deep agent

- Replace langgraph create_react_agent with deepagents create_deep_agent
- Sub-agents now configured as SubAgent dicts dispatched via built-in task tool
- Stream filter updated: langgraph_node 'agent' → 'model'
- Accept both AIMessage and AIMessageChunk in stream filter
- Collector only captures write mutations (insert/update/delete)
- Add deepagents>=0.4.10 to requirements.txt

.env.example

@@ -2,7 +2,7 @@
 ENV=dev
 
 # ── Database ──────────────────────────────────────────────────────────────────
-DATABASE_URL=postgresql+asyncpg://postgres:postgres@localhost:5432/adiuvai
+DATABASE_URL=postgresql+asyncpg://postgres:postgres@localhost:5432/adiuva
 
 # ── Auth ──────────────────────────────────────────────────────────────────────
 JWT_SECRET=replace-with-a-long-random-secret
@@ -13,82 +13,31 @@ 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=
-CEREBRAS_API_KEY=
+LLM_MODEL=gpt-4o
-GROQ_API_KEY=
+LLM_ROUTER_MODEL=gpt-4o-mini
-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
 
-# ── Langfuse (leave empty to disable observability) ───────────────────────────
+# ── Vector Store ──────────────────────────────────────────────────────────────
-LANGFUSE_SECRET_KEY=
+# Pinecone is used when PINECONE_API_KEY is set; otherwise falls back to Qdrant.
-LANGFUSE_PUBLIC_KEY=
+PINECONE_API_KEY=
-# LANGFUSE_BASE_URL=https://cloud.langfuse.com        # EU (default)
+PINECONE_INDEX=adiuva
-# LANGFUSE_BASE_URL=https://us.cloud.langfuse.com     # US
+QDRANT_URL=
-# LANGFUSE_BASE_URL=http://localhost:3000             # Self-hosted
+QDRANT_API_KEY=
+# For local Qdrant (homelab): QDRANT_URL=http://qdrant:6333
 
 # ── CORS ──────────────────────────────────────────────────────────────────────
 # Comma-separated list parsed by Settings (override default if needed)

.gitea/workflows/deploy.yaml

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

.gitignore

@@ -21,16 +21,12 @@ 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

@@ -1,5 +1,793 @@
-## DEV
+# Adiuva Cloud API
-Run in DEV with command:
+
+**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
+
 ```
-uvicorn app.main:app --host 0.0.0.0 --port 8000 --reload --log-config logging.conf
+┌──────────────┐      ┌────────────────────────────────────────────────────────┐
+│  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.*

alembic/env.py

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

alembic/versions/001_initial_schema.py

@@ -1,4 +1,5 @@
-"""Initial schema: users, refresh_tokens, subscriptions.
+"""Initial schema: users, refresh_tokens, subscriptions, storage_records,
+backup_metadata, plugins, plugin_installations, plugin_reviews, revenue_events.
 
 Revision ID: 001
 Revises:
@@ -27,6 +28,18 @@ 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(
@@ -75,10 +88,122 @@ 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

@@ -0,0 +1,92 @@
+"""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] = "001"
+down_revision: Union[str, None] = "002"
 branch_labels: Union[str, Sequence[str], None] = None
 depends_on: Union[str, Sequence[str], None] = None
 

alembic/versions/005_associative_pgvector.py

@@ -1,54 +0,0 @@
-"""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

@@ -1,74 +0,0 @@
-"""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

@@ -1,38 +0,0 @@
-"""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/9a1f2d0b6c7e_deprecate_backend_agent_config_tables.py

@@ -1,92 +0,0 @@
-"""Deprecate backend agent config tables.
-
-The Electron client is now the source of truth for agent configuration
-(directory, extract targets, batch interval, custom prompt). Backend keeps
-billing checks and trigger/run logs only.
-
-Revision ID: 9a1f2d0b6c7e
-Revises: 818478c251dc
-Create Date: 2026-03-16
-"""
-
-from __future__ import annotations
-
-from typing import Sequence, Union
-
-import sqlalchemy as sa
-from alembic import op
-from sqlalchemy.dialects import postgresql
-
-revision: str = "9a1f2d0b6c7e"
-down_revision: Union[str, None] = "818478c251dc"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
-    bind = op.get_bind()
-    inspector = sa.inspect(bind)
-    existing = set(inspector.get_table_names())
-
-    if "cloud_agent_configs" in existing:
-        op.drop_index("ix_cloud_agent_configs_user_id", table_name="cloud_agent_configs")
-        op.drop_table("cloud_agent_configs")
-
-    if "local_agent_configs" in existing:
-        op.drop_index("ix_local_agent_configs_user_id", table_name="local_agent_configs")
-        op.drop_table("local_agent_configs")
-
-
-def downgrade() -> None:
-    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("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"])
-
-    op.execute(
-        """
-        DO $$ BEGIN
-            CREATE TYPE cloud_provider AS ENUM ('gmail', 'teams', 'outlook');
-        EXCEPTION WHEN duplicate_object THEN NULL;
-        END $$;
-        """
-    )
-
-    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"])

alembic/versions/a3b9c0d1e2f3_add_agent_config_to_local_agents.py

@@ -1,107 +0,0 @@
-"""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

@@ -1,56 +0,0 @@
-"""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

@@ -1,31 +0,0 @@
-"""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

@@ -1,46 +0,0 @@
-"""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

@@ -1,34 +0,0 @@
-"""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/__init__.py

@@ -1,5 +1,5 @@
-"""Expose tool modules used by deep orchestrator-worker graphs."""
+"""Agent tool modules — imported by deep_agent.py to build sub-agent graphs."""
 
-from app.agents import filesystem_agent, timeline_agent, note_agent, project_agent, task_agent
+from app.agents import timeline_agent, note_agent, project_agent, task_agent
 
-__all__ = ["filesystem_agent", "timeline_agent", "note_agent", "project_agent", "task_agent"]
+__all__ = ["timeline_agent", "note_agent", "project_agent", "task_agent"]

app/agents/client_agent.py

@@ -1,52 +0,0 @@
-"""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

@@ -1,194 +0,0 @@
-"""Filesystem agent — tools for reading local directories and files on Electron.
-
-These tools delegate to the Electron client via ``execute_on_client()`` using
-the same WS tool-call round-trip pattern as CRUD tools.  The Electron app
-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:
-    """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.
-    """
-    result = await execute_on_client(
-        action="list_directory",
-        data={"path": path},
-    )
-    entries: list[dict[str, Any]] = result.get("entries", [])
-    if not entries:
-        return f"Directory '{path}' 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 '{path}' ({len(entries)} entries):\n" + "\n".join(lines)
-
-
-@tool
-async def read_file_content(path: str) -> str:
-    """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.
-    """
-    result = await execute_on_client(
-        action="read_file_content",
-        data={"path": path},
-    )
-    content: str = result.get("content", "")
-    if not content:
-        return f"File '{path}' is empty or could not be read."
-    return content
-
-
-@tool
-async def get_file_metadata(path: str) -> str:
-    """Get metadata for a local file: size, creation date, modification date, extension.
-
-    Returns a formatted summary of the file's metadata.
-    """
-    result = await execute_on_client(
-        action="get_file_metadata",
-        data={"path": path},
-    )
-    size = result.get("size", "unknown")
-    created = result.get("createdAt", "unknown")
-    modified = result.get("modifiedAt", "unknown")
-    extension = result.get("extension", "unknown")
-    name = result.get("name", path)
-    return (
-        f"File: {name}\n"
-        f"  Extension: {extension}\n"
-        f"  Size: {size} bytes\n"
-        f"  Created: {created}\n"
-        f"  Modified: {modified}"
-    )
-
-
-FILESYSTEM_TOOLS: list[Any] = [
-    list_directory,
-    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

@@ -1,168 +0,0 @@
-"""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

@@ -1,50 +1,27 @@
-"""Note agent — Markdown note management (list, get, create, update, propose edit)."""
+"""Note agent — tool definitions for Markdown note CRUD."""
 
 from __future__ import annotations
 
-import asyncio
-import re
 from typing import Any
 
 from langchain_core.tools import tool
 
-from app.core.note_summarizer import generate_note_summary
+from app.core.llm import embed
 from app.core.ws_context import execute_on_client
 
-_UUID_RE = re.compile(
-    r"^[0-9a-fA-F]{8}-[0-9a-fA-F]{4}-[1-5][0-9a-fA-F]{3}-[89abAB][0-9a-fA-F]{3}-[0-9a-fA-F]{12}$"
-)
-
-
-def _is_uuid(value: str) -> bool:
-    return bool(_UUID_RE.match(value))
-
-
-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 with AI summaries, optionally scoped to a project by project_id.
+    """List notes, 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",
         table="notes",
-        filters={"projectId": normalized_project_id or None},
+        filters={"projectId": project_id or None},
     )
     rows = result.get("rows", [])
     if not rows:
         return "No notes found."
-    lines = [f"  - [{r['id']}] {r['title']}{_fmt_summary(r)}" for r in rows]
+    lines = [f"- {r['title']} (id: {r['id']})" for r in rows]
     return f"Found {len(rows)} note(s):\n" + "\n".join(lines)
 
 
@@ -79,10 +56,14 @@ async def create_note(
         },
     )
     row = result["row"]
-    note_id: str = row["id"]
+    # Index the note content in the vector store.
-    # Generate summary asynchronously — fire-and-forget.
+    vector = await embed(content)
-    asyncio.create_task(_refresh_summary(note_id, title, content))
+    await execute_on_client(
-    return f"Note created: '{row['title']}' (id: {note_id})."
+        action="vector_upsert",
+        data={"id": row["id"], "projectId": row.get("projectId"), "content": content},
+        vector=vector,
+    )
+    return f"Note created: '{row['title']}' (id: {row['id']})."
 
 
 @tool
@@ -91,8 +72,7 @@ async def update_note(
     title: str = "",
     content: str = "",
 ) -> str:
-    """Update an existing note directly (no approval required).
+    """Update an existing note. Only pass fields that should change.
-    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.
     """
@@ -107,63 +87,17 @@ async def update_note(
         data={"id": note_id, "updates": updates},
     )
     row = result["row"]
+    # Re-index if content changed.
     if content:
-        new_title = title or row.get("title", "")
+        vector = await embed(content)
-        asyncio.create_task(_refresh_summary(note_id, new_title, content))
+        await execute_on_client(
+            action="vector_upsert",
+            data={"id": note_id, "projectId": row.get("projectId"), "content": content},
+            vector=vector,
+        )
     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."""
@@ -171,36 +105,4 @@ 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

@@ -1,4 +1,4 @@
-"""Project agent — full lifecycle management (list, get, create, update, archive, delete)."""
+"""Project agent — tool definitions for project lifecycle CRUD."""
 
 from __future__ import annotations
 
@@ -117,17 +117,4 @@ async def delete_project(project_id: str) -> str:
     return f"Project {project_id} permanently deleted."
 
 
-PROJECT_TOOLS: list[Any] = [
-    list_projects,
-    list_all_projects,
-    get_project,
-    create_project,
-    update_project,
-    delete_project,
-]
 
-PROJECT_READ_TOOLS: list[Any] = [
-    list_projects,
-    list_all_projects,
-    get_project,
-]

app/agents/relations_agent.py

@@ -1,63 +0,0 @@
-"""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

@@ -1,23 +1,14 @@
-"""Task agent — full CRUD for tasks and task comments."""
+"""Task agent — tool definitions for task and task comment CRUD."""
 
 from __future__ import annotations
 
 from datetime import datetime, timezone
-import re
 from typing import Any
 
 from langchain_core.tools import tool
 
 from app.core.ws_context import execute_on_client
 
-_UUID_RE = re.compile(
-    r"^[0-9a-fA-F]{8}-[0-9a-fA-F]{4}-[1-5][0-9a-fA-F]{3}-[89abAB][0-9a-fA-F]{3}-[0-9a-fA-F]{12}$"
-)
-
-
-def _is_uuid(value: str) -> bool:
-    return bool(_UUID_RE.match(value))
-
 
 # ── Task tools ────────────────────────────────────────────────────────
 
@@ -26,141 +17,31 @@ def _is_uuid(value: str) -> bool:
 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 with optional filters. Returns up to `limit` results (default 50).
+    """List tasks, optionally filtered by project_id, status (todo|in_progress|done),
-
+    a search string, or an order_by field name (dueDate|priority|createdAt)."""
-    project_id: UUID of the project to scope results to.
+    result = await execute_on_client(
-    status: filter by status — todo | in_progress | done.
+        action="select",
-    priority: filter by priority — high | medium | low.
+        table="tasks",
-    assignee: substring to match against assignee names. OMIT unless the user explicitly
+        filters={
-              names a person or refers to themselves ("my tasks", "assigned to me", "mine").
+            "projectId": project_id or None,
-              Do NOT default to the current user.
+            "status": status or None,
-    search: substring search across title and description.
+            "search": search or None,
-    order_by: sort field — dueDate | priority | createdAt | completedAt.
+            "orderBy": order_by or None,
-    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 ""
-    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']}, "
+        f"- {r['title']} (status: {r['status']}, priority: {r['priority']}, id: {r['id']})"
-        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,
@@ -171,6 +52,7 @@ async def create_task(
     due_date: int = 0,
     project_id: str = "",
     is_ai_suggested: int = 0,
+    is_approved: int = 0,
 ) -> str:
     """Create a new task.
     title: task title (required)
@@ -181,8 +63,7 @@ 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
-
+    is_approved: 0 until the user confirms; 1 when confirmed
-    completedAt is set automatically when status is 'done'.
     """
     result = await execute_on_client(
         action="insert",
@@ -196,12 +77,13 @@ async def create_task(
             "dueDate": due_date or None,
             "projectId": project_id or None,
             "isAiSuggested": is_ai_suggested,
+            "isApproved": is_approved,
         },
     )
     row = result["row"]
     return (
         f"Task created: '{row['title']}' "
-        f"(id: {row['id']}, status: {row['status']}, priority: {row['priority']}, projectId: {row.get('projectId')})"
+        f"(id: {row['id']}, status: {row['status']}, priority: {row['priority']})"
     )
 
 
@@ -215,14 +97,12 @@ async def update_task(
     assignees: str = "",
     due_date: int = -1,
     project_id: str = "",
+    is_approved: int = -1,
 ) -> str:
     """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
-
+    is_approved: -1 means unchanged; 0 or 1 sets the value
-    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:
@@ -239,13 +119,15 @@ async def update_task(
         updates["dueDate"] = due_date or None
     if project_id:
         updates["projectId"] = project_id
+    if is_approved != -1:
+        updates["isApproved"] = is_approved
     result = await execute_on_client(
         action="update",
         table="tasks",
         data={"id": task_id, "updates": updates},
     )
     row = result["row"]
-    return f"Task updated: '{row['title']}' (id: {row['id']}, status: {row['status']}, projectId: {row.get('projectId')})"
+    return f"Task updated: '{row['title']}' (id: {row['id']}, status: {row['status']})"
 
 
 @tool
@@ -256,36 +138,21 @@ async def delete_task(task_id: str) -> str:
 
 
 @tool
-async def list_tasks_due_today(user_timezone: str = "UTC", include_done: bool = False) -> str:
+async def list_tasks_due_today() -> str:
-    """List all tasks whose due date falls on today's date.
+    """List all tasks whose due date falls on today's date."""
-
+    now = datetime.now(tz=timezone.utc)
-    user_timezone: IANA timezone name (e.g. 'Europe/Rome', 'America/New_York').
+    start_ms = int(datetime(now.year, now.month, now.day, tzinfo=timezone.utc).timestamp() * 1000)
-    Always pass the user's timezone so 'today' is computed in their local time.
+    end_ms = start_ms + 86_400_000 - 1  # last ms of today
-    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=filters,
+        filters={"dueDateFrom": start_ms, "dueDateTo": end_ms},
     )
     rows = result.get("rows", [])
     if not rows:
         return "No tasks are due today."
     lines = [
-        f"- {r['title']} (priority: {r['priority']}, status: {r['status']}, "
+        f"- {r['title']} (priority: {r['priority']}, status: {r['status']}, id: {r['id']})"
-        f"projectId: {r.get('projectId')}, id: {r['id']})"
         for r in rows
     ]
     return f"Tasks due today ({len(rows)}):\n" + "\n".join(lines)
@@ -321,11 +188,8 @@ async def add_task_comment(task_id: str, author: str, content: str) -> str:
         table="taskComments",
         data={"taskId": task_id, "author": author, "content": content},
     )
-    row = result.get("row", {})
+    row = result["row"]
-    row_author = row.get("author", author)
+    return f"Comment added by {row['author']} on task {row['taskId']} (comment id: {row['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})."
 
 
 @tool
@@ -335,24 +199,4 @@ async def delete_task_comment(comment_id: str) -> str:
     return f"Comment {comment_id} deleted."
 
 
-# ── Agent ─────────────────────────────────────────────────────────────
 
-
-TASK_TOOLS: list[Any] = [
-    list_tasks,
-    count_tasks,
-    create_task,
-    update_task,
-    delete_task,
-    list_tasks_due_today,
-    list_task_comments,
-    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

@@ -1,147 +1,27 @@
-"""Timeline agent — project milestone management (list, create, update, delete)."""
+"""Timeline agent — tool definitions for project milestone CRUD."""
 
 from __future__ import annotations
 
-import re
-from datetime import datetime, timezone
 from typing import Any
 
 from langchain_core.tools import tool
 
 from app.core.ws_context import execute_on_client
 
-_UUID_RE = re.compile(
-    r"^[0-9a-fA-F]{8}-[0-9a-fA-F]{4}-[1-5][0-9a-fA-F]{3}-[89abAB][0-9a-fA-F]{3}-[0-9a-fA-F]{12}$"
-)
-
-
-def _is_uuid(value: str) -> bool:
-    return bool(_UUID_RE.match(value))
-
 
 @tool
-async def list_timelines(
+async def list_timelines(project_id: str = "") -> str:
-    project_id: str = "",
+    """List timelines. Provide project_id to scope to a specific project."""
-    type: str = "",
+    result = await execute_on_client(
-    is_completed: int = -1,
+        action="select",
-    is_ai_suggested: int = -1,
+        table="timelines",
-    order_by: str = "",
+        filters={"projectId": project_id or None},
-    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."
+        return "No timelines found."
-    lines = [
+    lines = [f"- {r['title']} (date: {r['date']}, id: {r['id']})" for r in rows]
-        f"- {r['title']} (date: {r['date']}, type: {r.get('type')}, "
+    return f"Found {len(rows)} timeline(s):\n" + "\n".join(lines)
-        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 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 ""
-    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
@@ -149,19 +29,15 @@ async def create_timeline(
     project_id: str,
     title: str,
     date: int,
-    type: str = "milestone",
-    is_completed: int = 0,
     is_ai_suggested: int = 0,
+    is_approved: int = 0,
 ) -> str:
-    """Create a project timeline event.
+    """Create a project timeline (milestone).
     project_id: REQUIRED UUID of the parent project
-    title: descriptive name for the event
+    title: descriptive name for the milestone
-    date: Unix timestamp in milliseconds for the event date
+    date: Unix timestamp in milliseconds
-    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
-
+    is_approved: 0 until the user confirms
-    completedAt is set automatically when is_completed is 1.
     """
     result = await execute_on_client(
         action="insert",
@@ -170,13 +46,12 @@ async def create_timeline(
             "projectId": project_id,
             "title": title,
             "date": date,
-            "type": type,
-            "isCompleted": is_completed,
             "isAiSuggested": is_ai_suggested,
+            "isApproved": is_approved,
         },
     )
     row = result["row"]
-    return f"Timeline event created: '{row['title']}' (id: {row['id']}, date: {row['date']}, type: {row.get('type')})"
+    return f"Timeline created: '{row['title']}' (id: {row['id']}, date: {row['date']})"
 
 
 @tool
@@ -184,87 +59,34 @@ async def update_timeline(
     timeline_id: str,
     title: str = "",
     date: int = -1,
-    is_completed: int = -1,
+    is_approved: int = -1,
 ) -> str:
-    """Update a timeline event. Only pass fields that should change.
+    """Update a timeline. Only pass fields that should change.
-    timeline_id: UUID of the event (required)
+    timeline_id: UUID of the timeline (required)
     date: -1 means unchanged; any other value sets the new date (ms timestamp)
-    is_completed: 0 = mark incomplete, 1 = mark complete, -1 = unchanged
+    is_approved: -1 means unchanged; 0 or 1 sets the approval state
-
-    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:
+    if is_approved != -1:
-        updates["isCompleted"] = is_completed
+        updates["isApproved"] = is_approved
     result = await execute_on_client(
         action="update",
         table="timelines",
         data={"id": timeline_id, "updates": updates},
     )
     row = result["row"]
-    return f"Timeline event updated: '{row['title']}' (id: {row['id']})"
+    return f"Timeline updated: '{row['title']}' (id: {row['id']})"
 
 
 @tool
 async def delete_timeline(timeline_id: str) -> str:
-    """Delete a timeline event permanently by its UUID."""
+    """Delete a timeline permanently by its UUID."""
     await execute_on_client(action="delete", table="timelines", data={"id": timeline_id})
-    return f"Timeline event {timeline_id} deleted."
+    return f"Timeline {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

@@ -55,49 +55,23 @@ async def get_current_user(
         raise credentials_exc
 
     # Live tier lookup — subscription row is the authoritative source.
-    # In dev, fall back to 'power' (unlimited) so quota limits don't
-    # block local development when no Stripe subscription exists.
     from app.models import Subscription, User  # 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 "free"
-    tier: str = result.scalar_one_or_none() or default_tier
 
-    # Fetch name/surname/avatar_url/onboarding_completed_at/password_hash from user row.
+    # Fetch name/surname from user row.
     user_result = await db.execute(
-        select(
+        select(User.name, User.surname).where(User.id == user_id)
-            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

@@ -8,7 +8,8 @@ that could reveal server-side prompt IP:
   - Internal reasoning markers (<thinking>, <reasoning>, [INST], …)
   - Exact-match known prompt fingerprints
 
-The middleware only activates for paths under /api/v1/chat.
+Binary responses (storage blobs, backup data) are never touched — 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

@@ -1,71 +1,74 @@
-"""Chatbot Journey — WS-based guided conversation to build an AgentConfig.
+"""Chatbot Journey endpoints — guided conversation to build an agent prompt_template.
 
-The journey is driven entirely through WebSocket frames (no REST endpoints).
+Endpoints:
-The device WS handler dispatches ``journey_start`` and ``journey_message``
+  POST /agents/journey/start    — start a new journey session
-frames to the functions exported here.
+  POST /agents/journey/message  — continue the conversation
+
+Sessions are stored in-memory with a 30-minute TTL.  Stale entries are
+cleaned up lazily on access.  Upgrade to Redis for multi-instance deployments.
 
 Journey flow:
-  1. FE sends ``journey_start`` frame with basic agent info (directory,
+  1. Client sends ``{ agent_type, agent_id? }`` to ``/start``.
-     data_types, schedule).
+  2. Server creates a session, calls the LLM with a contextual system prompt,
-  2. Server creates an in-memory session, sets up a WS executor so the
+     and returns the first question.
-     setup LLM can use file-system tools, does a first directory scrape,
+  3. Client sends follow-up messages to ``/message``.
-     and sends back a ``journey_reply`` with the first question.
+  4. After 3-5 turns the LLM wraps up by emitting a ``prompt_template`` block
-  3. FE sends ``journey_message`` frames for each user reply.
+     delimited by ``PROMPT_TEMPLATE_START`` / ``PROMPT_TEMPLATE_END``.
-  4. Server appends the user message, calls the LLM (which may read files
+  5. Server parses the block, sets ``done=True``, and returns the template.
-     via tools), and sends back a ``journey_reply``.
+
-  5. After 3-5 turns the LLM wraps up by emitting an ``AgentConfig`` JSON
+The ``prompt_template`` from the final response is meant to be stored in
-     block delimited by ``AGENT_CONFIG_START`` / ``AGENT_CONFIG_END``.
+``LocalAgentConfig.prompt_template`` or ``CloudAgentConfig.prompt_template``
-  6. Server parses and validates the JSON with Pydantic, sends
+by the Electron client (via the agent CRUD endpoints).
-     ``journey_reply`` with ``done=True`` and the serialised config.
-     FE stores it locally.
 """
 
 from __future__ import annotations
 
-import json
 import logging
 import time
 import uuid
 from dataclasses import dataclass, field
 from typing import Any
 
-from langchain_core.messages import AIMessage, HumanMessage, SystemMessage, ToolMessage
+from fastapi import APIRouter, Depends, HTTPException, status
+from langchain_core.messages import AIMessage, HumanMessage, SystemMessage
+from sqlalchemy import select
+from sqlalchemy.ext.asyncio import AsyncSession
 
-from app.agents.filesystem_agent import make_directory_tools
+from app.api.deps import get_current_user
-from app.core.langfuse_client import compile_prompt, extract_usage, get_langfuse, get_prompt_or_fallback, langfuse_context
+from app.core.llm import get_llm
-from app.core.llm import get_agent_llm, model_for_agent
+from app.db import get_session
-from app.schemas import AgentConfig
+from app.models import CloudAgentConfig, LocalAgentConfig
+from app.schemas import (
+    JourneyMessageRequest,
+    JourneyResponse,
+    JourneyStartRequest,
+    UserProfile,
+)
 
 logger = logging.getLogger(__name__)
 
+router = APIRouter(prefix="/agents/journey", tags=["agents"])
+
 # ── Session TTL ───────────────────────────────────────────────────────────
 
 _SESSION_TTL_SECONDS: int = 1800  # 30 minutes
 
-# Sentinel strings used to delimit the LLM-produced AgentConfig JSON.
+# Sentinel strings used to delimit the LLM-produced prompt_template.
-_CONFIG_START = "AGENT_CONFIG_START"
+_TEMPLATE_START = "PROMPT_TEMPLATE_START"
-_CONFIG_END = "AGENT_CONFIG_END"
+_TEMPLATE_END = "PROMPT_TEMPLATE_END"
 
-# Minimum turns before we consider nudging the LLM to wrap up.
+# Maximum number of conversation turns before the LLM is nudged to wrap up.
-_MIN_TURNS_BEFORE_NUDGE: int = 3
+_MAX_TURNS: int = 5
-# Hard cap to avoid infinite loops (safety net, not the primary stopping criterion).
-_MAX_TURNS: int = 15
-# Max tool-calling steps per LLM invocation.
-_MAX_TOOL_STEPS: int = 6
 
 # ── In-memory session store ───────────────────────────────────────────────
 
 
 @dataclass
-class JourneySession:
+class _JourneySession:
     session_id: str
     user_id: str
     agent_type: str  # "local" | "cloud"
-    directory: str
-    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:
@@ -73,182 +76,103 @@ class JourneySession:
 
 
 # session_id → session
-_sessions: dict[str, JourneySession] = {}
+_sessions: dict[str, _JourneySession] = {}
 
 
-def get_journey_session(session_id: str, user_id: str) -> JourneySession | None:
+def _get_session(session_id: str, user_id: str) -> _JourneySession:
-    """Retrieve session; return None on missing, expired, or wrong owner."""
+    """Retrieve session; raise 404 on missing, expired, or wrong owner."""
     s = _sessions.get(session_id)
     if s is None or s.is_expired():
         _sessions.pop(session_id, None)
-        return None
+        raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail="Journey session not found or expired")
     if s.user_id != user_id:
-        return None
+        raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail="Journey session not found or expired")
     return s
 
 
-# ── System prompt ─────────────────────────────────────────────────────────
+# ── System prompt builder ─────────────────────────────────────────────────
 
-_JOURNEY_SYSTEM_PROMPT = """\
+_LOCAL_PREAMBLE = """\
+What kind of files are in the directories you want to monitor? \
+(for example: emails saved as .eml, documents in .pdf or .txt, markdown notes, etc.)"""
+
+_CLOUD_PREAMBLE = """\
+What kind of emails or messages should I look for? \
+(for example: client communications, invoices, meeting notes, project updates, etc.)"""
+
+_SYSTEM_PROMPT_TEMPLATE = """\
 You are a friendly assistant helping a freelancer configure a data-extraction agent.
-Your job is to understand what files the user has in their directory and produce a
+Your job is to understand exactly what data the user wants to extract from their {source_description} \
-structured AgentConfig JSON that the extraction agent will use as its instruction set.
+and produce a detailed prompt_template that a separate AI will use as its instruction set.
 
-You have access to file-system tools to explore the user's directory:
+Ask concise, focused questions one at a time.  Cover these topics (not necessarily in this order):
-- list_directory: see folder structure and file names
+  1. The type and format of the source content.
-- read_file_content: peek at a file's content
+  2. Which data types to extract: tasks, notes, timelines, and/or projects.
-- get_file_metadata: check file size, extension, dates
+  3. How fields should be mapped (e.g. email subject → task title).
+  4. Priority or status rules (e.g. "urgent" keyword → high priority).
+  5. Any special handling, date extraction, or exclusions.
 
-The user's configured directory is: {directory}
+After 3-5 questions (when you have enough information), output the final prompt_template between \
-Target data types: {data_types}
+these exact markers on their own lines:
 
-## Your process
+{template_start}
+<the complete extraction prompt here>
+{template_end}
 
-### Step 1 — Explore the directory
+The prompt_template must be a self-contained instruction for an AI that receives a document/email/message \
-Use list_directory and read_file_content to understand what types of files are present
+and must return a JSON array of records in this shape:
-(HTML emails, plain-text documents, CSVs, etc.).
+  [{{ "table": "<tasks|notes|timelines|projects>", "data": {{ <field: value> }} }}, ...]
-
-### 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
-
-### 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)
-
-### 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
 
+Rules for the generated template:
+  - Be explicit about field names (camelCase: title, status, priority, dueDate, projectId, content, etc.).
+  - Include concrete examples of mappings.
+  - Mention that Electron adds id/createdAt/updatedAt automatically.
+  - Set isAiSuggested: true and isApproved: false on every record.
 {existing_section}\
-Begin by exploring the directory, then ask your first question.\
+Do not ask more than {max_turns} questions total. Start with your first question now.\
 """
 
 
-def _build_system_prompt(
+def _build_system_prompt(agent_type: str, existing_template: str | None) -> str:
-    directory: str,
+    source_description = (
-    data_types: list[str],
+        "files in local directories" if agent_type == "local" else "emails and messages from cloud providers"
-    existing_config: str | None = None,
+    )
-) -> tuple[str, Any]:
-    """Return ``(compiled_system_prompt, langfuse_prompt_obj_or_None)``."""
     existing_section = (
-        "\nThe user already has the following AgentConfig — refine it based on their answers:\n"
+        f"\nThe user already has the following prompt_template — refine it based on their answers:\n"
-        f"```json\n{existing_config}\n```\n"
+        f"---\n{existing_template}\n---\n"
-        if existing_config
+        if existing_template
         else ""
     )
-    template, prompt_obj = get_prompt_or_fallback(
+    return _SYSTEM_PROMPT_TEMPLATE.format(
-        "journey_system", _JOURNEY_SYSTEM_PROMPT
+        source_description=source_description,
-    )
+        template_start=_TEMPLATE_START,
-    compiled = compile_prompt(
+        template_end=_TEMPLATE_END,
-        template,
-        prompt_obj,
-        directory=directory,
-        data_types=", ".join(data_types),
-        data_types_json=json.dumps(data_types),
-        config_start=_CONFIG_START,
-        config_end=_CONFIG_END,
         existing_section=existing_section,
+        max_turns=_MAX_TURNS,
     )
-    return compiled, prompt_obj
 
 
-# ── AgentConfig extraction ────────────────────────────────────────────────
+def _first_question(agent_type: str) -> str:
+    return _LOCAL_PREAMBLE if agent_type == "local" else _CLOUD_PREAMBLE
 
 
-def _extract_agent_config(text: str) -> str | None:
+# ── Template extraction ───────────────────────────────────────────────────
-    """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.
+def _extract_template(text: str) -> str | None:
-    """
+    """Return the text between PROMPT_TEMPLATE_START and PROMPT_TEMPLATE_END, or None."""
-    if _CONFIG_START not in text or _CONFIG_END not in text:
+    if _TEMPLATE_START not in text or _TEMPLATE_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 ───────────────────────────────────────────
+# ── LLM call ─────────────────────────────────────────────────────────────
 
 
-def _as_text(content: Any) -> str:
+async def _call_llm(system_prompt: str, history: list[dict[str, Any]]) -> str:
-    if content is None:
+    """Build LangChain messages from history and invoke the LLM."""
-        return ""
-    if isinstance(content, str):
-        return content
-    if isinstance(content, list):
-        parts: list[str] = []
-        for item in content:
-            if isinstance(item, str):
-                parts.append(item)
-            elif isinstance(item, dict):
-                text = item.get("text")
-                if isinstance(text, str):
-                    parts.append(text)
-        return "".join(parts)
-    return str(content)
-
-
-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":
@@ -256,258 +180,138 @@ async def _call_llm_with_tools(
         else:
             messages.append(AIMessage(content=turn["content"]))
 
-    llm = get_agent_llm("setup", temperature=0.4)
+    llm = get_llm(model=None, temperature=0.4)
-    llm_with_tools = llm.bind_tools(tools)
+    response = await llm.ainvoke(messages)
-    tool_map = {tool_def.name: tool_def for tool_def in tools}
+    return response.content  # type: ignore[return-value]
-
-    _lf_ctx = langfuse_context(user_id=user_id or None, session_id=session_id or None)
-    _lf_ctx.__enter__()
-
-    _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
-
-    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)
-
-            resp_text = _as_text(response.content)
-
-            # 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
-
-            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) ──────────────────────────
+# ── Existing-config loader ────────────────────────────────────────────────
 
 
-async def handle_journey_start(
+async def _load_existing_template(
+    agent_id: str,
     user_id: str,
-    frame: dict[str, Any],
+    db: AsyncSession,
-) -> dict[str, Any]:
+) -> str | None:
-    """Handle a ``journey_start`` WS frame.
+    """Return the prompt_template of an existing agent config, or None."""
+    # Try local first, then cloud.
+    local_result = await db.execute(
+        select(LocalAgentConfig).where(
+            LocalAgentConfig.id == agent_id,
+            LocalAgentConfig.user_id == user_id,
+        )
+    )
+    local = local_result.scalar_one_or_none()
+    if local is not None:
+        return local.prompt_template
 
-    Creates a session, runs the setup LLM with directory exploration,
+    cloud_result = await db.execute(
-    and returns the ``journey_reply`` payload.
+        select(CloudAgentConfig).where(
+            CloudAgentConfig.id == agent_id,
+            CloudAgentConfig.user_id == user_id,
+        )
+    )
+    cloud = cloud_result.scalar_one_or_none()
+    return cloud.prompt_template if cloud is not None else None
+
+
+# ── Routes ────────────────────────────────────────────────────────────────
+
+
+@router.post("/start", response_model=JourneyResponse, status_code=status.HTTP_200_OK)
+async def start_journey(
+    body: JourneyStartRequest,
+    current_user: UserProfile = Depends(get_current_user),
+    db: AsyncSession = Depends(get_session),
+) -> JourneyResponse:
+    """Start a new Chatbot Journey session.
+
+    If ``agent_id`` is provided the session is pre-seeded with the existing
+    agent's ``prompt_template`` so the user can refine it.
     """
-    agent_type = frame.get("agent_type", "local")
+    # Load existing template (may be None).
-    directory = frame.get("directory", "")
+    existing_template: str | None = None
-    data_types = frame.get("data_types", [])
+    if body.agent_id:
-    existing_config = frame.get("existing_config")
+        existing_template = await _load_existing_template(body.agent_id, current_user.id, db)
+        # If agent_id was given but not found, proceed without seeding (don't 404 —
+        # the user may be starting a fresh journey for a not-yet-persisted config).
 
-    # Use the session_id provided by the FE so the reply matches the
+    system_prompt = _build_system_prompt(body.agent_type, existing_template)
-    # listener key; fall back to a generated one if absent.
+    first_question = _first_question(body.agent_type)
-    session_id = frame.get("session_id") or str(uuid.uuid4())
-    system_prompt, langfuse_prompt = _build_system_prompt(directory, data_types, existing_config)
 
-    session = JourneySession(
+    session_id = str(uuid.uuid4())
+    session = _JourneySession(
         session_id=session_id,
-        user_id=user_id,
+        user_id=current_user.id,
-        agent_type=agent_type,
+        agent_type=body.agent_type,
-        directory=directory,
+        # Seed history with the AI's first question so it stays consistent.
-        data_types=data_types,
+        history=[{"role": "assistant", "content": first_question}],
-        system_prompt=system_prompt,
-        langfuse_prompt=langfuse_prompt,
     )
-
+    # Store the system prompt inside the session for reuse in /message.
-    # Seed with an initial user message — some providers require at least one
+    session.__dict__["_system_prompt"] = system_prompt  # type: ignore[index]
-    # 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=make_directory_tools(directory),
-        user_id=user_id,
-        session_id=session_id,
-        langfuse_prompt=langfuse_prompt,
-    )
-
-    session.history.extend(seed_history)
-    session.history.append({"role": "assistant", "content": ai_reply})
     _sessions[session_id] = session
 
-    logger.info(
+    logger.info("Journey session %s started for user %s (agent_type=%s)", session_id, current_user.id, body.agent_type)
-        "agent_setup: journey session %s started for user %s (directory=%s)",
+    return JourneyResponse(session_id=session_id, message=first_question, done=False)
-        session_id,
-        user_id,
-        directory,
-    )
-
-    # 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(_CONFIG_START)].strip()
-            or "Here is your agent configuration. You can save it or continue refining."
-        )
-        _sessions.pop(session_id, None)
-
-    return {
-        "type": "journey_reply",
-        "session_id": session_id,
-        "message": display_message,
-        "done": done,
-        "agent_config": agent_config,
-    }
 
 
-async def handle_journey_message(
+@router.post("/message", response_model=JourneyResponse, status_code=status.HTTP_200_OK)
-    user_id: str,
+async def send_journey_message(
-    frame: dict[str, Any],
+    body: JourneyMessageRequest,
-) -> dict[str, Any]:
+    current_user: UserProfile = Depends(get_current_user),
-    """Handle a ``journey_message`` WS frame.
+    db: AsyncSession = Depends(get_session),
+) -> JourneyResponse:
+    """Send a message in an existing Chatbot Journey session.
 
-    Appends the user message, calls the LLM, and returns the
+    The server appends the user's message to the conversation history,
-    ``journey_reply`` payload.
+    calls the LLM, and appends the AI reply.  When the LLM wraps up with a
+    ``prompt_template`` block the response includes ``done=True`` and the
+    extracted template.
     """
-    session_id = frame.get("session_id", "")
+    session = _get_session(body.session_id, current_user.id)
-    message = frame.get("message", "")
+    system_prompt: str = session.__dict__.get("_system_prompt", _build_system_prompt(session.agent_type, None))  # type: ignore[assignment]
 
-    session = get_journey_session(session_id, user_id)
+    # Append user turn to history.
-    if session is None:
+    session.history.append({"role": "user", "content": body.message})
-        return {
-            "type": "journey_reply",
-            "session_id": session_id,
-            "message": "Journey session not found or expired. Please start a new setup.",
-            "done": True,
-            "agent_config": None,
-        }
 
-    # Append user turn.
+    # Call the LLM with the full conversation so far.
-    session.history.append({"role": "user", "content": message})
+    ai_reply = await _call_llm(system_prompt, session.history)
-
-    # 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=session_tools,
-        user_id=session.user_id,
-        session_id=session_id,
-        langfuse_prompt=session.langfuse_prompt,
-    )
 
+    # Append AI turn.
     session.history.append({"role": "assistant", "content": ai_reply})
 
-    # Check if the LLM produced the final config.
+    # Check if the LLM produced the final template.
-    agent_config = _extract_agent_config(ai_reply)
+    prompt_template = _extract_template(ai_reply)
-    done = agent_config is not None
+    done = prompt_template is not None
-
-    # 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"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=session_tools,
-                user_id=session.user_id,
-                session_id=session_id,
-                langfuse_prompt=session.langfuse_prompt,
-            )
-            session.history.append({"role": "assistant", "content": nudge_reply})
-
-            agent_config = _extract_agent_config(nudge_reply)
-            if agent_config is not None:
-                done = True
-                ai_reply = nudge_reply
 
+    # Strip the sentinel markers from the message shown to the user.
     display_message = ai_reply
     if done:
         display_message = (
-            ai_reply[: ai_reply.index(_CONFIG_START)].strip()
+            ai_reply[: ai_reply.index(_TEMPLATE_START)].strip()
-            if _CONFIG_START in ai_reply
+            or "Here is your agent configuration. You can save it or continue refining."
-            else "Here is your agent configuration. You can save it or continue refining."
         )
-        _sessions.pop(session_id, None)
-        logger.info("agent_setup: journey session %s completed for user %s", session_id, user_id)
 
-    return {
+    if done:
-        "type": "journey_reply",
+        logger.info("Journey session %s completed for user %s", body.session_id, current_user.id)
-        "session_id": session_id,
+        # Clean up the session immediately on completion.
-        "message": display_message,
+        _sessions.pop(body.session_id, None)
-        "done": done,
+    else:
-        "agent_config": agent_config,
+        # Nudge the LLM to wrap up after max turns.
-    }
+        turns = sum(1 for t in session.history if t["role"] == "user")
+        if turns >= _MAX_TURNS:
+            # Add a system-level nudge as a hidden user message.
+            session.history.append({
+                "role": "user",
+                "content": (
+                    "[System: You have enough information. Please generate the final "
+                    f"prompt_template now, wrapped in {_TEMPLATE_START} / {_TEMPLATE_END} markers.]"
+                ),
+            })
+
+    return JourneyResponse(
+        session_id=body.session_id,
+        message=display_message,
+        done=done,
+        prompt_template=prompt_template,
+    )

app/api/routes/agents.py

@@ -1,45 +1,48 @@
-"""Agent routes.
+"""Agent CRUD routes: local directory agents and cloud connector agents.
 
-Backend responsibilities are intentionally minimal:
+Endpoints:
-    GET  /agents/catalog         — static catalog for UI display
+  GET    /agents/catalog            — hardcoded agent type catalog
-    POST /agents/can-create      — billing eligibility check
+  GET    /agents/local              — list user's local agent configs
-    POST /agents/trigger         — trigger a local agent run
+  POST   /agents/local              — create local agent (tier-gated)
-
+  PUT    /agents/local/{agent_id}   — partial update (ownership check)
-Agent configuration is owned by the Electron app and is not persisted
+  DELETE /agents/local/{agent_id}   — delete + cascade run logs
-in backend agent-config tables.
+  GET    /agents/cloud              — list user's cloud agent configs
+  POST   /agents/cloud              — create cloud agent (tier-gated)
+  PUT    /agents/cloud/{agent_id}   — partial update (ownership check)
+  DELETE /agents/cloud/{agent_id}   — delete + cascade run logs
+  GET    /agents/runs               — paginated run logs (agent_id, page, limit)
+  POST   /agents/{agent_id}/run     — manual trigger stub (dispatch in Step 3.4)
 """
 
 from __future__ import annotations
 
 import asyncio
-import logging
+from datetime import datetime
-import uuid
+from typing import Any
-from datetime import datetime, timezone
-
-from fastapi import APIRouter, Depends, HTTPException, status
-from sqlalchemy import func, select
-from sqlalchemy.ext.asyncio import AsyncSession
 
+from fastapi import APIRouter, Depends, HTTPException, Query, status
 from pydantic import BaseModel
+from sqlalchemy import func, or_, select
+from sqlalchemy.ext.asyncio import AsyncSession
 
 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.agent_runner import run_cloud_agent, 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.models import AgentRunLog, CloudAgentConfig, LocalAgentConfig
 from app.schemas import (
     AgentCatalogItem,
-    AgentCreationCheckRequest,
-    AgentCreationCheckResponse,
     AgentRunLogResponse,
-    AgentTriggerRequest,
+    CloudAgentConfigCreate,
+    CloudAgentConfigResponse,
+    CloudAgentConfigUpdate,
+    LocalAgentConfigCreate,
+    LocalAgentConfigResponse,
+    LocalAgentConfigUpdate,
     UserProfile,
 )
 
-logger = logging.getLogger(__name__)
-
 router = APIRouter(prefix="/agents", tags=["agents"])
 
 
@@ -53,21 +56,39 @@ def _dt_ms_opt(dt: datetime | None) -> int | None:
     return int(dt.timestamp() * 1000) if dt else None
 
 
-def _to_data_types(values: list[str]) -> list[str]:
+# ── Model → schema converters ─────────────────────────────────────────
-    normalize = {
+
-        "task": "tasks",           "tasks": "tasks",
+def _to_local_response(a: LocalAgentConfig) -> LocalAgentConfigResponse:
-        "note": "notes",           "notes": "notes",
+    return LocalAgentConfigResponse(
-        "timeline": "timelines",   "timelines": "timelines",   "timelineEvents": "timelines",
+        id=a.id,
-        "project": "projects",     "projects": "projects",
+        name=a.name,
-    }
+        device_id=a.device_id,
-    seen: set[str] = set()
+        directory_paths=a.directory_paths,
-    result: list[str] = []
+        data_types=a.data_types,
-    for v in values:
+        prompt_template=a.prompt_template,
-        mapped = normalize.get(v)
+        file_extensions=a.file_extensions,
-        if mapped and mapped not in seen:
+        schedule_cron=a.schedule_cron,
-            seen.add(mapped)
+        enabled=a.enabled,
-            result.append(mapped)
+        last_run_at=_dt_ms_opt(a.last_run_at),
-    return result
+        created_at=_dt_ms(a.created_at),
+        updated_at=_dt_ms(a.updated_at),
+    )
+
+
+def _to_cloud_response(a: CloudAgentConfig) -> CloudAgentConfigResponse:
+    return CloudAgentConfigResponse(
+        id=a.id,
+        provider=a.provider,  # type: ignore[arg-type]
+        name=a.name,
+        data_types=a.data_types,
+        prompt_template=a.prompt_template,
+        schedule_cron=a.schedule_cron,
+        filter_config=a.filter_config,
+        enabled=a.enabled,
+        last_run_at=_dt_ms_opt(a.last_run_at),
+        created_at=_dt_ms(a.created_at),
+        updated_at=_dt_ms(a.updated_at),
+    )
 
 
 def _to_run_log_response(log: AgentRunLog) -> AgentRunLogResponse:
@@ -84,42 +105,77 @@ def _to_run_log_response(log: AgentRunLog) -> AgentRunLogResponse:
     )
 
 
-def _enforce_agent_limit(tier: str, current_count: int) -> int:
+# ── Ownership-checked lookups ─────────────────────────────────────────
+
+async def _get_local_agent_for_user(
+    agent_id: str, user_id: str, db: AsyncSession
+) -> LocalAgentConfig:
+    result = await db.execute(
+        select(LocalAgentConfig).where(
+            LocalAgentConfig.id == agent_id,
+            LocalAgentConfig.user_id == user_id,
+        )
+    )
+    record = result.scalar_one_or_none()
+    if record is None:
+        raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail="Agent not found")
+    return record
+
+
+async def _get_cloud_agent_for_user(
+    agent_id: str, user_id: str, db: AsyncSession
+) -> CloudAgentConfig:
+    result = await db.execute(
+        select(CloudAgentConfig).where(
+            CloudAgentConfig.id == agent_id,
+            CloudAgentConfig.user_id == user_id,
+        )
+    )
+    record = result.scalar_one_or_none()
+    if record is None:
+        raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail="Agent not found")
+    return record
+
+
+# ── Tier limit helper ─────────────────────────────────────────────────
+
+async def _count_enabled_agents(user_id: str, db: AsyncSession) -> int:
+    """Return combined enabled local + cloud agent count for the user."""
+    local_count = (
+        await db.execute(
+            select(func.count(LocalAgentConfig.id)).where(
+                LocalAgentConfig.user_id == user_id,
+                LocalAgentConfig.enabled == True,  # noqa: E712
+            )
+        )
+    ).scalar_one()
+    cloud_count = (
+        await db.execute(
+            select(func.count(CloudAgentConfig.id)).where(
+                CloudAgentConfig.user_id == user_id,
+                CloudAgentConfig.enabled == True,  # noqa: E712
+            )
+        )
+    ).scalar_one()
+    return local_count + cloud_count
+
+
+def _enforce_agent_limit(tier: str, current_count: int) -> None:
     limit: int = FEATURES.get(tier, FEATURES["free"])["batch_active"]
     if limit != -1 and current_count >= limit:
         raise HTTPException(
             status_code=status.HTTP_403_FORBIDDEN,
             detail=f"Agent limit ({limit}) reached for your tier. Upgrade to create more.",
         )
-    return limit
 
 
-async def _enforce_run_frequency(
+# ── Local page schema (used by runs endpoint) ─────────────────────────
-    tier: str,
-    user_id: str,
-    db: AsyncSession,
-) -> None:
-    """Raise HTTP 402 if the user has exceeded their daily batch run limit."""
-    limit: int = FEATURES.get(tier, FEATURES["free"])["batch_runs_per_day"]
-    if limit == -1:
-        return  # unlimited
 
-    today_start = datetime.now(timezone.utc).replace(
+class _RunsPage(BaseModel):
-        hour=0, minute=0, second=0, microsecond=0
+    total: int
-    )
+    page: int
-    result = await db.execute(
+    limit: int
-        select(func.count(AgentRunLog.id)).where(
+    items: list[AgentRunLogResponse]
-            AgentRunLog.user_id == user_id,
-            AgentRunLog.started_at >= today_start,
-        )
-    )
-    runs_today: int = result.scalar_one()
-
-    if runs_today >= limit:
-        raise HTTPException(
-            status_code=status.HTTP_402_PAYMENT_REQUIRED,
-            detail=f"Daily batch run limit ({limit}) reached for your tier. Upgrade for more runs.",
-        )
 
 
 # ── Catalog ───────────────────────────────────────────────────────────
@@ -153,68 +209,229 @@ async def get_agent_catalog(
     ]
 
 
-@router.post("/can-create", response_model=AgentCreationCheckResponse)
+# ── Local agent CRUD ──────────────────────────────────────────────────
-async def can_create_agent(
+
-    body: AgentCreationCheckRequest,
+@router.get("/local", response_model=list[LocalAgentConfigResponse])
+async def list_local_agents(
     current_user: UserProfile = Depends(get_current_user),
-) -> AgentCreationCheckResponse:
+    db: AsyncSession = Depends(get_session),
-    """Check if the user can create one more agent based on billing tier.
+) -> list[LocalAgentConfigResponse]:
-
+    """List all local directory agent configs owned by the authenticated user."""
-    Since configuration is client-owned, the Electron app sends its current
+    result = await db.execute(
-    active agent count and the backend applies tier limits.
+        select(LocalAgentConfig).where(LocalAgentConfig.user_id == current_user.id)
-    """
-    limit: int = FEATURES.get(current_user.tier, FEATURES["free"])["batch_active"]
-    allowed = limit == -1 or body.active_agents < limit
-    return AgentCreationCheckResponse(
-        allowed=allowed,
-        tier=current_user.tier,
-        active_agents=body.active_agents,
-        limit=limit,
     )
+    return [_to_local_response(a) for a in result.scalars().all()]
 
 
-@router.post("/trigger", response_model=AgentRunLogResponse, status_code=status.HTTP_202_ACCEPTED)
+@router.post("/local", response_model=LocalAgentConfigResponse, status_code=status.HTTP_201_CREATED)
+async def create_local_agent(
+    body: LocalAgentConfigCreate,
+    current_user: UserProfile = Depends(get_current_user),
+    db: AsyncSession = Depends(get_session),
+) -> LocalAgentConfigResponse:
+    """Create a new local directory agent config.
+
+    The combined count of enabled local and cloud agents for the user is
+    checked against the ``batch_active`` limit for their billing tier.
+    """
+    _enforce_agent_limit(current_user.tier, await _count_enabled_agents(current_user.id, db))
+    agent = LocalAgentConfig(
+        user_id=current_user.id,
+        name=body.name,
+        device_id=body.device_id,
+        directory_paths=body.directory_paths,
+        data_types=body.data_types,
+        prompt_template=body.prompt_template,
+        file_extensions=body.file_extensions,
+        schedule_cron=body.schedule_cron,
+    )
+    db.add(agent)
+    await db.commit()
+    await db.refresh(agent)
+    return _to_local_response(agent)
+
+
+@router.put("/local/{agent_id}", response_model=LocalAgentConfigResponse)
+async def update_local_agent(
+    agent_id: str,
+    body: LocalAgentConfigUpdate,
+    current_user: UserProfile = Depends(get_current_user),
+    db: AsyncSession = Depends(get_session),
+) -> LocalAgentConfigResponse:
+    """Partially update a local agent config. Only provided fields are changed."""
+    agent = await _get_local_agent_for_user(agent_id, current_user.id, db)
+    for field, value in body.model_dump(exclude_unset=True).items():
+        setattr(agent, field, value)
+    await db.commit()
+    await db.refresh(agent)
+    return _to_local_response(agent)
+
+
+@router.delete("/local/{agent_id}", response_model=dict)
+async def delete_local_agent(
+    agent_id: str,
+    current_user: UserProfile = Depends(get_current_user),
+    db: AsyncSession = Depends(get_session),
+) -> dict[str, bool]:
+    """Delete a local agent config. Associated run logs are cascade-deleted."""
+    agent = await _get_local_agent_for_user(agent_id, current_user.id, db)
+    await db.delete(agent)
+    await db.commit()
+    return {"ok": True}
+
+
+# ── Cloud agent CRUD ──────────────────────────────────────────────────
+
+@router.get("/cloud", response_model=list[CloudAgentConfigResponse])
+async def list_cloud_agents(
+    current_user: UserProfile = Depends(get_current_user),
+    db: AsyncSession = Depends(get_session),
+) -> list[CloudAgentConfigResponse]:
+    """List all cloud connector agent configs owned by the authenticated user."""
+    result = await db.execute(
+        select(CloudAgentConfig).where(CloudAgentConfig.user_id == current_user.id)
+    )
+    return [_to_cloud_response(a) for a in result.scalars().all()]
+
+
+@router.post("/cloud", response_model=CloudAgentConfigResponse, status_code=status.HTTP_201_CREATED)
+async def create_cloud_agent(
+    body: CloudAgentConfigCreate,
+    current_user: UserProfile = Depends(get_current_user),
+    db: AsyncSession = Depends(get_session),
+) -> CloudAgentConfigResponse:
+    """Create a new cloud connector agent config.
+
+    The combined count of enabled local and cloud agents for the user is
+    checked against the ``batch_active`` limit for their billing tier.
+    """
+    _enforce_agent_limit(current_user.tier, await _count_enabled_agents(current_user.id, db))
+    agent = CloudAgentConfig(
+        user_id=current_user.id,
+        provider=body.provider,
+        name=body.name,
+        data_types=body.data_types,
+        prompt_template=body.prompt_template,
+        oauth_token_encrypted=body.oauth_token_encrypted,
+        schedule_cron=body.schedule_cron,
+        filter_config=body.filter_config,
+    )
+    db.add(agent)
+    await db.commit()
+    await db.refresh(agent)
+    return _to_cloud_response(agent)
+
+
+@router.put("/cloud/{agent_id}", response_model=CloudAgentConfigResponse)
+async def update_cloud_agent(
+    agent_id: str,
+    body: CloudAgentConfigUpdate,
+    current_user: UserProfile = Depends(get_current_user),
+    db: AsyncSession = Depends(get_session),
+) -> CloudAgentConfigResponse:
+    """Partially update a cloud agent config. Only provided fields are changed."""
+    agent = await _get_cloud_agent_for_user(agent_id, current_user.id, db)
+    for field, value in body.model_dump(exclude_unset=True).items():
+        setattr(agent, field, value)
+    await db.commit()
+    await db.refresh(agent)
+    return _to_cloud_response(agent)
+
+
+@router.delete("/cloud/{agent_id}", response_model=dict)
+async def delete_cloud_agent(
+    agent_id: str,
+    current_user: UserProfile = Depends(get_current_user),
+    db: AsyncSession = Depends(get_session),
+) -> dict[str, bool]:
+    """Delete a cloud agent config. Associated run logs are cascade-deleted."""
+    agent = await _get_cloud_agent_for_user(agent_id, current_user.id, db)
+    await db.delete(agent)
+    await db.commit()
+    return {"ok": True}
+
+
+# ── Run logs ──────────────────────────────────────────────────────────
+
+@router.get("/runs", response_model=_RunsPage)
+async def list_run_logs(
+    agent_id: str | None = Query(default=None),
+    page: int = Query(default=1, ge=1),
+    limit: int = Query(default=20, ge=1, le=100),
+    current_user: UserProfile = Depends(get_current_user),
+    db: AsyncSession = Depends(get_session),
+) -> _RunsPage:
+    """Return paginated run logs for the authenticated user.
+
+    Optionally filter by ``agent_id``. Results are ordered from newest to oldest.
+    """
+    base_filter = [AgentRunLog.user_id == current_user.id]
+    if agent_id:
+        base_filter.append(AgentRunLog.agent_id == agent_id)
+
+    total = (
+        await db.execute(select(func.count(AgentRunLog.id)).where(*base_filter))
+    ).scalar_one()
+
+    result = await db.execute(
+        select(AgentRunLog)
+        .where(*base_filter)
+        .order_by(AgentRunLog.started_at.desc())
+        .offset((page - 1) * limit)
+        .limit(limit)
+    )
+    items = [_to_run_log_response(log) for log in result.scalars().all()]
+
+    return _RunsPage(total=total, page=page, limit=limit, items=items)
+
+
+# ── Manual trigger stub ───────────────────────────────────────────────
+
+@router.post("/{agent_id}/run", response_model=AgentRunLogResponse, status_code=status.HTTP_202_ACCEPTED)
 async def trigger_agent_run(
-    body: AgentTriggerRequest,
+    agent_id: str,
     current_user: UserProfile = Depends(get_current_user),
     db: AsyncSession = Depends(get_session),
 ) -> AgentRunLogResponse:
-    """Trigger a local agent run using client-provided configuration."""
+    """Manually trigger an 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 = (
+    Looks up the agent config (local or cloud) by ID with ownership check,
-        datetime.fromtimestamp(body.last_run_at / 1000, tz=timezone.utc)
+    creates a run log entry with ``status="running"``, and returns it.
-        if body.last_run_at
-        else None
-    )
-    config = LocalAgentConfig(
-        id=str(uuid.uuid4()),
-        user_id=current_user.id,
-        device_id=body.device_id,
-        name="Local Directory Monitor",
-        directory_paths=[body.directory],
-        data_types=_to_data_types(body.what_to_extract),
-        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.
+    Actual dispatch to the agent runner is wired in Step 3.4 once
-    stable_agent_id = body.agent_id or config.id
+    ``DeviceConnectionManager`` and ``agent_runner`` are available.
+    """
+    # Determine agent type by trying local first, then cloud.
+    # Keep the full config object so we can pass it to the agent runner.
+    local_config: LocalAgentConfig | None = None
+    cloud_config: CloudAgentConfig | None = None
 
-    if is_agent_running(stable_agent_id):
+    local_result = await db.execute(
-        raise HTTPException(
+        select(LocalAgentConfig).where(
-            status_code=status.HTTP_409_CONFLICT,
+            LocalAgentConfig.id == agent_id,
-            detail="Agent is already running. Only one run per agent is allowed at a time.",
+            LocalAgentConfig.user_id == current_user.id,
         )
+    )
+    local_config = local_result.scalar_one_or_none()
+
+    if local_config is not None:
+        agent_type = "local"
+    else:
+        cloud_result = await db.execute(
+            select(CloudAgentConfig).where(
+                CloudAgentConfig.id == agent_id,
+                CloudAgentConfig.user_id == current_user.id,
+            )
+        )
+        cloud_config = cloud_result.scalar_one_or_none()
+        if cloud_config is not None:
+            agent_type = "cloud"
+        else:
+            raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail="Agent not found")
 
     run_log = AgentRunLog(
-        agent_id=stable_agent_id,
+        agent_id=agent_id,
-        agent_type="local",
+        agent_type=agent_type,
         user_id=current_user.id,
         status="running",
     )
@@ -222,36 +439,14 @@ async def trigger_agent_run(
     await db.commit()
     await db.refresh(run_log)
 
-    run_context = {
+    # Dispatch the run as a background task — returns 202 immediately.
-        "type": "agent_batch",
+    if agent_type == "local" and local_config is not None:
-        "run_id": run_log.id,
+        asyncio.create_task(
-        "agent_id": stable_agent_id,
+            run_local_agent(current_user.id, local_config, run_log, device_manager)
-    }
+        )
-
+    elif agent_type == "cloud" and cloud_config is not None:
-    asyncio.create_task(
+        asyncio.create_task(
-        run_local_agent(current_user.id, config, run_log, device_manager, run_context)
+            run_cloud_agent(current_user.id, cloud_config, run_log, device_manager)
-    )
+        )
 
     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

@@ -1,68 +1,34 @@
-"""Auth routes: register, login, refresh, me, OAuth social login, onboarding.
+"""Auth routes: register, login, refresh, me.
 
 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, Field
+from pydantic import BaseModel
 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 OAuthAccount, RefreshToken, User
+from app.models import 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 ─────────────────────────────────────────────────
 
 
@@ -265,531 +231,5 @@ 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

@@ -0,0 +1,171 @@
+"""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, HTTPException, Request, status
+from fastapi import APIRouter, Depends, Header, Request, status
 from pydantic import BaseModel
 from sqlalchemy.ext.asyncio import AsyncSession
 
@@ -83,50 +83,3 @@ 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

@@ -1,116 +1,42 @@
-"""Chat routes: POST /chat (REST fallback) and POST /chat/embed (text → vector).
+"""Chat routes: POST /chat (REST fallback).
 
 WebSocket chat is handled by the unified device WS endpoint (/api/v1/ws/device).
 """
 
 from __future__ import annotations
 
-import uuid
+from fastapi import APIRouter, Depends
-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
+from app.schemas import ChatRequest, ChatResponse, 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,
     current_user: UserProfile = Depends(get_current_user),
 ) -> JSONResponse:
-    """REST fallback for home chat when websocket streaming is unavailable."""
+    """Route a chat message through the Home deep agent (non-streaming)."""
-    response = await run_home(
-        user_id=current_user.id,
-        message=body.message,
-        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(
+        memory_context = await memory.enrich_context(current_user.id, body.message)
-            current_user.id,
-            "",
-            trace_id=request_id,
-            session_id=request_id,
-        )
 
-    context: dict = {
+    context = {
-        "_debug": {"request_id": request_id, "user_id": current_user.id},
+        **body.context.model_dump(),
         **memory_context,
     }
 
-    chunks: list[str] = []
+    response_text = await run_home(
-    if body.mode == "project":
+        user_id=current_user.id,
-        stream = run_project_brief(current_user.id, body.project_id, context)  # type: ignore[arg-type]
+        message=body.message,
-    else:
+        context=context,
-        stream = run_home_brief(current_user.id, context)
+        db_session_factory=async_session,
-
+    )
-    async for event_type, data in stream:
+    result = ChatResponse(response=response_text)
-        if event_type == "token" and data:
+    return JSONResponse(content=result.model_dump())
-            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

@@ -14,11 +14,11 @@ Protocol:
   4. Session enters message dispatch loop + heartbeat.
 
 Incoming frame dispatch:
-  - ``tool_result``      → resolves a pending tool-call Future.
+  - ``tool_result``    → resolves a pending tool-call Future.
-  - ``journey_start``    → starts a guided setup journey session.
+  - ``agent_data``     → enqueued in the per-run agent data queue.
-  - ``journey_message``  → continues a journey conversation.
+  - ``agent_complete`` → sends None sentinel to close the queue stream.
-  - ``pong``             → heartbeat acknowledgement (updates last-seen).
+  - ``pong``           → heartbeat acknowledgement (updates last-seen).
-  - unknown types        → logged, ignored.
+  - unknown types      → logged, ignored.
 
 Outgoing heartbeat: ``{ "type": "ping" }`` every 30 s.
 
@@ -39,28 +39,21 @@ from fastapi import APIRouter, WebSocket, WebSocketDisconnect
 from jose import JWTError, jwt
 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.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.deep_agent import run_home_stream, run_floating_stream
+from app.core.output_formatter import HomeFormatter, FloatingFormatter
 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, WsStreamEnd
+from app.schemas import WsFrameType
 
 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
 
@@ -154,6 +147,37 @@ async def _message_loop(websocket: WebSocket, user_id: str) -> None:
                     "device_ws: tool_result missing id from user=%s", user_id
                 )
 
+        elif frame_type == WsFrameType.agent_data:
+            run_id = frame.get("run_id")
+            if run_id:
+                try:
+                    queue = device_manager.get_agent_data_queue(user_id, run_id)
+                    await queue.put(frame)
+                except RuntimeError:
+                    logger.warning(
+                        "device_ws: agent_data for unknown run user=%s run=%s",
+                        user_id,
+                        run_id,
+                    )
+            else:
+                logger.warning(
+                    "device_ws: agent_data missing run_id from user=%s", user_id
+                )
+
+        elif frame_type == WsFrameType.agent_complete:
+            run_id = frame.get("run_id")
+            if run_id:
+                try:
+                    queue = device_manager.get_agent_data_queue(user_id, run_id)
+                    # Sentinel: signals the agent data stream is finished.
+                    await queue.put(None)
+                except RuntimeError:
+                    pass
+            else:
+                logger.warning(
+                    "device_ws: agent_complete missing run_id from user=%s", user_id
+                )
+
         elif frame_type == WsFrameType.home_request:
             asyncio.create_task(
                 _handle_home_request(websocket, user_id, frame)
@@ -164,39 +188,6 @@ 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)
-            )
-
-        elif frame_type == WsFrameType.journey_message:
-            asyncio.create_task(
-                _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
@@ -209,13 +200,35 @@ async def _message_loop(websocket: WebSocket, user_id: str) -> None:
 
 # ── v3 Chat Handlers ──────────────────────────────────────────────────
 
+_WS_TOOL_CALL_TIMEOUT = 30  # seconds to wait for Electron tool_result
+
+
 async def _make_ws_executor(websocket: WebSocket, user_id: str):
     """Return a callback that sends tool_call frames and awaits tool_result."""
     async def _executor(payload: dict) -> dict:
         payload["type"] = WsFrameType.tool_call
+        call_id = payload["id"]
+        logger.info("ws_executor: sending tool_call id=%s action=%s", call_id, payload.get("action"))
         await websocket.send_text(json.dumps(payload))
-        future = device_manager.create_pending_call(user_id, payload["id"])
+        future = device_manager.create_pending_call(user_id, call_id)
-        return await future
+        try:
+            result = await asyncio.wait_for(future, timeout=_WS_TOOL_CALL_TIMEOUT)
+        except asyncio.TimeoutError:
+            logger.error(
+                "ws_executor: timeout waiting for tool_result id=%s action=%s user=%s",
+                call_id, payload.get("action"), user_id,
+            )
+            # Clean up the pending future so it doesn't leak
+            conn = device_manager._connections.get(user_id)
+            if conn:
+                conn.pending_calls.pop(call_id, None)
+            return {"error": f"Tool call timed out after {_WS_TOOL_CALL_TIMEOUT}s", "rows": []}
+        logger.info("ws_executor: tool_result id=%s result_type=%s result_keys=%s",
+                     call_id, type(result).__name__,
+                     list(result.keys()) if isinstance(result, dict) else "N/A")
+        if result is None:
+            logger.error("ws_executor: future resolved to None for call_id=%s user=%s", call_id, user_id)
+        return result
     return _executor
 
 
@@ -228,30 +241,14 @@ 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 project=%s msg=%s",
-        user_id,
-        request_id,
-        session_id,
-        project_id,
-        message[:200],
-    )
 
     # ── Memory: enrich context before LLM call ────────────────────────
     async with async_session() as db:
         memory = MemoryMiddleware(db)
-        memory_context = await memory.enrich_context(
+        memory_context = await memory.enrich_context(user_id, message)
-            user_id,
-            message,
-            trace_id=request_id,
-            session_id=session_id,
-        )
 
     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,
     }
 
@@ -259,11 +256,12 @@ async def _handle_home_request(
     set_client_executor(executor)
     response_chunks: list[str] = []
     try:
-        event_stream = run_home_stream(user_id, message, context, project_id=project_id)
+        event_stream = run_home_stream(
-        formatter = StreamFormatter(request_id=request_id)
+            user_id, message, context, db_session_factory=async_session
+        )
+        formatter = HomeFormatter(request_id=request_id)
         async for ws_frame in formatter.format(event_stream):
             await websocket.send_text(ws_frame.model_dump_json())
-            # Collect text chunks to build the full response for episode storage
             if ws_frame.type == "stream_text":  # type: ignore[union-attr]
                 response_chunks.append(ws_frame.chunk)  # type: ignore[union-attr]
     except Exception as exc:
@@ -278,15 +276,8 @@ async def _handle_home_request(
     async with async_session() as db:
         memory = MemoryMiddleware(db)
         await memory.store_episode(
-            user_id, session_id, message, "".join(response_chunks), trace_id=request_id
+            user_id, session_id, message, "".join(response_chunks)
         )
-    logger.info(
-        "device_ws: home_request_end user=%s req=%s session=%s response_chars=%d",
-        user_id,
-        request_id,
-        session_id,
-        len("".join(response_chunks)),
-    )
 
 
 async def _handle_floating_request(
@@ -299,39 +290,23 @@ async def _handle_floating_request(
     message: str = frame.get("message", "")
     session_id: str = frame.get("session_id") or str(uuid4())
     scope: dict = frame.get("scope", {})
-    logger.info(
-        "device_ws: floating_request_start user=%s req=%s session=%s scope=%s msg=%s",
-        user_id,
-        request_id,
-        session_id,
-        json.dumps(scope, ensure_ascii=True)[:200],
-        message[:200],
-    )
 
     # ── Memory: enrich context before LLM call ────────────────────────
     async with async_session() as db:
         memory = MemoryMiddleware(db)
-        memory_context = await memory.enrich_context(
+        memory_context = await memory.enrich_context(user_id, message)
-            user_id,
-            message,
-            trace_id=request_id,
-            session_id=session_id,
-        )
 
-    context: dict = {
+    context: dict = {"scope": scope, **memory_context}
-        "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,
-    }
 
     executor = await _make_ws_executor(websocket, user_id)
     set_client_executor(executor)
     response_chunks: list[str] = []
     try:
-        event_stream = run_floating_stream(user_id, message, context)
+        event_stream = run_floating_stream(
-        formatter = StreamFormatter(request_id=request_id)
+            user_id, message, context, scope=scope,
+            db_session_factory=async_session,
+        )
+        formatter = FloatingFormatter(request_id=request_id)
         async for ws_frame in formatter.format(event_stream):
             await websocket.send_text(ws_frame.model_dump_json())
             if ws_frame.type == "stream_text":  # type: ignore[union-attr]
@@ -348,413 +323,8 @@ async def _handle_floating_request(
     async with async_session() as db:
         memory = MemoryMiddleware(db)
         await memory.store_episode(
-            user_id, session_id, message, "".join(response_chunks), trace_id=request_id
+            user_id, session_id, message, "".join(response_chunks)
         )
-    logger.info(
-        "device_ws: floating_request_end user=%s req=%s session=%s response_chars=%d",
-        user_id,
-        request_id,
-        session_id,
-        len("".join(response_chunks)),
-    )
-
-
-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 ─────────────────────────────────────────────
-
-
-async def _handle_journey_start(
-    websocket: WebSocket,
-    user_id: str,
-    frame: dict,
-) -> None:
-    """Handle a journey_start frame — explores directory and sends first question."""
-    executor = await _make_ws_executor(websocket, user_id)
-    set_client_executor(executor)
-    try:
-        reply = await handle_journey_start(user_id, frame)
-        await websocket.send_text(json.dumps(reply))
-    except Exception as exc:
-        logger.error(
-            "device_ws: journey_start failed user=%s: %s", user_id, exc
-        )
-        await websocket.send_text(json.dumps({
-            "type": "journey_reply",
-            "session_id": frame.get("session_id", ""),
-            "message": f"Failed to start journey: {exc}",
-            "done": True,
-            "prompt_template": None,
-        }))
-    finally:
-        clear_client_executor()
-
-
-async def _handle_journey_message(
-    websocket: WebSocket,
-    user_id: str,
-    frame: dict,
-) -> None:
-    """Handle a journey_message frame — continues the journey conversation."""
-    executor = await _make_ws_executor(websocket, user_id)
-    set_client_executor(executor)
-    try:
-        reply = await handle_journey_message(user_id, frame)
-        await websocket.send_text(json.dumps(reply))
-    except Exception as exc:
-        session_id = frame.get("session_id", "")
-        logger.error(
-            "device_ws: journey_message failed user=%s session=%s: %s",
-            user_id, session_id, exc,
-        )
-        await websocket.send_text(json.dumps({
-            "type": "journey_reply",
-            "session_id": session_id,
-            "message": f"Journey error: {exc}",
-            "done": True,
-            "prompt_template": None,
-        }))
-    finally:
-        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 ─────────────────────────────────────────────────────────
@@ -790,3 +360,6 @@ async def _mark_runs_disconnected(user_id: str) -> None:
             user_id,
             exc,
         )
+
+
+

app/api/routes/memory.py

@@ -1,225 +0,0 @@
-"""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

@@ -0,0 +1,148 @@
+"""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

@@ -0,0 +1,195 @@
+"""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

@@ -0,0 +1,79 @@
+"""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

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

app/auth/oauth_providers.py

@@ -1,135 +0,0 @@
-"""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

@@ -1,139 +0,0 @@
-"""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.adiuvai.app/billing/success?session_id={CHECKOUT_SESSION_ID}",
+        success_url: str = "https://app.adiuva.app/billing/success?session_id={CHECKOUT_SESSION_ID}",
-        cancel_url: str = "https://app.adiuvai.app/billing/cancel",
+        cancel_url: str = "https://app.adiuva.app/billing/cancel",
     ) -> str:
         """Create a Stripe checkout session and return the URL.
 
@@ -200,45 +200,6 @@ 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

@@ -21,58 +21,42 @@ FEATURES: dict[str, dict[str, Any]] = {
     "free": {
         "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
     },
 }
 
@@ -93,18 +77,16 @@ class TierManager:
     async def get_tier(self, user_id: str, db: AsyncSession) -> BillingTier:
         """Return the current billing tier for ``user_id`` from the DB.
 
-        Falls back to ``'power'`` in dev (unlimited) or ``'free'`` in prod
+        Falls back to ``'free'`` when no subscription row exists.
-        when no subscription row exists.
         """
         from app.models import Subscription  # noqa: PLC0415
-        from app.config.settings import settings  # noqa: PLC0415
 
         result = await db.execute(
             select(Subscription.tier).where(Subscription.user_id == user_id)
         )
         tier: str | None = result.scalar_one_or_none()
         if tier is None or tier not in FEATURES:
-            return "power" if settings.ENV == "dev" else "free"
+            return "free"
         return tier  # type: ignore[return-value]
 
     # ── Feature access ───────────────────────────────────────────────────
@@ -131,19 +113,77 @@ 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/adiuvai"
+    DATABASE_URL: str = "postgresql+asyncpg://postgres:postgres@localhost:5432/adiuva"
     JWT_SECRET: str = "change-me-in-production"
     JWT_ALGORITHM: str = "HS256"
     JWT_ACCESS_TOKEN_EXPIRE_MINUTES: int = 30
@@ -12,29 +12,26 @@ 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.
@@ -48,39 +45,16 @@ 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] = [
+    CORS_ORIGINS: list[str] = ["app://.", "http://localhost:3000", "http://localhost:5173"]
-        "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", extra="ignore")
+    model_config = SettingsConfigDict(env_file=".env", env_file_encoding="utf-8")
 
 
 settings = Settings()

app/core/agent_registry.py

@@ -1,30 +0,0 @@
-"""Minimal agent base types retained for compatibility with batch runners."""
-
-from __future__ import annotations
-
-from abc import ABC, abstractmethod
-from typing import Any
-
-
-class BaseAgent(ABC):
-    """Common base for non-chat agents still using the old base contract."""
-
-    def __init__(
-        self,
-        user_id: str = "",
-        shared_memory: dict[str, Any] | None = None,
-        vector_store_context: list[str] | None = None,
-    ) -> None:
-        self.user_id = user_id
-        self.shared_memory: dict[str, Any] = shared_memory or {}
-        self.vector_store_context: list[str] = vector_store_context or []
-
-    @abstractmethod
-    def get_name(self) -> str: ...
-
-    @abstractmethod
-    def get_description(self) -> str: ...
-
-    @property
-    def skills(self) -> list[str]:
-        return []

app/core/agent_session_buffer.py

@@ -1,59 +0,0 @@
-"""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

@@ -1,228 +0,0 @@
-"""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/device_manager.py

@@ -3,15 +3,20 @@
 Maintains in-memory state for all active Electron → backend WebSocket
 connections.  One connection per user (latest replaces previous).
 
-The manager handles the **tool-call round-trip** pattern:
+The manager participates in two interaction patterns:
-  - Backend sends ``tool_call`` frame → Electron executes the action →
-    returns ``tool_result`` frame.
-  - ``create_pending_call`` registers a Future keyed by ``call_id``.
-  - ``resolve_pending_call`` fulfils the Future; callers awaiting it
-    receive the result dict from Electron.
 
-This pattern is used by all tools (CRUD, file-system, etc.) via
+1. **Tool-call round-trip** (bidirectional CRUD):
-``execute_on_client()`` in ``ws_context.py``.
+   - Backend sends ``tool_call`` frame → Electron executes CRUD → returns
+     ``tool_result`` frame.
+   - ``create_pending_call`` registers a Future keyed by ``call_id``.
+   - ``resolve_pending_call`` fulfils the Future; callers awaiting it
+     receive the result dict from Electron.
+
+2. **Agent-data streaming** (local directory agent runs):
+   - Backend sends ``agent_run`` frame → Electron reads files and sends
+     back a stream of ``agent_data`` frames followed by ``agent_complete``.
+   - ``get_agent_data_queue`` returns (or creates) an asyncio.Queue for
+     a specific ``run_id`` so the agent runner can iterate frames.
 
 The ``device_manager`` module-level singleton is imported by both the
 device WS route and the agent runner.
@@ -37,6 +42,8 @@ class DeviceConnection:
     device_id: str
     # Futures indexed by tool_call id — resolved when tool_result arrives.
     pending_calls: dict[str, asyncio.Future[dict]] = field(default_factory=dict)
+    # Per-run queues for agent_data / agent_complete frames.
+    agent_data_queues: dict[str, asyncio.Queue[dict | None]] = field(default_factory=dict)
 
 
 class DeviceConnectionManager:
@@ -146,6 +153,31 @@ class DeviceConnectionManager:
         if fut is not None and not fut.done():
             fut.set_result(result)
 
+    # ── Agent-data queue ──────────────────────────────────────────────
+
+    def get_agent_data_queue(
+        self, user_id: str, run_id: str
+    ) -> asyncio.Queue[dict | None]:
+        """Return (creating if absent) the queue for *run_id* agent frames.
+
+        The agent runner reads from this queue.  The device WS handler writes
+        to it.  ``None`` is the sentinel that signals the stream is finished.
+        """
+        conn = self._connections.get(user_id)
+        if conn is None:
+            raise RuntimeError(
+                f"get_agent_data_queue: user {user_id!r} is not connected"
+            )
+        if run_id not in conn.agent_data_queues:
+            conn.agent_data_queues[run_id] = asyncio.Queue()
+        return conn.agent_data_queues[run_id]
+
+    def cleanup_agent_data_queue(self, user_id: str, run_id: str) -> None:
+        """Remove the queue for *run_id* once a run has completed."""
+        conn = self._connections.get(user_id)
+        if conn:
+            conn.agent_data_queues.pop(run_id, None)
+
 
 # Module-level singleton — import this everywhere.
 device_manager = DeviceConnectionManager()

app/core/embeddings.py

@@ -1,34 +0,0 @@
-"""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

@@ -1,183 +0,0 @@
-"""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

@@ -1,190 +0,0 @@
-"""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()``
+Every agent and the deep-agent supervisors call ``get_llm()`` or ``get_router_llm()``
 instead of directly constructing a provider-specific class.  The model string
 follows the `LiteLLM model naming convention
 <https://docs.litellm.ai/docs/providers>`_:
@@ -11,15 +11,13 @@ follows the `LiteLLM model naming convention
 * Ollama:     ``ollama/llama3``
 * Bedrock:    ``bedrock/anthropic.claude-v2``
 
-Switch providers by changing **LLM_MODEL** in ``.env``
+Switch providers by changing **LLM_MODEL** / **LLM_ROUTER_MODEL** in ``.env``
 — no code changes required.
 """
 
 from __future__ import annotations
 
 import os
-import warnings
-from collections.abc import Callable
 
 from openai import AsyncOpenAI
 import litellm
@@ -34,14 +32,6 @@ from app.config.settings import settings
 # Drop them silently instead of raising UnsupportedParamsError.
 litellm.drop_params = True
 
-# Some provider responses include a plain dict in the `usage` field where a
-# richer Pydantic model is expected. This warning is noisy but non-fatal.
-warnings.filterwarnings(
-    "ignore",
-    message=r"PydanticSerializationUnexpectedValue\(Expected `ResponseAPIUsage`",
-    category=UserWarning,
-)
-
 
 def _api_key_for_model(model: str) -> str | None:
     """Return the most appropriate API key for the given LiteLLM model string."""
@@ -51,10 +41,6 @@ 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.
@@ -100,39 +86,12 @@ def get_llm(
     )
 
 
-_AGENT_MODEL_SETTINGS: dict[str, Callable[[], str]] = {
+def get_router_llm(
-    "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 an LLM configured for *agent_name*, respecting per-agent overrides.
+    """Return the lighter model used for intent classification / routing."""
-
+    return get_llm(model=settings.LLM_ROUTER_MODEL, temperature=temperature)
-    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

@@ -1,450 +0,0 @@
-"""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

@@ -1,581 +0,0 @@
-"""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

@@ -18,10 +18,8 @@ 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
@@ -29,22 +27,15 @@ 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
@@ -52,60 +43,36 @@ _PROACTIVE_CONFIDENCE_THRESHOLD = 0.6
 
 
 class MemoryMiddleware:
-    """Enrich orchestrator context with memory and persist interactions after."""
+    """Enrich agent context with memory and persist interactions after."""
 
     def __init__(self, db: AsyncSession) -> None:
         self._db = db
 
     # ── Public API ────────────────────────────────────────────────────────────
 
-    async def enrich_context(
+    async def enrich_context(self, user_id: str, message: str) -> dict[str, Any]:
-        self,
+        """Build memory context dict to inject into the agent before LLM call.
-        user_id: str,
-        message: str,
-        trace_id: str | None = None,
-        session_id: str | None = None,
-    ) -> dict[str, Any]:
-        """Build memory context dict to inject into the orchestrator before LLM call.
 
         Returns a dict with keys:
           core_memory        — {key: plaintext_value, ...}
           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, user_tier=user_tier)
+        associative = await self._load_associative(user_id, message, fernet)
-        episodic = await self._load_episodic(user_id, fernet, session_id=session_id)
+        episodic = await self._load_episodic(user_id, fernet)
         proactive = await self._load_proactive(user_id, fernet)
-        relational = await self._load_relational(user_id, user_tier=user_tier)
-
-        logger.info(
-            "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_tier,
-            len(core),
-            len(associative),
-            len(episodic),
-            len(proactive),
-            len(relational),
-        )
 
         return {
             "core_memory": core,
             "associative_memory": associative,
             "episodic_memory": episodic,
             "proactive_hints": proactive,
-            "relational_memory": relational,
         }
 
     async def store_episode(
@@ -114,15 +81,11 @@ class MemoryMiddleware:
         session_id: str,
         message: str,
         response: str,
-        trace_id: str | None = None,
     ) -> None:
         """Summarise and store a completed interaction in episodic memory.
 
         The summary is a simple heuristic concatenation (no LLM call) to keep
-        latency low. After committing the episode row, dispatches the Mem0-style
+        latency low. Full LLM summarisation can be added in a later step.
-        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:
@@ -131,97 +94,20 @@ class MemoryMiddleware:
         summary = f"User: {message[:200]}\nAssistant: {response[:200]}"
         encrypted = _encrypt(fernet, summary)
 
-        episode = MemoryEpisodic(
+        row = MemoryEpisodic(
             id=str(uuid.uuid4()),
             user_id=user_id,
             summary_encrypted=encrypted,
             session_id=session_id,
         )
-        self._db.add(episode)
+        self._db.add(row)
-        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,
-                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) ────────────────────────────
+    async def update_core(self, user_id: str, key: str, value: str) -> None:
-        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."""
         fernet = await self._get_fernet(user_id)
         if fernet is None:
@@ -247,313 +133,10 @@ class MemoryMiddleware:
             ))
         try:
             await self._db.commit()
-            user_dbg = await self._get_user_debug(user_id)
-            logger.info(
-                "memory: update_core trace=%s user=%s tier=%s key=%s",
-                trace_id or "-",
-                user_id,
-                user_dbg.get("tier") or "-",
-                key,
-            )
         except Exception as exc:
             logger.error("memory: update_core failed user=%s key=%s: %s", user_id, key, exc)
             await self._db.rollback()
 
-    async def list_core_blocks(self, user_id: str) -> list[dict[str, str]]:
-        """Return core memory as editable blocks (label/value)."""
-        fernet = await self._get_fernet(user_id)
-        if fernet is None:
-            return []
-
-        result = await self._db.execute(
-            select(MemoryCore)
-            .where(MemoryCore.user_id == user_id)
-            .order_by(MemoryCore.key.asc())
-        )
-        rows = result.scalars().all()
-        out: list[dict[str, str]] = []
-        for row in rows:
-            plaintext = _safe_decrypt(fernet, row.value_encrypted)
-            if plaintext is not None:
-                out.append({"label": row.key, "value": plaintext})
-        logger.debug("memory: list_core_blocks user=%s count=%d", user_id, len(out))
-        return out
-
-    async def get_core_block(self, user_id: str, label: str) -> str | None:
-        """Return a single core memory block value by label."""
-        fernet = await self._get_fernet(user_id)
-        if fernet is None:
-            return None
-
-        result = await self._db.execute(
-            select(MemoryCore).where(
-                MemoryCore.user_id == user_id,
-                MemoryCore.key == label,
-            )
-        )
-        row = result.scalar_one_or_none()
-        if row is None:
-            logger.debug("memory: get_core_block user=%s label=%s found=0", user_id, label)
-            return None
-        value = _safe_decrypt(fernet, row.value_encrypted)
-        logger.debug("memory: get_core_block user=%s label=%s found=%d", user_id, label, 1 if value is not None else 0)
-        return value
-
-    async def delete_core(self, user_id: str, label: str) -> bool:
-        """Delete a core memory block by label. Returns True if deleted."""
-        result = await self._db.execute(
-            select(MemoryCore).where(
-                MemoryCore.user_id == user_id,
-                MemoryCore.key == label,
-            )
-        )
-        row = result.scalar_one_or_none()
-        if row is None:
-            logger.debug("memory: delete_core user=%s label=%s found=0", user_id, label)
-            return False
-
-        await self._db.delete(row)
-        try:
-            await self._db.commit()
-            logger.info("memory: delete_core user=%s label=%s", user_id, label)
-            return True
-        except Exception as exc:
-            logger.error("memory: delete_core failed user=%s label=%s: %s", user_id, label, exc)
-            await self._db.rollback()
-            return False
-
-    async def append_core(self, user_id: str, label: str, content: str) -> None:
-        """Append content to a core block, creating it if missing."""
-        current = await self.get_core_block(user_id, label)
-        if current is None:
-            await self.update_core(user_id, label, content)
-            logger.info("memory: append_core user=%s label=%s created=1", user_id, label)
-            return
-        await self.update_core(user_id, label, f"{current}\n{content}")
-        logger.info("memory: append_core user=%s label=%s created=0", user_id, label)
-
-    async def replace_core(self, user_id: str, label: str, old: str, new: str) -> bool:
-        """Replace one exact string inside a core block. Returns False if not found."""
-        current = await self.get_core_block(user_id, label)
-        if current is None or old not in current:
-            logger.debug("memory: replace_core user=%s label=%s changed=0", user_id, label)
-            return False
-        await self.update_core(user_id, label, current.replace(old, new, 1))
-        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)
-        if fernet is None:
-            return
-
-        encrypted = _encrypt(fernet, content)
-        row = MemoryAssociative(
-            id=str(uuid.uuid4()),
-            user_id=user_id,
-            content_encrypted=encrypted,
-            embedding=None,
-            entity_type=source,
-            entity_id=None,
-        )
-        self._db.add(row)
-        try:
-            await self._db.commit()
-            logger.info("memory: insert_archival user=%s source=%s", user_id, source)
-        except Exception as exc:
-            logger.error("memory: insert_archival failed user=%s: %s", user_id, exc)
-            await self._db.rollback()
-
-    async def search_archival(self, user_id: str, query: str, top_k: int = 5) -> list[str]:
-        """Search archival memory (keyword fallback; semantic ranking can replace this)."""
-        fernet = await self._get_fernet(user_id)
-        if fernet is None:
-            return []
-
-        result = await self._db.execute(
-            select(MemoryAssociative)
-            .where(MemoryAssociative.user_id == user_id)
-            .order_by(MemoryAssociative.updated_at.desc())
-            .limit(100)
-        )
-        rows = result.scalars().all()
-        needle = query.strip().lower()
-        out: list[str] = []
-        for row in rows:
-            plaintext = _safe_decrypt(fernet, row.content_encrypted)
-            if plaintext is None:
-                continue
-            if not needle or needle in plaintext.lower():
-                out.append(plaintext)
-            if len(out) >= max(top_k, 1):
-                break
-        logger.info("memory: search_archival user=%s query=%s hits=%d", user_id, query[:80], len(out))
-        return out
-
-    async def search_recall(self, user_id: str, query: str, top_k: int = 5) -> list[str]:
-        """Search recall memory (episodic summaries) by keyword."""
-        fernet = await self._get_fernet(user_id)
-        if fernet is None:
-            return []
-
-        result = await self._db.execute(
-            select(MemoryEpisodic)
-            .where(MemoryEpisodic.user_id == user_id)
-            .order_by(MemoryEpisodic.created_at.desc())
-            .limit(100)
-        )
-        rows = result.scalars().all()
-        needle = query.strip().lower()
-        out: list[str] = []
-        for row in rows:
-            plaintext = _safe_decrypt(fernet, row.summary_encrypted)
-            if plaintext is None:
-                continue
-            if not needle or needle in plaintext.lower():
-                out.append(plaintext)
-            if len(out) >= max(top_k, 1):
-                break
-        logger.info("memory: search_recall user=%s query=%s hits=%d", user_id, query[:80], len(out))
-        return out
-
     # ── Private helpers ───────────────────────────────────────────────────────
 
     async def _get_fernet(self, user_id: str) -> Fernet | None:
@@ -565,29 +148,6 @@ class MemoryMiddleware:
             return None
         return Fernet(user.encryption_key.encode())
 
-    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}
-
-        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(
             select(MemoryCore).where(MemoryCore.user_id == user_id)
@@ -601,49 +161,14 @@ class MemoryMiddleware:
         return out
 
     async def _load_associative(
-        self, user_id: str, message: str, fernet: Fernet, *, user_tier: str = "free"
+        self, user_id: str, message: str, fernet: Fernet
     ) -> list[str]:
         """Load top-k associative memories.
 
-        Pro+: pgvector cosine similarity on the message embedding (real_embeddings feature).
+        Production: uses pgvector cosine similarity on the message embedding.
-        Free / embedding failure: keyword-ordered fallback (most recent rows).
+        Current implementation: keyword-based fallback (no external embedding call)
+        so tests pass without a live OpenAI key.
         """
-        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)
@@ -651,24 +176,17 @@ class MemoryMiddleware:
             .limit(_ASSOCIATIVE_TOP_K)
         )
         rows = result.scalars().all()
-        out = []
+        out: list[str] = []
         for row in rows:
             plaintext = _safe_decrypt(fernet, row.content_encrypted)
             if plaintext is not None:
                 out.append(plaintext)
         return out
 
-    async def _load_episodic(
+    async def _load_episodic(self, user_id: str, fernet: Fernet) -> list[str]:
-        self,
-        user_id: str,
-        fernet: Fernet,
-        session_id: str | None = None,
-    ) -> list[str]:
-        query = select(MemoryEpisodic).where(MemoryEpisodic.user_id == user_id)
-        if session_id:
-            query = query.where(MemoryEpisodic.session_id == session_id)
         result = await self._db.execute(
-            query
+            select(MemoryEpisodic)
+            .where(MemoryEpisodic.user_id == user_id)
             .order_by(MemoryEpisodic.created_at.desc())
             .limit(_EPISODIC_RECENT_N)
         )
@@ -680,26 +198,6 @@ 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

@@ -1,51 +0,0 @@
-"""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

@@ -1,71 +1,141 @@
-"""Output formatter for deep-agent stream events."""
+"""Output Formatter — transforms deep-agent event streams into WS frame sequences.
+
+Consumes ``(event_type, data)`` tuples yielded by ``deep_agent.run_*_stream()``:
+  * ``("token", str)``       — supervisor text token
+  * ``("tool_end", dict)``   — sub-agent finished: ``{name, result}``
+  * ``("mutations", list)``  — collected CRUD mutations for ``stream_end``
+
+HomeFormatter:
+  * Streams text tokens as-is → emits ``WsStreamText``
+    (text may contain inline ``<type>[id,...]</type>`` entity tags
+    for the frontend to parse and render as interactive components)
+  * Attaches mutations        → injects into ``WsStreamEnd``
+
+FloatingFormatter:
+  * Sniffs first ``tool_end`` name → emits ``WsFloatingDomain``
+  * Streams text tokens             → emits ``WsStreamText``
+  * Attaches mutations              → injects into ``WsStreamEnd``
+"""
 
 from __future__ import annotations
 
-import re
+import logging
 from collections.abc import AsyncGenerator
 from typing import Any
 
-from app.schemas import WsFloatingDomain, WsStreamEnd, WsStreamStart, WsStreamText
+from app.schemas import (
-
+    WsFloatingDomain,
-# Matches <canvas kind="...">...</canvas> blocks (single-line or multiline).
+    WsStreamEnd,
-_CANVAS_BLOCK_RE = re.compile(
+    WsStreamStart,
-    r'<canvas\s+kind=["\']([^"\']+)["\']>(.*?)</canvas>',
+    WsStreamText,
-    re.DOTALL | re.IGNORECASE,
 )
 
+logger = logging.getLogger(__name__)
 
-def extract_canvas_block(text: str) -> tuple[str, str | None, str | None]:
+# Map sub-agent tool name → floating domain / entity type
-    """Strip the first <canvas kind="...">...</canvas> block from *text*.
+_AGENT_DOMAIN: dict[str, str] = {
-
+    "task_agent": "tasks",
-    Returns ``(visible_text, canvas_content, canvas_kind)``.
+    "timeline_agent": "timelines",
-    ``canvas_content`` and ``canvas_kind`` are ``None`` when no block is found.
+    "note_agent": "notes",
-    """
+    "project_agent": "projects",
-    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
 
 
-class StreamFormatter:
+class HomeFormatter:
-    """Convert `(event_type, data)` stream events into websocket frame models."""
+    """Consumes a deep-agent event stream and yields WS frames for the Home view.
+
+    Text tokens are forwarded as-is via ``WsStreamText``.  The supervisor
+    embeds ``<type>[id1,id2]</type>`` entity tags inline — the frontend
+    is responsible for parsing those and rendering interactive components.
+    Mutations are attached to ``WsStreamEnd``.
+    """
 
     def __init__(self, request_id: str) -> None:
         self.request_id = request_id
+        self._mutations: list[dict] = []
 
     async def format(
         self,
         event_stream: AsyncGenerator[tuple[str, Any], None],
     ) -> AsyncGenerator[WsFrame, None]:
-        started = False
+        yield WsStreamStart(request_id=self.request_id)
 
         async for event_type, data in event_stream:
-            if event_type == "floating_domain":
+            if event_type == "token":
-                if isinstance(data, dict):
+                if data:
+                    yield WsStreamText(request_id=self.request_id, chunk=data)
+
+            elif event_type == "mutations":
+                self._mutations = data or []
+
+        yield WsStreamEnd(
+            request_id=self.request_id,
+            mutations=[
+                {"action": m["action"], "table": m["table"], "data": m["data"]}
+                for m in self._mutations
+            ],
+        )
+
+
+class FloatingFormatter:
+    """Consumes a deep-agent event stream and yields WS frames for the Floating view.
+
+    Sniffs the first ``tool_end`` event name to derive the domain (e.g.
+    ``task_agent`` → ``"tasks"``), then streams text tokens as plain
+    ``WsStreamText``.  No block parsing for floating context.
+    """
+
+    def __init__(self, request_id: str) -> None:
+        self.request_id = request_id
+        self._mutations: list[dict] = []
+
+    async def format(
+        self,
+        event_stream: AsyncGenerator[tuple[str, Any], None],
+    ) -> AsyncGenerator[WsFrame, None]:
+        domain_sent = False
+
+        async for event_type, data in event_stream:
+            if event_type == "tool_end" and not domain_sent:
+                # Sniff domain from the first sub-agent that completes
+                name = data.get("name", "")
+                domain = _AGENT_DOMAIN.get(name, "tasks")
+                yield WsFloatingDomain(
+                    request_id=self.request_id,
+                    domain=domain,  # type: ignore[arg-type]
+                )
+                yield WsStreamStart(request_id=self.request_id)
+                domain_sent = True
+
+            elif event_type == "token":
+                if not domain_sent:
+                    # First token arrived before any tool_end — default domain
                     yield WsFloatingDomain(
                         request_id=self.request_id,
-                        domain=data,
+                        domain="tasks",  # type: ignore[arg-type]
                     )
-                continue
+                    yield WsStreamStart(request_id=self.request_id)
+                    domain_sent = True
+                if data:
+                    yield WsStreamText(request_id=self.request_id, chunk=data)
 
-            if event_type != "token":
+            elif event_type == "mutations":
-                continue
+                self._mutations = data or []
 
-            if not started:
+        # If no events triggered domain_sent (edge case), still emit structure
-                yield WsStreamStart(request_id=self.request_id)
+        if not domain_sent:
-                started = True
+            yield WsFloatingDomain(
-
+                request_id=self.request_id,
-            text = str(data or "")
+                domain="tasks",  # type: ignore[arg-type]
-            if text:
+            )
-                yield WsStreamText(request_id=self.request_id, chunk=text)
-
-        if not started:
             yield WsStreamStart(request_id=self.request_id)
-        yield WsStreamEnd(request_id=self.request_id)
+
+        yield WsStreamEnd(
+            request_id=self.request_id,
+            mutations=[
+                {"action": m["action"], "table": m["table"], "data": m["data"]}
+                for m in self._mutations
+            ],
+        )

app/core/preprocessors/__init__.py

@@ -1,104 +0,0 @@
-"""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

@@ -1,25 +0,0 @@
-"""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

@@ -1,111 +0,0 @@
-"""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

@@ -7,40 +7,21 @@ The callback sends a `tool_call` WS frame and awaits the `tool_result`.
 
 from __future__ import annotations
 
-import re
+import logging
 from contextvars import ContextVar
 from typing import Any, Callable, Coroutine
 from uuid import uuid4
 
-_SNAKE_TO_CAMEL_RE = re.compile(r"_([a-z])")
+logger = logging.getLogger(__name__)
-
-
-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.
+# Set by the chat WS handler before the deep agent runs; cleared after.
 _client_executor: ContextVar[Callable[[dict], Coroutine[Any, Any, dict]]] = ContextVar(
     "_client_executor"
 )
 
 # Optional collector that captures raw execute_on_client results.
-# Set by _tool_loop / _tool_loop_stream to populate ChatAgent.tool_results.
+# Set by the deep agent tool loop to capture CRUD mutations.
 _tool_result_collector: ContextVar[list[dict] | None] = ContextVar(
     "_tool_result_collector", default=None
 )
@@ -103,13 +84,17 @@ async def execute_on_client(
     if limit is not None:
         payload["limit"] = limit
 
+    logger.info("execute_on_client: sending payload action=%s table=%s id=%s", action, table, payload["id"])
     result = await callback(payload)
-    result = _keys_to_camel(result)
+    if result is None:
+        logger.error("execute_on_client: callback returned None for action=%s table=%s id=%s", action, table, payload["id"])
+    else:
+        logger.info("execute_on_client: got result type=%s keys=%s", type(result).__name__, list(result.keys()) if isinstance(result, dict) else "N/A")
     collector = _tool_result_collector.get(None)
-    if collector is not None:
+    if collector is not None and action in ("insert", "update", "delete"):
         collector.append({
             "action": action,
             "table": table,
-            "data": result,
+            "data": data or {},
         })
     return result

app/integrations/ms_graph.py

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

app/main.py

@@ -4,10 +4,6 @@ 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",
@@ -15,88 +11,16 @@ logging.basicConfig(
 logging.getLogger("sqlalchemy.engine").setLevel(logging.WARNING)
 logging.getLogger("sqlalchemy.pool").setLevel(logging.WARNING)
 
-
+from app.api.middleware.rate_limit import TierRateLimitMiddleware
-async def _memory_audit_cron_tick() -> None:
+from app.api.middleware.sanitizer import SanitizerMiddleware
-    """Weekly cron: contradiction scan + label canonicalization for all users (Phase 7)."""
+from app.config.settings import settings
-    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
 async def lifespan(app: FastAPI):
-    # Startup: ensure agent tool modules are loaded.
+    # Startup: initialise DB connection pool
-    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()
@@ -104,7 +28,7 @@ async def lifespan(app: FastAPI):
 
 def create_app() -> FastAPI:
     app = FastAPI(
-        title="AdiuvAI Cloud API",
+        title="Adiuva Cloud API",
         version="0.1.0",
         docs_url="/docs" if settings.ENV == "dev" else None,
         redoc_url=None,
@@ -124,14 +48,18 @@ def create_app() -> FastAPI:
     app.add_middleware(SanitizerMiddleware)
     app.add_middleware(TierRateLimitMiddleware)
 
-    from app.api.routes import agents, auth, billing, chat, device_ws, memory
+    from app.api.routes import agent_setup, agents, auth, backup, billing, chat, device_ws, plugins, storage, vectors
 
     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(agent_setup.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

@@ -0,0 +1,7 @@
+"""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

@@ -0,0 +1,212 @@
+"""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

@@ -0,0 +1,125 @@
+"""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

@@ -0,0 +1,233 @@
+"""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

@@ -1,20 +1,23 @@
 """SQLAlchemy ORM models for all persistent tables.
 
-Only auth, billing, agent config, and memory data live here.
+Only auth, billing, storage metadata, and marketplace data live here.
-User content (notes, tasks, etc.) lives exclusively on the client.
+User content (notes, tasks, etc.) is NEVER persisted server-side —
+it lives in E2E-encrypted blobs in S3, referenced by storage_records.
 
 Table inventory:
   users               — account credentials + tier
   refresh_tokens      — hashed refresh token store
   subscriptions       — Stripe subscription records
-  local_agent_configs — per-device batch agent configs
+  storage_records     — S3 blob metadata (no plaintext)
-  cloud_agent_configs — OAuth-backed cloud agent configs
+  backup_metadata     — encrypted backup manifests
-  agent_run_logs      — execution history for all agents
+  plugins             — marketplace plugin catalog
+  plugin_installations — per-user install records
+  plugin_reviews      — admin review decisions
+  revenue_events      — Stripe Connect 70/30 split ledger
   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
@@ -22,8 +25,8 @@ from __future__ import annotations
 import uuid
 from datetime import datetime, timezone
 
-from pgvector.sqlalchemy import Vector
 from sqlalchemy import (
+    BigInteger,
     Boolean,
     DateTime,
     Enum,
@@ -31,9 +34,9 @@ from sqlalchemy import (
     ForeignKey,
     Integer,
     JSON,
-    LargeBinary,
     String,
     Text,
+    UniqueConstraint,
     Uuid,
     func,
 )
@@ -55,6 +58,8 @@ 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")
@@ -72,8 +77,7 @@ 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 | None] = mapped_column(String(255), nullable=True)
+    password_hash: Mapped[str] = mapped_column(String(255), nullable=False)
-    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.
@@ -82,9 +86,6 @@ 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()
     )
@@ -95,9 +96,6 @@ 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):
@@ -118,25 +116,6 @@ 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"
 
@@ -158,6 +137,151 @@ 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"
 
@@ -172,7 +296,6 @@ 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)
@@ -243,7 +366,6 @@ 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()
@@ -264,17 +386,6 @@ 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 ─────────────────────────────────────────────────────────────
 
 
@@ -314,8 +425,8 @@ class MemoryAssociative(Base):
         nullable=False, index=True,
     )
     content_encrypted: Mapped[str] = mapped_column(Text, nullable=False)
-    # vector(1536) via pgvector; SQLite tests use NULL embeddings so no dialect issue.
+    # JSON-encoded float list in SQLite tests; vector(1536) in Postgres via migration.
-    embedding: Mapped[list | None] = mapped_column(Vector(1536), nullable=True)
+    embedding: Mapped[list | None] = mapped_column(JSON, 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(
@@ -363,85 +474,3 @@ 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

@@ -30,16 +30,6 @@ 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 ─────────────────────────────────────────────────────────────
@@ -60,6 +50,88 @@ 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):
@@ -70,6 +142,9 @@ class WsFrameType(str, Enum):
     tool_result = "tool_result"
     final = "final"
     ping = "ping"
+    agent_run = "agent_run"
+    agent_data = "agent_data"
+    agent_complete = "agent_complete"
     device_hello = "device_hello"
     # ── v3 frame types ─────────────────────────────────────────────────
     home_request = "home_request"
@@ -81,21 +156,6 @@ class WsFrameType(str, Enum):
     data_request = "data_request"
     data_response = "data_response"
     mutation = "mutation"
-    # ── v4 journey frame types ────────────────────────────────────────
-    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):
@@ -148,19 +208,34 @@ class WsDeviceHello(BaseModel):
     agent_ids: list[str] = Field(default_factory=list)
 
 
+class WsAgentRun(BaseModel):
+    """Server → Client: trigger an agent run on the connected device."""
+
+    type: Literal[WsFrameType.agent_run] = WsFrameType.agent_run
+    run_id: str
+    agent_id: str
+    config: dict[str, Any]
+
+
+class WsAgentData(BaseModel):
+    """Client → Server: files read by the local agent."""
+
+    type: Literal[WsFrameType.agent_data] = WsFrameType.agent_data
+    run_id: str
+    files: list[dict[str, Any]]
+
+
+class WsAgentComplete(BaseModel):
+    """Client → Server: Electron signals it has finished reading files."""
+
+    type: Literal[WsFrameType.agent_complete] = WsFrameType.agent_complete
+    run_id: str
+    files_read: int
+    errors: list[str] = Field(default_factory=list)
+
 
 # ── 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."""
 
@@ -174,7 +249,6 @@ 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):
@@ -183,18 +257,6 @@ 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):
@@ -217,16 +279,7 @@ class WsStreamEnd(BaseModel):
 
     type: Literal[WsFrameType.stream_end] = WsFrameType.stream_end
     request_id: str
-    error: str | None = None
+    mutations: list[dict[str, Any]] = Field(default_factory=list)
-    mutations: list[dict[str, Any]] | None = None
-
-
-class WsDomain(BaseModel):
-    """Structured floating domain payload for UI routing decisions."""
-
-    type: Literal["task", "timeline", "project", "node"]
-    id: str | None = None
-    section: Literal["task", "timeline", "note"] | None = None
 
 
 class WsFloatingDomain(BaseModel):
@@ -234,28 +287,7 @@ class WsFloatingDomain(BaseModel):
 
     type: Literal[WsFrameType.floating_domain] = WsFrameType.floating_domain
     request_id: str
-    domain: WsDomain
+    domain: Literal["tasks", "timelines", "notes", "projects"]
-
-
-# ── 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 ─────────────────────────────────────────────────────
@@ -264,29 +296,84 @@ class AgentCatalogItem(BaseModel):
     type: str
     name: str
     description: str
+    config_schema: dict[str, Any] = Field(default_factory=dict)
 
 
-class AgentCreationCheckRequest(BaseModel):
+# ── Local Agent Config ────────────────────────────────────────────────
-    active_agents: int = Field(ge=0, default=0)
+
+class LocalAgentConfigCreate(BaseModel):
+    name: str
+    device_id: str
+    directory_paths: list[str]
+    data_types: list[str]
+    prompt_template: str
+    file_extensions: list[str]
+    schedule_cron: str
 
 
-class AgentCreationCheckResponse(BaseModel):
+class LocalAgentConfigUpdate(BaseModel):
-    allowed: bool
+    name: str | None = None
-    tier: BillingTier
+    device_id: str | None = None
-    active_agents: int
+    directory_paths: list[str] | None = None
-    limit: int
+    data_types: list[str] | None = None
+    prompt_template: str | None = None
+    file_extensions: list[str] | None = None
+    schedule_cron: str | None = None
+    enabled: bool | None = None
 
 
-class AgentTriggerRequest(BaseModel):
+class LocalAgentConfigResponse(BaseModel):
-    directory: str = Field(min_length=1)
+    id: str
-    device_id: str = Field(default="")
+    name: str
-    agent_id: str | None = None  # FE stable agent ID (electron-store UUID)
+    device_id: str
-    what_to_extract: list[str] = Field(min_length=1)
+    directory_paths: list[str]
-    batch_interval: str = Field(min_length=1)
+    data_types: list[str]
-    custom_agent_prompt: str | None = None
+    prompt_template: str
-    agent_config: dict | None = None
+    file_extensions: list[str]
-    active_agents: int = Field(ge=0, default=0)
+    schedule_cron: str
-    last_run_at: int | None = None  # epoch ms from FE — enables incremental scanning
+    enabled: bool
+    last_run_at: int | None
+    created_at: int
+    updated_at: int
+
+
+# ── Cloud Agent Config ────────────────────────────────────────────────
+
+class CloudAgentConfigCreate(BaseModel):
+    provider: Literal["gmail", "teams", "outlook"]
+    name: str
+    data_types: list[str]
+    prompt_template: str
+    oauth_token_encrypted: str
+    schedule_cron: str
+    filter_config: dict[str, Any] | None = None
+
+
+class CloudAgentConfigUpdate(BaseModel):
+    provider: Literal["gmail", "teams", "outlook"] | None = None
+    name: str | None = None
+    data_types: list[str] | None = None
+    prompt_template: str | None = None
+    oauth_token_encrypted: str | None = None
+    schedule_cron: str | None = None
+    filter_config: dict[str, Any] | None = None
+    enabled: bool | None = None
+
+
+class CloudAgentConfigResponse(BaseModel):
+    """oauth_token_encrypted is intentionally excluded — never returned to clients."""
+
+    id: str
+    provider: Literal["gmail", "teams", "outlook"]
+    name: str
+    data_types: list[str]
+    prompt_template: str
+    schedule_cron: str
+    filter_config: dict[str, Any] | None
+    enabled: bool
+    last_run_at: int | None
+    created_at: int
+    updated_at: int
 
 
 # ── Agent Run Log ─────────────────────────────────────────────────────
@@ -305,3 +392,18 @@ class AgentRunLogResponse(BaseModel):
 
 # ── Chatbot Journey ───────────────────────────────────────────────────
 
+class JourneyStartRequest(BaseModel):
+    agent_type: Literal["local", "cloud"]
+    agent_id: str | None = None
+
+
+class JourneyMessageRequest(BaseModel):
+    session_id: str
+    message: str
+
+
+class JourneyResponse(BaseModel):
+    session_id: str
+    message: str
+    done: bool
+    prompt_template: str | None = None

app/storage/__init__.py

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

app/storage/blob_store.py

@@ -0,0 +1,106 @@
+"""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

@@ -0,0 +1,32 @@
+"""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

@@ -0,0 +1,205 @@
+"""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/adiuvai
+      DATABASE_URL: postgresql+asyncpg://postgres:postgres@db:5432/adiuva
       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: adiuvai
+      POSTGRES_DB: adiuva
     volumes:
       - postgres_data:/var/lib/postgresql/data
     healthcheck:
@@ -36,6 +36,37 @@ 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:

requirements.txt

@@ -4,6 +4,8 @@ gunicorn>=22.0.0
 langchain>=0.3.0
 langchain-openai>=0.3.0
 langchain-litellm>=0.1.0
+langgraph>=0.3.0
+deepagents>=0.4.10
 litellm>=1.50.0
 pydantic>=2.10.0
 pydantic-settings>=2.7.0
@@ -32,12 +34,4 @@ 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

@@ -6,23 +6,26 @@ 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 Subscription, User
+from app.models import Plugin, Subscription, User
 
 # ── Fixed test user IDs (one per tier) ───────────────────────────────
 
@@ -106,6 +109,79 @@ 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 ──────────────────────────────────────────────────────
 
 
@@ -136,53 +212,24 @@ def auth_header(tier: str = "power", user_id: str | None = None) -> dict[str, st
     return {"Authorization": f"Bearer {make_jwt(tier, user_id)}"}
 
 
-# ── Convenience aliases and per-tier user fixtures ────────────────────
+# ── S3 mock fixture ──────────────────────────────────────────────────
 
-@pytest_asyncio.fixture
+S3_TEST_BUCKET = "test-bucket"
-async def db(db_session: AsyncSession) -> AsyncSession:
+S3_TEST_REGION = "us-east-1"
-    """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 auth_headers_free() -> dict[str, str]:
+def s3_bucket():
-    """Authorization header for the seeded free-tier user."""
+    """Create a mocked S3 bucket via moto and patch BlobStore settings."""
-    return auth_header("free")
+    with mock_aws():
-
+        os.environ.setdefault("AWS_ACCESS_KEY_ID", "testing")
-
+        os.environ.setdefault("AWS_SECRET_ACCESS_KEY", "testing")
-# ── CLI options ───────────────────────────────────────────────────────
+        os.environ.setdefault("AWS_DEFAULT_REGION", S3_TEST_REGION)
-
+        client = boto3.client("s3", region_name=S3_TEST_REGION)
-def pytest_addoption(parser):
+        client.create_bucket(Bucket=S3_TEST_BUCKET)
-    parser.addoption(
+        with patch("app.storage.blob_store.settings") as mock_settings:
-        "--preprocess-dir",
+            mock_settings.S3_BUCKET = S3_TEST_BUCKET
-        default=None,
+            mock_settings.S3_REGION = S3_TEST_REGION
-        help="Override fixture folder for preprocessor tests (must contain cases.yaml + data/)",
+            mock_settings.AWS_ACCESS_KEY_ID = "testing"
-    )
+            mock_settings.AWS_SECRET_ACCESS_KEY = "testing"
-    parser.addoption(
+            yield S3_TEST_BUCKET
-        "--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

@@ -1,86 +0,0 @@
-# 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

@@ -1,7 +0,0 @@
-<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

@@ -1,5 +0,0 @@
-<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

@@ -1,7 +0,0 @@
-<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

@@ -1,5 +0,0 @@
-<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

@@ -1,19 +0,0 @@
-# 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

@@ -1,23 +0,0 @@
-<!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

@@ -1,23 +0,0 @@
-<!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

@@ -1,68 +0,0 @@
-# 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

@@ -1,25 +0,0 @@
-<!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

@@ -1,49 +0,0 @@
-<!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

@@ -1,8 +0,0 @@
-<!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

@@ -1,24 +0,0 @@
-<!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

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

tests/fixtures/preprocessors/data/generic_page.html

@@ -1,35 +0,0 @@
-<!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

@@ -1,15 +0,0 @@
-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

@@ -0,0 +1,871 @@
+"""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        — overdue local + cloud dispatched
+    - trigger_pending_runs        — non-overdue skipped
+    - trigger_pending_runs        — device_id filter for local agents
+
+  Integration:
+    - POST /agents/{id}/run       — 404 on unknown agent
+    - POST /agents/{id}/run       — 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 True
+
+    # 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():
+    """If no agents are overdue trigger_pending_runs does nothing."""
+    from datetime import timedelta
+
+    config = _make_local_config()
+    config.last_run_at = datetime.now(timezone.utc) - timedelta(minutes=30)  # ran 30m ago
+    config.schedule_cron = "0 */6 * * *"  # every 6h — not due yet
+
+    mock_db_result_local = MagicMock()
+    mock_db_result_local.scalars.return_value.all.return_value = [config]
+
+    mock_db_result_cloud = MagicMock()
+    mock_db_result_cloud.scalars.return_value.all.return_value = []
+
+    mgr = _make_manager()
+
+    with patch("app.core.agent_runner.async_session") as mock_session_factory, \
+         patch("app.core.agent_runner.run_local_agent", new_callable=AsyncMock) as mock_run:
+        mock_ctx = AsyncMock()
+        mock_ctx.__aenter__ = AsyncMock(return_value=mock_ctx)
+        mock_ctx.__aexit__ = AsyncMock(return_value=False)
+        mock_ctx.execute = AsyncMock(
+            side_effect=[mock_db_result_local, mock_db_result_cloud]
+        )
+        mock_session_factory.return_value = mock_ctx
+
+        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():
+    """Local agents are only triggered for the matching device_id."""
+    # The DB query already filters by device_id, so we verify the SELECT
+    # includes the device_id filter by checking that a config bound to a
+    # different device is never dispatched.
+    #
+    # Since trigger_pending_runs queries with device_id == "dev-001",
+    # simulate the DB returning an empty list (as it would for a mismatch).
+    mock_db_result_local = MagicMock()
+    mock_db_result_local.scalars.return_value.all.return_value = []  # no match
+
+    mock_db_result_cloud = MagicMock()
+    mock_db_result_cloud.scalars.return_value.all.return_value = []
+
+    mgr = _make_manager(device_id="dev-001")
+
+    with patch("app.core.agent_runner.async_session") as mock_session_factory, \
+         patch("app.core.agent_runner.run_local_agent", new_callable=AsyncMock) as mock_run:
+        mock_ctx = AsyncMock()
+        mock_ctx.__aenter__ = AsyncMock(return_value=mock_ctx)
+        mock_ctx.__aexit__ = AsyncMock(return_value=False)
+        mock_ctx.execute = AsyncMock(
+            side_effect=[mock_db_result_local, mock_db_result_cloud]
+        )
+        mock_session_factory.return_value = mock_ctx
+
+        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():
+    """Overdue local agent triggers run_local_agent sequentially."""
+    config = _make_local_config()  # last_run_at=None → always overdue
+
+    mock_db_result_local = MagicMock()
+    mock_db_result_local.scalars.return_value.all.return_value = [config]
+
+    mock_db_result_cloud = MagicMock()
+    mock_db_result_cloud.scalars.return_value.all.return_value = []
+
+    mgr = _make_manager()
+
+    call_order: list[str] = []
+
+    async def _mock_run_local(user_id, cfg, run_log, device_mgr):
+        call_order.append("run_local")
+
+    with patch("app.core.agent_runner.async_session") as mock_session_factory, \
+         patch("app.core.agent_runner.run_local_agent", side_effect=_mock_run_local):
+        # First call: query configs. Subsequent calls: create run_log.
+        mock_query_ctx = AsyncMock()
+        mock_query_ctx.__aenter__ = AsyncMock(return_value=mock_query_ctx)
+        mock_query_ctx.__aexit__ = AsyncMock(return_value=False)
+        mock_query_ctx.execute = AsyncMock(
+            side_effect=[mock_db_result_local, mock_db_result_cloud]
+        )
+
+        run_log_obj = AgentRunLog(
+            id=str(uuid.uuid4()),
+            agent_id=config.id,
+            agent_type="local",
+            user_id=_FREE_UID,
+            status="running",
+            started_at=datetime.now(timezone.utc),
+        )
+        mock_insert_ctx = AsyncMock()
+        mock_insert_ctx.__aenter__ = AsyncMock(return_value=mock_insert_ctx)
+        mock_insert_ctx.__aexit__ = AsyncMock(return_value=False)
+        mock_insert_ctx.add = MagicMock()
+        mock_insert_ctx.commit = AsyncMock()
+        mock_insert_ctx.refresh = AsyncMock(side_effect=lambda obj: None)
+
+        mock_session_factory.side_effect = [mock_query_ctx, mock_insert_ctx]
+
+        await trigger_pending_runs(_FREE_UID, "dev-001", mgr)
+
+    assert call_order == ["run_local"]
+
+
+# ---------------------------------------------------------------------------
+# Integration: POST /agents/{id}/run
+# ---------------------------------------------------------------------------
+
+
+@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_trigger_run_unknown_agent(client):
+    """POST /agents/{id}/run returns 404 for unknown agent id."""
+    resp = client.post(
+        f"/api/v1/agents/{uuid.uuid4()}/run",
+        headers=auth_header("power"),
+    )
+    assert resp.status_code == 404
+
+
+@pytest.mark.asyncio
+async def test_trigger_run_local_agent_creates_run_log(client, db_session):
+    """POST /agents/{id}/run creates a run log and dispatches a background task."""
+    # Create the local agent config in the DB.
+    config = LocalAgentConfig(
+        id=str(uuid.uuid4()),
+        user_id=TEST_USER_IDS["power"],
+        device_id="dev-001",
+        name="My Agent",
+        directory_paths=["/home/user/docs"],
+        data_types=["tasks"],
+        prompt_template="Extract tasks.",
+        file_extensions=[".txt"],
+        schedule_cron="0 */6 * * *",
+        enabled=True,
+    )
+    db_session.add(config)
+    await db_session.commit()
+
+    dispatched: list = []
+
+    async def _fake_run(user_id, cfg, run_log, device_mgr):
+        dispatched.append((user_id, cfg.id))
+
+    with patch("app.api.routes.agents.run_local_agent", new_callable=AsyncMock, side_effect=_fake_run), \
+         patch("app.api.routes.agents.run_cloud_agent", new_callable=AsyncMock), \
+         patch("asyncio.create_task") as mock_create_task:
+        resp = client.post(
+            f"/api/v1/agents/{config.id}/run",
+            headers=auth_header("power"),
+        )
+
+    assert resp.status_code == 202
+    data = resp.json()
+    assert data["agent_id"] == config.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

@@ -1,430 +0,0 @@
-"""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

@@ -0,0 +1,243 @@
+"""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