now()")),
-        sa.PrimaryKeyConstraint("id"),
-        sa.ForeignKeyConstraint(["author_id"], ["users.id"], ondelete="SET NULL"),
-    )
-
-    # ── plugin_installations ──────────────────────────────────────────────
-    op.create_table(
-        "plugin_installations",
-        sa.Column("id", postgresql.UUID(as_uuid=False), nullable=False),
-        sa.Column("plugin_id", sa.String(255), nullable=False),
-        sa.Column("user_id", postgresql.UUID(as_uuid=False), nullable=False),
-        sa.Column("installed_at", sa.DateTime(timezone=True), nullable=False, server_default=sa.text("now()")),
-        sa.PrimaryKeyConstraint("id"),
-        sa.ForeignKeyConstraint(["plugin_id"], ["plugins.id"], ondelete="CASCADE"),
-        sa.ForeignKeyConstraint(["user_id"], ["users.id"], ondelete="CASCADE"),
-        sa.UniqueConstraint("plugin_id", "user_id", name="uq_plugin_user"),
-    )
-    op.create_index("ix_plugin_installations_plugin_id", "plugin_installations", ["plugin_id"])
-    op.create_index("ix_plugin_installations_user_id", "plugin_installations", ["user_id"])
-
-    # ── plugin_reviews ────────────────────────────────────────────────────
-    op.create_table(
-        "plugin_reviews",
-        sa.Column("id", postgresql.UUID(as_uuid=False), nullable=False),
-        sa.Column("plugin_id", sa.String(255), nullable=False),
-        sa.Column("reviewer_id", postgresql.UUID(as_uuid=False), nullable=True),
-        sa.Column("decision", postgresql.ENUM("approved", "rejected", name="review_decision", create_type=False), nullable=False),
-        sa.Column("notes", sa.Text, nullable=True),
-        sa.Column("reviewed_at", sa.DateTime(timezone=True), nullable=False, server_default=sa.text("now()")),
-        sa.Column("created_at", sa.DateTime(timezone=True), nullable=False, server_default=sa.text("now()")),
-        sa.PrimaryKeyConstraint("id"),
-        sa.ForeignKeyConstraint(["plugin_id"], ["plugins.id"], ondelete="CASCADE"),
-        sa.ForeignKeyConstraint(["reviewer_id"], ["users.id"], ondelete="SET NULL"),
-    )
-    op.create_index("ix_plugin_reviews_plugin_id", "plugin_reviews", ["plugin_id"])
-
-    # ── revenue_events ────────────────────────────────────────────────────
-    op.create_table(
-        "revenue_events",
-        sa.Column("id", postgresql.UUID(as_uuid=False), nullable=False),
-        sa.Column("plugin_id", sa.String(255), nullable=False),
-        sa.Column("user_id", postgresql.UUID(as_uuid=False), nullable=False),
-        sa.Column("amount_cents", sa.Integer, nullable=False, server_default="0"),
-        sa.Column("developer_share_cents", sa.Integer, nullable=False, server_default="0"),
-        sa.Column("stripe_transfer_id", sa.String(255), nullable=True),
-        sa.Column("paid_at", sa.DateTime(timezone=True), nullable=True),
-        sa.Column("created_at", sa.DateTime(timezone=True), nullable=False, server_default=sa.text("now()")),
-        sa.PrimaryKeyConstraint("id"),
-        sa.ForeignKeyConstraint(["plugin_id"], ["plugins.id"], ondelete="CASCADE"),
-        sa.ForeignKeyConstraint(["user_id"], ["users.id"], ondelete="CASCADE"),
-    )
-    op.create_index("ix_revenue_events_plugin_id", "revenue_events", ["plugin_id"])
-    op.create_index("ix_revenue_events_user_id", "revenue_events", ["user_id"])
-
 
 def downgrade() -> None:
-    op.drop_table("revenue_events")
-    op.drop_table("plugin_reviews")
-    op.drop_table("plugin_installations")
-    op.drop_table("plugins")
-    op.drop_table("backup_metadata")
-    op.drop_table("storage_records")
     op.drop_table("subscriptions")
     op.drop_table("refresh_tokens")
     op.drop_table("users")
 
-    op.execute("DROP TYPE IF EXISTS review_decision")
-    op.execute("DROP TYPE IF EXISTS plugin_status")
     op.execute("DROP TYPE IF EXISTS billing_tier")

alembic/versions/002_seed_plugins.py

@@ -1,92 +0,0 @@
-"""Seed approved plugins: GitHub Sync, Slack Notifier, Time Tracker.
-
-Revision ID: 002
-Revises: 001
-Create Date: 2026-03-03
-"""
-
-from __future__ import annotations
-
-import json
-from datetime import datetime, timezone
-from typing import Sequence, Union
-
-import sqlalchemy as sa
-from alembic import op
-
-revision: str = "002"
-down_revision: Union[str, None] = "001"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-_SEED_PLUGINS = [
-    {
-        "id": "plugin-github-sync",
-        "name": "GitHub Sync",
-        "description": "Sync tasks with GitHub Issues and pull requests.",
-        "version": "1.0.0",
-        "author_name": "Adiuva",
-        "category": "productivity",
-        "price_cents": 0,
-        "permissions": json.dumps(["read:tasks", "write:tasks"]),
-        "status": "approved",
-        "s3_package_key": "plugins/plugin-github-sync/1.0.0/package.zip",
-        "install_count": 0,
-        "avg_rating": 0.0,
-    },
-    {
-        "id": "plugin-slack-notify",
-        "name": "Slack Notifier",
-        "description": "Post task and timeline updates to Slack channels.",
-        "version": "1.2.0",
-        "author_name": "Adiuva",
-        "category": "communication",
-        "price_cents": 499,
-        "permissions": json.dumps(["read:tasks", "read:timelines"]),
-        "status": "approved",
-        "s3_package_key": "plugins/plugin-slack-notify/1.2.0/package.zip",
-        "install_count": 0,
-        "avg_rating": 0.0,
-    },
-    {
-        "id": "plugin-time-tracker",
-        "name": "Time Tracker",
-        "description": "Track time spent on tasks with automatic reporting.",
-        "version": "0.9.1",
-        "author_name": "Third Party",
-        "category": "productivity",
-        "price_cents": 999,
-        "permissions": json.dumps(["read:tasks", "write:tasks"]),
-        "status": "approved",
-        "s3_package_key": "plugins/plugin-time-tracker/0.9.1/package.zip",
-        "install_count": 0,
-        "avg_rating": 0.0,
-    },
-]
-
-
-def upgrade() -> None:
-    plugins = sa.table(
-        "plugins",
-        sa.column("id", sa.String),
-        sa.column("name", sa.String),
-        sa.column("description", sa.Text),
-        sa.column("version", sa.String),
-        sa.column("author_name", sa.String),
-        sa.column("category", sa.String),
-        sa.column("price_cents", sa.Integer),
-        sa.column("permissions", sa.Text),
-        sa.column("status", sa.Enum("pending_review", "approved", "rejected", name="plugin_status")),
-        sa.column("s3_package_key", sa.String),
-        sa.column("install_count", sa.Integer),
-        sa.column("avg_rating", sa.Float),
-    )
-    op.bulk_insert(plugins, _SEED_PLUGINS)
-
-
-def downgrade() -> None:
-    op.execute(
-        "DELETE FROM plugins WHERE id IN ("
-        "'plugin-github-sync', 'plugin-slack-notify', 'plugin-time-tracker'"
-        ")"
-    )

alembic/versions/003_agent_tables.py

@@ -14,7 +14,7 @@ from alembic import op
 from sqlalchemy.dialects import postgresql
 
 revision: str = "003"
-down_revision: Union[str, None] = "002"
+down_revision: Union[str, None] = "001"
 branch_labels: Union[str, Sequence[str], None] = None
 depends_on: Union[str, Sequence[str], None] = None
 

alembic/versions/005_associative_pgvector.py

@@ -0,0 +1,54 @@
+"""Phase 1 — confirm pgvector activation on memory_associative.
+
+Migration 004 created the embedding column as vector(1536) and added the
+IVFFlat index.  This migration is the Phase-1 checkpoint:
+  1. Ensures the pgvector extension is enabled (idempotent).
+  2. Ensures the canonical Phase-1 IVFFlat index exists under the name
+     memory_associative_embedding_idx (creates it only if absent).
+
+Revision ID: 005
+Revises: 9a1f2d0b6c7e
+Create Date: 2026-04-15
+"""
+
+from __future__ import annotations
+
+from typing import Sequence, Union
+
+from alembic import op
+
+revision: str = "005"
+down_revision: Union[str, None] = "e04100e88ace"
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+def upgrade() -> None:
+    # Ensure pgvector extension is enabled (also done in 004, idempotent).
+    op.execute("CREATE EXTENSION IF NOT EXISTS vector;")
+
+    # Ensure the canonical Phase-1 IVFFlat index exists.
+    # 004 may have created ix_memory_associative_embedding; this adds the
+    # Phase-1 name memory_associative_embedding_idx if it is missing.
+    op.execute(
+        """
+        DO $$
+        BEGIN
+            IF NOT EXISTS (
+                SELECT 1
+                FROM   pg_indexes
+                WHERE  tablename  = 'memory_associative'
+                  AND  indexname  = 'memory_associative_embedding_idx'
+            ) THEN
+                CREATE INDEX memory_associative_embedding_idx
+                ON memory_associative
+                USING ivfflat (embedding vector_cosine_ops)
+                WITH  (lists = 100);
+            END IF;
+        END $$;
+        """
+    )
+
+
+def downgrade() -> None:
+    op.execute("DROP INDEX IF EXISTS memory_associative_embedding_idx;")

alembic/versions/006_memory_relations.py

@@ -0,0 +1,74 @@
+"""Add memory_relations table (Phase 3 — relational tier).
+
+Revision ID: 006
+Revises: 1f5975a4f3f4
+Create Date: 2026-04-16
+"""
+
+from __future__ import annotations
+
+from typing import Sequence, Union
+
+import sqlalchemy as sa
+from alembic import op
+from sqlalchemy.dialects import postgresql
+
+revision: str = "006"
+down_revision: Union[str, None] = "1f5975a4f3f4"
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+def upgrade() -> None:
+    op.create_table(
+        "memory_relations",
+        sa.Column("id", postgresql.UUID(as_uuid=False), primary_key=True),
+        sa.Column(
+            "user_id",
+            postgresql.UUID(as_uuid=False),
+            sa.ForeignKey("users.id", ondelete="CASCADE"),
+            nullable=False,
+        ),
+        sa.Column("subject_label", sa.String(128), nullable=False),
+        sa.Column("subject_type", sa.String(32), nullable=False),
+        sa.Column("predicate", sa.String(64), nullable=False),
+        sa.Column("object_label", sa.String(128), nullable=False),
+        sa.Column("object_type", sa.String(32), nullable=False),
+        sa.Column("confidence", sa.Float, nullable=False, server_default="0.7"),
+        sa.Column(
+            "source_episode_id",
+            postgresql.UUID(as_uuid=False),
+            sa.ForeignKey("memory_episodic.id", ondelete="SET NULL"),
+            nullable=True,
+        ),
+        sa.Column("notes_encrypted", sa.LargeBinary, nullable=True),
+        sa.Column(
+            "created_at",
+            sa.DateTime(timezone=True),
+            nullable=False,
+            server_default=sa.func.now(),
+        ),
+        sa.Column(
+            "updated_at",
+            sa.DateTime(timezone=True),
+            nullable=False,
+            server_default=sa.func.now(),
+        ),
+        sa.Column("last_confirmed_at", sa.DateTime(timezone=True), nullable=True),
+    )
+    op.create_index(
+        "memory_relations_user_subject_idx",
+        "memory_relations",
+        ["user_id", "subject_label"],
+    )
+    op.create_index(
+        "memory_relations_user_predicate_idx",
+        "memory_relations",
+        ["user_id", "predicate"],
+    )
+
+
+def downgrade() -> None:
+    op.drop_index("memory_relations_user_predicate_idx", "memory_relations")
+    op.drop_index("memory_relations_user_subject_idx", "memory_relations")
+    op.drop_table("memory_relations")

alembic/versions/1f5975a4f3f4_add_extraction_queue.py

@@ -0,0 +1,38 @@
+"""add extraction_queue
+
+Revision ID: 1f5975a4f3f4
+Revises: 005
+Create Date: 2026-04-16 17:26:25.790870
+
+"""
+from __future__ import annotations
+
+from typing import Sequence, Union
+
+from alembic import op
+import sqlalchemy as sa
+
+
+# revision identifiers, used by Alembic.
+revision: str = '1f5975a4f3f4'
+down_revision: Union[str, None] = '005'
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+def upgrade() -> None:
+    op.create_table(
+        'extraction_queue',
+        sa.Column('id', sa.Uuid(as_uuid=False), nullable=False),
+        sa.Column('user_id', sa.Uuid(as_uuid=False), nullable=False),
+        sa.Column('episode_id', sa.Uuid(as_uuid=False), nullable=True),
+        sa.Column('created_at', sa.DateTime(timezone=True), server_default=sa.text('now()'), nullable=False),
+        sa.ForeignKeyConstraint(['user_id'], ['users.id'], ondelete='CASCADE'),
+        sa.PrimaryKeyConstraint('id'),
+    )
+    op.create_index(op.f('ix_extraction_queue_user_id'), 'extraction_queue', ['user_id'], unique=False)
+
+
+def downgrade() -> None:
+    op.drop_index(op.f('ix_extraction_queue_user_id'), table_name='extraction_queue')
+    op.drop_table('extraction_queue')

alembic/versions/a3b9c0d1e2f3_add_agent_config_to_local_agents.py

@@ -0,0 +1,107 @@
+"""Restore agent config tables and add agent_config column.
+
+9a1f2d0b6c7e dropped local_agent_configs and cloud_agent_configs, but both
+ORM models are still active. This migration recreates them with agent_config
+added to local_agent_configs.
+
+Revision ID: a3b9c0d1e2f3
+Revises: 9a1f2d0b6c7e
+Create Date: 2026-04-07 00:00:00.000000
+
+"""
+from __future__ import annotations
+
+from typing import Sequence, Union
+
+import sqlalchemy as sa
+from alembic import op
+from sqlalchemy.dialects import postgresql
+
+
+# revision identifiers, used by Alembic.
+revision: str = "a3b9c0d1e2f3"
+down_revision: Union[str, None] = "9a1f2d0b6c7e"
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+def upgrade() -> None:
+    # Recreate enum types (idempotent — they may already exist from migration 003)
+    op.execute("""
+        DO $$ BEGIN
+            CREATE TYPE agent_type AS ENUM ('local', 'cloud');
+        EXCEPTION WHEN duplicate_object THEN NULL;
+        END $$;
+    """)
+    op.execute("""
+        DO $$ BEGIN
+            CREATE TYPE agent_run_status AS ENUM ('running', 'success', 'error', 'partial');
+        EXCEPTION WHEN duplicate_object THEN NULL;
+        END $$;
+    """)
+    op.execute("""
+        DO $$ BEGIN
+            CREATE TYPE cloud_provider AS ENUM ('gmail', 'teams', 'outlook');
+        EXCEPTION WHEN duplicate_object THEN NULL;
+        END $$;
+    """)
+
+    bind = op.get_bind()
+    inspector = sa.inspect(bind)
+    existing = set(inspector.get_table_names())
+
+    # ── local_agent_configs (with agent_config column) ────────────────────
+    if "local_agent_configs" not in existing:
+        op.create_table(
+            "local_agent_configs",
+            sa.Column("id", postgresql.UUID(as_uuid=False), nullable=False),
+            sa.Column("user_id", postgresql.UUID(as_uuid=False), nullable=False),
+            sa.Column("device_id", sa.String(255), nullable=False),
+            sa.Column("name", sa.String(255), nullable=False),
+            sa.Column("directory_paths", sa.JSON, nullable=False, server_default="[]"),
+            sa.Column("data_types", sa.JSON, nullable=False, server_default="[]"),
+            sa.Column("prompt_template", sa.Text, nullable=False, server_default=""),
+            sa.Column("agent_config", sa.JSON, nullable=True),
+            sa.Column("file_extensions", sa.JSON, nullable=False, server_default="[]"),
+            sa.Column("schedule_cron", sa.String(100), nullable=False, server_default="0 */6 * * *"),
+            sa.Column("enabled", sa.Boolean, nullable=False, server_default=sa.true()),
+            sa.Column("last_run_at", sa.DateTime(timezone=True), nullable=True),
+            sa.Column("created_at", sa.DateTime(timezone=True), nullable=False, server_default=sa.text("now()")),
+            sa.Column("updated_at", sa.DateTime(timezone=True), nullable=False, server_default=sa.text("now()")),
+            sa.PrimaryKeyConstraint("id"),
+            sa.ForeignKeyConstraint(["user_id"], ["users.id"], ondelete="CASCADE"),
+        )
+        op.create_index("ix_local_agent_configs_user_id", "local_agent_configs", ["user_id"])
+
+    # ── cloud_agent_configs ───────────────────────────────────────────────
+    if "cloud_agent_configs" not in existing:
+        op.create_table(
+            "cloud_agent_configs",
+            sa.Column("id", postgresql.UUID(as_uuid=False), nullable=False),
+            sa.Column("user_id", postgresql.UUID(as_uuid=False), nullable=False),
+            sa.Column(
+                "provider",
+                postgresql.ENUM("gmail", "teams", "outlook", name="cloud_provider", create_type=False),
+                nullable=False,
+            ),
+            sa.Column("name", sa.String(255), nullable=False),
+            sa.Column("data_types", sa.JSON, nullable=False, server_default="[]"),
+            sa.Column("prompt_template", sa.Text, nullable=False, server_default=""),
+            sa.Column("oauth_token_encrypted", sa.Text, nullable=True),
+            sa.Column("filter_config", sa.JSON, nullable=True),
+            sa.Column("schedule_cron", sa.String(100), nullable=False, server_default="0 */6 * * *"),
+            sa.Column("enabled", sa.Boolean, nullable=False, server_default=sa.true()),
+            sa.Column("last_run_at", sa.DateTime(timezone=True), nullable=True),
+            sa.Column("created_at", sa.DateTime(timezone=True), nullable=False, server_default=sa.text("now()")),
+            sa.Column("updated_at", sa.DateTime(timezone=True), nullable=False, server_default=sa.text("now()")),
+            sa.PrimaryKeyConstraint("id"),
+            sa.ForeignKeyConstraint(["user_id"], ["users.id"], ondelete="CASCADE"),
+        )
+        op.create_index("ix_cloud_agent_configs_user_id", "cloud_agent_configs", ["user_id"])
+
+
+def downgrade() -> None:
+    op.drop_index("ix_cloud_agent_configs_user_id", table_name="cloud_agent_configs")
+    op.drop_table("cloud_agent_configs")
+    op.drop_index("ix_local_agent_configs_user_id", table_name="local_agent_configs")
+    op.drop_table("local_agent_configs")

alembic/versions/b4c0d1e2f3a4_add_oauth_and_avatar.py

@@ -0,0 +1,56 @@
+"""Add oauth_accounts table, nullable password_hash, avatar_url to users.
+
+Revision ID: b4c0d1e2f3a4
+Revises: a3b9c0d1e2f3
+Create Date: 2026-04-10 00:00:00.000000
+
+"""
+from __future__ import annotations
+
+from typing import Sequence, Union
+
+import sqlalchemy as sa
+from alembic import op
+from sqlalchemy.dialects import postgresql
+
+
+# revision identifiers, used by Alembic.
+revision: str = "b4c0d1e2f3a4"
+down_revision: Union[str, None] = "a3b9c0d1e2f3"
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+def upgrade() -> None:
+    # ── users: make password_hash nullable (social users have no password) ──
+    op.alter_column("users", "password_hash", existing_type=sa.String(255), nullable=True)
+
+    # ── users: add avatar_url ─────────────────────────────────────────────
+    op.add_column("users", sa.Column("avatar_url", sa.String(2048), nullable=True))
+
+    # ── oauth_accounts ────────────────────────────────────────────────────
+    op.create_table(
+        "oauth_accounts",
+        sa.Column("id", postgresql.UUID(as_uuid=False), nullable=False),
+        sa.Column("user_id", postgresql.UUID(as_uuid=False), nullable=False),
+        sa.Column("provider", sa.String(50), nullable=False),
+        sa.Column("provider_user_id", sa.String(255), nullable=False),
+        sa.Column("provider_email", sa.String(255), nullable=True),
+        sa.Column(
+            "created_at",
+            sa.DateTime(timezone=True),
+            nullable=False,
+            server_default=sa.text("now()"),
+        ),
+        sa.PrimaryKeyConstraint("id"),
+        sa.ForeignKeyConstraint(["user_id"], ["users.id"], ondelete="CASCADE"),
+        sa.UniqueConstraint("provider", "provider_user_id", name="uq_oauth_provider_user"),
+    )
+    op.create_index("ix_oauth_accounts_user_id", "oauth_accounts", ["user_id"])
+
+
+def downgrade() -> None:
+    op.drop_index("ix_oauth_accounts_user_id", table_name="oauth_accounts")
+    op.drop_table("oauth_accounts")
+    op.drop_column("users", "avatar_url")
+    op.alter_column("users", "password_hash", existing_type=sa.String(255), nullable=False)

alembic/versions/c5d1e2f3a4b5_add_onboarding_completed_at.py

@@ -0,0 +1,31 @@
+"""Add onboarding_completed_at column to users table.
+
+Revision ID: c5d1e2f3a4b5
+Revises: b4c0d1e2f3a4
+Create Date: 2026-04-11 00:00:00.000000
+
+"""
+from __future__ import annotations
+
+from typing import Sequence, Union
+
+import sqlalchemy as sa
+from alembic import op
+
+
+# revision identifiers, used by Alembic.
+revision: str = "c5d1e2f3a4b5"
+down_revision: Union[str, None] = "b4c0d1e2f3a4"
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+def upgrade() -> None:
+    op.add_column(
+        "users",
+        sa.Column("onboarding_completed_at", sa.DateTime(timezone=True), nullable=True),
+    )
+
+
+def downgrade() -> None:
+    op.drop_column("users", "onboarding_completed_at")

alembic/versions/d6e3f4a5b6c7_folder_index_tables.py

@@ -0,0 +1,46 @@
+"""Add token tracking columns for folder integration.
+
+Revision ID: d6e3f4a5b6c7
+Revises: 006
+Create Date: 2026-05-11 00:00:00.000000
+
+"""
+from __future__ import annotations
+
+from typing import Sequence, Union
+
+import sqlalchemy as sa
+from alembic import op
+from sqlalchemy.dialects.postgresql import UUID
+
+# revision identifiers, used by Alembic.
+revision: str = "d6e3f4a5b6c7"
+down_revision: Union[str, None] = "006"
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+def upgrade() -> None:
+    op.add_column(
+        "agent_run_logs",
+        sa.Column("tokens_used", sa.Integer(), nullable=False, server_default="0"),
+    )
+    op.create_table(
+        "monthly_token_usage",
+        sa.Column("user_id", UUID(as_uuid=False), sa.ForeignKey("users.id", ondelete="CASCADE"), nullable=False),
+        sa.Column("year_month", sa.String(7), nullable=False),
+        sa.Column("feature", sa.String(64), nullable=False),
+        sa.Column("tokens_used", sa.Integer(), nullable=False, server_default="0"),
+        sa.PrimaryKeyConstraint("user_id", "year_month", "feature"),
+    )
+    op.create_index(
+        "ix_monthly_token_usage_user_month",
+        "monthly_token_usage",
+        ["user_id", "year_month"],
+    )
+
+
+def downgrade() -> None:
+    op.drop_index("ix_monthly_token_usage_user_month", table_name="monthly_token_usage")
+    op.drop_table("monthly_token_usage")
+    op.drop_column("agent_run_logs", "tokens_used")

alembic/versions/e04100e88ace_avatar_url_varchar_to_text.py

@@ -0,0 +1,34 @@
+"""avatar_url_varchar_to_text
+
+Revision ID: e04100e88ace
+Revises: c5d1e2f3a4b5
+Create Date: 2026-04-13 09:13:06.733674
+
+"""
+from __future__ import annotations
+
+from typing import Sequence, Union
+
+from alembic import op
+import sqlalchemy as sa
+
+
+# revision identifiers, used by Alembic.
+revision: str = 'e04100e88ace'
+down_revision: Union[str, None] = 'c5d1e2f3a4b5'
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+def upgrade() -> None:
+    op.alter_column('users', 'avatar_url',
+               existing_type=sa.VARCHAR(length=2048),
+               type_=sa.Text(),
+               existing_nullable=True)
+
+
+def downgrade() -> None:
+    op.alter_column('users', 'avatar_url',
+               existing_type=sa.Text(),
+               type_=sa.VARCHAR(length=2048),
+               existing_nullable=True)

app/agents/client_agent.py

@@ -0,0 +1,52 @@
+"""Client agent — read-only tools for the clients table."""
+
+from __future__ import annotations
+
+import json
+from typing import Any
+
+from langchain_core.tools import tool
+
+from app.core.ws_context import execute_on_client
+
+
+@tool
+async def list_clients(search: str = "", limit: int = 20) -> str:
+    """List clients, optionally filtered by a name/email substring search.
+
+    search: optional substring to match against client name or email.
+    limit: max rows to return (default 20).
+    """
+    filters: dict[str, Any] = {"limit": limit}
+    if search:
+        filters["search"] = search
+
+    result = await execute_on_client(action="select", table="clients", filters=filters)
+    rows = result.get("rows", [])
+    if not rows:
+        return "No clients found."
+    lines = [
+        f"- {r.get('name', '?')} (id: {r.get('id')}, email: {r.get('email', '')}, "
+        f"company: {r.get('company', '')})"
+        for r in rows
+    ]
+    return f"Found {len(rows)} client(s):\n" + "\n".join(lines)
+
+
+@tool
+async def get_client(id: str) -> str:
+    """Get full details for one client by UUID.
+
+    id: the client's UUID.
+    """
+    if not id:
+        return "Client id is required."
+
+    result = await execute_on_client(action="get", table="clients", data={"id": id})
+    row = result.get("row") or result.get("rows", [None])[0] if result else None
+    if not row:
+        return f"Client '{id}' not found."
+    return f"Client details:\n{json.dumps(row, ensure_ascii=False, indent=2)}"
+
+
+CLIENT_TOOLS: list[Any] = [list_clients, get_client]

app/agents/filesystem_agent.py

@@ -7,12 +7,31 @@ handles actual disk I/O and responds with ``tool_result`` frames.
 
 from __future__ import annotations
 
+import os
+import re
+from pathlib import Path
 from typing import Any
 
 from langchain_core.tools import tool
 
 from app.core.ws_context import execute_on_client
 
+# Max characters returned by read_file_content in journey (exploration) tools.
+# The journey only needs to understand file structure, not full content.
+_JOURNEY_READ_MAX_CHARS: int = 4000
+
+
+def _resolve_path(path: str, base: str) -> str:
+    """Resolve *path* against *base* when *path* is relative.
+
+    The LLM often passes ``"."`` meaning "the configured directory".
+    Without this, Electron resolves ``"."`` relative to its own CWD instead
+    of the user's chosen directory.
+    """
+    if os.path.isabs(path):
+        return path
+    return str(Path(base) / path)
+
 
 @tool
 async def list_directory(path: str) -> str:
@@ -83,3 +102,93 @@ FILESYSTEM_TOOLS: list[Any] = [
     read_file_content,
     get_file_metadata,
 ]
+
+
+def make_directory_tools(base_directory: str) -> list[Any]:
+    """Return filesystem tools that resolve relative paths against *base_directory*.
+
+    Use this instead of ``FILESYSTEM_TOOLS`` whenever you know the user's target
+    directory upfront (e.g., journey setup sessions).  Relative paths like ``"."``
+    from the LLM are resolved to the correct absolute path before being sent to
+    the Electron client, preventing it from falling back to its own CWD.
+    """
+
+    def _compact_for_journey(raw: str) -> str:
+        """Strip HTML noise and truncate for journey exploration.
+
+        The journey LLM only needs to understand file structure (headers,
+        first paragraphs).  Full CSS/style blocks are pure noise that eat
+        up context window budget.
+        """
+        text = re.sub(r"<style[^>]*>.*?</style>", "", raw, flags=re.DOTALL | re.IGNORECASE)
+        text = re.sub(r"<script[^>]*>.*?</script>", "", text, flags=re.DOTALL | re.IGNORECASE)
+        text = re.sub(r"<!--.*?-->", "", text, flags=re.DOTALL)
+        if len(text) > _JOURNEY_READ_MAX_CHARS:
+            text = text[:_JOURNEY_READ_MAX_CHARS] + "\n[…truncated for exploration]"
+        return text
+
+    @tool
+    async def list_directory(path: str) -> str:  # noqa: F811
+        """List files and folders in a local directory on the user's device.
+
+        Returns a formatted listing of entries with name, type (file/directory),
+        and full path.
+        """
+        resolved = _resolve_path(path, base_directory)
+        result = await execute_on_client(
+            action="list_directory",
+            data={"path": resolved},
+        )
+        entries: list[dict[str, Any]] = result.get("entries", [])
+        if not entries:
+            return f"Directory '{resolved}' is empty or does not exist."
+        lines: list[str] = []
+        for entry in entries:
+            entry_type = entry.get("type", "unknown")
+            entry_name = entry.get("name", "")
+            entry_path = entry.get("path", "")
+            lines.append(f"- [{entry_type}] {entry_name}  ({entry_path})")
+        return f"Directory listing for '{resolved}' ({len(entries)} entries):\n" + "\n".join(lines)
+
+    @tool
+    async def read_file_content(path: str) -> str:  # noqa: F811
+        """Read the text content of a local file on the user's device.
+
+        Returns the file content as a string.  Large files may be truncated
+        by the Electron client.
+        """
+        resolved = _resolve_path(path, base_directory)
+        result = await execute_on_client(
+            action="read_file_content",
+            data={"path": resolved},
+        )
+        content: str = result.get("content", "")
+        if not content:
+            return f"File '{resolved}' is empty or could not be read."
+        return _compact_for_journey(content)
+
+    @tool
+    async def get_file_metadata(path: str) -> str:  # noqa: F811
+        """Get metadata for a local file: size, creation date, modification date, extension.
+
+        Returns a formatted summary of the file's metadata.
+        """
+        resolved = _resolve_path(path, base_directory)
+        result = await execute_on_client(
+            action="get_file_metadata",
+            data={"path": resolved},
+        )
+        size = result.get("size", "unknown")
+        created = result.get("createdAt", "unknown")
+        modified = result.get("modifiedAt", "unknown")
+        extension = result.get("extension", "unknown")
+        name = result.get("name", resolved)
+        return (
+            f"File: {name}\n"
+            f"  Extension: {extension}\n"
+            f"  Size: {size} bytes\n"
+            f"  Created: {created}\n"
+            f"  Modified: {modified}"
+        )
+
+    return [list_directory, read_file_content, get_file_metadata]

app/agents/folder_agent.py

@@ -0,0 +1,168 @@
+"""Scoped file-read and search tools for the project folder feature."""
+from __future__ import annotations
+
+from langchain_core.tools import tool
+
+from app.core.folder_indexer import _extract_docx_text, _extract_pdf_text
+from app.core.ws_context import execute_on_client
+
+# Cap returned slice size to keep tool output under control.
+_MAX_RETURN_CHARS = 50_000
+_MAX_SEARCH_MATCHES = 20
+
+
+def _is_unsafe_path(rel: str) -> bool:
+    if not rel:
+        return True
+    norm = rel.replace("\\", "/")
+    if norm.startswith("/"):
+        return True
+    # Windows drive letter
+    if len(rel) >= 2 and rel[1] == ":":
+        return True
+    parts = norm.split("/")
+    return ".." in parts
+
+
+async def _fetch_file(project_id: str, relative_path: str, offset: int, length: int) -> dict:
+    """Return the raw Electron tool_result dict for a file read."""
+    return await execute_on_client(
+        action="read_project_folder_file",
+        data={
+            "projectId": project_id,
+            "relativePath": relative_path,
+            "offset": offset,
+            "length": length,
+        },
+    )
+
+
+def _decode(result: dict) -> tuple[str, str, int]:
+    """Decode a tool_result into (text, kind, total_size). For pdf/docx,
+    extracts text from base64. For images, returns a placeholder string.
+    For text, content is already a sliced utf-8 string.
+    """
+    kind = result.get("kind", "text")
+    content = result.get("content", "") or ""
+    total = int(result.get("totalSize", 0) or 0)
+    if kind == "image":
+        return ("[Image file — cannot be navigated as text. See manifest summary.]", kind, total)
+    if kind == "pdf":
+        return (_extract_pdf_text(content), kind, total)
+    if kind == "docx":
+        return (_extract_docx_text(content), kind, total)
+    return (content, kind, total)
+
+
+@tool
+async def read_project_folder_file(
+    project_id: str,
+    relative_path: str,
+    offset: int = 0,
+    length: int = _MAX_RETURN_CHARS,
+) -> str:
+    """Read a slice of a file inside the project's linked folder.
+
+    Args:
+        project_id: project ID.
+        relative_path: path relative to the linked folder root.
+        offset: char offset to start reading from (0 = beginning).
+        length: max chars to return. Default 50000. Use smaller values to save tokens.
+
+    Returns text content slice with a header showing position. Header tells you
+    when more content is available; call again with the suggested next offset.
+
+    For PDF / DOCX files the backend extracts text first, then applies offset/length
+    on the extracted text. For images returns a placeholder; navigate with the
+    manifest summary instead.
+    """
+    if _is_unsafe_path(relative_path):
+        return "Access denied"
+
+    result = await _fetch_file(project_id, relative_path, offset, length)
+    text, kind, total_size = _decode(result)
+
+    if not text and kind in ("missing", "error"):
+        return f"File not found or unreadable: {relative_path}"
+
+    if kind in ("pdf", "docx"):
+        # Backend extracted full text — apply offset/length on chars.
+        sliced = text[offset:offset + length]
+        slice_end = min(offset + length, len(text))
+        header = (
+            f"[file={relative_path} kind={kind} offset={offset} end={slice_end} "
+            f"totalChars={len(text)}]"
+        )
+        if slice_end < len(text):
+            header += f"\n[More content available — call again with offset={slice_end}.]"
+        return header + "\n" + sliced
+
+    if kind == "text":
+        slice_end = offset + len(text)
+        header = (
+            f"[file={relative_path} kind=text offset={offset} end={slice_end} "
+            f"totalBytes={total_size}]"
+        )
+        if slice_end < total_size:
+            header += f"\n[More content available — call again with offset={slice_end}.]"
+        return header + "\n" + text
+
+    # image or unknown
+    return text
+
+
+@tool
+async def search_project_folder_file(
+    project_id: str,
+    relative_path: str,
+    query: str,
+    context_lines: int = 3,
+) -> str:
+    """Search a project folder file for a query string (case-insensitive substring).
+
+    Args:
+        project_id: project ID.
+        relative_path: path relative to the linked folder root.
+        query: text to search for.
+        context_lines: number of lines of context around each match (default 3).
+
+    Returns matching line ranges with surrounding context and 1-based line numbers.
+    Capped at 20 matches; if more exist the header shows the total.
+
+    Works on text, code, markdown, PDF (extracted), and DOCX (extracted).
+    Images and binary files are not searchable.
+    """
+    if _is_unsafe_path(relative_path):
+        return "Access denied"
+    if not query:
+        return "Empty query."
+
+    # For text we still need full file; pass length=very large.
+    result = await _fetch_file(project_id, relative_path, offset=0, length=10_000_000)
+    text, kind, _ = _decode(result)
+
+    if not text and kind in ("missing", "error"):
+        return f"File not found or unreadable: {relative_path}"
+    if kind == "image":
+        return "Cannot search inside images."
+
+    lines = text.splitlines()
+    q = query.lower()
+    matches = [i for i, line in enumerate(lines) if q in line.lower()]
+    if not matches:
+        return f"No matches for '{query}' in {relative_path}."
+
+    shown = matches[:_MAX_SEARCH_MATCHES]
+    snippets: list[str] = []
+    for i in shown:
+        start = max(0, i - context_lines)
+        end = min(len(lines), i + context_lines + 1)
+        block = "\n".join(f"{n + 1:5d}: {lines[n]}" for n in range(start, end))
+        snippets.append(block)
+
+    header = f"[file={relative_path} matches={len(matches)} showing={len(shown)} query='{query}']"
+    body = "\n---\n".join(snippets)
+    return header + "\n" + body
+
+
+FOLDER_TOOLS = [read_project_folder_file, search_project_folder_file]

app/agents/note_agent.py

@@ -1,13 +1,14 @@
-"""Note agent — Markdown note management (list, get, create, update, delete)."""
+"""Note agent — Markdown note management (list, get, create, update, propose edit)."""
 
 from __future__ import annotations
 
+import asyncio
 import re
 from typing import Any
 
 from langchain_core.tools import tool
 
-from app.core.llm import embed
+from app.core.note_summarizer import generate_note_summary
 from app.core.ws_context import execute_on_client
 
 _UUID_RE = re.compile(
@@ -18,25 +19,22 @@ _UUID_RE = re.compile(
 def _is_uuid(value: str) -> bool:
     return bool(_UUID_RE.match(value))
 
-NOTE_SYSTEM_PROMPT = (
-    "You are a note-taking assistant. You help users create, retrieve, update,\n"
-    "and delete Markdown notes in their workspace.\n\n"
-    "Rules:\n"
-    "  - content is always Markdown; preserve formatting when updating\n"
-    "  - project_id is optional; link a note to a project when mentioned\n"
-    "  - When updating, call get_note first if you need to read existing content\n"
-    "    before appending or replacing sections\n"
-    "  - list_notes without project_id returns all notes; scope with project_id\n"
-    "    when the user is working within a specific project\n"
-    "  - project_id must be a UUID; if you only know a project name, do not pass it as project_id\n"
-    "  - Do not fabricate note content — reflect what the user provides or what\n"
-    "    is already in the note (retrieved via get_note)."
-)
+
+def _fmt_summary(row: dict) -> str:
+    summary = (row.get("aiSummary") or row.get("ai_summary") or "").strip()
+    if summary:
+        return f" — {summary}"
+    snippet = (row.get("content") or "")[:120].replace("\n", " ").strip()
+    return f" — {snippet}" if snippet else ""
 
 
 @tool
 async def list_notes(project_id: str = "") -> str:
-    """List notes, optionally scoped to a project by project_id."""
+    """List notes with AI summaries, optionally scoped to a project by project_id.
+
+    Returns id, title, and ai_summary for each note so you can decide which
+    note to read in full with get_note before creating or updating.
+    """
     normalized_project_id = project_id if (project_id and _is_uuid(project_id)) else ""
     result = await execute_on_client(
         action="select",
@@ -46,7 +44,7 @@ async def list_notes(project_id: str = "") -> str:
     rows = result.get("rows", [])
     if not rows:
         return "No notes found."
-    lines = [f"- {r['title']} (id: {r['id']})" for r in rows]
+    lines = [f"  - [{r['id']}] {r['title']}{_fmt_summary(r)}" for r in rows]
     return f"Found {len(rows)} note(s):\n" + "\n".join(lines)
 
 
@@ -81,14 +79,10 @@ async def create_note(
         },
     )
     row = result["row"]
-    # Index the note content in the vector store.
-    vector = await embed(content)
-    await execute_on_client(
-        action="vector_upsert",
-        data={"id": row["id"], "projectId": row.get("projectId"), "content": content},
-        vector=vector,
-    )
-    return f"Note created: '{row['title']}' (id: {row['id']})."
+    note_id: str = row["id"]
+    # Generate summary asynchronously — fire-and-forget.
+    asyncio.create_task(_refresh_summary(note_id, title, content))
+    return f"Note created: '{row['title']}' (id: {note_id})."
 
 
 @tool
@@ -97,7 +91,8 @@ async def update_note(
     title: str = "",
     content: str = "",
 ) -> str:
-    """Update an existing note. Only pass fields that should change.
+    """Update an existing note directly (no approval required).
+    Use propose_note_edit instead when human review is needed.
     note_id: UUID of the note (required)
     If you need to preserve existing content, call get_note first.
     """
@@ -112,17 +107,63 @@ async def update_note(
         data={"id": note_id, "updates": updates},
     )
     row = result["row"]
-    # Re-index if content changed.
     if content:
-        vector = await embed(content)
-        await execute_on_client(
-            action="vector_upsert",
-            data={"id": note_id, "projectId": row.get("projectId"), "content": content},
-            vector=vector,
-        )
+        new_title = title or row.get("title", "")
+        asyncio.create_task(_refresh_summary(note_id, new_title, content))
     return f"Note updated: '{row['title']}' (id: {row['id']})."
 
 
+@tool
+async def propose_note_edit(
+    note_id: str,
+    edit_type: str,
+    proposed_content: str,
+    reasoning: str = "",
+    anchor_before: str = "",
+    anchor_text: str = "",
+    agent_id: str = "",
+    run_id: str = "",
+) -> str:
+    """Propose an AI edit to an existing note, pending human approval.
+
+    Use this instead of update_note when review_required is true.
+    The user will see the proposal highlighted before it is merged.
+
+    note_id: UUID of the target note (required)
+    edit_type: 'append' | 'insert' | 'replace'
+      - append: adds proposed_content at the end of the note
+      - insert: inserts proposed_content immediately after anchor_before text
+      - replace: replaces the first occurrence of anchor_text with proposed_content
+    proposed_content: the new Markdown text to add or substitute (required)
+    reasoning: brief explanation shown to the user (recommended)
+    anchor_before: for 'insert' — the text snippet that precedes the insertion point
+    anchor_text: for 'replace' — the exact text to be replaced
+    agent_id: agent identifier (for traceability)
+    run_id: run identifier (for traceability)
+    """
+    if edit_type not in ("append", "insert", "replace"):
+        return f"Invalid edit_type '{edit_type}'. Use 'append', 'insert', or 'replace'."
+
+    result = await execute_on_client(
+        action="propose_note_edit",
+        data={
+            "noteId": note_id,
+            "type": edit_type,
+            "proposedContent": proposed_content,
+            "reasoning": reasoning or None,
+            "anchorBefore": anchor_before or None,
+            "anchorText": anchor_text or None,
+            "agentId": agent_id or None,
+            "runId": run_id or None,
+        },
+    )
+    edit_id = result.get("id", "?")
+    return (
+        f"Edit proposal created (id: {edit_id}) for note {note_id}. "
+        f"Status: pending user approval."
+    )
+
+
 @tool
 async def delete_note(note_id: str) -> str:
     """Delete a note permanently by its UUID."""
@@ -130,10 +171,36 @@ async def delete_note(note_id: str) -> str:
     return f"Note {note_id} deleted."
 
 
+async def _refresh_summary(note_id: str, title: str, content: str) -> None:
+    """Generate and persist the AI summary for a note.  Fire-and-forget."""
+    try:
+        summary = await generate_note_summary(title, content)
+        if summary:
+            await execute_on_client(
+                action="update",
+                table="notes",
+                data={
+                    "id": note_id,
+                    "updates": {
+                        "aiSummary": summary,
+                        "aiSummaryUpdatedAt": int(__import__("time").time() * 1000),
+                    },
+                },
+            )
+    except Exception:
+        pass  # fire-and-forget; errors logged by generate_note_summary
+
+
 NOTE_TOOLS: list[Any] = [
     list_notes,
     get_note,
     create_note,
     update_note,
+    propose_note_edit,
     delete_note,
 ]
+
+NOTE_READ_TOOLS: list[Any] = [
+    list_notes,
+    get_note,
+]

app/agents/project_agent.py

@@ -8,22 +8,6 @@ from langchain_core.tools import tool
 
 from app.core.ws_context import execute_on_client
 
-PROJECT_SYSTEM_PROMPT = (
-    "You are a project management assistant. You help users create, find,\n"
-    "update, and archive projects in their workspace.\n\n"
-    "Rules:\n"
-    "  - status must be one of: active, archived\n"
-    "  - client_id is optional; link to a client only when explicitly mentioned\n"
-    "  - ai_summary is populated only when the user asks for a project summary;\n"
-    "    derive it from context data — do not fabricate content\n"
-    "  - Use list_projects for scoped queries; list_all_projects only when the\n"
-    "    user wants a complete cross-client view including archived projects\n"
-    "  - get_project requires a project UUID; resolve the ID first by calling\n"
-    "    list_projects if you only have a project name\n"
-    "  - Prefer archiving (update_project status=archived) over deletion;\n"
-    "    only call delete_project when the user explicitly confirms deletion."
-)
-
 
 @tool
 async def list_projects(
@@ -141,3 +125,9 @@ PROJECT_TOOLS: list[Any] = [
     update_project,
     delete_project,
 ]
+
+PROJECT_READ_TOOLS: list[Any] = [
+    list_projects,
+    list_all_projects,
+    get_project,
+]

app/agents/relations_agent.py

@@ -0,0 +1,63 @@
+"""Relations agent — read-only tool wrapping MemoryMiddleware.query_relations."""
+
+from __future__ import annotations
+
+from typing import Any
+
+from langchain_core.tools import tool
+
+from app.core.memory_middleware import MemoryMiddleware
+from app.db import async_session
+
+# Injected at tool-factory time by _brief_research_tools(); not a module-level global.
+# Each tool closure captures the user_id bound at factory time.
+
+
+def make_query_relations_tool(user_id: str, trace_id: str | None = None) -> Any:
+    """Return a query_relations tool bound to *user_id*."""
+
+    @tool
+    async def query_relations(
+        subject_label: str = "",
+        predicate: str = "",
+        object_label: str = "",
+        limit: int = 10,
+    ) -> str:
+        """Query the relational memory graph for entity relationships.
+
+        Returns rows where subject ↔ predicate ↔ object match the given filters.
+        All parameters are optional — omit to retrieve all relations up to limit.
+
+        subject_label: entity label on the left side (e.g. a client name, "Acme Corp").
+        predicate: relationship type (e.g. "mentioned_in", "works_at", "related_to").
+        object_label: entity label on the right side (e.g. a project name, "Website Redesign").
+        limit: max rows to return (default 10).
+        """
+        import logging
+        logger = logging.getLogger(__name__)
+        logger.info(
+            "relations_agent: query_relations trace=%s user=%s subject=%r predicate=%r object=%r",
+            trace_id or "-", user_id, subject_label, predicate, object_label,
+        )
+
+        async with async_session() as db:
+            memory = MemoryMiddleware(db)
+            rows = await memory.query_relations(
+                user_id=user_id,
+                subject=subject_label or None,
+                predicate=predicate or None,
+                object_=object_label or None,
+                limit=limit,
+            )
+
+        if not rows:
+            return "No relational memory entries found for the given filters."
+
+        lines = [
+            f"- {r.subject_label} —[{r.predicate}]→ {r.object_label}"
+            + (f" (confidence: {r.confidence:.2f})" if r.confidence is not None else "")
+            for r in rows
+        ]
+        return f"Found {len(rows)} relation(s):\n" + "\n".join(lines)
+
+    return query_relations

app/agents/task_agent.py

@@ -18,23 +18,6 @@ _UUID_RE = re.compile(
 def _is_uuid(value: str) -> bool:
     return bool(_UUID_RE.match(value))
 
-TASK_SYSTEM_PROMPT = (
-    "You are a task management assistant for a project workspace.\n"
-    "You create, update, list, and track tasks and their comments.\n\n"
-    "Rules:\n"
-    "  - status must be one of: todo, in_progress, done\n"
-    "  - priority must be one of: high, medium, low\n"
-    "  - due_date is a Unix timestamp in milliseconds; convert human dates\n"
-    "  - assignees is a JSON-encoded array of strings (e.g. '[\"Alice\",\"Bob\"]')\n"
-    "  - project_id is optional; link to a project when the user mentions one\n"
-    "  - is_ai_suggested: 1 only when proactively proposing a task the user\n"
-    "    did not explicitly request; 0 otherwise\n"
-    "  - is_ai_suggested: 1 only when proactively proposing a task the user did not explicitly request; 0 otherwise\n"
-    "  - Use list_tasks_due_today for 'what's due today' queries\n"
-    "  - For update_task, use -1 for integer fields you do not want to change\n"
-    "  - Always confirm the action in plain, user-friendly language."
-)
-
 
 # ── Task tools ────────────────────────────────────────────────────────
 
@@ -43,32 +26,141 @@ TASK_SYSTEM_PROMPT = (
 async def list_tasks(
     project_id: str = "",
     status: str = "",
+    priority: str = "",
+    assignee: str = "",
     search: str = "",
     order_by: str = "",
+    order_dir: str = "",
+    due_date_from: int = -1,
+    due_date_to: int = -1,
+    created_at_from: int = -1,
+    created_at_to: int = -1,
+    completed_at_from: int = -1,
+    completed_at_to: int = -1,
+    is_ai_suggested: int = -1,
+    limit: int = 50,
+    offset: int = 0,
 ) -> str:
-    """List tasks, optionally filtered by project_id, status (todo|in_progress|done),
-    a search string, or an order_by field name (dueDate|priority|createdAt)."""
+    """List tasks with optional filters. Returns up to `limit` results (default 50).
+
+    project_id: UUID of the project to scope results to.
+    status: filter by status — todo | in_progress | done.
+    priority: filter by priority — high | medium | low.
+    assignee: substring to match against assignee names. OMIT unless the user explicitly
+              names a person or refers to themselves ("my tasks", "assigned to me", "mine").
+              Do NOT default to the current user.
+    search: substring search across title and description.
+    order_by: sort field — dueDate | priority | createdAt | completedAt.
+    order_dir: asc (default) | desc.
+    due_date_from / due_date_to: ms epoch range for dueDate. Use -1 to omit.
+    created_at_from / created_at_to: ms epoch range for createdAt. Use -1 to omit.
+    completed_at_from / completed_at_to: ms epoch range for completedAt. Use -1 to omit.
+    is_ai_suggested: 0 or 1 to filter by AI-suggested flag; -1 = any.
+    limit: max rows to return (default 50). Use with offset to paginate.
+    offset: skip first N rows (default 0).
+
+    Tip — combine *_from and *_to for a closed range; pass only one for open-ended.
+    Tip — prefer count_tasks for "how many" questions to avoid listing rows.
+    Tip — for natural-language windows ("today", "tomorrow", "this week", "last month", etc.)
+    take due_date_from / due_date_to verbatim from the DATE CONTEXT block in the system prompt;
+    do not compute boundaries from the current UTC instant.
+    """
     normalized_project_id = project_id if (project_id and _is_uuid(project_id)) else ""
-    result = await execute_on_client(
-        action="select",
-        table="tasks",
-        filters={
-            "projectId": normalized_project_id or None,
-            "status": status or None,
-            "search": search or None,
-            "orderBy": order_by or None,
-        },
-    )
+    filters: dict[str, Any] = {
+        "projectId": normalized_project_id or None,
+        "status": status or None,
+        "priority": priority or None,
+        "search": search or None,
+        "orderBy": order_by or None,
+        "orderDir": order_dir or None,
+        "limit": limit,
+        "offset": offset,
+    }
+    if assignee:
+        filters["assignee"] = assignee
+    if due_date_from != -1:
+        filters["dueDateFrom"] = due_date_from
+    if due_date_to != -1:
+        filters["dueDateTo"] = due_date_to
+    if created_at_from != -1:
+        filters["createdAtFrom"] = created_at_from
+    if created_at_to != -1:
+        filters["createdAtTo"] = created_at_to
+    if completed_at_from != -1:
+        filters["completedAtFrom"] = completed_at_from
+    if completed_at_to != -1:
+        filters["completedAtTo"] = completed_at_to
+    if is_ai_suggested != -1:
+        filters["isAiSuggested"] = is_ai_suggested
+
+    result = await execute_on_client(action="select", table="tasks", filters=filters)
     rows = result.get("rows", [])
     if not rows:
         return "No tasks found matching the given filters."
     lines = [
-        f"- {r['title']} (status: {r['status']}, priority: {r['priority']}, id: {r['id']})"
+        f"- {r['title']} (status: {r['status']}, priority: {r['priority']}, "
+        f"dueDate: {r.get('dueDate')}, completedAt: {r.get('completedAt')}, "
+        f"projectId: {r.get('projectId')}, id: {r['id']})"
         for r in rows
     ]
     return f"Found {len(rows)} task(s):\n" + "\n".join(lines)
 
 
+@tool
+async def count_tasks(
+    project_id: str = "",
+    status: str = "",
+    priority: str = "",
+    assignee: str = "",
+    search: str = "",
+    due_date_from: int = -1,
+    due_date_to: int = -1,
+    created_at_from: int = -1,
+    created_at_to: int = -1,
+    completed_at_from: int = -1,
+    completed_at_to: int = -1,
+    is_ai_suggested: int = -1,
+) -> str:
+    """Count tasks matching the given filters without returning rows.
+
+    Use this instead of list_tasks for "how many" questions — it is much cheaper.
+    Same filter parameters as list_tasks (no limit/offset/order_by needed).
+    assignee: OMIT unless the user explicitly names a person or refers to themselves
+              ("my tasks"). Do NOT default to the current user.
+    due_date_from / due_date_to: ms epoch range for dueDate. Use -1 to omit.
+    created_at_from / created_at_to: ms epoch range for createdAt. Use -1 to omit.
+    completed_at_from / completed_at_to: ms epoch range for completedAt. Use -1 to omit.
+    Tip — for natural-language windows take due_date_from / due_date_to from the DATE CONTEXT block;
+    do not compute boundaries from the current UTC instant.
+    """
+    normalized_project_id = project_id if (project_id and _is_uuid(project_id)) else ""
+    filters: dict[str, Any] = {
+        "projectId": normalized_project_id or None,
+        "status": status or None,
+        "priority": priority or None,
+        "search": search or None,
+    }
+    if assignee:
+        filters["assignee"] = assignee
+    if due_date_from != -1:
+        filters["dueDateFrom"] = due_date_from
+    if due_date_to != -1:
+        filters["dueDateTo"] = due_date_to
+    if created_at_from != -1:
+        filters["createdAtFrom"] = created_at_from
+    if created_at_to != -1:
+        filters["createdAtTo"] = created_at_to
+    if completed_at_from != -1:
+        filters["completedAtFrom"] = completed_at_from
+    if completed_at_to != -1:
+        filters["completedAtTo"] = completed_at_to
+    if is_ai_suggested != -1:
+        filters["isAiSuggested"] = is_ai_suggested
+
+    result = await execute_on_client(action="count", table="tasks", filters=filters)
+    return f"Task count: {result.get('count', 0)}"
+
+
 @tool
 async def create_task(
     title: str,
@@ -89,6 +181,8 @@ async def create_task(
     due_date: Unix timestamp in milliseconds; 0 means no due date
     project_id: optional UUID of the parent project
     is_ai_suggested: 1 if proactively suggested, 0 if user-requested
+
+    completedAt is set automatically when status is 'done'.
     """
     result = await execute_on_client(
         action="insert",
@@ -107,7 +201,7 @@ async def create_task(
     row = result["row"]
     return (
         f"Task created: '{row['title']}' "
-        f"(id: {row['id']}, status: {row['status']}, priority: {row['priority']})"
+        f"(id: {row['id']}, status: {row['status']}, priority: {row['priority']}, projectId: {row.get('projectId')})"
     )
 
 
@@ -125,6 +219,10 @@ async def update_task(
     """Update fields on an existing task. Only pass fields you want to change.
     task_id: the task's UUID (required)
     due_date: -1 means unchanged; 0 clears the due date; any positive value sets it
+
+    completedAt is managed automatically:
+      - setting status to 'done' records the current timestamp
+      - changing status away from 'done' clears completedAt
     """
     updates: dict[str, Any] = {}
     if title:
@@ -147,7 +245,7 @@ async def update_task(
         data={"id": task_id, "updates": updates},
     )
     row = result["row"]
-    return f"Task updated: '{row['title']}' (id: {row['id']}, status: {row['status']})"
+    return f"Task updated: '{row['title']}' (id: {row['id']}, status: {row['status']}, projectId: {row.get('projectId')})"
 
 
 @tool
@@ -158,21 +256,36 @@ async def delete_task(task_id: str) -> str:
 
 
 @tool
-async def list_tasks_due_today() -> str:
-    """List all tasks whose due date falls on today's date."""
-    now = datetime.now(tz=timezone.utc)
-    start_ms = int(datetime(now.year, now.month, now.day, tzinfo=timezone.utc).timestamp() * 1000)
-    end_ms = start_ms + 86_400_000 - 1  # last ms of today
+async def list_tasks_due_today(user_timezone: str = "UTC", include_done: bool = False) -> str:
+    """List all tasks whose due date falls on today's date.
+
+    user_timezone: IANA timezone name (e.g. 'Europe/Rome', 'America/New_York').
+    Always pass the user's timezone so 'today' is computed in their local time.
+    include_done: set True to also include already-completed tasks due today (default False).
+    """
+    try:
+        from zoneinfo import ZoneInfo
+        tz = ZoneInfo(user_timezone or "UTC")
+    except Exception:
+        tz = timezone.utc
+    now_local = datetime.now(tz=tz)
+    start_dt = datetime(now_local.year, now_local.month, now_local.day, tzinfo=tz)
+    start_ms = int(start_dt.timestamp() * 1000)
+    end_ms = start_ms + 86_400_000 - 1
+    filters: dict[str, Any] = {"dueDateFrom": start_ms, "dueDateTo": end_ms}
+    if not include_done:
+        filters["status"] = "todo"
     result = await execute_on_client(
         action="select",
         table="tasks",
-        filters={"dueDateFrom": start_ms, "dueDateTo": end_ms},
+        filters=filters,
     )
     rows = result.get("rows", [])
     if not rows:
         return "No tasks are due today."
     lines = [
-        f"- {r['title']} (priority: {r['priority']}, status: {r['status']}, id: {r['id']})"
+        f"- {r['title']} (priority: {r['priority']}, status: {r['status']}, "
+        f"projectId: {r.get('projectId')}, id: {r['id']})"
         for r in rows
     ]
     return f"Tasks due today ({len(rows)}):\n" + "\n".join(lines)
@@ -210,7 +323,6 @@ async def add_task_comment(task_id: str, author: str, content: str) -> str:
     )
     row = result.get("row", {})
     row_author = row.get("author", author)
-    # Electron payloads can vary (taskId vs task_id). Fall back to input task_id.
     row_task_id = row.get("taskId") or row.get("task_id") or task_id
     row_comment_id = row.get("id", "unknown")
     return f"Comment added by {row_author} on task {row_task_id} (comment id: {row_comment_id})."
@@ -228,6 +340,7 @@ async def delete_task_comment(comment_id: str) -> str:
 
 TASK_TOOLS: list[Any] = [
     list_tasks,
+    count_tasks,
     create_task,
     update_task,
     delete_task,
@@ -236,3 +349,10 @@ TASK_TOOLS: list[Any] = [
     add_task_comment,
     delete_task_comment,
 ]
+
+TASK_READ_TOOLS: list[Any] = [
+    list_tasks,
+    count_tasks,
+    list_tasks_due_today,
+    list_task_comments,
+]

app/agents/timeline_agent.py

@@ -3,6 +3,7 @@
 from __future__ import annotations
 
 import re
+from datetime import datetime, timezone
 from typing import Any
 
 from langchain_core.tools import tool
@@ -17,35 +18,130 @@ _UUID_RE = re.compile(
 def _is_uuid(value: str) -> bool:
     return bool(_UUID_RE.match(value))
 
-TIMELINE_SYSTEM_PROMPT = (
-    "You are a project timeline assistant. Timelines are milestone dates that\n"
-    "track progress on a project — they are not calendar events.\n\n"
-    "Rules:\n"
-    "  - project_id is REQUIRED for every create; confirm with the user if unknown\n"
-    "  - For listing, project_id must be a UUID; never pass plain names as project_id\n"
-    "  - date is a Unix timestamp in milliseconds; convert human-readable dates\n"
-    "  - is_ai_suggested: 1 when proactively proposing a timeline, 0 otherwise\n"
-    "  - is_ai_suggested: 1 when proactively proposing a timeline, 0 otherwise\n"
-    "  - For update_timeline, use -1 for integer fields you do not want to change\n"
-    "  - Listing without a project_id returns all timelines across projects\n"
-    "  - Always echo the title and formatted date in your confirmation."
-)
+
+@tool
+async def list_timelines(
+    project_id: str = "",
+    type: str = "",
+    is_completed: int = -1,
+    is_ai_suggested: int = -1,
+    order_by: str = "",
+    order_dir: str = "",
+    date_from: int = -1,
+    date_to: int = -1,
+    created_at_from: int = -1,
+    created_at_to: int = -1,
+    completed_at_from: int = -1,
+    completed_at_to: int = -1,
+    limit: int = 50,
+    offset: int = 0,
+) -> str:
+    """List timeline events (milestones, checkpoints, activities) with optional filters.
+
+    project_id: UUID to scope results to a specific project.
+    type: filter by event type — milestone | checkpoint | activity.
+    is_completed: 0 = incomplete only, 1 = completed only, -1 = any (default).
+    is_ai_suggested: 0 or 1 to filter by AI-suggested flag; -1 = any.
+    order_by: sort field — date (default) | createdAt | completedAt.
+    order_dir: asc (default) | desc.
+    date_from / date_to: ms epoch range for the event date. Use -1 to omit.
+    created_at_from / created_at_to: ms epoch range for createdAt. Use -1 to omit.
+    completed_at_from / completed_at_to: ms epoch range for completedAt. Use -1 to omit.
+    limit: max rows to return (default 50). Use with offset to paginate.
+    offset: skip first N rows (default 0).
+
+    Tip — combine *_from and *_to for a closed range; pass only one for open-ended.
+    Tip — prefer count_timelines for "how many" questions to avoid listing rows.
+    Tip — for natural-language windows ("today", "this week", "last month", etc.)
+    take date_from / date_to verbatim from the DATE CONTEXT block in the system prompt;
+    do not compute boundaries from the current UTC instant.
+    """
+    normalized_project_id = project_id if (project_id and _is_uuid(project_id)) else ""
+    filters: dict[str, Any] = {
+        "projectId": normalized_project_id or None,
+        "orderBy": order_by or None,
+        "orderDir": order_dir or None,
+        "limit": limit,
+        "offset": offset,
+    }
+    if type:
+        filters["type"] = type
+    if is_completed != -1:
+        filters["isCompleted"] = is_completed
+    if is_ai_suggested != -1:
+        filters["isAiSuggested"] = is_ai_suggested
+    if date_from != -1:
+        filters["dateFrom"] = date_from
+    if date_to != -1:
+        filters["dateTo"] = date_to
+    if created_at_from != -1:
+        filters["createdAtFrom"] = created_at_from
+    if created_at_to != -1:
+        filters["createdAtTo"] = created_at_to
+    if completed_at_from != -1:
+        filters["completedAtFrom"] = completed_at_from
+    if completed_at_to != -1:
+        filters["completedAtTo"] = completed_at_to
+
+    result = await execute_on_client(action="select", table="timelines", filters=filters)
+    rows = result.get("rows", [])
+    if not rows:
+        return "No timeline events found."
+    lines = [
+        f"- {r['title']} (date: {r['date']}, type: {r.get('type')}, "
+        f"completed: {bool(r.get('isCompleted'))}, completedAt: {r.get('completedAt')}, "
+        f"projectId: {r.get('projectId')}, id: {r['id']})"
+        for r in rows
+    ]
+    return f"Found {len(rows)} timeline event(s):\n" + "\n".join(lines)
 
 
 @tool
-async def list_timelines(project_id: str = "") -> str:
-    """List timelines. Provide project_id to scope to a specific project."""
+async def count_timelines(
+    project_id: str = "",
+    type: str = "",
+    is_completed: int = -1,
+    is_ai_suggested: int = -1,
+    date_from: int = -1,
+    date_to: int = -1,
+    created_at_from: int = -1,
+    created_at_to: int = -1,
+    completed_at_from: int = -1,
+    completed_at_to: int = -1,
+) -> str:
+    """Count timeline events matching the given filters without returning rows.
+
+    Use this instead of list_timelines for "how many" questions — it is much cheaper.
+    Same filter parameters as list_timelines (no limit/offset/order_by needed).
+
+    date_from / date_to: ms epoch range for the event date. Use -1 to omit.
+    completed_at_from / completed_at_to: ms epoch range for completedAt. Use -1 to omit.
+    Tip — for natural-language windows take date_from / date_to from the DATE CONTEXT block;
+    do not compute boundaries from the current UTC instant.
+    """
     normalized_project_id = project_id if (project_id and _is_uuid(project_id)) else ""
-    result = await execute_on_client(
-        action="select",
-        table="timelines",
-        filters={"projectId": normalized_project_id or None},
-    )
-    rows = result.get("rows", [])
-    if not rows:
-        return "No timelines found."
-    lines = [f"- {r['title']} (date: {r['date']}, id: {r['id']})" for r in rows]
-    return f"Found {len(rows)} timeline(s):\n" + "\n".join(lines)
+    filters: dict[str, Any] = {"projectId": normalized_project_id or None}
+    if type:
+        filters["type"] = type
+    if is_completed != -1:
+        filters["isCompleted"] = is_completed
+    if is_ai_suggested != -1:
+        filters["isAiSuggested"] = is_ai_suggested
+    if date_from != -1:
+        filters["dateFrom"] = date_from
+    if date_to != -1:
+        filters["dateTo"] = date_to
+    if created_at_from != -1:
+        filters["createdAtFrom"] = created_at_from
+    if created_at_to != -1:
+        filters["createdAtTo"] = created_at_to
+    if completed_at_from != -1:
+        filters["completedAtFrom"] = completed_at_from
+    if completed_at_to != -1:
+        filters["completedAtTo"] = completed_at_to
+
+    result = await execute_on_client(action="count", table="timelines", filters=filters)
+    return f"Timeline event count: {result.get('count', 0)}"
 
 
 @tool
@@ -53,13 +149,19 @@ async def create_timeline(
     project_id: str,
     title: str,
     date: int,
+    type: str = "milestone",
+    is_completed: int = 0,
     is_ai_suggested: int = 0,
 ) -> str:
-    """Create a project timeline (milestone).
+    """Create a project timeline event.
     project_id: REQUIRED UUID of the parent project
-    title: descriptive name for the milestone
-    date: Unix timestamp in milliseconds
+    title: descriptive name for the event
+    date: Unix timestamp in milliseconds for the event date
+    type: milestone (default) | checkpoint | activity
+    is_completed: 1 if already completed, 0 if not (default 0)
     is_ai_suggested: 1 if proactively suggested, 0 if user-requested
+
+    completedAt is set automatically when is_completed is 1.
     """
     result = await execute_on_client(
         action="insert",
@@ -68,11 +170,13 @@ async def create_timeline(
             "projectId": project_id,
             "title": title,
             "date": date,
+            "type": type,
+            "isCompleted": is_completed,
             "isAiSuggested": is_ai_suggested,
         },
     )
     row = result["row"]
-    return f"Timeline created: '{row['title']}' (id: {row['id']}, date: {row['date']})"
+    return f"Timeline event created: '{row['title']}' (id: {row['id']}, date: {row['date']}, type: {row.get('type')})"
 
 
 @tool
@@ -80,35 +184,87 @@ async def update_timeline(
     timeline_id: str,
     title: str = "",
     date: int = -1,
+    is_completed: int = -1,
 ) -> str:
-    """Update a timeline. Only pass fields that should change.
-    timeline_id: UUID of the timeline (required)
+    """Update a timeline event. Only pass fields that should change.
+    timeline_id: UUID of the event (required)
     date: -1 means unchanged; any other value sets the new date (ms timestamp)
+    is_completed: 0 = mark incomplete, 1 = mark complete, -1 = unchanged
+
+    completedAt is managed automatically:
+      - setting is_completed to 1 records the current timestamp
+      - setting is_completed to 0 clears completedAt
     """
     updates: dict[str, Any] = {}
     if title:
         updates["title"] = title
     if date != -1:
         updates["date"] = date
+    if is_completed != -1:
+        updates["isCompleted"] = is_completed
     result = await execute_on_client(
         action="update",
         table="timelines",
         data={"id": timeline_id, "updates": updates},
     )
     row = result["row"]
-    return f"Timeline updated: '{row['title']}' (id: {row['id']})"
+    return f"Timeline event updated: '{row['title']}' (id: {row['id']})"
 
 
 @tool
 async def delete_timeline(timeline_id: str) -> str:
-    """Delete a timeline permanently by its UUID."""
+    """Delete a timeline event permanently by its UUID."""
     await execute_on_client(action="delete", table="timelines", data={"id": timeline_id})
-    return f"Timeline {timeline_id} deleted."
+    return f"Timeline event {timeline_id} deleted."
+
+
+@tool
+async def list_timelines_today(user_timezone: str = "UTC", include_completed: bool = True) -> str:
+    """List all timeline events whose date falls on today.
+
+    user_timezone: IANA timezone name (e.g. 'Europe/Rome', 'America/New_York').
+    Always pass the user's timezone so 'today' is computed in their local time.
+    include_completed: set False to exclude already-completed events (default True).
+    """
+    try:
+        from zoneinfo import ZoneInfo
+        tz = ZoneInfo(user_timezone or "UTC")
+    except Exception:
+        tz = timezone.utc
+    now_local = datetime.now(tz=tz)
+    start_dt = datetime(now_local.year, now_local.month, now_local.day, tzinfo=tz)
+    start_ms = int(start_dt.timestamp() * 1000)
+    end_ms = start_ms + 86_400_000 - 1
+    filters: dict[str, Any] = {"dateFrom": start_ms, "dateTo": end_ms}
+    if not include_completed:
+        filters["isCompleted"] = 0
+    result = await execute_on_client(
+        action="select",
+        table="timelines",
+        filters=filters,
+    )
+    rows = result.get("rows", [])
+    if not rows:
+        return "No timeline events today."
+    lines = [
+        f"- {r['title']} (date: {r['date']}, type: {r.get('type')}, "
+        f"completed: {bool(r.get('isCompleted'))}, projectId: {r.get('projectId')}, id: {r['id']})"
+        for r in rows
+    ]
+    return f"Timeline events today ({len(rows)}):\n" + "\n".join(lines)
 
 
 TIMELINE_TOOLS: list[Any] = [
     list_timelines,
+    count_timelines,
+    list_timelines_today,
     create_timeline,
     update_timeline,
     delete_timeline,
 ]
+
+TIMELINE_READ_TOOLS: list[Any] = [
+    list_timelines,
+    count_timelines,
+    list_timelines_today,
+]

app/api/middleware/auth.py

@@ -65,16 +65,39 @@ async def get_current_user(
     default_tier = "power" if settings.ENV == "dev" else "free"
     tier: str = result.scalar_one_or_none() or default_tier
 
-    # Fetch name/surname from user row.
+    # Fetch name/surname/avatar_url/onboarding_completed_at/password_hash from user row.
     user_result = await db.execute(
-        select(User.name, User.surname).where(User.id == user_id)
+        select(
+            User.name, User.surname, User.avatar_url, User.onboarding_completed_at,
+            User.password_hash,
+        ).where(User.id == user_id)
     )
     user_row = user_result.one_or_none()
 
+    # Convert onboarding_completed_at to epoch ms (int) or None.
+    onboarding_ms: int | None = None
+    if user_row and user_row.onboarding_completed_at is not None:
+        onboarding_ms = int(user_row.onboarding_completed_at.timestamp() * 1000)
+
+    # Load decrypted core memory.
+    from app.core.memory_middleware import MemoryMiddleware  # noqa: PLC0415
+
+    memory_dict: dict[str, str] = {}
+    try:
+        mw = MemoryMiddleware(db)
+        blocks = await mw.list_core_blocks(user_id)
+        memory_dict = {b["label"]: b["value"] for b in blocks}
+    except Exception:
+        pass  # Non-critical — return empty memory on failure
+
     return UserProfile(
         id=user_id,
         email=email,
         name=user_row.name if user_row else None,
         surname=user_row.surname if user_row else None,
+        avatar_url=user_row.avatar_url if user_row else None,
+        has_password=bool(user_row.password_hash) if user_row else False,
         tier=tier,
+        onboarding_completed_at=onboarding_ms,
+        memory=memory_dict,
     )  # type: ignore[arg-type]

app/api/middleware/sanitizer.py

@@ -8,8 +8,7 @@ that could reveal server-side prompt IP:
   - Internal reasoning markers (<thinking>, <reasoning>, [INST], …)
   - Exact-match known prompt fingerprints
 
-Binary responses (storage blobs, backup data) are never touched — the
-middleware only activates for paths under /api/v1/chat.
+The middleware only activates for paths under /api/v1/chat.
 
 Any sanitisation event is logged as a WARNING with the request path and the
 names of the fields that were modified.

app/api/routes/agent_setup.py

@@ -1,11 +1,11 @@
-"""Chatbot Journey — WS-based guided conversation to build an agent prompt_template.
+"""Chatbot Journey — WS-based guided conversation to build an AgentConfig.
 
 The journey is driven entirely through WebSocket frames (no REST endpoints).
 The device WS handler dispatches ``journey_start`` and ``journey_message``
 frames to the functions exported here.
 
 Journey flow:
-  1. FE sends ``journey_start`` frame with basic agent config (directory,
+  1. FE sends ``journey_start`` frame with basic agent info (directory,
      data_types, schedule).
   2. Server creates an in-memory session, sets up a WS executor so the
      setup LLM can use file-system tools, does a first directory scrape,
@@ -13,10 +13,11 @@ Journey flow:
   3. FE sends ``journey_message`` frames for each user reply.
   4. Server appends the user message, calls the LLM (which may read files
      via tools), and sends back a ``journey_reply``.
-  5. After 3-5 turns the LLM wraps up by emitting a ``prompt_template``
-     block delimited by ``PROMPT_TEMPLATE_START`` / ``PROMPT_TEMPLATE_END``.
-  6. Server parses the block, sends ``journey_reply`` with ``done=True``
-     and the template.  FE stores it locally.
+  5. After 3-5 turns the LLM wraps up by emitting an ``AgentConfig`` JSON
+     block delimited by ``AGENT_CONFIG_START`` / ``AGENT_CONFIG_END``.
+  6. Server parses and validates the JSON with Pydantic, sends
+     ``journey_reply`` with ``done=True`` and the serialised config.
+     FE stores it locally.
 """
 
 from __future__ import annotations
@@ -30,8 +31,10 @@ from typing import Any
 
 from langchain_core.messages import AIMessage, HumanMessage, SystemMessage, ToolMessage
 
-from app.agents.filesystem_agent import FILESYSTEM_TOOLS
-from app.core.llm import get_llm
+from app.agents.filesystem_agent import make_directory_tools
+from app.core.langfuse_client import compile_prompt, extract_usage, get_langfuse, get_prompt_or_fallback, langfuse_context
+from app.core.llm import get_agent_llm, model_for_agent
+from app.schemas import AgentConfig
 
 logger = logging.getLogger(__name__)
 
@@ -39,9 +42,9 @@ logger = logging.getLogger(__name__)
 
 _SESSION_TTL_SECONDS: int = 1800  # 30 minutes
 
-# Sentinel strings used to delimit the LLM-produced prompt_template.
-_TEMPLATE_START = "PROMPT_TEMPLATE_START"
-_TEMPLATE_END = "PROMPT_TEMPLATE_END"
+# Sentinel strings used to delimit the LLM-produced AgentConfig JSON.
+_CONFIG_START = "AGENT_CONFIG_START"
+_CONFIG_END = "AGENT_CONFIG_END"
 
 # Minimum turns before we consider nudging the LLM to wrap up.
 _MIN_TURNS_BEFORE_NUDGE: int = 3
@@ -62,6 +65,7 @@ class JourneySession:
     data_types: list[str]
     history: list[dict[str, Any]] = field(default_factory=list)
     system_prompt: str = ""
+    langfuse_prompt: Any = None
     created_at: float = field(default_factory=time.monotonic)
 
     def is_expired(self) -> bool:
@@ -83,61 +87,76 @@ def get_journey_session(session_id: str, user_id: str) -> JourneySession | None:
     return s
 
 
-# ── System prompt builder ─────────────────────────────────────────────────
+# ── System prompt ─────────────────────────────────────────────────────────
 
-_SYSTEM_PROMPT_TEMPLATE = """\
+_JOURNEY_SYSTEM_PROMPT = """\
 You are a friendly assistant helping a freelancer configure a data-extraction agent.
-Your job is to understand exactly what data the user wants to extract from their
-local directory and produce a detailed prompt_template that a separate AI will use
-as its instruction set.
-
-The extraction agent already has this base behaviour built in:
-  - Reads each file using file-system tools.
-  - Creates records (tasks, notes, timelines, projects) via CRUD tools.
-  - Sets isAiSuggested=1 on every new record.
-  - Only extracts data explicitly present in the files — it never invents information.
-The user's custom prompt is appended AFTER this base behaviour, so focus on
-what to look for and how to map it — not on the general extraction mechanics.
+Your job is to understand what files the user has in their directory and produce a
+structured AgentConfig JSON that the extraction agent will use as its instruction set.
 
 You have access to file-system tools to explore the user's directory:
-- list_directory: to see folder structure
-- read_file_content: to peek at file contents
-- get_file_metadata: to check file info
+- list_directory: see folder structure and file names
+- read_file_content: peek at a file's content
+- get_file_metadata: check file size, extension, dates
 
 The user's configured directory is: {directory}
 Target data types: {data_types}
 
-IMPORTANT — project assignment is handled automatically by the main agent runner
-before the custom prompt is ever used.  You MUST NOT ask the user about projects,
-projectId, or how to link records to projects.  Never include projectId logic or
-project creation instructions in the generated prompt_template.
+## Your process
 
-Start by exploring the directory to understand its structure.  Then ask concise,
-focused questions one at a time.  Cover these topics (not necessarily in this order):
-  1. The type and format of the source content (confirmed by your exploration).
-  2. How fields should be mapped (e.g. filename → task title).
-  3. Priority or status rules (e.g. "urgent" keyword → high priority).
-  4. Any special handling, date extraction, or exclusions.
+### Step 1 — Explore the directory
+Use list_directory and read_file_content to understand what types of files are present
+(HTML emails, plain-text documents, CSVs, etc.).
 
-Once you reach 90% confidence, output the final prompt_template between these exact
-markers on their own lines:
+### Step 2 — Identify content types
+For each distinct file type found, decide:
+- A short id (e.g. "email_html", "plain_text", "csv")
+- Which preprocessing handler to use: "email_html" for HTML emails, "generic" for everything else
+- A human-readable label and optional detection_hint
 
-{template_start}
-<the complete extraction prompt here>
-{template_end}
+### Step 3 — Ask focused questions (one at a time)
+Cover these topics based on what you discovered:
+1. How to map content to entity types (task / note / timeline entry)
+2. Field mapping rules (e.g. email Subject → task title, filename → note title)
+3. Priority or status rules (e.g. "urgent" in subject → high priority)
+4. Date extraction (e.g. "by Friday" → dueDate)
+5. Exclusion rules (e.g. skip newsletters, skip files with no project match)
 
-The prompt_template must be a self-contained instruction for an AI that reads files
-and must perform CRUD operations using tools to create records.  It should specify:
-  - What entity types to create (tasks, notes, timelines) — never projects.
-  - How to map file content to record fields (camelCase: title, status, priority,
-    dueDate, content, etc.) — never include projectId.
-  - That isAiSuggested must be set to 1 on every new record.
-  - Concrete examples of mappings based on what you discovered in the directory.
+### Step 4 — Produce the AgentConfig JSON
+Once you are ≥ 90% confident, output the final config between these exact markers
+(each on its own line):
+
+{config_start}
+{{
+  "content_types": [
+    {{
+      "id": "email_html",
+      "label": "Email HTML",
+      "detection_hint": "HTML file with From/To/Subject headers",
+      "preprocessing": "email_html",
+      "extraction_prompt": "Detailed extraction instructions for this content type..."
+    }}
+  ],
+  "global_rules": [
+    "If the file cannot be matched to any project, do not create any entity."
+  ],
+  "data_types": {data_types_json}
+}}
+{config_end}
+
+## Rules for the extraction_prompt field
+- Describe when to create a task vs note vs timeline entry (be specific and concrete)
+- Include field mapping rules based on what you found in the directory
+- Include priority/status/date rules if applicable
+- Do NOT include projectId logic — the runner handles project assignment automatically
+- Do NOT mention isAiSuggested — the runner always sets it to 1
+
+## Constraints
+- Never ask about projects, projectId, or how to link records to projects
+- Never include projectId or project creation logic in the generated config
+- Keep asking questions until ≥ 90% confident, then output the JSON immediately
 
 {existing_section}\
-Keep asking clarifying questions until you are at least 90% confident you have
-enough information to generate an accurate prompt_template.  Once you reach that
-confidence level, stop asking and produce the final template immediately.
 Begin by exploring the directory, then ask your first question.\
 """
 
@@ -145,33 +164,53 @@ Begin by exploring the directory, then ask your first question.\
 def _build_system_prompt(
     directory: str,
     data_types: list[str],
-    existing_template: str | None = None,
-) -> str:
+    existing_config: str | None = None,
+) -> tuple[str, Any]:
+    """Return ``(compiled_system_prompt, langfuse_prompt_obj_or_None)``."""
     existing_section = (
-        f"\nThe user already has the following prompt_template — refine it based on their answers:\n"
-        f"---\n{existing_template}\n---\n"
-        if existing_template
+        "\nThe user already has the following AgentConfig — refine it based on their answers:\n"
+        f"```json\n{existing_config}\n```\n"
+        if existing_config
         else ""
     )
-    return _SYSTEM_PROMPT_TEMPLATE.format(
+    template, prompt_obj = get_prompt_or_fallback(
+        "journey_system", _JOURNEY_SYSTEM_PROMPT
+    )
+    compiled = compile_prompt(
+        template,
+        prompt_obj,
         directory=directory,
         data_types=", ".join(data_types),
-        template_start=_TEMPLATE_START,
-        template_end=_TEMPLATE_END,
+        data_types_json=json.dumps(data_types),
+        config_start=_CONFIG_START,
+        config_end=_CONFIG_END,
         existing_section=existing_section,
     )
+    return compiled, prompt_obj
 
 
-# ── Template extraction ───────────────────────────────────────────────────
+# ── AgentConfig extraction ────────────────────────────────────────────────
 
 
-def _extract_template(text: str) -> str | None:
-    """Return the text between PROMPT_TEMPLATE_START and PROMPT_TEMPLATE_END, or None."""
-    if _TEMPLATE_START not in text or _TEMPLATE_END not in text:
+def _extract_agent_config(text: str) -> str | None:
+    """Return validated AgentConfig JSON string from between markers, or None.
+
+    Parses the JSON with Pydantic to ensure it conforms to the schema before
+    returning.  Returns None if markers are absent or JSON is invalid.
+    """
+    if _CONFIG_START not in text or _CONFIG_END not in text:
+        return None
+    start_idx = text.index(_CONFIG_START) + len(_CONFIG_START)
+    end_idx = text.index(_CONFIG_END)
+    raw = text[start_idx:end_idx].strip()
+    if not raw:
+        return None
+    try:
+        parsed = AgentConfig.model_validate_json(raw)
+        return parsed.model_dump_json()
+    except Exception as exc:
+        logger.warning("agent_setup: failed to parse AgentConfig JSON: %s", exc)
         return None
-    start_idx = text.index(_TEMPLATE_START) + len(_TEMPLATE_START)
-    end_idx = text.index(_TEMPLATE_END)
-    return text[start_idx:end_idx].strip() or None
 
 
 # ── LLM call with tool support ───────────────────────────────────────────
@@ -199,12 +238,17 @@ async def _call_llm_with_tools(
     system_prompt: str,
     history: list[dict[str, Any]],
     tools: list[Any],
+    *,
+    user_id: str = "",
+    session_id: str = "",
+    langfuse_prompt: Any = None,
 ) -> str:
     """Build LangChain messages from history and invoke the LLM with tools.
 
     Handles tool-calling loops: if the LLM calls tools, execute them and
     continue until a final text response is produced.
     """
+    lf = get_langfuse()
     messages: list[Any] = [SystemMessage(content=system_prompt)]
     for turn in history:
         if turn["role"] == "user":
@@ -212,42 +256,97 @@ async def _call_llm_with_tools(
         else:
             messages.append(AIMessage(content=turn["content"]))
 
-    llm = get_llm(model=None, temperature=0.4)
+    llm = get_agent_llm("setup", temperature=0.4)
     llm_with_tools = llm.bind_tools(tools)
     tool_map = {tool_def.name: tool_def for tool_def in tools}
 
-    for _ in range(_MAX_TOOL_STEPS):
-        response: AIMessage = await llm_with_tools.ainvoke(messages)
-        messages.append(response)
+    _lf_ctx = langfuse_context(user_id=user_id or None, session_id=session_id or None)
+    _lf_ctx.__enter__()
 
-        if not response.tool_calls:
-            return _as_text(response.content)
+    _span_ctx = (
+        lf.start_as_current_observation(
+            as_type="span",
+            name="journey-setup",
+            input=history[-1]["content"] if history else "",
+        )
+        if lf else None
+    )
+    _span = _span_ctx.__enter__() if _span_ctx else None
 
-        for call in response.tool_calls:
-            call_name = str(call.get("name", ""))
-            call_args = call.get("args", {})
-            logger.info(
-                "agent_setup: journey tool_call name=%s args=%s",
-                call_name,
-                json.dumps(call_args, ensure_ascii=True)[:500],
+    try:
+        for step in range(_MAX_TOOL_STEPS):
+            _gen_ctx = (
+                lf.start_as_current_observation(
+                    as_type="generation",
+                    name="journey-setup-llm",
+                    model=model_for_agent("setup"),
+                    prompt=langfuse_prompt,
+                    input=messages,
+                )
+                if lf else None
             )
+            _gen = _gen_ctx.__enter__() if _gen_ctx else None
+            response: AIMessage = await llm_with_tools.ainvoke(messages)
+            if _gen_ctx:
+                _gen.update(output=_as_text(response.content), usage_details=extract_usage(response))
+                _gen_ctx.__exit__(None, None, None)
 
-            tool_fn = tool_map.get(call_name)
-            if tool_fn is None:
-                tool_output = f"Unknown tool: {call_name}"
-            else:
-                tool_output = await tool_fn.ainvoke(call_args)
+            resp_text = _as_text(response.content)
 
-            logger.info(
-                "agent_setup: journey tool_result name=%s output=%s",
-                call_name,
-                str(tool_output)[:800],
-            )
-            messages.append(ToolMessage(content=str(tool_output), tool_call_id=call["id"]))
+            # Guard against empty responses (e.g. model returned finish_reason
+            # 'error' which LiteLLM maps to 'stop' with empty content).
+            if not response.tool_calls and not resp_text.strip():
+                logger.warning(
+                    "agent_setup: journey LLM returned empty response at step %d — retrying",
+                    step,
+                )
+                # Drop the empty AIMessage so we don't pollute history, and retry.
+                continue
 
-    # Fallback: exceeded max steps.
-    final = await llm.ainvoke(messages)
-    return _as_text(final.content)
+            messages.append(response)
+
+            if not response.tool_calls:
+                if _span:
+                    _span.update(output=resp_text)
+                return resp_text
+
+            for call in response.tool_calls:
+                call_name = str(call.get("name", ""))
+                call_args = call.get("args", {})
+                logger.info(
+                    "agent_setup: journey tool_call name=%s args=%s",
+                    call_name,
+                    json.dumps(call_args, ensure_ascii=True)[:500],
+                )
+
+                tool_fn = tool_map.get(call_name)
+                if tool_fn is None:
+                    tool_output = f"Unknown tool: {call_name}"
+                else:
+                    tool_output = await tool_fn.ainvoke(call_args)
+
+                logger.info(
+                    "agent_setup: journey tool_result name=%s output=%s",
+                    call_name,
+                    str(tool_output)[:800],
+                )
+                messages.append(ToolMessage(content=str(tool_output), tool_call_id=call["id"]))
+
+        # Fallback: exceeded max steps.
+        final = await llm.ainvoke(messages)
+        final_text = _as_text(final.content)
+        if _span:
+            _span.update(output=final_text)
+        return final_text or (
+            "Sorry, I had trouble processing the files. "
+            "Could you try again? If the issue persists, the files might be too large for me to analyse."
+        )
+    finally:
+        if _span_ctx:
+            _span_ctx.__exit__(None, None, None)
+        _lf_ctx.__exit__(None, None, None)
+        if lf:
+            lf.flush()
 
 
 # ── Journey handlers (called from device_ws.py) ──────────────────────────
@@ -265,12 +364,12 @@ async def handle_journey_start(
     agent_type = frame.get("agent_type", "local")
     directory = frame.get("directory", "")
     data_types = frame.get("data_types", [])
-    existing_template = frame.get("existing_template")
+    existing_config = frame.get("existing_config")
 
     # Use the session_id provided by the FE so the reply matches the
     # listener key; fall back to a generated one if absent.
     session_id = frame.get("session_id") or str(uuid.uuid4())
-    system_prompt = _build_system_prompt(directory, data_types, existing_template)
+    system_prompt, langfuse_prompt = _build_system_prompt(directory, data_types, existing_config)
 
     session = JourneySession(
         session_id=session_id,
@@ -279,19 +378,21 @@ async def handle_journey_start(
         directory=directory,
         data_types=data_types,
         system_prompt=system_prompt,
+        langfuse_prompt=langfuse_prompt,
     )
 
-    # The LLM will explore the directory using FILESYSTEM_TOOLS via the
-    # ws_context executor (already set by the WS handler before calling us).
-    # Seed with an initial user message — some providers (e.g. GitHub Copilot)
-    # require at least one user/input message to be present.
+    # Seed with an initial user message — some providers require at least one
+    # user/input message to be present.
     seed_history: list[dict[str, Any]] = [
         {"role": "user", "content": "Hi, I'm ready to set up my agent. Please explore my directory and ask me your first question."},
     ]
     ai_reply = await _call_llm_with_tools(
         system_prompt=system_prompt,
         history=seed_history,
-        tools=list(FILESYSTEM_TOOLS),
+        tools=make_directory_tools(directory),
+        user_id=user_id,
+        session_id=session_id,
+        langfuse_prompt=langfuse_prompt,
     )
 
     session.history.extend(seed_history)
@@ -305,14 +406,14 @@ async def handle_journey_start(
         directory,
     )
 
-    # Check if the LLM produced the template on the first turn (unlikely but possible).
-    prompt_template = _extract_template(ai_reply)
-    done = prompt_template is not None
+    # Check if the LLM produced the config on the first turn (unlikely but possible).
+    agent_config = _extract_agent_config(ai_reply)
+    done = agent_config is not None
 
     display_message = ai_reply
     if done:
         display_message = (
-            ai_reply[: ai_reply.index(_TEMPLATE_START)].strip()
+            ai_reply[: ai_reply.index(_CONFIG_START)].strip()
             or "Here is your agent configuration. You can save it or continue refining."
         )
         _sessions.pop(session_id, None)
@@ -322,7 +423,7 @@ async def handle_journey_start(
         "session_id": session_id,
         "message": display_message,
         "done": done,
-        "prompt_template": prompt_template,
+        "agent_config": agent_config,
     }
 
 
@@ -345,53 +446,59 @@ async def handle_journey_message(
             "session_id": session_id,
             "message": "Journey session not found or expired. Please start a new setup.",
             "done": True,
-            "prompt_template": None,
+            "agent_config": None,
         }
 
     # Append user turn.
     session.history.append({"role": "user", "content": message})
 
     # Call the LLM with tools.
+    session_tools = make_directory_tools(session.directory)
     ai_reply = await _call_llm_with_tools(
         system_prompt=session.system_prompt,
         history=session.history,
-        tools=list(FILESYSTEM_TOOLS),
+        tools=session_tools,
+        user_id=session.user_id,
+        session_id=session_id,
+        langfuse_prompt=session.langfuse_prompt,
     )
 
     session.history.append({"role": "assistant", "content": ai_reply})
 
-    # Check if the LLM produced the final template.
-    prompt_template = _extract_template(ai_reply)
-    done = prompt_template is not None
+    # Check if the LLM produced the final config.
+    agent_config = _extract_agent_config(ai_reply)
+    done = agent_config is not None
 
-    # If the LLM didn't produce a template, nudge it once it has asked enough
-    # questions (>= _MIN_TURNS_BEFORE_NUDGE) or hits the hard safety cap.
+    # If the LLM didn't produce a config, nudge it once it hits the hard safety cap.
     if not done:
         turns = sum(1 for t in session.history if t["role"] == "user")
         if turns >= _MAX_TURNS:
             nudge_content = (
                 "[System: You have enough information. Please generate the final "
-                f"prompt_template now, wrapped in {_TEMPLATE_START} / {_TEMPLATE_END} markers.]"
+                f"AgentConfig JSON now, wrapped in {_CONFIG_START} / {_CONFIG_END} markers.]"
             )
             session.history.append({"role": "user", "content": nudge_content})
 
             nudge_reply = await _call_llm_with_tools(
                 system_prompt=session.system_prompt,
                 history=session.history,
-                tools=list(FILESYSTEM_TOOLS),
+                tools=session_tools,
+                user_id=session.user_id,
+                session_id=session_id,
+                langfuse_prompt=session.langfuse_prompt,
             )
             session.history.append({"role": "assistant", "content": nudge_reply})
 
-            prompt_template = _extract_template(nudge_reply)
-            if prompt_template is not None:
+            agent_config = _extract_agent_config(nudge_reply)
+            if agent_config is not None:
                 done = True
                 ai_reply = nudge_reply
 
     display_message = ai_reply
     if done:
         display_message = (
-            ai_reply[: ai_reply.index(_TEMPLATE_START)].strip()
-            if _TEMPLATE_START in ai_reply
+            ai_reply[: ai_reply.index(_CONFIG_START)].strip()
+            if _CONFIG_START in ai_reply
             else "Here is your agent configuration. You can save it or continue refining."
         )
         _sessions.pop(session_id, None)
@@ -402,5 +509,5 @@ async def handle_journey_message(
         "session_id": session_id,
         "message": display_message,
         "done": done,
-        "prompt_template": prompt_template,
+        "agent_config": agent_config,
     }

app/api/routes/agents.py

@@ -12,17 +12,21 @@ in backend agent-config tables.
 from __future__ import annotations
 
 import asyncio
+import logging
 import uuid
-from datetime import datetime, timedelta, timezone
+from datetime import datetime, timezone
 
 from fastapi import APIRouter, Depends, HTTPException, status
 from sqlalchemy import func, select
 from sqlalchemy.ext.asyncio import AsyncSession
 
+from pydantic import BaseModel
+
 from app.api.deps import get_current_user
 from app.billing.tier_manager import FEATURES
 from app.core.agent_runner import is_agent_running, run_local_agent
 from app.core.device_manager import device_manager
+from app.core.note_summarizer import generate_note_summary
 from app.db import get_session
 from app.models import AgentRunLog, LocalAgentConfig
 from app.schemas import (
@@ -34,6 +38,8 @@ from app.schemas import (
     UserProfile,
 )
 
+logger = logging.getLogger(__name__)
+
 router = APIRouter(prefix="/agents", tags=["agents"])
 
 
@@ -177,6 +183,11 @@ async def trigger_agent_run(
     _enforce_agent_limit(current_user.tier, body.active_agents)
     await _enforce_run_frequency(current_user.tier, current_user.id, db)
 
+    last_run_dt = (
+        datetime.fromtimestamp(body.last_run_at / 1000, tz=timezone.utc)
+        if body.last_run_at
+        else None
+    )
     config = LocalAgentConfig(
         id=str(uuid.uuid4()),
         user_id=current_user.id,
@@ -184,10 +195,12 @@ async def trigger_agent_run(
         name="Local Directory Monitor",
         directory_paths=[body.directory],
         data_types=_to_data_types(body.what_to_extract),
-        prompt_template=body.custom_agent_prompt,
+        prompt_template=body.custom_agent_prompt or "",
+        agent_config=body.agent_config,
         file_extensions=[],
         schedule_cron=body.batch_interval,
         enabled=True,
+        last_run_at=last_run_dt,
     )
 
     # Use the FE's stable agent_id if provided, fall back to the ephemeral config id.
@@ -220,3 +233,25 @@ async def trigger_agent_run(
     )
 
     return _to_run_log_response(run_log)
+
+
+# ── Note summary endpoint ──────────────────────────────────────────────────────
+
+
+class NoteSummarizeRequest(BaseModel):
+    title: str
+    content: str
+
+
+class NoteSummarizeResponse(BaseModel):
+    summary: str
+
+
+@router.post("/notes/summarize", response_model=NoteSummarizeResponse)
+async def summarize_note(
+    body: NoteSummarizeRequest,
+    current_user: UserProfile = Depends(get_current_user),
+) -> NoteSummarizeResponse:
+    """Generate an AI summary for a note.  Used by the Electron backfill on startup."""
+    summary = await generate_note_summary(body.title, body.content)
+    return NoteSummarizeResponse(summary=summary)

app/api/routes/auth.py

@@ -1,34 +1,68 @@
-"""Auth routes: register, login, refresh, me.
+"""Auth routes: register, login, refresh, me, OAuth social login, onboarding.
 
 Users and refresh tokens are persisted in PostgreSQL (users + refresh_tokens
 tables).  Passwords are hashed with bcrypt; refresh tokens are stored as
 SHA-256 hashes so plaintext never reaches the DB.
+
+OAuth (Google):
+  GET  /auth/oauth/{provider}/authorize  — returns consent-screen URL + state
+  POST /auth/oauth/{provider}/callback   — exchanges code, issues JWT tokens
 """
 
 from __future__ import annotations
 
 import hashlib
+import json
 import time
+import urllib.parse
 import uuid
 from datetime import datetime, timedelta, timezone
+from typing import Literal
 
 import bcrypt
 from cryptography.fernet import Fernet
 from fastapi import APIRouter, Depends, HTTPException, status
+from fastapi.responses import RedirectResponse
 from jose import jwt
-from pydantic import BaseModel
+from pydantic import BaseModel, Field
 from sqlalchemy import select
 from sqlalchemy.ext.asyncio import AsyncSession
 
 from app.api.deps import get_current_user
+from app.auth.oauth_providers import GoogleOAuthProvider, generate_pkce_pair
 from app.config.settings import settings
+from app.core.llm import get_llm
+from app.core.memory_middleware import MemoryMiddleware
 from app.db import get_session
-from app.models import RefreshToken, User
+from app.models import OAuthAccount, RefreshToken, User
 from app.schemas import AuthTokens, UserProfile
 
 router = APIRouter(prefix="/auth", tags=["auth"])
 
 
+# ── OAuth provider registry ───────────────────────────────────────────
+
+def _get_google_provider() -> GoogleOAuthProvider:
+    if not settings.GOOGLE_AUTH_CLIENT_ID or not settings.GOOGLE_AUTH_CLIENT_SECRET:
+        raise HTTPException(
+            status.HTTP_503_SERVICE_UNAVAILABLE,
+            "Google login is not configured on this server",
+        )
+    return GoogleOAuthProvider(
+        client_id=settings.GOOGLE_AUTH_CLIENT_ID,
+        client_secret=settings.GOOGLE_AUTH_CLIENT_SECRET,
+        redirect_uri=settings.OAUTH_REDIRECT_URI,
+    )
+
+
+_PROVIDERS = {"google": _get_google_provider}
+
+# In-memory state store: state → (code_verifier, expires_at_epoch_s)
+# Production note: replace with Redis for multi-process deployments.
+_pending_states: dict[str, tuple[str, float]] = {}
+_STATE_TTL_SECONDS = 600  # 10 minutes
+
+
 # ── Internal helpers ─────────────────────────────────────────────────
 
 
@@ -231,5 +265,531 @@ async def update_profile(
         email=user.email,
         name=user.name,
         surname=user.surname,
+        avatar_url=user.avatar_url,
         tier=current_user.tier,
     )
+
+
+# ── OAuth helpers ─────────────────────────────────────────────────────
+
+
+async def _issue_refresh_token(user: User, db: AsyncSession) -> tuple[str, AuthTokens]:
+    """Create a refresh token row and return (plain_token, AuthTokens)."""
+    plain_token = str(uuid.uuid4())
+    expires_at = datetime.now(timezone.utc) + timedelta(
+        days=settings.JWT_REFRESH_TOKEN_EXPIRE_DAYS
+    )
+    rt = RefreshToken(
+        user_id=user.id,
+        token_hash=_hash_token(plain_token),
+        expires_at=expires_at,
+    )
+    db.add(rt)
+    access_token, expires_at_ms = _make_access_token(user.id, user.email, user.tier)
+    return plain_token, AuthTokens(
+        access_token=access_token,
+        refresh_token=plain_token,
+        expires_at=expires_at_ms,
+    )
+
+
+# ── OAuth request/response schemas ───────────────────────────────────
+
+
+class _OAuthAuthorizeResponse(BaseModel):
+    url: str
+    state: str
+
+
+class _OAuthCallbackRequest(BaseModel):
+    code: str
+    state: str
+
+
+# ── OAuth routes ──────────────────────────────────────────────────────
+
+
+@router.get(
+    "/oauth/{provider}/web-callback",
+    summary="Web-facing OAuth redirect — bounces to the adiuvai:// deep link",
+    include_in_schema=False,
+)
+async def oauth_web_callback(
+    provider: Literal["google"],
+    code: str,
+    state: str,
+) -> RedirectResponse:
+    """Google redirects here after user consent.
+
+    This endpoint immediately redirects to the Electron deep-link URI so the
+    desktop app receives the authorization code.  It is intentionally simple —
+    no state validation here (the Electron app + backend callback do that).
+
+    Registered in Google Cloud Console as:
+      http://localhost:8000/api/v1/auth/oauth/google/web-callback  (dev)
+      https://api.adiuvai.com/api/v1/auth/oauth/google/web-callback  (prod)
+    """
+    params = urllib.parse.urlencode({"code": code, "state": state, "provider": provider})
+    deep_link = f"adiuvai://oauth/callback?{params}"
+    return RedirectResponse(url=deep_link, status_code=302)
+
+
+@router.get(
+    "/oauth/{provider}/authorize",
+    response_model=_OAuthAuthorizeResponse,
+    summary="Start OAuth flow — returns the provider consent-screen URL",
+)
+async def oauth_authorize(
+    provider: Literal["google"],
+) -> _OAuthAuthorizeResponse:
+    """Generate a PKCE state + code_challenge and return the authorization URL.
+
+    The client opens this URL in the system browser.  After the user grants
+    consent, the provider redirects to the deep-link URI (adiuvai://oauth/callback)
+    with ``code`` and ``state`` query params.  The client then calls
+    ``POST /auth/oauth/{provider}/callback`` with those values.
+    """
+    provider_factory = _PROVIDERS.get(provider)
+    if provider_factory is None:
+        raise HTTPException(status.HTTP_400_BAD_REQUEST, f"Unknown provider: {provider}")
+
+    oauth_provider = provider_factory()
+    state = str(uuid.uuid4())
+    code_verifier, code_challenge = generate_pkce_pair()
+
+    # Purge expired states to prevent unbounded growth.
+    now = time.time()
+    expired = [s for s, (_, exp) in _pending_states.items() if exp < now]
+    for s in expired:
+        del _pending_states[s]
+
+    _pending_states[state] = (code_verifier, now + _STATE_TTL_SECONDS)
+
+    url = oauth_provider.get_authorization_url(state=state, code_challenge=code_challenge)
+    return _OAuthAuthorizeResponse(url=url, state=state)
+
+
+@router.post(
+    "/oauth/{provider}/callback",
+    response_model=AuthTokens,
+    summary="Complete OAuth flow — exchange code and issue JWT tokens",
+)
+async def oauth_callback(
+    provider: Literal["google"],
+    body: _OAuthCallbackRequest,
+    db: AsyncSession = Depends(get_session),
+) -> AuthTokens:
+    """Validate state, exchange the authorization code, and sign in (or register) the user.
+
+    Resolution order:
+      1. ``oauth_accounts`` row match → existing user, log in.
+      2. Email match + ``email_verified=True`` → link OAuth account to existing user.
+      3. No match → create new user (password_hash=None, avatar from provider).
+    """
+    provider_factory = _PROVIDERS.get(provider)
+    if provider_factory is None:
+        raise HTTPException(status.HTTP_400_BAD_REQUEST, f"Unknown provider: {provider}")
+
+    # Validate state (CSRF protection).
+    now = time.time()
+    entry = _pending_states.pop(body.state, None)
+    if entry is None or entry[1] < now:
+        raise HTTPException(status.HTTP_401_UNAUTHORIZED, "Invalid or expired OAuth state")
+
+    code_verifier, _ = entry
+
+    oauth_provider = provider_factory()
+
+    # Exchange code for tokens.
+    try:
+        token_data = await oauth_provider.exchange_code(
+            code=body.code,
+            code_verifier=code_verifier,
+            redirect_uri=settings.OAUTH_REDIRECT_URI,
+        )
+    except Exception:
+        raise HTTPException(
+            status.HTTP_400_BAD_REQUEST, "Failed to exchange authorization code"
+        )
+
+    access_token_google = token_data.get("access_token")
+    if not access_token_google:
+        raise HTTPException(status.HTTP_400_BAD_REQUEST, "No access token in provider response")
+
+    # Fetch user identity.
+    try:
+        userinfo = await oauth_provider.get_userinfo(access_token_google)
+    except Exception:
+        raise HTTPException(status.HTTP_400_BAD_REQUEST, "Failed to fetch user info from provider")
+
+    # ── Resolution order ──────────────────────────────────────────────
+
+    # 1. Existing OAuth link?
+    oauth_result = await db.execute(
+        select(OAuthAccount).where(
+            OAuthAccount.provider == provider,
+            OAuthAccount.provider_user_id == userinfo.provider_user_id,
+        )
+    )
+    oauth_account = oauth_result.scalar_one_or_none()
+
+    if oauth_account is not None:
+        user_result = await db.execute(select(User).where(User.id == oauth_account.user_id))
+        user = user_result.scalar_one()
+        # Backfill avatar if the user doesn't have one yet.
+        if user.avatar_url is None and userinfo.avatar_url:
+            user.avatar_url = userinfo.avatar_url
+            await db.commit()
+        plain_token, tokens = await _issue_refresh_token(user, db)
+        await db.commit()
+        return tokens
+
+    # 2. Email match with a verified Google email → link accounts.
+    if userinfo.email_verified:
+        email_result = await db.execute(select(User).where(User.email == userinfo.email))
+        existing_user = email_result.scalar_one_or_none()
+
+        if existing_user is not None:
+            new_link = OAuthAccount(
+                user_id=existing_user.id,
+                provider=provider,
+                provider_user_id=userinfo.provider_user_id,
+                provider_email=userinfo.email,
+            )
+            db.add(new_link)
+            if existing_user.avatar_url is None and userinfo.avatar_url:
+                existing_user.avatar_url = userinfo.avatar_url
+            plain_token, tokens = await _issue_refresh_token(existing_user, db)
+            await db.commit()
+            return tokens
+
+    # Guard: if the email is already taken but we couldn't auto-link (e.g.
+    # email_verified=False), refuse with 409 instead of hitting a DB constraint.
+    if not userinfo.email_verified:
+        conflict = await db.execute(select(User).where(User.email == userinfo.email))
+        if conflict.scalar_one_or_none() is not None:
+            raise HTTPException(
+                status.HTTP_409_CONFLICT,
+                "An account with this email already exists. "
+                "Please sign in with your password.",
+            )
+
+    # 3. New user — social-only account (no password).
+    new_user = User(
+        id=str(uuid.uuid4()),
+        email=userinfo.email,
+        name=userinfo.name,
+        password_hash=None,
+        avatar_url=userinfo.avatar_url,
+        tier="free",
+        encryption_key=Fernet.generate_key().decode(),
+    )
+    db.add(new_user)
+    await db.flush()  # populate new_user.id
+
+    new_oauth = OAuthAccount(
+        user_id=new_user.id,
+        provider=provider,
+        provider_user_id=userinfo.provider_user_id,
+        provider_email=userinfo.email,
+    )
+    db.add(new_oauth)
+
+    plain_token, tokens = await _issue_refresh_token(new_user, db)
+    await db.commit()
+    return tokens
+
+
+# ── Onboarding helpers ────────────────────────────────────────────────
+
+
+async def _build_profile(user_id: str, email: str, db: AsyncSession) -> UserProfile:
+    """Re-fetch and return a full UserProfile (reuses get_current_user logic)."""
+
+    # We can't call the FastAPI dependency directly, but we can replicate
+    # the core logic inline.  Instead, we just re-query the same way.
+    from app.models import Subscription  # noqa: PLC0415
+
+    result = await db.execute(
+        select(Subscription.tier).where(Subscription.user_id == user_id)
+    )
+    default_tier = "power" if settings.ENV == "dev" else "free"
+    tier: str = result.scalar_one_or_none() or default_tier
+
+    user_result = await db.execute(
+        select(
+            User.name, User.surname, User.avatar_url, User.onboarding_completed_at,
+            User.password_hash,
+        ).where(User.id == user_id)
+    )
+    user_row = user_result.one_or_none()
+
+    onboarding_ms: int | None = None
+    if user_row and user_row.onboarding_completed_at is not None:
+        onboarding_ms = int(user_row.onboarding_completed_at.timestamp() * 1000)
+
+    memory_dict: dict[str, str] = {}
+    try:
+        mw = MemoryMiddleware(db)
+        blocks = await mw.list_core_blocks(user_id)
+        memory_dict = {b["label"]: b["value"] for b in blocks}
+    except Exception:
+        pass
+
+    return UserProfile(
+        id=user_id,
+        email=email,
+        name=user_row.name if user_row else None,
+        surname=user_row.surname if user_row else None,
+        avatar_url=user_row.avatar_url if user_row else None,
+        has_password=bool(user_row.password_hash) if user_row else False,
+        tier=tier,
+        onboarding_completed_at=onboarding_ms,
+        memory=memory_dict,
+    )
+
+
+# ── Onboarding routes ────────────────────────────────────────────────
+
+
+class _UpdateMemoryRequest(BaseModel):
+    memory: dict[str, str] = Field(default_factory=dict)
+    mark_onboarded: bool = False
+
+
+@router.put("/me/memory", response_model=UserProfile)
+async def update_memory(
+    body: _UpdateMemoryRequest,
+    current_user: UserProfile = Depends(get_current_user),
+    db: AsyncSession = Depends(get_session),
+) -> UserProfile:
+    """Update core memory key/value pairs and optionally mark onboarding complete."""
+    mw = MemoryMiddleware(db)
+    for key, value in body.memory.items():
+        await mw.update_core(current_user.id, key, value)
+    if body.mark_onboarded:
+        result = await db.execute(select(User).where(User.id == current_user.id))
+        user = result.scalar_one()
+        user.onboarding_completed_at = datetime.now(timezone.utc)
+        await db.commit()
+    return await _build_profile(current_user.id, current_user.email, db)
+
+
+@router.post("/me/onboarding/reset")
+async def reset_onboarding(
+    current_user: UserProfile = Depends(get_current_user),
+    db: AsyncSession = Depends(get_session),
+):
+    """Reset onboarding so the wizard runs again on next login."""
+    result = await db.execute(select(User).where(User.id == current_user.id))
+    user = result.scalar_one()
+    user.onboarding_completed_at = None
+    await db.commit()
+    return {"status": "reset"}
+
+
+class _NormalizeRequest(BaseModel):
+    inputs: dict[str, str]
+
+
+class _NormalizeResponse(BaseModel):
+    normalized: dict[str, str]
+
+
+@router.post("/onboarding/normalize", response_model=_NormalizeResponse)
+async def normalize_onboarding(
+    body: _NormalizeRequest,
+    current_user: UserProfile = Depends(get_current_user),
+) -> _NormalizeResponse:
+    """One-shot LLM normalization for free-text onboarding answers."""
+    if not body.inputs:
+        return _NormalizeResponse(normalized={})
+    try:
+        llm = get_llm(model="gpt-4o-mini", temperature=0)
+        prompt = (
+            "You normalize user onboarding answers into clean, ≤3-word canonical labels.\n"
+            "Return a JSON object with the same keys and normalized values.\n"
+            "Examples: 'i build websites' → 'Web Developer', 'tech-ish stuff' → 'Technology'\n"
+            f"Input: {json.dumps(body.inputs)}"
+        )
+        response = await llm.ainvoke(
+            [
+                {"role": "system", "content": "You normalize user inputs. Return JSON only."},
+                {"role": "user", "content": prompt},
+            ],
+        )
+        normalized = json.loads(response.content)
+        return _NormalizeResponse(normalized=normalized)
+    except Exception:
+        # LLM failure must never block onboarding — return inputs unchanged
+        return _NormalizeResponse(normalized=body.inputs)
+
+
+# ── Password management ───────────────────────────────────────────────
+
+
+class _ChangePasswordRequest(BaseModel):
+    current_password: str = Field(min_length=1)
+    new_password: str = Field(min_length=8)
+
+
+@router.put("/me/password", status_code=status.HTTP_200_OK)
+async def change_password(
+    body: _ChangePasswordRequest,
+    current_user: UserProfile = Depends(get_current_user),
+    db: AsyncSession = Depends(get_session),
+) -> dict[str, bool]:
+    """Change the authenticated user's password.
+
+    Requires the current password for verification.
+    Returns 400 for social-only users (no password set).
+    """
+    result = await db.execute(select(User).where(User.id == current_user.id))
+    user = result.scalar_one()
+
+    if user.password_hash is None:
+        raise HTTPException(
+            status.HTTP_400_BAD_REQUEST,
+            "This account uses social login and has no password to change",
+        )
+
+    if not _verify_password(body.current_password, user.password_hash):
+        raise HTTPException(status.HTTP_400_BAD_REQUEST, "Current password is incorrect")
+
+    user.password_hash = _hash_password(body.new_password)
+    await db.commit()
+    return {"ok": True}
+
+
+# ── OAuth account management ─────────────────────────────────────────
+
+
+@router.get("/me/oauth-accounts", response_model=list[dict])
+async def list_oauth_accounts(
+    current_user: UserProfile = Depends(get_current_user),
+    db: AsyncSession = Depends(get_session),
+) -> list[dict]:
+    """List all OAuth providers linked to the authenticated user."""
+    result = await db.execute(
+        select(OAuthAccount).where(OAuthAccount.user_id == current_user.id)
+    )
+    accounts = result.scalars().all()
+    return [
+        {
+            "provider": a.provider,
+            "provider_email": a.provider_email,
+            "created_at": int(a.created_at.timestamp() * 1000),
+        }
+        for a in accounts
+    ]
+
+
+@router.delete("/me/oauth-accounts/{provider}", status_code=status.HTTP_200_OK)
+async def unlink_oauth_account(
+    provider: str,
+    current_user: UserProfile = Depends(get_current_user),
+    db: AsyncSession = Depends(get_session),
+) -> dict[str, bool]:
+    """Unlink an OAuth provider from the authenticated user.
+
+    Refuses if the user has no password and this is their only login method.
+    """
+    result = await db.execute(select(User).where(User.id == current_user.id))
+    user = result.scalar_one()
+
+    oauth_result = await db.execute(
+        select(OAuthAccount).where(
+            OAuthAccount.user_id == current_user.id,
+            OAuthAccount.provider == provider,
+        )
+    )
+    account = oauth_result.scalar_one_or_none()
+    if account is None:
+        raise HTTPException(status.HTTP_404_NOT_FOUND, f"No linked {provider} account found")
+
+    # Safety: don't let users lock themselves out.
+    all_oauth = await db.execute(
+        select(OAuthAccount).where(OAuthAccount.user_id == current_user.id)
+    )
+    oauth_count = len(all_oauth.scalars().all())
+
+    if user.password_hash is None and oauth_count <= 1:
+        raise HTTPException(
+            status.HTTP_400_BAD_REQUEST,
+            "Cannot unlink the only login method. Set a password first.",
+        )
+
+    await db.delete(account)
+    await db.commit()
+    return {"ok": True}
+
+
+# ── Avatar update ─────────────────────────────────────────────────────
+
+
+class _UpdateAvatarRequest(BaseModel):
+    avatar_url: str = Field(min_length=1)
+
+
+@router.put("/me/avatar", response_model=UserProfile)
+async def update_avatar(
+    body: _UpdateAvatarRequest,
+    current_user: UserProfile = Depends(get_current_user),
+    db: AsyncSession = Depends(get_session),
+) -> UserProfile:
+    """Update the authenticated user's avatar URL.
+
+    Accepts {"avatar_url": "https://..."} — the client uploads the image
+    to its own storage and passes the resulting URL here.
+    """
+    if not body.avatar_url.startswith(("https://", "http://", "data:image/")):
+        raise HTTPException(status.HTTP_400_BAD_REQUEST, "Invalid avatar URL")
+
+    result = await db.execute(select(User).where(User.id == current_user.id))
+    user = result.scalar_one()
+    user.avatar_url = body.avatar_url
+    await db.commit()
+
+    return await _build_profile(current_user.id, current_user.email, db)
+
+
+# ── Account deletion ─────────────────────────────────────────────────
+
+
+@router.delete("/me", status_code=status.HTTP_200_OK)
+async def delete_account(
+    current_user: UserProfile = Depends(get_current_user),
+    db: AsyncSession = Depends(get_session),
+) -> dict[str, bool]:
+    """Permanently delete the authenticated user's account.
+
+    Cascades: refresh tokens, OAuth accounts, subscription, and all memory
+    rows are deleted via SQLAlchemy relationship cascades.  Stripe subscription
+    is cancelled if active.
+    """
+    # Cancel Stripe subscription if present.
+    try:
+        from app.billing.stripe_service import stripe_service  # noqa: PLC0415
+        await stripe_service.cancel_subscription(current_user.id, db)
+    except HTTPException:
+        pass  # No subscription — that's fine
+
+    # Delete all memory rows (core, associative, episodic, proactive).
+    try:
+        from app.models import (  # noqa: PLC0415
+            MemoryAssociative, MemoryCore, MemoryEpisodic, MemoryProactive,
+        )
+        for model in (MemoryCore, MemoryAssociative, MemoryEpisodic, MemoryProactive):
+            await db.execute(
+                model.__table__.delete().where(model.user_id == current_user.id)
+            )
+    except Exception:
+        pass  # Non-critical — cascade on User will handle most
+
+    # Delete the user row — cascades handle refresh_tokens, oauth_accounts, subscription.
+    result = await db.execute(select(User).where(User.id == current_user.id))
+    user = result.scalar_one()
+    await db.delete(user)
+    await db.commit()
+
+    return {"ok": True}

app/api/routes/backup.py

@@ -1,171 +0,0 @@
-"""Backup routes: upload, download, history, and delete E2E-encrypted backups.
-
-Blobs are stored in S3 via BlobStore. Backup metadata is persisted in the
-PostgreSQL ``backup_metadata`` table.
-
-IMPORTANT: GET /history must be declared BEFORE GET / to avoid FastAPI
-treating "history" as a ``{backup_id}`` path parameter.
-"""
-
-from __future__ import annotations
-
-import uuid
-from email.utils import parsedate_to_datetime
-
-from fastapi import APIRouter, Depends, Header, HTTPException, Request, Response, status
-from sqlalchemy import func, select
-from sqlalchemy.ext.asyncio import AsyncSession
-
-from app.api.deps import get_current_user
-from app.billing.tier_manager import tier_manager
-from app.db import get_session
-from app.models import BackupMetadata as BackupMetadataModel
-from app.schemas import BackupMetadata, UserProfile
-from app.storage.blob_store import BlobStore
-from app.storage.encryption import reject_if_tampered
-
-router = APIRouter(prefix="/backup", tags=["backup"])
-
-_blob_store = BlobStore()
-
-
-async def _current_backup_bytes(user_id: str, db: AsyncSession) -> int:
-    """Return total backup bytes stored by *user_id*."""
-    result = await db.execute(
-        select(func.coalesce(func.sum(BackupMetadataModel.size_bytes), 0)).where(
-            BackupMetadataModel.user_id == user_id
-        )
-    )
-    return int(result.scalar_one())
-
-
-async def _check_backup_quota(
-    user: UserProfile, size_bytes: int, db: AsyncSession
-) -> None:
-    """Raise HTTP 402 if the upload would exceed the tier's backup limit."""
-    current = await _current_backup_bytes(user.id, db)
-    tier_manager.enforce_backup_quota(
-        user.tier, current_bytes=current, additional_bytes=size_bytes
-    )
-
-
-@router.put("")
-async def upload_backup(
-    request: Request,
-    x_backup_version: int = Header(..., alias="X-Backup-Version"),
-    x_backup_timestamp: int = Header(..., alias="X-Backup-Timestamp"),
-    x_backup_checksum: str = Header(..., alias="X-Backup-Checksum"),
-    current_user: UserProfile = Depends(get_current_user),
-    db: AsyncSession = Depends(get_session),
-) -> dict[str, bool]:
-    """Upload an E2E-encrypted backup blob.
-
-    Metadata is passed via custom headers; the raw body is the encrypted blob.
-    """
-    blob = await request.body()
-    reject_if_tampered(blob, x_backup_checksum)
-    await _check_backup_quota(current_user, len(blob), db)
-
-    s3_key = await _blob_store.upload(
-        current_user.id, "backup", str(x_backup_timestamp), blob, x_backup_checksum
-    )
-
-    row = BackupMetadataModel(
-        id=str(uuid.uuid4()),
-        user_id=current_user.id,
-        s3_key=s3_key,
-        version=x_backup_version,
-        timestamp=x_backup_timestamp,
-        checksum=x_backup_checksum,
-        size_bytes=len(blob),
-    )
-    db.add(row)
-    await db.commit()
-
-    return {"ok": True}
-
-
-@router.get("/history", response_model=list[BackupMetadata])
-async def backup_history(
-    current_user: UserProfile = Depends(get_current_user),
-    db: AsyncSession = Depends(get_session),
-) -> list[BackupMetadata]:
-    """Return backup metadata records for the authenticated user (no blob bytes)."""
-    result = await db.execute(
-        select(BackupMetadataModel)
-        .where(BackupMetadataModel.user_id == current_user.id)
-        .order_by(BackupMetadataModel.timestamp.desc())
-    )
-    rows = result.scalars().all()
-    return [
-        BackupMetadata(
-            version=r.version,
-            timestamp=r.timestamp,
-            checksum=r.checksum,
-            chunk_count=1,
-        )
-        for r in rows
-    ]
-
-
-@router.get("")
-async def download_backup(
-    request: Request,
-    current_user: UserProfile = Depends(get_current_user),
-    db: AsyncSession = Depends(get_session),
-) -> Response:
-    """Download the latest backup blob. Supports ``If-Modified-Since``."""
-    result = await db.execute(
-        select(BackupMetadataModel)
-        .where(BackupMetadataModel.user_id == current_user.id)
-        .order_by(BackupMetadataModel.timestamp.desc())
-        .limit(1)
-    )
-    latest = result.scalar_one_or_none()
-    if latest is None:
-        raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail="No backup found")
-
-    ims_header = request.headers.get("If-Modified-Since")
-    if ims_header:
-        try:
-            ims_dt = parsedate_to_datetime(ims_header)
-            ims_ms = int(ims_dt.timestamp() * 1000)
-            if latest.timestamp <= ims_ms:
-                return Response(status_code=status.HTTP_304_NOT_MODIFIED)
-        except Exception:
-            pass  # malformed header — ignore and serve the blob
-
-    blob = await _blob_store.download(current_user.id, latest.s3_key)
-    return Response(
-        content=blob,
-        media_type="application/octet-stream",
-        headers={
-            "X-Backup-Version": str(latest.version),
-            "X-Backup-Timestamp": str(latest.timestamp),
-            "X-Checksum": latest.checksum,
-        },
-    )
-
-
-@router.delete("/{backup_id}", response_model=dict)
-async def delete_backup(
-    backup_id: str,
-    current_user: UserProfile = Depends(get_current_user),
-    db: AsyncSession = Depends(get_session),
-) -> dict[str, bool]:
-    """Delete a specific backup by ID."""
-    result = await db.execute(
-        select(BackupMetadataModel).where(
-            BackupMetadataModel.id == backup_id,
-            BackupMetadataModel.user_id == current_user.id,
-        )
-    )
-    target = result.scalar_one_or_none()
-    if target is None:
-        raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail="Backup not found")
-
-    await _blob_store.delete(current_user.id, target.s3_key)
-    await db.delete(target)
-    await db.commit()
-
-    return {"ok": True}

app/api/routes/billing.py

@@ -9,7 +9,7 @@ from __future__ import annotations
 
 from typing import Any
 
-from fastapi import APIRouter, Depends, Header, Request, status
+from fastapi import APIRouter, Depends, Header, HTTPException, Request, status
 from pydantic import BaseModel
 from sqlalchemy.ext.asyncio import AsyncSession
 
@@ -83,3 +83,50 @@ async def cancel_subscription(
     """Cancel the active subscription."""
     await stripe_service.cancel_subscription(current_user.id, db)
     return {"ok": True}
+
+
+@router.get("/invoices", response_model=list[dict])
+async def list_invoices(
+    current_user: UserProfile = Depends(get_current_user),
+    db: AsyncSession = Depends(get_session),
+) -> list[dict[str, Any]]:
+    """Return billing history (invoices) from Stripe.
+
+    Returns an empty list when Stripe is not configured.
+    """
+    invoices = await stripe_service.list_invoices(current_user.id, db)
+    return invoices
+
+
+# ── Quota check ────────────────────────────────────────────────────────
+
+from app.billing.quota import check_folder_quota, QuotaExceeded  # noqa: E402
+
+
+class QuotaCheckRequest(BaseModel):
+    feature: str
+    estimated_files: int
+
+
+@router.post("/quota/check")
+async def quota_check(
+    payload: QuotaCheckRequest,
+    current_user: UserProfile = Depends(get_current_user),
+    db: AsyncSession = Depends(get_session),
+) -> dict:
+    """Pre-flight folder quota check. 402 if tier limits would be exceeded."""
+    if payload.feature != "folder_index":
+        raise HTTPException(status_code=400, detail="Unknown feature")
+    try:
+        await check_folder_quota(
+            user_id=current_user.id,
+            tier=current_user.tier,
+            estimated_files=payload.estimated_files,
+            db=db,
+        )
+    except QuotaExceeded as exc:
+        raise HTTPException(
+            status_code=402,
+            detail={"reason": exc.reason, "message": str(exc)},
+        )
+    return {"ok": True}

app/api/routes/chat.py

@@ -1,20 +1,42 @@
-"""Chat routes: POST /chat (REST fallback).
+"""Chat routes: POST /chat (REST fallback) and POST /chat/embed (text → vector).
 
 WebSocket chat is handled by the unified device WS endpoint (/api/v1/ws/device).
 """
 
 from __future__ import annotations
 
-from fastapi import APIRouter, Depends
+import uuid
+from typing import Literal
+
+from fastapi import APIRouter, Depends, HTTPException
 from fastapi.responses import JSONResponse
+from pydantic import BaseModel
 
 from app.api.deps import get_current_user
+from app.core.brief_agent import run_home_brief, run_project_brief
 from app.core.deep_agent import run_home
+from app.core.llm import embed
+from app.core.memory_middleware import MemoryMiddleware
+from app.db import async_session
 from app.schemas import ChatRequest, UserProfile
 
 router = APIRouter(prefix="/chat", tags=["chat"])
 
 
+# ── Embed helpers ─────────────────────────────────────────────────────────
+
+
+class _EmbedRequest(BaseModel):
+    text: str
+
+
+class _EmbedResponse(BaseModel):
+    vector: list[float]
+
+
+# ── Endpoints ─────────────────────────────────────────────────────────────
+
+
 @router.post("")
 async def chat(
     body: ChatRequest,
@@ -27,3 +49,68 @@ async def chat(
         context=body.context.model_dump(),
     )
     return JSONResponse(content={"response": response})
+
+
+class _BriefRequest(BaseModel):
+    mode: Literal["home", "project"]
+    project_id: str | None = None
+
+
+class _BriefResponse(BaseModel):
+    response: str
+
+
+@router.post("/brief", response_model=_BriefResponse)
+async def brief(
+    body: _BriefRequest,
+    current_user: UserProfile = Depends(get_current_user),
+) -> _BriefResponse:
+    """REST fallback for brief when the device WebSocket is not ready."""
+    if body.mode == "project":
+        if not body.project_id:
+            raise HTTPException(status_code=422, detail="project_id required for project mode")
+        try:
+            uuid.UUID(body.project_id)
+        except ValueError:
+            raise HTTPException(status_code=422, detail="project_id must be a valid UUID")
+
+    request_id = str(uuid.uuid4())
+    async with async_session() as db:
+        memory = MemoryMiddleware(db)
+        memory_context = await memory.enrich_context(
+            current_user.id,
+            "",
+            trace_id=request_id,
+            session_id=request_id,
+        )
+
+    context: dict = {
+        "_debug": {"request_id": request_id, "user_id": current_user.id},
+        **memory_context,
+    }
+
+    chunks: list[str] = []
+    if body.mode == "project":
+        stream = run_project_brief(current_user.id, body.project_id, context)  # type: ignore[arg-type]
+    else:
+        stream = run_home_brief(current_user.id, context)
+
+    async for event_type, data in stream:
+        if event_type == "token" and data:
+            chunks.append(str(data))
+
+    return _BriefResponse(response="".join(chunks))
+
+
+@router.post("/embed", response_model=_EmbedResponse)
+async def embed_text(
+    body: _EmbedRequest,
+    current_user: UserProfile = Depends(get_current_user),
+) -> _EmbedResponse:
+    """Generate a 1536-dim embedding vector for the given text.
+
+    Uses ``text-embedding-3-small`` via OpenAI.  Auth required (JWT).
+    Used by Electron (vectordb.ts) for local note search.
+    """
+    vector = await embed(body.text)
+    return _EmbedResponse(vector=vector)

app/api/routes/device_ws.py

@@ -42,19 +42,25 @@ from sqlalchemy import update
 from app.api.routes.agent_setup import handle_journey_message, handle_journey_start
 from app.config.settings import settings
 from app.core.agent_runner import trigger_pending_runs
-from app.core.deep_agent import run_floating_stream, run_home_stream
+from app.core.brief_agent import run_home_brief, run_project_brief
+from app.core.deep_agent import run_floating_stream, run_home_stream, run_task_brief_research_stream
+from app.core.output_formatter import extract_canvas_block
 from app.core.device_manager import device_manager
 from app.core.memory_middleware import MemoryMiddleware
 from app.core.output_formatter import StreamFormatter
 from app.core.ws_context import clear_client_executor, set_client_executor
 from app.db import async_session
 from app.models import AgentRunLog
-from app.schemas import WsFrameType
+from app.schemas import WsFrameType, WsStreamEnd
 
 logger = logging.getLogger(__name__)
 
 router = APIRouter(prefix="/ws", tags=["device-ws"])
 
+# ── v7 folder index session state ─────────────────────────────────────
+# Keyed by sessionId; value: { user_id, project_id, processed, total, cancelled }
+_index_sessions: dict[str, dict] = {}
+
 _HEARTBEAT_INTERVAL = 30  # seconds
 _PONG_TIMEOUT = 10  # seconds — grace window after a ping
 
@@ -158,6 +164,16 @@ async def _message_loop(websocket: WebSocket, user_id: str) -> None:
                 _handle_floating_request(websocket, user_id, frame)
             )
 
+        elif frame_type == WsFrameType.brief_request:
+            asyncio.create_task(
+                _handle_brief_request(websocket, user_id, frame)
+            )
+
+        elif frame_type == WsFrameType.task_brief_request:
+            asyncio.create_task(
+                _handle_task_brief_request(websocket, user_id, frame)
+            )
+
         elif frame_type == WsFrameType.journey_start:
             asyncio.create_task(
                 _handle_journey_start(websocket, user_id, frame)
@@ -168,6 +184,19 @@ async def _message_loop(websocket: WebSocket, user_id: str) -> None:
                 _handle_journey_message(websocket, user_id, frame)
             )
 
+        elif frame_type == WsFrameType.index_session_start:
+            asyncio.create_task(
+                _handle_index_session_start(websocket, user_id, frame)
+            )
+
+        elif frame_type == WsFrameType.index_file_batch:
+            asyncio.create_task(
+                _handle_index_file_batch(websocket, user_id, frame)
+            )
+
+        elif frame_type == WsFrameType.index_session_cancel:
+            await _handle_index_session_cancel(websocket, frame)
+
         elif frame_type == "pong":
             # Heartbeat ack — nothing to do, connection is alive.
             pass
@@ -199,11 +228,13 @@ async def _handle_home_request(
     request_id = frame.get("request_id") or str(uuid4())
     message: str = frame.get("message", "")
     session_id: str = frame.get("session_id") or str(uuid4())
+    project_id: str | None = frame.get("project_id") or frame.get("projectId") or None
     logger.info(
-        "device_ws: home_request_start user=%s req=%s session=%s msg=%s",
+        "device_ws: home_request_start user=%s req=%s session=%s project=%s msg=%s",
         user_id,
         request_id,
         session_id,
+        project_id,
         message[:200],
     )
 
@@ -220,6 +251,7 @@ async def _handle_home_request(
     context: dict = {
         "conversation_history": frame.get("conversation_history", []),
         "_debug": {"request_id": request_id, "session_id": session_id, "user_id": user_id},
+        "format_prefs": frame.get("format_prefs"),
         **memory_context,
     }
 
@@ -227,7 +259,7 @@ async def _handle_home_request(
     set_client_executor(executor)
     response_chunks: list[str] = []
     try:
-        event_stream = run_home_stream(user_id, message, context)
+        event_stream = run_home_stream(user_id, message, context, project_id=project_id)
         formatter = StreamFormatter(request_id=request_id)
         async for ws_frame in formatter.format(event_stream):
             await websocket.send_text(ws_frame.model_dump_json())
@@ -287,8 +319,10 @@ async def _handle_floating_request(
         )
 
     context: dict = {
+        "conversation_history": frame.get("conversation_history", []),
         "scope": scope,
         "_debug": {"request_id": request_id, "session_id": session_id, "user_id": user_id},
+        "format_prefs": frame.get("format_prefs"),
         **memory_context,
     }
 
@@ -325,6 +359,179 @@ async def _handle_floating_request(
     )
 
 
+async def _handle_brief_request(
+    websocket: WebSocket,
+    user_id: str,
+    frame: dict,
+) -> None:
+    """Handle a brief_request frame — streams plain-text brief back on the socket.
+
+    No episode storage — briefs are not conversations.
+    """
+    import uuid as _uuid
+
+    request_id = frame.get("request_id") or str(uuid4())
+    session_id = frame.get("session_id") or str(uuid4())
+    mode: str = frame.get("mode", "home")
+    project_id: str | None = frame.get("project_id")
+
+    logger.info(
+        "device_ws: brief_request_start user=%s req=%s mode=%s project_id=%s",
+        user_id, request_id, mode, project_id,
+    )
+
+    # Validate project_id for project mode before touching LLM.
+    if mode == "project":
+        try:
+            if not project_id:
+                raise ValueError("project_id required for project mode")
+            _uuid.UUID(project_id)
+        except (ValueError, AttributeError) as exc:
+            logger.warning(
+                "device_ws: brief_request invalid project_id user=%s req=%s: %s",
+                user_id, request_id, exc,
+            )
+            await websocket.send_text(
+                WsStreamEnd(request_id=request_id, error=str(exc)).model_dump_json()
+            )
+            return
+
+    # Enrich context with memory (no user message — use empty string as probe).
+    async with async_session() as db:
+        memory = MemoryMiddleware(db)
+        memory_context = await memory.enrich_context(
+            user_id,
+            "",
+            trace_id=request_id,
+            session_id=session_id,
+        )
+
+    context: dict = {
+        "_debug": {"request_id": request_id, "session_id": session_id, "user_id": user_id},
+        "format_prefs": frame.get("format_prefs"),
+        **memory_context,
+    }
+
+    executor = await _make_ws_executor(websocket, user_id)
+    set_client_executor(executor)
+    try:
+        if mode == "project":
+            event_stream = run_project_brief(user_id, project_id, context)  # type: ignore[arg-type]
+        else:
+            event_stream = run_home_brief(user_id, context)
+
+        formatter = StreamFormatter(request_id=request_id)
+        async for ws_frame in formatter.format(event_stream):
+            await websocket.send_text(ws_frame.model_dump_json())
+    except Exception as exc:
+        logger.error(
+            "device_ws: brief_request failed user=%s req=%s: %s",
+            user_id, request_id, exc,
+        )
+        await websocket.send_text(
+            WsStreamEnd(request_id=request_id, error=str(exc)).model_dump_json()
+        )
+    finally:
+        clear_client_executor()
+
+    logger.info(
+        "device_ws: brief_request_end user=%s req=%s mode=%s",
+        user_id, request_id, mode,
+    )
+
+
+# ── v6 Task Brief Handler ────────────────────────────────────────────
+
+
+async def _handle_task_brief_request(
+    websocket: WebSocket,
+    user_id: str,
+    frame: dict,
+) -> None:
+    """Handle a task_brief_request frame — Stage-1 executive assistant deep research.
+
+    Streams the briefing markdown back to the client.
+    On stream_end, emits a ``canvas_draft`` mutation if the agent produced one.
+    """
+    request_id = frame.get("request_id") or str(uuid4())
+    session_id = frame.get("session_id") or str(uuid4())
+    task_id: str = frame.get("task_id") or frame.get("taskId") or ""
+    project_id: str | None = frame.get("project_id") or frame.get("projectId") or None
+
+    logger.info(
+        "device_ws: task_brief_request_start user=%s req=%s task=%s project=%s [cache_miss]",
+        user_id, request_id, task_id, project_id,
+    )
+
+    if not task_id:
+        await websocket.send_text(
+            WsStreamEnd(request_id=request_id, error="task_id is required").model_dump_json()
+        )
+        return
+
+    async with async_session() as db:
+        memory = MemoryMiddleware(db)
+        memory_context = await memory.enrich_context(
+            user_id,
+            f"task brief: {task_id}",
+            trace_id=request_id,
+            session_id=session_id,
+        )
+
+    context: dict = {
+        "_debug": {"request_id": request_id, "session_id": session_id, "user_id": user_id},
+        "format_prefs": frame.get("format_prefs"),
+        **memory_context,
+    }
+
+    executor = await _make_ws_executor(websocket, user_id)
+    set_client_executor(executor)
+    response_chunks: list[str] = []
+
+    try:
+        event_stream = run_task_brief_research_stream(user_id, task_id, context, project_id=project_id)
+        formatter = StreamFormatter(request_id=request_id)
+        async for ws_frame in formatter.format(event_stream):
+            if ws_frame.type == "stream_text":  # type: ignore[union-attr]
+                response_chunks.append(ws_frame.chunk)  # type: ignore[union-attr]
+                await websocket.send_text(ws_frame.model_dump_json())
+            elif ws_frame.type == "stream_start":
+                await websocket.send_text(ws_frame.model_dump_json())
+            # stream_end is emitted below with mutations — skip formatter's version
+    except Exception as exc:
+        logger.error(
+            "device_ws: task_brief_request failed user=%s req=%s task=%s: %s",
+            user_id, request_id, task_id, exc,
+        )
+        await websocket.send_text(
+            WsStreamEnd(request_id=request_id, error=str(exc)).model_dump_json()
+        )
+        return
+    finally:
+        clear_client_executor()
+
+    # Extract canvas block then emit stream_end with optional mutations.
+    full_response = "".join(response_chunks)
+    _visible, canvas_content, canvas_kind = extract_canvas_block(full_response)
+
+    mutations: list[dict] = []
+    if canvas_content:
+        mutations.append({
+            "type": "canvas_draft",
+            "content": canvas_content,
+            "kind": canvas_kind,
+        })
+
+    await websocket.send_text(
+        WsStreamEnd(request_id=request_id, mutations=mutations or None).model_dump_json()
+    )
+
+    logger.info(
+        "device_ws: task_brief_request_end user=%s req=%s task=%s response_chars=%d canvas=%s",
+        user_id, request_id, task_id, len(full_response), canvas_kind or "none",
+    )
+
+
 # ── v4 Journey Handlers ─────────────────────────────────────────────
 
 
@@ -382,6 +589,174 @@ async def _handle_journey_message(
         clear_client_executor()
 
 
+# ── v7 Folder Index Handlers ──────────────────────────────────────────
+
+
+async def _handle_index_session_start(
+    websocket: WebSocket,
+    user_id: str,
+    frame: dict,
+) -> None:
+    """Register a new folder index session.  No response sent — client is declaring intent."""
+    session_id: str = frame.get("sessionId") or frame.get("session_id") or ""
+    project_id: str | None = frame.get("projectId") or frame.get("project_id")
+    total: int = int(frame.get("totalFiles") or frame.get("total_files") or 0)
+
+    if not session_id:
+        logger.warning("device_ws: index_session_start missing sessionId user=%s", user_id)
+        return
+
+    _index_sessions[session_id] = {
+        "user_id": user_id,
+        "project_id": project_id,
+        "processed": 0,
+        "total": total,
+        "cancelled": False,
+    }
+    logger.info(
+        "device_ws: index_session_start user=%s session=%s project=%s total=%d",
+        user_id, session_id, project_id, total,
+    )
+
+
+async def _handle_index_session_cancel(
+    websocket: WebSocket,
+    frame: dict,
+) -> None:
+    """Mark a session as cancelled and emit index_session_done(cancelled)."""
+    session_id: str = frame.get("sessionId") or frame.get("session_id") or ""
+    session = _index_sessions.get(session_id)
+    if session:
+        session["cancelled"] = True
+
+    await websocket.send_text(json.dumps({
+        "type": WsFrameType.index_session_done,
+        "sessionId": session_id,
+        "status": "cancelled",
+    }))
+    _index_sessions.pop(session_id, None)
+    logger.info("device_ws: index_session_cancel session=%s", session_id)
+
+
+async def _handle_index_file_batch(
+    websocket: WebSocket,
+    user_id: str,
+    frame: dict,
+) -> None:
+    """Process a batch of files for an index session, streaming results back."""
+    # Lazy imports to avoid heavy load at module startup.
+    from app.core.folder_indexer import (  # noqa: PLC0415
+        summarize_image,
+        summarize_pdf,
+        summarize_docx,
+        summarize_text,
+    )
+    from app.billing.tier_manager import tier_manager  # noqa: PLC0415
+    from app.billing.quota import add_token_usage  # noqa: PLC0415
+
+    session_id: str = frame.get("sessionId") or frame.get("session_id") or ""
+    files: list[dict] = frame.get("files", [])
+
+    session = _index_sessions.get(session_id)
+    if not session or session.get("cancelled"):
+        return
+
+    async with async_session() as db:
+        tier = await tier_manager.get_tier(user_id, db)
+        raw_cap = tier_manager.get_feature_value(tier, "folder_monthly_tokens")
+        cap: int | None = None if raw_cap == -1 else raw_cap
+
+        for file_info in files:
+            if session.get("cancelled"):
+                return
+
+            # Electron's toSnakeCase converts payload keys, so accept both forms.
+            rel_path: str = file_info.get("relPath") or file_info.get("rel_path") or ""
+            kind: str = file_info.get("kind") or "text"
+            content: str = file_info.get("content") or ""
+            ext: str = file_info.get("ext") or ""
+            mime: str = file_info.get("mime") or "application/octet-stream"
+            name: str = rel_path.split("/")[-1] or rel_path
+
+            try:
+                if kind == "image":
+                    res = await summarize_image(image_b64=content, mime=mime)
+                elif kind == "pdf":
+                    res = await summarize_pdf(pdf_b64=content, name=name)
+                elif kind == "docx":
+                    res = await summarize_docx(docx_b64=content, name=name)
+                else:
+                    res = await summarize_text(content=content, ext=ext, name=name)
+            except Exception as exc:
+                logger.warning(
+                    "device_ws: index_file_batch summarize failed session=%s path=%s: %s",
+                    session_id, rel_path, exc,
+                )
+                await websocket.send_text(json.dumps({
+                    "type": WsFrameType.index_file_result,
+                    "sessionId": session_id,
+                    "relPath": rel_path,
+                    "summary": None,
+                    "tokensUsed": 0,
+                    "error": str(exc),
+                }))
+                session["processed"] += 1
+                continue
+
+            # Account for token usage and check cap.
+            usage = await add_token_usage(
+                user_id=user_id,
+                feature="folder_index",
+                tokens=res.tokens_used,
+                db=db,
+                cap=cap,
+            )
+
+            await websocket.send_text(json.dumps({
+                "type": WsFrameType.index_file_result,
+                "sessionId": session_id,
+                "relPath": rel_path,
+                "summary": res.summary,
+                "tokensUsed": res.tokens_used,
+            }))
+            session["processed"] += 1
+
+            if usage.exhausted:
+                await websocket.send_text(json.dumps({
+                    "type": WsFrameType.index_session_done,
+                    "sessionId": session_id,
+                    "status": "quota_exceeded",
+                }))
+                _index_sessions.pop(session_id, None)
+                logger.info(
+                    "device_ws: index_session quota_exceeded user=%s session=%s",
+                    user_id, session_id,
+                )
+                return
+
+        # After processing the batch, emit progress.
+        processed = session["processed"]
+        total = session["total"]
+        await websocket.send_text(json.dumps({
+            "type": WsFrameType.index_session_progress,
+            "sessionId": session_id,
+            "processed": processed,
+            "total": total,
+        }))
+
+        if processed >= total:
+            await websocket.send_text(json.dumps({
+                "type": WsFrameType.index_session_done,
+                "sessionId": session_id,
+                "status": "completed",
+            }))
+            _index_sessions.pop(session_id, None)
+            logger.info(
+                "device_ws: index_session_done completed user=%s session=%s processed=%d",
+                user_id, session_id, processed,
+            )
+
+
 # ── Heartbeat ─────────────────────────────────────────────────────────
 
 async def _heartbeat_loop(websocket: WebSocket) -> None:

app/api/routes/memory.py

@@ -0,0 +1,225 @@
+"""Memory management routes — view/edit/delete user memory tiers.
+
+All routes require authentication. Data is always user-scoped.
+"""
+
+from __future__ import annotations
+
+import logging
+from datetime import datetime, timezone
+from typing import Annotated
+
+from fastapi import APIRouter, Depends, Header, HTTPException, status
+from pydantic import BaseModel, Field
+from sqlalchemy import delete, select
+from sqlalchemy.ext.asyncio import AsyncSession
+
+from app.api.deps import get_current_user
+from app.core.memory_middleware import MemoryMiddleware
+from app.db import get_session
+from app.models import (
+    ExtractionQueue,
+    MemoryAssociative,
+    MemoryCore,
+    MemoryEpisodic,
+    MemoryProactive,
+    MemoryRelation,
+)
+from app.schemas import UserProfile
+
+router = APIRouter(prefix="/memory", tags=["memory"])
+
+logger = logging.getLogger(__name__)
+
+_ALLOWED_PREDICATES = {
+    "works_at",
+    "reports_to",
+    "stakeholder_of",
+    "last_contacted_on",
+    "owes_followup",
+    "manages",
+    "collaborates_with",
+    "owns",
+    "member_of",
+    "custom",
+}
+
+
+# ── Response schemas ─────────────────────────────────────────────────────────
+
+class RelationOut(BaseModel):
+    id: str
+    subject_label: str
+    subject_type: str
+    predicate: str
+    object_label: str
+    object_type: str
+    confidence: float
+    last_confirmed_at: int | None = None  # epoch ms
+
+
+class RelationPatch(BaseModel):
+    subject_label: str | None = None
+    object_label: str | None = None
+    predicate: str | None = None
+    confidence: float | None = Field(None, ge=0.0, le=1.0)
+
+
+class CoreAddBody(BaseModel):
+    key: str = Field(..., min_length=1, max_length=255)
+    value: str = Field(..., min_length=1)
+
+
+# ── Helpers ──────────────────────────────────────────────────────────────────
+
+def _relation_to_out(row: MemoryRelation) -> RelationOut:
+    last_ms: int | None = None
+    if row.last_confirmed_at is not None:
+        last_ms = int(row.last_confirmed_at.timestamp() * 1000)
+    return RelationOut(
+        id=row.id,
+        subject_label=row.subject_label,
+        subject_type=row.subject_type,
+        predicate=row.predicate,
+        object_label=row.object_label,
+        object_type=row.object_type,
+        confidence=row.confidence,
+        last_confirmed_at=last_ms,
+    )
+
+
+# ── Routes ───────────────────────────────────────────────────────────────────
+
+@router.get("/core", response_model=dict[str, str])
+async def get_core_memory(
+    current_user: UserProfile = Depends(get_current_user),
+    db: AsyncSession = Depends(get_session),
+) -> dict[str, str]:
+    """Return all core memory k/v pairs (plaintext) for the current user."""
+    mw = MemoryMiddleware(db)
+    blocks = await mw.list_core_blocks(current_user.id)
+    return {b["label"]: b["value"] for b in blocks}
+
+
+@router.delete("/core/{key}", status_code=status.HTTP_204_NO_CONTENT)
+async def delete_core_key(
+    key: str,
+    current_user: UserProfile = Depends(get_current_user),
+    db: AsyncSession = Depends(get_session),
+) -> None:
+    """Delete a single core memory key (GDPR Art. 17)."""
+    mw = MemoryMiddleware(db)
+    deleted = await mw.delete_core(current_user.id, key)
+    if not deleted:
+        raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail="Key not found")
+
+
+@router.post("/core", status_code=status.HTTP_201_CREATED, response_model=dict[str, str])
+async def add_core_key(
+    body: CoreAddBody,
+    current_user: UserProfile = Depends(get_current_user),
+    db: AsyncSession = Depends(get_session),
+) -> dict[str, str]:
+    """Add or overwrite a core memory key/value pair."""
+    mw = MemoryMiddleware(db)
+    await mw.update_core(current_user.id, body.key, body.value)
+    return {body.key: body.value}
+
+
+@router.get("/relational", response_model=list[RelationOut])
+async def get_relational_memory(
+    current_user: UserProfile = Depends(get_current_user),
+    db: AsyncSession = Depends(get_session),
+) -> list[RelationOut]:
+    """Return all relational memory rows for the current user."""
+    mw = MemoryMiddleware(db)
+    rows = await mw.query_relations(current_user.id, limit=200)
+    return [_relation_to_out(r) for r in rows]
+
+
+@router.patch("/relational/{relation_id}", response_model=RelationOut)
+async def patch_relation(
+    relation_id: str,
+    body: RelationPatch,
+    current_user: UserProfile = Depends(get_current_user),
+    db: AsyncSession = Depends(get_session),
+) -> RelationOut:
+    """Edit a relation row's labels, predicate, or confidence."""
+    if body.predicate is not None and body.predicate not in _ALLOWED_PREDICATES:
+        raise HTTPException(
+            status_code=status.HTTP_422_UNPROCESSABLE_ENTITY,
+            detail=f"predicate must be one of: {sorted(_ALLOWED_PREDICATES)}",
+        )
+
+    result = await db.execute(
+        select(MemoryRelation).where(
+            MemoryRelation.id == relation_id,
+            MemoryRelation.user_id == current_user.id,
+        )
+    )
+    row = result.scalar_one_or_none()
+    if row is None:
+        raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail="Relation not found")
+
+    if body.subject_label is not None:
+        row.subject_label = body.subject_label
+    if body.object_label is not None:
+        row.object_label = body.object_label
+    if body.predicate is not None:
+        row.predicate = body.predicate
+    if body.confidence is not None:
+        row.confidence = body.confidence
+        row.last_confirmed_at = datetime.now(timezone.utc)
+
+    await db.commit()
+    await db.refresh(row)
+    logger.info("memory: patch_relation user=%s relation=%s", current_user.id, relation_id)
+    return _relation_to_out(row)
+
+
+@router.delete("/relational/{relation_id}", status_code=status.HTTP_204_NO_CONTENT)
+async def delete_relation(
+    relation_id: str,
+    current_user: UserProfile = Depends(get_current_user),
+    db: AsyncSession = Depends(get_session),
+) -> None:
+    """Hard-delete a relation row (GDPR Art. 17)."""
+    result = await db.execute(
+        select(MemoryRelation).where(
+            MemoryRelation.id == relation_id,
+            MemoryRelation.user_id == current_user.id,
+        )
+    )
+    row = result.scalar_one_or_none()
+    if row is None:
+        raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail="Relation not found")
+    await db.delete(row)
+    await db.commit()
+    logger.info("memory: delete_relation user=%s relation=%s", current_user.id, relation_id)
+
+
+@router.post("/forget-all", status_code=status.HTTP_204_NO_CONTENT)
+async def forget_all(
+    x_confirm: Annotated[str | None, Header(alias="X-Confirm")] = None,
+    current_user: UserProfile = Depends(get_current_user),
+    db: AsyncSession = Depends(get_session),
+) -> None:
+    """Wipe all memory tiers for the current user (GDPR Art. 17).
+
+    Requires ``X-Confirm: true`` header. Does NOT delete the user account.
+    """
+    if x_confirm != "true":
+        raise HTTPException(
+            status_code=status.HTTP_400_BAD_REQUEST,
+            detail="Missing or invalid X-Confirm header. Send X-Confirm: true to confirm.",
+        )
+
+    uid = current_user.id
+    await db.execute(delete(MemoryCore).where(MemoryCore.user_id == uid))
+    await db.execute(delete(MemoryAssociative).where(MemoryAssociative.user_id == uid))
+    await db.execute(delete(MemoryEpisodic).where(MemoryEpisodic.user_id == uid))
+    await db.execute(delete(MemoryProactive).where(MemoryProactive.user_id == uid))
+    await db.execute(delete(MemoryRelation).where(MemoryRelation.user_id == uid))
+    await db.execute(delete(ExtractionQueue).where(ExtractionQueue.user_id == uid))
+    await db.commit()
+    logger.warning("memory: forget_all GDPR wipe user=%s", uid)

app/api/routes/plugins.py

@@ -1,148 +0,0 @@
-"""Plugins routes: browse and install plugins from the marketplace.
-
-Backed by ``PluginRegistry`` and ``RevenueShare`` service classes that
-persist data in the PostgreSQL ``plugins`` and ``revenue_events`` tables.
-"""
-
-from __future__ import annotations
-
-from typing import Any, Literal
-
-from fastapi import APIRouter, Depends, HTTPException, Query, status
-from pydantic import BaseModel
-from sqlalchemy import select
-from sqlalchemy.ext.asyncio import AsyncSession
-
-from app.api.deps import get_current_user
-from app.db import get_session
-from app.marketplace.plugin_registry import registry
-from app.marketplace.revenue_share import revenue_share
-from app.models import PluginInstallation, PluginReview as PluginReviewModel
-from app.schemas import PluginInstallRequest, PluginListResponse, PluginManifest, UserProfile
-
-router = APIRouter(prefix="/plugins", tags=["plugins"])
-
-
-# ── Tier gate ─────────────────────────────────────────────────────────
-
-def _require_plugin_tier(user: UserProfile) -> None:
-    """Raise HTTP 403 for users below Power tier."""
-    if user.tier not in ("power", "team"):
-        raise HTTPException(
-            status_code=status.HTTP_403_FORBIDDEN,
-            detail="Plugin marketplace requires Power tier or above",
-        )
-
-
-# ── Local detail schema ────────────────────────────────────────────────
-
-class _PluginDetail(BaseModel):
-    plugin: PluginManifest
-    install_count: int
-    ratings: list[Any]
-
-
-# ── Routes ────────────────────────────────────────────────────────────
-
-@router.get("", response_model=PluginListResponse)
-async def list_plugins(
-    category: str | None = Query(default=None),
-    q: str | None = Query(default=None),
-    page: int = Query(default=1, ge=1),
-    sort: Literal["rating", "installs", "newest"] = Query(default="newest"),
-    current_user: UserProfile = Depends(get_current_user),
-    db: AsyncSession = Depends(get_session),
-) -> PluginListResponse:
-    """Browse the plugin marketplace. Requires Power tier or above."""
-    _require_plugin_tier(current_user)
-    return await registry.list_plugins(db, category=category, query=q, page=page, sort=sort)
-
-
-@router.get("/{plugin_id}", response_model=_PluginDetail)
-async def get_plugin(
-    plugin_id: str,
-    current_user: UserProfile = Depends(get_current_user),
-    db: AsyncSession = Depends(get_session),
-) -> _PluginDetail:
-    """Get full plugin details including install count. Requires Power tier or above."""
-    _require_plugin_tier(current_user)
-    entry = await registry.get_plugin(db, plugin_id)
-    if entry is None:
-        raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail="Plugin not found")
-
-    # Fetch review ratings for this plugin
-    review_result = await db.execute(
-        select(PluginReviewModel).where(PluginReviewModel.plugin_id == plugin_id)
-    )
-    reviews = review_result.scalars().all()
-    ratings = [
-        {
-            "reviewer_id": r.reviewer_id,
-            "decision": r.decision,
-            "notes": r.notes,
-            "reviewed_at": int(r.reviewed_at.timestamp() * 1000) if r.reviewed_at else None,
-        }
-        for r in reviews
-    ]
-
-    return _PluginDetail(
-        plugin=entry["manifest"],
-        install_count=entry["install_count"],
-        ratings=ratings,
-    )
-
-
-@router.post("/{plugin_id}/install", response_model=dict)
-async def install_plugin(
-    plugin_id: str,
-    body: PluginInstallRequest,  # noqa: ARG001 — reserved for future fields
-    current_user: UserProfile = Depends(get_current_user),
-    db: AsyncSession = Depends(get_session),
-) -> dict[str, Any]:
-    """Install a plugin. Triggers Stripe Connect revenue split for paid plugins.
-
-    Requires Power tier or above.
-    """
-    _require_plugin_tier(current_user)
-    entry = await registry.get_plugin(db, plugin_id)
-    if entry is None:
-        raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail="Plugin not found")
-
-    # Record the installation in plugin_installations
-    installation = PluginInstallation(
-        plugin_id=plugin_id,
-        user_id=current_user.id,
-    )
-    db.add(installation)
-    await db.flush()
-
-    await revenue_share.record_install(
-        db,
-        plugin_id=plugin_id,
-        user_id=current_user.id,
-        amount_cents=entry["manifest"].price_cents,
-    )
-
-    download_url = f"https://cdn.adiuva.app/plugins/{plugin_id}/package.zip"
-    return {"ok": True, "download_url": download_url}
-
-
-@router.delete("/{plugin_id}/install", response_model=dict)
-async def uninstall_plugin(
-    plugin_id: str,
-    current_user: UserProfile = Depends(get_current_user),
-    db: AsyncSession = Depends(get_session),
-) -> dict[str, bool]:
-    """Unregister a plugin installation."""
-    result = await db.execute(
-        select(PluginInstallation).where(
-            PluginInstallation.plugin_id == plugin_id,
-            PluginInstallation.user_id == current_user.id,
-        )
-    )
-    installation = result.scalar_one_or_none()
-    if installation is not None:
-        await db.delete(installation)
-        await db.commit()
-    await registry.record_uninstall(db, plugin_id)
-    return {"ok": True}

app/api/routes/storage.py

@@ -1,195 +0,0 @@
-"""Storage routes: CRUD for E2E-encrypted cloud records.
-
-Blobs are stored in S3 via BlobStore. Record metadata is persisted in the
-PostgreSQL ``storage_records`` table.
-"""
-
-from __future__ import annotations
-
-import uuid
-
-from fastapi import APIRouter, Depends, HTTPException, Query, Response, status
-from pydantic import BaseModel
-from sqlalchemy import func, select
-from sqlalchemy.ext.asyncio import AsyncSession
-
-from app.api.deps import get_current_user
-from app.billing.tier_manager import tier_manager
-from app.db import get_session
-from app.models import StorageRecord
-from app.schemas import StorageRecordCreate, StorageRecordUpdate, UserProfile
-from app.storage.blob_store import BlobStore
-from app.storage.encryption import reject_if_tampered
-
-router = APIRouter(prefix="/storage", tags=["storage"])
-
-_blob_store = BlobStore()
-
-
-# ── Local response schemas ─────────────────────────────────────────────
-
-class _CreateResponse(BaseModel):
-    id: str
-    created_at: int
-
-
-class _RecordMeta(BaseModel):
-    id: str
-    table: str
-    checksum: str
-    created_at: int
-    updated_at: int
-
-
-# ── Helpers ────────────────────────────────────────────────────────────
-
-async def _current_usage_bytes(user_id: str, db: AsyncSession) -> int:
-    """Return total bytes stored by *user_id*."""
-    result = await db.execute(
-        select(func.coalesce(func.sum(StorageRecord.size_bytes), 0)).where(
-            StorageRecord.user_id == user_id
-        )
-    )
-    return int(result.scalar_one())
-
-
-async def _check_quota(user: UserProfile, additional_bytes: int, db: AsyncSession) -> None:
-    """Raise HTTP 402 if adding *additional_bytes* would exceed the tier limit."""
-    current = await _current_usage_bytes(user.id, db)
-    tier_manager.enforce_quota(user.tier, current_bytes=current, additional_bytes=additional_bytes)
-
-
-async def _get_record_for_user(
-    record_id: str, user_id: str, db: AsyncSession
-) -> StorageRecord:
-    """Look up a record and verify ownership. Returns 404 on mismatch
-    to prevent user enumeration attacks."""
-    result = await db.execute(
-        select(StorageRecord).where(
-            StorageRecord.id == record_id, StorageRecord.user_id == user_id
-        )
-    )
-    record = result.scalar_one_or_none()
-    if record is None:
-        raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail="Record not found")
-    return record
-
-
-# ── Routes ─────────────────────────────────────────────────────────────
-
-@router.post("/records", response_model=_CreateResponse, status_code=status.HTTP_201_CREATED)
-async def create_record(
-    body: StorageRecordCreate,
-    current_user: UserProfile = Depends(get_current_user),
-    db: AsyncSession = Depends(get_session),
-) -> _CreateResponse:
-    """Upload a new E2E-encrypted blob. Verifies checksum before storing."""
-    reject_if_tampered(body.blob, body.checksum)
-    await _check_quota(current_user, len(body.blob), db)
-
-    record_id = str(uuid.uuid4())
-
-    s3_key = await _blob_store.upload(
-        current_user.id, body.table, record_id, body.blob, body.checksum
-    )
-
-    record = StorageRecord(
-        id=record_id,
-        user_id=current_user.id,
-        table_name=body.table,
-        s3_key=s3_key,
-        checksum=body.checksum,
-        size_bytes=len(body.blob),
-    )
-    db.add(record)
-    await db.commit()
-    await db.refresh(record)
-
-    created_at_ms = int(record.created_at.timestamp() * 1000)
-    return _CreateResponse(id=record_id, created_at=created_at_ms)
-
-
-@router.get("/records", response_model=list[_RecordMeta])
-async def list_records(
-    table: str | None = Query(default=None),
-    page: int = Query(default=1, ge=1),
-    limit: int = Query(default=50, ge=1, le=200),
-    current_user: UserProfile = Depends(get_current_user),
-    db: AsyncSession = Depends(get_session),
-) -> list[_RecordMeta]:
-    """List record metadata for the authenticated user. Blob bytes are never returned."""
-    query = select(StorageRecord).where(StorageRecord.user_id == current_user.id)
-    if table is not None:
-        query = query.where(StorageRecord.table_name == table)
-    query = query.offset((page - 1) * limit).limit(limit)
-
-    result = await db.execute(query)
-    rows = result.scalars().all()
-
-    return [
-        _RecordMeta(
-            id=r.id,
-            table=r.table_name,
-            checksum=r.checksum,
-            created_at=int(r.created_at.timestamp() * 1000),
-            updated_at=int(r.updated_at.timestamp() * 1000),
-        )
-        for r in rows
-    ]
-
-
-@router.get("/records/{record_id}")
-async def download_record(
-    record_id: str,
-    current_user: UserProfile = Depends(get_current_user),
-    db: AsyncSession = Depends(get_session),
-) -> Response:
-    """Download an E2E-encrypted blob. Returns raw bytes with ``X-Checksum`` header."""
-    record = await _get_record_for_user(record_id, current_user.id, db)
-    blob = await _blob_store.download(current_user.id, record.s3_key)
-    return Response(
-        content=blob,
-        media_type="application/octet-stream",
-        headers={"X-Checksum": record.checksum},
-    )
-
-
-@router.put("/records/{record_id}", response_model=dict)
-async def update_record(
-    record_id: str,
-    body: StorageRecordUpdate,
-    current_user: UserProfile = Depends(get_current_user),
-    db: AsyncSession = Depends(get_session),
-) -> dict[str, bool]:
-    """Replace the blob for an existing record. Verifies checksum before storing."""
-    record = await _get_record_for_user(record_id, current_user.id, db)
-    reject_if_tampered(body.blob, body.checksum)
-
-    delta = len(body.blob) - record.size_bytes
-    if delta > 0:
-        await _check_quota(current_user, delta, db)
-
-    s3_key = await _blob_store.upload(
-        current_user.id, record.table_name, record_id, body.blob, body.checksum
-    )
-
-    record.s3_key = s3_key
-    record.checksum = body.checksum
-    record.size_bytes = len(body.blob)
-    await db.commit()
-
-    return {"ok": True}
-
-
-@router.delete("/records/{record_id}", response_model=dict)
-async def delete_record(
-    record_id: str,
-    current_user: UserProfile = Depends(get_current_user),
-    db: AsyncSession = Depends(get_session),
-) -> dict[str, bool]:
-    """Delete a record and its S3 blob."""
-    record = await _get_record_for_user(record_id, current_user.id, db)
-    await _blob_store.delete(current_user.id, record.s3_key)
-    await db.delete(record)
-    await db.commit()
-    return {"ok": True}

app/api/routes/vectors.py

@@ -1,79 +0,0 @@
-"""Vectors routes: upsert, search, delete cloud vector store entries, and embed text."""
-
-from __future__ import annotations
-
-from fastapi import APIRouter, Depends
-from pydantic import BaseModel
-
-from app.api.deps import get_current_user
-from app.core.llm import embed
-from app.schemas import (
-    UserProfile,
-    VectorSearchRequest,
-    VectorSearchResponse,
-    VectorUpsertRequest,
-)
-from app.storage.encryption import reject_if_tampered
-from app.storage.vector_store import VectorStore
-
-router = APIRouter(prefix="/storage", tags=["vectors"])
-
-_vector_store = VectorStore()
-
-
-class _VectorDeleteRequest(BaseModel):
-    ids: list[str]
-
-
-class _EmbedRequest(BaseModel):
-    text: str
-
-
-class _EmbedResponse(BaseModel):
-    vector: list[float]
-
-
-@router.post("/vectors/upsert", response_model=dict)
-async def upsert_vectors(
-    body: VectorUpsertRequest,
-    current_user: UserProfile = Depends(get_current_user),
-) -> dict[str, int]:
-    """Verify checksums and store encrypted vectors in the user-scoped namespace."""
-    for item in body.vectors:
-        reject_if_tampered(item.blob, item.checksum)
-    await _vector_store.upsert(current_user.id, body.vectors)
-    return {"upserted": len(body.vectors)}
-
-
-@router.post("/vectors/search", response_model=VectorSearchResponse)
-async def search_vectors(
-    body: VectorSearchRequest,
-    current_user: UserProfile = Depends(get_current_user),
-) -> VectorSearchResponse:
-    """Search the user-scoped vector namespace with an encrypted query blob."""
-    results = await _vector_store.search(current_user.id, body.query_blob, body.top_k)
-    return VectorSearchResponse(results=results)
-
-
-@router.delete("/vectors", response_model=dict)
-async def delete_vectors(
-    body: _VectorDeleteRequest,
-    current_user: UserProfile = Depends(get_current_user),
-) -> dict[str, bool]:
-    """Delete vectors by ID, scoped to the authenticated user."""
-    await _vector_store.delete(current_user.id, body.ids)
-    return {"ok": True}
-
-
-@router.post("/vectors/embed", response_model=_EmbedResponse)
-async def embed_text(
-    body: _EmbedRequest,
-    current_user: UserProfile = Depends(get_current_user),
-) -> _EmbedResponse:
-    """Generate a 1536-dim embedding vector for the given text.
-
-    Uses ``text-embedding-3-small`` via OpenAI.  Auth required (JWT).
-    Used by backend tools (note_agent) and Electron (vectordb.ts) alike.
-    """
-    vector = await embed(body.text)
-    return _EmbedResponse(vector=vector)

app/auth/__init__.py

@@ -0,0 +1 @@
+"OAuth provider abstractions and utilities."

app/auth/oauth_providers.py

@@ -0,0 +1,135 @@
+"""OAuth 2.0 + PKCE provider abstractions.
+
+Each provider implements a three-step flow designed for a desktop (public) client:
+
+  1. get_authorization_url(state, code_challenge) → str
+       Build the provider's consent-screen URL.  State and code_challenge are
+       generated server-side; the client opens this URL in the system browser.
+
+  2. exchange_code(code, code_verifier, redirect_uri) → dict
+       Exchange the short-lived authorization code for an access token.
+       The code_verifier proves ownership of the PKCE challenge.
+
+  3. get_userinfo(access_token) → OAuthUserInfo
+       Fetch the canonical user identity from the provider.
+
+Currently supported providers:
+  - GoogleOAuthProvider  (scope: openid email profile)
+
+Adding a new provider:
+  - Implement the three methods above.
+  - Register in _PROVIDERS inside routes/auth.py.
+"""
+
+from __future__ import annotations
+
+import base64
+import hashlib
+import os
+import urllib.parse
+from dataclasses import dataclass
+
+import httpx
+
+
+# ── Data transfer objects ─────────────────────────────────────────────
+
+
+@dataclass
+class OAuthUserInfo:
+    """Normalized user identity returned by any provider."""
+
+    provider_user_id: str
+    email: str
+    email_verified: bool
+    avatar_url: str | None
+    name: str | None
+
+
+# ── PKCE helpers ──────────────────────────────────────────────────────
+
+
+def generate_pkce_pair() -> tuple[str, str]:
+    """Generate a (code_verifier, code_challenge) pair for PKCE S256.
+
+    The code_verifier is a random 32-byte URL-safe base64 string.
+    The code_challenge is SHA-256(code_verifier) base64url-encoded (no padding).
+    """
+    code_verifier = base64.urlsafe_b64encode(os.urandom(32)).rstrip(b"=").decode()
+    digest = hashlib.sha256(code_verifier.encode()).digest()
+    code_challenge = base64.urlsafe_b64encode(digest).rstrip(b"=").decode()
+    return code_verifier, code_challenge
+
+
+# ── Google provider ───────────────────────────────────────────────────
+
+
+class GoogleOAuthProvider:
+    """Google OAuth 2.0 provider (openid email profile scope).
+
+    Uses Google's standard authorization endpoint with PKCE S256.
+    Does NOT use google-auth-oauthlib to keep the flow generic and async.
+    """
+
+    name = "google"
+
+    _AUTH_URL = "https://accounts.google.com/o/oauth2/v2/auth"
+    _TOKEN_URL = "https://oauth2.googleapis.com/token"
+    _USERINFO_URL = "https://www.googleapis.com/oauth2/v3/userinfo"
+
+    def __init__(self, client_id: str, client_secret: str, redirect_uri: str) -> None:
+        self.client_id = client_id
+        self.client_secret = client_secret
+        self.redirect_uri = redirect_uri
+
+    def get_authorization_url(self, state: str, code_challenge: str) -> str:
+        """Build the Google consent-screen URL."""
+        params = {
+            "client_id": self.client_id,
+            "redirect_uri": self.redirect_uri,
+            "response_type": "code",
+            "scope": "openid email profile",
+            "state": state,
+            "code_challenge": code_challenge,
+            "code_challenge_method": "S256",
+            "access_type": "offline",
+            "prompt": "select_account",
+        }
+        return f"{self._AUTH_URL}?{urllib.parse.urlencode(params)}"
+
+    async def exchange_code(
+        self, code: str, code_verifier: str, redirect_uri: str
+    ) -> dict:
+        """Exchange authorization code for an access token."""
+        async with httpx.AsyncClient() as client:
+            response = await client.post(
+                self._TOKEN_URL,
+                data={
+                    "client_id": self.client_id,
+                    "client_secret": self.client_secret,
+                    "code": code,
+                    "code_verifier": code_verifier,
+                    "grant_type": "authorization_code",
+                    "redirect_uri": redirect_uri,
+                },
+            )
+        response.raise_for_status()
+        return response.json()
+
+    async def get_userinfo(self, access_token: str) -> OAuthUserInfo:
+        """Fetch the authenticated user's identity from Google."""
+        async with httpx.AsyncClient() as client:
+            response = await client.get(
+                self._USERINFO_URL,
+                headers={"Authorization": f"Bearer {access_token}"},
+            )
+        response.raise_for_status()
+        data = response.json()
+
+        return OAuthUserInfo(
+            provider_user_id=data["sub"],
+            email=data["email"],
+            email_verified=data.get("email_verified", False),
+            avatar_url=data.get("picture"),
+            name=data.get("name"),
+        )

app/billing/quota.py

@@ -0,0 +1,139 @@
+"""Quota checks and atomic token-usage accounting for folder integration."""
+from __future__ import annotations
+
+from dataclasses import dataclass
+from datetime import datetime, timezone
+
+from sqlalchemy import select, update
+from sqlalchemy.dialects.postgresql import insert as pg_insert
+from sqlalchemy.ext.asyncio import AsyncSession
+
+from app.billing.tier_manager import TierManager
+from app.models import MonthlyTokenUsage
+from app.schemas import BillingTier
+
+
+class QuotaExceeded(Exception):
+    """Raised when a folder operation cannot proceed under the user's tier."""
+
+    def __init__(self, reason: str, message: str) -> None:
+        super().__init__(message)
+        self.reason = reason  # "max_files" | "monthly_tokens"
+
+
+@dataclass
+class TokenUsageResult:
+    tokens_used: int
+    exhausted: bool
+
+
+def _current_year_month() -> str:
+    return datetime.now(timezone.utc).strftime("%Y-%m")
+
+
+_tier_manager = TierManager()
+
+
+async def check_folder_quota(
+    *,
+    user_id: str,
+    tier: BillingTier,
+    estimated_files: int,
+    db: AsyncSession,
+) -> None:
+    """Raise QuotaExceeded if folder_max_files or folder_monthly_tokens
+    would be violated. -1 in either feature means unlimited."""
+    max_files = _tier_manager.get_feature_value(tier, "folder_max_files")
+    if max_files != -1 and estimated_files > max_files:
+        raise QuotaExceeded(
+            "max_files",
+            f"Folder has {estimated_files} files; tier '{tier}' allows max {max_files}.",
+        )
+
+    cap = _tier_manager.get_feature_value(tier, "folder_monthly_tokens")
+    if cap == -1:
+        return
+    ym = _current_year_month()
+    row = (
+        await db.execute(
+            select(MonthlyTokenUsage).where(
+                MonthlyTokenUsage.user_id == user_id,
+                MonthlyTokenUsage.year_month == ym,
+                MonthlyTokenUsage.feature == "folder_index",
+            )
+        )
+    ).scalar_one_or_none()
+    used = row.tokens_used if row else 0
+    if used >= cap:
+        raise QuotaExceeded(
+            "monthly_tokens",
+            f"Monthly token budget exhausted ({used}/{cap}); resets next month.",
+        )
+
+
+async def add_token_usage(
+    *,
+    user_id: str,
+    feature: str,
+    tokens: int,
+    db: AsyncSession,
+    cap: int | None = None,
+) -> TokenUsageResult:
+    """Atomically add `tokens` to MonthlyTokenUsage row for (user, current month, feature).
+
+    Uses PostgreSQL ``INSERT … ON CONFLICT DO UPDATE`` when available; falls
+    back to a read-then-write on other engines (e.g. aiosqlite in tests).
+    Returns post-update total and whether cap is exhausted.
+    """
+    ym = _current_year_month()
+
+    # Detect dialect to choose between native upsert and portable fallback.
+    dialect_name: str = db.bind.dialect.name if db.bind is not None else ""  # type: ignore[union-attr]
+
+    if dialect_name == "postgresql":
+        # Native atomic upsert — production path.
+        stmt = (
+            pg_insert(MonthlyTokenUsage)
+            .values(
+                user_id=user_id,
+                year_month=ym,
+                feature=feature,
+                tokens_used=tokens,
+            )
+            .on_conflict_do_update(
+                index_elements=["user_id", "year_month", "feature"],
+                set_={"tokens_used": MonthlyTokenUsage.tokens_used + tokens},
+            )
+            .returning(MonthlyTokenUsage.tokens_used)
+        )
+        used: int = (await db.execute(stmt)).scalar_one()
+        await db.commit()
+    else:
+        # Portable fallback — used in tests (SQLite) and any non-PG engine.
+        row = (
+            await db.execute(
+                select(MonthlyTokenUsage).where(
+                    MonthlyTokenUsage.user_id == user_id,
+                    MonthlyTokenUsage.year_month == ym,
+                    MonthlyTokenUsage.feature == feature,
+                )
+            )
+        ).scalar_one_or_none()
+
+        if row is None:
+            row = MonthlyTokenUsage(
+                user_id=user_id,
+                year_month=ym,
+                feature=feature,
+                tokens_used=tokens,
+            )
+            db.add(row)
+        else:
+            row.tokens_used += tokens
+
+        await db.commit()
+        await db.refresh(row)
+        used = row.tokens_used
+
+    exhausted = cap is not None and cap != -1 and used >= cap
+    return TokenUsageResult(tokens_used=used, exhausted=exhausted)

app/billing/stripe_service.py

@@ -43,8 +43,8 @@ class StripeService:
         self,
         user_id: str,
         tier: str,
-        success_url: str = "https://app.adiuva.app/billing/success?session_id={CHECKOUT_SESSION_ID}",
-        cancel_url: str = "https://app.adiuva.app/billing/cancel",
+        success_url: str = "https://app.adiuvai.app/billing/success?session_id={CHECKOUT_SESSION_ID}",
+        cancel_url: str = "https://app.adiuvai.app/billing/cancel",
     ) -> str:
         """Create a Stripe checkout session and return the URL.
 
@@ -200,6 +200,45 @@ class StripeService:
         sub.status = "canceled"
         await db.commit()
 
+    async def list_invoices(
+        self, user_id: str, db: AsyncSession, limit: int = 24
+    ) -> list[dict[str, Any]]:
+        """Return recent invoices for the user from Stripe.
+
+        Returns an empty list when Stripe is not configured or the user has
+        no ``stripe_customer_id``.
+        """
+        if not self._configured():
+            return []
+
+        from app.models import User  # noqa: PLC0415
+
+        result = await db.execute(
+            select(User.stripe_customer_id).where(User.id == user_id)
+        )
+        customer_id = result.scalar_one_or_none()
+        if not customer_id:
+            return []
+
+        try:
+            s = self._client()
+            invoices = s.Invoice.list(customer=customer_id, limit=limit)
+            return [
+                {
+                    "id": inv.id,
+                    "amount_due": inv.amount_due,
+                    "amount_paid": inv.amount_paid,
+                    "currency": inv.currency,
+                    "status": inv.status,
+                    "created": inv.created * 1000,  # epoch ms
+                    "invoice_url": inv.hosted_invoice_url,
+                    "invoice_pdf": inv.invoice_pdf,
+                }
+                for inv in invoices.auto_paging_iter()
+            ]
+        except Exception:
+            return []
+
     # ── Private DB helpers ───────────────────────────────────────────────
 
     async def _upsert_subscription(

app/billing/tier_manager.py

@@ -22,45 +22,57 @@ FEATURES: dict[str, dict[str, Any]] = {
         "agents": 3,
         "batch_active": 2,
         "batch_runs_per_day": 5,
-        "cloud_storage_gb": 0,
-        "backup_gb": 0,
         "providers": 1,
         "batch_builder": False,
-        "plugin_marketplace": False,
         "sso": False,
+        "real_embeddings": False,       # keyword fallback only
+        "realtime_extraction": False,   # batch queue (Phase 2)
+        "relational_memory": False,     # relational tier (Phase 3) — Pro+
+        "proactive_mining": False,      # Power+ only (Phase 5)
+        "folder_max_files": 200,
+        "folder_monthly_tokens": 100_000,
     },
     "pro": {
         "agents": -1,           # unlimited
         "batch_active": 10,
         "batch_runs_per_day": 50,
-        "cloud_storage_gb": 5,
-        "backup_gb": 5,
         "providers": -1,
         "batch_builder": False,
-        "plugin_marketplace": False,
         "sso": False,
+        "real_embeddings": True,        # pgvector cosine search
+        "realtime_extraction": True,    # fire-and-forget asyncio.create_task
+        "relational_memory": True,      # person/project predicates
+        "proactive_mining": False,      # Power+ only (Phase 5)
+        "folder_max_files": 5000,
+        "folder_monthly_tokens": 2_000_000,
     },
     "power": {
         "agents": -1,
         "batch_active": -1,     # unlimited
         "batch_runs_per_day": -1,  # unlimited
-        "cloud_storage_gb": 25,
-        "backup_gb": 25,
         "providers": -1,
         "batch_builder": True,
-        "plugin_marketplace": True,
         "sso": False,
+        "real_embeddings": True,
+        "realtime_extraction": True,
+        "relational_memory": True,      # all predicates incl. custom
+        "proactive_mining": True,       # scheduled pattern mining (Phase 5)
+        "folder_max_files": -1,         # unlimited
+        "folder_monthly_tokens": -1,    # unlimited
     },
     "team": {
         "agents": -1,
         "batch_active": -1,
         "batch_runs_per_day": -1,  # unlimited
-        "cloud_storage_gb": -1,  # unlimited
-        "backup_gb": -1,         # unlimited
         "providers": -1,
         "batch_builder": True,
-        "plugin_marketplace": True,
         "sso": True,
+        "real_embeddings": True,
+        "realtime_extraction": True,
+        "relational_memory": True,      # all predicates incl. custom
+        "proactive_mining": True,       # scheduled pattern mining (Phase 5)
+        "folder_max_files": -1,         # unlimited
+        "folder_monthly_tokens": -1,    # unlimited
     },
 }
 
@@ -119,77 +131,19 @@ class TierManager:
             )
             raise HTTPException(status_code=status.HTTP_403_FORBIDDEN, detail=detail)
 
+    def get_feature_value(self, tier: BillingTier, feature: str) -> int:
+        """Return integer feature value for tier. -1 means unlimited."""
+        value = FEATURES.get(tier, FEATURES["free"]).get(feature)
+        if not isinstance(value, int):
+            return 0
+        return value
+
     # ── Rate limiting ────────────────────────────────────────────────────
 
     def get_rate_limit(self, tier: BillingTier) -> int:
         """Return the requests-per-minute limit for ``tier``."""
         return RATE_LIMITS.get(tier, RATE_LIMITS["free"])
 
-    # ── Storage quota ────────────────────────────────────────────────────
-
-    def enforce_quota(
-        self,
-        tier: BillingTier,
-        current_bytes: int = 0,
-        additional_bytes: int = 0,
-    ) -> None:
-        """Raise ``HTTP 402`` if the user would exceed their cloud storage quota.
-
-        ``tier`` is the caller's current tier (from ``current_user.tier``).
-        ``current_bytes`` is the total bytes already stored (queried by caller).
-        """
-        limit_gb: int = FEATURES[tier]["cloud_storage_gb"]
-        if limit_gb == 0:
-            raise HTTPException(
-                status_code=status.HTTP_402_PAYMENT_REQUIRED,
-                detail=f"Cloud storage is not available on the '{tier}' tier",
-            )
-        if limit_gb == -1:
-            return  # unlimited
-        limit_bytes = limit_gb * 1024 ** 3
-        if current_bytes + additional_bytes > limit_bytes:
-            raise HTTPException(
-                status_code=status.HTTP_402_PAYMENT_REQUIRED,
-                detail=f"Storage quota exceeded for tier '{tier}'",
-            )
-
-    def enforce_backup_quota(
-        self,
-        tier: BillingTier,
-        current_bytes: int = 0,
-        additional_bytes: int = 0,
-    ) -> None:
-        """Raise ``HTTP 402`` if the user would exceed their backup quota."""
-        limit_gb: int = FEATURES[tier]["backup_gb"]
-        if limit_gb == 0:
-            raise HTTPException(
-                status_code=status.HTTP_402_PAYMENT_REQUIRED,
-                detail=f"Backup is not available on the '{tier}' tier",
-            )
-        if limit_gb == -1:
-            return  # unlimited
-        limit_bytes = limit_gb * 1024 ** 3
-        if current_bytes + additional_bytes > limit_bytes:
-            raise HTTPException(
-                status_code=status.HTTP_402_PAYMENT_REQUIRED,
-                detail=f"Backup quota exceeded for tier '{tier}'",
-            )
-
-    def check_quota(
-        self,
-        tier: BillingTier,
-        current_bytes: int = 0,
-        additional_bytes: int = 0,
-    ) -> bool:
-        """Return ``True`` if the user can store ``additional_bytes`` more data."""
-        limit_gb: int = FEATURES[tier]["cloud_storage_gb"]
-        if limit_gb == 0:
-            return False
-        if limit_gb == -1:
-            return True
-        limit_bytes = limit_gb * 1024 ** 3
-        return current_bytes + additional_bytes <= limit_bytes
-
 
 # Module-level singleton shared across the app.
 tier_manager = TierManager()

app/config/settings.py

@@ -3,7 +3,7 @@ from pydantic_settings import BaseSettings, SettingsConfigDict
 
 
 class Settings(BaseSettings):
-    DATABASE_URL: str = "postgresql+asyncpg://postgres:postgres@localhost:5432/adiuva"
+    DATABASE_URL: str = "postgresql+asyncpg://postgres:postgres@localhost:5432/adiuvai"
     JWT_SECRET: str = "change-me-in-production"
     JWT_ALGORITHM: str = "HS256"
     JWT_ACCESS_TOKEN_EXPIRE_MINUTES: int = 30
@@ -12,26 +12,29 @@ class Settings(BaseSettings):
     STRIPE_SECRET_KEY: str = ""
     STRIPE_WEBHOOK_SECRET: str = ""
 
-    S3_BUCKET: str = ""
-    S3_REGION: str = "us-east-1"
-    S3_ENDPOINT_URL: str = ""
-    AWS_ACCESS_KEY_ID: str = ""
-    AWS_SECRET_ACCESS_KEY: str = ""
-
-    PINECONE_API_KEY: str = ""
-    PINECONE_INDEX: str = "adiuva"
-    QDRANT_URL: str = ""
-    QDRANT_API_KEY: str = ""
-
     OPENAI_API_KEY: str = ""
     ANTHROPIC_API_KEY: str = ""
     GOOGLE_API_KEY: str = ""
     CEREBRAS_API_KEY: str = ""
+    GROQ_API_KEY: str = ""
+    DEEPSEEK_API_KEY: str = ""
 
     LLM_MODEL: str = "gpt-4o"
-    LLM_ROUTER_MODEL: str = "gpt-4o-mini"
     LLM_EMBED_MODEL: str = "text-embedding-3-small"
 
+    # Per-agent model overrides. Leave empty to fall back to LLM_MODEL.
+    LLM_MODEL_CLASSIFIER: str = ""        # _infer_floating_domain (intent routing)
+    LLM_MODEL_HOME_AGENT: str = ""        # home-agent (run_single_agent / stream)
+    LLM_MODEL_FLOATING_AGENT: str = ""    # floating-agent (contextual chat)
+    LLM_MODEL_UNIFIED_PROCESSOR: str = "" # unified-processor (agent_runner)
+    LLM_MODEL_CLOUD_PROCESSOR: str = ""   # cloud-processor (agent_runner)
+    LLM_MODEL_BRIEF_AGENT: str = ""            # brief-agent (home + project text briefs)
+    LLM_MODEL_TASK_BRIEF_AGENT: str = ""      # task-brief-agent (per-task deep research)
+    LLM_MODEL_SETUP_AGENT: str = ""           # agent-setup journey
+    LLM_MODEL_MEMORY_EXTRACTOR: str = ""  # memory-extractor (Phase 2 extract/decide)
+    LLM_MODEL_MEMORY_MINER: str = ""      # memory-miner (Phase 5 proactive mining)
+    LLM_MODEL_MEMORY_AUDITOR: str = ""    # memory-auditor (Phase 7 weekly audit)
+
     # GitHub Copilot OAuth token storage directory.
     # Leave empty to use the LiteLLM default (~/.config/litellm/github_copilot).
     # In Docker, set this to a path backed by a named volume so tokens survive restarts.
@@ -45,16 +48,39 @@ class Settings(BaseSettings):
     # MS_TENANT_ID: set to 'common' to allow multi-tenant (personal + work accounts).
     MS_TENANT_ID: str = "common"
 
+    # Google Login OAuth credentials — scope: openid email profile.
+    # Separate from GMAIL_CLIENT_ID/SECRET (which uses gmail.readonly scope).
+    GOOGLE_AUTH_CLIENT_ID: str = ""
+    GOOGLE_AUTH_CLIENT_SECRET: str = ""
+    # The redirect URI registered in Google Cloud Console.
+    # Google redirects here after consent; this backend route then bounces to
+    # the adiuvai:// deep link so the Electron app receives the code.
+    # Dev:  http://localhost:8000/api/v1/auth/oauth/google/web-callback
+    # Prod: https://api.adiuvai.com/api/v1/auth/oauth/google/web-callback
+    OAUTH_REDIRECT_URI: str = "http://localhost:8000/api/v1/auth/oauth/google/web-callback"
+
     # Fernet key (URL-safe base64, 32-byte key) for at-rest encryption of OAuth
     # tokens stored in cloud_agent_configs.oauth_token_encrypted.
     # Generate with: from cryptography.fernet import Fernet; Fernet.generate_key()
     OAUTH_ENCRYPTION_KEY: str = ""
 
-    CORS_ORIGINS: list[str] = ["app://.", "http://localhost:3000", "http://localhost:5173"]
+    CORS_ORIGINS: list[str] = [
+        "app://.",
+        "http://localhost:3000",
+        "http://localhost:5173",
+        "http://localhost:4173",      # Vite preview (web SPA)
+        "https://app.adiuvai.com",    # Production web portal
+    ]
+
+    LANGFUSE_SECRET_KEY: str = ""
+    LANGFUSE_PUBLIC_KEY: str = ""
+    LANGFUSE_BASE_URL: str = "https://cloud.langfuse.com"
+
+    SCHEDULER_ENABLED: bool = True
 
     ENV: Literal["dev", "prod"] = "dev"
 
-    model_config = SettingsConfigDict(env_file=".env", env_file_encoding="utf-8")
+    model_config = SettingsConfigDict(env_file=".env", env_file_encoding="utf-8", extra="ignore")
 
 
 settings = Settings()

app/core/agent_runner.py

@@ -2,12 +2,12 @@
 
 Drives two agent types:
 
-* **Local directory agent** — two-step execution per file:
-  Step 1 (Classification) uses code to fetch all projects and asks the LLM
-  to identify which project the file belongs to and which domains are relevant.
-  Step 2 (Processing) fetches existing entities for that project/domains via
-  code and runs an LLM with tools — existing data in context enforces
-  update-first naturally.
+* **Local directory agent** — V2 unified flow per file:
+  Phase A (Detect + Preprocess, zero LLM): Python detects the content type
+  and strips markup/noise, producing clean text + metadata.
+  Phase B (Single LLM call with tools): the LLM identifies the project,
+  checks for duplicates via list_* tools, and creates/updates records.
+  ``items_created`` is counted from ``create_*`` tool calls.
 
 * **Cloud connector agent** — fetches data from third-party APIs (Gmail,
   Teams, Outlook) and pushes extracted items to Electron.
@@ -29,7 +29,7 @@ from __future__ import annotations
 import asyncio
 import json
 import logging
-import uuid
+import os
 from datetime import datetime, timedelta, timezone
 from typing import Any
 
@@ -43,7 +43,9 @@ from app.agents.project_agent import PROJECT_TOOLS
 from app.agents.task_agent import TASK_TOOLS
 from app.agents.timeline_agent import TIMELINE_TOOLS
 from app.core.device_manager import DeviceConnectionManager
-from app.core.llm import get_llm
+from app.core.langfuse_client import compile_prompt, extract_usage, get_langfuse, get_prompt_or_fallback, langfuse_context
+from app.core.llm import get_agent_llm, model_for_agent
+from app.core.preprocessors import detect_content_type, preprocess
 from app.core.ws_context import clear_client_executor, execute_on_client, set_client_executor
 from app.db import async_session
 from app.models import AgentRunLog, CloudAgentConfig, LocalAgentConfig
@@ -70,97 +72,52 @@ _MAX_PROCESSING_STEPS: int = 12
 _MAX_SCAN_DEPTH: int = 5
 
 # ── Data-type to tool mapping ─────────────────────────────────────────────
-# NOTE: "projects" is intentionally excluded — project creation/assignment is
-# handled in code by the runner, never delegated to the Step 2 LLM.
 
 _DATA_TYPE_TOOLS: dict[str, list[Any]] = {
     "tasks": TASK_TOOLS,
     "notes": NOTE_TOOLS,
     "timelines": TIMELINE_TOOLS,
+    "timelineEvents": TIMELINE_TOOLS,
+    "projects": PROJECT_TOOLS,
 }
 
-# ── Step 1: Classification prompt ─────────────────────────────────────────
+# ── V2: Unified processing prompt (hot-swappable via Langfuse "unified_processing") ──
 
-_DOMAIN_DESCRIPTIONS: dict[str, str] = {
-    "tasks": (
-        "Action items, to-dos, deliverables — anything that describes work to be done, "
-        "assigned to someone, or tracked with a due date or status."
-    ),
-    "notes": (
-        "Documentation, meeting notes, summaries, reference material — "
-        "written content meant to be read and referenced rather than acted on."
-    ),
-    "timelines": (
-        "Project milestones, deadlines, scheduled events — "
-        "specific dates that mark a point in the progress of a project."
-    ),
-    "projects": (
-        "High-level project entities — only relevant if the file clearly introduces "
-        "a new project or updates the scope of an existing one."
-    ),
-}
-
-_STEP1_SYSTEM_PROMPT = """\
-You are a file classifier for a freelance project management tool.
-
-Your job is to match a file to an existing project and identify which data domains to extract.
-
-## Project matching rules (STRICT — follow in order)
-
-1. Search the file content for any mention of a project name, client name, acronym, or topic
-   that overlaps with the existing projects listed below.
-2. The match does NOT need to be exact — partial name, abbreviation, or topic similarity is enough.
-3. STRONGLY PREFER matching an existing project. Only return "new" as an absolute last resort
-   when the file has zero meaningful connection to any listed project.
-4. When in doubt, pick the closest match from the list.
-
-## Response format
-
-Respond ONLY with a JSON object — no markdown, no explanation:
-
-{{"project_id": "<exact id from the list below, or new>", "new_project_name": "<concise 2-5 word name, only when project_id is new>", "domains": ["tasks", "notes"]}}
-
-## Domain definitions (only consider domains in the allowed list)
-
-{domain_definitions}
-
-## Existing projects
-
-{projects_list}
-"""
-
-# ── Step 2: Processing prompt ─────────────────────────────────────────────
-
-_PROCESSING_SYSTEM_PROMPT = """\
+_UNIFIED_PROCESSING_PROMPT = """\
 You are a data extraction assistant for a freelance project management tool.
 
-Your task: extract structured data from the file content and persist it using the available tools.
+## Your process (follow this exact order)
 
-## Mandatory process — follow this order for EVERY item you extract
+### 1. Identify the project
+File: {filename}
+{metadata_section}
 
-1. READ the existing records listed below for the relevant domain.
-2. SEARCH for a match by title, topic, or semantic similarity.
-3. If a match exists → call the update_* tool with the existing record's id.
-4. If no match exists → call the create_* tool and set isAiSuggested=1.
+Existing projects:
+{projects_list}
 
-NEVER call create_* without first checking the existing records.
-NEVER duplicate a record that already exists under a different wording.
+Match this file to an existing project using the filename and content clues.
+If no project matches, {no_match_behavior}.
 
-## Existing records (source of truth)
+### 2. Check existing records
+Once you identify the project, use list_tasks / list_notes / list_timelines
+(filtered by projectId) to see what already exists.
+NEVER create a record that already exists under the same or similar title.
 
-{existing_context}
+### 3. Extract and create / update
+{extraction_rules}
 
-## Context
-
-Project: {project_context}
-Domains to extract: {data_types}
-
-{custom_prompt_section}
+### Rules
+- Set isAiSuggested=1 on every new record.
+- Set projectId on every record (use the id from the project list above).
+- Update existing records when a match is found by title or topic.
+- Do NOT invent data — only extract what is clearly stated in the content.
+- Target entity types: {data_types}.
+{global_rules}
 """
 
 # ── Cloud processing prompt (kept separate for cloud agent) ───────────────
 
-_CLOUD_PROCESSING_PROMPT = """\
+_BATCH_CLOUD_PROCESSING_PROMPT = """\
 You are a data extraction and management assistant for a freelance project
 management tool.
 
@@ -268,9 +225,19 @@ async def _run_agent_with_tools(
     user_message: str,
     tools: list[Any],
     max_steps: int,
+    user_id: str = "",
+    session_id: str = "",
+    langfuse_prompt: Any = None,
+    agent_name: str = "batch-agent",
+    _tool_calls_out: list[str] | None = None,
 ) -> str:
-    """Run an LLM agent with tool-calling, returning the final text response."""
-    llm = get_llm()
+    """Run an LLM agent with tool-calling, returning the final text response.
+
+    If *_tool_calls_out* is provided, the name of every tool called during the
+    run is appended to it (used by the caller to count ``create_*`` calls).
+    """
+    lf = get_langfuse()
+    llm = get_agent_llm(agent_name)
     llm_with_tools = llm.bind_tools(tools)
     messages: list[Any] = [
         SystemMessage(content=system_prompt),
@@ -279,38 +246,82 @@ async def _run_agent_with_tools(
 
     tool_map = {tool_def.name: tool_def for tool_def in tools}
 
-    for _ in range(max_steps):
-        response: AIMessage = await llm_with_tools.ainvoke(messages)
-        messages.append(response)
+    _lf_ctx = langfuse_context(user_id=user_id or None, session_id=session_id or None)
+    _lf_ctx.__enter__()
 
-        if not response.tool_calls:
-            return _as_text(response.content)
+    _span_ctx = (
+        lf.start_as_current_observation(
+            as_type="span",
+            name=agent_name,
+            metadata={"user_id": user_id} if user_id else None,
+            input=user_message,
+        )
+        if lf else None
+    )
+    _span = _span_ctx.__enter__() if _span_ctx else None
 
-        for call in response.tool_calls:
-            call_id = str(call.get("id", ""))
-            call_name = str(call.get("name", ""))
-            call_args = call.get("args", {})
-            logger.info(
-                "agent_runner: tool_call name=%s args=%s",
-                call_name,
-                json.dumps(call_args, ensure_ascii=True)[:800],
+    try:
+        for _ in range(max_steps):
+            _gen_ctx = (
+                lf.start_as_current_observation(
+                    as_type="generation",
+                    name=f"{agent_name}-llm",
+                    model=model_for_agent(agent_name),
+                    prompt=langfuse_prompt,
+                    input=messages,
+                )
+                if lf else None
             )
+            _gen = _gen_ctx.__enter__() if _gen_ctx else None
+            response: AIMessage = await llm_with_tools.ainvoke(messages)
+            if _gen_ctx:
+                _gen.update(output=_as_text(response.content), usage_details=extract_usage(response))
+                _gen_ctx.__exit__(None, None, None)
 
-            tool_fn = tool_map.get(call_name)
-            if tool_fn is None:
-                tool_output = f"Unknown tool: {call_name}"
-            else:
-                tool_output = await tool_fn.ainvoke(call_args)
+            messages.append(response)
 
-            logger.info(
-                "agent_runner: tool_result name=%s output=%s",
-                call_name,
-                str(tool_output)[:200],
-            )
-            messages.append(ToolMessage(content=str(tool_output), tool_call_id=call["id"]))
+            if not response.tool_calls:
+                final_text = _as_text(response.content)
+                if _span:
+                    _span.update(output=final_text)
+                return final_text
 
-    final = await llm.ainvoke(messages)
-    return _as_text(final.content)
+            for call in response.tool_calls:
+                call_name = str(call.get("name", ""))
+                call_args = call.get("args", {})
+                logger.info(
+                    "agent_runner: tool_call name=%s args=%s",
+                    call_name,
+                    json.dumps(call_args, ensure_ascii=True)[:800],
+                )
+
+                if _tool_calls_out is not None:
+                    _tool_calls_out.append(call_name)
+
+                tool_fn = tool_map.get(call_name)
+                if tool_fn is None:
+                    tool_output = f"Unknown tool: {call_name}"
+                else:
+                    tool_output = await tool_fn.ainvoke(call_args)
+
+                logger.info(
+                    "agent_runner: tool_result name=%s output=%s",
+                    call_name,
+                    str(tool_output)[:200],
+                )
+                messages.append(ToolMessage(content=str(tool_output), tool_call_id=call["id"]))
+
+        final = await llm.ainvoke(messages)
+        final_text = _as_text(final.content)
+        if _span:
+            _span.update(output=final_text)
+        return final_text
+    finally:
+        if _span_ctx:
+            _span_ctx.__exit__(None, None, None)
+        _lf_ctx.__exit__(None, None, None)
+        if lf:
+            lf.flush()
 
 
 # ── Tool list builder ─────────────────────────────────────────────────────
@@ -377,7 +388,8 @@ async def _scan_directories(
     for file_path in all_files:
         try:
             meta = await execute_on_client(action="get_file_metadata", data={"path": file_path})
-            modified_at = meta.get("modifiedAt")
+            # FE sends snake_case keys on the wire (toSnakeCase transform)
+            modified_at = meta.get("modified_at") or meta.get("modifiedAt")
             if modified_at is None:
                 filtered.append(file_path)
                 continue
@@ -479,83 +491,66 @@ def _format_entities_for_context(domain: str, rows: list[dict]) -> str:
     return f"Existing {domain}:\n" + "\n".join(lines)
 
 
-# ── Step 1: LLM file classifier ───────────────────────────────────────────
+# ── V2 helper functions ───────────────────────────────────────────────────
 
 
-async def _classify_file(
-    file_path: str,
-    file_content: str,
-    projects: list[dict],
-    config_data_types: list[str],
-) -> tuple[str, list[str], str | None]:
-    """Call the LLM to classify a file by project and relevant domains.
-
-    Returns ``(project_id_or_"new", domains, new_project_name_or_None)``.
-    - ``project_id`` is an existing project UUID, or ``"new"`` when no match found.
-    - ``new_project_name`` is only set when ``project_id == "new"``.
-    Falls back to ``("new", config_data_types, None)`` on any error.
-    """
-    fallback: tuple[str, list[str], str | None] = ("new", list(config_data_types), None)
-
-    if not file_content.strip():
-        return fallback
-
-    valid_project_ids = {p["id"] for p in projects}
-
-    def _fmt_project(p: dict) -> str:
+def _format_projects(projects: list[dict]) -> str:
+    """Format the project list for the unified system prompt."""
+    if not projects:
+        return "  (no projects yet)"
+    lines: list[str] = []
+    for p in projects:
         summary = (p.get("aiSummary") or p.get("ai_summary") or "").strip()
         summary_part = f" — {summary[:100]}" if summary else ""
-        return f"  - id={p['id']} | name={p.get('name', '')} | status={p.get('status', '')}{summary_part}"
+        lines.append(
+            f"  - id={p['id']} | name={p.get('name', '')} | "
+            f"status={p.get('status', '')}{summary_part}"
+        )
+    return "\n".join(lines)
 
-    projects_list = "\n".join(_fmt_project(p) for p in projects) or "  (none yet)"
 
-    domain_definitions = "\n".join(
-        f"  - {d}: {_DOMAIN_DESCRIPTIONS[d]}"
-        for d in config_data_types
-        if d in _DOMAIN_DESCRIPTIONS
+def _format_metadata(metadata: dict) -> str:
+    """Format preprocessor metadata as a compact context block."""
+    if not metadata:
+        return ""
+    parts: list[str] = []
+    for key in ("subject", "from", "to", "date"):
+        if metadata.get(key):
+            parts.append(f"{key.capitalize()}: {metadata[key]}")
+    # any remaining keys
+    for key, val in metadata.items():
+        if key not in ("subject", "from", "to", "date") and val:
+            parts.append(f"{key}: {val}")
+    return "\n".join(parts)
+
+
+def _get_extraction_rules(agent_config: dict, content_type: str) -> str:
+    """Return the extraction_prompt for *content_type* from *agent_config*.
+
+    Falls back to a generic instruction when the type is not configured.
+    """
+    for ct in agent_config.get("content_types", []):
+        if ct.get("id") == content_type:
+            prompt = ct.get("extraction_prompt", "").strip()
+            if prompt:
+                return prompt
+    return (
+        "Extract relevant information as tasks (action items), notes "
+        "(informational content), or timelines (dated events)."
     )
 
-    system = _STEP1_SYSTEM_PROMPT.format(
-        domain_definitions=domain_definitions,
-        projects_list=projects_list,
-    )
 
-    llm = get_llm()
-    try:
-        response = await llm.ainvoke([
-            SystemMessage(content=system),
-            HumanMessage(content=f"File: {file_path}\n\nContent:\n{file_content[:4000]}"),
-        ])
-        raw = _as_text(response.content).strip()
-        # Strip markdown fences if the model wraps the JSON.
-        if raw.startswith("```"):
-            raw = raw.split("```")[1]
-            if raw.startswith("json"):
-                raw = raw[4:]
-        parsed = json.loads(raw.strip())
-        raw_project_id: str = str(parsed.get("project_id") or "new")
-        # Reject hallucinated UUIDs — only accept ids that exist in the fetched list.
-        project_id = raw_project_id if raw_project_id in valid_project_ids else "new"
-        new_project_name: str | None = (
-            str(parsed["new_project_name"]).strip() or None
-            if project_id == "new" and parsed.get("new_project_name")
-            else None
-        )
-        domains: list[str] = [
-            d for d in parsed.get("domains", [])
-            if d in config_data_types
-        ]
-        if not domains:
-            domains = list(config_data_types)
-        return project_id, domains, new_project_name
-    except Exception as exc:
-        logger.warning(
-            "agent_runner: step1 classification failed for %r: %s", file_path, exc
-        )
-        return fallback
+def _get_no_match_behavior(agent_config: dict) -> str:
+    """Derive the 'no project match' instruction from global_rules."""
+    rules = agent_config.get("global_rules", [])
+    for rule in rules:
+        lower = rule.lower()
+        if "no project" in lower or "no match" in lower or "skip" in lower:
+            return rule
+    return "create a new project with a concise name derived from the file content"
 
 
-# ── Local agent runner (two-step per file) ────────────────────────────────
+# ── Local agent runner (V2 — unified per-file flow) ───────────────────────
 
 
 async def run_local_agent(
@@ -565,16 +560,17 @@ async def run_local_agent(
     device_mgr: DeviceConnectionManager,
     run_context: dict | None = None,
 ) -> None:
-    """Execute a local directory agent run using a two-step approach per file.
+    """Execute a local directory agent run — V2 unified flow.
 
-    Step 1 — Classification (code + 1 LLM call per file, no tools):
-        Code scans directories and fetches all projects via WS.
-        For each file, LLM identifies the project and relevant domains.
+    Phase A — Detect + Preprocess (zero LLM, per file):
+        Python detects the content type from filename + content patterns and
+        runs the appropriate handler (e.g. email_html) to produce clean text
+        and structured metadata.
 
-    Step 2 — Processing (code + 1 LLM call per file, with tools):
-        Code fetches existing entities for the identified project/domains.
-        LLM receives file content + existing entities in context and uses
-        tools to update existing records or create new ones.
+    Phase B — Single LLM call with tools (per file):
+        One LLM call handles project identification, duplicate checking, and
+        record creation/update.  ``create_*`` tool calls are counted to
+        produce the accurate ``items_created`` metric.
     """
     run_id = run_log.id
     agent_id = (run_context or {}).get("agent_id") or config.id
@@ -609,16 +605,11 @@ async def run_local_agent(
     errors: list[str] = []
     items_processed = 0
     items_created = 0
-
-    custom_section = (
-        f"User instructions:\n{config.prompt_template}"
-        if config.prompt_template
-        else ""
-    )
+    agent_config: dict = config.agent_config or {}
+    processing_tools = _build_processing_tools(config.data_types)
 
     try:
         # ── Code: scan directories ───────────────────────────────────
-        logger.info("agent_runner: run=%s scanning directories user=%s", run_id, user_id)
         file_paths = await _scan_directories(
             paths=config.directory_paths,
             extensions=config.file_extensions or [],
@@ -634,108 +625,94 @@ async def run_local_agent(
 
         # ── Code: fetch all projects once ────────────────────────────
         projects = await _fetch_projects()
+        projects_block = _format_projects(projects)
+
+        # Prompt template + Langfuse version linking (hot-swappable from UI).
+        unified_template, prompt_obj = get_prompt_or_fallback(
+            "unified_processing", _UNIFIED_PROCESSING_PROMPT
+        )
 
         for file_path in file_paths:
             try:
-                # Read file content via code.
+                # ── Phase A: read + detect + preprocess ─────────────
                 file_result = await execute_on_client(
                     action="read_file_content", data={"path": file_path}
                 )
-                file_content: str = file_result.get("content", "")
-                if not file_content:
-                    logger.debug("agent_runner: run=%s skipping empty file %r", run_id, file_path)
+                raw_content: str = file_result.get("content", "")
+                if not raw_content.strip():
+                    logger.debug(
+                        "agent_runner: run=%s skipping empty file %r", run_id, file_path
+                    )
                     continue
 
                 items_processed += 1
+                filename = os.path.basename(file_path)
+                content_type = detect_content_type(filename, raw_content)
+                preprocessed = preprocess(content_type, raw_content)
 
-                # Step 1 — classify file.
-                project_id, domains, new_project_name = await _classify_file(
-                    file_path=file_path,
-                    file_content=file_content,
-                    projects=projects,
-                    config_data_types=config.data_types,
-                )
                 logger.info(
-                    "agent_runner: run=%s file=%r → project=%s new_name=%r domains=%s",
-                    run_id,
-                    file_path,
-                    project_id,
-                    new_project_name,
-                    domains,
+                    "agent_runner: run=%s file=%r content_type=%s clean_len=%d",
+                    run_id, file_path, content_type, len(preprocessed.clean_text),
                 )
 
-                # Step 2 — resolve project_id via CODE, then fetch entities.
-                # Project creation is NEVER delegated to the Step 2 LLM.
-                if project_id == "new":
-                    proj_name = new_project_name or "Untitled Project"
-                    try:
-                        proj_result = await execute_on_client(
-                            action="insert",
-                            table="projects",
-                            data={"name": proj_name, "clientId": None},
-                        )
-                        created = proj_result.get("row", {})
-                        effective_project_id = created.get("id", "standalone")
-                        # Add to local list so subsequent files can match it.
-                        if "id" in created:
-                            projects.append(created)
-                        logger.info(
-                            "agent_runner: run=%s created project %r id=%s",
-                            run_id, proj_name, effective_project_id,
-                        )
-                    except Exception as exc:
-                        logger.warning(
-                            "agent_runner: run=%s failed to create project %r: %s",
-                            run_id, proj_name, exc,
-                        )
-                        effective_project_id = "standalone"
-                        proj_name = "unknown"
-                    project_context = (
-                        f"Project: {proj_name} (id: {effective_project_id}). "
-                        "Always set projectId to this id on every record you create."
-                    )
-                else:
-                    effective_project_id = project_id
-                    proj = next((p for p in projects if p["id"] == project_id), None)
-                    proj_name = proj.get("name", project_id) if proj else project_id
-                    project_context = (
-                        f"Project: {proj_name} (id: {project_id}). "
-                        "Always set projectId to this id on every record you create."
+                # ── Phase B: single LLM call ─────────────────────────
+                extraction_rules = _get_extraction_rules(agent_config, content_type)
+                no_match_behavior = _get_no_match_behavior(agent_config)
+                base_global_rules = list(agent_config.get("global_rules", []))
+                if "notes" in config.data_types:
+                    base_global_rules.append(
+                        "For notes: when updating an existing note use `propose_note_edit` "
+                        "(type=append/insert/replace) so the user can review AI changes. "
+                        "Only call `update_note` for complete content replacement without review."
                     )
+                global_rules_lines = "\n".join(f"- {r}" for r in base_global_rules)
+                metadata_section = _format_metadata(preprocessed.metadata)
 
-                # "projects" domain is never passed to Step 2 — handled above in code.
-                domains = [d for d in domains if d != "projects"]
-
-                existing_blocks: list[str] = []
-                for domain in domains:
-                    rows = await _fetch_domain_entities(domain, effective_project_id)
-                    existing_blocks.append(_format_entities_for_context(domain, rows))
-
-                existing_context = "\n\n".join(existing_blocks)
-
-                system_prompt = _PROCESSING_SYSTEM_PROMPT.format(
-                    existing_context=existing_context,
-                    project_context=project_context,
-                    data_types=", ".join(domains),
-                    custom_prompt_section=custom_section,
+                system_prompt = compile_prompt(
+                    unified_template,
+                    prompt_obj,
+                    filename=filename,
+                    metadata_section=metadata_section,
+                    projects_list=projects_block,
+                    no_match_behavior=no_match_behavior,
+                    extraction_rules=extraction_rules,
+                    global_rules=global_rules_lines,
+                    data_types=", ".join(config.data_types),
                 )
 
-                processing_tools = _build_processing_tools(domains)
+                user_message = (
+                    f"Process this file and extract relevant information.\n\n"
+                    f"File: {file_path}\n\n"
+                    f"Content:\n{preprocessed.clean_text}"
+                )
 
+                file_tool_calls: list[str] = []
                 result_text = await _run_agent_with_tools(
                     system_prompt=system_prompt,
-                    user_message=(
-                        f"Process this file and extract relevant information.\n\n"
-                        f"File: {file_path}\n\nContent:\n{file_content}"
-                    ),
+                    user_message=user_message,
                     tools=processing_tools,
                     max_steps=_MAX_PROCESSING_STEPS,
+                    user_id=user_id,
+                    session_id=run_id,
+                    langfuse_prompt=prompt_obj,
+                    agent_name="unified-processor",
+                    _tool_calls_out=file_tool_calls,
                 )
+
+                file_created = sum(
+                    1 for name in file_tool_calls if name.startswith("create_")
+                )
+                items_created += file_created
+
+                # Refresh project list when a project was created so
+                # subsequent files see it in the prompt context.
+                if "create_project" in file_tool_calls:
+                    projects = await _fetch_projects()
+                    projects_block = _format_projects(projects)
+
                 logger.info(
-                    "agent_runner: run=%s file=%r result=%s",
-                    run_id,
-                    file_path,
-                    result_text[:200],
+                    "agent_runner: run=%s file=%r created=%d result=%s",
+                    run_id, file_path, file_created, result_text[:200],
                 )
 
             except Exception as exc:
@@ -767,10 +744,11 @@ async def run_local_agent(
         errors=errors,
     )
     logger.info(
-        "agent_runner: run=%s done status=%s processed=%d errors=%d",
+        "agent_runner: run=%s done status=%s processed=%d created=%d errors=%d",
         run_id,
         final_status,
         items_processed,
+        items_created,
         len(errors),
     )
 
@@ -928,7 +906,12 @@ async def run_cloud_agent(
                 continue
             items_processed += 1
 
-            processing_prompt = _CLOUD_PROCESSING_PROMPT.format(
+            cloud_template, cloud_prompt_obj = get_prompt_or_fallback(
+                "batch_cloud_processing", _BATCH_CLOUD_PROCESSING_PROMPT
+            )
+            processing_prompt = compile_prompt(
+                cloud_template,
+                cloud_prompt_obj,
                 data_types=", ".join(config.data_types),
                 project_context="Determine the appropriate project from the message context.",
                 file_list=f"Message from {config.provider} (id: {msg.id})",
@@ -941,6 +924,10 @@ async def run_cloud_agent(
                     user_message=f"Process this message content:\n\n{content_text[:8000]}",
                     tools=processing_tools,
                     max_steps=_MAX_PROCESSING_STEPS,
+                    user_id=user_id,
+                    session_id=run_id,
+                    langfuse_prompt=cloud_prompt_obj,
+                    agent_name="cloud-processor",
                 )
             except Exception as exc:
                 errors.append(f"LLM processing error for message {msg.id!r}: {exc}")

app/core/agent_session_buffer.py

@@ -0,0 +1,59 @@
+"""In-process TTL buffer for per-session LangChain message history.
+
+Stores the full message list (including AIMessage with tool_calls and ToolMessage)
+keyed by (user_id, session_id), so agents can reconstruct tool-call context across
+conversation turns without it being lossy through the wire.
+
+Single-process only. For multi-worker deployments, replace the _SessionBuffer
+implementation with one backed by Redis (serialize LangChain messages to dicts via
+message_to_dict / messages_from_dict from langchain_core.messages).
+"""
+from __future__ import annotations
+
+import time
+from threading import Lock
+
+from langchain_core.messages import BaseMessage
+
+SESSION_TTL_SECONDS = 1800  # 30-minute idle expiry
+MAX_MESSAGES_PER_SESSION = 80  # cap to avoid unbounded memory growth
+
+
+class _SessionBuffer:
+    def __init__(self) -> None:
+        self._store: dict[tuple[str, str], tuple[float, list[BaseMessage]]] = {}
+        self._lock = Lock()
+
+    def _evict_stale(self) -> None:
+        now = time.monotonic()
+        stale = [k for k, (ts, _) in self._store.items() if now - ts > SESSION_TTL_SECONDS]
+        for k in stale:
+            del self._store[k]
+
+    def get(self, user_id: str, session_id: str) -> list[BaseMessage] | None:
+        key = (user_id, session_id)
+        with self._lock:
+            entry = self._store.get(key)
+            if entry is None:
+                return None
+            ts, msgs = entry
+            if time.monotonic() - ts > SESSION_TTL_SECONDS:
+                del self._store[key]
+                return None
+            self._store[key] = (time.monotonic(), msgs)
+            return list(msgs)
+
+    def set(self, user_id: str, session_id: str, messages: list[BaseMessage]) -> None:
+        key = (user_id, session_id)
+        capped = messages[-MAX_MESSAGES_PER_SESSION:]
+        with self._lock:
+            self._evict_stale()
+            self._store[key] = (time.monotonic(), capped)
+
+    def clear(self, user_id: str, session_id: str) -> None:
+        with self._lock:
+            self._store.pop((user_id, session_id), None)
+
+
+# Module-level singleton — same pattern as _pending_states in api/app/api/routes/auth.py
+session_buffer = _SessionBuffer()

app/core/brief_agent.py

@@ -0,0 +1,228 @@
+"""Brief agent — produces plain-text home and project status briefs.
+
+Read-only tool subset only. Never calls _normalize_tagged_list_lines —
+the brief prompt forbids XML tags, so skipping post-processing is intentional.
+"""
+
+from __future__ import annotations
+
+from collections.abc import AsyncGenerator
+from datetime import date
+from typing import Any
+
+from app.agents.note_agent import NOTE_READ_TOOLS
+from app.agents.project_agent import PROJECT_READ_TOOLS
+from app.agents.task_agent import TASK_READ_TOOLS
+from app.agents.timeline_agent import TIMELINE_READ_TOOLS
+from app.core.deep_agent import (
+    _language_instruction,
+    _proactive_hints_injection,
+    _read_only_memory_tools,
+    _relational_memory_injection,
+    _run_single_agent_stream,
+    _trace_id_from_context,
+    build_brief_multi_project_manifest,
+)
+from app.core.langfuse_client import compile_prompt, get_prompt_or_fallback
+
+_LANGUAGE_NAMES: dict[str, str] = {
+    "en": "English", "it": "Italian", "es": "Spanish",
+    "fr": "French", "de": "German",
+    "english": "English", "italian": "Italian", "italiano": "Italian",
+    "spanish": "Spanish", "español": "Spanish",
+    "french": "French", "français": "French",
+    "german": "German", "deutsch": "German",
+}
+
+_HOME_BRIEF_FALLBACK = """\
+You are the user's personal assistant producing a short daily brief.
+
+ROLE
+Act like a calm, attentive secretary writing a stand-up note for your boss.
+Warm and human, never breezy. Never cheerful filler, never emojis, never
+"here is your brief" meta-text. The user is opening the app mid-workday and
+is probably stressed — your job is to lower cognitive load, not add noise.
+
+TOOLS — always call before writing
+Pull fresh data every run. Do not invent counts or titles. Use at minimum:
+- list_tasks_due_today — tasks the user owes today
+- list_timelines_today — events starting or ending today
+- list_all_projects — projects currently in progress or at risk
+- memory_list_blocks / memory_get — personal context about people, clients,
+  payment habits, working preferences
+If a tool returns nothing, simply omit that topic. Never report zeros.
+
+WHAT TO INCLUDE
+1. Tasks due today (title + priority; group the 1-2 most important).
+2. Timeline events starting or ending today (and anything that starts/ends
+   tomorrow if the user has a very light day).
+3. Active projects that need a nudge — stalled, blocked, or awaiting input.
+4. Memory-aware colour where it sharpens the brief. Examples:
+   - "Client Rossi tends to pay late — the Acme invoice is 6 days out."
+   - "You usually dislike meetings before 10:00 — the call at 09:30 is unusual."
+   Only add a memory line when it changes what the user does. Do not pad.
+
+WHAT TO OMIT
+- Zero-counts ("no overdue items", "0 meetings today").
+- Statistics ("2 active projects, 3 completed tasks").
+- Headers, titles, greetings, sign-offs, dates, emojis, slang.
+- Meta-phrases ("here is", "let me know if", "hope this helps").
+- XML/HTML tags of any kind. Plain prose only.
+
+LIGHT-DAY CLAUSE
+If tasks + events + active-project-nudges together produce fewer than two
+sentences of content, also list 1-2 projects in status on_hold or waiting
+and ask a single, specific question about them — e.g. "Is the Bianchi
+redesign still paused, or ready to pick back up?" One question max, grounded
+in a real project name.
+
+VOICE
+- Calm. Concise. Human. Short sentences.
+- Use **bold** sparingly for task titles, project names, and people's names.
+- No bullet lists. Flow as 2-4 sentences of prose.
+
+LENGTH
+2-4 sentences total. Hard cap 4. If the day is truly empty, one sentence.
+
+Respond in the user's language ({language}). Today is {today}.\
+"""
+
+_PROJECT_BRIEF_FALLBACK = """\
+You are the project assistant producing a short status brief for ONE project.
+
+ROLE
+A senior project manager summarising state-of-play for the owner. Factual,
+sharp, forward-looking. Never reassuring filler, never emojis.
+
+SCOPE
+Work only with project_id = {project_id}. Do not mention or pull data from
+other projects. Use tools to fetch fresh data:
+- get_project — current status, dates, description
+- list_tasks(project_id) — open work, split by status
+- list_timelines(project_id) — milestones hit, upcoming, overdue
+- list_notes(project_id) — any recent decisions or blockers
+- memory_get — relevant context about the client, collaborators, constraints
+
+STRUCTURE — follow exactly, one short paragraph per section, no headers
+1. **State.** One sentence: current phase, health (on track / at risk / blocked),
+   and why. Cite the concrete signal (overdue milestone, stalled tasks, recent
+   blocker note).
+2. **What's moving.** What was completed or progressed recently. Name specific
+   tasks or milestones.
+3. **Next steps.** The 1-3 most important things the user should do next, in
+   priority order. Be concrete — task name, who owns it, when due if known.
+   If waiting on someone else, name them and what the ask is.
+4. **Risks / memory-flagged items.** One line max. Only include when there is
+   a real risk or a relevant memory (e.g. late-paying client, tight deadline,
+   scope change). Omit the section entirely if nothing to say.
+
+WHAT TO OMIT
+- Zero-counts ("no overdue tasks").
+- Generic advice ("keep up the good work").
+- Greetings, headers, bullet lists, emojis, sign-offs, meta-phrases.
+- XML/HTML tags or bracketed id lists. Plain prose only.
+
+VOICE
+- Direct. Factual. No fluff.
+- Use **bold** sparingly for task titles, milestone names, and the owner's name.
+- Short sentences. Prefer verbs over nouns ("Client review is blocking release"
+  not "There is a blocker which is the client review").
+
+LENGTH
+4-8 sentences total across the 3-4 sections. Hard cap 8.
+
+Respond in the user's language ({language}). Today is {today}.\
+"""
+
+
+def _resolve_language(context: dict[str, Any]) -> str:
+    core = context.get("core_memory") or {}
+    raw = (core.get("language") or "en").strip().lower()
+    return _LANGUAGE_NAMES.get(raw, raw.title()) or "English"
+
+
+def _build_read_tools(user_id: str, trace_id: str | None) -> list[Any]:
+    return [
+        *TASK_READ_TOOLS,
+        *PROJECT_READ_TOOLS,
+        *TIMELINE_READ_TOOLS,
+        *NOTE_READ_TOOLS,
+        *_read_only_memory_tools(user_id, trace_id),
+    ]
+
+
+async def run_home_brief(
+    user_id: str,
+    context: dict[str, Any],
+) -> AsyncGenerator[tuple[str, Any], None]:
+    """Stream a plain-text daily home brief.
+
+    Yields (event_type, data) tuples identical to _run_single_agent_stream.
+    Do NOT post-process output through _normalize_tagged_list_lines.
+    """
+    from app.agents.folder_agent import FOLDER_TOOLS
+
+    trace_id = _trace_id_from_context(context)
+    today = date.today().isoformat()
+    language = _resolve_language(context)
+
+    raw_template, langfuse_prompt = get_prompt_or_fallback("home_brief", _HOME_BRIEF_FALLBACK)
+    system_prompt = compile_prompt(raw_template, langfuse_prompt, language=language, today=today)
+    system_prompt += _relational_memory_injection(context)
+    system_prompt += _proactive_hints_injection(context)
+    system_prompt += _language_instruction(context)
+    if today not in system_prompt:
+        system_prompt += f"\nToday is {today}."
+
+    brief_manifest = await build_brief_multi_project_manifest()
+    system_prompt = system_prompt + ("\n\n" + brief_manifest if brief_manifest else "")
+
+    tools = [*_build_read_tools(user_id, trace_id), *FOLDER_TOOLS]
+    async for event in _run_single_agent_stream(
+        user_id=user_id,
+        system_prompt=system_prompt,
+        message="Generate the daily brief.",
+        context=context,
+        langfuse_prompt=langfuse_prompt,
+        agent_name="brief-agent",
+        tools=tools,
+    ):
+        yield event
+
+
+async def run_project_brief(
+    user_id: str,
+    project_id: str,
+    context: dict[str, Any],
+) -> AsyncGenerator[tuple[str, Any], None]:
+    """Stream a plain-text project status brief for project_id.
+
+    Yields (event_type, data) tuples identical to _run_single_agent_stream.
+    Do NOT post-process output through _normalize_tagged_list_lines.
+    """
+    trace_id = _trace_id_from_context(context)
+    today = date.today().isoformat()
+    language = _resolve_language(context)
+
+    raw_template, langfuse_prompt = get_prompt_or_fallback("project_brief", _PROJECT_BRIEF_FALLBACK)
+    system_prompt = compile_prompt(
+        raw_template, langfuse_prompt,
+        language=language, today=today, project_id=project_id,
+    )
+    system_prompt += _relational_memory_injection(context)
+    system_prompt += _proactive_hints_injection(context)
+    system_prompt += _language_instruction(context)
+    if today not in system_prompt:
+        system_prompt += f"\nToday is {today}."
+
+    tools = _build_read_tools(user_id, trace_id)
+    async for event in _run_single_agent_stream(
+        user_id=user_id,
+        system_prompt=system_prompt,
+        message=f"Generate the project status brief for project {project_id}.",
+        context=context,
+        langfuse_prompt=langfuse_prompt,
+        agent_name="brief-agent",
+        tools=tools,
+    ):
+        yield event

app/core/embeddings.py

@@ -0,0 +1,34 @@
+"""OpenAI embedding helper for associative memory tier.
+
+Single public function: ``embed_text(text) -> list[float] | None``.
+Returns None on any failure — callers must implement a keyword fallback.
+Never raises; all exceptions are logged as warnings.
+"""
+
+from __future__ import annotations
+
+import logging
+
+from openai import AsyncOpenAI
+
+logger = logging.getLogger(__name__)
+
+_MAX_INPUT_CHARS = 8000
+_EMBEDDING_MODEL = "text-embedding-3-small"
+
+
+async def embed_text(text: str) -> list[float] | None:
+    """Call OpenAI text-embedding-3-small. Return None on failure (caller falls back to keyword)."""
+    try:
+        client = AsyncOpenAI()
+        truncated = text[:_MAX_INPUT_CHARS]
+        response = await client.embeddings.create(
+            input=truncated,
+            model=_EMBEDDING_MODEL,
+        )
+        result: list[float] = response.data[0].embedding
+        logger.debug("embeddings: embed_text dims=%d", len(result))
+        return result
+    except Exception as exc:
+        logger.warning("embeddings: embed_text failed: %s", exc)
+        return None

app/core/folder_indexer.py

@@ -0,0 +1,183 @@
+"""Per-file summarisation for project folder integration."""
+from __future__ import annotations
+
+import base64
+import io
+from dataclasses import dataclass
+
+from langchain_core.messages import HumanMessage, SystemMessage
+from pypdf import PdfReader
+from docx import Document as DocxDocument
+
+from app.core.langfuse_client import (
+    compile_prompt,
+    extract_usage,
+    get_langfuse,
+    get_prompt_or_fallback,
+)
+from app.core.llm import get_llm
+
+_TEXT_FALLBACK = (
+    "You are summarising a file for an AI assistant that helps the user manage a project.\n"
+    "Produce a single sentence (<=30 words, <=200 chars) that captures the file's purpose "
+    "and most important detail.\nFile extension: {ext}\nFile name: {name}\nContent (truncated if long):\n{content}"
+)
+_IMAGE_FALLBACK = (
+    "You are summarising an image attached to a project folder.\n"
+    "Produce a single sentence (<=30 words, <=200 chars) describing what the image shows "
+    "and any obvious purpose (logo, screenshot, diagram, photo of a whiteboard, etc.)."
+)
+_MAX_INPUT_CHARS = 6000
+
+
+@dataclass
+class IndexResult:
+    summary: str
+    tokens_used: int
+
+
+async def _llm_text(messages: list) -> object:
+    """Make the LLM call for text summarisation.
+
+    Defined as a standalone async function so tests can patch it cleanly
+    without needing to mock the LLM object itself.
+    """
+    llm = get_llm(model="gpt-4o-mini", temperature=0.2)
+    return await llm.ainvoke(messages)
+
+
+async def _llm_vision(messages: list) -> object:
+    """Make the LLM call for vision (image) summarisation.
+
+    Accepts the message list and returns the response directly, mirroring
+    the ``_llm_text`` caller pattern so tests can patch it at the module level.
+    """
+    llm = get_llm(model="gpt-4o-mini", temperature=0.2)
+    return await llm.ainvoke(messages)
+
+
+async def summarize_image(*, image_b64: str, mime: str, file_name: str | None = None) -> IndexResult:
+    """Return a compact summary of an image file using vision.
+
+    Parameters
+    ----------
+    image_b64:
+        Base64-encoded image bytes.
+    mime:
+        MIME type of the image, e.g. ``"image/png"``.
+    file_name:
+        Optional file name, attached to the Langfuse trace as input metadata.
+    """
+    template, prompt_obj = get_prompt_or_fallback("folder_file_summary_image", _IMAGE_FALLBACK)
+    messages = [
+        SystemMessage(content=template),
+        HumanMessage(content=[
+            {"type": "text", "text": "Summarise this image."},
+            {"type": "image_url", "image_url": {"url": f"data:{mime};base64,{image_b64}"}},
+        ]),
+    ]
+    lf = get_langfuse()
+    if lf is not None:
+        with lf.start_as_current_observation(
+            as_type="generation",
+            name="folder-summarize-image",
+            model="gpt-4o-mini",
+            prompt=prompt_obj,
+            input={"file_name": file_name, "mime": mime},
+        ) as gen:
+            response = await _llm_vision(messages)
+            usage = extract_usage(response)
+            gen.update(output=response.content, usage_details=usage)
+    else:
+        response = await _llm_vision(messages)
+        usage = extract_usage(response)
+    summary = (response.content or "").strip()[:500]
+    return IndexResult(summary=summary, tokens_used=usage.get("total", 0))
+
+
+async def summarize_text(*, content: str, ext: str, name: str) -> IndexResult:
+    """Return a compact summary of a text file.
+
+    Parameters
+    ----------
+    content:
+        Raw text content of the file (will be truncated to _MAX_INPUT_CHARS).
+    ext:
+        File extension including the leading dot, e.g. ``".md"``.
+    name:
+        File name, e.g. ``"kickoff.md"``.
+    """
+    template, prompt_obj = get_prompt_or_fallback("folder_file_summary_text", _TEXT_FALLBACK)
+    truncated = content[:_MAX_INPUT_CHARS]
+    compiled = compile_prompt(template, prompt_obj, ext=ext, name=name, content=truncated)
+    messages = [
+        SystemMessage(content=compiled),
+        HumanMessage(content="Summarise this file."),
+    ]
+    lf = get_langfuse()
+    if lf is not None:
+        with lf.start_as_current_observation(
+            as_type="generation",
+            name="folder-summarize-text",
+            model="gpt-4o-mini",
+            prompt=prompt_obj,
+            input={"file_name": name, "ext": ext, "content_chars": len(truncated)},
+        ) as gen:
+            response = await _llm_text(messages)
+            usage = extract_usage(response)
+            gen.update(output=response.content, usage_details=usage)
+    else:
+        response = await _llm_text(messages)
+        usage = extract_usage(response)
+    summary = (response.content or "").strip()[:500]
+    return IndexResult(summary=summary, tokens_used=usage.get("total", 0))
+
+
+def _extract_pdf_text(pdf_b64: str) -> str:
+    buf = io.BytesIO(base64.b64decode(pdf_b64))
+    reader = PdfReader(buf)
+    parts: list[str] = []
+    for page in reader.pages:
+        try:
+            parts.append(page.extract_text() or "")
+        except Exception:
+            continue
+    return "\n".join(parts).strip()
+
+
+def _extract_docx_text(docx_b64: str) -> str:
+    buf = io.BytesIO(base64.b64decode(docx_b64))
+    doc = DocxDocument(buf)
+    return "\n".join(p.text for p in doc.paragraphs if p.text).strip()
+
+
+async def summarize_pdf(*, pdf_b64: str, name: str) -> IndexResult:
+    """Return a compact summary of a PDF file.
+
+    Parameters
+    ----------
+    pdf_b64:
+        Base64-encoded PDF bytes.
+    name:
+        File name, e.g. ``"report.pdf"``.
+    """
+    text = _extract_pdf_text(pdf_b64)
+    if not text:
+        return IndexResult(summary="Could not extract text", tokens_used=0)
+    return await summarize_text(content=text, ext=".pdf", name=name)
+
+
+async def summarize_docx(*, docx_b64: str, name: str) -> IndexResult:
+    """Return a compact summary of a DOCX file.
+
+    Parameters
+    ----------
+    docx_b64:
+        Base64-encoded DOCX bytes.
+    name:
+        File name, e.g. ``"spec.docx"``.
+    """
+    text = _extract_docx_text(docx_b64)
+    if not text:
+        return IndexResult(summary="Could not extract text", tokens_used=0)
+    return await summarize_text(content=text, ext=".docx", name=name)

app/core/langfuse_client.py

@@ -0,0 +1,190 @@
+"""Langfuse observability — singleton client and prompt helpers.
+
+If LANGFUSE_SECRET_KEY / LANGFUSE_PUBLIC_KEY are not set,
+all helpers are no-ops so the app works without Langfuse configured.
+
+Usage
+-----
+Tracing::
+
+    from app.core.langfuse_client import get_langfuse
+
+    lf = get_langfuse()
+    if lf:
+        with lf.start_as_current_observation(as_type="span", name="my-agent") as span:
+            span.update(input=user_message)
+            # ... do work ...
+            span.update(output=result)
+        lf.flush()
+
+Prompt management::
+
+    from app.core.langfuse_client import get_prompt_or_fallback
+
+    text, prompt_obj = get_prompt_or_fallback("home_system", FALLBACK_PROMPT)
+    # Use text as the system prompt; pass prompt_obj to generations for linking.
+
+Linking a prompt to a generation::
+
+    with lf.start_as_current_observation(
+        as_type="generation",
+        name="llm-call",
+        model="gpt-4o",
+        prompt=prompt_obj,   # links generation → prompt version in the UI
+        input=messages,
+    ) as gen:
+        response = await llm.ainvoke(messages)
+        gen.update(output=response.content, usage=_usage(response))
+"""
+
+from __future__ import annotations
+
+import hashlib
+import logging
+from contextlib import contextmanager
+from typing import Any, Generator
+
+logger = logging.getLogger(__name__)
+
+_client: Any = None
+_initialized: bool = False
+
+
+def get_langfuse() -> Any | None:
+    """Return the Langfuse singleton, or ``None`` when not configured."""
+    global _client, _initialized
+    if _initialized:
+        return _client
+    _initialized = True
+
+    from app.config.settings import settings  # local import to avoid circular deps
+
+    if not settings.LANGFUSE_SECRET_KEY or not settings.LANGFUSE_PUBLIC_KEY:
+        logger.debug("langfuse: not configured — observability disabled")
+        return None
+
+    try:
+        from langfuse import Langfuse
+
+        _client = Langfuse(
+            secret_key=settings.LANGFUSE_SECRET_KEY,
+            public_key=settings.LANGFUSE_PUBLIC_KEY,
+            host=settings.LANGFUSE_BASE_URL,
+        )
+        logger.info("langfuse: client initialized host=%s", settings.LANGFUSE_BASE_URL)
+    except Exception as exc:
+        logger.warning("langfuse: failed to initialize: %s", exc)
+        _client = None
+
+    return _client
+
+
+def get_prompt_or_fallback(name: str, fallback: str) -> tuple[str, Any]:
+    """Fetch a text prompt from Langfuse; fall back to ``fallback`` on any error.
+
+    Returns ``(raw_template, prompt_obj_or_None)``.
+
+    * ``raw_template`` — the uncompiled template string.  Do NOT call ``.format()``
+      on it directly; use :func:`compile_prompt` instead so the correct variable
+      syntax is applied (``{{var}}`` for Langfuse, ``{var}`` for the fallback).
+    * ``prompt_obj`` — the Langfuse prompt object, or ``None`` when Langfuse is
+      unavailable / the fetch failed.  Pass this to generation observations so
+      Langfuse links the generation to the exact prompt version in the UI.
+    """
+    lf = get_langfuse()
+    if lf is None:
+        return fallback, None
+
+    try:
+        prompt = lf.get_prompt(name, label="production", fallback=fallback)
+        # For text-type prompts .prompt holds the raw template string.
+        raw = prompt.prompt if hasattr(prompt, "prompt") and isinstance(prompt.prompt, str) else fallback
+        return raw, prompt
+    except Exception as exc:
+        logger.warning("langfuse: get_prompt %r failed: %s — using fallback", name, exc)
+        return fallback, None
+
+
+def compile_prompt(template: str, prompt_obj: Any, **variables: Any) -> str:
+    """Compile *template* with *variables*, choosing the right syntax.
+
+    * When *prompt_obj* is a real Langfuse prompt object, calls
+      ``prompt_obj.compile(**variables)`` which handles ``{{variable}}``
+      substitution as defined in the Langfuse UI.
+    * When *prompt_obj* is ``None`` (Langfuse unavailable or fetch failed),
+      falls back to ``template.format(**variables)`` which handles the
+      ``{variable}`` syntax used in the hardcoded fallback strings.
+
+    This keeps callers oblivious to which syntax is in use.
+    """
+    if prompt_obj is not None:
+        try:
+            compiled = prompt_obj.compile(**variables)
+            # compile() returns a string for text prompts.
+            if isinstance(compiled, str):
+                return compiled
+            # Chat prompts return a list of dicts — join text parts.
+            if isinstance(compiled, list):
+                return "\n".join(
+                    m.get("content", "") for m in compiled if isinstance(m, dict)
+                )
+        except Exception as exc:
+            logger.warning(
+                "langfuse: compile failed for prompt %r: %s — falling back to .format()",
+                getattr(prompt_obj, "name", "?"),
+                exc,
+            )
+    return template.format(**variables)
+
+
+def extract_usage(response: Any) -> dict[str, int]:
+    """Extract token usage from a LangChain AI message into Langfuse format."""
+    meta = getattr(response, "usage_metadata", None)
+    if not meta:
+        return {}
+    return {
+        "input": int(meta.get("input_tokens", 0)),
+        "output": int(meta.get("output_tokens", 0)),
+        "total": int(meta.get("total_tokens", 0)),
+    }
+
+
+def hash_user_id(user_id: str) -> str:
+    """Return a SHA-256 hash of *user_id* for use as Langfuse ``user_id``.
+
+    This avoids sending raw database UUIDs to external observability services
+    while still providing a stable, deterministic identifier for per-user
+    metrics in the Langfuse dashboard.
+    """
+    return hashlib.sha256(user_id.encode()).hexdigest()
+
+
+@contextmanager
+def langfuse_context(
+    user_id: str | None = None,
+    session_id: str | None = None,
+) -> Generator[None, None, None]:
+    """Propagate ``user_id`` (hashed) and ``session_id`` to all Langfuse observations.
+
+    No-op when Langfuse is not configured or parameters are empty.
+    """
+    lf = get_langfuse()
+    if lf is None or (not user_id and not session_id):
+        yield
+        return
+
+    try:
+        from langfuse import propagate_attributes
+    except ImportError:
+        logger.debug("langfuse: propagate_attributes not available — skipping context")
+        yield
+        return
+
+    attrs: dict[str, str] = {}
+    if user_id:
+        attrs["user_id"] = hash_user_id(user_id)
+    if session_id:
+        attrs["session_id"] = session_id
+
+    with propagate_attributes(**attrs):
+        yield

app/core/llm.py

@@ -1,6 +1,6 @@
 """LLM factory — centralised model instantiation via LiteLLM.
 
-Every agent and the orchestrator call ``get_llm()`` or ``get_router_llm()``
+Every agent and the orchestrator call ``get_llm()``
 instead of directly constructing a provider-specific class.  The model string
 follows the `LiteLLM model naming convention
 <https://docs.litellm.ai/docs/providers>`_:
@@ -11,7 +11,7 @@ follows the `LiteLLM model naming convention
 * Ollama:     ``ollama/llama3``
 * Bedrock:    ``bedrock/anthropic.claude-v2``
 
-Switch providers by changing **LLM_MODEL** / **LLM_ROUTER_MODEL** in ``.env``
+Switch providers by changing **LLM_MODEL** in ``.env``
 — no code changes required.
 """
 
@@ -19,6 +19,7 @@ from __future__ import annotations
 
 import os
 import warnings
+from collections.abc import Callable
 
 from openai import AsyncOpenAI
 import litellm
@@ -50,6 +51,10 @@ def _api_key_for_model(model: str) -> str | None:
         return settings.GOOGLE_API_KEY or None
     if model.startswith("cerebras/"):
         return settings.CEREBRAS_API_KEY or None
+    if model.startswith("groq/"):
+        return settings.GROQ_API_KEY or None
+    if model.startswith("deepseek/"):
+        return settings.DEEPSEEK_API_KEY or None
     if model.startswith("github_copilot/"):
         # GitHub Copilot uses OAuth device-flow tokens managed by LiteLLM.
         # No API key is required; returning None lets LiteLLM handle auth.
@@ -95,12 +100,39 @@ def get_llm(
     )
 
 
-def get_router_llm(
+_AGENT_MODEL_SETTINGS: dict[str, Callable[[], str]] = {
+    "classifier":          lambda: settings.LLM_MODEL_CLASSIFIER or settings.LLM_MODEL,
+    "home-agent":          lambda: settings.LLM_MODEL_HOME_AGENT or settings.LLM_MODEL,
+    "floating-agent":      lambda: settings.LLM_MODEL_FLOATING_AGENT or settings.LLM_MODEL,
+    "unified-processor":   lambda: settings.LLM_MODEL_UNIFIED_PROCESSOR or settings.LLM_MODEL,
+    "cloud-processor":     lambda: settings.LLM_MODEL_CLOUD_PROCESSOR or settings.LLM_MODEL,
+    "brief-agent":         lambda: settings.LLM_MODEL_BRIEF_AGENT or settings.LLM_MODEL,
+    "task-brief-agent":    lambda: settings.LLM_MODEL_TASK_BRIEF_AGENT or settings.LLM_MODEL,
+    "setup":               lambda: settings.LLM_MODEL_SETUP_AGENT or settings.LLM_MODEL,
+    "memory-extractor":    lambda: settings.LLM_MODEL_MEMORY_EXTRACTOR or "gpt-4o-mini",
+    "memory-miner":        lambda: settings.LLM_MODEL_MEMORY_MINER or "gpt-4o-mini",
+    "memory-auditor":      lambda: settings.LLM_MODEL_MEMORY_AUDITOR or settings.LLM_MODEL,
+    "note-summarizer":     lambda: "gpt-4o-mini",
+}
+
+
+def model_for_agent(agent_name: str) -> str:
+    """Return the resolved model string for *agent_name* (for Langfuse tracking)."""
+    return _AGENT_MODEL_SETTINGS.get(agent_name, lambda: settings.LLM_MODEL)()
+
+
+def get_agent_llm(
+    agent_name: str,
     *,
     temperature: float = 0,
 ) -> ChatOpenAI | ChatLiteLLM:
-    """Return the lighter model used for intent classification / routing."""
-    return get_llm(model=settings.LLM_ROUTER_MODEL, temperature=temperature)
+    """Return an LLM configured for *agent_name*, respecting per-agent overrides.
+
+    Falls back to ``settings.LLM_MODEL`` for unknown agent names or when the
+    per-agent override is left empty in ``.env``.
+    """
+    model = model_for_agent(agent_name)
+    return get_llm(model=model, temperature=temperature)
 
 
 async def embed(text: str) -> list[float]:

app/core/memory_extraction.py

@@ -0,0 +1,450 @@
+"""Mem0-style Extract/Update pipeline — Phase 2.
+
+Runs after every ``store_episode`` call to distil durable facts, preferences,
+routines, and relations from the latest conversation turn.
+
+Entry point: ``run_extraction(db, user_id, last_user_msg, last_assistant_msg, session_id)``
+
+Design notes
+------------
+- Two gpt-4o-mini calls per turn: extract candidates, then decide action per candidate.
+- Short-circuit: if no existing neighbours → ADD without a second LLM call (cost saving).
+- Zero-trust: never logs decrypted user content; relation subject/object labels are
+  treated as identifiers (safe to log per spec).
+- Must not raise into the request path — caller wraps in asyncio.create_task().
+"""
+
+from __future__ import annotations
+
+import json
+import logging
+from typing import Any, Literal
+
+from pydantic import BaseModel, Field
+from sqlalchemy.ext.asyncio import AsyncSession
+
+from app.core.langfuse_client import get_langfuse, get_prompt_or_fallback, extract_usage, langfuse_context
+from app.core.llm import get_agent_llm, model_for_agent
+
+logger = logging.getLogger(__name__)
+
+# ── Fallback prompts (used when Langfuse unavailable) ─────────────────────────
+
+_EXTRACTION_FALLBACK = (
+    "You are a memory extractor for a personal AI secretary. Given the last conversation "
+    "turn, the user's core memory, and recent episode summaries, identify durable facts, "
+    "preferences, routines, and person/project relations worth remembering.\n\n"
+    "Output JSON matching this schema exactly:\n"
+    '{{"candidates": [{{"type": "<fact|preference|relation|routine>", '
+    '"content": "<short canonical statement>", '
+    '"target_tier": "<core|associative|relational|proactive>", '
+    '"subject": null, "predicate": null, "object": null, "confidence": 0.7}}]}}\n\n'
+    "Rules:\n"
+    "- Skip small talk, greetings, one-off questions.\n"
+    "- Max 5 candidates per call.\n"
+    "- Only extract durable information (still true next week).\n"
+    "- For type=relation: subject/predicate/object required.\n"
+    "- Default confidence=0.7.\n\n"
+    "## Last turn\n{last_turn}\n\n"
+    "## Core memory (current)\n{core_memory}\n\n"
+    "## Recent episodes\n{recent_episodes}"
+)
+
+_DECIDE_FALLBACK = (
+    "You are a memory update decision engine. Given a new memory candidate and a list of "
+    "existing memories from the same tier, decide what action to take.\n\n"
+    "Respond with exactly one word: ADD, UPDATE, DELETE, or NOOP.\n\n"
+    "- ADD: new information not in existing memories.\n"
+    "- UPDATE: contradicts or supersedes an existing memory.\n"
+    "- DELETE: states something is no longer true.\n"
+    "- NOOP: already captured accurately.\n\n"
+    "## New candidate\n{candidate}\n\n"
+    "## Existing memories (same tier, top neighbours)\n{existing_memories}"
+)
+
+
+# ── Pydantic schemas ───────────────────────────────────────────────────────────
+
+class MemoryCandidate(BaseModel):
+    type: Literal["fact", "preference", "relation", "routine"]
+    content: str
+    target_tier: Literal["core", "associative", "relational", "proactive"]
+    subject: str | None = None
+    predicate: str | None = None
+    object: str | None = None
+    confidence: float = Field(default=0.7, ge=0.0, le=1.0)
+
+
+class ExtractionResult(BaseModel):
+    candidates: list[MemoryCandidate] = Field(default_factory=list)
+
+
+# ── Task 2.1 — Extract candidates ─────────────────────────────────────────────
+
+async def extract_candidates(
+    last_turn: str,
+    core_memory: dict[str, str],
+    recent_episodes: list[str],
+) -> ExtractionResult:
+    """Call gpt-4o-mini to extract memory candidates from the latest turn.
+
+    Returns an ExtractionResult (may be empty on failure — never raises).
+    """
+    core_str = "\n".join(f"{k}: {v}" for k, v in core_memory.items()) or "(empty)"
+    episodes_str = "\n---\n".join(recent_episodes[-5:]) or "(none)"
+
+    template, prompt_obj = get_prompt_or_fallback("memory_extraction", _EXTRACTION_FALLBACK)
+
+    # Compile with Langfuse variable syntax ({{var}}) or fallback {var}
+    if prompt_obj is not None:
+        try:
+            system_text = prompt_obj.compile(
+                last_turn=last_turn,
+                core_memory=core_str,
+                recent_episodes=episodes_str,
+            )
+            if isinstance(system_text, list):
+                system_text = "\n".join(m.get("content", "") for m in system_text if isinstance(m, dict))
+        except Exception as exc:
+            logger.warning("memory_extraction: compile failed: %s", exc)
+            system_text = template.format(
+                last_turn=last_turn,
+                core_memory=core_str,
+                recent_episodes=episodes_str,
+            )
+    else:
+        system_text = template.format(
+            last_turn=last_turn,
+            core_memory=core_str,
+            recent_episodes=episodes_str,
+        )
+
+    llm = get_agent_llm("memory-extractor", temperature=0)
+    # Bind JSON mode so the model always returns parseable output.
+    llm_json = llm.bind(response_format={"type": "json_object"})  # type: ignore[attr-defined]
+
+    lf = get_langfuse()
+    try:
+        from langchain_core.messages import HumanMessage, SystemMessage  # noqa: PLC0415
+        messages = [
+            SystemMessage(content=system_text),
+            HumanMessage(content="Extract memory candidates as JSON."),
+        ]
+
+        if lf:
+            with lf.start_as_current_observation(
+                as_type="generation",
+                name="memory-extraction",
+                model=model_for_agent("memory-extractor"),
+                prompt=prompt_obj,
+                input=messages,
+            ) as gen:
+                response = await llm_json.ainvoke(messages)
+                gen.update(output=response.content, usage=extract_usage(response))
+        else:
+            response = await llm_json.ainvoke(messages)
+
+        raw = json.loads(response.content)
+        result = ExtractionResult.model_validate(raw)
+        logger.info("memory_extraction: extracted %d candidates", len(result.candidates))
+        return result
+
+    except Exception as exc:
+        logger.warning("memory_extraction: extract_candidates failed: %s", exc)
+        return ExtractionResult(candidates=[])
+
+
+# ── Task 2.2 — Decide action ──────────────────────────────────────────────────
+
+async def decide_action(
+    candidate: MemoryCandidate,
+    existing: list[str],
+) -> Literal["ADD", "UPDATE", "DELETE", "NOOP"]:
+    """Decide what to do with a candidate given existing memories in the same tier.
+
+    Short-circuits to ADD without an LLM call when existing is empty (cost saving).
+    Never raises.
+    """
+    if not existing:
+        return "ADD"
+
+    candidate_str = f"[{candidate.type}] {candidate.content}"
+    existing_str = "\n".join(f"- {m}" for m in existing)
+
+    template, prompt_obj = get_prompt_or_fallback("memory_decide_action", _DECIDE_FALLBACK)
+
+    if prompt_obj is not None:
+        try:
+            system_text = prompt_obj.compile(
+                candidate=candidate_str,
+                existing_memories=existing_str,
+            )
+            if isinstance(system_text, list):
+                system_text = "\n".join(m.get("content", "") for m in system_text if isinstance(m, dict))
+        except Exception as exc:
+            logger.warning("memory_extraction: decide compile failed: %s", exc)
+            system_text = template.format(candidate=candidate_str, existing_memories=existing_str)
+    else:
+        system_text = template.format(candidate=candidate_str, existing_memories=existing_str)
+
+    llm = get_agent_llm("memory-extractor", temperature=0)
+    lf = get_langfuse()
+
+    try:
+        from langchain_core.messages import HumanMessage, SystemMessage  # noqa: PLC0415
+        messages = [
+            SystemMessage(content=system_text),
+            HumanMessage(content="Decide action."),
+        ]
+
+        if lf:
+            with lf.start_as_current_observation(
+                as_type="generation",
+                name="memory-decide-action",
+                model=model_for_agent("memory-extractor"),
+                prompt=prompt_obj,
+                input=messages,
+            ) as gen:
+                response = await llm.ainvoke(messages)
+                gen.update(output=response.content, usage=extract_usage(response))
+        else:
+            response = await llm.ainvoke(messages)
+
+        verb = response.content.strip().upper()
+        if verb in ("ADD", "UPDATE", "DELETE", "NOOP"):
+            return verb  # type: ignore[return-value]
+        logger.warning("memory_extraction: unexpected decide verb=%r, defaulting ADD", verb)
+        return "ADD"
+
+    except Exception as exc:
+        logger.warning("memory_extraction: decide_action failed: %s", exc)
+        return "ADD"
+
+
+# ── Task 2.3 — Pipeline orchestrator ──────────────────────────────────────────
+
+async def run_extraction(
+    db: AsyncSession,
+    user_id: str,
+    last_user_msg: str,
+    last_assistant_msg: str,
+    session_id: str | None,
+) -> None:
+    """Full Mem0-style extract/update pipeline for one conversation turn.
+
+    Steps:
+    1. Load core memory + last 5 episodes.
+    2. extract_candidates() → up to 5 MemoryCandidate objects.
+    3. For each candidate: find top-3 neighbours → decide_action() → apply.
+    4. Trace via Langfuse.
+
+    Never raises — wraps everything in try/except.
+    """
+    try:
+        await _run_extraction_inner(db, user_id, last_user_msg, last_assistant_msg, session_id)
+    except Exception as exc:
+        logger.warning("memory_extraction: run_extraction failed user=%s: %s", user_id, exc)
+
+
+async def _run_extraction_inner(
+    db: AsyncSession,
+    user_id: str,
+    last_user_msg: str,
+    last_assistant_msg: str,
+    session_id: str | None,
+) -> None:
+    from app.core.memory_middleware import MemoryMiddleware  # noqa: PLC0415
+
+    middleware = MemoryMiddleware(db)
+    fernet = await middleware._get_fernet(user_id)
+    if fernet is None:
+        logger.warning("memory_extraction: no fernet for user=%s, skipping", user_id)
+        return
+
+    # 1. Load context
+    core: dict[str, str] = await middleware._load_core(user_id, fernet)
+    episodes: list[str] = await middleware._load_episodic(user_id, fernet, session_id=session_id)
+
+    last_turn = f"User: {last_user_msg}\nAssistant: {last_assistant_msg}"
+
+    lf = get_langfuse()
+
+    async def _run(trace_id: str | None) -> dict[str, Any]:
+        # 2. Extract candidates
+        result = await extract_candidates(last_turn, core, episodes)
+        if not result.candidates:
+            logger.info("memory_extraction: no candidates user=%s", user_id)
+            return {"candidates": 0, "applied": 0}
+
+        logger.info(
+            "memory_extraction: processing %d candidates user=%s trace=%s",
+            len(result.candidates),
+            user_id,
+            trace_id or "-",
+        )
+
+        # 3. Apply each candidate
+        applied = 0
+        actions: list[str] = []
+        for candidate in result.candidates:
+            try:
+                await _apply_candidate(middleware, db, user_id, fernet, candidate, trace_id)
+                applied += 1
+                actions.append(f"{candidate.type}:{candidate.target_tier}")
+            except Exception as exc:
+                logger.warning(
+                    "memory_extraction: apply failed candidate=%r user=%s: %s",
+                    candidate.content[:80],
+                    user_id,
+                    exc,
+                )
+
+        logger.info(
+            "memory_extraction: applied %d/%d candidates user=%s",
+            applied,
+            len(result.candidates),
+            user_id,
+        )
+        return {"candidates": len(result.candidates), "applied": applied, "actions": actions}
+
+    with langfuse_context(user_id=user_id, session_id=session_id):
+        if lf:
+            with lf.start_as_current_observation(
+                as_type="span",
+                name="memory-extraction-pipeline",
+                input={"last_turn_preview": last_turn[:200]},
+            ) as span:
+                summary = await _run(trace_id=span.id)
+                span.update(output=summary)
+            try:
+                lf.flush()
+            except Exception:
+                pass
+        else:
+            await _run(trace_id=None)
+
+
+async def _apply_candidate(
+    middleware: Any,
+    db: AsyncSession,
+    user_id: str,
+    fernet: Any,
+    candidate: MemoryCandidate,
+    trace_id: str | None,
+) -> None:
+    """Fetch neighbours, decide action, apply to the appropriate tier."""
+
+    neighbours: list[str] = []
+
+    if candidate.target_tier == "core":
+        # For core tier: neighbours are existing core block values for similar keys.
+        blocks = await middleware.list_core_blocks(user_id)
+        neighbours = [b["value"] for b in blocks[:3]]
+
+    elif candidate.target_tier == "associative":
+        neighbours = await middleware.search_archival(user_id, candidate.content, top_k=3)
+
+    elif candidate.target_tier == "relational":
+        # Relation candidates handled specially — passed to upsert_relation directly.
+        # Neighbours: search by subject label if available.
+        neighbours = []
+
+    elif candidate.target_tier == "proactive":
+        neighbours = await middleware.search_recall(user_id, candidate.content, top_k=3)
+
+    action = await decide_action(candidate, neighbours)
+    logger.info(
+        "memory_extraction: candidate type=%s tier=%s action=%s",
+        candidate.type,
+        candidate.target_tier,
+        action,
+    )
+
+    if action == "NOOP":
+        return
+
+    if candidate.target_tier == "relational":
+        # Always upsert relations — decide_action skipped (no neighbour search).
+        if candidate.subject and candidate.predicate and candidate.object:
+            await _upsert_relation(
+                middleware, db, user_id, candidate, trace_id
+            )
+        return
+
+    if action in ("ADD", "UPDATE"):
+        if candidate.target_tier == "core":
+            # Derive a short key from the content (first 40 chars, snake_cased).
+            key = _content_to_key(candidate.content)
+            await middleware.update_core(user_id, key, candidate.content, trace_id=trace_id)
+
+        elif candidate.target_tier == "associative":
+            await middleware.store_associative(user_id, candidate.content)
+
+        elif candidate.target_tier == "proactive":
+            await _store_proactive_stub(middleware, db, user_id, candidate, fernet)
+
+    elif action == "DELETE":
+        if candidate.target_tier == "core":
+            key = _content_to_key(candidate.content)
+            await middleware.delete_core(user_id, key)
+
+
+def _content_to_key(content: str) -> str:
+    """Derive a short snake_case key from a content string (first 40 chars)."""
+    import re  # noqa: PLC0415
+    slug = re.sub(r"[^a-z0-9]+", "_", content[:40].lower()).strip("_")
+    return slug or "memory"
+
+
+async def _upsert_relation(
+    middleware: Any,
+    db: AsyncSession,
+    user_id: str,
+    candidate: MemoryCandidate,
+    trace_id: str | None,
+) -> None:
+    """Upsert a relation row via MemoryMiddleware.upsert_relation (Phase 3)."""
+    await middleware.upsert_relation(
+        user_id=user_id,
+        subject=candidate.subject or "unknown",
+        subject_type="unknown",
+        predicate=candidate.predicate or "related_to",
+        object_=candidate.object or "unknown",
+        object_type="unknown",
+        confidence=candidate.confidence,
+    )
+    logger.info(
+        "memory_extraction: upserted relation subject=%s predicate=%s object=%s",
+        candidate.subject,
+        candidate.predicate,
+        candidate.object,
+    )
+
+
+async def _store_proactive_stub(
+    middleware: Any,
+    db: AsyncSession,
+    user_id: str,
+    candidate: MemoryCandidate,
+    fernet: Any,
+) -> None:
+    """Store a proactive pattern row directly (MemoryProactive model)."""
+    import uuid  # noqa: PLC0415
+    from app.models import MemoryProactive  # noqa: PLC0415
+    from app.core.memory_middleware import _encrypt  # noqa: PLC0415
+
+    encrypted = _encrypt(fernet, candidate.content)
+    row = MemoryProactive(
+        id=str(uuid.uuid4()),
+        user_id=user_id,
+        pattern_encrypted=encrypted,
+        confidence=candidate.confidence,
+        source="inferred",
+    )
+    db.add(row)
+    try:
+        await db.commit()
+        logger.info("memory_extraction: stored proactive pattern user=%s", user_id)
+    except Exception as exc:
+        logger.warning("memory_extraction: store proactive failed: %s", exc)
+        await db.rollback()

app/core/memory_maintenance.py

@@ -0,0 +1,581 @@
+"""Memory maintenance jobs — Phase 3/5.
+
+Three entrypoints called by the scheduler (APScheduler) registered in app/main.py:
+
+  drain_extraction_queue(db) — Free-tier batch extraction (Phase 2/5).
+  mine_proactive_patterns(db, user_id) — Power+ pattern mining (Phase 5).
+  decay_relations(db, user_id) — confidence decay + pruning for memory_relations (Phase 3).
+
+All are safe to call manually or from tests; they never raise.
+"""
+
+from __future__ import annotations
+
+import json
+import logging
+import uuid
+from datetime import datetime, timedelta, timezone
+
+from cryptography.fernet import Fernet
+from sqlalchemy import select
+from sqlalchemy.ext.asyncio import AsyncSession
+
+from app.core.langfuse_client import compile_prompt, extract_usage, get_langfuse, get_prompt_or_fallback
+from app.models import MemoryAssociative, MemoryEpisodic, MemoryProactive, MemoryRelation, User
+
+logger = logging.getLogger(__name__)
+
+# Decay parameters for relations
+_DECAY_FACTOR = 0.95
+_DECAY_PERIOD_DAYS = 30
+_PRUNE_THRESHOLD = 0.2
+
+# Proactive pattern decay: 10 % per 7 days since last sighting
+_PROACTIVE_DECAY_FACTOR = 0.9
+_PROACTIVE_DECAY_PERIOD_DAYS = 7
+_PROACTIVE_PRUNE_THRESHOLD = 0.2
+
+# Mining: require at least this many episodes to attempt pattern extraction
+_MIN_EPISODES_FOR_MINING = 3
+_MINING_LOOKBACK_DAYS = 30
+
+# Audit: caps to control token cost
+_AUDIT_MAX_FACTS = 50
+_AUDIT_MAX_LABELS = 100
+
+
+async def decay_relations(db: AsyncSession, user_id: str) -> None:
+    """Apply confidence decay to all relation rows for a user.
+
+    Decay rule: confidence *= 0.95 for every 30 days since last_confirmed_at.
+    Rows whose confidence falls below 0.2 are deleted.
+
+    Never raises — wraps in try/except.
+    """
+    try:
+        await _decay_relations_inner(db, user_id)
+    except Exception as exc:
+        logger.warning("memory_maintenance: decay_relations failed user=%s: %s", user_id, exc)
+
+
+async def _decay_relations_inner(db: AsyncSession, user_id: str) -> None:
+    result = await db.execute(
+        select(MemoryRelation).where(MemoryRelation.user_id == user_id)
+    )
+    rows = result.scalars().all()
+    now = datetime.now(timezone.utc)
+    deleted = 0
+    decayed = 0
+
+    for row in rows:
+        reference = row.last_confirmed_at or row.created_at
+        if reference is None:
+            continue
+        if reference.tzinfo is None:
+            reference = reference.replace(tzinfo=timezone.utc)
+
+        days_elapsed = (now - reference).days
+        if days_elapsed < _DECAY_PERIOD_DAYS:
+            continue
+
+        periods = days_elapsed // _DECAY_PERIOD_DAYS
+        new_confidence = row.confidence * (_DECAY_FACTOR ** periods)
+
+        if new_confidence < _PRUNE_THRESHOLD:
+            await db.delete(row)
+            deleted += 1
+            logger.info(
+                "memory_maintenance: pruned relation id=%s user=%s subject=%s predicate=%s "
+                "confidence=%.3f (below threshold)",
+                row.id, user_id, row.subject_label, row.predicate, new_confidence,
+            )
+        else:
+            row.confidence = new_confidence
+            decayed += 1
+
+    try:
+        await db.commit()
+        logger.info(
+            "memory_maintenance: decay_relations user=%s decayed=%d deleted=%d",
+            user_id, decayed, deleted,
+        )
+    except Exception as exc:
+        logger.warning("memory_maintenance: decay_relations commit failed user=%s: %s", user_id, exc)
+        await db.rollback()
+
+
+async def drain_extraction_queue(db: AsyncSession) -> None:
+    """Process pending ExtractionQueue rows for Free-tier users.
+
+    Each row corresponds to a stored episode that should be fed through the
+    Mem0-style extraction pipeline. Rows are deleted after successful processing.
+    Never raises — wraps in try/except.
+    """
+    try:
+        await _drain_extraction_queue_inner(db)
+    except Exception as exc:
+        logger.warning("memory_maintenance: drain_extraction_queue failed: %s", exc)
+
+
+async def _drain_extraction_queue_inner(db: AsyncSession) -> None:
+    from app.models import ExtractionQueue  # noqa: PLC0415
+
+    result = await db.execute(select(ExtractionQueue))
+    rows = result.scalars().all()
+
+    if not rows:
+        logger.debug("memory_maintenance: drain_extraction_queue nothing to drain")
+        return
+
+    logger.info("memory_maintenance: drain_extraction_queue pending=%d", len(rows))
+
+    from app.core.memory_extraction import run_extraction  # noqa: PLC0415
+
+    processed = 0
+    for row in rows:
+        try:
+            await run_extraction(
+                db=db,
+                user_id=row.user_id,
+                last_user_msg="",
+                last_assistant_msg="",
+                session_id=None,
+            )
+            await db.delete(row)
+            await db.commit()
+            processed += 1
+        except Exception as exc:
+            logger.warning(
+                "memory_maintenance: drain failed row=%s user=%s: %s",
+                row.id, row.user_id, exc,
+            )
+            await db.rollback()
+
+    logger.info("memory_maintenance: drain_extraction_queue processed=%d/%d", processed, len(rows))
+
+
+async def mine_proactive_patterns(db: AsyncSession, user_id: str) -> None:
+    """Mine recurring behavioral patterns from last 30 days of episodes (Power+ only).
+
+    Steps:
+    1. Gate on proactive_mining tier feature.
+    2. Load + decrypt last 30 days of episodic summaries.
+    3. Call gpt-4o-mini to identify recurring patterns.
+    4. Encrypt and store each pattern in memory_proactive.
+    5. Apply decay to existing proactive rows.
+
+    Never raises — wraps in try/except.
+    """
+    try:
+        await _mine_proactive_patterns_inner(db, user_id)
+    except Exception as exc:
+        logger.warning("memory_maintenance: mine_proactive_patterns failed user=%s: %s", user_id, exc)
+
+
+async def _mine_proactive_patterns_inner(db: AsyncSession, user_id: str) -> None:
+    from app.billing.tier_manager import tier_manager  # noqa: PLC0415
+
+    tier = await tier_manager.get_tier(user_id, db)
+    if not tier_manager.check_feature(tier, "proactive_mining"):
+        logger.debug("memory_maintenance: mine_proactive_patterns skipped (tier=%s)", tier)
+        return
+
+    # Load user Fernet key
+    result = await db.execute(select(User).where(User.id == user_id))
+    user = result.scalar_one_or_none()
+    if user is None or not user.encryption_key:
+        logger.warning("memory_maintenance: mine_proactive_patterns no encryption_key user=%s", user_id)
+        return
+
+    fernet = Fernet(user.encryption_key.encode())
+    cutoff = datetime.now(timezone.utc) - timedelta(days=_MINING_LOOKBACK_DAYS)
+
+    episodes_result = await db.execute(
+        select(MemoryEpisodic)
+        .where(
+            MemoryEpisodic.user_id == user_id,
+            MemoryEpisodic.created_at >= cutoff,
+        )
+        .order_by(MemoryEpisodic.created_at.asc())
+    )
+    episode_rows = episodes_result.scalars().all()
+
+    if len(episode_rows) < _MIN_EPISODES_FOR_MINING:
+        logger.info(
+            "memory_maintenance: mine_proactive_patterns skipped user=%s episodes=%d (< %d)",
+            user_id, len(episode_rows), _MIN_EPISODES_FOR_MINING,
+        )
+        return
+
+    summaries: list[str] = []
+    for ep in episode_rows:
+        try:
+            plaintext = fernet.decrypt(ep.summary_encrypted.encode()).decode()
+            summaries.append(plaintext)
+        except Exception:
+            pass
+
+    if not summaries:
+        return
+
+    patterns = await _extract_proactive_patterns(summaries)
+    if not patterns:
+        logger.info("memory_maintenance: mine_proactive_patterns user=%s no patterns extracted", user_id)
+        return
+
+    stored = 0
+    for pattern_text in patterns:
+        try:
+            encrypted = fernet.encrypt(pattern_text.encode()).decode()
+            row = MemoryProactive(
+                id=str(uuid.uuid4()),
+                user_id=user_id,
+                pattern_encrypted=encrypted,
+                confidence=0.7,
+                source="inferred",
+            )
+            db.add(row)
+            stored += 1
+        except Exception as exc:
+            logger.warning("memory_maintenance: failed to store pattern user=%s: %s", user_id, exc)
+
+    try:
+        await db.commit()
+        logger.info(
+            "memory_maintenance: mine_proactive_patterns user=%s stored=%d",
+            user_id, stored,
+        )
+    except Exception as exc:
+        logger.warning("memory_maintenance: mine_proactive_patterns commit failed user=%s: %s", user_id, exc)
+        await db.rollback()
+        return
+
+    await _decay_proactive_patterns(db, user_id, fernet)
+
+
+async def _extract_proactive_patterns(summaries: list[str]) -> list[str]:
+    """Call memory-miner LLM to identify recurring behavioral/temporal patterns."""
+    from app.core.llm import get_agent_llm  # noqa: PLC0415
+
+    llm = get_agent_llm("memory-miner", temperature=0)
+    combined = "\n---\n".join(summaries[-20:])  # cap at last 20 to control token usage
+    prompt = (
+        "You are analyzing conversation history for a personal AI secretary. "
+        "Identify 3-5 recurring temporal or behavioral patterns (e.g. 'always works late on Thursdays', "
+        "'prefers bullet-point summaries', 'frequently asks about Project Acme status'). "
+        "Return each pattern as a plain, short English sentence on its own line. "
+        "No numbering, no bullet points, no extra text.\n\n"
+        f"Conversation history:\n{combined}"
+    )
+    try:
+        response = await llm.ainvoke(prompt)
+        text = response.content if hasattr(response, "content") else str(response)
+        lines = [line.strip() for line in str(text).splitlines() if line.strip()]
+        return lines[:5]
+    except Exception as exc:
+        logger.warning("memory_maintenance: _extract_proactive_patterns LLM failed: %s", exc)
+        return []
+
+
+async def _decay_proactive_patterns(db: AsyncSession, user_id: str, fernet: Fernet) -> None:
+    """Decay confidence of existing proactive patterns; prune below threshold."""
+    result = await db.execute(
+        select(MemoryProactive).where(MemoryProactive.user_id == user_id)
+    )
+    rows = result.scalars().all()
+    now = datetime.now(timezone.utc)
+    deleted = 0
+    decayed = 0
+
+    for row in rows:
+        reference = row.created_at
+        if reference is None:
+            continue
+        if reference.tzinfo is None:
+            reference = reference.replace(tzinfo=timezone.utc)
+
+        days_elapsed = (now - reference).days
+        if days_elapsed < _PROACTIVE_DECAY_PERIOD_DAYS:
+            continue
+
+        periods = days_elapsed // _PROACTIVE_DECAY_PERIOD_DAYS
+        new_confidence = row.confidence * (_PROACTIVE_DECAY_FACTOR ** periods)
+
+        if new_confidence < _PROACTIVE_PRUNE_THRESHOLD:
+            await db.delete(row)
+            deleted += 1
+        else:
+            row.confidence = new_confidence
+            decayed += 1
+
+    try:
+        await db.commit()
+        logger.info(
+            "memory_maintenance: decay_proactive user=%s decayed=%d deleted=%d",
+            user_id, decayed, deleted,
+        )
+    except Exception as exc:
+        logger.warning("memory_maintenance: decay_proactive commit failed user=%s: %s", user_id, exc)
+        await db.rollback()
+
+
+# ── Phase 7: weekly memory audit ──────────────────────────────────────────────
+
+_AUDIT_CONTRADICTIONS_FALLBACK = (
+    "You are auditing a personal AI assistant's memory bank. "
+    "Each fact has an ID in brackets. "
+    "Find pairs that directly contradict each other "
+    "(e.g. 'prefers morning meetings' vs 'never schedules before noon'). "
+    "For each contradiction, pick the ID to DELETE (the older or less specific one). "
+    'Return ONLY a valid JSON array, no markdown fences: '
+    '[{{"delete": "<id>", "reason": "<one line>"}}]. '
+    "If no contradictions, return [].\n\n"
+    "Facts:\n{facts}"
+)
+
+_AUDIT_CANONICALIZE_FALLBACK = (
+    "You are auditing entity labels in a personal AI assistant's relational memory. "
+    "These are names of people, companies, projects, or topics. "
+    "Group labels that clearly refer to the same real-world entity "
+    "(e.g. 'giulia', 'Giulia', 'Giulia R.' → canonical 'Giulia'). "
+    "Return ONLY a valid JSON array, no markdown fences: "
+    '[{{"canonical": "<best label>", "variants": ["<v1>", "<v2>"]}}]. '
+    "Only include groups with at least one variant. Singletons: omit.\n\n"
+    "Labels:\n{labels}"
+)
+
+
+async def audit_memory(db: AsyncSession, user_id: str) -> None:
+    """Weekly audit: contradiction scan on associative facts + label canonicalization on relations.
+
+    Steps:
+    1. Decrypt up to _AUDIT_MAX_FACTS associative rows; send list to memory-auditor LLM.
+    2. LLM flags rows to delete (direct contradictions); hard-delete them.
+    3. Collect unique subject/object labels from memory_relations; ask LLM to group duplicates.
+    4. Rewrite variant labels to their canonical form in-place.
+
+    Never raises — wraps in try/except.
+    """
+    try:
+        await _audit_memory_inner(db, user_id)
+    except Exception as exc:
+        logger.warning("memory_maintenance: audit_memory failed user=%s: %s", user_id, exc)
+
+
+async def _audit_memory_inner(db: AsyncSession, user_id: str) -> None:
+    result = await db.execute(select(User).where(User.id == user_id))
+    user = result.scalar_one_or_none()
+    if user is None or not user.encryption_key:
+        logger.warning("memory_maintenance: audit_memory no encryption_key user=%s", user_id)
+        return
+
+    fernet = Fernet(user.encryption_key.encode())
+    await _scan_associative_contradictions(db, user_id, fernet)
+    await _canonicalize_relation_labels(db, user_id)
+
+
+async def _scan_associative_contradictions(
+    db: AsyncSession,
+    user_id: str,
+    fernet: Fernet,
+) -> None:
+    """Decrypt associative facts, ask LLM to flag contradictions, delete superseded rows."""
+    result = await db.execute(
+        select(MemoryAssociative)
+        .where(MemoryAssociative.user_id == user_id)
+        .order_by(MemoryAssociative.updated_at.desc())
+        .limit(_AUDIT_MAX_FACTS)
+    )
+    rows = result.scalars().all()
+    if len(rows) < 2:
+        return
+
+    id_to_text: dict[str, str] = {}
+    for row in rows:
+        try:
+            plaintext = fernet.decrypt(row.content_encrypted.encode()).decode()
+            id_to_text[row.id] = plaintext
+        except Exception:
+            pass
+
+    if len(id_to_text) < 2:
+        return
+
+    id_list = list(id_to_text.keys())
+    numbered = "\n".join(
+        f"{i + 1}. [{rid}] {id_to_text[rid]}" for i, rid in enumerate(id_list)
+    )
+
+    template, prompt_obj = get_prompt_or_fallback(
+        "memory_audit_contradictions", _AUDIT_CONTRADICTIONS_FALLBACK
+    )
+    system_text = compile_prompt(template, prompt_obj, facts=numbered)
+
+    from app.core.llm import get_agent_llm, model_for_agent  # noqa: PLC0415
+    from langchain_core.messages import HumanMessage, SystemMessage  # noqa: PLC0415
+
+    llm = get_agent_llm("memory-auditor", temperature=0)
+    lf = get_langfuse()
+    messages = [
+        SystemMessage(content=system_text),
+        HumanMessage(content="Audit facts for contradictions."),
+    ]
+    try:
+        if lf:
+            with lf.start_as_current_observation(
+                as_type="generation",
+                name="memory-audit-contradictions",
+                model=model_for_agent("memory-auditor"),
+                prompt=prompt_obj,
+                input=messages,
+            ) as gen:
+                response = await llm.ainvoke(messages)
+                gen.update(output=response.content, usage=extract_usage(response))
+        else:
+            response = await llm.ainvoke(messages)
+
+        text = response.content if hasattr(response, "content") else str(response)
+        deletions = json.loads(text.strip())
+        if not isinstance(deletions, list):
+            return
+    except Exception as exc:
+        logger.warning(
+            "memory_maintenance: _scan_associative_contradictions LLM/parse failed user=%s: %s",
+            user_id, exc,
+        )
+        return
+
+    deleted = 0
+    for item in deletions:
+        if not isinstance(item, dict):
+            continue
+        rid = item.get("delete")
+        if not rid or rid not in id_to_text:
+            continue
+        result2 = await db.execute(
+            select(MemoryAssociative).where(
+                MemoryAssociative.id == rid,
+                MemoryAssociative.user_id == user_id,
+            )
+        )
+        target = result2.scalar_one_or_none()
+        if target:
+            await db.delete(target)
+            deleted += 1
+            logger.info(
+                "memory_maintenance: audit deleted contradiction id=%s user=%s reason=%s",
+                rid, user_id, item.get("reason", ""),
+            )
+
+    if deleted:
+        try:
+            await db.commit()
+        except Exception as exc:
+            logger.warning(
+                "memory_maintenance: audit contradiction commit failed user=%s: %s", user_id, exc
+            )
+            await db.rollback()
+
+    logger.info(
+        "memory_maintenance: _scan_associative_contradictions user=%s deleted=%d", user_id, deleted
+    )
+
+
+async def _canonicalize_relation_labels(db: AsyncSession, user_id: str) -> None:
+    """Group near-duplicate entity labels in memory_relations and unify to canonical form."""
+    result = await db.execute(
+        select(MemoryRelation).where(MemoryRelation.user_id == user_id)
+    )
+    rows = result.scalars().all()
+    if not rows:
+        return
+
+    all_labels: set[str] = set()
+    for row in rows:
+        all_labels.add(row.subject_label)
+        all_labels.add(row.object_label)
+
+    labels_list = sorted(all_labels)[:_AUDIT_MAX_LABELS]
+    if len(labels_list) < 2:
+        return
+
+    labels_block = "\n".join(f"- {lbl}" for lbl in labels_list)
+    template, prompt_obj = get_prompt_or_fallback(
+        "memory_audit_canonicalize", _AUDIT_CANONICALIZE_FALLBACK
+    )
+    system_text = compile_prompt(template, prompt_obj, labels=labels_block)
+
+    from app.core.llm import get_agent_llm, model_for_agent  # noqa: PLC0415
+    from langchain_core.messages import HumanMessage, SystemMessage  # noqa: PLC0415
+
+    llm = get_agent_llm("memory-auditor", temperature=0)
+    lf = get_langfuse()
+    messages = [
+        SystemMessage(content=system_text),
+        HumanMessage(content="Canonicalize entity labels."),
+    ]
+    try:
+        if lf:
+            with lf.start_as_current_observation(
+                as_type="generation",
+                name="memory-audit-canonicalize",
+                model=model_for_agent("memory-auditor"),
+                prompt=prompt_obj,
+                input=messages,
+            ) as gen:
+                response = await llm.ainvoke(messages)
+                gen.update(output=response.content, usage=extract_usage(response))
+        else:
+            response = await llm.ainvoke(messages)
+
+        text = response.content if hasattr(response, "content") else str(response)
+        groups = json.loads(text.strip())
+        if not isinstance(groups, list):
+            return
+    except Exception as exc:
+        logger.warning(
+            "memory_maintenance: _canonicalize_relation_labels LLM/parse failed user=%s: %s",
+            user_id, exc,
+        )
+        return
+
+    # Build variant → canonical map
+    remap: dict[str, str] = {}
+    for group in groups:
+        if not isinstance(group, dict):
+            continue
+        canonical = group.get("canonical", "")
+        variants = group.get("variants") or []
+        if not canonical:
+            continue
+        for v in variants:
+            if isinstance(v, str) and v != canonical:
+                remap[v] = canonical
+
+    if not remap:
+        return
+
+    updated = 0
+    for row in rows:
+        changed = False
+        if row.subject_label in remap:
+            row.subject_label = remap[row.subject_label]
+            changed = True
+        if row.object_label in remap:
+            row.object_label = remap[row.object_label]
+            changed = True
+        if changed:
+            updated += 1
+
+    if updated:
+        try:
+            await db.commit()
+            logger.info(
+                "memory_maintenance: _canonicalize_relation_labels user=%s updated=%d",
+                user_id, updated,
+            )
+        except Exception as exc:
+            logger.warning(
+                "memory_maintenance: canonicalize commit failed user=%s: %s", user_id, exc
+            )
+            await db.rollback()

app/core/memory_middleware.py

@@ -18,8 +18,10 @@ Usage:
 
 from __future__ import annotations
 
+import asyncio
 import logging
 import uuid
+from datetime import datetime, timezone
 from typing import Any
 
 from cryptography.fernet import Fernet, InvalidToken
@@ -27,15 +29,22 @@ from sqlalchemy import select
 from sqlalchemy.ext.asyncio import AsyncSession
 
 from app.models import (
+    ExtractionQueue,
     MemoryAssociative,
     MemoryCore,
     MemoryEpisodic,
     MemoryProactive,
+    MemoryRelation,
     User,
 )
 
 logger = logging.getLogger(__name__)
 
+
+def _now() -> datetime:
+    return datetime.now(timezone.utc)
+
+
 # Tuning constants
 _ASSOCIATIVE_TOP_K = 5
 _EPISODIC_RECENT_N = 10
@@ -64,26 +73,31 @@ class MemoryMiddleware:
           associative_memory — [plaintext_content, ...]  (top-k by keyword match)
           episodic_memory    — [plaintext_summary, ...]  (most recent N)
           proactive_hints    — [plaintext_pattern, ...]  (above threshold)
+          relational_memory  — ["subject --predicate--> object", ...] (top 10, Pro+)
         """
         fernet = await self._get_fernet(user_id)
         if fernet is None:
             return {}
 
+        user_dbg = await self._get_user_debug(user_id)
+        user_tier: str = user_dbg.get("tier") or "free"
+
         core = await self._load_core(user_id, fernet)
-        associative = await self._load_associative(user_id, message, fernet)
+        associative = await self._load_associative(user_id, message, fernet, user_tier=user_tier)
         episodic = await self._load_episodic(user_id, fernet, session_id=session_id)
         proactive = await self._load_proactive(user_id, fernet)
+        relational = await self._load_relational(user_id, user_tier=user_tier)
 
-        user_dbg = await self._get_user_debug(user_id)
         logger.info(
-            "memory: enrich_context trace=%s user=%s tier=%s core=%d associative=%d episodic=%d proactive=%d",
+            "memory: enrich_context trace=%s user=%s tier=%s core=%d associative=%d episodic=%d proactive=%d relational=%d",
             trace_id or "-",
             user_id,
-            user_dbg.get("tier") or "-",
+            user_tier,
             len(core),
             len(associative),
             len(episodic),
             len(proactive),
+            len(relational),
         )
 
         return {
@@ -91,6 +105,7 @@ class MemoryMiddleware:
             "associative_memory": associative,
             "episodic_memory": episodic,
             "proactive_hints": proactive,
+            "relational_memory": relational,
         }
 
     async def store_episode(
@@ -104,7 +119,10 @@ class MemoryMiddleware:
         """Summarise and store a completed interaction in episodic memory.
 
         The summary is a simple heuristic concatenation (no LLM call) to keep
-        latency low. Full LLM summarisation can be added in a later step.
+        latency low. After committing the episode row, dispatches the Mem0-style
+        extraction pipeline:
+          - Pro/Power/Team → asyncio.create_task (fire-and-forget, realtime).
+          - Free → enqueue an ExtractionQueue row for the daily cron.
         """
         fernet = await self._get_fernet(user_id)
         if fernet is None:
@@ -113,26 +131,95 @@ class MemoryMiddleware:
         summary = f"User: {message[:200]}\nAssistant: {response[:200]}"
         encrypted = _encrypt(fernet, summary)
 
-        row = MemoryEpisodic(
+        episode = MemoryEpisodic(
             id=str(uuid.uuid4()),
             user_id=user_id,
             summary_encrypted=encrypted,
             session_id=session_id,
         )
-        self._db.add(row)
+        self._db.add(episode)
+        episode_id: str = episode.id
         try:
             await self._db.commit()
             user_dbg = await self._get_user_debug(user_id)
+            tier = user_dbg.get("tier") or "free"
             logger.info(
                 "memory: store_episode trace=%s user=%s tier=%s session=%s",
                 trace_id or "-",
                 user_id,
-                user_dbg.get("tier") or "-",
+                tier,
                 session_id,
             )
         except Exception as exc:
             logger.error("memory: store_episode failed user=%s: %s", user_id, exc)
             await self._db.rollback()
+            return
+
+        # ── Dispatch extraction pipeline (Phase 2) ────────────────────────────
+        await self._dispatch_extraction(
+            user_id=user_id,
+            episode_id=episode_id,
+            last_user_msg=message,
+            last_assistant_msg=response,
+            session_id=session_id,
+        )
+
+    async def _dispatch_extraction(
+        self,
+        user_id: str,
+        episode_id: str,
+        last_user_msg: str,
+        last_assistant_msg: str,
+        session_id: str | None,
+    ) -> None:
+        """Route extraction to realtime task or batch queue based on user tier."""
+        from app.billing.tier_manager import tier_manager  # noqa: PLC0415
+
+        tier = await tier_manager.get_tier(user_id, self._db)
+
+        if tier_manager.check_feature(tier, "realtime_extraction"):
+            # Pro/Power/Team: fire-and-forget in the background.
+            # Must open a fresh session — request session closes after handler returns.
+            from app.core.memory_extraction import run_extraction  # noqa: PLC0415
+            from app.db import async_session  # noqa: PLC0415
+
+            async def _task() -> None:
+                try:
+                    async with async_session() as fresh_db:
+                        await run_extraction(
+                            db=fresh_db,
+                            user_id=user_id,
+                            last_user_msg=last_user_msg,
+                            last_assistant_msg=last_assistant_msg,
+                            session_id=session_id,
+                        )
+                except Exception as exc:
+                    logger.warning(
+                        "memory: extraction task failed user=%s: %s", user_id, exc
+                    )
+
+            asyncio.create_task(_task())
+            logger.info("memory: realtime extraction dispatched user=%s", user_id)
+        else:
+            # Free tier: enqueue for daily batch cron.
+            queue_row = ExtractionQueue(
+                id=str(uuid.uuid4()),
+                user_id=user_id,
+                episode_id=episode_id,
+            )
+            self._db.add(queue_row)
+            try:
+                await self._db.commit()
+                logger.info(
+                    "memory: extraction enqueued (batch) user=%s episode=%s",
+                    user_id,
+                    episode_id,
+                )
+            except Exception as exc:
+                logger.warning(
+                    "memory: extraction queue insert failed user=%s: %s", user_id, exc
+                )
+                await self._db.rollback()
 
     async def update_core(self, user_id: str, key: str, value: str, trace_id: str | None = None) -> None:
         """Upsert a core memory key/value for a user."""
@@ -255,6 +342,143 @@ class MemoryMiddleware:
         logger.info("memory: replace_core user=%s label=%s changed=1", user_id, label)
         return True
 
+    async def store_associative(
+        self,
+        user_id: str,
+        content: str,
+        entity_type: str | None = None,
+        entity_id: str | None = None,
+    ) -> None:
+        """Store associative memory; embed if user tier has real_embeddings."""
+        from app.billing.tier_manager import tier_manager  # noqa: PLC0415
+        from app.core.embeddings import embed_text  # noqa: PLC0415
+
+        fernet = await self._get_fernet(user_id)
+        if fernet is None:
+            return
+
+        encrypted = _encrypt(fernet, content)
+
+        user_dbg = await self._get_user_debug(user_id)
+        user_tier = user_dbg.get("tier") or "free"
+
+        embedding: list[float] | None = None
+        if tier_manager.check_feature(user_tier, "real_embeddings"):
+            embedding = await embed_text(content)
+
+        row = MemoryAssociative(
+            id=str(uuid.uuid4()),
+            user_id=user_id,
+            content_encrypted=encrypted,
+            embedding=embedding,
+            entity_type=entity_type,
+            entity_id=entity_id,
+        )
+        self._db.add(row)
+        try:
+            await self._db.commit()
+            logger.info(
+                "memory: store_associative user=%s embedded=%s",
+                user_id,
+                embedding is not None,
+            )
+        except Exception as exc:
+            logger.error("memory: store_associative failed user=%s: %s", user_id, exc)
+            await self._db.rollback()
+
+    async def upsert_relation(
+        self,
+        user_id: str,
+        subject: str,
+        subject_type: str,
+        predicate: str,
+        object_: str,
+        object_type: str,
+        *,
+        confidence: float = 0.7,
+        source_episode_id: str | None = None,
+        notes: str | None = None,
+    ) -> None:
+        """Insert or update a relation row.  Matches on (user_id, subject_label, predicate, object_label).
+
+        subject_label / object_label are plaintext entity identifiers — not encrypted.
+        notes is optional; encrypted with user Fernet if provided.
+        """
+        from app.billing.tier_manager import tier_manager  # noqa: PLC0415
+
+        user_dbg = await self._get_user_debug(user_id)
+        user_tier = user_dbg.get("tier") or "free"
+        if not tier_manager.check_feature(user_tier, "relational_memory"):
+            logger.debug("memory: upsert_relation skipped (tier=%s no relational_memory)", user_tier)
+            return
+
+        notes_encrypted: bytes | None = None
+        if notes:
+            fernet = await self._get_fernet(user_id)
+            if fernet:
+                notes_encrypted = fernet.encrypt(notes.encode())
+
+        result = await self._db.execute(
+            select(MemoryRelation).where(
+                MemoryRelation.user_id == user_id,
+                MemoryRelation.subject_label == subject,
+                MemoryRelation.predicate == predicate,
+                MemoryRelation.object_label == object_,
+            )
+        )
+        existing = result.scalar_one_or_none()
+
+        if existing is not None:
+            existing.subject_type = subject_type
+            existing.object_type = object_type
+            existing.confidence = confidence
+            existing.last_confirmed_at = _now()
+            if notes_encrypted is not None:
+                existing.notes_encrypted = notes_encrypted
+        else:
+            self._db.add(MemoryRelation(
+                id=str(uuid.uuid4()),
+                user_id=user_id,
+                subject_label=subject,
+                subject_type=subject_type,
+                predicate=predicate,
+                object_label=object_,
+                object_type=object_type,
+                confidence=confidence,
+                source_episode_id=source_episode_id,
+                notes_encrypted=notes_encrypted,
+            ))
+
+        try:
+            await self._db.commit()
+            logger.info(
+                "memory: upsert_relation user=%s subject=%s predicate=%s object=%s",
+                user_id, subject, predicate, object_,
+            )
+        except Exception as exc:
+            logger.error("memory: upsert_relation failed user=%s: %s", user_id, exc)
+            await self._db.rollback()
+
+    async def query_relations(
+        self,
+        user_id: str,
+        subject: str | None = None,
+        predicate: str | None = None,
+        object_: str | None = None,
+        limit: int = 20,
+    ) -> list[MemoryRelation]:
+        """Query relation rows for a user with optional filters."""
+        q = select(MemoryRelation).where(MemoryRelation.user_id == user_id)
+        if subject is not None:
+            q = q.where(MemoryRelation.subject_label == subject)
+        if predicate is not None:
+            q = q.where(MemoryRelation.predicate == predicate)
+        if object_ is not None:
+            q = q.where(MemoryRelation.object_label == object_)
+        q = q.order_by(MemoryRelation.confidence.desc()).limit(limit)
+        result = await self._db.execute(q)
+        return list(result.scalars().all())
+
     async def insert_archival(self, user_id: str, content: str, source: str = "manual") -> None:
         """Insert a long-term archival memory entry."""
         fernet = await self._get_fernet(user_id)
@@ -343,13 +567,26 @@ class MemoryMiddleware:
 
     async def _get_user_debug(self, user_id: str) -> dict[str, str | None]:
         """Load lightweight user debug fields for trace logs."""
+        from app.config.settings import settings  # noqa: PLC0415
+        from app.models import Subscription  # noqa: PLC0415
+
         result = await self._db.execute(select(User).where(User.id == user_id))
         user = result.scalar_one_or_none()
         if user is None:
             return {"tier": None}
-        return {
-            "tier": user.tier,
-        }
+
+        sub_result = await self._db.execute(
+            select(Subscription.tier).where(Subscription.user_id == user_id)
+        )
+        sub_tier: str | None = sub_result.scalar_one_or_none()
+        if sub_tier:
+            tier = sub_tier
+        elif settings.ENV == "dev":
+            tier = "power"
+        else:
+            tier = user.tier or "free"
+
+        return {"tier": tier}
 
     async def _load_core(self, user_id: str, fernet: Fernet) -> dict[str, str]:
         result = await self._db.execute(
@@ -364,14 +601,49 @@ class MemoryMiddleware:
         return out
 
     async def _load_associative(
-        self, user_id: str, message: str, fernet: Fernet
+        self, user_id: str, message: str, fernet: Fernet, *, user_tier: str = "free"
     ) -> list[str]:
         """Load top-k associative memories.
 
-        Production: uses pgvector cosine similarity on the message embedding.
-        Current implementation: keyword-based fallback (no external embedding call)
-        so tests pass without a live OpenAI key.
+        Pro+: pgvector cosine similarity on the message embedding (real_embeddings feature).
+        Free / embedding failure: keyword-ordered fallback (most recent rows).
         """
+        from app.billing.tier_manager import tier_manager  # noqa: PLC0415
+        from app.core.embeddings import embed_text  # noqa: PLC0415
+
+        if tier_manager.check_feature(user_tier, "real_embeddings"):
+            vec = await embed_text(message)
+            if vec is not None:
+                try:
+                    result = await self._db.execute(
+                        select(MemoryAssociative)
+                        .where(
+                            MemoryAssociative.user_id == user_id,
+                            MemoryAssociative.embedding.isnot(None),
+                        )
+                        .order_by(MemoryAssociative.embedding.cosine_distance(vec))
+                        .limit(_ASSOCIATIVE_TOP_K)
+                    )
+                    rows = result.scalars().all()
+                    out: list[str] = []
+                    for row in rows:
+                        plaintext = _safe_decrypt(fernet, row.content_encrypted)
+                        if plaintext is not None:
+                            out.append(plaintext)
+                    logger.info(
+                        "memory: _load_associative user=%s mode=vector hits=%d",
+                        user_id,
+                        len(out),
+                    )
+                    return out
+                except Exception as exc:
+                    logger.warning(
+                        "memory: vector search failed user=%s, falling back to keyword: %s",
+                        user_id,
+                        exc,
+                    )
+
+        # Keyword fallback: most recent rows
         result = await self._db.execute(
             select(MemoryAssociative)
             .where(MemoryAssociative.user_id == user_id)
@@ -379,7 +651,7 @@ class MemoryMiddleware:
             .limit(_ASSOCIATIVE_TOP_K)
         )
         rows = result.scalars().all()
-        out: list[str] = []
+        out = []
         for row in rows:
             plaintext = _safe_decrypt(fernet, row.content_encrypted)
             if plaintext is not None:
@@ -408,6 +680,26 @@ class MemoryMiddleware:
                 out.append(plaintext)
         return out
 
+    async def _load_relational(self, user_id: str, *, user_tier: str = "free") -> list[str]:
+        """Return top-10 relation strings for Pro+ users; empty list for Free."""
+        from app.billing.tier_manager import tier_manager  # noqa: PLC0415
+
+        if not tier_manager.check_feature(user_tier, "relational_memory"):
+            return []
+
+        result = await self._db.execute(
+            select(MemoryRelation)
+            .where(MemoryRelation.user_id == user_id)
+            .order_by(MemoryRelation.confidence.desc())
+            .limit(10)
+        )
+        rows = result.scalars().all()
+        out = [
+            f"{r.subject_label} --{r.predicate}--> {r.object_label}"
+            for r in rows
+        ]
+        return out
+
     async def _load_proactive(self, user_id: str, fernet: Fernet) -> list[str]:
         result = await self._db.execute(
             select(MemoryProactive)

app/core/note_summarizer.py

@@ -0,0 +1,51 @@
+"""Note summarizer — generates a compact AI summary for a note.
+
+Called fire-and-forget from create_note / update_note tools so the
+``notes.ai_summary`` column stays current without blocking the agent loop.
+"""
+
+from __future__ import annotations
+
+import logging
+
+from langchain_core.messages import HumanMessage, SystemMessage
+
+from app.core.langfuse_client import get_prompt_or_fallback
+from app.core.llm import get_agent_llm
+
+logger = logging.getLogger(__name__)
+
+_FALLBACK_PROMPT = """\
+Summarize this note in <=250 characters. Be terse and dense.
+Keep proper nouns, dates, decisions, and action items.
+Do not start with "This note".
+Respond with the summary text only — no intro, no labels.
+
+Title: {title}
+Content: {content}"""
+
+_MAX_CONTENT_CHARS = 4000
+
+
+async def generate_note_summary(title: str, content: str) -> str:
+    """Return a <=250-char summary of *title* + *content*.
+
+    Uses the Langfuse ``note_summary`` prompt (hot-swappable) with a local
+    fallback.  Truncates *content* to 4000 chars before sending to avoid
+    token waste on large notes.
+    """
+    template, _ = get_prompt_or_fallback("note_summary", _FALLBACK_PROMPT)
+    trimmed = content[:_MAX_CONTENT_CHARS]
+    system_prompt = template.format(title=title, content=trimmed)
+
+    try:
+        llm = get_agent_llm("note-summarizer")
+        response = await llm.ainvoke([
+            SystemMessage(content=system_prompt),
+            HumanMessage(content="Generate the summary."),
+        ])
+        text = response.content if isinstance(response.content, str) else ""
+        return text.strip()[:250]
+    except Exception as exc:
+        logger.warning("note_summarizer: failed to generate summary: %s", exc)
+        return ""

app/core/output_formatter.py

@@ -2,11 +2,35 @@
 
 from __future__ import annotations
 
+import re
 from collections.abc import AsyncGenerator
 from typing import Any
 
 from app.schemas import WsFloatingDomain, WsStreamEnd, WsStreamStart, WsStreamText
 
+# Matches <canvas kind="...">...</canvas> blocks (single-line or multiline).
+_CANVAS_BLOCK_RE = re.compile(
+    r'<canvas\s+kind=["\']([^"\']+)["\']>(.*?)</canvas>',
+    re.DOTALL | re.IGNORECASE,
+)
+
+
+def extract_canvas_block(text: str) -> tuple[str, str | None, str | None]:
+    """Strip the first <canvas kind="...">...</canvas> block from *text*.
+
+    Returns ``(visible_text, canvas_content, canvas_kind)``.
+    ``canvas_content`` and ``canvas_kind`` are ``None`` when no block is found.
+    """
+    match = _CANVAS_BLOCK_RE.search(text)
+    if not match:
+        return text, None, None
+
+    canvas_kind = match.group(1).strip()
+    canvas_content = match.group(2).strip()
+    visible = text[: match.start()] + text[match.end() :]
+    visible = visible.strip()
+    return visible, canvas_content, canvas_kind
+
 WsFrame = WsStreamStart | WsStreamText | WsStreamEnd | WsFloatingDomain
 
 

app/core/preprocessors/__init__.py

@@ -0,0 +1,104 @@
+"""Preprocessor registry: detect content type and dispatch to handlers.
+
+Public API
+----------
+detect_content_type(filename, raw_content) -> str
+    Heuristic detection based on file extension and content patterns.
+
+preprocess(content_type, raw_content) -> PreprocessResult
+    Dispatch to the appropriate handler.
+"""
+
+from __future__ import annotations
+
+import re
+
+from app.core.preprocessors.base import PreprocessResult
+
+# ── Heuristics ────────────────────────────────────────────────────────
+
+# Patterns that strongly suggest an email HTML file
+_EMAIL_SIGNALS = re.compile(
+    r"(Subject:|From:|To:|Date:|Sent:|MIME-Version:|Content-Type:\s*text/html)",
+    re.IGNORECASE,
+)
+
+# Patterns that suggest a generic HTML page (not an email)
+_GENERIC_HTML_SIGNALS = re.compile(
+    r"<(nav|main|header|footer|article|section)\b",
+    re.IGNORECASE,
+)
+
+
+def detect_content_type(filename: str, raw_content: str) -> str:
+    """Return a content-type string for the given file.
+
+    Supported types: ``"email_html"``, ``"generic_html"``,
+    ``"plain_text"``, ``"unknown"``.
+    """
+    ext = filename.rsplit(".", 1)[-1].lower() if "." in filename else ""
+
+    if ext == "txt":
+        return "plain_text"
+
+    if ext in ("html", "htm", "eml", "mhtml", "mht"):
+        # Prefer email detection over generic HTML
+        if _EMAIL_SIGNALS.search(raw_content[:4096]):
+            return "email_html"
+        if _GENERIC_HTML_SIGNALS.search(raw_content[:4096]) or "<html" in raw_content[:200].lower():
+            return "generic_html"
+        # .html without clear signals — check for any email header
+        if re.search(r"^(From|To|Subject|Date):", raw_content[:2048], re.MULTILINE | re.IGNORECASE):
+            return "email_html"
+        return "generic_html"
+
+    # Plain text files with email headers
+    if ext in ("", "txt") or not ext:
+        if _EMAIL_SIGNALS.search(raw_content[:4096]):
+            return "email_html"
+
+    # Detect binary content
+    try:
+        raw_content.encode("utf-8")
+    except (UnicodeEncodeError, AttributeError):
+        return "unknown"
+
+    # Non-text bytes heuristic: high ratio of non-printable chars
+    sample = raw_content[:512]
+    non_printable = sum(1 for c in sample if ord(c) < 32 and c not in "\r\n\t")
+    if len(sample) > 0 and non_printable / len(sample) > 0.1:
+        return "unknown"
+
+    return "unknown"
+
+
+# ── Generic fallback handler ──────────────────────────────────────────
+
+def _preprocess_generic(raw_content: str, content_type: str) -> PreprocessResult:
+    """Strip HTML tags if present, return text as-is."""
+    try:
+        from bs4 import BeautifulSoup
+        text = BeautifulSoup(raw_content, "html.parser").get_text(separator="\n")
+    except ImportError:
+        # No BeautifulSoup — strip tags with a simple regex
+        text = re.sub(r"<[^>]+>", "", raw_content)
+
+    text = re.sub(r"\n{3,}", "\n\n", text).strip()
+    return PreprocessResult(content_type=content_type, clean_text=text, metadata={})
+
+
+# ── Dispatch ──────────────────────────────────────────────────────────
+
+def preprocess(content_type: str, raw_content: str) -> PreprocessResult:
+    """Dispatch *raw_content* to the handler registered for *content_type*.
+
+    Falls back to the generic handler for unknown types.
+    """
+    if content_type == "email_html":
+        from app.core.preprocessors.email_html import preprocess_email_html
+        return preprocess_email_html(raw_content)
+
+    return _preprocess_generic(raw_content, content_type)
+
+
+__all__ = ["detect_content_type", "preprocess", "PreprocessResult"]

app/core/preprocessors/base.py

@@ -0,0 +1,25 @@
+"""Base types for the preprocessor system."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+
+
+@dataclass
+class PreprocessResult:
+    """Output of a preprocessor handler.
+
+    Attributes
+    ----------
+    content_type:
+        The detected content type (e.g. ``"email_html"``, ``"plain_text"``).
+    clean_text:
+        Human-readable text stripped of markup/binary noise.
+    metadata:
+        Dict of extracted metadata (keys vary by handler).
+        Common keys: ``subject``, ``from``, ``to``, ``date``, ``filename``.
+    """
+
+    content_type: str
+    clean_text: str
+    metadata: dict = field(default_factory=dict)

app/core/preprocessors/email_html.py

@@ -0,0 +1,111 @@
+"""Preprocessor for email HTML files.
+
+Handles:
+- HTML stripping via BeautifulSoup
+- Metadata extraction (Subject, From, To, Date)
+- Thread splitting — isolates the latest reply
+"""
+
+from __future__ import annotations
+
+import re
+from typing import TYPE_CHECKING
+
+from app.core.preprocessors.base import PreprocessResult
+
+if TYPE_CHECKING:
+    pass
+
+# ── Thread split markers ──────────────────────────────────────────────
+
+# Matches patterns like:
+#   "On Mon, Apr 7, 2026 at 10:00 AM, Alice <alice@co.com> wrote:"
+#   "-----Original Message-----"
+#   "> " (plain-text quote prefix)
+_THREAD_PATTERNS = [
+    re.compile(r"^On\s+.+wrote\s*:", re.IGNORECASE | re.MULTILINE),
+    re.compile(r"^-{3,}\s*(original message|forwarded message)\s*-{3,}", re.IGNORECASE | re.MULTILINE),
+    re.compile(r"^>{1,}\s+\S", re.MULTILINE),
+    re.compile(r"^From:\s+.+\nSent:\s+", re.IGNORECASE | re.MULTILINE),
+]
+
+# ── Metadata patterns (applied on raw HTML / plain fallback) ──────────
+
+_META_PATTERNS: dict[str, list[re.Pattern]] = {
+    "subject": [
+        re.compile(r"<title>(.+?)</title>", re.IGNORECASE | re.DOTALL),
+        re.compile(r"Subject:\s*(.+)", re.IGNORECASE),
+    ],
+    "from": [
+        re.compile(r'<meta[^>]+name=["\']?from["\']?[^>]+content=["\']([^"\']+)["\']', re.IGNORECASE),
+        re.compile(r"From:\s*(.+)", re.IGNORECASE),
+    ],
+    "to": [
+        re.compile(r'<meta[^>]+name=["\']?to["\']?[^>]+content=["\']([^"\']+)["\']', re.IGNORECASE),
+        re.compile(r"To:\s*(.+)", re.IGNORECASE),
+    ],
+    "date": [
+        re.compile(r'<meta[^>]+name=["\']?date["\']?[^>]+content=["\']([^"\']+)["\']', re.IGNORECASE),
+        re.compile(r"Date:\s*(.+)", re.IGNORECASE),
+        re.compile(r"Sent:\s*(.+)", re.IGNORECASE),
+    ],
+}
+
+
+def _extract_metadata(raw_html: str, text: str) -> dict:
+    """Extract Subject/From/To/Date from raw HTML or plain text."""
+    metadata: dict[str, str] = {}
+    for field, patterns in _META_PATTERNS.items():
+        for pat in patterns:
+            m = pat.search(raw_html) or pat.search(text)
+            if m:
+                metadata[field] = m.group(1).strip()
+                break
+    return metadata
+
+
+def _split_thread(text: str) -> str:
+    """Return only the latest message in a threaded email."""
+    earliest_pos: int | None = None
+    for pat in _THREAD_PATTERNS:
+        m = pat.search(text)
+        if m and (earliest_pos is None or m.start() < earliest_pos):
+            earliest_pos = m.start()
+
+    if earliest_pos is not None and earliest_pos > 0:
+        return text[:earliest_pos].strip()
+    return text.strip()
+
+
+def preprocess_email_html(raw_content: str) -> PreprocessResult:
+    """Strip HTML, extract metadata, split thread from an email HTML file."""
+    try:
+        from bs4 import BeautifulSoup  # lazy import — optional dep
+    except ImportError as exc:
+        raise ImportError(
+            "beautifulsoup4 is required for email_html preprocessing. "
+            "Install it with: pip install beautifulsoup4"
+        ) from exc
+
+    # Parse with lxml if available, fall back to html.parser
+    try:
+        soup = BeautifulSoup(raw_content, "lxml")
+    except Exception:
+        soup = BeautifulSoup(raw_content, "html.parser")
+
+    # Remove noise tags
+    for tag in soup(["style", "script", "head", "noscript"]):
+        tag.decompose()
+
+    clean_text = soup.get_text(separator="\n")
+    # Collapse excessive blank lines
+    clean_text = re.sub(r"\n{3,}", "\n\n", clean_text).strip()
+
+    metadata = _extract_metadata(raw_content, clean_text)
+    latest_message = _split_thread(clean_text)
+
+    return PreprocessResult(
+        content_type="email_html",
+        clean_text=latest_message,
+        metadata=metadata,
+    )

app/core/ws_context.py

@@ -7,10 +7,32 @@ The callback sends a `tool_call` WS frame and awaits the `tool_result`.
 
 from __future__ import annotations
 
+import re
 from contextvars import ContextVar
 from typing import Any, Callable, Coroutine
 from uuid import uuid4
 
+_SNAKE_TO_CAMEL_RE = re.compile(r"_([a-z])")
+
+
+def _key_to_camel(key: str) -> str:
+    return _SNAKE_TO_CAMEL_RE.sub(lambda m: m.group(1).upper(), key)
+
+
+def _keys_to_camel(obj: Any) -> Any:
+    """Recursively convert dict keys from snake_case to camelCase.
+
+    Mirrors the JS-side ``toCamelCase`` applied to incoming WS frames in
+    ``adiuvAI/src/main/api/backend-client.ts``. The Electron executor wraps
+    tool_result payloads in ``toSnakeCase`` before sending; this restores the
+    camelCase schema property names that the tool code expects to read.
+    """
+    if isinstance(obj, dict):
+        return {_key_to_camel(k): _keys_to_camel(v) for k, v in obj.items()}
+    if isinstance(obj, list):
+        return [_keys_to_camel(v) for v in obj]
+    return obj
+
 # Holds the execute callback for the current WS session.
 # Set by the chat WS handler before the orchestrator runs; cleared after.
 _client_executor: ContextVar[Callable[[dict], Coroutine[Any, Any, dict]]] = ContextVar(
@@ -82,6 +104,7 @@ async def execute_on_client(
         payload["limit"] = limit
 
     result = await callback(payload)
+    result = _keys_to_camel(result)
     collector = _tool_result_collector.get(None)
     if collector is not None:
         collector.append({

app/integrations/ms_graph.py

@@ -25,7 +25,7 @@ from __future__ import annotations
 
 import logging
 import re
-from datetime import datetime, timedelta, timezone
+from datetime import datetime, timezone
 from typing import Any
 
 import httpx

app/main.py

@@ -4,6 +4,10 @@ import logging
 from fastapi import FastAPI
 from fastapi.middleware.cors import CORSMiddleware
 
+from app.api.middleware.rate_limit import TierRateLimitMiddleware
+from app.api.middleware.sanitizer import SanitizerMiddleware
+from app.config.settings import settings
+
 logging.basicConfig(
     level=logging.INFO,
     format="%(asctime)s %(levelname)s %(name)s: %(message)s",
@@ -11,9 +15,66 @@ logging.basicConfig(
 logging.getLogger("sqlalchemy.engine").setLevel(logging.WARNING)
 logging.getLogger("sqlalchemy.pool").setLevel(logging.WARNING)
 
-from app.api.middleware.rate_limit import TierRateLimitMiddleware
-from app.api.middleware.sanitizer import SanitizerMiddleware
-from app.config.settings import settings
+
+async def _memory_audit_cron_tick() -> None:
+    """Weekly cron: contradiction scan + label canonicalization for all users (Phase 7)."""
+    import logging  # noqa: PLC0415
+    _log = logging.getLogger(__name__)
+    _log.info("memory audit cron tick: starting")
+    try:
+        from app.db import async_session  # noqa: PLC0415
+        from app.core.memory_maintenance import audit_memory  # noqa: PLC0415
+        from app.models import User  # noqa: PLC0415
+        from sqlalchemy import select  # noqa: PLC0415
+
+        async with async_session() as db:
+            result = await db.execute(select(User.id))
+            user_ids: list[str] = list(result.scalars().all())
+
+        for uid in user_ids:
+            try:
+                async with async_session() as db:
+                    await audit_memory(db, uid)
+            except Exception as exc:
+                _log.warning("memory audit cron tick: audit_memory failed user=%s: %s", uid, exc)
+
+        _log.info("memory audit cron tick: done users=%d", len(user_ids))
+    except Exception as exc:
+        _log.warning("memory audit cron tick: failed: %s", exc)
+
+
+async def _memory_cron_tick() -> None:
+    """Hourly cron: drain Free-tier extraction queue + mine proactive patterns for Power+ users."""
+    import logging  # noqa: PLC0415
+    _log = logging.getLogger(__name__)
+    _log.info("memory cron tick: starting")
+    try:
+        from app.db import async_session  # noqa: PLC0415
+        from app.core.memory_maintenance import drain_extraction_queue, mine_proactive_patterns  # noqa: PLC0415
+        from app.billing.tier_manager import tier_manager  # noqa: PLC0415
+        from app.models import User  # noqa: PLC0415
+        from sqlalchemy import select  # noqa: PLC0415
+
+        async with async_session() as db:
+            await drain_extraction_queue(db)
+
+        # mine proactive patterns for every Power+ user
+        async with async_session() as db:
+            result = await db.execute(select(User.id))
+            user_ids: list[str] = list(result.scalars().all())
+
+        for uid in user_ids:
+            try:
+                async with async_session() as db:
+                    tier = await tier_manager.get_tier(uid, db)
+                    if tier_manager.check_feature(tier, "proactive_mining"):
+                        await mine_proactive_patterns(db, uid)
+            except Exception as exc:
+                _log.warning("memory cron tick: mine_proactive_patterns failed user=%s: %s", uid, exc)
+
+        _log.info("memory cron tick: done users=%d", len(user_ids))
+    except Exception as exc:
+        _log.warning("memory cron tick: failed: %s", exc)
 
 
 @asynccontextmanager
@@ -21,8 +82,21 @@ async def lifespan(app: FastAPI):
     # Startup: ensure agent tool modules are loaded.
     import app.agents  # noqa: F401
 
+    scheduler = None
+    if settings.SCHEDULER_ENABLED:
+        from apscheduler.schedulers.asyncio import AsyncIOScheduler  # noqa: PLC0415
+
+        scheduler = AsyncIOScheduler()
+        scheduler.add_job(_memory_cron_tick, "interval", hours=1, id="memory_cron")
+        scheduler.add_job(_memory_audit_cron_tick, "interval", weeks=1, id="memory_audit_cron")
+        scheduler.start()
+        logging.getLogger(__name__).info("memory cron scheduler started (interval=1h)")
+
     yield
 
+    if scheduler is not None:
+        scheduler.shutdown(wait=False)
+
     # Shutdown: dispose SQLAlchemy connection pool
     from app.db import engine
     await engine.dispose()
@@ -30,7 +104,7 @@ async def lifespan(app: FastAPI):
 
 def create_app() -> FastAPI:
     app = FastAPI(
-        title="Adiuva Cloud API",
+        title="AdiuvAI Cloud API",
         version="0.1.0",
         docs_url="/docs" if settings.ENV == "dev" else None,
         redoc_url=None,
@@ -50,17 +124,14 @@ def create_app() -> FastAPI:
     app.add_middleware(SanitizerMiddleware)
     app.add_middleware(TierRateLimitMiddleware)
 
-    from app.api.routes import agents, auth, backup, billing, chat, device_ws, plugins, storage, vectors
+    from app.api.routes import agents, auth, billing, chat, device_ws, memory
 
     app.include_router(auth.router,       prefix="/api/v1")
     app.include_router(chat.router,       prefix="/api/v1")
-    app.include_router(storage.router,    prefix="/api/v1")
-    app.include_router(vectors.router,    prefix="/api/v1")
-    app.include_router(backup.router,     prefix="/api/v1")
-    app.include_router(plugins.router,    prefix="/api/v1")
     app.include_router(billing.router,    prefix="/api/v1")
     app.include_router(agents.router,     prefix="/api/v1")
     app.include_router(device_ws.router,  prefix="/api/v1")
+    app.include_router(memory.router,     prefix="/api/v1")
 
     @app.get("/api/v1/health", tags=["health"])
     async def health() -> dict:

app/marketplace/__init__.py

@@ -1,7 +0,0 @@
-"""Plugin marketplace package.
-
-Three service classes introduced in Step 10:
-  - ``PluginRegistry``  — catalog, submit/approve/reject, install counts
-  - ``ReviewQueue``     — approval workflow + security checklist
-  - ``RevenueShare``    — 70/30 split tracking and Stripe Connect payouts
-"""

app/marketplace/plugin_registry.py

@@ -1,212 +0,0 @@
-"""Plugin catalog registry backed by PostgreSQL.
-
-Maintains the authoritative list of plugins, their review status, and
-aggregate install counts.  All data is persisted in the ``plugins`` table.
-
-Module-level singleton::
-
-    from app.marketplace.plugin_registry import registry
-"""
-
-from __future__ import annotations
-
-import json
-from typing import Any, Literal
-
-from sqlalchemy import select, func
-from sqlalchemy.ext.asyncio import AsyncSession
-
-from app.models import Plugin
-from app.schemas import PluginListResponse, PluginManifest
-
-_PAGE_SIZE = 20
-
-
-def _plugin_to_manifest(p: Plugin) -> PluginManifest:
-    """Convert an ORM ``Plugin`` row to a Pydantic ``PluginManifest``."""
-    try:
-        permissions = json.loads(p.permissions) if p.permissions else []
-    except (json.JSONDecodeError, TypeError):
-        permissions = []
-    return PluginManifest(
-        id=p.id,
-        name=p.name,
-        description=p.description,
-        version=p.version,
-        author=p.author_name,
-        permissions=permissions,
-        category=p.category,
-        price_cents=p.price_cents,
-    )
-
-
-class PluginRegistry:
-    """PostgreSQL-backed plugin catalog.
-
-    All methods accept an ``AsyncSession`` parameter so the calling route
-    controls the session lifecycle.
-    """
-
-    # ── Queries ──────────────────────────────────────────────────────
-
-    async def list_plugins(
-        self,
-        db: AsyncSession,
-        category: str | None = None,
-        query: str | None = None,
-        page: int = 1,
-        sort: Literal["rating", "installs", "newest"] = "newest",
-    ) -> PluginListResponse:
-        """Return a page of approved plugins, optionally filtered and sorted."""
-        base = select(Plugin).where(Plugin.status == "approved")
-
-        if category:
-            base = base.where(Plugin.category == category)
-        if query:
-            pattern = f"%{query}%"
-            base = base.where(
-                Plugin.name.ilike(pattern) | Plugin.description.ilike(pattern)
-            )
-
-        # Count
-        count_q = select(func.count()).select_from(base.subquery())
-        total = (await db.execute(count_q)).scalar_one()
-
-        # Sort
-        if sort == "installs":
-            base = base.order_by(Plugin.install_count.desc())
-        elif sort == "rating":
-            base = base.order_by(Plugin.avg_rating.desc())
-        else:  # newest
-            base = base.order_by(Plugin.created_at.desc())
-
-        base = base.offset((page - 1) * _PAGE_SIZE).limit(_PAGE_SIZE)
-        rows = (await db.execute(base)).scalars().all()
-
-        return PluginListResponse(
-            plugins=[_plugin_to_manifest(r) for r in rows],
-            total=total,
-            page=page,
-        )
-
-    async def get_plugin(self, db: AsyncSession, plugin_id: str) -> dict[str, Any] | None:
-        """Return ``{manifest, status, install_count, avg_rating}`` or ``None``."""
-        result = await db.execute(select(Plugin).where(Plugin.id == plugin_id))
-        p = result.scalar_one_or_none()
-        if p is None:
-            return None
-        return {
-            "manifest": _plugin_to_manifest(p),
-            "status": p.status,
-            "install_count": p.install_count,
-            "avg_rating": p.avg_rating,
-        }
-
-    # ── Mutations ────────────────────────────────────────────────────
-
-    async def submit_plugin(
-        self,
-        db: AsyncSession,
-        manifest: PluginManifest,
-        package_s3_key: str,
-    ) -> str:
-        """Add *manifest* to the catalog with ``status='pending_review'``.
-
-        Returns the plugin_id.  If a plugin with the same id already exists
-        it is overwritten (re-submission after rejection).
-        """
-        plugin_id = manifest.id
-        existing = await db.execute(select(Plugin).where(Plugin.id == plugin_id))
-        row = existing.scalar_one_or_none()
-
-        if row is not None:
-            row.name = manifest.name
-            row.description = manifest.description
-            row.version = manifest.version
-            row.author_name = manifest.author
-            row.category = manifest.category
-            row.price_cents = manifest.price_cents
-            row.permissions = json.dumps(manifest.permissions)
-            row.status = "pending_review"
-            row.s3_package_key = package_s3_key
-            row.rejection_reason = None
-        else:
-            row = Plugin(
-                id=plugin_id,
-                name=manifest.name,
-                description=manifest.description,
-                version=manifest.version,
-                author_name=manifest.author,
-                category=manifest.category,
-                price_cents=manifest.price_cents,
-                permissions=json.dumps(manifest.permissions),
-                status="pending_review",
-                s3_package_key=package_s3_key,
-                install_count=0,
-                avg_rating=0.0,
-            )
-            db.add(row)
-        await db.commit()
-        return plugin_id
-
-    async def approve_plugin(self, db: AsyncSession, plugin_id: str) -> None:
-        """Set *plugin_id* status to ``'approved'``.
-
-        Raises ``KeyError`` if the plugin is not found.
-        """
-        result = await db.execute(select(Plugin).where(Plugin.id == plugin_id))
-        row = result.scalar_one_or_none()
-        if row is None:
-            raise KeyError(f"Plugin not found: {plugin_id}")
-        row.status = "approved"
-        row.rejection_reason = None
-        await db.commit()
-
-    async def reject_plugin(self, db: AsyncSession, plugin_id: str, reason: str) -> None:
-        """Set *plugin_id* status to ``'rejected'`` and record the reason.
-
-        Raises ``KeyError`` if the plugin is not found.
-        """
-        result = await db.execute(select(Plugin).where(Plugin.id == plugin_id))
-        row = result.scalar_one_or_none()
-        if row is None:
-            raise KeyError(f"Plugin not found: {plugin_id}")
-        row.status = "rejected"
-        row.rejection_reason = reason
-        await db.commit()
-
-    async def record_install(self, db: AsyncSession, plugin_id: str) -> None:
-        """Increment the install count for *plugin_id* (no-op if not found)."""
-        result = await db.execute(select(Plugin).where(Plugin.id == plugin_id))
-        row = result.scalar_one_or_none()
-        if row is not None:
-            row.install_count = row.install_count + 1
-            await db.commit()
-
-    async def record_uninstall(self, db: AsyncSession, plugin_id: str) -> None:
-        """Decrement the install count for *plugin_id*, floored at 0."""
-        result = await db.execute(select(Plugin).where(Plugin.id == plugin_id))
-        row = result.scalar_one_or_none()
-        if row is not None:
-            row.install_count = max(0, row.install_count - 1)
-            await db.commit()
-
-    # ── Internal helpers used by ReviewQueue ─────────────────────────
-
-    async def get_pending_entries(self, db: AsyncSession) -> list[dict[str, Any]]:
-        """Return all entries with status='pending_review'."""
-        result = await db.execute(
-            select(Plugin).where(Plugin.status == "pending_review")
-        )
-        rows = result.scalars().all()
-        return [
-            {
-                "manifest": _plugin_to_manifest(r),
-                "submitted_at": int(r.submitted_at.timestamp()) if r.submitted_at else 0,
-            }
-            for r in rows
-        ]
-
-
-# Module-level singleton
-registry = PluginRegistry()

app/marketplace/plugin_review.py

@@ -1,125 +0,0 @@
-"""Plugin review workflow backed by PostgreSQL.
-
-Manages the approval queue for newly submitted plugins and enforces a
-security checklist before any plugin is made visible in the marketplace.
-
-Module-level singleton::
-
-    from app.marketplace.plugin_review import review_queue
-"""
-
-from __future__ import annotations
-
-import re
-from typing import Any, Literal
-
-from sqlalchemy.ext.asyncio import AsyncSession
-
-from app.marketplace.plugin_registry import registry
-from app.models import PluginReview as PluginReviewModel
-from app.schemas import PluginManifest
-
-# ── Security policy ───────────────────────────────────────────────────
-
-ALLOWED_PERMISSIONS: frozenset[str] = frozenset(
-    {
-        "read:tasks",
-        "write:tasks",
-        "read:projects",
-        "write:projects",
-        "read:notes",
-        "write:notes",
-        "read:timelines",
-        "write:timelines",
-        "read:calendar",
-        "write:calendar",
-    }
-)
-
-_PLUGIN_ID_RE = re.compile(r"^[a-z0-9-]+$")
-
-
-def validate_manifest(manifest: PluginManifest) -> None:
-    """Enforce the plugin security checklist.
-
-    Raises:
-        ``ValueError`` on the first violation found.  Callers should catch
-        this and return HTTP 422 / reject the submission.
-
-    Checks:
-      1. Plugin id matches ``^[a-z0-9-]+$``
-      2. All declared permissions are in ``ALLOWED_PERMISSIONS``
-      3. No manifest field contains raw binary data
-    """
-    if not _PLUGIN_ID_RE.match(manifest.id):
-        raise ValueError(
-            f"Invalid plugin id format: '{manifest.id}'. "
-            "Only lowercase letters, digits, and hyphens are allowed."
-        )
-
-    for perm in manifest.permissions:
-        if perm not in ALLOWED_PERMISSIONS:
-            raise ValueError(
-                f"Unknown permission: '{perm}'. "
-                f"Allowed permissions: {sorted(ALLOWED_PERMISSIONS)}"
-            )
-
-    for field_name, value in manifest.model_dump().items():
-        if isinstance(value, (bytes, bytearray)):
-            raise ValueError(
-                f"Binary content is not allowed in manifest field '{field_name}'."
-            )
-
-
-class ReviewQueue:
-    """Approval queue for pending plugin submissions.
-
-    Delegates status changes to the shared ``PluginRegistry`` singleton.
-    Review records are persisted in the ``plugin_reviews`` table.
-    """
-
-    async def get_pending(self, db: AsyncSession) -> list[dict[str, Any]]:
-        """Return all plugins currently awaiting review.
-
-        Each item is ``{plugin_id, manifest, submitted_at}``.
-        """
-        entries = await registry.get_pending_entries(db)
-        return [
-            {
-                "plugin_id": e["manifest"].id,
-                "manifest": e["manifest"],
-                "submitted_at": e["submitted_at"],
-            }
-            for e in entries
-        ]
-
-    async def submit_review(
-        self,
-        db: AsyncSession,
-        plugin_id: str,
-        reviewer_id: str,
-        decision: Literal["approved", "rejected"],
-        notes: str = "",
-    ) -> None:
-        """Record a review decision and update the plugin's status.
-
-        Raises:
-            ``KeyError`` if *plugin_id* is not found in the registry.
-        """
-        if decision == "approved":
-            await registry.approve_plugin(db, plugin_id)
-        else:
-            await registry.reject_plugin(db, plugin_id, reason=notes)
-
-        review = PluginReviewModel(
-            plugin_id=plugin_id,
-            reviewer_id=reviewer_id,
-            decision=decision,
-            notes=notes,
-        )
-        db.add(review)
-        await db.commit()
-
-
-# Module-level singleton
-review_queue = ReviewQueue()

app/marketplace/revenue_share.py

@@ -1,233 +0,0 @@
-"""Revenue share tracking and Stripe Connect payouts backed by PostgreSQL.
-
-Records every plugin installation as a revenue event and facilitates
-70 % / 30 % payouts to developers via Stripe Connect.  Data is persisted
-in the ``revenue_events`` table.
-
-Module-level singleton::
-
-    from app.marketplace.revenue_share import revenue_share
-"""
-
-from __future__ import annotations
-
-import logging
-from datetime import datetime, timezone
-from typing import Any
-
-import stripe as stripe_lib
-from sqlalchemy import extract, func, select
-from sqlalchemy.ext.asyncio import AsyncSession
-
-from app.config.settings import settings
-from app.marketplace.plugin_registry import registry
-from app.models import Plugin, RevenueEvent
-
-logger = logging.getLogger(__name__)
-
-# ── Revenue split constants ───────────────────────────────────────────
-
-DEVELOPER_SHARE: float = 0.70
-PLATFORM_SHARE: float = 0.30
-
-
-class RevenueShare:
-    """Records installation revenue events and coordinates developer payouts.
-
-    Stripe Connect calls are gracefully stubbed when ``STRIPE_SECRET_KEY``
-    is not configured, consistent with the rest of the billing layer.
-    """
-
-    # ── Helpers ──────────────────────────────────────────────────────
-
-    @staticmethod
-    def _stripe_configured() -> bool:
-        return bool(settings.STRIPE_SECRET_KEY)
-
-    @staticmethod
-    def _stripe() -> Any:
-        stripe_lib.api_key = settings.STRIPE_SECRET_KEY
-        return stripe_lib
-
-    # ── Core operations ──────────────────────────────────────────────
-
-    async def record_install(
-        self,
-        db: AsyncSession,
-        plugin_id: str,
-        user_id: str,
-        amount_cents: int,
-    ) -> None:
-        """Record a plugin installation and trigger a Stripe Connect charge if paid.
-
-        For free plugins (``amount_cents == 0``) no payment is initiated but
-        the event is still recorded for analytics.
-
-        For paid plugins the developer receives 70 % via a Stripe Connect
-        destination charge.  If Stripe is not configured or the charge fails
-        the installation still succeeds (the event is recorded and the install
-        count is incremented) — a warning is logged for monitoring.
-        """
-        developer_share_cents = int(amount_cents * DEVELOPER_SHARE)
-        stripe_transfer_id: str | None = None
-
-        if amount_cents > 0 and self._stripe_configured():
-            # Look up the plugin's author Stripe account from the DB
-            result = await db.execute(select(Plugin).where(Plugin.id == plugin_id))
-            plugin_row = result.scalar_one_or_none()
-            developer_stripe_account: str | None = None
-            if plugin_row and plugin_row.author_id:
-                # Future: look up user.stripe_connect_account_id
-                developer_stripe_account = None  # no real account yet
-
-            if developer_stripe_account:
-                try:
-                    s = self._stripe()
-                    transfer = s.Transfer.create(
-                        amount=developer_share_cents,
-                        currency="eur",
-                        destination=developer_stripe_account,
-                        description=f"Revenue share for plugin {plugin_id}",
-                        metadata={"plugin_id": plugin_id, "user_id": user_id},
-                    )
-                    stripe_transfer_id = transfer["id"]
-                except Exception as exc:
-                    logger.warning(
-                        "Stripe Connect transfer failed for plugin %s: %s",
-                        plugin_id,
-                        exc,
-                    )
-            else:
-                logger.debug(
-                    "No Stripe account on file for plugin %s developer; "
-                    "skipping transfer.",
-                    plugin_id,
-                )
-
-        event = RevenueEvent(
-            plugin_id=plugin_id,
-            user_id=user_id,
-            amount_cents=amount_cents,
-            developer_share_cents=developer_share_cents,
-            stripe_transfer_id=stripe_transfer_id,
-        )
-        db.add(event)
-        await db.commit()
-
-        await registry.record_install(db, plugin_id)
-
-    async def get_earnings(
-        self,
-        db: AsyncSession,
-        developer_id: str,
-        period: str | None = None,
-    ) -> dict[str, Any]:
-        """Return aggregated earnings for *developer_id*.
-
-        ``period`` is an optional ``YYYY-MM`` string to restrict the window.
-
-        Returns::
-
-            {
-                "developer_id": str,
-                "period": str | None,
-                "total_installs": int,
-                "total_revenue_cents": int,
-                "developer_share_cents": int,
-            }
-        """
-        # Find plugin ids belonging to this developer (by author_name match)
-        plugin_q = select(Plugin.id).where(Plugin.author_name == developer_id)
-        plugin_result = await db.execute(plugin_q)
-        developer_plugin_ids = [row[0] for row in plugin_result.all()]
-
-        if not developer_plugin_ids:
-            return {
-                "developer_id": developer_id,
-                "period": period,
-                "total_installs": 0,
-                "total_revenue_cents": 0,
-                "developer_share_cents": 0,
-            }
-
-        query = select(
-            func.count().label("total_installs"),
-            func.coalesce(func.sum(RevenueEvent.amount_cents), 0).label("total_revenue"),
-            func.coalesce(func.sum(RevenueEvent.developer_share_cents), 0).label("dev_share"),
-        ).where(RevenueEvent.plugin_id.in_(developer_plugin_ids))
-
-        if period:
-            # Filter by YYYY-MM: extract year and month from created_at
-            try:
-                year, month = period.split("-")
-                query = query.where(
-                    extract("year", RevenueEvent.created_at) == int(year),
-                    extract("month", RevenueEvent.created_at) == int(month),
-                )
-            except ValueError:
-                pass  # invalid period format — return all
-
-        result = await db.execute(query)
-        row = result.one()
-
-        return {
-            "developer_id": developer_id,
-            "period": period,
-            "total_installs": row.total_installs,
-            "total_revenue_cents": row.total_revenue,
-            "developer_share_cents": row.dev_share,
-        }
-
-    async def payout_developer(self, db: AsyncSession, plugin_id: str, period: str) -> None:
-        """Aggregate unpaid revenue for *period* and issue a Stripe Transfer.
-
-        Marks processed events with ``paid_at`` timestamp.
-        Stubs gracefully when Stripe is not configured.
-        """
-        try:
-            year, month = period.split("-")
-            year_int, month_int = int(year), int(month)
-        except ValueError:
-            logger.warning("Invalid period format: %s", period)
-            return
-
-        result = await db.execute(
-            select(RevenueEvent).where(
-                RevenueEvent.plugin_id == plugin_id,
-                RevenueEvent.paid_at.is_(None),
-                extract("year", RevenueEvent.created_at) == year_int,
-                extract("month", RevenueEvent.created_at) == month_int,
-            )
-        )
-        unpaid = list(result.scalars().all())
-
-        total_dev_share = sum(e.developer_share_cents for e in unpaid)
-        if total_dev_share <= 0 or not unpaid:
-            logger.debug("Nothing to pay out for plugin %s in period %s", plugin_id, period)
-            return
-
-        if self._stripe_configured():
-            plugin_result = await db.execute(select(Plugin).where(Plugin.id == plugin_id))
-            plugin_row = plugin_result.scalar_one_or_none()
-            developer_stripe_account: str | None = None  # Future: fetch from DB
-            if plugin_row and developer_stripe_account:
-                try:
-                    s = self._stripe()
-                    s.Transfer.create(
-                        amount=total_dev_share,
-                        currency="eur",
-                        destination=developer_stripe_account,
-                        description=f"Payout for plugin {plugin_id} period {period}",
-                    )
-                except Exception as exc:
-                    logger.warning("Payout transfer failed for plugin %s: %s", plugin_id, exc)
-                    return
-
-        paid_ts = datetime.now(timezone.utc)
-        for event in unpaid:
-            event.paid_at = paid_ts
-        await db.commit()
-
-
-# Module-level singleton
-revenue_share = RevenueShare()

app/models.py

@@ -1,23 +1,20 @@
 """SQLAlchemy ORM models for all persistent tables.
 
-Only auth, billing, storage metadata, and marketplace data live here.
-User content (notes, tasks, etc.) is NEVER persisted server-side —
-it lives in E2E-encrypted blobs in S3, referenced by storage_records.
+Only auth, billing, agent config, and memory data live here.
+User content (notes, tasks, etc.) lives exclusively on the client.
 
 Table inventory:
   users               — account credentials + tier
   refresh_tokens      — hashed refresh token store
   subscriptions       — Stripe subscription records
-  storage_records     — S3 blob metadata (no plaintext)
-  backup_metadata     — encrypted backup manifests
-  plugins             — marketplace plugin catalog
-  plugin_installations — per-user install records
-  plugin_reviews      — admin review decisions
-  revenue_events      — Stripe Connect 70/30 split ledger
+  local_agent_configs — per-device batch agent configs
+  cloud_agent_configs — OAuth-backed cloud agent configs
+  agent_run_logs      — execution history for all agents
   memory_core         — per-user persistent key/value preferences (encrypted)
   memory_associative  — per-user semantic memory with embeddings (encrypted)
   memory_episodic     — per-user session summaries (encrypted)
   memory_proactive    — per-user behavioral patterns (encrypted)
+  memory_relations    — per-user entity/relation graph (Mem0g-light, Phase 3)
 """
 
 from __future__ import annotations
@@ -25,8 +22,8 @@ from __future__ import annotations
 import uuid
 from datetime import datetime, timezone
 
+from pgvector.sqlalchemy import Vector
 from sqlalchemy import (
-    BigInteger,
     Boolean,
     DateTime,
     Enum,
@@ -34,9 +31,9 @@ from sqlalchemy import (
     ForeignKey,
     Integer,
     JSON,
+    LargeBinary,
     String,
     Text,
-    UniqueConstraint,
     Uuid,
     func,
 )
@@ -58,8 +55,6 @@ def _now() -> datetime:
 # ── Enum types ────────────────────────────────────────────────────────────
 
 TierEnum = Enum("free", "pro", "power", "team", name="billing_tier")
-PluginStatusEnum = Enum("pending_review", "approved", "rejected", name="plugin_status")
-ReviewDecisionEnum = Enum("approved", "rejected", name="review_decision")
 AgentTypeEnum = Enum("local", "cloud", name="agent_type")
 AgentStatusEnum = Enum("running", "success", "error", "partial", name="agent_run_status")
 CloudProviderEnum = Enum("gmail", "teams", "outlook", name="cloud_provider")
@@ -77,7 +72,8 @@ class User(Base):
     email: Mapped[str] = mapped_column(String(255), unique=True, nullable=False, index=True)
     name: Mapped[str | None] = mapped_column(String(100), nullable=True)
     surname: Mapped[str | None] = mapped_column(String(100), nullable=True)
-    password_hash: Mapped[str] = mapped_column(String(255), nullable=False)
+    password_hash: Mapped[str | None] = mapped_column(String(255), nullable=True)
+    avatar_url: Mapped[str | None] = mapped_column(Text, nullable=True)
     tier: Mapped[str] = mapped_column(TierEnum, nullable=False, default="free")
     stripe_customer_id: Mapped[str | None] = mapped_column(String(255), nullable=True)
     # Per-user Fernet key (base64-urlsafe, 44 chars). Generated on registration.
@@ -86,6 +82,9 @@ class User(Base):
     created_at: Mapped[datetime] = mapped_column(
         DateTime(timezone=True), nullable=False, server_default=func.now()
     )
+    onboarding_completed_at: Mapped[datetime | None] = mapped_column(
+        DateTime(timezone=True), nullable=True, default=None
+    )
     updated_at: Mapped[datetime] = mapped_column(
         DateTime(timezone=True), nullable=False, server_default=func.now(), onupdate=func.now()
     )
@@ -96,6 +95,9 @@ class User(Base):
     subscription: Mapped[Subscription | None] = relationship(
         back_populates="user", uselist=False, cascade="all, delete-orphan"
     )
+    oauth_accounts: Mapped[list[OAuthAccount]] = relationship(
+        back_populates="user", cascade="all, delete-orphan"
+    )
 
 
 class RefreshToken(Base):
@@ -116,6 +118,25 @@ class RefreshToken(Base):
     user: Mapped[User] = relationship(back_populates="refresh_tokens")
 
 
+class OAuthAccount(Base):
+    __tablename__ = "oauth_accounts"
+
+    id: Mapped[str] = mapped_column(
+        Uuid(as_uuid=False), primary_key=True, default=_uuid
+    )
+    user_id: Mapped[str] = mapped_column(
+        Uuid(as_uuid=False), ForeignKey("users.id", ondelete="CASCADE"), nullable=False, index=True
+    )
+    provider: Mapped[str] = mapped_column(String(50), nullable=False)
+    provider_user_id: Mapped[str] = mapped_column(String(255), nullable=False)
+    provider_email: Mapped[str | None] = mapped_column(String(255), nullable=True)
+    created_at: Mapped[datetime] = mapped_column(
+        DateTime(timezone=True), nullable=False, server_default=func.now()
+    )
+
+    user: Mapped[User] = relationship(back_populates="oauth_accounts")
+
+
 class Subscription(Base):
     __tablename__ = "subscriptions"
 
@@ -137,151 +158,6 @@ class Subscription(Base):
     user: Mapped[User] = relationship(back_populates="subscription")
 
 
-class StorageRecord(Base):
-    __tablename__ = "storage_records"
-
-    id: Mapped[str] = mapped_column(
-        Uuid(as_uuid=False), primary_key=True, default=_uuid
-    )
-    user_id: Mapped[str] = mapped_column(
-        Uuid(as_uuid=False), ForeignKey("users.id", ondelete="CASCADE"), nullable=False, index=True
-    )
-    table_name: Mapped[str] = mapped_column(String(100), nullable=False)
-    s3_key: Mapped[str] = mapped_column(String(500), nullable=False)
-    checksum: Mapped[str] = mapped_column(String(64), nullable=False)
-    size_bytes: Mapped[int] = mapped_column(Integer, nullable=False)
-    created_at: Mapped[datetime] = mapped_column(
-        DateTime(timezone=True), nullable=False, server_default=func.now()
-    )
-    updated_at: Mapped[datetime] = mapped_column(
-        DateTime(timezone=True), nullable=False, server_default=func.now(), onupdate=func.now()
-    )
-
-
-class BackupMetadata(Base):
-    __tablename__ = "backup_metadata"
-
-    id: Mapped[str] = mapped_column(
-        Uuid(as_uuid=False), primary_key=True, default=_uuid
-    )
-    user_id: Mapped[str] = mapped_column(
-        Uuid(as_uuid=False), ForeignKey("users.id", ondelete="CASCADE"), nullable=False, index=True
-    )
-    s3_key: Mapped[str] = mapped_column(String(500), nullable=False)
-    version: Mapped[int] = mapped_column(Integer, nullable=False)
-    timestamp: Mapped[int] = mapped_column(BigInteger, nullable=False)
-    checksum: Mapped[str] = mapped_column(String(64), nullable=False)
-    size_bytes: Mapped[int] = mapped_column(Integer, nullable=False)
-    created_at: Mapped[datetime] = mapped_column(
-        DateTime(timezone=True), nullable=False, server_default=func.now()
-    )
-
-
-class Plugin(Base):
-    __tablename__ = "plugins"
-
-    id: Mapped[str] = mapped_column(String(255), primary_key=True)
-    name: Mapped[str] = mapped_column(String(255), nullable=False)
-    description: Mapped[str] = mapped_column(Text, nullable=False, default="")
-    version: Mapped[str] = mapped_column(String(50), nullable=False, default="1.0.0")
-    # nullable until developer account system is built
-    author_id: Mapped[str | None] = mapped_column(
-        Uuid(as_uuid=False), ForeignKey("users.id", ondelete="SET NULL"), nullable=True
-    )
-    author_name: Mapped[str] = mapped_column(String(255), nullable=False, default="")
-    category: Mapped[str] = mapped_column(String(100), nullable=False, default="")
-    price_cents: Mapped[int] = mapped_column(Integer, nullable=False, default=0)
-    permissions: Mapped[str] = mapped_column(Text, nullable=False, default="[]")  # JSON list
-    status: Mapped[str] = mapped_column(PluginStatusEnum, nullable=False, default="pending_review")
-    s3_package_key: Mapped[str | None] = mapped_column(String(500), nullable=True)
-    install_count: Mapped[int] = mapped_column(Integer, nullable=False, default=0)
-    avg_rating: Mapped[float] = mapped_column(Float, nullable=False, default=0.0)
-    rejection_reason: Mapped[str | None] = mapped_column(Text, nullable=True)
-    submitted_at: Mapped[datetime] = mapped_column(
-        DateTime(timezone=True), nullable=False, server_default=func.now()
-    )
-    created_at: Mapped[datetime] = mapped_column(
-        DateTime(timezone=True), nullable=False, server_default=func.now()
-    )
-
-    installations: Mapped[list[PluginInstallation]] = relationship(
-        back_populates="plugin", cascade="all, delete-orphan"
-    )
-    reviews: Mapped[list[PluginReview]] = relationship(
-        back_populates="plugin", cascade="all, delete-orphan"
-    )
-    revenue_events: Mapped[list[RevenueEvent]] = relationship(
-        back_populates="plugin", cascade="all, delete-orphan"
-    )
-
-
-class PluginInstallation(Base):
-    __tablename__ = "plugin_installations"
-    __table_args__ = (UniqueConstraint("plugin_id", "user_id", name="uq_plugin_user"),)
-
-    id: Mapped[str] = mapped_column(
-        Uuid(as_uuid=False), primary_key=True, default=_uuid
-    )
-    plugin_id: Mapped[str] = mapped_column(
-        String(255), ForeignKey("plugins.id", ondelete="CASCADE"), nullable=False, index=True
-    )
-    user_id: Mapped[str] = mapped_column(
-        Uuid(as_uuid=False), ForeignKey("users.id", ondelete="CASCADE"), nullable=False, index=True
-    )
-    installed_at: Mapped[datetime] = mapped_column(
-        DateTime(timezone=True), nullable=False, server_default=func.now()
-    )
-
-    plugin: Mapped[Plugin] = relationship(back_populates="installations")
-
-
-class PluginReview(Base):
-    __tablename__ = "plugin_reviews"
-
-    id: Mapped[str] = mapped_column(
-        Uuid(as_uuid=False), primary_key=True, default=_uuid
-    )
-    plugin_id: Mapped[str] = mapped_column(
-        String(255), ForeignKey("plugins.id", ondelete="CASCADE"), nullable=False, index=True
-    )
-    reviewer_id: Mapped[str | None] = mapped_column(
-        Uuid(as_uuid=False), ForeignKey("users.id", ondelete="SET NULL"), nullable=True
-    )
-    decision: Mapped[str] = mapped_column(ReviewDecisionEnum, nullable=False)
-    notes: Mapped[str | None] = mapped_column(Text, nullable=True)
-    reviewed_at: Mapped[datetime] = mapped_column(
-        DateTime(timezone=True), nullable=False, server_default=func.now()
-    )
-    created_at: Mapped[datetime] = mapped_column(
-        DateTime(timezone=True), nullable=False, server_default=func.now()
-    )
-
-    plugin: Mapped[Plugin] = relationship(back_populates="reviews")
-
-
-class RevenueEvent(Base):
-    __tablename__ = "revenue_events"
-
-    id: Mapped[str] = mapped_column(
-        Uuid(as_uuid=False), primary_key=True, default=_uuid
-    )
-    plugin_id: Mapped[str] = mapped_column(
-        String(255), ForeignKey("plugins.id", ondelete="CASCADE"), nullable=False, index=True
-    )
-    user_id: Mapped[str] = mapped_column(
-        Uuid(as_uuid=False), ForeignKey("users.id", ondelete="CASCADE"), nullable=False, index=True
-    )
-    amount_cents: Mapped[int] = mapped_column(Integer, nullable=False, default=0)
-    developer_share_cents: Mapped[int] = mapped_column(Integer, nullable=False, default=0)
-    stripe_transfer_id: Mapped[str | None] = mapped_column(String(255), nullable=True)
-    paid_at: Mapped[datetime | None] = mapped_column(DateTime(timezone=True), nullable=True)
-    created_at: Mapped[datetime] = mapped_column(
-        DateTime(timezone=True), nullable=False, server_default=func.now()
-    )
-
-    plugin: Mapped[Plugin] = relationship(back_populates="revenue_events")
-
-
 class LocalAgentConfig(Base):
     __tablename__ = "local_agent_configs"
 
@@ -296,6 +172,7 @@ class LocalAgentConfig(Base):
     directory_paths: Mapped[list] = mapped_column(JSON, nullable=False, default=list)
     data_types: Mapped[list] = mapped_column(JSON, nullable=False, default=list)
     prompt_template: Mapped[str] = mapped_column(Text, nullable=False, default="")
+    agent_config: Mapped[dict | None] = mapped_column(JSON, nullable=True)
     file_extensions: Mapped[list] = mapped_column(JSON, nullable=False, default=list)
     schedule_cron: Mapped[str] = mapped_column(String(100), nullable=False, default="0 */6 * * *")
     enabled: Mapped[bool] = mapped_column(Boolean, nullable=False, default=True)
@@ -366,6 +243,7 @@ class AgentRunLog(Base):
     status: Mapped[str] = mapped_column(AgentStatusEnum, nullable=False, default="running")
     items_processed: Mapped[int] = mapped_column(Integer, nullable=False, default=0)
     items_created: Mapped[int] = mapped_column(Integer, nullable=False, default=0)
+    tokens_used: Mapped[int] = mapped_column(Integer, nullable=False, default=0, server_default="0")
     errors: Mapped[list | None] = mapped_column(JSON, nullable=True)
     started_at: Mapped[datetime] = mapped_column(
         DateTime(timezone=True), nullable=False, server_default=func.now()
@@ -386,6 +264,17 @@ class AgentRunLog(Base):
     )
 
 
+class MonthlyTokenUsage(Base):
+    __tablename__ = "monthly_token_usage"
+
+    user_id: Mapped[str] = mapped_column(
+        Uuid(as_uuid=False), ForeignKey("users.id", ondelete="CASCADE"), primary_key=True
+    )
+    year_month: Mapped[str] = mapped_column(String(7), primary_key=True)  # 'YYYY-MM'
+    feature: Mapped[str] = mapped_column(String(64), primary_key=True)
+    tokens_used: Mapped[int] = mapped_column(Integer, nullable=False, default=0, server_default="0")
+
+
 # ── Memory models ─────────────────────────────────────────────────────────────
 
 
@@ -425,8 +314,8 @@ class MemoryAssociative(Base):
         nullable=False, index=True,
     )
     content_encrypted: Mapped[str] = mapped_column(Text, nullable=False)
-    # JSON-encoded float list in SQLite tests; vector(1536) in Postgres via migration.
-    embedding: Mapped[list | None] = mapped_column(JSON, nullable=True)
+    # vector(1536) via pgvector; SQLite tests use NULL embeddings so no dialect issue.
+    embedding: Mapped[list | None] = mapped_column(Vector(1536), nullable=True)
     entity_type: Mapped[str | None] = mapped_column(String(100), nullable=True)
     entity_id: Mapped[str | None] = mapped_column(String(255), nullable=True)
     updated_at: Mapped[datetime] = mapped_column(
@@ -474,3 +363,85 @@ class MemoryProactive(Base):
     created_at: Mapped[datetime] = mapped_column(
         DateTime(timezone=True), nullable=False, server_default=func.now()
     )
+
+
+class ExtractionQueue(Base):
+    """Batch extraction queue for Free-tier users (Phase 2).
+
+    Pro/Power/Team users get realtime asyncio.create_task() extraction.
+    Free users get a queue row here; a daily cron (Phase 5) drains it.
+    """
+
+    __tablename__ = "extraction_queue"
+
+    id: Mapped[str] = mapped_column(Uuid(as_uuid=False), primary_key=True, default=_uuid)
+    user_id: Mapped[str] = mapped_column(
+        Uuid(as_uuid=False), ForeignKey("users.id", ondelete="CASCADE"),
+        nullable=False, index=True,
+    )
+    episode_id: Mapped[str | None] = mapped_column(
+        Uuid(as_uuid=False), nullable=True,
+    )
+    created_at: Mapped[datetime] = mapped_column(
+        DateTime(timezone=True), nullable=False, server_default=func.now()
+    )
+
+
+class MemoryRelation(Base):
+    """Per-user entity/relation graph row (Mem0g-light, Phase 3).
+
+    subject_label/object_label are plaintext entity identifiers (not user content).
+    notes_encrypted is optional Fernet-encrypted per-user commentary.
+    confidence in [0.0, 1.0] — decays 5 % per 30 days since last_confirmed_at.
+    """
+
+    __tablename__ = "memory_relations"
+
+    id: Mapped[str] = mapped_column(Uuid(as_uuid=False), primary_key=True, default=_uuid)
+    user_id: Mapped[str] = mapped_column(
+        Uuid(as_uuid=False), ForeignKey("users.id", ondelete="CASCADE"),
+        nullable=False, index=True,
+    )
+    subject_label: Mapped[str] = mapped_column(String(128), nullable=False)
+    subject_type: Mapped[str] = mapped_column(String(32), nullable=False)
+    predicate: Mapped[str] = mapped_column(String(64), nullable=False)
+    object_label: Mapped[str] = mapped_column(String(128), nullable=False)
+    object_type: Mapped[str] = mapped_column(String(32), nullable=False)
+    confidence: Mapped[float] = mapped_column(Float, nullable=False, default=0.7)
+    source_episode_id: Mapped[str | None] = mapped_column(
+        Uuid(as_uuid=False),
+        ForeignKey("memory_episodic.id", ondelete="SET NULL"),
+        nullable=True,
+    )
+    notes_encrypted: Mapped[bytes | None] = mapped_column(LargeBinary, nullable=True)
+    created_at: Mapped[datetime] = mapped_column(
+        DateTime(timezone=True), nullable=False, server_default=func.now()
+    )
+    updated_at: Mapped[datetime] = mapped_column(
+        DateTime(timezone=True), nullable=False, server_default=func.now(), onupdate=func.now()
+    )
+    last_confirmed_at: Mapped[datetime | None] = mapped_column(
+        DateTime(timezone=True), nullable=True
+    )
+
+
+class Plugin(Base):
+    """Plugin marketplace catalog entry."""
+
+    __tablename__ = "plugins"
+
+    id: Mapped[str] = mapped_column(String(255), primary_key=True)
+    name: Mapped[str] = mapped_column(String(255), nullable=False)
+    description: Mapped[str] = mapped_column(Text, nullable=False)
+    version: Mapped[str] = mapped_column(String(50), nullable=False)
+    author_name: Mapped[str] = mapped_column(String(255), nullable=False)
+    category: Mapped[str] = mapped_column(String(100), nullable=False)
+    price_cents: Mapped[int] = mapped_column(Integer, nullable=False, default=0)
+    permissions: Mapped[str] = mapped_column(Text, nullable=False, default="[]")
+    status: Mapped[str] = mapped_column(String(50), nullable=False, default="pending")
+    s3_package_key: Mapped[str | None] = mapped_column(String(500), nullable=True)
+    install_count: Mapped[int] = mapped_column(Integer, nullable=False, default=0)
+    avg_rating: Mapped[float] = mapped_column(Float, nullable=False, default=0.0)
+    created_at: Mapped[datetime] = mapped_column(
+        DateTime(timezone=True), nullable=False, server_default=func.now()
+    )

app/schemas.py

@@ -30,6 +30,16 @@ class UserProfile(BaseModel):
     name: str | None = None
     surname: str | None = None
     tier: BillingTier
+    avatar_url: str | None = None
+    has_password: bool = True
+    onboarding_completed_at: int | None = None  # epoch ms, null = not onboarded
+    memory: dict[str, str] = Field(default_factory=dict)  # decrypted core memory k/v
+
+
+class OAuthAccountInfo(BaseModel):
+    provider: str
+    provider_email: str | None = None
+    created_at: int  # epoch ms
 
 
 # ── Chat ─────────────────────────────────────────────────────────────
@@ -50,88 +60,6 @@ class ChatResponse(BaseModel):
     response: str
 
 
-# ── Backup ───────────────────────────────────────────────────────────
-
-class BackupMetadata(BaseModel):
-    version: int
-    timestamp: int
-    checksum: str
-    chunk_count: int
-
-
-# ── Cloud Storage (E2E encrypted blobs) ──────────────────────────────
-
-class StorageRecord(BaseModel):
-    id: str
-    user_id: str
-    table: str
-    blob: bytes
-    checksum: str
-    created_at: int
-    updated_at: int
-
-
-class StorageRecordCreate(BaseModel):
-    table: str
-    blob: bytes
-    checksum: str
-
-
-class StorageRecordUpdate(BaseModel):
-    blob: bytes
-    checksum: str
-
-
-# ── Cloud Vector Store (E2E encrypted vectors) ────────────────────────
-
-class VectorItem(BaseModel):
-    id: str
-    blob: bytes   # encrypted vector + metadata — backend never decrypts
-    checksum: str
-
-
-class VectorUpsertRequest(BaseModel):
-    vectors: list[VectorItem]
-
-
-class VectorSearchRequest(BaseModel):
-    query_blob: bytes   # encrypted query — backend never decrypts
-    top_k: int = 10
-
-
-class VectorSearchResult(BaseModel):
-    id: str
-    score: float
-    blob: bytes
-
-
-class VectorSearchResponse(BaseModel):
-    results: list[VectorSearchResult]
-
-
-# ── Plugin Marketplace ────────────────────────────────────────────────
-
-class PluginManifest(BaseModel):
-    id: str
-    name: str
-    description: str
-    version: str
-    author: str
-    permissions: list[str]
-    category: str
-    price_cents: int = 0
-
-
-class PluginListResponse(BaseModel):
-    plugins: list[PluginManifest]
-    total: int
-    page: int
-
-
-class PluginInstallRequest(BaseModel):
-    plugin_id: str
-
-
 # ── WebSocket Frame Protocol ──────────────────────────────────────────
 
 class WsFrameType(str, Enum):
@@ -157,6 +85,17 @@ class WsFrameType(str, Enum):
     journey_start = "journey_start"
     journey_message = "journey_message"
     journey_reply = "journey_reply"
+    # ── v5 brief frame types ──────────────────────────────────────────
+    brief_request = "brief_request"
+    # ── v6 task brief frame types ─────────────────────────────────────
+    task_brief_request = "task_brief_request"
+    # ── v7 folder index frame types ───────────────────────────────────
+    index_session_start = "index_session_start"
+    index_file_batch = "index_file_batch"
+    index_session_cancel = "index_session_cancel"
+    index_file_result = "index_file_result"
+    index_session_progress = "index_session_progress"
+    index_session_done = "index_session_done"
 
 
 class WsToolCall(BaseModel):
@@ -212,6 +151,16 @@ class WsDeviceHello(BaseModel):
 
 # ── WebSocket v3 Frame Models ─────────────────────────────────────────
 
+class FormatPrefsModel(BaseModel):
+    """User display preferences sent by Electron on each request."""
+
+    timezone: str = "UTC"
+    date_format: str = "dd/MM/yyyy"
+    time_format: str = "24h"
+    locale: str = "en-US"
+    now_iso: str = ""
+
+
 class WsFloatingScope(BaseModel):
     """Scope for a floating request — narrows the agent to a specific entity."""
 
@@ -225,6 +174,7 @@ class WsHomeRequest(BaseModel):
     type: Literal[WsFrameType.home_request] = WsFrameType.home_request
     message: str
     conversation_history: list[dict[str, Any]] = Field(default_factory=list)
+    format_prefs: FormatPrefsModel | None = None
 
 
 class WsFloatingRequest(BaseModel):
@@ -233,6 +183,18 @@ class WsFloatingRequest(BaseModel):
     type: Literal[WsFrameType.floating_request] = WsFrameType.floating_request
     message: str
     scope: WsFloatingScope
+    format_prefs: FormatPrefsModel | None = None
+
+
+class WsBriefRequest(BaseModel):
+    """Client → Server: Request a plain-text brief (home or project)."""
+
+    type: Literal[WsFrameType.brief_request] = WsFrameType.brief_request
+    request_id: str | None = None
+    session_id: str | None = None
+    mode: Literal["home", "project"]
+    project_id: str | None = None
+    format_prefs: FormatPrefsModel | None = None
 
 
 class WsStreamStart(BaseModel):
@@ -255,6 +217,8 @@ class WsStreamEnd(BaseModel):
 
     type: Literal[WsFrameType.stream_end] = WsFrameType.stream_end
     request_id: str
+    error: str | None = None
+    mutations: list[dict[str, Any]] | None = None
 
 
 class WsDomain(BaseModel):
@@ -273,6 +237,27 @@ class WsFloatingDomain(BaseModel):
     domain: WsDomain
 
 
+# ── Agent Config V2 ───────────────────────────────────────────────────
+
+
+class ContentTypeConfig(BaseModel):
+    """Per-type extraction config produced by the journey chatbot."""
+
+    id: str
+    label: str = ""
+    detection_hint: str = ""
+    preprocessing: str = "generic"  # handler name: "email_html", "plain_text", ...
+    extraction_prompt: str
+
+
+class AgentConfig(BaseModel):
+    """Structured agent configuration (replaces freeform prompt_template)."""
+
+    content_types: list[ContentTypeConfig] = []
+    global_rules: list[str] = []
+    data_types: list[str] = []
+
+
 # ── Agent Catalog ─────────────────────────────────────────────────────
 
 class AgentCatalogItem(BaseModel):
@@ -297,10 +282,11 @@ class AgentTriggerRequest(BaseModel):
     device_id: str = Field(default="")
     agent_id: str | None = None  # FE stable agent ID (electron-store UUID)
     what_to_extract: list[str] = Field(min_length=1)
-    actions_by_type: dict[str, list[str]] | None = None
     batch_interval: str = Field(min_length=1)
-    custom_agent_prompt: str = Field(min_length=1)
+    custom_agent_prompt: str | None = None
+    agent_config: dict | None = None
     active_agents: int = Field(ge=0, default=0)
+    last_run_at: int | None = None  # epoch ms from FE — enables incremental scanning
 
 
 # ── Agent Run Log ─────────────────────────────────────────────────────

app/storage/__init__.py

@@ -1 +0,0 @@
-"""Cloud storage layer — E2E encrypted blobs and vectors."""

app/storage/blob_store.py

@@ -1,106 +0,0 @@
-"""S3-backed store for E2E-encrypted blobs.
-
-Keys are structured as ``{user_id}/{table}/{record_id}``.
-The backend never inspects blob content — it stores and retrieves opaque bytes.
-"""
-
-from __future__ import annotations
-
-from typing import Any
-
-import boto3
-
-from app.config.settings import settings
-
-
-class BlobStore:
-    """Thin wrapper around boto3 S3.
-
-    All blobs must be E2E encrypted by the client before upload.
-    The backend adds SSE-S3 as an extra layer of at-rest encryption
-    but cannot decrypt the inner client-side payload.
-    """
-
-    def _client(self) -> Any:
-        kwargs: dict[str, Any] = {
-            "region_name": settings.S3_REGION,
-            "aws_access_key_id": settings.AWS_ACCESS_KEY_ID,
-            "aws_secret_access_key": settings.AWS_SECRET_ACCESS_KEY,
-        }
-        if settings.S3_ENDPOINT_URL and isinstance(settings.S3_ENDPOINT_URL, str):
-            kwargs["endpoint_url"] = settings.S3_ENDPOINT_URL
-        return boto3.client("s3", **kwargs)
-
-    @staticmethod
-    def _key(user_id: str, table: str, record_id: str) -> str:
-        return f"{user_id}/{table}/{record_id}"
-
-    async def upload(
-        self,
-        user_id: str,
-        table: str,
-        record_id: str,
-        blob: bytes,
-        checksum: str,
-    ) -> str:
-        """Store *blob* in S3 and return the S3 key.
-
-        Args:
-            user_id:   Owner of the blob (used as key prefix).
-            table:     Logical table name (e.g. ``"tasks"``).
-            record_id: Record UUID.
-            blob:      Raw bytes (pre-encrypted by client).
-            checksum:  SHA-256 hex digest supplied by the client; stored as
-                       object metadata for download-time verification.
-
-        Returns:
-            The S3 key under which the blob was stored.
-        """
-        key = self._key(user_id, table, record_id)
-        self._client().put_object(
-            Bucket=settings.S3_BUCKET,
-            Key=key,
-            Body=blob,
-            ServerSideEncryption="AES256",  # SSE-S3 at rest
-            Metadata={"checksum": checksum},
-        )
-        return key
-
-    async def download(self, user_id: str, s3_key: str) -> bytes:
-        """Retrieve the blob stored at *s3_key*.
-
-        *user_id* is retained in the signature so higher-level code can
-        enforce ownership without re-parsing the key.
-
-        Raises:
-            ``botocore.exceptions.ClientError`` with code ``NoSuchKey`` if the
-            object does not exist.
-        """
-        response = self._client().get_object(
-            Bucket=settings.S3_BUCKET,
-            Key=s3_key,
-        )
-        return response["Body"].read()
-
-    async def delete(self, user_id: str, s3_key: str) -> None:
-        """Delete the object at *s3_key*.
-
-        S3 ``delete_object`` is idempotent — it succeeds even if the key does
-        not exist.
-        """
-        self._client().delete_object(
-            Bucket=settings.S3_BUCKET,
-            Key=s3_key,
-        )
-
-    async def list_keys(self, user_id: str, table: str) -> list[str]:
-        """Return all S3 keys for a given user + table combination.
-
-        Uses the prefix ``{user_id}/{table}/`` to scope the listing.
-        """
-        prefix = f"{user_id}/{table}/"
-        response = self._client().list_objects_v2(
-            Bucket=settings.S3_BUCKET,
-            Prefix=prefix,
-        )
-        return [obj["Key"] for obj in response.get("Contents", [])]

app/storage/encryption.py

@@ -1,32 +0,0 @@
-"""Integrity verification only — the backend NEVER decrypts user data."""
-
-from __future__ import annotations
-
-import hashlib
-import hmac
-
-from fastapi import HTTPException
-
-
-def verify_checksum(blob: bytes, checksum: str) -> bool:
-    """Return ``True`` if SHA-256(blob) matches *checksum*.
-
-    Uses ``hmac.compare_digest`` for constant-time comparison to prevent
-    timing-based side-channel attacks.
-    """
-    computed = hashlib.sha256(blob).hexdigest()
-    return hmac.compare_digest(computed, checksum)
-
-
-def reject_if_tampered(blob: bytes, checksum: str) -> None:
-    """Raise ``HTTP 400`` if the blob does not match its checksum.
-
-    Call this before storing or forwarding any client-provided blob.
-    The backend never holds decryption keys — this check only verifies
-    that the opaque bytes arrived intact.
-    """
-    if not verify_checksum(blob, checksum):
-        raise HTTPException(
-            status_code=400,
-            detail="Checksum mismatch: blob integrity check failed",
-        )

app/storage/vector_store.py

@@ -1,205 +0,0 @@
-"""Cloud vector store — wraps Pinecone (default) or Qdrant.
-
-Vectors are pre-encrypted blobs from the client.  The backend stores them
-alongside a deterministic 32-dim float representation derived from the blob's
-SHA-256 hash.  Semantic ANN search is not meaningful on encrypted data — this
-is a known trade-off documented in the backend plan.
-
-Isolation: Pinecone uses ``namespace=user_id``; Qdrant filters by
-``user_id`` payload field on a shared collection.
-"""
-
-from __future__ import annotations
-
-import base64
-import hashlib
-from typing import Any
-
-from pinecone import Pinecone
-from qdrant_client import QdrantClient
-from qdrant_client.models import FieldCondition, Filter, MatchValue, PointIdsList, PointStruct
-
-from app.config.settings import settings
-from app.schemas import VectorItem, VectorSearchResult
-
-_QDRANT_COLLECTION = "adiuva_vectors"
-
-
-def _blob_to_vector(blob: bytes) -> list[float]:
-    """Derive a 32-dim float vector from *blob* for storage purposes only.
-
-    Uses SHA-256 to produce a deterministic 32-byte fingerprint, then
-    normalises each byte to the range [-1.0, 1.0].  This vector carries no
-    semantic meaning on encrypted data.
-    """
-    return [(b - 128) / 128.0 for b in hashlib.sha256(blob).digest()]
-
-
-class VectorStore:
-    """Thin wrapper around Pinecone or Qdrant.
-
-    The backend to use is selected at runtime:
-    - Pinecone: when ``settings.PINECONE_API_KEY`` is non-empty.
-    - Qdrant: otherwise (requires ``settings.QDRANT_URL``).
-    """
-
-    def _use_pinecone(self) -> bool:
-        return bool(settings.PINECONE_API_KEY)
-
-    # ── Pinecone helpers ──────────────────────────────────────────────
-
-    def _pinecone_index(self) -> Any:
-        pc = Pinecone(api_key=settings.PINECONE_API_KEY)
-        return pc.Index(settings.PINECONE_INDEX)
-
-    # ── Qdrant helpers ────────────────────────────────────────────────
-
-    def _qdrant_client(self) -> Any:
-        return QdrantClient(
-            url=settings.QDRANT_URL,
-            api_key=settings.QDRANT_API_KEY or None,
-        )
-
-    # ── Public API ────────────────────────────────────────────────────
-
-    async def upsert(self, user_id: str, vectors: list[VectorItem]) -> None:
-        """Store encrypted vectors in the backend.
-
-        Each ``VectorItem.blob`` is base64-encoded and kept in metadata/payload
-        so it can be returned verbatim during search.
-
-        Args:
-            user_id: Used as Pinecone namespace or Qdrant payload field.
-            vectors: List of encrypted vector items from the client.
-        """
-        if self._use_pinecone():
-            await self._pinecone_upsert(user_id, vectors)
-        else:
-            await self._qdrant_upsert(user_id, vectors)
-
-    async def search(
-        self,
-        user_id: str,
-        query_blob: bytes,
-        top_k: int,
-    ) -> list[VectorSearchResult]:
-        """Query the vector store and return encrypted result blobs.
-
-        The query vector is derived from *query_blob* using the same
-        deterministic mapping as upsert.
-
-        Args:
-            user_id:    Scopes the search to this user's namespace.
-            query_blob: Encrypted query from the client.
-            top_k:      Maximum number of results to return.
-
-        Returns:
-            List of ``VectorSearchResult`` with ``id``, ``score``, and ``blob``.
-        """
-        if self._use_pinecone():
-            return await self._pinecone_search(user_id, query_blob, top_k)
-        return await self._qdrant_search(user_id, query_blob, top_k)
-
-    async def delete(self, user_id: str, vector_ids: list[str]) -> None:
-        """Remove vectors by ID, scoped to *user_id*.
-
-        Args:
-            user_id:    Namespace / payload filter to prevent cross-user deletion.
-            vector_ids: List of vector IDs to remove.
-        """
-        if self._use_pinecone():
-            await self._pinecone_delete(user_id, vector_ids)
-        else:
-            await self._qdrant_delete(user_id, vector_ids)
-
-    # ── Pinecone implementation ───────────────────────────────────────
-
-    async def _pinecone_upsert(self, user_id: str, vectors: list[VectorItem]) -> None:
-        index = self._pinecone_index()
-        records = [
-            {
-                "id": v.id,
-                "values": _blob_to_vector(v.blob),
-                "metadata": {
-                    "blob": base64.b64encode(v.blob).decode(),
-                    "checksum": v.checksum,
-                    "user_id": user_id,
-                },
-            }
-            for v in vectors
-        ]
-        index.upsert(vectors=records, namespace=user_id)
-
-    async def _pinecone_search(
-        self, user_id: str, query_blob: bytes, top_k: int
-    ) -> list[VectorSearchResult]:
-        index = self._pinecone_index()
-        query_vector = _blob_to_vector(query_blob)
-        response = index.query(
-            vector=query_vector,
-            top_k=top_k,
-            namespace=user_id,
-            include_metadata=True,
-        )
-        results: list[VectorSearchResult] = []
-        for match in response.get("matches", []):
-            blob_bytes = base64.b64decode(match["metadata"]["blob"])
-            results.append(
-                VectorSearchResult(
-                    id=match["id"],
-                    score=match["score"],
-                    blob=blob_bytes,
-                )
-            )
-        return results
-
-    async def _pinecone_delete(self, user_id: str, vector_ids: list[str]) -> None:
-        index = self._pinecone_index()
-        index.delete(ids=vector_ids, namespace=user_id)
-
-    # ── Qdrant implementation ─────────────────────────────────────────
-
-    async def _qdrant_upsert(self, user_id: str, vectors: list[VectorItem]) -> None:
-        client = self._qdrant_client()
-        points = [
-            PointStruct(
-                id=v.id,
-                vector=_blob_to_vector(v.blob),
-                payload={
-                    "blob": base64.b64encode(v.blob).decode(),
-                    "checksum": v.checksum,
-                    "user_id": user_id,
-                },
-            )
-            for v in vectors
-        ]
-        client.upsert(collection_name=_QDRANT_COLLECTION, points=points)
-
-    async def _qdrant_search(
-        self, user_id: str, query_blob: bytes, top_k: int
-    ) -> list[VectorSearchResult]:
-        client = self._qdrant_client()
-        query_vector = _blob_to_vector(query_blob)
-        hits = client.search(
-            collection_name=_QDRANT_COLLECTION,
-            query_vector=query_vector,
-            query_filter=Filter(
-                must=[FieldCondition(key="user_id", match=MatchValue(value=user_id))]
-            ),
-            limit=top_k,
-        )
-        return [
-            VectorSearchResult(
-                id=str(hit.id),
-                score=hit.score,
-                blob=base64.b64decode(hit.payload["blob"]),
-            )
-            for hit in hits
-        ]
-
-    async def _qdrant_delete(self, user_id: str, vector_ids: list[str]) -> None:
-        client = self._qdrant_client()
-        client.delete(
-            collection_name=_QDRANT_COLLECTION,
-            points_selector=PointIdsList(points=vector_ids),
-        )

docker-compose.yml

@@ -7,7 +7,7 @@ services:
       - path: .env
         required: false
     environment:
-      DATABASE_URL: postgresql+asyncpg://postgres:postgres@db:5432/adiuva
+      DATABASE_URL: postgresql+asyncpg://postgres:postgres@db:5432/adiuvai
       GITHUB_COPILOT_TOKEN_DIR: /root/.config/litellm/github_copilot
     volumes:
       - copilot_tokens:/root/.config/litellm/github_copilot
@@ -21,7 +21,7 @@ services:
     environment:
       POSTGRES_USER: postgres
       POSTGRES_PASSWORD: postgres
-      POSTGRES_DB: adiuva
+      POSTGRES_DB: adiuvai
     volumes:
       - postgres_data:/var/lib/postgresql/data
     healthcheck:
@@ -36,37 +36,6 @@ services:
   #   image: redis:7-alpine
   #   restart: unless-stopped
 
-  # ── Local S3-compatible storage (MinIO) ──
-  minio:
-    image: minio/minio:latest
-    command: server /data --console-address ":9001"
-    ports:
-      - "9000:9000"
-      - "9001:9001"
-    environment:
-      MINIO_ROOT_USER: minioadmin
-      MINIO_ROOT_PASSWORD: minioadmin
-    volumes:
-      - minio_data:/data
-    healthcheck:
-      test: ["CMD", "mc", "ready", "local"]
-      interval: 5s
-      timeout: 5s
-      retries: 5
-    restart: unless-stopped
-
-  # ── Local vector store (Qdrant) ──
-  qdrant:
-    image: qdrant/qdrant:latest
-    ports:
-      - "6333:6333"
-      - "6334:6334"
-    volumes:
-      - qdrant_data:/qdrant/storage
-    restart: unless-stopped
-
 volumes:
   postgres_data:
-  minio_data:
-  qdrant_data:
   copilot_tokens:

docs/MICROSERVICES_ARCHITECTURE.md

@@ -1,941 +0,0 @@
-# Adiuva — Architettura Microservizi (MVP)
-
-## Panoramica
-
-Il monolite viene suddiviso in **4 servizi MVP** + un **API Gateway (Traefik)**, orchestrati con Docker Compose su un singolo VPS raggiungibile via Cloudflare.
-
-> **Fuori dall'MVP**: Storage Service (S3/backup CRUD) e Plugin Service (marketplace). Verranno aggiunti come servizi indipendenti in una fase successiva.
-
-```
-                          ┌──────────────┐
-                          │  Cloudflare  │
-                          │  (DNS + CDN) │
-                          └──────┬───────┘
-                                 │ HTTPS / WSS
-                          ┌──────▼───────┐
-                          │   Traefik    │
-                          │ API Gateway  │
-                          │  (routing,   │
-                          │   TLS, rate  │
-                          │   limiting)  │
-                          └──────┬───────┘
-                                 │
-          ┌──────────┬───────────┼───────────┐
-          │          │           │           │
-    ┌─────▼────┐ ┌───▼───┐ ┌────▼────┐ ┌────▼───┐
-    │  Auth    │ │  Chat │ │  Agent  │ │Billing │
-    │ Service  │ │Service│ │ Service │ │Service │
-    └─────┬────┘ └───┬───┘ └────┬────┘ └────┬───┘
-          │          │          │           │
-    ┌─────▼──────────▼──────────▼───────────▼────┐
-    │              Infrastruttura                 │
-    │  PostgreSQL  │  Redis  │  Qdrant            │
-    └─────────────────────────────────────────────┘
-```
-
----
-
-## 1. Suddivisione dei Servizi
-
-### 1.1 Auth Service (`auth-service`)
-
-**Responsabilità**: Registrazione, login, refresh token, profilo utente, encryption key.
-
-| Endpoint originale | Metodo |
-|---|---|
-| `/api/v1/auth/register` | POST |
-| `/api/v1/auth/login` | POST |
-| `/api/v1/auth/refresh` | POST |
-| `/api/v1/auth/me` | GET / PUT |
-
-**Database**: Tabelle `users`, `refresh_tokens` (PostgreSQL condiviso, schema `auth`).
-
-**Modifica chiave — JWT con RS256**:
-Il monolite usa un `SECRET_KEY` simmetrico (HS256). Con i microservizi, passare a **RS256** (asimmetrico):
-- L'Auth Service firma i JWT con la **chiave privata**.
-- Tutti gli altri servizi verificano i JWT con la **chiave pubblica** senza mai contattare l'Auth Service.
-- La chiave pubblica viene esposta via `GET /api/v1/auth/.well-known/jwks.json` oppure montata come volume condiviso.
-
-```python
-# auth-service/app/auth/jwt.py
-from cryptography.hazmat.primitives.asymmetric import rsa
-from jose import jwt
-
-PRIVATE_KEY = ...  # Da env/secret
-PUBLIC_KEY = ...   # Derivata o da env
-
-def create_access_token(user_id: str, tier: str) -> str:
-    return jwt.encode(
-        {"sub": user_id, "tier": tier, "exp": ...},
-        PRIVATE_KEY,
-        algorithm="RS256",
-    )
-```
-
-```python
-# shared/auth.py  (usato da tutti gli altri servizi)
-from jose import jwt
-
-PUBLIC_KEY = ...  # Volume montato o fetched da JWKS endpoint
-
-def verify_token(token: str) -> dict:
-    return jwt.decode(token, PUBLIC_KEY, algorithms=["RS256"])
-```
-
-**Scaling**: 2 repliche sufficienti, stateless. Rate-limit dedicato su `/login` e `/register`.
-
----
-
-### 1.2 Chat Service (`chat-service`) ⭐ Real-time
-
-**Responsabilità**: WebSocket device connection, home chat, floating chat, memory middleware, streaming LLM responses verso il client.
-
-Questo servizio gestisce la **connessione persistente** con l'app Electron e le interazioni **real-time** dell'utente (chat home, floating chat). È il proprietario della WebSocket.
-
-| Endpoint | Tipo |
-|---|---|
-| `/api/v1/ws/device` | WebSocket (connessione persistente) |
-| `/api/v1/chat` | POST (REST fallback) |
-
-**Moduli inclusi**: `deep_agent`, `memory_middleware`, `ws_context`, `device_manager` (Redis-backed), `output_formatter`, `llm`, tutti gli agent tools (`task_agent`, `project_agent`, `note_agent`, `timeline_agent`).
-
-**Perché separato dall'Agent Service**: Il Chat Service tiene la WebSocket aperta e risponde in tempo reale (streaming). Scalare aggiungendo repliche è semplice con sticky sessions + Redis pub/sub per il cross-instance routing dei tool_call.
-
-**Scaling**: 2–N repliche. Sticky cookies per le WS + Redis per cross-instance.
-
----
-
-### 1.3 Agent Service (`agent-service`) ⭐ Batch
-
-**Responsabilità**: Batch agent processing (directory scanning, file classification, entity extraction), agent setup journeys, agent configuration CRUD.
-
-Questo servizio gestisce i processi **long-running** e **CPU-intensive**: scansione filesystem, classificazione file con LLM, estrazione entità in batch. Non possiede la WebSocket — comunica con il device dell'utente tramite **Redis pub/sub** passando per il Chat Service.
-
-| Endpoint | Tipo |
-|---|---|
-| `/api/v1/agents/catalog` | GET |
-| `/api/v1/agents/can-create` | POST |
-| `/api/v1/agents/trigger` | POST |
-| `/api/v1/agents/journey/start` | POST (o WS relay) |
-| `/api/v1/agents/journey/message` | POST (o WS relay) |
-
-**Moduli inclusi**: `agent_runner`, `agent_registry`, `filesystem_agent`, `llm`.
-
-**Flusso tool-call cross-service** (l'Agent Service non ha la WS):
-
-```
-┌──────────────┐            ┌──────────────┐            ┌──────────┐
-│ Agent Service│            │    Redis     │            │  Chat    │
-│ (batch run)  │            │              │            │ Service  │
-│              │            │              │            │ (ha WS)  │
-│ 1. Needs to  │  PUBLISH   │              │ SUBSCRIBE  │          │
-│    read file ├───────────►│tool_call:u123├───────────►│ 2. Invia │
-│    from      │            │              │            │    al    │
-│    device    │            │              │            │    device│
-│              │            │              │            │    via WS│
-│              │  SUBSCRIBE │              │  PUBLISH   │          │
-│ 4. Riceve   ◄────────────┤tool_result:id│◄───────────┤ 3. Device│
-│    risultato │            │              │            │    reply │
-└──────────────┘            └──────────────┘            └──────────┘
-```
-
-**Scaling**: 1–N repliche. Completamente stateless, scala indipendentemente dalla chat. Ogni replica processa batch job diversi. Può essere scalato a 0 se non ci sono agent attivi (risparmio risorse).
-
-**Vantaggio dello split**: Se 50 utenti triggerano agenti batch contemporaneamente, il Chat Service non ne risente — le risposte real-time rimangono veloci.
-
----
-
-### 1.4 Billing Service (`billing-service`)
-
-**Responsabilità**: Stripe checkout, webhook, subscription management.
-
-| Endpoint originale | Metodo |
-|---|---|
-| `/api/v1/billing/checkout` | POST |
-| `/api/v1/billing/webhook` | POST |
-| `/api/v1/billing/subscription` | GET / DELETE |
-
-**Database**: Tabelle `subscriptions` (schema `billing`).
-
-**Comunicazione inter-servizio**: Quando Stripe invia un webhook e il tier cambia, il Billing Service pubblica un evento su **Redis pub/sub** channel `tier_changed:{user_id}`. L'Auth Service aggiorna il campo `tier` nella tabella users. Al prossimo token refresh il JWT conterrà il tier aggiornato.
-
-**Scaling**: 1 replica sufficiente. Basso traffico.
-
----
-
-### 1.5 Servizi esclusi dall'MVP
-
-I seguenti servizi verranno aggiunti post-MVP come servizi indipendenti:
-
-| Servizio | Responsabilità | Note |
-|---|---|---|
-| **Storage Service** | S3 blobs CRUD, vector ops, backup | Le funzionalità vector/embed possono restare nel Chat Service per il MVP |
-| **Plugin Service** | Marketplace, install, revenue split | Feature non critica per il lancio |
-
----
-
-## 2. Tier Check — Dove e Come
-
-Il tier dell'utente (free/pro/power/team) determina rate-limiting, quote e accesso a funzionalità. Con i microservizi, **ogni servizio controlla il tier autonomamente** senza chiamare l'Auth Service.
-
-### Strategia: Tier nel JWT
-
-L'Auth Service include il `tier` come claim nel JWT al momento del login/refresh:
-
-```json
-{
-  "sub": "user_123",
-  "tier": "pro",
-  "exp": 1742515200,
-  "iat": 1742511600
-}
-```
-
-Ogni servizio:
-1. Decodifica il JWT con la chiave pubblica (già lo fa per l'auth)
-2. Legge `payload["tier"]` — **zero chiamate extra**
-3. Applica le sue regole di enforcement localmente
-
-```python
-# shared/auth.py — dependency FastAPI condivisa
-from fastapi import Depends, HTTPException, Request
-from jose import jwt
-
-PUBLIC_KEY = ...
-
-class CurrentUser:
-    def __init__(self, user_id: str, tier: str):
-        self.user_id = user_id
-        self.tier = tier
-
-async def get_current_user(request: Request) -> CurrentUser:
-    token = request.headers.get("Authorization", "").removeprefix("Bearer ")
-    payload = jwt.decode(token, PUBLIC_KEY, algorithms=["RS256"])
-    return CurrentUser(user_id=payload["sub"], tier=payload["tier"])
-
-def require_tier(*allowed_tiers: str):
-    """Dependency che blocca se il tier non è tra quelli ammessi."""
-    async def check(user: CurrentUser = Depends(get_current_user)):
-        if user.tier not in allowed_tiers:
-            raise HTTPException(403, "Tier insufficient")
-        return user
-    return check
-```
-
-### Cosa succede quando il tier cambia (upgrade/downgrade)?
-
-```
-┌──────────┐  Stripe webhook   ┌──────────┐  tier_changed   ┌──────────┐
-│  Stripe  │ ─────────────────►│ Billing  │ ───────────────►│   Auth   │
-│          │                    │ Service  │  (Redis pub/sub) │ Service  │
-└──────────┘                    └──────────┘                  └────┬─────┘
-                                                                   │
-                                                          UPDATE users
-                                                          SET tier = 'power'
-                                                                   │
-                                                    Al prossimo /refresh
-                                                    il JWT conterrà tier='power'
-```
-
-**Latenza del cambio**: Il tier si propaga al prossimo token refresh (tipicamente 15–30 min, o il client può forzare un refresh immediato dopo il checkout). Per il billing webhook, il downgrade può essere forzato invalidando il refresh token su Redis → il client è obbligato a ri-autenticarsi.
-
-### Dove si applica in ciascun servizio
-
-| Servizio | Enforcement |
-|---|---|
-| **Auth Service** | Nessuno (è lui che scrive il tier) |
-| **Chat Service** | Rate-limit per tier (req/min), quota messaggi |
-| **Agent Service** | Max agent configs, max runs/day, max concurrent batches |
-| **Billing Service** | Nessuno (gestisce i tier, non li consuma) |
-
-### Rate-limit distribuito via Redis
-
-Poiché ogni servizio ha le sue repliche, il rate-limiting deve essere **condiviso** via Redis:
-
-```python
-# shared/middleware/rate_limit.py
-import redis.asyncio as aioredis
-
-class DistributedRateLimiter:
-    def __init__(self, redis: aioredis.Redis):
-        self._redis = redis
-
-    async def check(self, user_id: str, tier: str, service: str) -> bool:
-        limits = {"free": 20, "pro": 60, "power": 120, "team": 200}
-        max_req = limits.get(tier, 20)
-        key = f"rate:{service}:{user_id}"
-
-        pipe = self._redis.pipeline()
-        pipe.incr(key)
-        pipe.expire(key, 60)
-        count, _ = await pipe.execute()
-
-        return count <= max_req
-```
-
----
-
-## 3. WebSocket con Scaling Orizzontale — Il Problema Chiave
-
-`DeviceConnectionManager` è un **singleton in-memory**:
-
-```python
-class DeviceConnectionManager:
-    def __init__(self):
-        self._connections: dict[str, DeviceConnection] = {}  # ← In-memory!
-```
-
-Con N istanze del Chat Service, il device si connette a **una sola** istanza. Quando un'altra istanza deve inviare un `tool_call` a quel device (es. un agent trigger da un'API call), non trova la connessione.
-
-### La soluzione: Redis Pub/Sub + Registry
-
-```
-┌──────────────────────────────────────────────────────────────┐
-│                     Redis                                    │
-│                                                              │
-│  Hash: ws:connections                                        │
-│    user_123 → instance_A                                     │
-│    user_456 → instance_B                                     │
-│                                                              │
-│  Pub/Sub channels:                                           │
-│    tool_call:{user_id}  → tool call payloads                 │
-│    tool_result:{call_id} → tool result payloads              │
-│    stream:{user_id}     → text_chunk streaming               │
-└──────────────────────────────────────────────────────────────┘
-
- Instance A (ha WS di user_123)     Instance B (deve chiamare tool su user_123)
- ┌───────────────────────┐          ┌───────────────────────┐
- │  1. Sottoscrive a     │          │  1. Lookup Redis Hash │
- │     tool_call:user_123│          │     → user_123 è su A │
- │                       │          │                       │
- │  2. Riceve tool_call  │◄─────────│  2. PUBLISH           │
- │     da Redis channel  │          │    tool_call:user_123 │
- │                       │          │    {id, action, ...}  │
- │  3. Invia al device   │          │                       │
- │     via WS            │          │  4. SUBSCRIBE         │
- │                       │          │    tool_result:{id}   │
- │  4. Device risponde   │          │                       │
- │     tool_result       │──────────│► 5. Riceve risultato  │
- │                       │          │                       │
- │  5. PUBLISH           │          │                       │
- │    tool_result:{id}   │          │                       │
- └───────────────────────┘          └───────────────────────┘
-```
-
-### Implementazione: `RedisDeviceManager`
-
-```python
-# chat-service/app/core/device_manager.py
-
-import asyncio
-import json
-import os
-import redis.asyncio as aioredis
-from dataclasses import dataclass, field
-from fastapi import WebSocket
-
-INSTANCE_ID = os.environ.get("INSTANCE_ID", os.urandom(8).hex())
-
-@dataclass
-class LocalConnection:
-    ws: WebSocket
-    device_id: str
-    pending_calls: dict[str, asyncio.Future[dict]] = field(default_factory=dict)
-
-
-class RedisDeviceManager:
-    """Device manager backed by Redis for cross-instance communication."""
-
-    def __init__(self, redis_url: str = "redis://redis:6379"):
-        self._redis = aioredis.from_url(redis_url)
-        self._pubsub = self._redis.pubsub()
-        self._local: dict[str, LocalConnection] = {}  # Solo connessioni locali
-        self._remote_futures: dict[str, asyncio.Future[dict]] = {}
-
-    async def start(self):
-        """Avvia il listener Redis per tool_call in arrivo."""
-        asyncio.create_task(self._listen_tool_calls())
-
-    # ── Registrazione ──
-
-    async def register(self, user_id: str, device_id: str, ws: WebSocket):
-        # Registra localmente
-        self._local[user_id] = LocalConnection(ws=ws, device_id=device_id)
-        # Registra in Redis quale istanza ha la connessione
-        await self._redis.hset("ws:connections", user_id, INSTANCE_ID)
-        # Sottoscrivi ai tool_call per questo utente
-        await self._pubsub.subscribe(f"tool_call:{user_id}")
-
-    async def unregister(self, user_id: str):
-        conn = self._local.pop(user_id, None)
-        if conn:
-            for fut in conn.pending_calls.values():
-                if not fut.done():
-                    fut.cancel()
-        await self._redis.hdel("ws:connections", user_id)
-        await self._pubsub.unsubscribe(f"tool_call:{user_id}")
-
-    # ── Presenza ──
-
-    async def is_online(self, user_id: str) -> bool:
-        return await self._redis.hexists("ws:connections", user_id)
-
-    # ── Tool-call round-trip (cross-instance) ──
-
-    async def execute_tool_call(self, user_id: str, payload: dict) -> dict:
-        """
-        Invia un tool_call al device dell'utente.
-        Funziona sia che la WS sia locale che su un'altra istanza.
-        """
-        call_id = payload["id"]
-
-        # Caso 1: connessione locale → invio diretto
-        if user_id in self._local:
-            conn = self._local[user_id]
-            loop = asyncio.get_event_loop()
-            fut: asyncio.Future[dict] = loop.create_future()
-            conn.pending_calls[call_id] = fut
-            await conn.ws.send_text(json.dumps({"type": "tool_call", **payload}))
-            return await asyncio.wait_for(fut, timeout=30.0)
-
-        # Caso 2: connessione remota → Redis pub/sub
-        loop = asyncio.get_event_loop()
-        fut = loop.create_future()
-        self._remote_futures[call_id] = fut
-
-        # Sottoscrivi al canale di risposta
-        result_channel = f"tool_result:{call_id}"
-        await self._pubsub.subscribe(result_channel)
-
-        # Pubblica il tool_call
-        await self._redis.publish(
-            f"tool_call:{user_id}",
-            json.dumps(payload),
-        )
-
-        try:
-            return await asyncio.wait_for(fut, timeout=30.0)
-        finally:
-            self._remote_futures.pop(call_id, None)
-            await self._pubsub.unsubscribe(result_channel)
-
-    # ── Risoluzione tool_result (da WS locale) ──
-
-    def resolve_local(self, user_id: str, call_id: str, result: dict):
-        conn = self._local.get(user_id)
-        if conn:
-            fut = conn.pending_calls.pop(call_id, None)
-            if fut and not fut.done():
-                fut.set_result(result)
-
-    async def resolve_and_publish(self, user_id: str, call_id: str, result: dict):
-        """Chiamato quando il device locale invia un tool_result."""
-        self.resolve_local(user_id, call_id, result)
-        # Pubblica anche su Redis per l'istanza remota che aspetta
-        await self._redis.publish(
-            f"tool_result:{call_id}",
-            json.dumps(result),
-        )
-
-    # ── Listener Redis ──
-
-    async def _listen_tool_calls(self):
-        """Loop che ascolta i tool_call in arrivo da altre istanze."""
-        async for message in self._pubsub.listen():
-            if message["type"] != "message":
-                continue
-            channel = message["channel"]
-            if isinstance(channel, bytes):
-                channel = channel.decode()
-
-            data = json.loads(message["data"])
-
-            if channel.startswith("tool_call:"):
-                # Un'altra istanza vuole che inviamo un tool_call al nostro device
-                user_id = channel.split(":", 1)[1]
-                conn = self._local.get(user_id)
-                if conn:
-                    await conn.ws.send_text(json.dumps({"type": "tool_call", **data}))
-
-            elif channel.startswith("tool_result:"):
-                # Risposta a un tool_call che abbiamo inviato tramite Redis
-                call_id = channel.split(":", 1)[1]
-                fut = self._remote_futures.pop(call_id, None)
-                if fut and not fut.done():
-                    fut.set_result(data)
-
-    # ── Stream cross-instance ──
-
-    async def publish_stream_chunk(self, user_id: str, chunk: dict):
-        """Pubblica un chunk di streaming su Redis (per REST→WS relay)."""
-        await self._redis.publish(f"stream:{user_id}", json.dumps(chunk))
-```
-
----
-
-## 4. Struttura Directory Proposta (MVP)
-
-```
-adiuva-api/
-├── docker-compose.yml          # Orchestrazione completa
-├── docker-compose.dev.yml      # Override per sviluppo locale
-├── shared/                     # Codice condiviso (montato come volume)
-│   ├── auth.py                 # JWT verification (chiave pubblica)
-│   ├── schemas.py              # Pydantic schemas condivisi
-│   ├── middleware/
-│   │   ├── rate_limit.py       # DistributedRateLimiter (Redis)
-│   │   └── sanitizer.py
-│   └── models/
-│       └── base.py             # SQLAlchemy base condivisa
-│
-├── auth-service/
-│   ├── Dockerfile
-│   ├── requirements.txt
-│   └── app/
-│       ├── main.py
-│       ├── config.py
-│       ├── db.py
-│       ├── models.py           # users, refresh_tokens
-│       ├── routes/
-│       │   └── auth.py
-│       └── services/
-│           ├── jwt_service.py  # RS256 signing
-│           └── user_service.py
-│
-├── chat-service/
-│   ├── Dockerfile
-│   ├── requirements.txt
-│   └── app/
-│       ├── main.py
-│       ├── config.py
-│       ├── db.py
-│       ├── models.py           # memory_*
-│       ├── routes/
-│       │   ├── device_ws.py    # WS connection owner
-│       │   └── chat.py         # REST fallback
-│       ├── core/
-│       │   ├── device_manager.py   # RedisDeviceManager
-│       │   ├── deep_agent.py       # Home + floating chat
-│       │   ├── memory_middleware.py
-│       │   ├── ws_context.py
-│       │   ├── output_formatter.py
-│       │   └── llm.py
-│       └── agents/                 # Tool definitions (used by deep_agent)
-│           ├── task_agent.py
-│           ├── project_agent.py
-│           ├── note_agent.py
-│           └── timeline_agent.py
-│
-├── agent-service/
-│   ├── Dockerfile
-│   ├── requirements.txt
-│   └── app/
-│       ├── main.py
-│       ├── config.py
-│       ├── db.py
-│       ├── models.py           # agent_run_logs, local/cloud_agent_configs
-│       ├── routes/
-│       │   ├── agents.py       # catalog, can-create, trigger
-│       │   └── agent_setup.py  # journey start/message
-│       ├── core/
-│       │   ├── agent_runner.py     # Batch classify → process
-│       │   ├── agent_registry.py
-│       │   ├── redis_executor.py   # execute_on_client via Redis pub/sub
-│       │   └── llm.py
-│       └── agents/
-│           ├── task_agent.py       # Tool definitions (batch context)
-│           ├── project_agent.py
-│           ├── note_agent.py
-│           ├── timeline_agent.py
-│           └── filesystem_agent.py
-│
-├── billing-service/
-│   ├── Dockerfile
-│   ├── requirements.txt
-│   └── app/
-│       ├── main.py
-│       ├── config.py
-│       ├── db.py
-│       ├── models.py           # subscriptions
-│       ├── routes/
-│       │   └── billing.py
-│       └── services/
-│           ├── stripe_service.py
-│           └── tier_manager.py
-│
-└── infra/
-    ├── traefik/
-    │   └── traefik.yml
-    ├── keys/
-    │   ├── jwt_private.pem     # Solo auth-service
-    │   └── jwt_public.pem      # Tutti i servizi
-    └── alembic/                # Migrazioni condivise o per-servizio
-```
-
----
-
-## 5. Docker Compose — Configurazione MVP
-
-```yaml
-# docker-compose.yml
-
-services:
-
-  # ══════════════════════════════════════════════════════════
-  # API Gateway
-  # ══════════════════════════════════════════════════════════
-  traefik:
-    image: traefik:v3.2
-    command:
-      - "--api.insecure=true"
-      - "--providers.docker=true"
-      - "--providers.docker.exposedbydefault=false"
-      - "--entrypoints.web.address=:80"
-      - "--entrypoints.websecure.address=:443"
-      - "--entrypoints.web.http.redirections.entrypoint.to=websecure"
-    ports:
-      - "80:80"
-      - "443:443"
-      - "8080:8080"   # Dashboard Traefik (disabilitare in prod)
-    volumes:
-      - /var/run/docker.sock:/var/run/docker.sock:ro
-      - ./infra/certs:/certs:ro
-    restart: unless-stopped
-
-  # ══════════════════════════════════════════════════════════
-  # Auth Service (2 repliche)
-  # ══════════════════════════════════════════════════════════
-  auth-service:
-    build: ./auth-service
-    deploy:
-      replicas: 2
-    env_file: .env
-    environment:
-      DATABASE_URL: postgresql+asyncpg://postgres:postgres@db:5432/adiuva
-      REDIS_URL: redis://redis:6379
-      JWT_PRIVATE_KEY_FILE: /run/secrets/jwt_private_key
-      SERVICE_NAME: auth
-    secrets:
-      - jwt_private_key
-      - jwt_public_key
-    labels:
-      - "traefik.enable=true"
-      - "traefik.http.routers.auth.rule=PathPrefix(`/api/v1/auth`)"
-      - "traefik.http.services.auth.loadbalancer.server.port=8000"
-    depends_on:
-      db:
-        condition: service_healthy
-      redis:
-        condition: service_healthy
-
-  # ══════════════════════════════════════════════════════════
-  # Chat Service — Real-time WS + Chat (scalabile)
-  # ══════════════════════════════════════════════════════════
-  chat-service:
-    build: ./chat-service
-    deploy:
-      replicas: 2
-    env_file: .env
-    environment:
-      DATABASE_URL: postgresql+asyncpg://postgres:postgres@db:5432/adiuva
-      REDIS_URL: redis://redis:6379
-      JWT_PUBLIC_KEY_FILE: /run/secrets/jwt_public_key
-      SERVICE_NAME: chat
-    secrets:
-      - jwt_public_key
-    labels:
-      - "traefik.enable=true"
-      # REST chat endpoint
-      - "traefik.http.routers.chat.rule=PathPrefix(`/api/v1/chat`)"
-      - "traefik.http.services.chat.loadbalancer.server.port=8000"
-      # WebSocket route con sticky session
-      - "traefik.http.routers.ws.rule=PathPrefix(`/api/v1/ws`)"
-      - "traefik.http.routers.ws.service=chat-ws"
-      - "traefik.http.services.chat-ws.loadbalancer.server.port=8000"
-      - "traefik.http.services.chat-ws.loadbalancer.sticky.cookie.name=ws_affinity"
-      - "traefik.http.services.chat-ws.loadbalancer.sticky.cookie.httpOnly=true"
-    depends_on:
-      db:
-        condition: service_healthy
-      redis:
-        condition: service_healthy
-
-  # ══════════════════════════════════════════════════════════
-  # Agent Service — Batch processing (scalabile indipendentemente)
-  # ══════════════════════════════════════════════════════════
-  agent-service:
-    build: ./agent-service
-    deploy:
-      replicas: 2
-    env_file: .env
-    environment:
-      DATABASE_URL: postgresql+asyncpg://postgres:postgres@db:5432/adiuva
-      REDIS_URL: redis://redis:6379
-      JWT_PUBLIC_KEY_FILE: /run/secrets/jwt_public_key
-      SERVICE_NAME: agent
-    secrets:
-      - jwt_public_key
-    labels:
-      - "traefik.enable=true"
-      - "traefik.http.routers.agents.rule=PathPrefix(`/api/v1/agents`)"
-      - "traefik.http.services.agents.loadbalancer.server.port=8000"
-    depends_on:
-      db:
-        condition: service_healthy
-      redis:
-        condition: service_healthy
-
-  # ══════════════════════════════════════════════════════════
-  # Billing Service (1 replica)
-  # ══════════════════════════════════════════════════════════
-  billing-service:
-    build: ./billing-service
-    deploy:
-      replicas: 1
-    env_file: .env
-    environment:
-      DATABASE_URL: postgresql+asyncpg://postgres:postgres@db:5432/adiuva
-      REDIS_URL: redis://redis:6379
-      JWT_PUBLIC_KEY_FILE: /run/secrets/jwt_public_key
-      SERVICE_NAME: billing
-    secrets:
-      - jwt_public_key
-    labels:
-      - "traefik.enable=true"
-      - "traefik.http.routers.billing.rule=PathPrefix(`/api/v1/billing`)"
-      - "traefik.http.services.billing.loadbalancer.server.port=8000"
-    depends_on:
-      db:
-        condition: service_healthy
-      redis:
-        condition: service_healthy
-
-  # ══════════════════════════════════════════════════════════
-  # Infrastruttura
-  # ══════════════════════════════════════════════════════════
-  db:
-    image: pgvector/pgvector:pg16
-    environment:
-      POSTGRES_USER: postgres
-      POSTGRES_PASSWORD: postgres
-      POSTGRES_DB: adiuva
-    volumes:
-      - postgres_data:/var/lib/postgresql/data
-    healthcheck:
-      test: ["CMD-SHELL", "pg_isready -U postgres"]
-      interval: 5s
-      timeout: 5s
-      retries: 5
-    restart: unless-stopped
-
-  redis:
-    image: redis:7-alpine
-    command: redis-server --maxmemory 256mb --maxmemory-policy allkeys-lru
-    volumes:
-      - redis_data:/data
-    healthcheck:
-      test: ["CMD", "redis-cli", "ping"]
-      interval: 5s
-      timeout: 3s
-      retries: 5
-    restart: unless-stopped
-
-  qdrant:
-    image: qdrant/qdrant:latest
-    volumes:
-      - qdrant_data:/qdrant/storage
-    restart: unless-stopped
-
-secrets:
-  jwt_private_key:
-    file: ./infra/keys/jwt_private.pem
-  jwt_public_key:
-    file: ./infra/keys/jwt_public.pem
-
-volumes:
-  postgres_data:
-  redis_data:
-  qdrant_data:
-```
-
----
-
-## 6. Configurazione Cloudflare + VPS
-
-### 6.1 DNS
-
-```
-api.tuodominio.com  →  A record  →  IP del VPS
-                    →  Proxy: ON (orange cloud)
-```
-
-### 6.2 Cloudflare Settings
-
-| Setting | Valore | Motivo |
-|---------|--------|--------|
-| SSL/TLS mode | **Full (Strict)** | Cloudflare ↔ VPS con certificato valido |
-| WebSocket | **ON** | Necessario per `/api/v1/ws/device` |
-| Proxy timeout | **100s** (Enterprise) o default | Le LLM calls possono durare 30s+ |
-| Under Attack Mode | Off (attivare se necessario) | |
-
-### 6.3 TLS sul VPS
-
-Due opzioni:
-- **Opzione A (consigliata)**: Cloudflare Origin Certificate → montato in Traefik
-- **Opzione B**: Let's Encrypt via Traefik (con DNS challenge Cloudflare)
-
-```yaml
-# traefik.yml — con Cloudflare Origin Certificate
-entryPoints:
-  websecure:
-    address: ":443"
-
-tls:
-  certificates:
-    - certFile: /certs/origin.pem
-      keyFile: /certs/origin-key.pem
-```
-
-### 6.4 Rete VPS
-
-```bash
-# UFW firewall — solo Cloudflare può raggiungere le porte 80/443
-# https://www.cloudflare.com/ips/
-ufw default deny incoming
-ufw allow from 173.245.48.0/20 to any port 443
-ufw allow from 103.21.244.0/22 to any port 443
-# ... (tutti gli IP range di Cloudflare)
-ufw allow ssh
-ufw enable
-```
-
----
-
-## 7. Comunicazione Inter-Servizio
-
-### 7.1 Redis Pub/Sub — Event Bus
-
-```
-┌──────────┐  tier_changed:user_123   ┌──────────┐
-│ Billing  │ ────────────────────────► │   Auth   │
-│ Service  │                           │ Service  │
-└──────────┘                           └──────────┘
-
-┌──────────┐  tool_call:user_123      ┌──────────┐
-│  Agent   │ ────────────────────────► │   Chat   │
-│ Service  │                           │ Service  │
-│ (batch)  │ ◄────────────────────────│ (ha WS)  │
-└──────────┘  tool_result:{call_id}    └──────────┘
-```
-
-### 7.2 Health Checks e Service Discovery
-
-Traefik gestisce automaticamente il service discovery via Docker labels. I servizi non devono conoscersi tra loro — comunicano solo via:
-- **Redis pub/sub** (tool-call cross-instance, tier events)
-- **Redis hash** (stato condiviso: `ws:connections`, rate-limit counters)
-- **PostgreSQL** (dati persistenti condivisi)
-
----
-
-## 8. Piano di Migrazione Incrementale (MVP)
-
-### Fase 1 — Preparazione (nel monolite attuale)
-1. Aggiungere Redis al `docker-compose.yml` attuale
-2. Migrare JWT da HS256 → RS256 (backward-compatible: accetta entrambi per un periodo)
-3. Implementare `RedisDeviceManager` come drop-in replacement del singleton in-memory
-4. Estrarre `shared/` con auth verification, schemas, middleware
-
-### Fase 2 — Auth Service (primo split)
-1. Estrarre `auth.py` routes + models in `auth-service/`
-2. Verificare che i JWT firmati da `auth-service` vengano validati dal monolite
-3. Aggiungere Traefik e routare `/api/v1/auth/*` al nuovo servizio
-4. Il monolite continua a servire tutto il resto
-
-### Fase 3 — Billing Service
-1. Estrarre billing routes, Stripe service, tier manager
-2. Configurare Redis pub/sub per `tier_changed` events
-3. Routare via Traefik
-
-### Fase 4 — Split Chat + Agent (il più delicato)
-1. Il monolite residuo contiene WS + chat + agents
-2. Separare Agent Service: estrarre `agent_runner`, `agent_registry`, `agent_setup`, route `/agents/*`
-3. Implementare `redis_executor.py` nell'Agent Service per tool-call via Redis
-4. Il Chat Service resta proprietario della WS e sottoscrive i canali `tool_call:{user_id}`
-5. Testare: trigger agent dall'Agent Service → tool_call via Redis → Chat Service → WS → device → risposta
-
-### Fase 5 — Scaling test
-1. Scalare Chat Service a 2 repliche, verificare sticky sessions
-2. Scalare Agent Service a 2 repliche, verificare batch processing distribuito
-3. Monitoring (Prometheus + Grafana) per ogni servizio
-
----
-
-## 9. Monitoraggio e Logging
-
-```yaml
-# Aggiungere al docker-compose.yml
-
-  prometheus:
-    image: prom/prometheus:latest
-    volumes:
-      - ./infra/prometheus/prometheus.yml:/etc/prometheus/prometheus.yml
-    restart: unless-stopped
-
-  grafana:
-    image: grafana/grafana:latest
-    ports:
-      - "3000:3000"
-    volumes:
-      - grafana_data:/var/lib/grafana
-    restart: unless-stopped
-
-  loki:
-    image: grafana/loki:latest
-    restart: unless-stopped
-```
-
-Ogni servizio espone `/metrics` (Prometheus) e scrive log strutturati (JSON) raccolti da Loki.
-
----
-
-## 10. Sizing VPS Minimo Consigliato (MVP)
-
-| Componente | CPU | RAM | Note |
-|---|---|---|---|
-| Traefik | 0.25 | 128MB | |
-| Auth Service ×2 | 0.25 ×2 | 128MB ×2 | Stateless, leggero |
-| Chat Service ×2 | 1.0 ×2 | 1GB ×2 | WS + streaming LLM |
-| Agent Service ×2 | 0.75 ×2 | 512MB ×2 | Batch LLM, CPU-bound |
-| Billing Service | 0.25 | 128MB | |
-| PostgreSQL | 1.0 | 1GB | |
-| Redis | 0.25 | 256MB | |
-| Qdrant | 0.5 | 512MB | |
-| **Totale MVP** | **~5.5 vCPU** | **~5 GB** | |
-
-**Raccomandazione**: VPS con **8 vCPU / 16 GB RAM** per avere margine. Hetzner CPX41 (~€30/mese) o equivalente. Senza Storage/Plugin si risparmia ~1 vCPU e 512MB rispetto alla versione completa.
-
----
-
-## Riepilogo Architettura MVP
-
-| Servizio | Repliche | Proprietario di |
-|---|---|---|
-| **Traefik** | 1 | Routing, TLS, sticky sessions |
-| **Auth Service** | 2 | JWT RS256, registrazione, login, profilo |
-| **Chat Service** | 2–N | WebSocket, home/floating chat, streaming |
-| **Agent Service** | 2–N | Batch processing, directory scan, agent setup |
-| **Billing Service** | 1 | Stripe, subscriptions, tier management |
-
-| Decisione | Scelta | Motivazione |
-|---|---|---|
-| API Gateway | Traefik | Nativo Docker, WebSocket support, service discovery automatico |
-| JWT | RS256 (asimmetrico) | Verifica distribuita senza contattare Auth Service |
-| Tier check | Claim nel JWT | Ogni servizio verifica localmente, zero roundtrip |
-| WebSocket scaling | Redis pub/sub + sticky cookies | Cross-instance tool-call routing |
-| Chat ↔ Agent split | Servizi separati | Batch CPU-bound non impatta real-time chat |
-| Agent → Device comms | Redis pub/sub via Chat Service | Agent non possiede la WS, usa un relay |
-| Rate limiting | Redis contatori distribuiti | Sliding window condivisa tra repliche |
-| Database | PostgreSQL condiviso | Semplicità MVP; split DB futuro facile |
-| TLS | Cloudflare Origin Certificate | Zero maintenance |
-| Orchestrazione | Docker Compose | Sufficiente per un singolo VPS |
-| Storage / Plugin | Post-MVP | Non critici per il lancio |

requirements.txt

@@ -32,4 +32,12 @@ google-auth-oauthlib>=1.2.0
 google-auth-httplib2>=0.2.0
 msal>=1.28.0
 cryptography>=42.0.0
+pgvector>=0.2.5
+langfuse>=3.3.1
+beautifulsoup4>=4.12.0
+lxml>=5.0.0
+PyYAML>=6.0.0
+apscheduler>=3.10.0
 ruff>=0.8.0
+pypdf>=4.0
+python-docx>=1.1

tests/conftest.py

@@ -6,26 +6,23 @@ a per-test session, and a FastAPI ``TestClient`` wired to use it.
 
 from __future__ import annotations
 
-import json
-import os
 import time
 import uuid
 from collections.abc import AsyncGenerator, Generator
-from unittest.mock import patch
 
-import boto3
 import pytest
 import pytest_asyncio
 from fastapi.testclient import TestClient
 from jose import jwt
-from moto import mock_aws
 from sqlalchemy import StaticPool, event
 from sqlalchemy.ext.asyncio import AsyncSession, async_sessionmaker, create_async_engine
 
+from sqlalchemy import select
+
 from app.config.settings import settings
 from app.db import Base, get_session
 from app.main import app
-from app.models import Plugin, Subscription, User
+from app.models import Subscription, User
 
 # ── Fixed test user IDs (one per tier) ───────────────────────────────
 
@@ -109,79 +106,6 @@ def client(db_session: AsyncSession) -> Generator[TestClient, None, None]:   # n
     app.dependency_overrides.pop(get_session, None)
 
 
-# ── Seed data helpers ────────────────────────────────────────────────
-
-_SEED_PLUGINS = [
-    Plugin(
-        id="plugin-github-sync",
-        name="GitHub Sync",
-        description="Sync tasks with GitHub Issues and pull requests.",
-        version="1.0.0",
-        author_name="Adiuva",
-        category="productivity",
-        price_cents=0,
-        permissions=json.dumps(["read:tasks", "write:tasks"]),
-        status="approved",
-        s3_package_key="plugins/plugin-github-sync/1.0.0/package.zip",
-        install_count=0,
-        avg_rating=0.0,
-    ),
-    Plugin(
-        id="plugin-slack-notify",
-        name="Slack Notifier",
-        description="Post task and timeline updates to Slack channels.",
-        version="1.2.0",
-        author_name="Adiuva",
-        category="communication",
-        price_cents=499,
-        permissions=json.dumps(["read:tasks", "read:timelines"]),
-        status="approved",
-        s3_package_key="plugins/plugin-slack-notify/1.2.0/package.zip",
-        install_count=0,
-        avg_rating=0.0,
-    ),
-    Plugin(
-        id="plugin-time-tracker",
-        name="Time Tracker",
-        description="Track time spent on tasks with automatic reporting.",
-        version="0.9.1",
-        author_name="Third Party",
-        category="productivity",
-        price_cents=999,
-        permissions=json.dumps(["read:tasks", "write:tasks"]),
-        status="approved",
-        s3_package_key="plugins/plugin-time-tracker/0.9.1/package.zip",
-        install_count=0,
-        avg_rating=0.0,
-    ),
-]
-
-
-@pytest_asyncio.fixture
-async def seed_plugins(db_session: AsyncSession) -> list[Plugin]:
-    """Insert the 3 default approved plugins and return them."""
-    plugins = []
-    for template in _SEED_PLUGINS:
-        p = Plugin(
-            id=template.id,
-            name=template.name,
-            description=template.description,
-            version=template.version,
-            author_name=template.author_name,
-            category=template.category,
-            price_cents=template.price_cents,
-            permissions=template.permissions,
-            status=template.status,
-            s3_package_key=template.s3_package_key,
-            install_count=template.install_count,
-            avg_rating=template.avg_rating,
-        )
-        db_session.add(p)
-        plugins.append(p)
-    await db_session.commit()
-    return plugins
-
-
 # ── JWT helpers ──────────────────────────────────────────────────────
 
 
@@ -212,24 +136,53 @@ def auth_header(tier: str = "power", user_id: str | None = None) -> dict[str, st
     return {"Authorization": f"Bearer {make_jwt(tier, user_id)}"}
 
 
-# ── S3 mock fixture ──────────────────────────────────────────────────
+# ── Convenience aliases and per-tier user fixtures ────────────────────
 
-S3_TEST_BUCKET = "test-bucket"
-S3_TEST_REGION = "us-east-1"
+@pytest_asyncio.fixture
+async def db(db_session: AsyncSession) -> AsyncSession:
+    """Alias for db_session — used by folder quota tests."""
+    return db_session
+
+
+@pytest_asyncio.fixture
+async def test_user_free(db_session: AsyncSession):
+    """Return the seeded free-tier User row."""
+    result = await db_session.execute(
+        select(User).where(User.id == TEST_USER_IDS["free"])
+    )
+    return result.scalar_one()
+
+
+@pytest_asyncio.fixture
+async def test_user_power(db_session: AsyncSession):
+    """Return the seeded power-tier User row."""
+    result = await db_session.execute(
+        select(User).where(User.id == TEST_USER_IDS["power"])
+    )
+    return result.scalar_one()
 
 
 @pytest.fixture
-def s3_bucket():
-    """Create a mocked S3 bucket via moto and patch BlobStore settings."""
-    with mock_aws():
-        os.environ.setdefault("AWS_ACCESS_KEY_ID", "testing")
-        os.environ.setdefault("AWS_SECRET_ACCESS_KEY", "testing")
-        os.environ.setdefault("AWS_DEFAULT_REGION", S3_TEST_REGION)
-        client = boto3.client("s3", region_name=S3_TEST_REGION)
-        client.create_bucket(Bucket=S3_TEST_BUCKET)
-        with patch("app.storage.blob_store.settings") as mock_settings:
-            mock_settings.S3_BUCKET = S3_TEST_BUCKET
-            mock_settings.S3_REGION = S3_TEST_REGION
-            mock_settings.AWS_ACCESS_KEY_ID = "testing"
-            mock_settings.AWS_SECRET_ACCESS_KEY = "testing"
-            yield S3_TEST_BUCKET
+def auth_headers_free() -> dict[str, str]:
+    """Authorization header for the seeded free-tier user."""
+    return auth_header("free")
+
+
+# ── CLI options ───────────────────────────────────────────────────────
+
+def pytest_addoption(parser):
+    parser.addoption(
+        "--preprocess-dir",
+        default=None,
+        help="Override fixture folder for preprocessor tests (must contain cases.yaml + data/)",
+    )
+    parser.addoption(
+        "--runner-dir",
+        default=None,
+        help="Override fixture folder for agent_runner_v2 eval tests (must contain cases.yaml + data/)",
+    )
+    parser.addoption(
+        "--journey-dir",
+        default=None,
+        help="Override fixture folder for journey_v2 eval tests (must contain cases.yaml + data/)",
+    )

tests/fixtures/agent_runner_v2/cases.yaml

@@ -0,0 +1,86 @@
+# Agent Runner V2 — eval test cases (Step 2, requires real LLM)
+#
+# Each case drives one parametrized `test_eval_runner` invocation.
+#
+# Keys
+# ----
+# id: str                     unique identifier shown in pytest output
+# description: str            human-readable label
+# file: str                   filename inside data/
+# file_path: str              path reported to the executor (affects project-matching via filename)
+# projects: [alpha|beta]      symbolic project names resolved by the test helper
+#
+# Optional pre-existing records (dedup tests)
+# existing_tasks:             list of {id, title, status, priority}
+# existing_notes:             list of {id, title, content}
+# existing_timelines:         list of {id, title, date}
+#
+# Assertions (one or more)
+# expect_insert: <table>      at least 1 insert row in this table (tasks|notes|timelines)
+# expect_no_insert: true      zero inserts in any table
+# expect_project_id: <id>     any insert must carry this projectId
+# expect_dedup: true          task inserts == 0 OR task updates >= 1 (dedup check)
+#
+# Langfuse
+# score_name: str             observation score name
+
+- id: "2.1"
+  description: "Action email → create_task"
+  file: email_action.html
+  file_path: /emails/ProjectAlpha_action.html
+  projects: [alpha, beta]
+  expect_insert: tasks
+  score_name: runner.email_to_task
+
+- id: "2.2"
+  description: "Informational email → create_note"
+  file: email_info.html
+  file_path: /emails/ProjectAlpha_info.html
+  projects: [alpha, beta]
+  expect_insert: notes
+  score_name: runner.email_to_note
+
+- id: "2.3"
+  description: "Email with meeting date → create_timeline"
+  file: email_date.html
+  file_path: /emails/ProjectAlpha_kickoff.html
+  projects: [alpha, beta]
+  expect_insert: timelines
+  score_name: runner.email_to_timeline
+
+- id: "2.4"
+  description: "Filename contains project name → correct project assigned"
+  file: email_action.html
+  file_path: /emails/ProjectAlpha_report.html
+  projects: [alpha, beta]
+  expect_project_id: proj-alpha
+  score_name: runner.project_filename
+
+- id: "2.5"
+  description: "Email body mentions project → correct project assigned"
+  file: email_action.html
+  file_path: /emails/email_001.html
+  projects: [alpha, beta]
+  expect_project_id: proj-alpha
+  score_name: runner.project_content
+
+- id: "2.6"
+  description: "Newsletter + global rule no-project → no creates"
+  file: email_no_project.html
+  file_path: /emails/newsletter.html
+  projects: [alpha, beta]
+  expect_no_insert: true
+  score_name: runner.no_project
+
+- id: "2.7"
+  description: "Existing task with same title → dedup (update not create)"
+  file: email_action.html
+  file_path: /emails/ProjectAlpha_followup.html
+  projects: [alpha]
+  existing_tasks:
+    - id: task-existing
+      title: Fix the login bug
+      status: todo
+      priority: medium
+  expect_dedup: true
+  score_name: runner.dedup

tests/fixtures/agent_runner_v2/data/email_action.html

@@ -0,0 +1,7 @@
+<html><head></head><body>
+<p><b>From:</b> boss@company.com</p>
+<p><b>To:</b> dev@company.com</p>
+<p><b>Subject:</b> Fix the login bug</p>
+<p><b>Date:</b> 2026-04-07</p>
+<p>Hi,<br>Please fix the login bug in Project Alpha by Friday. High priority!</p>
+</body></html>

tests/fixtures/agent_runner_v2/data/email_date.html

@@ -0,0 +1,5 @@
+<html><head></head><body>
+<p><b>From:</b> pm@company.com</p>
+<p><b>Subject:</b> Project Alpha kick-off meeting</p>
+<p>The kick-off meeting for Project Alpha is scheduled for 2026-04-15 at 10:00.</p>
+</body></html>

tests/fixtures/agent_runner_v2/data/email_info.html

@@ -0,0 +1,7 @@
+<html><head></head><body>
+<p><b>From:</b> pm@company.com</p>
+<p><b>To:</b> team@company.com</p>
+<p><b>Subject:</b> FYI: New policy for Project Alpha</p>
+<p>Just a heads-up that starting next week all code reviews must be done
+within 24 hours for Project Alpha. No action needed from you now.</p>
+</body></html>

tests/fixtures/agent_runner_v2/data/email_no_project.html

@@ -0,0 +1,5 @@
+<html><head></head><body>
+<p><b>From:</b> newsletter@ads.com</p>
+<p><b>Subject:</b> Weekly newsletter</p>
+<p>Check out our latest deals on electronics!</p>
+</body></html>

tests/fixtures/journey_v2/cases.yaml

@@ -0,0 +1,19 @@
+# Journey V2 eval test cases — Step 4
+#
+# Only case 4.1 is kept as an automated eval. Cases 4.2–4.5 (multi-turn
+# conversations that expect the LLM to produce a complete AgentConfig)
+# are non-deterministic and tested manually — results tracked in Langfuse.
+#
+# Assertion keys:
+#   expect_question: true → first reply must contain "?"
+
+- id: "4.1"
+  description: "Journey start explores directory, first reply contains a question"
+  directory: "/test/emails"
+  data_types: ["tasks", "notes", "timelines"]
+  directory_files:
+    - path: "/test/emails/outlook_export_2024.html"
+      content_file: "email_action.html"
+  user_messages: []
+  score_name: "journey.start"
+  expect_question: true

tests/fixtures/journey_v2/data/email_action.html

@@ -0,0 +1,23 @@
+<!DOCTYPE html>
+<html>
+<head>
+  <meta charset="UTF-8">
+  <title>Email: Fix the login bug</title>
+  <style>body { font-family: Arial; } .header { color: #666; }</style>
+</head>
+<body>
+  <div class="header">
+    <p><strong>From:</strong> boss@company.com</p>
+    <p><strong>To:</strong> dev@company.com</p>
+    <p><strong>Subject:</strong> Fix the login bug</p>
+    <p><strong>Date:</strong> Mon, 7 Apr 2026 09:15:00 +0000</p>
+  </div>
+  <div class="body">
+    <p>Hi,</p>
+    <p>Please fix the login bug in Project Alpha as soon as possible.
+    Users are reporting that they can't log in with their Google accounts.
+    This is blocking the whole team. Please resolve it by Friday.</p>
+    <p>Thanks,<br>Boss</p>
+  </div>
+</body>
+</html>

tests/fixtures/journey_v2/data/email_info.html

@@ -0,0 +1,23 @@
+<!DOCTYPE html>
+<html>
+<head>
+  <meta charset="UTF-8">
+  <title>Email: New policy update</title>
+  <style>body { font-family: Arial; }</style>
+</head>
+<body>
+  <div class="header">
+    <p><strong>From:</strong> hr@company.com</p>
+    <p><strong>To:</strong> all@company.com</p>
+    <p><strong>Subject:</strong> FYI: New remote work policy effective May 1</p>
+    <p><strong>Date:</strong> Tue, 8 Apr 2026 10:00:00 +0000</p>
+  </div>
+  <div class="body">
+    <p>Hi everyone,</p>
+    <p>Just a heads-up that starting May 1, 2026 the company will be moving to
+    a hybrid work model. You will be expected to come into the office at least
+    two days per week. More details will follow in the employee handbook.</p>
+    <p>Best,<br>HR Team</p>
+  </div>
+</body>
+</html>

tests/fixtures/preprocessors/cases.yaml

@@ -0,0 +1,68 @@
+# Preprocessor test cases
+#
+# detect: <expected_type>   → chiama detect_content_type(filename, content)
+# process: <content_type>   → chiama preprocess(content_type, content)
+#
+# Sorgente: file: <nome in data/>  oppure  generate: binary_noise
+#
+# Assertions piatte (solo per process):
+#   no_html: true           clean_text senza tag HTML
+#   min_chars: N            len(clean_text) >= N
+#   ratio_lt: F             len(clean) / len(raw) < F
+#   has_meta: [k, ...]      chiavi presenti in metadata
+#   contains: str | [str]   substring(s) presenti in clean_text
+#   excludes: str | [str]   substring(s) assenti da clean_text
+#   content_type: str       result.content_type == questo valore
+
+- id: "1.1"
+  file: email_action.html
+  detect: email_html
+
+- id: "1.2"
+  file: generic_page.html
+  detect: generic_html
+
+- id: "1.3"
+  file: notes.txt
+  detect: plain_text
+
+- id: "1.4"
+  file: archive.xyz
+  generate: binary_noise
+  detect: unknown
+
+- id: "1.5"
+  file: email_action.html
+  process: email_html
+  no_html: true
+  min_chars: 50
+  ratio_lt: 0.8
+
+- id: "1.6"
+  file: email_action.html
+  process: email_html
+  has_meta: [subject, from]
+
+- id: "1.7"
+  file: email_thread.html
+  process: email_html
+  contains: "Sure, I'll handle the deploy"
+  excludes: "Let's plan the deploy"
+
+- id: "1.8"
+  file: email_single.html
+  process: email_html
+  contains: "deploy is done"
+
+- id: "1.9"
+  file: email_heavy.html
+  process: email_html
+  no_html: true
+  min_chars: 30
+  excludes: [border-collapse, font-size]
+
+- id: "1.10"
+  file: fallback.txt
+  process: unknown
+  min_chars: 1
+  content_type: unknown

tests/fixtures/preprocessors/data/email_action.html

@@ -0,0 +1,25 @@
+<!DOCTYPE html>
+<html>
+<head>
+  <title>Fix the login bug</title>
+  <style>
+    body { font-family: Arial, sans-serif; color: #333; margin: 0; padding: 20px; }
+    .header { background: #f5f5f5; padding: 10px; border-bottom: 1px solid #ddd; }
+    .body { padding: 20px; }
+  </style>
+</head>
+<body>
+  <div class="header">
+    <p><strong>From:</strong> boss@company.com</p>
+    <p><strong>To:</strong> dev@company.com</p>
+    <p><strong>Subject:</strong> Fix the login bug</p>
+    <p><strong>Date:</strong> Mon, 7 Apr 2026 09:00:00 +0200</p>
+  </div>
+  <div class="body">
+    <p>Hi,</p>
+    <p>Please fix the login bug by Friday. It is blocking the release.</p>
+    <p>Priority: high. Let me know if you need anything.</p>
+    <p>Thanks,<br>Boss</p>
+  </div>
+</body>
+</html>

tests/fixtures/preprocessors/data/email_heavy.html

@@ -0,0 +1,49 @@
+<!DOCTYPE html>
+<html>
+<head>
+<style>
+  table { border-collapse: collapse; width: 100%; max-width: 600px; margin: 0 auto; }
+  td { padding: 8px 12px; border: 1px solid #dddddd; font-size: 12px; color: #444444; }
+  .header-row { background-color: #003366; color: #ffffff; font-weight: bold; }
+  .label-col { background-color: #f0f0f0; width: 80px; font-weight: bold; }
+  .footer-row { font-size: 10px; color: #999999; text-align: center; }
+</style>
+</head>
+<body bgcolor="#eeeeee">
+<center>
+<table cellpadding="0" cellspacing="0">
+  <tr class="header-row">
+    <td colspan="2">Company Internal Update</td>
+  </tr>
+  <tr>
+    <td class="label-col">From:</td>
+    <td>newsletter@corp.com</td>
+  </tr>
+  <tr>
+    <td class="label-col">Subject:</td>
+    <td>Q1 Results Update</td>
+  </tr>
+  <tr>
+    <td class="label-col">Date:</td>
+    <td>Apr 7, 2026</td>
+  </tr>
+  <tr>
+    <td colspan="2">
+      <table width="100%" cellpadding="10">
+        <tr>
+          <td>
+            <p style="font-size:14px; font-weight:bold;">Dear Team,</p>
+            <p>Q1 results are in. Revenue up 15% year-over-year.</p>
+            <p>Please review the attached report and share any feedback by EOW.</p>
+          </td>
+        </tr>
+      </table>
+    </td>
+  </tr>
+  <tr class="footer-row">
+    <td colspan="2">Confidential — do not forward outside the company.</td>
+  </tr>
+</table>
+</center>
+</body>
+</html>

tests/fixtures/preprocessors/data/email_single.html

@@ -0,0 +1,8 @@
+<!DOCTYPE html>
+<html><body>
+  <p><strong>From:</strong> alice@co.com</p>
+  <p><strong>To:</strong> team@co.com</p>
+  <p><strong>Subject:</strong> Quick update</p>
+  <p><strong>Date:</strong> Tue, 7 Apr 2026 10:30:00 +0200</p>
+  <p>The deploy is done. Everything looks good. No issues so far.</p>
+</body></html>

tests/fixtures/preprocessors/data/email_thread.html

@@ -0,0 +1,24 @@
+<!DOCTYPE html>
+<html><body>
+  <div class="message-latest">
+    <p><strong>From:</strong> alice@co.com</p>
+    <p><strong>Subject:</strong> Re: Re: Deploy plan</p>
+    <p>Sure, I'll handle the deploy.</p>
+  </div>
+
+  <p>On Mon, Apr 6, 2026 at 3:00 PM, Bob &lt;bob@co.com&gt; wrote:</p>
+  <blockquote>
+    <p>From: bob@co.com</p>
+    <p>Can you handle the deploy?</p>
+    <p>On Sun, Apr 5, 2026 at 1:00 PM, Alice &lt;alice@co.com&gt; wrote:</p>
+    <blockquote>
+      <p>From: alice@co.com</p>
+      <p>Let's plan the deploy for Monday.</p>
+      <p>On Sat, Apr 4, 2026 at 11:00 AM, Charlie &lt;charlie@co.com&gt; wrote:</p>
+      <blockquote>
+        <p>From: charlie@co.com</p>
+        <p>We need to schedule the deploy. What day works?</p>
+      </blockquote>
+    </blockquote>
+  </blockquote>
+</body></html>

tests/fixtures/preprocessors/data/fallback.txt

@@ -0,0 +1,3 @@
+random text content without any structure
+line two with some words
+line three and more content here

tests/fixtures/preprocessors/data/generic_page.html

@@ -0,0 +1,35 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+  <meta charset="UTF-8">
+  <title>My Web App</title>
+  <link rel="stylesheet" href="styles.css">
+</head>
+<body>
+  <nav>
+    <a href="/">Home</a>
+    <a href="/about">About</a>
+    <a href="/contact">Contact</a>
+  </nav>
+  <main>
+    <header>
+      <h1>Welcome to My App</h1>
+    </header>
+    <article>
+      <p>This is a generic web page with no email headers.</p>
+      <p>It has navigation, main content, and a footer.</p>
+    </article>
+    <section>
+      <h2>Features</h2>
+      <ul>
+        <li>Fast</li>
+        <li>Reliable</li>
+        <li>Secure</li>
+      </ul>
+    </section>
+  </main>
+  <footer>
+    <p>&copy; 2026 My App</p>
+  </footer>
+</body>
+</html>

tests/fixtures/preprocessors/data/notes.txt

@@ -0,0 +1,15 @@
+Meeting notes - April 7, 2026
+
+Attendees: Alice, Bob, Charlie
+
+Discussion points:
+- Deploy scheduled for Friday
+- Bug fix for login must be completed by Thursday
+- Review Q1 numbers before EOW
+
+Action items:
+- Alice: fix login bug
+- Bob: prepare deploy checklist
+- Charlie: send Q1 report
+
+Next meeting: April 14, 2026

tests/test_agent_runner.py

@@ -1,810 +0,0 @@
-"""Tests for Step 3.4: agent_runner module.
-
-Coverage:
-  Unit:
-    - _is_overdue      — cron schedule overdue detection
-    - _extract_items_from_content — LLM extraction + JSON parsing + validation
-    - _send_insert_to_client      — tool_call frame construction + timeout
-    - run_local_agent             — end-to-end local agent happy path
-    - run_local_agent             — device offline path
-    - run_local_agent             — file-read timeout path
-    - run_local_agent             — LLM extraction error path
-    - run_cloud_agent             — stub returns error immediately
-    - trigger_pending_runs        — skipped when config is client-owned
-    - trigger_pending_runs        — non-overdue skipped
-    - trigger_pending_runs        — device_id filter for local agents
-
-    Integration:
-        - POST /agents/can-create     — billing eligibility check
-        - POST /agents/trigger        — creates run log + dispatches background task
-"""
-
-from __future__ import annotations
-
-import asyncio
-import json
-import uuid
-from datetime import datetime, timezone
-from unittest.mock import AsyncMock, MagicMock, patch
-
-import pytest
-import pytest_asyncio
-
-from app.core.agent_runner import (
-    _extract_items_from_content,
-    _is_overdue,
-    _send_insert_to_client,
-    run_cloud_agent,
-    run_local_agent,
-    trigger_pending_runs,
-)
-from app.core.device_manager import DeviceConnectionManager
-from app.db import get_session
-from app.main import app
-from app.models import AgentRunLog, CloudAgentConfig, LocalAgentConfig
-from tests.conftest import TEST_USER_IDS, auth_header
-
-# ---------------------------------------------------------------------------
-# Helpers
-# ---------------------------------------------------------------------------
-
-_FREE_UID = TEST_USER_IDS["free"]
-_PRO_UID = TEST_USER_IDS["pro"]
-
-
-def _make_local_config(user_id: str = _FREE_UID, device_id: str = "dev-001") -> LocalAgentConfig:
-    return LocalAgentConfig(
-        id=str(uuid.uuid4()),
-        user_id=user_id,
-        device_id=device_id,
-        name="Test Local Agent",
-        directory_paths=["/home/user/emails"],
-        data_types=["tasks", "notes"],
-        prompt_template="Extract tasks and notes from this document.",
-        file_extensions=[".txt", ".eml"],
-        schedule_cron="0 */6 * * *",
-        enabled=True,
-        last_run_at=None,
-    )
-
-
-def _make_cloud_config(user_id: str = _FREE_UID) -> CloudAgentConfig:
-    return CloudAgentConfig(
-        id=str(uuid.uuid4()),
-        user_id=user_id,
-        provider="gmail",
-        name="Test Gmail Agent",
-        data_types=["tasks"],
-        prompt_template="Extract tasks from email.",
-        schedule_cron="0 */6 * * *",
-        enabled=True,
-        last_run_at=None,
-    )
-
-
-def _make_run_log(agent_id: str, agent_type: str = "local", user_id: str = _FREE_UID) -> AgentRunLog:
-    return AgentRunLog(
-        id=str(uuid.uuid4()),
-        agent_id=agent_id,
-        agent_type=agent_type,
-        user_id=user_id,
-        status="running",
-        started_at=datetime.now(timezone.utc),
-    )
-
-
-def _make_manager(user_id: str = _FREE_UID, device_id: str = "dev-001") -> DeviceConnectionManager:
-    mgr = DeviceConnectionManager()
-    ws = MagicMock()
-    ws.send_text = AsyncMock()
-    mgr.register(user_id, device_id, ws)
-    return mgr
-
-
-# ---------------------------------------------------------------------------
-# _is_overdue
-# ---------------------------------------------------------------------------
-
-def test_is_overdue_never_run():
-    """An agent that has never run is always overdue."""
-    assert _is_overdue("0 */6 * * *", None) is True
-
-
-def test_is_overdue_very_recently_run():
-    """An agent that just ran is not overdue."""
-    last = datetime.now(timezone.utc)
-    assert _is_overdue("0 */6 * * *", last) is False
-
-
-def test_is_overdue_long_ago():
-    """An agent last run 2 days ago with a 6-hour schedule is overdue."""
-    from datetime import timedelta
-    last = datetime.now(timezone.utc) - timedelta(days=2)
-    assert _is_overdue("0 */6 * * *", last) is True
-
-
-def test_is_overdue_invalid_cron_returns_false():
-    """Unparseable cron must not raise and should return False (fail-safe)."""
-    assert _is_overdue("not a cron", None) is False
-
-
-def test_is_overdue_naive_datetime():
-    """Naive datetime objects are handled without raising."""
-    from datetime import timedelta
-    last = datetime.utcnow() - timedelta(days=1)  # naive
-    # Should not raise.
-    result = _is_overdue("0 */6 * * *", last)
-    assert isinstance(result, bool)
-
-
-# ---------------------------------------------------------------------------
-# _extract_items_from_content
-# ---------------------------------------------------------------------------
-
-
-@pytest.mark.asyncio
-async def test_extract_items_happy_path():
-    """LLM returns valid JSON array; items with allowed tables are returned."""
-    mock_llm = MagicMock()
-    mock_response = MagicMock()
-    mock_response.content = json.dumps([
-        {"table": "tasks", "data": {"title": "Buy milk", "priority": "high"}},
-        {"table": "notes", "data": {"title": "Meeting recap", "content": "Discussed roadmap"}},
-    ])
-    mock_llm.ainvoke = AsyncMock(return_value=mock_response)
-
-    with patch("app.core.agent_runner.get_llm", return_value=mock_llm):
-        items = await _extract_items_from_content(
-            "Extract tasks and notes.",
-            "Email body: Buy milk urgently. Notes from meeting: discussed roadmap.",
-            ["tasks", "notes"],
-        )
-
-    assert len(items) == 2
-    assert items[0]["table"] == "tasks"
-    assert items[0]["data"]["title"] == "Buy milk"
-    assert items[1]["table"] == "notes"
-
-
-@pytest.mark.asyncio
-async def test_extract_items_strips_forbidden_fields():
-    """Fields like id, createdAt, isAiSuggested must be stripped from extracted data."""
-    mock_llm = MagicMock()
-    mock_response = MagicMock()
-    mock_response.content = json.dumps([
-        {
-            "table": "tasks",
-            "data": {
-                "title": "Review PR",
-                "id": "should-be-removed",
-                "createdAt": 99999,
-                "isAiSuggested": 0,
-                "isApproved": 1,
-            },
-        }
-    ])
-    mock_llm.ainvoke = AsyncMock(return_value=mock_response)
-
-    with patch("app.core.agent_runner.get_llm", return_value=mock_llm):
-        items = await _extract_items_from_content("Extract tasks.", "Review the PR.", ["tasks"])
-
-    assert len(items) == 1
-    data = items[0]["data"]
-    assert "id" not in data
-    assert "createdAt" not in data
-    assert "isAiSuggested" not in data
-    assert "isApproved" not in data
-    assert data["title"] == "Review PR"
-
-
-@pytest.mark.asyncio
-async def test_extract_items_invalid_json_returns_empty():
-    """LLM returning invalid JSON must return empty list without raising."""
-    mock_llm = MagicMock()
-    mock_response = MagicMock()
-    mock_response.content = "Sorry, I cannot extract anything."
-    mock_llm.ainvoke = AsyncMock(return_value=mock_response)
-
-    with patch("app.core.agent_runner.get_llm", return_value=mock_llm):
-        items = await _extract_items_from_content("Extract tasks.", "content", ["tasks"])
-
-    assert items == []
-
-
-@pytest.mark.asyncio
-async def test_extract_items_disallowed_table_filtered():
-    """Items whose table is not in data_types are discarded."""
-    mock_llm = MagicMock()
-    mock_response = MagicMock()
-    mock_response.content = json.dumps([
-        {"table": "tasks", "data": {"title": "Valid task"}},
-        {"table": "projects", "data": {"name": "Should be filtered"}},
-    ])
-    mock_llm.ainvoke = AsyncMock(return_value=mock_response)
-
-    with patch("app.core.agent_runner.get_llm", return_value=mock_llm):
-        # Only "tasks" is in data_types — "projects" should be filtered.
-        items = await _extract_items_from_content("Extract.", "content", ["tasks"])
-
-    assert len(items) == 1
-    assert items[0]["table"] == "tasks"
-
-
-@pytest.mark.asyncio
-async def test_extract_items_empty_data_types_returns_empty():
-    """If no allowed data_types match, skip LLM call and return immediately."""
-    mock_llm = MagicMock()
-    mock_llm.ainvoke = AsyncMock()
-
-    with patch("app.core.agent_runner.get_llm", return_value=mock_llm):
-        items = await _extract_items_from_content("Extract.", "content", [])
-
-    mock_llm.ainvoke.assert_not_called()
-    assert items == []
-
-
-@pytest.mark.asyncio
-async def test_extract_items_llm_error_propagates():
-    """LLM API errors propagate so the caller (run_local_agent) can record them."""
-    mock_llm = MagicMock()
-    mock_llm.ainvoke = AsyncMock(side_effect=RuntimeError("API unavailable"))
-
-    with patch("app.core.agent_runner.get_llm", return_value=mock_llm):
-        with pytest.raises(RuntimeError, match="API unavailable"):
-            await _extract_items_from_content("Extract tasks.", "content", ["tasks"])
-
-
-# ---------------------------------------------------------------------------
-# _send_insert_to_client
-# ---------------------------------------------------------------------------
-
-
-@pytest.mark.asyncio
-async def test_send_insert_to_client_happy_path():
-    """Frame is sent with isAiSuggested/isApproved added; result is returned."""
-    mgr = _make_manager()
-
-    sent_payloads: list[dict] = []
-    original_send = mgr.send_frame
-
-    async def _capture_send(uid: str, frame: dict) -> None:
-        sent_payloads.append(frame)
-        # Immediately resolve the pending call with a success result.
-        call_id = frame["id"]
-        mgr.resolve_pending_call(uid, call_id, {"row": {"id": "new-id", "title": "Buy milk"}})
-
-    mgr.send_frame = _capture_send  # type: ignore[method-assign]
-
-    result = await _send_insert_to_client(
-        _FREE_UID, "tasks", {"title": "Buy milk", "priority": "high"}, mgr
-    )
-
-    assert len(sent_payloads) == 1
-    payload = sent_payloads[0]
-    assert payload["action"] == "insert"
-    assert payload["table"] == "tasks"
-    assert payload["data"]["title"] == "Buy milk"
-    assert payload["data"]["isAiSuggested"] == 1
-    assert payload["data"]["isApproved"] == 0
-    assert result["row"]["title"] == "Buy milk"
-
-
-@pytest.mark.asyncio
-async def test_send_insert_to_client_timeout():
-    """asyncio.TimeoutError is raised when Electron does not respond."""
-    mgr = _make_manager()
-
-    async def _slow_send(uid: str, frame: dict) -> None:
-        # Never resolve the pending call.
-        pass
-
-    mgr.send_frame = _slow_send  # type: ignore[method-assign]
-
-    with patch("app.core.agent_runner._INSERT_TIMEOUT", 0.05):
-        with pytest.raises(asyncio.TimeoutError):
-            await _send_insert_to_client(_FREE_UID, "tasks", {"title": "X"}, mgr)
-
-
-# ---------------------------------------------------------------------------
-# run_local_agent
-# ---------------------------------------------------------------------------
-
-
-@pytest.mark.asyncio
-async def test_run_local_agent_device_offline():
-    """run_local_agent marks run as error when device is offline."""
-    config = _make_local_config()
-    run_log = _make_run_log(config.id)
-    mgr = DeviceConnectionManager()  # Empty — no device registered.
-
-    with patch("app.core.agent_runner._finalize_run", new_callable=AsyncMock) as mock_finalize:
-        await run_local_agent(_FREE_UID, config, run_log, mgr)
-
-    mock_finalize.assert_called_once()
-    _args, kwargs = mock_finalize.call_args
-    assert kwargs["status"] == "error"
-    assert any("not connected" in e for e in kwargs["errors"])
-
-
-@pytest.mark.asyncio
-async def test_run_local_agent_happy_path():
-    """End-to-end: files received, LLM extracts one task, insert sent + ack'd."""
-    config = _make_local_config()
-    run_log = _make_run_log(config.id)
-    mgr = _make_manager()
-
-    # Build a fake agent_data frame (will be queued after send).
-    file_frame = {
-        "type": "agent_data",
-        "run_id": run_log.id,
-        "files": [{"path": "/email.eml", "content": "Urgent: fix the bug by Friday."}],
-    }
-    agent_complete_frame = None  # sentinel
-
-    sent_frames: list[dict] = []
-
-    async def _mock_send(uid: str, frame: dict) -> None:
-        sent_frames.append(frame)
-        if frame.get("type") == "agent_run":
-            # Simulate Electron responding with file data then agent_complete.
-            q = mgr.get_agent_data_queue(uid, frame["run_id"])
-            await q.put(file_frame)
-            await q.put(agent_complete_frame)
-        elif frame.get("type") == "tool_call":
-            # Resolve the pending insert immediately.
-            mgr.resolve_pending_call(uid, frame["id"], {"row": {"id": "new-task", "title": "Fix the bug"}})
-
-    mgr.send_frame = _mock_send  # type: ignore[method-assign]
-
-    mock_llm = MagicMock()
-    mock_response = MagicMock()
-    mock_response.content = json.dumps([
-        {"table": "tasks", "data": {"title": "Fix the bug", "priority": "high"}}
-    ])
-    mock_llm.ainvoke = AsyncMock(return_value=mock_response)
-
-    with patch("app.core.agent_runner.get_llm", return_value=mock_llm), \
-         patch("app.core.agent_runner._finalize_run", new_callable=AsyncMock) as mock_finalize:
-        await run_local_agent(_FREE_UID, config, run_log, mgr)
-
-    mock_finalize.assert_called_once()
-    _args, kwargs = mock_finalize.call_args
-    assert kwargs["status"] == "success"
-    assert kwargs["items_processed"] == 1
-    assert kwargs["items_created"] == 1
-    assert kwargs["errors"] == []
-    assert kwargs["update_config_last_run"] is False
-
-    # Verify agent_run frame was sent.
-    agent_run_frames = [f for f in sent_frames if f.get("type") == "agent_run"]
-    assert len(agent_run_frames) == 1
-    assert agent_run_frames[0]["agent_id"] == config.id
-    assert "paths" in agent_run_frames[0]["config"]
-
-    # Verify insert frame was sent with AI flags.
-    insert_frames = [f for f in sent_frames if f.get("type") == "tool_call"]
-    assert len(insert_frames) == 1
-    assert insert_frames[0]["data"]["isAiSuggested"] == 1
-    assert insert_frames[0]["data"]["isApproved"] == 0
-
-
-@pytest.mark.asyncio
-async def test_run_local_agent_file_read_timeout():
-    """run_local_agent marks run as partial/error when device stops sending files."""
-    config = _make_local_config()
-    run_log = _make_run_log(config.id)
-    mgr = _make_manager()
-
-    async def _mock_send(uid: str, frame: dict) -> None:
-        # Don't put anything in the queue — simulate stalled device.
-        pass
-
-    mgr.send_frame = _mock_send  # type: ignore[method-assign]
-
-    with patch("app.core.agent_runner._FILE_READ_TIMEOUT", 0.1), \
-         patch("app.core.agent_runner._finalize_run", new_callable=AsyncMock) as mock_finalize:
-        await run_local_agent(_FREE_UID, config, run_log, mgr)
-
-    mock_finalize.assert_called_once()
-    _args, kwargs = mock_finalize.call_args
-    assert kwargs["status"] == "error"  # No items created, so error (not partial).
-    assert any("timed out" in e.lower() for e in kwargs["errors"])
-
-
-@pytest.mark.asyncio
-async def test_run_local_agent_llm_extraction_error():
-    """LLM errors per-file are recorded; run continues for remaining files."""
-    config = _make_local_config()
-    run_log = _make_run_log(config.id)
-    mgr = _make_manager()
-
-    file_frame = {
-        "type": "agent_data",
-        "run_id": run_log.id,
-        "files": [
-            {"path": "/file1.eml", "content": "Email one."},
-            {"path": "/file2.eml", "content": "Email two."},
-        ],
-    }
-
-    async def _mock_send(uid: str, frame: dict) -> None:
-        if frame.get("type") == "agent_run":
-            q = mgr.get_agent_data_queue(uid, frame["run_id"])
-            await q.put(file_frame)
-            await q.put(None)  # agent_complete sentinel
-
-    mgr.send_frame = _mock_send  # type: ignore[method-assign]
-
-    mock_llm = MagicMock()
-    mock_llm.ainvoke = AsyncMock(side_effect=RuntimeError("LLM boom"))
-
-    with patch("app.core.agent_runner.get_llm", return_value=mock_llm), \
-         patch("app.core.agent_runner._finalize_run", new_callable=AsyncMock) as mock_finalize:
-        await run_local_agent(_FREE_UID, config, run_log, mgr)
-
-    _args, kwargs = mock_finalize.call_args
-    assert kwargs["status"] == "error"
-    assert kwargs["items_processed"] == 2  # Both files attempted.
-    assert kwargs["items_created"] == 0
-    assert len(kwargs["errors"]) == 2  # One error per file.
-
-
-# ---------------------------------------------------------------------------
-# run_cloud_agent (stub)
-# ---------------------------------------------------------------------------
-
-
-@pytest.mark.asyncio
-async def test_run_cloud_agent_device_offline():
-    """Cloud agent aborts immediately when no device is connected."""
-    config = _make_cloud_config()
-    run_log = _make_run_log(config.id, agent_type="cloud")
-    mgr = DeviceConnectionManager()  # empty — no devices registered
-
-    with patch("app.core.agent_runner._finalize_run", new_callable=AsyncMock) as mock_finalize:
-        await run_cloud_agent(_FREE_UID, config, run_log, mgr)
-
-    mock_finalize.assert_called_once()
-    _, kwargs = mock_finalize.call_args
-    assert kwargs["status"] == "error"
-    assert any("device" in e.lower() or "connected" in e.lower() for e in kwargs["errors"])
-
-
-@pytest.mark.asyncio
-async def test_run_cloud_agent_no_oauth_token():
-    """Cloud agent errors when no OAuth token is stored."""
-    config = _make_cloud_config()
-    config.oauth_token_encrypted = None
-    run_log = _make_run_log(config.id, agent_type="cloud")
-    mgr = _make_manager()
-
-    with patch("app.core.agent_runner._finalize_run", new_callable=AsyncMock) as mock_finalize:
-        await run_cloud_agent(_FREE_UID, config, run_log, mgr)
-
-    _, kwargs = mock_finalize.call_args
-    assert kwargs["status"] == "error"
-    assert any("oauth" in e.lower() or "token" in e.lower() for e in kwargs["errors"])
-
-
-@pytest.mark.asyncio
-async def test_run_cloud_agent_token_decrypt_failure():
-    """Cloud agent errors gracefully when the stored token cannot be decrypted."""
-    config = _make_cloud_config()
-    config.oauth_token_encrypted = "this-is-not-valid-fernet-ciphertext"
-    run_log = _make_run_log(config.id, agent_type="cloud")
-    mgr = _make_manager()
-
-    from cryptography.fernet import Fernet as _Fernet
-    valid_key = _Fernet.generate_key().decode()
-
-    with patch("app.core.agent_runner._finalize_run", new_callable=AsyncMock) as mock_finalize, \
-         patch("app.integrations.settings") as mock_settings:
-        mock_settings.OAUTH_ENCRYPTION_KEY = valid_key
-        await run_cloud_agent(_FREE_UID, config, run_log, mgr)
-
-    _, kwargs = mock_finalize.call_args
-    assert kwargs["status"] == "error"
-    assert any("decrypt" in e.lower() for e in kwargs["errors"])
-
-
-@pytest.mark.asyncio
-async def test_run_cloud_agent_happy_path_gmail():
-    """Cloud agent happy path: Gmail fetch → LLM extraction → inserts → success."""
-    from app.integrations import EmailMessage, encrypt_token
-    from cryptography.fernet import Fernet as _Fernet
-
-    fernet_key = _Fernet.generate_key().decode()
-    credentials = {
-        "token": "access_abc",
-        "refresh_token": "refresh_xyz",
-        "token_uri": "https://oauth2.googleapis.com/token",
-        "client_id": "cid",
-        "client_secret": "csec",
-    }
-
-    config = _make_cloud_config()
-    config.provider = "gmail"
-    config.prompt_template = "Extract tasks from this email."
-    config.data_types = ["tasks"]
-
-    with patch("app.integrations.settings") as ms:
-        ms.OAUTH_ENCRYPTION_KEY = fernet_key
-        config.oauth_token_encrypted = encrypt_token(credentials)
-
-    run_log = _make_run_log(config.id, agent_type="cloud")
-    mgr = _make_manager()
-
-    sample_email = EmailMessage(
-        id="msg001",
-        subject="Action required",
-        sender="boss@company.com",
-        body_text="Please fix the bug by Friday.",
-        date=datetime(2025, 6, 1, 10, 0, tzinfo=timezone.utc),
-    )
-
-    extracted_items = [{"table": "tasks", "data": {"title": "Fix the bug", "priority": "high"}}]
-
-    with patch("app.integrations.settings") as mock_int_settings, \
-         patch("app.core.agent_runner._finalize_run", new_callable=AsyncMock) as mock_finalize, \
-         patch("app.core.agent_runner._extract_items_from_content", new_callable=AsyncMock, return_value=extracted_items) as mock_extract, \
-         patch("app.core.agent_runner._send_insert_to_client", new_callable=AsyncMock, return_value={"ok": True}) as mock_insert, \
-         patch("app.core.agent_runner.async_session"):
-        mock_int_settings.OAUTH_ENCRYPTION_KEY = fernet_key
-
-        mock_gmail = AsyncMock()
-        mock_gmail.fetch_messages = AsyncMock(return_value=[sample_email])
-        mock_gmail.refreshed_credentials = None
-
-        with patch("app.integrations.decrypt_token", return_value=credentials), \
-             patch("app.integrations.get_provider", return_value=mock_gmail):
-            await run_cloud_agent(_FREE_UID, config, run_log, mgr)
-
-    mock_extract.assert_called_once()
-    mock_insert.assert_called_once()
-    _, kwargs = mock_finalize.call_args
-    assert kwargs["status"] == "success"
-    assert kwargs["items_processed"] == 1
-    assert kwargs["items_created"] == 1
-    assert kwargs["config_type"] == "cloud"
-
-
-@pytest.mark.asyncio
-async def test_run_cloud_agent_provider_fetch_error():
-    """Cloud agent records error status when provider fetch raises RuntimeError."""
-    credentials = {"token": "abc"}
-    config = _make_cloud_config()
-    config.oauth_token_encrypted = "some_encrypted_value"  # non-empty so decrypt step is reached
-    config.prompt_template = "Extract tasks."
-    config.data_types = ["tasks"]
-    run_log = _make_run_log(config.id, agent_type="cloud")
-    mgr = _make_manager()
-
-    mock_provider = AsyncMock()
-    mock_provider.fetch_messages = AsyncMock(side_effect=RuntimeError("API quota exceeded"))
-    mock_provider.refreshed_credentials = None
-
-    with patch("app.core.agent_runner._finalize_run", new_callable=AsyncMock) as mock_finalize, \
-         patch("app.integrations.decrypt_token", return_value=credentials), \
-         patch("app.integrations.get_provider", return_value=mock_provider), \
-         patch("app.core.agent_runner.async_session"):
-        await run_cloud_agent(_FREE_UID, config, run_log, mgr)
-
-    _, kwargs = mock_finalize.call_args
-    assert kwargs["status"] == "error"
-    assert any("quota" in e.lower() or "fetch" in e.lower() for e in kwargs["errors"])
-
-
-@pytest.mark.asyncio
-async def test_run_cloud_agent_refreshed_token_persisted():
-    """When the provider refreshes its token, the new ciphertext is written to DB."""
-    from app.integrations import EmailMessage, encrypt_token
-    from cryptography.fernet import Fernet as _Fernet
-
-    fernet_key = _Fernet.generate_key().decode()
-    credentials = {"token": "old_token", "refresh_token": "rt_old"}
-    fresh_credentials = {"token": "new_token", "refresh_token": "rt_new"}
-
-    config = _make_cloud_config()
-    config.prompt_template = "Extract tasks."
-    config.data_types = ["tasks"]
-
-    with patch("app.integrations.settings") as ms:
-        ms.OAUTH_ENCRYPTION_KEY = fernet_key
-        config.oauth_token_encrypted = encrypt_token(credentials)
-
-    run_log = _make_run_log(config.id, agent_type="cloud")
-    mgr = _make_manager()
-
-    mock_provider = AsyncMock()
-    mock_provider.fetch_messages = AsyncMock(return_value=[])
-    mock_provider.refreshed_credentials = fresh_credentials  # token was refreshed
-
-    # Track DB writes via mock async_session.
-    mock_cfg_row = MagicMock()
-    mock_cfg_row.oauth_token_encrypted = None
-
-    mock_db = AsyncMock()
-    mock_db.__aenter__ = AsyncMock(return_value=mock_db)
-    mock_db.__aexit__ = AsyncMock(return_value=False)
-    mock_db.scalar_one_or_none = AsyncMock(return_value=mock_cfg_row)
-    cfg_result = MagicMock()
-    cfg_result.scalar_one_or_none.return_value = mock_cfg_row
-    mock_db.execute = AsyncMock(return_value=cfg_result)
-    mock_db.commit = AsyncMock()
-
-    with patch("app.core.agent_runner._finalize_run", new_callable=AsyncMock), \
-         patch("app.integrations.decrypt_token", return_value=credentials), \
-         patch("app.integrations.get_provider", return_value=mock_provider), \
-         patch("app.integrations.encrypt_token", return_value="new_encrypted") as mock_encrypt, \
-         patch("app.core.agent_runner.async_session", return_value=mock_db), \
-         patch("app.integrations.settings") as mock_int_settings:
-        mock_int_settings.OAUTH_ENCRYPTION_KEY = fernet_key
-        await run_cloud_agent(_FREE_UID, config, run_log, mgr)
-
-    # The new encrypted token should have been written to the config row.
-    mock_encrypt.assert_called_once_with(fresh_credentials)
-    assert mock_cfg_row.oauth_token_encrypted == "new_encrypted"
-
-
-@pytest.mark.asyncio
-async def test_finalize_run_updates_cloud_config_last_run_at():
-    """_finalize_run with config_type='cloud' updates CloudAgentConfig.last_run_at."""
-    from app.core.agent_runner import _finalize_run
-
-    run_log = _make_run_log(str(uuid.uuid4()), agent_type="cloud")
-    run_log.id = str(uuid.uuid4())
-
-    mock_cfg = MagicMock()
-    mock_cfg.last_run_at = None
-
-    cfg_result = MagicMock()
-    cfg_result.scalar_one_or_none.return_value = mock_cfg
-
-    mock_db = AsyncMock()
-    mock_db.__aenter__ = AsyncMock(return_value=mock_db)
-    mock_db.__aexit__ = AsyncMock(return_value=False)
-    mock_db.merge = AsyncMock(return_value=run_log)
-    mock_db.execute = AsyncMock(return_value=cfg_result)
-    mock_db.commit = AsyncMock()
-
-    config_id = str(uuid.uuid4())
-
-    with patch("app.core.agent_runner.async_session", return_value=mock_db):
-        await _finalize_run(
-            run_log,
-            status="success",
-            update_config_last_run=True,
-            config_id=config_id,
-            config_type="cloud",
-        )
-
-    # CloudAgentConfig.last_run_at should have been set.
-    assert mock_cfg.last_run_at is not None
-    mock_db.commit.assert_called()
-
-
-# ---------------------------------------------------------------------------
-# trigger_pending_runs
-# ---------------------------------------------------------------------------
-
-
-@pytest.mark.asyncio
-async def test_trigger_pending_runs_no_overdue():
-    """Pending-run scan is skipped because agent config is client-owned."""
-
-    mgr = _make_manager()
-
-    with patch("app.core.agent_runner.run_local_agent", new_callable=AsyncMock) as mock_run:
-        await trigger_pending_runs(_FREE_UID, "dev-001", mgr)
-
-    mock_run.assert_not_called()
-
-
-@pytest.mark.asyncio
-async def test_trigger_pending_runs_device_id_filter():
-    """Device filtering is no longer backend-managed in pending runs."""
-
-    mgr = _make_manager(device_id="dev-001")
-
-    with patch("app.core.agent_runner.run_local_agent", new_callable=AsyncMock) as mock_run:
-        await trigger_pending_runs(_FREE_UID, "dev-001", mgr)
-
-    mock_run.assert_not_called()
-
-
-@pytest.mark.asyncio
-async def test_trigger_pending_runs_dispatches_overdue():
-    """No pending runs are dispatched by backend after config deprecation."""
-
-    mgr = _make_manager()
-
-    with patch("app.core.agent_runner.run_local_agent", new_callable=AsyncMock) as mock_run:
-        await trigger_pending_runs(_FREE_UID, "dev-001", mgr)
-
-    mock_run.assert_not_called()
-
-
-# ---------------------------------------------------------------------------
-# Integration: POST /agents/can-create and /agents/trigger
-# ---------------------------------------------------------------------------
-
-
-@pytest.fixture(autouse=True)
-def _override_db(db_session):
-    """Route all get_session calls to the test SQLite session."""
-
-    async def _gen():
-        yield db_session
-
-    app.dependency_overrides[get_session] = _gen
-    yield
-    app.dependency_overrides.pop(get_session, None)
-
-
-@pytest.mark.asyncio
-async def test_can_create_agent_allows_when_under_limit(client):
-    """POST /agents/can-create returns allowed=True when under tier limit."""
-    resp = client.post(
-        "/api/v1/agents/can-create",
-        json={"active_agents": 0},
-        headers=auth_header("free"),
-    )
-    assert resp.status_code == 200
-    body = resp.json()
-    assert body["allowed"] is True
-    assert body["tier"] == "free"
-    assert body["active_agents"] == 0
-    assert body["limit"] == 2
-
-
-@pytest.mark.asyncio
-async def test_can_create_agent_denies_when_at_limit(client):
-    """POST /agents/can-create returns allowed=False at free-tier limit."""
-    resp = client.post(
-        "/api/v1/agents/can-create",
-        json={"active_agents": 2},
-        headers=auth_header("free"),
-    )
-    assert resp.status_code == 200
-    body = resp.json()
-    assert body["allowed"] is False
-    assert body["limit"] == 2
-
-
-@pytest.mark.asyncio
-async def test_trigger_run_local_agent_creates_run_log(client, db_session):
-    """POST /agents/trigger creates a local run log and dispatches background task."""
-    dispatched: list[tuple[str, str]] = []
-
-    async def _fake_run(user_id, cfg, run_log, device_mgr):
-        dispatched.append((user_id, cfg.id))
-
-    def _fake_create_task(coro):
-        coro.close()
-        return MagicMock()
-
-    with patch("app.api.routes.agents.run_local_agent", new_callable=AsyncMock, side_effect=_fake_run), \
-         patch("asyncio.create_task") as mock_create_task:
-        mock_create_task.side_effect = _fake_create_task
-        resp = client.post(
-            "/api/v1/agents/trigger",
-            json={
-                "directory": "/home/user/docs",
-                "what_to_extract": ["task", "note"],
-                "actions_by_type": {"task": ["add", "update"], "note": ["add"]},
-                "batch_interval": "0 */6 * * *",
-                "custom_agent_prompt": "Extract tasks and notes.",
-                "active_agents": 0,
-            },
-            headers=auth_header("power"),
-        )
-
-    assert resp.status_code == 202
-    data = resp.json()
-    assert isinstance(data["agent_id"], str)
-    assert data["agent_id"]
-    assert data["status"] == "running"
-    assert data["agent_type"] == "local"
-
-    # Verify create_task was called (dispatching background run).
-    mock_create_task.assert_called_once()

tests/test_agent_runner_v2.py

@@ -0,0 +1,430 @@
+"""Tests for Local Agent V2 runner (Step 2).
+
+Covers the unified per-file flow:
+  Phase A — detect + preprocess (Python, zero LLM)
+  Phase B — single LLM call with tools (classify + extract + create)
+
+Fixture-based eval tests (2.1–2.7)
+-----------------------------------
+Cases are defined in tests/fixtures/agent_runner_v2/cases.yaml.
+Email HTML files live in tests/fixtures/agent_runner_v2/data/.
+Use --runner-dir to point at a custom folder (same structure required).
+
+Unit tests (no LLM)
+--------------------
+  2.8  items_created count   → items_created == N create_* calls
+  2.9  Device offline        → status=error
+  2.10 Empty file            → items_processed=0, status=success
+
+Run:
+    pytest tests/test_agent_runner_v2.py -v
+    pytest tests/test_agent_runner_v2.py -v -k "2_9 or 2_10 or 2_8"   # unit only
+    pytest tests/test_agent_runner_v2.py -v -k "eval"                  # LLM evals only
+    pytest tests/test_agent_runner_v2.py -v --runner-dir /path/to/dir  # custom fixtures
+"""
+
+from __future__ import annotations
+
+import uuid
+from contextlib import nullcontext
+from datetime import datetime, timezone
+from pathlib import Path
+from typing import Any
+from unittest.mock import AsyncMock, MagicMock, patch
+
+import pytest
+import yaml
+
+from app.core.agent_runner import (
+    _format_metadata,
+    _format_projects,
+    _get_extraction_rules,
+    _get_no_match_behavior,
+    run_local_agent,
+)
+from app.core.device_manager import DeviceConnectionManager
+from app.core.langfuse_client import get_langfuse
+from app.models import AgentRunLog, LocalAgentConfig
+from tests.conftest import TEST_USER_IDS
+
+# ── Constants ─────────────────────────────────────────────────────────────
+
+_USER_ID = TEST_USER_IDS["power"]
+
+_DEFAULT_FIXTURE_DIR = Path(__file__).parent / "fixtures" / "agent_runner_v2"
+
+_AGENT_CONFIG = {
+    "content_types": [
+        {
+            "id": "email_html",
+            "label": "Email HTML",
+            "detection_hint": "HTML file with From/To/Subject headers",
+            "preprocessing": "email_html",
+            "extraction_prompt": (
+                "If the email contains a direct action request or task assignment → create a task. "
+                "If the email contains informational content, updates, or FYI → create a note. "
+                "If the email mentions a specific date for a meeting or deadline → create a timeline entry."
+            ),
+        }
+    ],
+    "global_rules": [
+        "Se il file non è riconducibile a nessun progetto, non creare alcuna entità."
+    ],
+    "data_types": ["tasks", "notes", "timelines"],
+}
+
+# Canonical project definitions, referenced symbolically in cases.yaml.
+_PROJECTS: dict[str, dict] = {
+    "alpha": {"id": "proj-alpha", "name": "Project Alpha", "status": "active"},
+    "beta":  {"id": "proj-beta",  "name": "Project Beta",  "status": "active"},
+}
+
+
+# ── Fixture loading ───────────────────────────────────────────────────────
+
+
+def _fixtures_dir(config) -> Path:
+    override = config.getoption("--runner-dir")
+    return Path(override) if override else _DEFAULT_FIXTURE_DIR
+
+
+def _load_cases(config) -> list[dict]:
+    return yaml.safe_load(
+        (_fixtures_dir(config) / "cases.yaml").read_text(encoding="utf-8")
+    )
+
+
+def _read_case_file(case: dict, data_dir: Path) -> str:
+    return (data_dir / case["file"]).read_text(encoding="utf-8")
+
+
+def _resolve_projects(entries: list[str | dict]) -> list[dict]:
+    """Resolve project list from YAML: symbolic names and/or inline dicts."""
+    result = []
+    for entry in entries:
+        if isinstance(entry, str):
+            if entry in _PROJECTS:
+                result.append(_PROJECTS[entry])
+        elif isinstance(entry, dict):
+            result.append(entry)
+    return result
+
+
+# ── pytest_generate_tests — parametrize eval tests from YAML ─────────────
+
+
+def pytest_generate_tests(metafunc):
+    if "runner_case" not in metafunc.fixturenames:
+        return
+    cases = _load_cases(metafunc.config)
+    metafunc.parametrize("runner_case", cases, ids=[c["id"] for c in cases])
+
+
+# ── Test helpers ──────────────────────────────────────────────────────────
+
+
+def _make_config(
+    agent_config: dict | None = None,
+    directory: str = "/emails",
+    device_id: str = "dev-001",
+) -> LocalAgentConfig:
+    return LocalAgentConfig(
+        id=str(uuid.uuid4()),
+        user_id=_USER_ID,
+        device_id=device_id,
+        name="Test V2 Agent",
+        directory_paths=[directory],
+        data_types=["tasks", "notes", "timelines"],
+        prompt_template="",
+        agent_config=agent_config or _AGENT_CONFIG,
+        file_extensions=[".html", ".eml"],
+        schedule_cron="0 */6 * * *",
+        enabled=True,
+        last_run_at=None,
+    )
+
+
+def _make_run_log(agent_id: str) -> AgentRunLog:
+    return AgentRunLog(
+        id=str(uuid.uuid4()),
+        agent_id=agent_id,
+        agent_type="local",
+        user_id=_USER_ID,
+        status="running",
+        started_at=datetime.now(timezone.utc),
+    )
+
+
+def _make_manager(online: bool = True) -> DeviceConnectionManager:
+    mgr = DeviceConnectionManager()
+    if online:
+        ws = MagicMock()
+        ws.send_text = AsyncMock()
+        mgr.register(_USER_ID, "dev-001", ws)
+    return mgr
+
+
+def _make_executor(
+    file_path: str,
+    file_content: str,
+    projects: list[dict] | None = None,
+    existing_tasks: list[dict] | None = None,
+    existing_notes: list[dict] | None = None,
+    existing_timelines: list[dict] | None = None,
+) -> tuple[Any, list[dict]]:
+    """Return (async_executor, captured_calls).
+
+    The executor handles all ``execute_on_client`` payloads:
+    directory listing, file reading, project/entity fetching, and CRUD.
+    """
+    calls: list[dict] = []
+    _projects = projects if projects is not None else list(_PROJECTS.values())
+
+    async def _executor(payload: dict) -> dict:
+        action = payload.get("action", "")
+        table = payload.get("table", "")
+        data = payload.get("data") or {}
+        calls.append({"action": action, "table": table, "data": data})
+
+        if action == "list_directory":
+            return {"entries": [{"type": "file", "path": file_path}]}
+
+        if action == "get_file_metadata":
+            return {"modifiedAt": None}
+
+        if action == "read_file_content":
+            return {"content": file_content}
+
+        if action == "select":
+            if table == "projects":
+                return {"rows": _projects}
+            if table == "tasks":
+                return {"rows": existing_tasks or []}
+            if table == "notes":
+                return {"rows": existing_notes or []}
+            if table == "timelines":
+                return {"rows": existing_timelines or []}
+            return {"rows": []}
+
+        if action == "insert":
+            return {"row": {"id": str(uuid.uuid4()), **data}}
+
+        if action == "update":
+            return {"success": True}
+
+        return {}
+
+    return _executor, calls
+
+
+# ── Unit: helper functions ────────────────────────────────────────────────
+
+
+def test_format_projects_empty():
+    assert "(no projects" in _format_projects([])
+
+
+def test_format_projects_with_data():
+    result = _format_projects([_PROJECTS["alpha"]])
+    assert "proj-alpha" in result
+    assert "Project Alpha" in result
+
+
+def test_format_metadata_empty():
+    assert _format_metadata({}) == ""
+
+
+def test_format_metadata_email():
+    meta = {"subject": "Fix bug", "from": "boss@co.com", "date": "2026-04-07"}
+    result = _format_metadata(meta)
+    assert "Fix bug" in result
+    assert "boss@co.com" in result
+
+
+def test_get_extraction_rules_match():
+    rules = _get_extraction_rules(_AGENT_CONFIG, "email_html")
+    assert "task" in rules.lower()
+
+
+def test_get_extraction_rules_fallback():
+    rules = _get_extraction_rules(_AGENT_CONFIG, "plain_text")
+    assert "extract" in rules.lower()
+
+
+def test_get_no_match_behavior_from_global_rules():
+    behavior = _get_no_match_behavior(_AGENT_CONFIG)
+    assert behavior  # non-empty
+
+
+def test_get_no_match_behavior_default():
+    behavior = _get_no_match_behavior({})
+    assert "project" in behavior.lower()
+
+
+# ── Unit: 2.9 — device offline ───────────────────────────────────────────
+
+
+@pytest.mark.asyncio
+async def test_2_9_device_offline():
+    """2.9 No device online → status=error, no executor created."""
+    config = _make_config()
+    run_log = _make_run_log(config.id)
+    mgr = _make_manager(online=False)
+
+    with patch("app.core.agent_runner._finalize_run", new_callable=AsyncMock) as mock_fin:
+        await run_local_agent(_USER_ID, config, run_log, mgr)
+
+    _, kwargs = mock_fin.call_args
+    assert kwargs["status"] == "error"
+    assert any("not connected" in e for e in kwargs.get("errors", []))
+
+
+# ── Unit: 2.10 — empty file ──────────────────────────────────────────────
+
+
+@pytest.mark.asyncio
+async def test_2_10_empty_file():
+    """2.10 File with empty content → skipped, items_processed=0, success."""
+    config = _make_config()
+    run_log = _make_run_log(config.id)
+    mgr = _make_manager()
+
+    executor, calls = _make_executor(
+        file_path="/emails/empty.html",
+        file_content="",
+        projects=[_PROJECTS["alpha"]],
+    )
+
+    with patch("app.core.agent_runner._make_agent_executor", return_value=executor), \
+         patch("app.core.agent_runner._finalize_run", new_callable=AsyncMock) as mock_fin:
+        await run_local_agent(_USER_ID, config, run_log, mgr)
+
+    _, kwargs = mock_fin.call_args
+    assert kwargs["items_processed"] == 0
+    assert kwargs["status"] == "success"
+    assert kwargs["items_created"] == 0
+
+
+# ── Unit: 2.8 — items_created count ─────────────────────────────────────
+
+
+@pytest.mark.asyncio
+async def test_2_8_items_created_count():
+    """2.8 items_created == number of create_* tool calls per run."""
+    config = _make_config()
+    run_log = _make_run_log(config.id)
+    mgr = _make_manager()
+
+    executor, _calls = _make_executor(
+        file_path="/emails/action.html",
+        file_content="<html><body><p>Fix the login bug in Project Alpha.</p></body></html>",
+        projects=[_PROJECTS["alpha"]],
+    )
+
+    async def mock_run_agent(*, _tool_calls_out=None, **kw) -> str:
+        if _tool_calls_out is not None:
+            _tool_calls_out.extend(["create_task", "create_note", "update_task"])
+        return "Done."
+
+    with patch("app.core.agent_runner._make_agent_executor", return_value=executor), \
+         patch("app.core.agent_runner._run_agent_with_tools", side_effect=mock_run_agent), \
+         patch("app.core.agent_runner._finalize_run", new_callable=AsyncMock) as mock_fin:
+        await run_local_agent(_USER_ID, config, run_log, mgr)
+
+    _, kwargs = mock_fin.call_args
+    # Only create_task + create_note count (not update_task).
+    assert kwargs["items_created"] == 2
+    assert kwargs["items_processed"] == 1
+
+
+# ── Eval: 2.1–2.7 — fixture-driven, real LLM + Langfuse scoring ──────────
+#
+# Cases loaded from tests/fixtures/agent_runner_v2/cases.yaml.
+# Supported assertions (from YAML):
+#   expect_insert: <table>   → at least 1 insert in that table
+#   expect_no_insert: true   → zero inserts in any table
+#   expect_project_id: <id>  → any insert carries this projectId
+#   expect_dedup: true       → task inserts == 0 OR task updates >= 1
+# ─────────────────────────────────────────────────────────────────────────
+
+
+@pytest.mark.asyncio
+@pytest.mark.eval
+async def test_eval_runner(runner_case, pytestconfig):
+    """Parametrized eval test — one invocation per YAML case."""
+    case: dict = runner_case
+    data_dir = _fixtures_dir(pytestconfig) / "data"
+    file_content = _read_case_file(case, data_dir)
+    projects = _resolve_projects(case.get("projects", []))
+
+    config = _make_config()
+    run_log = _make_run_log(config.id)
+    mgr = _make_manager()
+
+    executor, calls = _make_executor(
+        file_path=case["file_path"],
+        file_content=file_content,
+        projects=projects,
+        existing_tasks=case.get("existing_tasks"),
+        existing_notes=case.get("existing_notes"),
+        existing_timelines=case.get("existing_timelines"),
+    )
+
+    lf = get_langfuse()
+    obs_ctx = lf.start_as_current_observation(
+        name=f"eval-runner-{case['id']}-{case.get('score_name', 'unknown').replace('.', '-')}",
+        metadata={"step": "2", "case_id": case["id"]},
+    ) if lf else nullcontext()
+
+    with obs_ctx as obs:
+        with patch("app.core.agent_runner._make_agent_executor", return_value=executor), \
+             patch("app.core.agent_runner._finalize_run", new_callable=AsyncMock) as mock_fin:
+            await run_local_agent(_USER_ID, config, run_log, mgr)
+
+        _, kwargs = mock_fin.call_args
+        score, comment = _evaluate_case(case, calls, kwargs)
+
+        if obs is not None:
+            obs.score(
+                name=case.get("score_name", f"runner.case_{case['id']}"),
+                value=score,
+                comment=comment,
+            )
+
+    if lf:
+        lf.flush()
+
+    assert score == 1.0, f"[{case['id']}] {case.get('description', '')} — {comment}"
+
+
+def _evaluate_case(case: dict, calls: list[dict], finalize_kwargs: dict) -> tuple[float, str]:
+    """Return (score, comment) for a YAML case given the captured executor calls."""
+    inserts = [c for c in calls if c["action"] == "insert"]
+
+    if case.get("expect_no_insert"):
+        score = 1.0 if len(inserts) == 0 else 0.0
+        return score, f"inserts={len(inserts)} (expected 0)"
+
+    if "expect_insert" in case:
+        tables = case["expect_insert"]
+        if isinstance(tables, str):
+            tables = [tables]
+        missing = [t for t in tables if not any(c["table"] == t for c in inserts)]
+        score = 1.0 if not missing else 0.0
+        counts = {t: sum(1 for c in inserts if c["table"] == t) for t in tables}
+        return score, f"inserts={counts}" + (f" missing={missing}" if missing else "")
+
+    if "expect_project_id" in case:
+        expected_pid = case["expect_project_id"]
+        correct = any(c.get("data", {}).get("projectId") == expected_pid for c in inserts)
+        score = 1.0 if correct else 0.0
+        all_pids = [c.get("data", {}).get("projectId") for c in inserts]
+        return score, f"projectIds={all_pids} (expected {expected_pid!r})"
+
+    if case.get("expect_dedup"):
+        task_creates = [c for c in inserts if c["table"] == "tasks"]
+        task_updates = [c for c in calls if c["action"] == "update" and c["table"] == "tasks"]
+        score = 1.0 if len(task_creates) == 0 or len(task_updates) >= 1 else 0.0
+        return score, f"task_creates={len(task_creates)} task_updates={len(task_updates)}"
+
+    return 0.0, "no assertion defined in case"

tests/test_agent_setup.py

@@ -1,243 +0,0 @@
-"""Tests for the Chatbot Journey endpoints.
-
-Covers:
-  1. Start journey for local agent → session_id + first question, done=False
-  2. Start journey for cloud agent → contextual email-focused question
-  3. Start journey with existing agent_id → session seeded, first question returned
-  4. Start journey with non-existent agent_id → still succeeds (graceful fallback)
-  5. Message: continue conversation → done=False, follow-up question returned
-  6. Message: LLM wraps up → done=True + prompt_template extracted correctly
-  7. Message with max-turns nudge → no crash, returns response
-  8. Invalid session_id → 404
-  9. Expired session → 404
-  10. Session ownership: user B cannot access user A's session
-  11. No JWT on /start → 401
-  12. No JWT on /message → 401
-"""
-
-from __future__ import annotations
-
-import time
-import uuid
-from unittest.mock import AsyncMock, patch
-
-import pytest
-from fastapi.testclient import TestClient
-from sqlalchemy.ext.asyncio import AsyncSession
-
-from app.api.routes.agent_setup import (
-    _SESSION_TTL_SECONDS,
-    _TEMPLATE_END,
-    _TEMPLATE_START,
-    _extract_template,
-    _sessions,
-)
-from app.models import LocalAgentConfig
-from tests.conftest import TEST_USER_IDS, auth_header
-
-# ── Helpers ──────────────────────────────────────────────────────────────
-
-
-def _start(client: TestClient, agent_type: str = "local", agent_id: str | None = None, tier: str = "power") -> dict:
-    body: dict = {"agent_type": agent_type}
-    if agent_id:
-        body["agent_id"] = agent_id
-    resp = client.post("/api/v1/agents/journey/start", json=body, headers=auth_header(tier))
-    return resp
-
-
-def _message(client: TestClient, session_id: str, message: str, tier: str = "power") -> dict:
-    return client.post(
-        "/api/v1/agents/journey/message",
-        json={"session_id": session_id, "message": message},
-        headers=auth_header(tier),
-    )
-
-
-# ── Unit: _extract_template ───────────────────────────────────────────────
-
-
-def test_extract_template_present():
-    text = f"Some preamble.\n{_TEMPLATE_START}\nExtract tasks from emails.\n{_TEMPLATE_END}\nTrailing text."
-    result = _extract_template(text)
-    assert result == "Extract tasks from emails."
-
-
-def test_extract_template_absent():
-    assert _extract_template("No markers here.") is None
-
-
-def test_extract_template_empty_content():
-    text = f"{_TEMPLATE_START}\n{_TEMPLATE_END}"
-    assert _extract_template(text) is None
-
-
-# ── Start journey ─────────────────────────────────────────────────────────
-
-
-def test_start_journey_local(client: TestClient):
-    resp = _start(client, agent_type="local")
-    assert resp.status_code == 200
-    body = resp.json()
-    assert "session_id" in body
-    assert body["done"] is False
-    assert body["prompt_template"] is None
-    assert len(body["message"]) > 0
-    # Local question should be about files/directories
-    assert any(w in body["message"].lower() for w in ("file", "director", "document", "monitor"))
-
-
-def test_start_journey_cloud(client: TestClient):
-    resp = _start(client, agent_type="cloud")
-    assert resp.status_code == 200
-    body = resp.json()
-    assert body["done"] is False
-    # Cloud question should mention emails or messages
-    assert any(w in body["message"].lower() for w in ("email", "message", "communication"))
-
-
-def test_start_journey_with_agent_id(client: TestClient, db_session: AsyncSession):
-    """When agent_id is provided, session should be created even if agent doesn't exist."""
-    fake_agent_id = str(uuid.uuid4())
-    resp = _start(client, agent_type="local", agent_id=fake_agent_id)
-    # Should succeed gracefully even if the agent_id doesn't exist
-    assert resp.status_code == 200
-    body = resp.json()
-    assert body["done"] is False
-
-
-def test_start_journey_with_existing_agent(client: TestClient, db_session: AsyncSession):
-    """When a real local agent is provided, session is seeded with its prompt_template."""
-    import asyncio
-
-    user_id = TEST_USER_IDS["power"]
-    agent = LocalAgentConfig(
-        id=str(uuid.uuid4()),
-        user_id=user_id,
-        name="Test Agent",
-        device_id="device-1",
-        directory_paths=["/home/user/emails"],
-        data_types=["tasks"],
-        prompt_template="Extract tasks from .eml files.",
-        file_extensions=[".eml"],
-        schedule_cron="0 */6 * * *",
-        enabled=True,
-    )
-
-    async def _seed():
-        db_session.add(agent)
-        await db_session.commit()
-
-    asyncio.get_event_loop().run_until_complete(_seed())
-
-    resp = _start(client, agent_type="local", agent_id=agent.id)
-    assert resp.status_code == 200
-    body = resp.json()
-    assert body["done"] is False
-    # The session should be stored
-    assert body["session_id"] in _sessions
-
-
-def test_start_journey_requires_auth(client: TestClient):
-    resp = client.post("/api/v1/agents/journey/start", json={"agent_type": "local"})
-    assert resp.status_code == 401
-
-
-# ── Message ───────────────────────────────────────────────────────────────
-
-
-def test_message_continues_conversation(client: TestClient):
-    """A mid-journey reply (no template markers) returns done=False."""
-    follow_up = "That looks good. Can you tell me more about priority rules?"
-
-    with patch("app.api.routes.agent_setup._call_llm", new=AsyncMock(return_value=follow_up)):
-        start_resp = _start(client, agent_type="local")
-        assert start_resp.status_code == 200
-        session_id = start_resp.json()["session_id"]
-
-        msg_resp = _message(client, session_id, "I have .eml and .txt files")
-        assert msg_resp.status_code == 200
-        body = msg_resp.json()
-        assert body["done"] is False
-        assert body["prompt_template"] is None
-        assert body["message"] == follow_up
-        assert body["session_id"] == session_id
-
-
-def test_message_produces_template(client: TestClient):
-    """When the LLM includes PROMPT_TEMPLATE markers, done=True and prompt_template is set."""
-    final_template = "Extract tasks from email. Subject → title. 'urgent' → high priority."
-    llm_response = (
-        "Great, I have all the information I need.\n"
-        f"{_TEMPLATE_START}\n{final_template}\n{_TEMPLATE_END}\n"
-    )
-
-    with patch("app.api.routes.agent_setup._call_llm", new=AsyncMock(return_value=llm_response)):
-        start_resp = _start(client, agent_type="cloud")
-        assert start_resp.status_code == 200
-        session_id = start_resp.json()["session_id"]
-
-        msg_resp = _message(client, session_id, "Only invoices from clients")
-        assert msg_resp.status_code == 200
-        body = msg_resp.json()
-        assert body["done"] is True
-        assert body["prompt_template"] == final_template
-        # Session should be cleaned up
-        assert session_id not in _sessions
-
-
-def test_message_invalid_session(client: TestClient):
-    resp = _message(client, "nonexistent-session-id", "hello")
-    assert resp.status_code == 404
-
-
-def test_message_wrong_owner(client: TestClient):
-    """User B cannot access user A's session."""
-    start_resp = _start(client, agent_type="local", tier="power")
-    session_id = start_resp.json()["session_id"]
-
-    # user with "pro" tier (different user_id) tries to send a message
-    resp = client.post(
-        "/api/v1/agents/journey/message",
-        json={"session_id": session_id, "message": "hello"},
-        headers=auth_header("pro"),  # different user
-    )
-    assert resp.status_code == 404
-
-
-def test_message_expired_session(client: TestClient):
-    """Expired sessions return 404."""
-    start_resp = _start(client, agent_type="local")
-    session_id = start_resp.json()["session_id"]
-
-    # Manually expire the session
-    _sessions[session_id].created_at = time.monotonic() - _SESSION_TTL_SECONDS - 1
-
-    resp = _message(client, session_id, "hello")
-    assert resp.status_code == 404
-
-
-def test_message_requires_auth(client: TestClient):
-    resp = client.post(
-        "/api/v1/agents/journey/message",
-        json={"session_id": "any", "message": "hello"},
-    )
-    assert resp.status_code == 401
-
-
-def test_message_max_turns_nudge(client: TestClient):
-    """After _MAX_TURNS user messages, a system nudge is appended but no crash occurs."""
-    from app.api.routes.agent_setup import _MAX_TURNS
-
-    follow_up = "Tell me more about priority rules."
-
-    with patch("app.api.routes.agent_setup._call_llm", new=AsyncMock(return_value=follow_up)):
-        start_resp = _start(client, agent_type="local")
-        session_id = start_resp.json()["session_id"]
-
-        for i in range(_MAX_TURNS):
-            resp = _message(client, session_id, f"Answer {i + 1}")
-            assert resp.status_code == 200
-            # While no template produced, session must still exist
-            if resp.json()["done"]:
-                break  # LLM decided to wrap up early — also fine

tests/test_auth.py

@@ -1,4 +1,4 @@
-"""Tests for auth routes: register, login, refresh, me.
+"""Tests for auth routes: register, login, refresh, me, OAuth social login.
 
 Exercises the full auth lifecycle through the FastAPI TestClient against the
 in-memory SQLite test database seeded by ``conftest.py``.
@@ -7,9 +7,11 @@ in-memory SQLite test database seeded by ``conftest.py``.
 from __future__ import annotations
 
 import time
+from unittest.mock import AsyncMock, patch
 
 from jose import jwt
 
+from app.auth.oauth_providers import GoogleOAuthProvider, OAuthUserInfo
 from app.config.settings import settings
 from tests.conftest import auth_header, TEST_USER_IDS
 
@@ -204,3 +206,153 @@ class TestMe:
         token = jwt.encode(payload, "wrong-secret", algorithm="HS256")
         resp = client.get("/api/v1/auth/me", headers={"Authorization": f"Bearer {token}"})
         assert resp.status_code == 401
+
+
+# ── TestOAuth ─────────────────────────────────────────────────────────
+
+
+class TestOAuth:
+    """GET /auth/oauth/google/authorize and POST /auth/oauth/google/callback."""
+
+    FAKE_PROVIDER_USER_ID = "google-sub-12345"
+    FAKE_EMAIL = "oauth@example.com"
+    FAKE_AVATAR = "https://lh3.googleusercontent.com/photo.jpg"
+
+    def _patch_google(self, monkeypatch) -> None:
+        monkeypatch.setattr(settings, "GOOGLE_AUTH_CLIENT_ID", "fake-client-id")
+        monkeypatch.setattr(settings, "GOOGLE_AUTH_CLIENT_SECRET", "fake-client-secret")
+
+    def _userinfo(
+        self,
+        email: str | None = None,
+        email_verified: bool = True,
+    ) -> OAuthUserInfo:
+        return OAuthUserInfo(
+            provider_user_id=self.FAKE_PROVIDER_USER_ID,
+            email=email or self.FAKE_EMAIL,
+            email_verified=email_verified,
+            avatar_url=self.FAKE_AVATAR,
+            name="OAuth User",
+        )
+
+    def _authorize(self, client) -> str:
+        """Call /authorize and return the fresh state token."""
+        resp = client.get("/api/v1/auth/oauth/google/authorize")
+        assert resp.status_code == 200
+        return resp.json()["state"]
+
+    def _callback(self, client, state: str, userinfo: OAuthUserInfo):
+        """POST /callback with mocked provider exchange_code + get_userinfo."""
+        with (
+            patch.object(
+                GoogleOAuthProvider,
+                "exchange_code",
+                new=AsyncMock(return_value={"access_token": "google-access-tok"}),
+            ),
+            patch.object(
+                GoogleOAuthProvider,
+                "get_userinfo",
+                new=AsyncMock(return_value=userinfo),
+            ),
+        ):
+            return client.post(
+                "/api/v1/auth/oauth/google/callback",
+                json={"code": "auth-code", "state": state},
+            )
+
+    def _decode_sub(self, access_token: str) -> str:
+        return jwt.decode(
+            access_token, settings.JWT_SECRET, algorithms=[settings.JWT_ALGORITHM]
+        )["sub"]
+
+    # -- authorize --
+
+    def test_authorize_returns_url_and_state(self, client, monkeypatch) -> None:
+        self._patch_google(monkeypatch)
+        resp = client.get("/api/v1/auth/oauth/google/authorize")
+        assert resp.status_code == 200
+        data = resp.json()
+        assert "url" in data and "state" in data
+        assert "accounts.google.com" in data["url"]
+        assert len(data["state"]) > 0
+
+    def test_authorize_unconfigured_returns_503(self, client, monkeypatch) -> None:
+        monkeypatch.setattr(settings, "GOOGLE_AUTH_CLIENT_ID", "")
+        monkeypatch.setattr(settings, "GOOGLE_AUTH_CLIENT_SECRET", "")
+        resp = client.get("/api/v1/auth/oauth/google/authorize")
+        assert resp.status_code == 503
+
+    # -- callback --
+
+    def test_callback_state_mismatch_returns_401(self, client, monkeypatch) -> None:
+        self._patch_google(monkeypatch)
+        resp = client.post(
+            "/api/v1/auth/oauth/google/callback",
+            json={"code": "code", "state": "not-a-real-state"},
+        )
+        assert resp.status_code == 401
+
+    def test_callback_creates_new_user(self, client, monkeypatch) -> None:
+        """First-time Google login creates a new user and returns valid tokens."""
+        self._patch_google(monkeypatch)
+        state = self._authorize(client)
+        resp = self._callback(client, state, self._userinfo())
+
+        assert resp.status_code == 200
+        data = resp.json()
+        assert "access_token" in data and "refresh_token" in data
+        payload = jwt.decode(
+            data["access_token"], settings.JWT_SECRET, algorithms=[settings.JWT_ALGORITHM]
+        )
+        assert payload["email"] == self.FAKE_EMAIL
+
+    def test_callback_existing_oauth_link_logs_in(self, client, monkeypatch) -> None:
+        """Second Google login with the same account re-uses the existing user."""
+        self._patch_google(monkeypatch)
+        userinfo = self._userinfo()
+
+        # First login — creates user + oauth_accounts row
+        resp1 = self._callback(client, self._authorize(client), userinfo)
+        assert resp1.status_code == 200
+        sub1 = self._decode_sub(resp1.json()["access_token"])
+
+        # Second login — finds existing oauth_accounts row → same user
+        resp2 = self._callback(client, self._authorize(client), userinfo)
+        assert resp2.status_code == 200
+        sub2 = self._decode_sub(resp2.json()["access_token"])
+
+        assert sub1 == sub2
+
+    def test_callback_email_match_links_account(self, client, monkeypatch) -> None:
+        """Verified Google email matching an existing password user links the accounts."""
+        email = "link-target@example.com"
+        reg_resp = client.post(
+            "/api/v1/auth/register",
+            json={"email": email, "password": "TestPass123!"},
+        )
+        assert reg_resp.status_code == 201
+        orig_sub = self._decode_sub(reg_resp.json()["access_token"])
+
+        self._patch_google(monkeypatch)
+        state = self._authorize(client)
+        resp = self._callback(client, state, self._userinfo(email=email, email_verified=True))
+
+        assert resp.status_code == 200
+        oauth_sub = self._decode_sub(resp.json()["access_token"])
+        # OAuth login must resolve to the same user as the original registration
+        assert orig_sub == oauth_sub
+
+    def test_callback_unverified_email_conflict_returns_409(self, client, monkeypatch) -> None:
+        """Unverified Google email matching an existing account returns 409, not 500."""
+        email = "conflict@example.com"
+        reg_resp = client.post(
+            "/api/v1/auth/register",
+            json={"email": email, "password": "TestPass123!"},
+        )
+        assert reg_resp.status_code == 201
+
+        self._patch_google(monkeypatch)
+        state = self._authorize(client)
+        resp = self._callback(client, state, self._userinfo(email=email, email_verified=False))
+
+        assert resp.status_code == 409

tests/test_backup.py

@@ -1,243 +0,0 @@
-"""Tests for backup routes: upload, download, history, delete.
-
-Exercises the backup lifecycle through the FastAPI TestClient against the
-in-memory SQLite test database and moto-mocked S3 bucket.
-"""
-
-from __future__ import annotations
-
-import hashlib
-
-
-from tests.conftest import auth_header, TEST_USER_IDS
-
-
-# ── Helpers ───────────────────────────────────────────────────────────
-
-_BLOB = b"encrypted-backup-blob-opaque-bytes"
-_CHECKSUM = hashlib.sha256(_BLOB).hexdigest()
-_VERSION = 1
-_TIMESTAMP = 1700000000000  # arbitrary ms timestamp
-
-
-def _backup_headers(tier: str = "power", **overrides) -> dict[str, str]:
-    """Return auth + backup metadata headers."""
-    headers = auth_header(tier)
-    headers["X-Backup-Version"] = str(overrides.get("version", _VERSION))
-    headers["X-Backup-Timestamp"] = str(overrides.get("timestamp", _TIMESTAMP))
-    headers["X-Backup-Checksum"] = overrides.get("checksum", _CHECKSUM)
-    headers["Content-Type"] = "application/octet-stream"
-    return headers
-
-
-def _upload(client, tier="power", **overrides) -> "Response":  # noqa: F821
-    """Upload a backup blob and return the response."""
-    return client.put(
-        "/api/v1/backup",
-        content=overrides.pop("blob", _BLOB),
-        headers=_backup_headers(tier, **overrides),
-    )
-
-
-# ── TestUploadBackup ──────────────────────────────────────────────────
-
-
-class TestUploadBackup:
-    """PUT /api/v1/backup"""
-
-    def test_upload_success(self, client, s3_bucket) -> None:
-        resp = _upload(client, tier="power")
-        assert resp.status_code == 200
-        assert resp.json() == {"ok": True}
-
-    def test_upload_creates_history_entry(self, client, s3_bucket) -> None:
-        _upload(client, tier="power")
-        history = client.get(
-            "/api/v1/backup/history", headers=auth_header("power")
-        ).json()
-        assert len(history) == 1
-        assert history[0]["version"] == _VERSION
-        assert history[0]["timestamp"] == _TIMESTAMP
-        assert history[0]["checksum"] == _CHECKSUM
-
-    def test_upload_bad_checksum(self, client, s3_bucket) -> None:
-        resp = _upload(client, tier="power", checksum="0" * 64)
-        assert resp.status_code == 400
-
-    def test_upload_free_tier_blocked(self, client, s3_bucket) -> None:
-        """Free tier has backup_gb=0 → should return 402."""
-        resp = _upload(client, tier="free")
-        assert resp.status_code == 402
-
-    def test_upload_pro_tier_allowed(self, client, s3_bucket) -> None:
-        """Pro tier has backup_gb=5 → small blob succeeds."""
-        resp = _upload(client, tier="pro")
-        assert resp.status_code == 200
-
-
-# ── TestDownloadBackup ────────────────────────────────────────────────
-
-
-class TestDownloadBackup:
-    """GET /api/v1/backup"""
-
-    def test_download_latest(self, client, s3_bucket) -> None:
-        _upload(client, tier="power")
-        resp = client.get("/api/v1/backup", headers=auth_header("power"))
-        assert resp.status_code == 200
-        assert resp.content == _BLOB
-        assert resp.headers["X-Checksum"] == _CHECKSUM
-        assert resp.headers["X-Backup-Version"] == str(_VERSION)
-
-    def test_download_no_backup_returns_404(self, client, s3_bucket) -> None:
-        resp = client.get("/api/v1/backup", headers=auth_header("power"))
-        assert resp.status_code == 404
-
-    def test_download_if_modified_since_returns_304(self, client, s3_bucket) -> None:
-        """When If-Modified-Since is after the backup timestamp → 304."""
-        _upload(client, tier="power", timestamp=1700000000000)
-        resp = client.get(
-            "/api/v1/backup",
-            headers={
-                **auth_header("power"),
-                "If-Modified-Since": "Thu, 01 Jan 2099 00:00:00 GMT",
-            },
-        )
-        assert resp.status_code == 304
-
-    def test_download_if_modified_since_returns_200(self, client, s3_bucket) -> None:
-        """When If-Modified-Since is before the backup timestamp → serve blob."""
-        _upload(client, tier="power", timestamp=1700000000000)
-        resp = client.get(
-            "/api/v1/backup",
-            headers={
-                **auth_header("power"),
-                "If-Modified-Since": "Thu, 01 Jan 2000 00:00:00 GMT",
-            },
-        )
-        assert resp.status_code == 200
-        assert resp.content == _BLOB
-
-    def test_download_multiple_returns_latest(self, client, s3_bucket) -> None:
-        """When multiple backups exist, GET returns the one with the highest timestamp."""
-        _upload(client, tier="power", timestamp=1000)
-        blob2 = b"second-encrypted-backup"
-        checksum2 = hashlib.sha256(blob2).hexdigest()
-        _upload(client, tier="power", timestamp=2000, blob=blob2, checksum=checksum2)
-        resp = client.get("/api/v1/backup", headers=auth_header("power"))
-        assert resp.status_code == 200
-        assert resp.content == blob2
-
-
-# ── TestBackupHistory ─────────────────────────────────────────────────
-
-
-class TestBackupHistory:
-    """GET /api/v1/backup/history"""
-
-    def test_history_empty(self, client, s3_bucket) -> None:
-        resp = client.get("/api/v1/backup/history", headers=auth_header("power"))
-        assert resp.status_code == 200
-        assert resp.json() == []
-
-    def test_history_returns_entries(self, client, s3_bucket) -> None:
-        _upload(client, tier="power", timestamp=1000)
-        _upload(client, tier="power", timestamp=2000)
-        history = client.get(
-            "/api/v1/backup/history", headers=auth_header("power")
-        ).json()
-        assert len(history) == 2
-        # Ordered by timestamp descending
-        assert history[0]["timestamp"] == 2000
-        assert history[1]["timestamp"] == 1000
-
-    def test_history_isolated_per_user(self, client, s3_bucket) -> None:
-        """One user's backups should not appear in another user's history."""
-        _upload(client, tier="power")
-        resp = client.get("/api/v1/backup/history", headers=auth_header("team"))
-        assert resp.json() == []
-
-
-# ── TestDeleteBackup ──────────────────────────────────────────────────
-
-
-class TestDeleteBackup:
-    """DELETE /api/v1/backup/{backup_id}"""
-
-    def _get_backup_id(self, client, tier="power") -> str:
-        """Upload a backup and return its DB id from history."""
-        _upload(client, tier=tier)
-        client.get(
-            "/api/v1/backup/history", headers=auth_header(tier)
-        ).json()
-        # History returns BackupMetadata schema which doesn't have `id`.
-        # We need to look it up via a different means.
-        # Since there's only 1 backup, find via history length.
-        # Actually the schema doesn't return id — let's verify via re-download.
-        # We'll use a workaround: upload, then list history to confirm it exists,
-        # then try to delete — but we need the id...
-        # Let's check if history includes an id field.
-        # The schema is: version, timestamp, checksum, chunk_count — no id.
-        # We'll need to query the DB directly or use a known ID.
-        # For testing, we'll search history then use the DB.
-        return None  # pragma: no cover — overridden below
-
-    def test_delete_success(self, client, s3_bucket, db_session) -> None:
-        _upload(client, tier="power")
-
-        # Discover the backup_id via direct DB query
-        import asyncio
-        from sqlalchemy import select
-        from app.models import BackupMetadata
-
-        async def _get_id():
-            result = await db_session.execute(
-                select(BackupMetadata.id).where(
-                    BackupMetadata.user_id == TEST_USER_IDS["power"]
-                )
-            )
-            return result.scalar_one()
-
-        backup_id = asyncio.get_event_loop().run_until_complete(_get_id())
-
-        resp = client.delete(
-            f"/api/v1/backup/{backup_id}", headers=auth_header("power")
-        )
-        assert resp.status_code == 200
-        assert resp.json() == {"ok": True}
-
-        # History should now be empty
-        history = client.get(
-            "/api/v1/backup/history", headers=auth_header("power")
-        ).json()
-        assert history == []
-
-    def test_delete_nonexistent(self, client, s3_bucket) -> None:
-        resp = client.delete(
-            "/api/v1/backup/no-such-id", headers=auth_header("power")
-        )
-        assert resp.status_code == 404
-
-    def test_delete_other_users_backup(self, client, s3_bucket, db_session) -> None:
-        """Cannot delete another user's backup (ownership check returns 404)."""
-        _upload(client, tier="power")
-
-        import asyncio
-        from sqlalchemy import select
-        from app.models import BackupMetadata
-
-        async def _get_id():
-            result = await db_session.execute(
-                select(BackupMetadata.id).where(
-                    BackupMetadata.user_id == TEST_USER_IDS["power"]
-                )
-            )
-            return result.scalar_one()
-
-        backup_id = asyncio.get_event_loop().run_until_complete(_get_id())
-
-        # team user tries to delete power user's backup → 404
-        resp = client.delete(
-            f"/api/v1/backup/{backup_id}", headers=auth_header("team")
-        )
-        assert resp.status_code == 404

tests/test_brief_agent.py

@@ -0,0 +1,163 @@
+"""Tests for Phase 3: brief agent WS frame + REST fallback.
+
+Coverage:
+  - run_home_brief streams non-empty text (mocked _run_single_agent_stream)
+  - run_project_brief with bogus UUID → WS returns stream_end with error, no crash
+  - _build_read_tools uses read-only subset only (no mutating tools)
+  - POST /chat/brief home mode returns {response: "..."}
+  - POST /chat/brief project mode with invalid UUID → 422
+"""
+
+from __future__ import annotations
+
+import uuid
+from collections.abc import AsyncGenerator
+from typing import Any
+from unittest.mock import AsyncMock, patch
+
+import pytest
+
+from tests.conftest import TEST_USER_IDS, auth_header
+
+
+# ---------------------------------------------------------------------------
+# Helpers
+# ---------------------------------------------------------------------------
+
+_USER_ID = TEST_USER_IDS["pro"]
+_EMPTY_CONTEXT: dict[str, Any] = {"core_memory": {}}
+
+
+async def _fake_token_stream(*_args, **_kwargs) -> AsyncGenerator[tuple[str, Any], None]:
+    """Fake _run_single_agent_stream that yields two token events."""
+    yield ("token", "Hello")
+    yield ("token", " world")
+
+
+# ---------------------------------------------------------------------------
+# Unit: run_home_brief streams non-empty text
+# ---------------------------------------------------------------------------
+
+@pytest.mark.asyncio
+async def test_run_home_brief_streams_text():
+    with patch(
+        "app.core.brief_agent._run_single_agent_stream",
+        side_effect=_fake_token_stream,
+    ):
+        from app.core.brief_agent import run_home_brief
+
+        chunks: list[str] = []
+        async for event_type, data in run_home_brief(_USER_ID, _EMPTY_CONTEXT):
+            if event_type == "token":
+                chunks.append(str(data))
+
+    assert "".join(chunks) == "Hello world"
+
+
+# ---------------------------------------------------------------------------
+# Unit: run_project_brief streams text with valid UUID
+# ---------------------------------------------------------------------------
+
+@pytest.mark.asyncio
+async def test_run_project_brief_streams_text():
+    project_id = str(uuid.uuid4())
+    with patch(
+        "app.core.brief_agent._run_single_agent_stream",
+        side_effect=_fake_token_stream,
+    ):
+        from app.core.brief_agent import run_project_brief
+
+        chunks: list[str] = []
+        async for event_type, data in run_project_brief(_USER_ID, project_id, _EMPTY_CONTEXT):
+            if event_type == "token":
+                chunks.append(str(data))
+
+    assert "".join(chunks) == "Hello world"
+
+
+# ---------------------------------------------------------------------------
+# Unit: _build_read_tools uses read-only subset (no write tools)
+# ---------------------------------------------------------------------------
+
+def test_build_read_tools_read_only_subset():
+    from app.agents.note_agent import NOTE_READ_TOOLS
+    from app.agents.project_agent import PROJECT_READ_TOOLS
+    from app.agents.task_agent import TASK_READ_TOOLS
+    from app.agents.timeline_agent import TIMELINE_READ_TOOLS
+    from app.core.brief_agent import _build_read_tools
+
+    tools = _build_read_tools(_USER_ID, None)
+    tool_names = {getattr(t, "name", None) or getattr(t, "__name__", str(t)) for t in tools}
+
+    # Read-only exports must be present.
+    for read_list in (TASK_READ_TOOLS, PROJECT_READ_TOOLS, TIMELINE_READ_TOOLS, NOTE_READ_TOOLS):
+        for t in read_list:
+            name = getattr(t, "name", None) or getattr(t, "__name__", str(t))
+            assert name in tool_names, f"Read tool {name!r} missing from _build_read_tools"
+
+    # No mutating tools (e.g. create_task, update_task, delete_task).
+    mutating = {"create_task", "update_task", "delete_task", "create_project",
+                "update_project", "delete_project", "create_note", "update_note",
+                "delete_note", "memory_add", "memory_update", "memory_delete"}
+    overlap = tool_names & mutating
+    assert not overlap, f"Mutating tools in brief read-only subset: {overlap}"
+
+
+# ---------------------------------------------------------------------------
+# Integration: POST /chat/brief — home mode
+# ---------------------------------------------------------------------------
+
+@pytest.fixture(autouse=True)
+def _override_db(db_session):
+    from app.db import get_session
+    from app.main import app
+
+    async def _gen():
+        yield db_session
+
+    app.dependency_overrides[get_session] = _gen
+    yield
+    app.dependency_overrides.pop(get_session, None)
+
+
+@pytest.mark.asyncio
+async def test_rest_brief_home_returns_response(client):
+    async def _fake_home_brief(user_id, context):
+        yield ("token", "Today looks light.")
+
+    with (
+        patch("app.api.routes.chat.run_home_brief", side_effect=_fake_home_brief),
+        patch(
+            "app.api.routes.chat.MemoryMiddleware.enrich_context",
+            new=AsyncMock(return_value={}),
+        ),
+    ):
+        res = client.post(
+            "/api/v1/chat/brief",
+            json={"mode": "home"},
+            headers=auth_header("pro"),
+        )
+
+    assert res.status_code == 200
+    data = res.json()
+    assert data["response"] == "Today looks light."
+
+
+@pytest.mark.asyncio
+async def test_rest_brief_project_invalid_uuid_returns_422(client):
+    res = client.post(
+        "/api/v1/chat/brief",
+        json={"mode": "project", "project_id": "not-a-uuid"},
+        headers=auth_header("pro"),
+    )
+    assert res.status_code == 422
+
+
+@pytest.mark.asyncio
+async def test_rest_brief_project_missing_uuid_returns_422(client):
+    res = client.post(
+        "/api/v1/chat/brief",
+        json={"mode": "project"},
+        headers=auth_header("pro"),
+    )
+    assert res.status_code == 422