memory evolution phase 1

- Add _language_instruction() to deep_agent.py, reads language from core memory
- Append language directive to all 4 run_* functions (task/project/checkpoint/note)
- Minor fixes: alembic env, route imports, test cleanup

.env.example

@@ -2,7 +2,7 @@
 ENV=dev
 
 # ── Database ──────────────────────────────────────────────────────────────────
-DATABASE_URL=postgresql+asyncpg://postgres:postgres@localhost:5432/adiuva
+DATABASE_URL=postgresql+asyncpg://postgres:postgres@localhost:5432/adiuvai
 
 # ── Auth ──────────────────────────────────────────────────────────────────────
 JWT_SECRET=replace-with-a-long-random-secret
@@ -13,11 +13,45 @@ JWT_REFRESH_TOKEN_EXPIRE_DAYS=30
 # ── LLM ───────────────────────────────────────────────────────────────────────
 # LiteLLM model identifiers — change to swap providers without code changes.
 # Examples: gpt-4o, anthropic/claude-sonnet-4-20250514, gemini/gemini-pro, ollama/llama3
+#
+# API keys — only the key(s) matching your chosen provider(s) are required.
+# The correct key is picked automatically from the model prefix (e.g.
+# "anthropic/..." → ANTHROPIC_API_KEY, "gemini/..." → GOOGLE_API_KEY).
 OPENAI_API_KEY=
 ANTHROPIC_API_KEY=
 GOOGLE_API_KEY=
-LLM_MODEL=gpt-4o
+CEREBRAS_API_KEY=
-LLM_ROUTER_MODEL=gpt-4o-mini
+
+# 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=
+
+# Setup-agent — guided journey to build an AgentConfig via WebSocket chat.
+LLM_MODEL_SETUP_AGENT=
 
 # ── Stripe (leave empty to stub billing) ──────────────────────────────────────
 STRIPE_SECRET_KEY=
@@ -27,9 +61,9 @@ STRIPE_WEBHOOK_SECRET=
 # ── Langfuse (leave empty to disable observability) ───────────────────────────
 LANGFUSE_SECRET_KEY=
 LANGFUSE_PUBLIC_KEY=
-# LANGFUSE_HOST=https://cloud.langfuse.com        # EU (default)
+# LANGFUSE_BASE_URL=https://cloud.langfuse.com        # EU (default)
-# LANGFUSE_HOST=https://us.cloud.langfuse.com     # US
+# LANGFUSE_BASE_URL=https://us.cloud.langfuse.com     # US
-# LANGFUSE_HOST=http://localhost:3000             # Self-hosted
+# LANGFUSE_BASE_URL=http://localhost:3000             # Self-hosted
 
 # ── CORS ──────────────────────────────────────────────────────────────────────
 # Comma-separated list parsed by Settings (override default if needed)

.gitea/workflows/deploy.yaml

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

.github/workflows/ci.yml

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

README.md

@@ -1,591 +0,0 @@
-# Adiuva Cloud API
-
-**AI-powered project management backend with LLM orchestration and subscription billing.**
-
-Built with FastAPI · Python 3.12 · PostgreSQL · LangChain · Stripe
-
----
-
-## 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)
-- [Billing & Tiers](#billing--tiers)
-- [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, text embedding generation, and Stripe-based subscription billing across four tiers.
-
-### Design Principles
-
-1. **Never expose prompts** — system prompts stay server-side; responses are sanitized to strip any leaked prompt fragments.
-2. **Stateless request handling** — all context comes from the client and JWT; no server-side session state.
-3. **Tier gates enforced server-side** — the server always reads the current tier from the database, never trusting client-reported values.
-
----
-
-## Architecture
-
-```
-┌──────────────┐      ┌────────────────────────────────────────────────────────┐
-│  Electron    │      │  FastAPI  (Uvicorn / Gunicorn)                         │
-│  Desktop App │────▶│                                                        │
-│  (Client)    │◀────│  Middleware: RateLimit → Sanitizer → CORS → Router     │
-└──────────────┘      │                                                        │
-                      │  ┌──────────────────┐  ┌────────────────────────────┐  │
-                      │  │  Auth Routes     │  │  Chat Routes               │  │
-                      │  │  Billing Routes  │  │    ↓                       │  │
-                      │  │  Agent Routes    │  │  Orchestrator (GPT-4o-mini)│  │
-                      │  │  Device WS       │  │    ↓ classify intent       │  │
-                      │  └──────────────────┘  │  Agent Registry            │  │
-                      │                        │    ↓                       │  │
-                      │                        │  TaskAgent  | ProjectAgent │  │
-                      │                        │  NoteAgent  | CheckptAgent │  │
-                      │                        │  (GPT-4o + LangChain)      │  │
-                      │                        └────────────────────────────┘  │
-                      └────────────────────────────────────────────────────────┘
-                               │
-                      ┌────────▼───┐
-                      │ PostgreSQL │
-                      │ (Auth,     │
-                      │  Billing,  │
-                      │  Agents)   │
-                      └────────────┘
-                               │
-                      ┌────────▼───┐
-                      │  Stripe    │
-                      │  (Billing) │
-                      └────────────┘
-```
-
----
-
-## 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. **Text embeddings** — Generates text-embedding-3-small vectors for local client-side note search.
-5. **Stripe billing** — Four-tier subscription model (Free / Pro / Power / Team) with checkout sessions and full webhook lifecycle handling.
-6. **JWT authentication** — Access + refresh tokens with bcrypt password hashing, SHA-256 token hashing, and automatic rotation.
-7. **Prompt IP protection** — Sanitizer middleware strips system prompts, reasoning markers, tool schemas, and agent routing metadata from all chat responses.
-8. **Tier-based rate limiting** — Sliding-window per-user limiter scaling from 20 to 200 requests/min by subscription tier.
-9. **WebSocket streaming** — Real-time chat with 30-second heartbeat keep-alive and chunked text delivery.
-10. **Alembic migrations** — Versioned schema management.
-11. **Comprehensive test suite** — In-memory SQLite, 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 |
-| `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) |
-| `pytest` | ≥ 8.0.0 | Test framework |
-| `pytest-asyncio` | ≥ 0.24.0 | Async test support |
-| `aiosqlite` | ≥ 0.20.0 | In-memory SQLite 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)
-
-### 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
-
-### 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**.
-
-### 1. Start all services
-
-```bash
-docker compose up -d
-```
-
-This starts PostgreSQL alongside the app.
-
-### 2. Configure your `.env`
-
-```bash
-# Database (uses the compose PostgreSQL)
-DATABASE_URL=postgresql+asyncpg://postgres:postgres@db:5432/adiuva
-
-# 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
-```
-
-### 3. 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, agents |
-| 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 |\n| `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 |
-| `POST` | `/api/v1/chat/embed` | JWT | Generate a 1536-dim text embedding vector (`text-embedding-3-small`). Used by Electron for local note search. |
-| `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 |
-
-### 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
-
-3 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 |
-
-### Enum Types
-
-| Enum | Values |
-|---|---|
-| `billing_tier` | `free`, `pro`, `power`, `team` |
-
-### Migrations
-
-| Version | Description |
-|---|---|
-| `001_initial_schema` | Creates core auth and billing tables with indexes and foreign key constraints |
-
----
-
-## 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` and `shared_memory`.
-- **`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`.
-
----
-
-## 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 |
-| LLM Providers | 1 | Unlimited | Unlimited | Unlimited |
-| Batch Builder | — | — | ✓ | ✓ |
-| 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.
-
----
-
-## 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.
-- **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.
-- **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_middleware.py` | Rate limiting by tier, sanitizer prompt leak detection |
-
----
-
-## Project Structure
-
-```
-adiuva-api/
-├── alembic.ini                  # Alembic configuration
-├── 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
-│
-├── app/                         # Application source
-│   ├── main.py                  # FastAPI app factory, middleware, routes
-│   ├── db.py                    # Async SQLAlchemy engine & session
-│   ├── models.py                # SQLAlchemy ORM models
-│   ├── 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)
-│   │   └── deep_agent.py        # Deep agent orchestration
-│   │
-│   ├── api/                     # HTTP layer
-│   │   ├── deps.py              # Shared FastAPI dependencies
-│   │   ├── middleware/
-│   │   │   ├── rate_limit.py    # Sliding-window tier rate limiter
-│   │   │   └── sanitizer.py     # Prompt IP leak protection
-│   │   └── routes/
-│   │       ├── auth.py          # Register, login, refresh, me
-│   │       ├── chat.py          # Chat + embed endpoint
-│   │       ├── billing.py       # Stripe checkout, webhooks, subscription
-│   │       ├── agents.py        # Agent catalog, config, runs
-│   │       └── device_ws.py     # Persistent device WebSocket
-│   │
-│   └── billing/
-│       ├── stripe_service.py    # Stripe API wrapper
-│       └── tier_manager.py      # Feature matrix, rate limits
-│
-└── tests/                       # Test suite
-    ├── conftest.py              # Fixtures: DB, auth, seeds
-    ├── test_auth.py
-    ├── test_orchestrator.py
-    ├── test_agents.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 engine_from_config, pool
+from sqlalchemy import pool
 from sqlalchemy.ext.asyncio import create_async_engine
 
 # Alembic Config object (gives access to alembic.ini values).

alembic/versions/003_agent_tables.py

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

alembic/versions/005_associative_pgvector.py

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

alembic/versions/a3b9c0d1e2f3_add_agent_config_to_local_agents.py

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

alembic/versions/b4c0d1e2f3a4_add_oauth_and_avatar.py

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

alembic/versions/c5d1e2f3a4b5_add_onboarding_completed_at.py

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

alembic/versions/e04100e88ace_avatar_url_varchar_to_text.py

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

app/agents/filesystem_agent.py

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

app/agents/note_agent.py

@@ -18,21 +18,6 @@ _UUID_RE = re.compile(
 def _is_uuid(value: str) -> bool:
     return bool(_UUID_RE.match(value))
 
-NOTE_SYSTEM_PROMPT = (
-    "You are a note-taking assistant. You help users create, retrieve, update,\n"
-    "and delete Markdown notes in their workspace.\n\n"
-    "Rules:\n"
-    "  - content is always Markdown; preserve formatting when updating\n"
-    "  - project_id is optional; link a note to a project when mentioned\n"
-    "  - When updating, call get_note first if you need to read existing content\n"
-    "    before appending or replacing sections\n"
-    "  - list_notes without project_id returns all notes; scope with project_id\n"
-    "    when the user is working within a specific project\n"
-    "  - project_id must be a UUID; if you only know a project name, do not pass it as project_id\n"
-    "  - Do not fabricate note content — reflect what the user provides or what\n"
-    "    is already in the note (retrieved via get_note)."
-)
-
 
 @tool
 async def list_notes(project_id: str = "") -> str:

app/agents/project_agent.py

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

app/agents/task_agent.py

@@ -18,23 +18,6 @@ _UUID_RE = re.compile(
 def _is_uuid(value: str) -> bool:
     return bool(_UUID_RE.match(value))
 
-TASK_SYSTEM_PROMPT = (
-    "You are a task management assistant for a project workspace.\n"
-    "You create, update, list, and track tasks and their comments.\n\n"
-    "Rules:\n"
-    "  - status must be one of: todo, in_progress, done\n"
-    "  - priority must be one of: high, medium, low\n"
-    "  - due_date is a Unix timestamp in milliseconds; convert human dates\n"
-    "  - assignees is a JSON-encoded array of strings (e.g. '[\"Alice\",\"Bob\"]')\n"
-    "  - project_id is optional; link to a project when the user mentions one\n"
-    "  - is_ai_suggested: 1 only when proactively proposing a task the user\n"
-    "    did not explicitly request; 0 otherwise\n"
-    "  - is_ai_suggested: 1 only when proactively proposing a task the user did not explicitly request; 0 otherwise\n"
-    "  - Use list_tasks_due_today for 'what's due today' queries\n"
-    "  - For update_task, use -1 for integer fields you do not want to change\n"
-    "  - Always confirm the action in plain, user-friendly language."
-)
-
 
 # ── Task tools ────────────────────────────────────────────────────────
 

app/agents/timeline_agent.py

@@ -17,20 +17,6 @@ _UUID_RE = re.compile(
 def _is_uuid(value: str) -> bool:
     return bool(_UUID_RE.match(value))
 
-TIMELINE_SYSTEM_PROMPT = (
-    "You are a project timeline assistant. Timelines are milestone dates that\n"
-    "track progress on a project — they are not calendar events.\n\n"
-    "Rules:\n"
-    "  - project_id is REQUIRED for every create; confirm with the user if unknown\n"
-    "  - For listing, project_id must be a UUID; never pass plain names as project_id\n"
-    "  - date is a Unix timestamp in milliseconds; convert human-readable dates\n"
-    "  - is_ai_suggested: 1 when proactively proposing a timeline, 0 otherwise\n"
-    "  - is_ai_suggested: 1 when proactively proposing a timeline, 0 otherwise\n"
-    "  - For update_timeline, use -1 for integer fields you do not want to change\n"
-    "  - Listing without a project_id returns all timelines across projects\n"
-    "  - Always echo the title and formatted date in your confirmation."
-)
-
 
 @tool
 async def list_timelines(project_id: str = "") -> str:

app/api/middleware/auth.py

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

app/api/routes/agent_setup.py

@@ -1,11 +1,11 @@
-"""Chatbot Journey — WS-based guided conversation to build an agent prompt_template.
+"""Chatbot Journey — WS-based guided conversation to build an AgentConfig.
 
 The journey is driven entirely through WebSocket frames (no REST endpoints).
 The device WS handler dispatches ``journey_start`` and ``journey_message``
 frames to the functions exported here.
 
 Journey flow:
-  1. FE sends ``journey_start`` frame with basic agent config (directory,
+  1. FE sends ``journey_start`` frame with basic agent info (directory,
      data_types, schedule).
   2. Server creates an in-memory session, sets up a WS executor so the
      setup LLM can use file-system tools, does a first directory scrape,
@@ -13,10 +13,11 @@ Journey flow:
   3. FE sends ``journey_message`` frames for each user reply.
   4. Server appends the user message, calls the LLM (which may read files
      via tools), and sends back a ``journey_reply``.
-  5. After 3-5 turns the LLM wraps up by emitting a ``prompt_template``
+  5. After 3-5 turns the LLM wraps up by emitting an ``AgentConfig`` JSON
-     block delimited by ``PROMPT_TEMPLATE_START`` / ``PROMPT_TEMPLATE_END``.
+     block delimited by ``AGENT_CONFIG_START`` / ``AGENT_CONFIG_END``.
-  6. Server parses the block, sends ``journey_reply`` with ``done=True``
+  6. Server parses and validates the JSON with Pydantic, sends
-     and the template.  FE stores it locally.
+     ``journey_reply`` with ``done=True`` and the serialised config.
+     FE stores it locally.
 """
 
 from __future__ import annotations
@@ -30,10 +31,10 @@ from typing import Any
 
 from langchain_core.messages import AIMessage, HumanMessage, SystemMessage, ToolMessage
 
-from app.agents.filesystem_agent import FILESYSTEM_TOOLS
+from app.agents.filesystem_agent import make_directory_tools
-from app.config.settings import settings
+from app.core.langfuse_client import compile_prompt, extract_usage, get_langfuse, get_prompt_or_fallback, langfuse_context
-from app.core.langfuse_client import extract_usage, get_langfuse, get_prompt_or_fallback
+from app.core.llm import get_agent_llm, model_for_agent
-from app.core.llm import get_llm
+from app.schemas import AgentConfig
 
 logger = logging.getLogger(__name__)
 
@@ -41,9 +42,9 @@ logger = logging.getLogger(__name__)
 
 _SESSION_TTL_SECONDS: int = 1800  # 30 minutes
 
-# Sentinel strings used to delimit the LLM-produced prompt_template.
+# Sentinel strings used to delimit the LLM-produced AgentConfig JSON.
-_TEMPLATE_START = "PROMPT_TEMPLATE_START"
+_CONFIG_START = "AGENT_CONFIG_START"
-_TEMPLATE_END = "PROMPT_TEMPLATE_END"
+_CONFIG_END = "AGENT_CONFIG_END"
 
 # Minimum turns before we consider nudging the LLM to wrap up.
 _MIN_TURNS_BEFORE_NUDGE: int = 3
@@ -86,61 +87,76 @@ def get_journey_session(session_id: str, user_id: str) -> JourneySession | None:
     return s
 
 
-# ── System prompt builder ─────────────────────────────────────────────────
+# ── System prompt ─────────────────────────────────────────────────────────
 
 _JOURNEY_SYSTEM_PROMPT = """\
 You are a friendly assistant helping a freelancer configure a data-extraction agent.
-Your job is to understand exactly what data the user wants to extract from their
+Your job is to understand what files the user has in their directory and produce a
-local directory and produce a detailed prompt_template that a separate AI will use
+structured AgentConfig JSON that the extraction agent will use as its instruction set.
-as its instruction set.
-
-The extraction agent already has this base behaviour built in:
-  - Reads each file using file-system tools.
-  - Creates records (tasks, notes, timelines, projects) via CRUD tools.
-  - Sets isAiSuggested=1 on every new record.
-  - Only extracts data explicitly present in the files — it never invents information.
-The user's custom prompt is appended AFTER this base behaviour, so focus on
-what to look for and how to map it — not on the general extraction mechanics.
 
 You have access to file-system tools to explore the user's directory:
-- list_directory: to see folder structure
+- list_directory: see folder structure and file names
-- read_file_content: to peek at file contents
+- read_file_content: peek at a file's content
-- get_file_metadata: to check file info
+- get_file_metadata: check file size, extension, dates
 
 The user's configured directory is: {directory}
 Target data types: {data_types}
 
-IMPORTANT — project assignment is handled automatically by the main agent runner
+## Your process
-before the custom prompt is ever used.  You MUST NOT ask the user about projects,
-projectId, or how to link records to projects.  Never include projectId logic or
-project creation instructions in the generated prompt_template.
 
-Start by exploring the directory to understand its structure.  Then ask concise,
+### Step 1 — Explore the directory
-focused questions one at a time.  Cover these topics (not necessarily in this order):
+Use list_directory and read_file_content to understand what types of files are present
-  1. The type and format of the source content (confirmed by your exploration).
+(HTML emails, plain-text documents, CSVs, etc.).
-  2. How fields should be mapped (e.g. filename → task title).
-  3. Priority or status rules (e.g. "urgent" keyword → high priority).
-  4. Any special handling, date extraction, or exclusions.
 
-Once you reach 90% confidence, output the final prompt_template between these exact
+### Step 2 — Identify content types
-markers on their own lines:
+For each distinct file type found, decide:
+- A short id (e.g. "email_html", "plain_text", "csv")
+- Which preprocessing handler to use: "email_html" for HTML emails, "generic" for everything else
+- A human-readable label and optional detection_hint
 
-{template_start}
+### Step 3 — Ask focused questions (one at a time)
-<the complete extraction prompt here>
+Cover these topics based on what you discovered:
-{template_end}
+1. How to map content to entity types (task / note / timeline entry)
+2. Field mapping rules (e.g. email Subject → task title, filename → note title)
+3. Priority or status rules (e.g. "urgent" in subject → high priority)
+4. Date extraction (e.g. "by Friday" → dueDate)
+5. Exclusion rules (e.g. skip newsletters, skip files with no project match)
 
-The prompt_template must be a self-contained instruction for an AI that reads files
+### Step 4 — Produce the AgentConfig JSON
-and must perform CRUD operations using tools to create records.  It should specify:
+Once you are ≥ 90% confident, output the final config between these exact markers
-  - What entity types to create (tasks, notes, timelines) — never projects.
+(each on its own line):
-  - How to map file content to record fields (camelCase: title, status, priority,
+
-    dueDate, content, etc.) — never include projectId.
+{config_start}
-  - That isAiSuggested must be set to 1 on every new record.
+{{
-  - Concrete examples of mappings based on what you discovered in the directory.
+  "content_types": [
+    {{
+      "id": "email_html",
+      "label": "Email HTML",
+      "detection_hint": "HTML file with From/To/Subject headers",
+      "preprocessing": "email_html",
+      "extraction_prompt": "Detailed extraction instructions for this content type..."
+    }}
+  ],
+  "global_rules": [
+    "If the file cannot be matched to any project, do not create any entity."
+  ],
+  "data_types": {data_types_json}
+}}
+{config_end}
+
+## Rules for the extraction_prompt field
+- Describe when to create a task vs note vs timeline entry (be specific and concrete)
+- Include field mapping rules based on what you found in the directory
+- Include priority/status/date rules if applicable
+- Do NOT include projectId logic — the runner handles project assignment automatically
+- Do NOT mention isAiSuggested — the runner always sets it to 1
+
+## Constraints
+- Never ask about projects, projectId, or how to link records to projects
+- Never include projectId or project creation logic in the generated config
+- Keep asking questions until ≥ 90% confident, then output the JSON immediately
 
 {existing_section}\
-Keep asking clarifying questions until you are at least 90% confident you have
-enough information to generate an accurate prompt_template.  Once you reach that
-confidence level, stop asking and produce the final template immediately.
 Begin by exploring the directory, then ask your first question.\
 """
 
@@ -148,38 +164,53 @@ Begin by exploring the directory, then ask your first question.\
 def _build_system_prompt(
     directory: str,
     data_types: list[str],
-    existing_template: str | None = None,
+    existing_config: str | None = None,
 ) -> tuple[str, Any]:
     """Return ``(compiled_system_prompt, langfuse_prompt_obj_or_None)``."""
     existing_section = (
-        f"\nThe user already has the following prompt_template — refine it based on their answers:\n"
+        "\nThe user already has the following AgentConfig — refine it based on their answers:\n"
-        f"---\n{existing_template}\n---\n"
+        f"```json\n{existing_config}\n```\n"
-        if existing_template
+        if existing_config
         else ""
     )
     template, prompt_obj = get_prompt_or_fallback(
         "journey_system", _JOURNEY_SYSTEM_PROMPT
     )
-    compiled = template.format(
+    compiled = compile_prompt(
+        template,
+        prompt_obj,
         directory=directory,
         data_types=", ".join(data_types),
-        template_start=_TEMPLATE_START,
+        data_types_json=json.dumps(data_types),
-        template_end=_TEMPLATE_END,
+        config_start=_CONFIG_START,
+        config_end=_CONFIG_END,
         existing_section=existing_section,
     )
     return compiled, prompt_obj
 
 
-# ── Template extraction ───────────────────────────────────────────────────
+# ── AgentConfig extraction ────────────────────────────────────────────────
 
 
-def _extract_template(text: str) -> str | None:
+def _extract_agent_config(text: str) -> str | None:
-    """Return the text between PROMPT_TEMPLATE_START and PROMPT_TEMPLATE_END, or None."""
+    """Return validated AgentConfig JSON string from between markers, or None.
-    if _TEMPLATE_START not in text or _TEMPLATE_END not in text:
+
+    Parses the JSON with Pydantic to ensure it conforms to the schema before
+    returning.  Returns None if markers are absent or JSON is invalid.
+    """
+    if _CONFIG_START not in text or _CONFIG_END not in text:
+        return None
+    start_idx = text.index(_CONFIG_START) + len(_CONFIG_START)
+    end_idx = text.index(_CONFIG_END)
+    raw = text[start_idx:end_idx].strip()
+    if not raw:
+        return None
+    try:
+        parsed = AgentConfig.model_validate_json(raw)
+        return parsed.model_dump_json()
+    except Exception as exc:
+        logger.warning("agent_setup: failed to parse AgentConfig JSON: %s", exc)
         return None
-    start_idx = text.index(_TEMPLATE_START) + len(_TEMPLATE_START)
-    end_idx = text.index(_TEMPLATE_END)
-    return text[start_idx:end_idx].strip() or None
 
 
 # ── LLM call with tool support ───────────────────────────────────────────
@@ -225,16 +256,17 @@ async def _call_llm_with_tools(
         else:
             messages.append(AIMessage(content=turn["content"]))
 
-    llm = get_llm(model=None, temperature=0.4)
+    llm = get_agent_llm("setup", temperature=0.4)
     llm_with_tools = llm.bind_tools(tools)
     tool_map = {tool_def.name: tool_def for tool_def in tools}
 
+    _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",
-            user_id=user_id or None,
-            session_id=session_id or None,
             input=history[-1]["content"] if history else "",
         )
         if lf else None
@@ -242,12 +274,12 @@ async def _call_llm_with_tools(
     _span = _span_ctx.__enter__() if _span_ctx else None
 
     try:
-        for _ in range(_MAX_TOOL_STEPS):
+        for step in range(_MAX_TOOL_STEPS):
             _gen_ctx = (
                 lf.start_as_current_observation(
                     as_type="generation",
                     name="journey-setup-llm",
-                    model=settings.LLM_MODEL,
+                    model=model_for_agent("setup"),
                     prompt=langfuse_prompt,
                     input=messages,
                 )
@@ -256,15 +288,27 @@ async def _call_llm_with_tools(
             _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=extract_usage(response))
+                _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=_as_text(response.content))
+                    _span.update(output=resp_text)
-                return _as_text(response.content)
+                return resp_text
 
             for call in response.tool_calls:
                 call_name = str(call.get("name", ""))
@@ -293,10 +337,14 @@ async def _call_llm_with_tools(
         final_text = _as_text(final.content)
         if _span:
             _span.update(output=final_text)
-        return 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()
 
@@ -316,12 +364,12 @@ async def handle_journey_start(
     agent_type = frame.get("agent_type", "local")
     directory = frame.get("directory", "")
     data_types = frame.get("data_types", [])
-    existing_template = frame.get("existing_template")
+    existing_config = frame.get("existing_config")
 
     # Use the session_id provided by the FE so the reply matches the
     # listener key; fall back to a generated one if absent.
     session_id = frame.get("session_id") or str(uuid.uuid4())
-    system_prompt, langfuse_prompt = _build_system_prompt(directory, data_types, existing_template)
+    system_prompt, langfuse_prompt = _build_system_prompt(directory, data_types, existing_config)
 
     session = JourneySession(
         session_id=session_id,
@@ -333,17 +381,15 @@ async def handle_journey_start(
         langfuse_prompt=langfuse_prompt,
     )
 
-    # The LLM will explore the directory using FILESYSTEM_TOOLS via the
+    # Seed with an initial user message — some providers require at least one
-    # ws_context executor (already set by the WS handler before calling us).
+    # user/input message to be present.
-    # Seed with an initial user message — some providers (e.g. GitHub Copilot)
-    # require at least one user/input message to be present.
     seed_history: list[dict[str, Any]] = [
         {"role": "user", "content": "Hi, I'm ready to set up my agent. Please explore my directory and ask me your first question."},
     ]
     ai_reply = await _call_llm_with_tools(
         system_prompt=system_prompt,
         history=seed_history,
-        tools=list(FILESYSTEM_TOOLS),
+        tools=make_directory_tools(directory),
         user_id=user_id,
         session_id=session_id,
         langfuse_prompt=langfuse_prompt,
@@ -360,14 +406,14 @@ async def handle_journey_start(
         directory,
     )
 
-    # Check if the LLM produced the template on the first turn (unlikely but possible).
+    # Check if the LLM produced the config on the first turn (unlikely but possible).
-    prompt_template = _extract_template(ai_reply)
+    agent_config = _extract_agent_config(ai_reply)
-    done = prompt_template is not None
+    done = agent_config is not None
 
     display_message = ai_reply
     if done:
         display_message = (
-            ai_reply[: ai_reply.index(_TEMPLATE_START)].strip()
+            ai_reply[: ai_reply.index(_CONFIG_START)].strip()
             or "Here is your agent configuration. You can save it or continue refining."
         )
         _sessions.pop(session_id, None)
@@ -377,7 +423,7 @@ async def handle_journey_start(
         "session_id": session_id,
         "message": display_message,
         "done": done,
-        "prompt_template": prompt_template,
+        "agent_config": agent_config,
     }
 
 
@@ -400,17 +446,18 @@ async def handle_journey_message(
             "session_id": session_id,
             "message": "Journey session not found or expired. Please start a new setup.",
             "done": True,
-            "prompt_template": None,
+            "agent_config": None,
         }
 
     # Append user turn.
     session.history.append({"role": "user", "content": message})
 
     # Call the LLM with tools.
+    session_tools = make_directory_tools(session.directory)
     ai_reply = await _call_llm_with_tools(
         system_prompt=session.system_prompt,
         history=session.history,
-        tools=list(FILESYSTEM_TOOLS),
+        tools=session_tools,
         user_id=session.user_id,
         session_id=session_id,
         langfuse_prompt=session.langfuse_prompt,
@@ -418,41 +465,40 @@ async def handle_journey_message(
 
     session.history.append({"role": "assistant", "content": ai_reply})
 
-    # Check if the LLM produced the final template.
+    # Check if the LLM produced the final config.
-    prompt_template = _extract_template(ai_reply)
+    agent_config = _extract_agent_config(ai_reply)
-    done = prompt_template is not None
+    done = agent_config is not None
 
-    # If the LLM didn't produce a template, nudge it once it has asked enough
+    # If the LLM didn't produce a config, nudge it once it hits the hard safety cap.
-    # questions (>= _MIN_TURNS_BEFORE_NUDGE) or hits the hard safety cap.
     if not done:
         turns = sum(1 for t in session.history if t["role"] == "user")
         if turns >= _MAX_TURNS:
             nudge_content = (
                 "[System: You have enough information. Please generate the final "
-                f"prompt_template now, wrapped in {_TEMPLATE_START} / {_TEMPLATE_END} markers.]"
+                f"AgentConfig JSON now, wrapped in {_CONFIG_START} / {_CONFIG_END} markers.]"
             )
             session.history.append({"role": "user", "content": nudge_content})
 
             nudge_reply = await _call_llm_with_tools(
                 system_prompt=session.system_prompt,
                 history=session.history,
-                tools=list(FILESYSTEM_TOOLS),
+                tools=session_tools,
                 user_id=session.user_id,
                 session_id=session_id,
                 langfuse_prompt=session.langfuse_prompt,
             )
             session.history.append({"role": "assistant", "content": nudge_reply})
 
-            prompt_template = _extract_template(nudge_reply)
+            agent_config = _extract_agent_config(nudge_reply)
-            if prompt_template is not None:
+            if agent_config is not None:
                 done = True
                 ai_reply = nudge_reply
 
     display_message = ai_reply
     if done:
         display_message = (
-            ai_reply[: ai_reply.index(_TEMPLATE_START)].strip()
+            ai_reply[: ai_reply.index(_CONFIG_START)].strip()
-            if _TEMPLATE_START in ai_reply
+            if _CONFIG_START in ai_reply
             else "Here is your agent configuration. You can save it or continue refining."
         )
         _sessions.pop(session_id, None)
@@ -463,5 +509,5 @@ async def handle_journey_message(
         "session_id": session_id,
         "message": display_message,
         "done": done,
-        "prompt_template": prompt_template,
+        "agent_config": agent_config,
     }

app/api/routes/agents.py

@@ -12,8 +12,11 @@ in backend agent-config tables.
 from __future__ import annotations
 
 import asyncio
+import logging
 import uuid
-from datetime import datetime, timedelta, timezone
+from datetime import datetime, timezone
+
+logger = logging.getLogger(__name__)
 
 from fastapi import APIRouter, Depends, HTTPException, status
 from sqlalchemy import func, select
@@ -177,6 +180,11 @@ async def trigger_agent_run(
     _enforce_agent_limit(current_user.tier, body.active_agents)
     await _enforce_run_frequency(current_user.tier, current_user.id, db)
 
+    last_run_dt = (
+        datetime.fromtimestamp(body.last_run_at / 1000, tz=timezone.utc)
+        if body.last_run_at
+        else None
+    )
     config = LocalAgentConfig(
         id=str(uuid.uuid4()),
         user_id=current_user.id,
@@ -184,10 +192,12 @@ async def trigger_agent_run(
         name="Local Directory Monitor",
         directory_paths=[body.directory],
         data_types=_to_data_types(body.what_to_extract),
-        prompt_template=body.custom_agent_prompt,
+        prompt_template=body.custom_agent_prompt or "",
+        agent_config=body.agent_config,
         file_extensions=[],
         schedule_cron=body.batch_interval,
         enabled=True,
+        last_run_at=last_run_dt,
     )
 
     # Use the FE's stable agent_id if provided, fall back to the ephemeral config id.

app/api/routes/auth.py

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

app/api/routes/billing.py

@@ -83,3 +83,16 @@ 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

app/auth/__init__.py

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

app/auth/oauth_providers.py

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

app/billing/stripe_service.py

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

app/billing/tier_manager.py

@@ -25,6 +25,7 @@ FEATURES: dict[str, dict[str, Any]] = {
         "providers": 1,
         "batch_builder": False,
         "sso": False,
+        "real_embeddings": False,   # keyword fallback only
     },
     "pro": {
         "agents": -1,           # unlimited
@@ -33,6 +34,7 @@ FEATURES: dict[str, dict[str, Any]] = {
         "providers": -1,
         "batch_builder": False,
         "sso": False,
+        "real_embeddings": True,    # pgvector cosine search
     },
     "power": {
         "agents": -1,
@@ -41,6 +43,7 @@ FEATURES: dict[str, dict[str, Any]] = {
         "providers": -1,
         "batch_builder": True,
         "sso": False,
+        "real_embeddings": True,
     },
     "team": {
         "agents": -1,
@@ -49,6 +52,7 @@ FEATURES: dict[str, dict[str, Any]] = {
         "providers": -1,
         "batch_builder": True,
         "sso": True,
+        "real_embeddings": True,
     },
 }
 

app/config/settings.py

@@ -3,7 +3,7 @@ from pydantic_settings import BaseSettings, SettingsConfigDict
 
 
 class Settings(BaseSettings):
-    DATABASE_URL: str = "postgresql+asyncpg://postgres:postgres@localhost:5432/adiuva"
+    DATABASE_URL: str = "postgresql+asyncpg://postgres:postgres@localhost:5432/adiuvai"
     JWT_SECRET: str = "change-me-in-production"
     JWT_ALGORITHM: str = "HS256"
     JWT_ACCESS_TOKEN_EXPIRE_MINUTES: int = 30
@@ -18,9 +18,16 @@ class Settings(BaseSettings):
     CEREBRAS_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_SETUP_AGENT: str = ""       # agent-setup journey
+
     # 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.
@@ -34,20 +41,37 @@ class Settings(BaseSettings):
     # MS_TENANT_ID: set to 'common' to allow multi-tenant (personal + work accounts).
     MS_TENANT_ID: str = "common"
 
+    # Google Login OAuth credentials — scope: openid email profile.
+    # Separate from GMAIL_CLIENT_ID/SECRET (which uses gmail.readonly scope).
+    GOOGLE_AUTH_CLIENT_ID: str = ""
+    GOOGLE_AUTH_CLIENT_SECRET: str = ""
+    # The redirect URI registered in Google Cloud Console.
+    # Google redirects here after consent; this backend route then bounces to
+    # the adiuvai:// deep link so the Electron app receives the code.
+    # Dev:  http://localhost:8000/api/v1/auth/oauth/google/web-callback
+    # Prod: https://api.adiuvai.com/api/v1/auth/oauth/google/web-callback
+    OAUTH_REDIRECT_URI: str = "http://localhost:8000/api/v1/auth/oauth/google/web-callback"
+
     # Fernet key (URL-safe base64, 32-byte key) for at-rest encryption of OAuth
     # tokens stored in cloud_agent_configs.oauth_token_encrypted.
     # Generate with: from cryptography.fernet import Fernet; Fernet.generate_key()
     OAUTH_ENCRYPTION_KEY: str = ""
 
-    CORS_ORIGINS: list[str] = ["app://.", "http://localhost:3000", "http://localhost:5173"]
+    CORS_ORIGINS: list[str] = [
+        "app://.",
+        "http://localhost:3000",
+        "http://localhost:5173",
+        "http://localhost:4173",      # Vite preview (web SPA)
+        "https://app.adiuvai.com",    # Production web portal
+    ]
 
     LANGFUSE_SECRET_KEY: str = ""
     LANGFUSE_PUBLIC_KEY: str = ""
-    LANGFUSE_HOST: str = "https://cloud.langfuse.com"
+    LANGFUSE_BASE_URL: str = "https://cloud.langfuse.com"
 
     ENV: Literal["dev", "prod"] = "dev"
 
-    model_config = SettingsConfigDict(env_file=".env", env_file_encoding="utf-8")
+    model_config = SettingsConfigDict(env_file=".env", env_file_encoding="utf-8", extra="ignore")
 
 
 settings = Settings()

app/core/agent_runner.py

@@ -2,12 +2,12 @@
 
 Drives two agent types:
 
-* **Local directory agent** — two-step execution per file:
+* **Local directory agent** — V2 unified flow per file:
-  Step 1 (Classification) uses code to fetch all projects and asks the LLM
+  Phase A (Detect + Preprocess, zero LLM): Python detects the content type
-  to identify which project the file belongs to and which domains are relevant.
+  and strips markup/noise, producing clean text + metadata.
-  Step 2 (Processing) fetches existing entities for that project/domains via
+  Phase B (Single LLM call with tools): the LLM identifies the project,
-  code and runs an LLM with tools — existing data in context enforces
+  checks for duplicates via list_* tools, and creates/updates records.
-  update-first naturally.
+  ``items_created`` is counted from ``create_*`` tool calls.
 
 * **Cloud connector agent** — fetches data from third-party APIs (Gmail,
   Teams, Outlook) and pushes extracted items to Electron.
@@ -29,7 +29,7 @@ from __future__ import annotations
 import asyncio
 import json
 import logging
-import uuid
+import os
 from datetime import datetime, timedelta, timezone
 from typing import Any
 
@@ -42,10 +42,10 @@ from app.agents.note_agent import NOTE_TOOLS
 from app.agents.project_agent import PROJECT_TOOLS
 from app.agents.task_agent import TASK_TOOLS
 from app.agents.timeline_agent import TIMELINE_TOOLS
-from app.config.settings import settings
 from app.core.device_manager import DeviceConnectionManager
-from app.core.langfuse_client import extract_usage, get_langfuse, get_prompt_or_fallback
+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.core.preprocessors import detect_content_type, preprocess
 from app.core.ws_context import clear_client_executor, execute_on_client, set_client_executor
 from app.db import async_session
 from app.models import AgentRunLog, CloudAgentConfig, LocalAgentConfig
@@ -72,92 +72,47 @@ _MAX_PROCESSING_STEPS: int = 12
 _MAX_SCAN_DEPTH: int = 5
 
 # ── Data-type to tool mapping ─────────────────────────────────────────────
-# NOTE: "projects" is intentionally excluded — project creation/assignment is
-# handled in code by the runner, never delegated to the Step 2 LLM.
 
 _DATA_TYPE_TOOLS: dict[str, list[Any]] = {
     "tasks": TASK_TOOLS,
     "notes": NOTE_TOOLS,
     "timelines": TIMELINE_TOOLS,
+    "timelineEvents": TIMELINE_TOOLS,
+    "projects": PROJECT_TOOLS,
 }
 
-# ── Step 1: Classification prompt ─────────────────────────────────────────
+# ── V2: Unified processing prompt (hot-swappable via Langfuse "unified_processing") ──
 
-_DOMAIN_DESCRIPTIONS: dict[str, str] = {
+_UNIFIED_PROCESSING_PROMPT = """\
-    "tasks": (
-        "Action items, to-dos, deliverables — anything that describes work to be done, "
-        "assigned to someone, or tracked with a due date or status."
-    ),
-    "notes": (
-        "Documentation, meeting notes, summaries, reference material — "
-        "written content meant to be read and referenced rather than acted on."
-    ),
-    "timelines": (
-        "Project milestones, deadlines, scheduled events — "
-        "specific dates that mark a point in the progress of a project."
-    ),
-    "projects": (
-        "High-level project entities — only relevant if the file clearly introduces "
-        "a new project or updates the scope of an existing one."
-    ),
-}
-
-_BATCH_FILE_CLASSIFIER_PROMPT = """\
-You are a file classifier for a freelance project management tool.
-
-Your job is to match a file to an existing project and identify which data domains to extract.
-
-## Project matching rules (STRICT — follow in order)
-
-1. Search the file content for any mention of a project name, client name, acronym, or topic
-   that overlaps with the existing projects listed below.
-2. The match does NOT need to be exact — partial name, abbreviation, or topic similarity is enough.
-3. STRONGLY PREFER matching an existing project. Only return "new" as an absolute last resort
-   when the file has zero meaningful connection to any listed project.
-4. When in doubt, pick the closest match from the list.
-
-## Response format
-
-Respond ONLY with a JSON object — no markdown, no explanation:
-
-{{"project_id": "<exact id from the list below, or new>", "new_project_name": "<concise 2-5 word name, only when project_id is new>", "domains": ["tasks", "notes"]}}
-
-## Domain definitions (only consider domains in the allowed list)
-
-{domain_definitions}
-
-## Existing projects
-
-{projects_list}
-"""
-
-# ── Step 2: Processing prompt ─────────────────────────────────────────────
-
-_BATCH_PROCESSING_PROMPT = """\
 You are a data extraction assistant for a freelance project management tool.
 
-Your task: extract structured data from the file content and persist it using the available tools.
+## Your process (follow this exact order)
 
-## Mandatory process — follow this order for EVERY item you extract
+### 1. Identify the project
+File: {filename}
+{metadata_section}
 
-1. READ the existing records listed below for the relevant domain.
+Existing projects:
-2. SEARCH for a match by title, topic, or semantic similarity.
+{projects_list}
-3. If a match exists → call the update_* tool with the existing record's id.
-4. If no match exists → call the create_* tool and set isAiSuggested=1.
 
-NEVER call create_* without first checking the existing records.
+Match this file to an existing project using the filename and content clues.
-NEVER duplicate a record that already exists under a different wording.
+If no project matches, {no_match_behavior}.
 
-## Existing records (source of truth)
+### 2. Check existing records
+Once you identify the project, use list_tasks / list_notes / list_timelines
+(filtered by projectId) to see what already exists.
+NEVER create a record that already exists under the same or similar title.
 
-{existing_context}
+### 3. Extract and create / update
+{extraction_rules}
 
-## Context
+### Rules
-
+- Set isAiSuggested=1 on every new record.
-Project: {project_context}
+- Set projectId on every record (use the id from the project list above).
-Domains to extract: {data_types}
+- Update existing records when a match is found by title or topic.
-
+- Do NOT invent data — only extract what is clearly stated in the content.
-{custom_prompt_section}
+- Target entity types: {data_types}.
+{global_rules}
 """
 
 # ── Cloud processing prompt (kept separate for cloud agent) ───────────────
@@ -271,12 +226,18 @@ async def _run_agent_with_tools(
     tools: list[Any],
     max_steps: int,
     user_id: str = "",
+    session_id: str = "",
     langfuse_prompt: Any = None,
     agent_name: str = "batch-agent",
+    _tool_calls_out: list[str] | None = None,
 ) -> str:
-    """Run an LLM agent with tool-calling, returning the final text response."""
+    """Run an LLM agent with tool-calling, returning the final text response.
+
+    If *_tool_calls_out* is provided, the name of every tool called during the
+    run is appended to it (used by the caller to count ``create_*`` calls).
+    """
     lf = get_langfuse()
-    llm = get_llm()
+    llm = get_agent_llm(agent_name)
     llm_with_tools = llm.bind_tools(tools)
     messages: list[Any] = [
         SystemMessage(content=system_prompt),
@@ -285,11 +246,14 @@ async def _run_agent_with_tools(
 
     tool_map = {tool_def.name: tool_def for tool_def in tools}
 
+    _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=agent_name,
-            user_id=user_id or None,
+            metadata={"user_id": user_id} if user_id else None,
             input=user_message,
         )
         if lf else None
@@ -302,7 +266,7 @@ async def _run_agent_with_tools(
                 lf.start_as_current_observation(
                     as_type="generation",
                     name=f"{agent_name}-llm",
-                    model=settings.LLM_MODEL,
+                    model=model_for_agent(agent_name),
                     prompt=langfuse_prompt,
                     input=messages,
                 )
@@ -311,7 +275,7 @@ async def _run_agent_with_tools(
             _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=extract_usage(response))
+                _gen.update(output=_as_text(response.content), usage_details=extract_usage(response))
                 _gen_ctx.__exit__(None, None, None)
 
             messages.append(response)
@@ -332,6 +296,9 @@ async def _run_agent_with_tools(
                     json.dumps(call_args, ensure_ascii=True)[:800],
                 )
 
+                if _tool_calls_out is not None:
+                    _tool_calls_out.append(call_name)
+
                 tool_fn = tool_map.get(call_name)
                 if tool_fn is None:
                     tool_output = f"Unknown tool: {call_name}"
@@ -353,6 +320,7 @@ async def _run_agent_with_tools(
     finally:
         if _span_ctx:
             _span_ctx.__exit__(None, None, None)
+        _lf_ctx.__exit__(None, None, None)
         if lf:
             lf.flush()
 
@@ -421,7 +389,8 @@ async def _scan_directories(
     for file_path in all_files:
         try:
             meta = await execute_on_client(action="get_file_metadata", data={"path": file_path})
-            modified_at = meta.get("modifiedAt")
+            # FE sends snake_case keys on the wire (toSnakeCase transform)
+            modified_at = meta.get("modified_at") or meta.get("modifiedAt")
             if modified_at is None:
                 filtered.append(file_path)
                 continue
@@ -523,99 +492,66 @@ def _format_entities_for_context(domain: str, rows: list[dict]) -> str:
     return f"Existing {domain}:\n" + "\n".join(lines)
 
 
-# ── Step 1: LLM file classifier ───────────────────────────────────────────
+# ── V2 helper functions ───────────────────────────────────────────────────
 
 
-async def _classify_file(
+def _format_projects(projects: list[dict]) -> str:
-    file_path: str,
+    """Format the project list for the unified system prompt."""
-    file_content: str,
+    if not projects:
-    projects: list[dict],
+        return "  (no projects yet)"
-    config_data_types: list[str],
+    lines: list[str] = []
-) -> tuple[str, list[str], str | None]:
+    for p in projects:
-    """Call the LLM to classify a file by project and relevant domains.
-
-    Returns ``(project_id_or_"new", domains, new_project_name_or_None)``.
-    - ``project_id`` is an existing project UUID, or ``"new"`` when no match found.
-    - ``new_project_name`` is only set when ``project_id == "new"``.
-    Falls back to ``("new", config_data_types, None)`` on any error.
-    """
-    fallback: tuple[str, list[str], str | None] = ("new", list(config_data_types), None)
-
-    if not file_content.strip():
-        return fallback
-
-    valid_project_ids = {p["id"] for p in projects}
-
-    def _fmt_project(p: dict) -> str:
         summary = (p.get("aiSummary") or p.get("ai_summary") or "").strip()
         summary_part = f" — {summary[:100]}" if summary else ""
-        return f"  - id={p['id']} | name={p.get('name', '')} | status={p.get('status', '')}{summary_part}"
+        lines.append(
+            f"  - id={p['id']} | name={p.get('name', '')} | "
+            f"status={p.get('status', '')}{summary_part}"
+        )
+    return "\n".join(lines)
 
-    projects_list = "\n".join(_fmt_project(p) for p in projects) or "  (none yet)"
 
-    domain_definitions = "\n".join(
+def _format_metadata(metadata: dict) -> str:
-        f"  - {d}: {_DOMAIN_DESCRIPTIONS[d]}"
+    """Format preprocessor metadata as a compact context block."""
-        for d in config_data_types
+    if not metadata:
-        if d in _DOMAIN_DESCRIPTIONS
+        return ""
+    parts: list[str] = []
+    for key in ("subject", "from", "to", "date"):
+        if metadata.get(key):
+            parts.append(f"{key.capitalize()}: {metadata[key]}")
+    # any remaining keys
+    for key, val in metadata.items():
+        if key not in ("subject", "from", "to", "date") and val:
+            parts.append(f"{key}: {val}")
+    return "\n".join(parts)
+
+
+def _get_extraction_rules(agent_config: dict, content_type: str) -> str:
+    """Return the extraction_prompt for *content_type* from *agent_config*.
+
+    Falls back to a generic instruction when the type is not configured.
+    """
+    for ct in agent_config.get("content_types", []):
+        if ct.get("id") == content_type:
+            prompt = ct.get("extraction_prompt", "").strip()
+            if prompt:
+                return prompt
+    return (
+        "Extract relevant information as tasks (action items), notes "
+        "(informational content), or timelines (dated events)."
     )
 
-    step1_template, step1_prompt_obj = get_prompt_or_fallback(
-        "batch_file_classifier", _BATCH_FILE_CLASSIFIER_PROMPT
-    )
-    system = step1_template.format(
-        domain_definitions=domain_definitions,
-        projects_list=projects_list,
-    )
 
-    lf = get_langfuse()
+def _get_no_match_behavior(agent_config: dict) -> str:
-    llm = get_llm()
+    """Derive the 'no project match' instruction from global_rules."""
-    classifier_messages = [
+    rules = agent_config.get("global_rules", [])
-        SystemMessage(content=system),
+    for rule in rules:
-        HumanMessage(content=f"File: {file_path}\n\nContent:\n{file_content[:4000]}"),
+        lower = rule.lower()
-    ]
+        if "no project" in lower or "no match" in lower or "skip" in lower:
-    try:
+            return rule
-        if lf:
+    return "create a new project with a concise name derived from the file content"
-            with lf.start_as_current_observation(
-                as_type="generation",
-                name="step1-classifier",
-                model=settings.LLM_ROUTER_MODEL,
-                prompt=step1_prompt_obj,
-                input=classifier_messages,
-            ) as gen:
-                response = await llm.ainvoke(classifier_messages)
-                gen.update(output=_as_text(response.content), usage=extract_usage(response))
-        else:
-            response = await llm.ainvoke(classifier_messages)
-        raw = _as_text(response.content).strip()
-        # Strip markdown fences if the model wraps the JSON.
-        if raw.startswith("```"):
-            raw = raw.split("```")[1]
-            if raw.startswith("json"):
-                raw = raw[4:]
-        parsed = json.loads(raw.strip())
-        raw_project_id: str = str(parsed.get("project_id") or "new")
-        # Reject hallucinated UUIDs — only accept ids that exist in the fetched list.
-        project_id = raw_project_id if raw_project_id in valid_project_ids else "new"
-        new_project_name: str | None = (
-            str(parsed["new_project_name"]).strip() or None
-            if project_id == "new" and parsed.get("new_project_name")
-            else None
-        )
-        domains: list[str] = [
-            d for d in parsed.get("domains", [])
-            if d in config_data_types
-        ]
-        if not domains:
-            domains = list(config_data_types)
-        return project_id, domains, new_project_name
-    except Exception as exc:
-        logger.warning(
-            "agent_runner: step1 classification failed for %r: %s", file_path, exc
-        )
-        return fallback
 
 
-# ── Local agent runner (two-step per file) ────────────────────────────────
+# ── Local agent runner (V2 — unified per-file flow) ───────────────────────
 
 
 async def run_local_agent(
@@ -625,16 +561,17 @@ async def run_local_agent(
     device_mgr: DeviceConnectionManager,
     run_context: dict | None = None,
 ) -> None:
-    """Execute a local directory agent run using a two-step approach per file.
+    """Execute a local directory agent run — V2 unified flow.
 
-    Step 1 — Classification (code + 1 LLM call per file, no tools):
+    Phase A — Detect + Preprocess (zero LLM, per file):
-        Code scans directories and fetches all projects via WS.
+        Python detects the content type from filename + content patterns and
-        For each file, LLM identifies the project and relevant domains.
+        runs the appropriate handler (e.g. email_html) to produce clean text
+        and structured metadata.
 
-    Step 2 — Processing (code + 1 LLM call per file, with tools):
+    Phase B — Single LLM call with tools (per file):
-        Code fetches existing entities for the identified project/domains.
+        One LLM call handles project identification, duplicate checking, and
-        LLM receives file content + existing entities in context and uses
+        record creation/update.  ``create_*`` tool calls are counted to
-        tools to update existing records or create new ones.
+        produce the accurate ``items_created`` metric.
     """
     run_id = run_log.id
     agent_id = (run_context or {}).get("agent_id") or config.id
@@ -669,16 +606,11 @@ async def run_local_agent(
     errors: list[str] = []
     items_processed = 0
     items_created = 0
-
+    agent_config: dict = config.agent_config or {}
-    custom_section = (
+    processing_tools = _build_processing_tools(config.data_types)
-        f"User instructions:\n{config.prompt_template}"
-        if config.prompt_template
-        else ""
-    )
 
     try:
         # ── Code: scan directories ───────────────────────────────────
-        logger.info("agent_runner: run=%s scanning directories user=%s", run_id, user_id)
         file_paths = await _scan_directories(
             paths=config.directory_paths,
             extensions=config.file_extensions or [],
@@ -694,114 +626,89 @@ async def run_local_agent(
 
         # ── Code: fetch all projects once ────────────────────────────
         projects = await _fetch_projects()
+        projects_block = _format_projects(projects)
+
+        # Prompt template + Langfuse version linking (hot-swappable from UI).
+        unified_template, prompt_obj = get_prompt_or_fallback(
+            "unified_processing", _UNIFIED_PROCESSING_PROMPT
+        )
 
         for file_path in file_paths:
             try:
-                # Read file content via code.
+                # ── Phase A: read + detect + preprocess ─────────────
                 file_result = await execute_on_client(
                     action="read_file_content", data={"path": file_path}
                 )
-                file_content: str = file_result.get("content", "")
+                raw_content: str = file_result.get("content", "")
-                if not file_content:
+                if not raw_content.strip():
-                    logger.debug("agent_runner: run=%s skipping empty file %r", run_id, file_path)
+                    logger.debug(
+                        "agent_runner: run=%s skipping empty file %r", run_id, file_path
+                    )
                     continue
 
                 items_processed += 1
+                filename = os.path.basename(file_path)
+                content_type = detect_content_type(filename, raw_content)
+                preprocessed = preprocess(content_type, raw_content)
 
-                # Step 1 — classify file.
-                project_id, domains, new_project_name = await _classify_file(
-                    file_path=file_path,
-                    file_content=file_content,
-                    projects=projects,
-                    config_data_types=config.data_types,
-                )
                 logger.info(
-                    "agent_runner: run=%s file=%r → project=%s new_name=%r domains=%s",
+                    "agent_runner: run=%s file=%r content_type=%s clean_len=%d",
-                    run_id,
+                    run_id, file_path, content_type, len(preprocessed.clean_text),
-                    file_path,
-                    project_id,
-                    new_project_name,
-                    domains,
                 )
 
-                # Step 2 — resolve project_id via CODE, then fetch entities.
+                # ── Phase B: single LLM call ─────────────────────────
-                # Project creation is NEVER delegated to the Step 2 LLM.
+                extraction_rules = _get_extraction_rules(agent_config, content_type)
-                if project_id == "new":
+                no_match_behavior = _get_no_match_behavior(agent_config)
-                    proj_name = new_project_name or "Untitled Project"
+                global_rules_lines = "\n".join(
-                    try:
+                    f"- {r}" for r in agent_config.get("global_rules", [])
-                        proj_result = await execute_on_client(
-                            action="insert",
-                            table="projects",
-                            data={"name": proj_name, "clientId": None},
                 )
-                        created = proj_result.get("row", {})
+                metadata_section = _format_metadata(preprocessed.metadata)
-                        effective_project_id = created.get("id", "standalone")
+
-                        # Add to local list so subsequent files can match it.
+                system_prompt = compile_prompt(
-                        if "id" in created:
+                    unified_template,
-                            projects.append(created)
+                    prompt_obj,
-                        logger.info(
+                    filename=filename,
-                            "agent_runner: run=%s created project %r id=%s",
+                    metadata_section=metadata_section,
-                            run_id, proj_name, effective_project_id,
+                    projects_list=projects_block,
-                        )
+                    no_match_behavior=no_match_behavior,
-                    except Exception as exc:
+                    extraction_rules=extraction_rules,
-                        logger.warning(
+                    global_rules=global_rules_lines,
-                            "agent_runner: run=%s failed to create project %r: %s",
+                    data_types=", ".join(config.data_types),
-                            run_id, proj_name, exc,
-                        )
-                        effective_project_id = "standalone"
-                        proj_name = "unknown"
-                    project_context = (
-                        f"Project: {proj_name} (id: {effective_project_id}). "
-                        "Always set projectId to this id on every record you create."
-                    )
-                else:
-                    effective_project_id = project_id
-                    proj = next((p for p in projects if p["id"] == project_id), None)
-                    proj_name = proj.get("name", project_id) if proj else project_id
-                    project_context = (
-                        f"Project: {proj_name} (id: {project_id}). "
-                        "Always set projectId to this id on every record you create."
                 )
 
-                # "projects" domain is never passed to Step 2 — handled above in code.
+                user_message = (
-                domains = [d for d in domains if d != "projects"]
+                    f"Process this file and extract relevant information.\n\n"
-
+                    f"File: {file_path}\n\n"
-                existing_blocks: list[str] = []
+                    f"Content:\n{preprocessed.clean_text}"
-                for domain in domains:
-                    rows = await _fetch_domain_entities(domain, effective_project_id)
-                    existing_blocks.append(_format_entities_for_context(domain, rows))
-
-                existing_context = "\n\n".join(existing_blocks)
-
-                step2_template, step2_prompt_obj = get_prompt_or_fallback(
-                    "batch_processing", _BATCH_PROCESSING_PROMPT
-                )
-                system_prompt = step2_template.format(
-                    existing_context=existing_context,
-                    project_context=project_context,
-                    data_types=", ".join(domains),
-                    custom_prompt_section=custom_section,
                 )
 
-                processing_tools = _build_processing_tools(domains)
+                file_tool_calls: list[str] = []
-
                 result_text = await _run_agent_with_tools(
                     system_prompt=system_prompt,
-                    user_message=(
+                    user_message=user_message,
-                        f"Process this file and extract relevant information.\n\n"
-                        f"File: {file_path}\n\nContent:\n{file_content}"
-                    ),
                     tools=processing_tools,
                     max_steps=_MAX_PROCESSING_STEPS,
                     user_id=user_id,
-                    langfuse_prompt=step2_prompt_obj,
+                    session_id=run_id,
-                    agent_name="step2-processor",
+                    langfuse_prompt=prompt_obj,
+                    agent_name="unified-processor",
+                    _tool_calls_out=file_tool_calls,
                 )
+
+                file_created = sum(
+                    1 for name in file_tool_calls if name.startswith("create_")
+                )
+                items_created += file_created
+
+                # Refresh project list when a project was created so
+                # subsequent files see it in the prompt context.
+                if "create_project" in file_tool_calls:
+                    projects = await _fetch_projects()
+                    projects_block = _format_projects(projects)
+
                 logger.info(
-                    "agent_runner: run=%s file=%r result=%s",
+                    "agent_runner: run=%s file=%r created=%d result=%s",
-                    run_id,
+                    run_id, file_path, file_created, result_text[:200],
-                    file_path,
-                    result_text[:200],
                 )
 
             except Exception as exc:
@@ -833,10 +740,11 @@ async def run_local_agent(
         errors=errors,
     )
     logger.info(
-        "agent_runner: run=%s done status=%s processed=%d errors=%d",
+        "agent_runner: run=%s done status=%s processed=%d created=%d errors=%d",
         run_id,
         final_status,
         items_processed,
+        items_created,
         len(errors),
     )
 
@@ -997,7 +905,9 @@ async def run_cloud_agent(
             cloud_template, cloud_prompt_obj = get_prompt_or_fallback(
                 "batch_cloud_processing", _BATCH_CLOUD_PROCESSING_PROMPT
             )
-            processing_prompt = cloud_template.format(
+            processing_prompt = compile_prompt(
+                cloud_template,
+                cloud_prompt_obj,
                 data_types=", ".join(config.data_types),
                 project_context="Determine the appropriate project from the message context.",
                 file_list=f"Message from {config.provider} (id: {msg.id})",
@@ -1011,6 +921,7 @@ async def run_cloud_agent(
                     tools=processing_tools,
                     max_steps=_MAX_PROCESSING_STEPS,
                     user_id=user_id,
+                    session_id=run_id,
                     langfuse_prompt=cloud_prompt_obj,
                     agent_name="cloud-processor",
                 )

app/core/deep_agent.py

@@ -16,9 +16,8 @@ from app.agents.note_agent import NOTE_TOOLS
 from app.agents.project_agent import PROJECT_TOOLS
 from app.agents.task_agent import TASK_TOOLS
 from app.agents.timeline_agent import TIMELINE_TOOLS
-from app.core.langfuse_client import extract_usage, get_langfuse, get_prompt_or_fallback
+from app.core.langfuse_client import 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.config.settings import settings
 from app.core.memory_middleware import MemoryMiddleware
 from app.core.ws_context import clear_tool_result_collector, execute_on_client, set_tool_result_collector
 from app.db import async_session
@@ -28,6 +27,34 @@ logger = logging.getLogger(__name__)
 FloatingDomainType = Literal["task", "timeline", "project", "node"]
 FloatingDomainSection = Literal["task", "timeline", "note"]
 
+# Mapping of core-memory language values to natural-language names for prompts.
+_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",
+}
+
+
+def _language_instruction(context: dict[str, Any]) -> str:
+    """Return a system-prompt suffix that tells the LLM to respond in the user's language.
+
+    Returns an empty string when the language is English or unknown — saves tokens.
+    """
+    core = context.get("core_memory") or {}
+    raw = (core.get("language") or "").strip().lower()
+    if not raw:
+        return ""
+    lang = _LANGUAGE_NAMES.get(raw, raw.title())  # best-effort capitalisation
+    if lang.lower() == "english":
+        return ""
+    return (
+        f"\n\nIMPORTANT: Always respond in {lang}. "
+        f"All your output text must be written in {lang}."
+    )
+
 _HOME_SYSTEM_PROMPT = (
     "You are the home assistant with direct access to all tools: tasks, projects, notes, timelines, and memory tools. "
     "Always use tools for factual data retrieval before answering. "
@@ -149,6 +176,15 @@ def _trace_id_from_context(context: dict[str, Any]) -> str | None:
     return None
 
 
+def _session_id_from_context(context: dict[str, Any]) -> str | None:
+    debug = context.get("_debug")
+    if isinstance(debug, dict):
+        session_id = debug.get("session_id")
+        if isinstance(session_id, str) and session_id:
+            return session_id
+    return None
+
+
 def _context_for_model(context: dict[str, Any]) -> dict[str, Any]:
     sanitized = dict(context)
     sanitized.pop("_debug", None)
@@ -537,7 +573,7 @@ async def _infer_floating_domain(message: str, context: dict[str, Any]) -> dict[
     }
 
     try:
-        llm = get_llm()
+        llm = get_agent_llm("classifier")
         classifier_messages = [
             SystemMessage(content=_FLOATING_DOMAIN_CLASSIFIER_PROMPT),
             HumanMessage(
@@ -551,16 +587,23 @@ async def _infer_floating_domain(message: str, context: dict[str, Any]) -> dict[
         _, classifier_prompt_obj = get_prompt_or_fallback(
             "floating_domain_classifier", _FLOATING_DOMAIN_CLASSIFIER_PROMPT
         )
+
+        # Extract user/session from context for Langfuse attribution
+        _debug = context.get("_debug") if isinstance(context, dict) else None
+        _lf_user = (_debug or {}).get("user_id") if isinstance(_debug, dict) else None
+        _lf_session = (_debug or {}).get("session_id") if isinstance(_debug, dict) else None
+
+        with langfuse_context(user_id=_lf_user, session_id=_lf_session):
             if lf:
                 with lf.start_as_current_observation(
                     as_type="generation",
                     name="floating-classifier",
-                model=settings.LLM_MODEL,
+                    model=model_for_agent("classifier"),
                     prompt=classifier_prompt_obj,
                     input=classifier_messages,
                 ) as gen:
                     response = await llm.ainvoke(classifier_messages)
-                gen.update(output=_as_text(response.content), usage=extract_usage(response))
+                    gen.update(output=_as_text(response.content), usage_details=extract_usage(response))
             else:
                 response = await llm.ainvoke(classifier_messages)
         parsed = _parse_json_object(_as_text(response.content))
@@ -591,8 +634,9 @@ async def _run_single_agent(
     agent_name: str = "agent",
 ) -> str:
     trace_id = _trace_id_from_context(context)
+    session_id = _session_id_from_context(context)
     lf = get_langfuse()
-    llm = get_llm()
+    llm = get_agent_llm(agent_name)
     tools = _all_tools_for_user(user_id, trace_id)
     model_context = _context_for_model(context)
     logger.info("deep_agent: run_single_agent_start trace=%s user=%s", trace_id or "-", user_id)
@@ -611,12 +655,14 @@ async def _run_single_agent(
     collected: list[dict[str, Any]] = []
     set_tool_result_collector(collected)
 
+    _lf_ctx = langfuse_context(user_id=user_id, session_id=session_id)
+    _lf_ctx.__enter__()
+
     _span_ctx = (
         lf.start_as_current_observation(
             as_type="span",
             name=agent_name,
-            user_id=user_id,
+            metadata={"user_id": user_id, "session_id": trace_id},
-            session_id=trace_id,
             input=message,
         )
         if lf else None
@@ -629,7 +675,7 @@ async def _run_single_agent(
                 lf.start_as_current_observation(
                     as_type="generation",
                     name=f"{agent_name}-llm",
-                    model=settings.LLM_MODEL,
+                    model=model_for_agent(agent_name),
                     prompt=langfuse_prompt,
                     input=messages,
                 )
@@ -638,7 +684,7 @@ async def _run_single_agent(
             _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=extract_usage(response))
+                _gen.update(output=_as_text(response.content), usage_details=extract_usage(response))
                 _gen_ctx.__exit__(None, None, None)
 
             messages.append(response)
@@ -700,6 +746,7 @@ async def _run_single_agent(
         clear_tool_result_collector()
         if _span_ctx:
             _span_ctx.__exit__(None, None, None)
+        _lf_ctx.__exit__(None, None, None)
         if lf:
             lf.flush()
 
@@ -715,8 +762,9 @@ async def _run_single_agent_stream(
     agent_name: str = "agent",
 ) -> AsyncGenerator[tuple[str, Any], None]:
     trace_id = _trace_id_from_context(context)
+    session_id = _session_id_from_context(context)
     lf = get_langfuse()
-    llm = get_llm()
+    llm = get_agent_llm(agent_name)
     tools = _all_tools_for_user(user_id, trace_id)
     model_context = _context_for_model(context)
     logger.info("deep_agent: run_single_agent_stream_start trace=%s user=%s", trace_id or "-", user_id)
@@ -736,12 +784,14 @@ async def _run_single_agent_stream(
     collected: list[dict[str, Any]] = []
     set_tool_result_collector(collected)
 
+    _lf_ctx = langfuse_context(user_id=user_id, session_id=session_id)
+    _lf_ctx.__enter__()
+
     _span_ctx = (
         lf.start_as_current_observation(
             as_type="span",
             name=f"{agent_name}-stream",
-            user_id=user_id,
+            metadata={"user_id": user_id, "session_id": trace_id},
-            session_id=trace_id,
             input=message,
         )
         if lf else None
@@ -755,7 +805,7 @@ async def _run_single_agent_stream(
                 lf.start_as_current_observation(
                     as_type="generation",
                     name=f"{agent_name}-llm",
-                    model=settings.LLM_MODEL,
+                    model=model_for_agent(agent_name),
                     prompt=langfuse_prompt,
                     input=messages,
                 )
@@ -764,7 +814,7 @@ async def _run_single_agent_stream(
             _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=extract_usage(response))
+                _gen.update(output=_as_text(response.content), usage_details=extract_usage(response))
                 _gen_ctx.__exit__(None, None, None)
 
             messages.append(response)
@@ -844,6 +894,7 @@ async def _run_single_agent_stream(
         clear_tool_result_collector()
         if _span_ctx:
             _span_ctx.__exit__(None, None, None)
+        _lf_ctx.__exit__(None, None, None)
         if lf:
             lf.flush()
 
@@ -853,6 +904,7 @@ async def run_home(user_id: str, message: str, context: dict[str, Any]) -> str:
     system_prompt, langfuse_prompt = get_prompt_or_fallback(
         "home_system", _HOME_SYSTEM_PROMPT
     )
+    system_prompt += _language_instruction(context)
     response = await _run_single_agent(
         user_id=user_id,
         system_prompt=system_prompt,
@@ -870,6 +922,7 @@ async def run_floating(user_id: str, message: str, context: dict[str, Any]) -> t
     system_prompt, langfuse_prompt = get_prompt_or_fallback(
         "floating_system", _FLOATING_SYSTEM_PROMPT
     )
+    system_prompt += _language_instruction(context)
     response = await _run_single_agent(
         user_id=user_id,
         system_prompt=system_prompt,
@@ -893,6 +946,7 @@ async def run_home_stream(
     system_prompt, langfuse_prompt = get_prompt_or_fallback(
         "home_system", _HOME_SYSTEM_PROMPT
     )
+    system_prompt += _language_instruction(context)
     text_chunks: list[str] = []
     async for event in _run_single_agent_stream(
         user_id=user_id,
@@ -925,6 +979,7 @@ async def run_floating_stream(
     system_prompt, langfuse_prompt = get_prompt_or_fallback(
         "floating_system", _FLOATING_SYSTEM_PROMPT
     )
+    system_prompt += _language_instruction(context)
     sanitizer = _FloatingStreamSanitizer()
     emitted_sanitized = False
     raw_chunks: list[str] = []

app/core/embeddings.py

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

app/core/langfuse_client.py

@@ -39,8 +39,10 @@ Linking a prompt to a generation::
 
 from __future__ import annotations
 
+import hashlib
 import logging
-from typing import Any
+from contextlib import contextmanager
+from typing import Any, Generator
 
 logger = logging.getLogger(__name__)
 
@@ -67,9 +69,9 @@ def get_langfuse() -> Any | None:
         _client = Langfuse(
             secret_key=settings.LANGFUSE_SECRET_KEY,
             public_key=settings.LANGFUSE_PUBLIC_KEY,
-            host=settings.LANGFUSE_HOST,
+            host=settings.LANGFUSE_BASE_URL,
         )
-        logger.info("langfuse: client initialized host=%s", settings.LANGFUSE_HOST)
+        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
@@ -80,10 +82,11 @@ def get_langfuse() -> Any | None:
 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 ``(prompt_text, prompt_obj_or_None)``.
+    Returns ``(raw_template, prompt_obj_or_None)``.
 
-    * ``prompt_text`` — the raw template string (variables not yet substituted).
+    * ``raw_template`` — the uncompiled template string.  Do NOT call ``.format()``
-      Callers perform variable substitution with Python's ``.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.
@@ -102,6 +105,38 @@ def get_prompt_or_fallback(name: str, fallback: str) -> tuple[str, Any]:
         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)
@@ -112,3 +147,44 @@ def extract_usage(response: Any) -> dict[str, int]:
         "output": int(meta.get("output_tokens", 0)),
         "total": int(meta.get("total_tokens", 0)),
     }
+
+
+def hash_user_id(user_id: str) -> str:
+    """Return a SHA-256 hash of *user_id* for use as Langfuse ``user_id``.
+
+    This avoids sending raw database UUIDs to external observability services
+    while still providing a stable, deterministic identifier for per-user
+    metrics in the Langfuse dashboard.
+    """
+    return hashlib.sha256(user_id.encode()).hexdigest()
+
+
+@contextmanager
+def langfuse_context(
+    user_id: str | None = None,
+    session_id: str | None = None,
+) -> Generator[None, None, None]:
+    """Propagate ``user_id`` (hashed) and ``session_id`` to all Langfuse observations.
+
+    No-op when Langfuse is not configured or parameters are empty.
+    """
+    lf = get_langfuse()
+    if lf is None or (not user_id and not session_id):
+        yield
+        return
+
+    try:
+        from langfuse import propagate_attributes
+    except ImportError:
+        logger.debug("langfuse: propagate_attributes not available — skipping context")
+        yield
+        return
+
+    attrs: dict[str, str] = {}
+    if user_id:
+        attrs["user_id"] = hash_user_id(user_id)
+    if session_id:
+        attrs["session_id"] = session_id
+
+    with propagate_attributes(**attrs):
+        yield

app/core/llm.py

@@ -1,6 +1,6 @@
 """LLM factory — centralised model instantiation via LiteLLM.
 
-Every agent and the orchestrator call ``get_llm()`` or ``get_router_llm()``
+Every agent and the orchestrator call ``get_llm()``
 instead of directly constructing a provider-specific class.  The model string
 follows the `LiteLLM model naming convention
 <https://docs.litellm.ai/docs/providers>`_:
@@ -11,7 +11,7 @@ follows the `LiteLLM model naming convention
 * Ollama:     ``ollama/llama3``
 * Bedrock:    ``bedrock/anthropic.claude-v2``
 
-Switch providers by changing **LLM_MODEL** / **LLM_ROUTER_MODEL** in ``.env``
+Switch providers by changing **LLM_MODEL** in ``.env``
 — no code changes required.
 """
 
@@ -19,6 +19,7 @@ from __future__ import annotations
 
 import os
 import warnings
+from collections.abc import Callable
 
 from openai import AsyncOpenAI
 import litellm
@@ -95,12 +96,33 @@ def get_llm(
     )
 
 
-def get_router_llm(
+_AGENT_MODEL_SETTINGS: dict[str, Callable[[], str]] = {
+    "classifier":          lambda: settings.LLM_MODEL_CLASSIFIER or settings.LLM_MODEL,
+    "home-agent":          lambda: settings.LLM_MODEL_HOME_AGENT or settings.LLM_MODEL,
+    "floating-agent":      lambda: settings.LLM_MODEL_FLOATING_AGENT or settings.LLM_MODEL,
+    "unified-processor":   lambda: settings.LLM_MODEL_UNIFIED_PROCESSOR or settings.LLM_MODEL,
+    "cloud-processor":     lambda: settings.LLM_MODEL_CLOUD_PROCESSOR or settings.LLM_MODEL,
+    "setup":               lambda: settings.LLM_MODEL_SETUP_AGENT or settings.LLM_MODEL,
+}
+
+
+def model_for_agent(agent_name: str) -> str:
+    """Return the resolved model string for *agent_name* (for Langfuse tracking)."""
+    return _AGENT_MODEL_SETTINGS.get(agent_name, lambda: settings.LLM_MODEL)()
+
+
+def get_agent_llm(
+    agent_name: str,
     *,
     temperature: float = 0,
 ) -> ChatOpenAI | ChatLiteLLM:
-    """Return the lighter model used for intent classification / routing."""
+    """Return an LLM configured for *agent_name*, respecting per-agent overrides.
-    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_middleware.py

@@ -69,17 +69,19 @@ class MemoryMiddleware:
         if fernet is None:
             return {}
 
+        user_dbg = await self._get_user_debug(user_id)
+        user_tier: str = user_dbg.get("tier") or "free"
+
         core = await self._load_core(user_id, fernet)
-        associative = await self._load_associative(user_id, message, fernet)
+        associative = await self._load_associative(user_id, message, fernet, user_tier=user_tier)
         episodic = await self._load_episodic(user_id, fernet, session_id=session_id)
         proactive = await self._load_proactive(user_id, fernet)
 
-        user_dbg = await self._get_user_debug(user_id)
         logger.info(
             "memory: enrich_context trace=%s user=%s tier=%s core=%d associative=%d episodic=%d proactive=%d",
             trace_id or "-",
             user_id,
-            user_dbg.get("tier") or "-",
+            user_tier,
             len(core),
             len(associative),
             len(episodic),
@@ -255,6 +257,50 @@ class MemoryMiddleware:
         logger.info("memory: replace_core user=%s label=%s changed=1", user_id, label)
         return True
 
+    async def store_associative(
+        self,
+        user_id: str,
+        content: str,
+        entity_type: str | None = None,
+        entity_id: str | None = None,
+    ) -> None:
+        """Store associative memory; embed if user tier has real_embeddings."""
+        from app.billing.tier_manager import tier_manager  # noqa: PLC0415
+        from app.core.embeddings import embed_text  # noqa: PLC0415
+
+        fernet = await self._get_fernet(user_id)
+        if fernet is None:
+            return
+
+        encrypted = _encrypt(fernet, content)
+
+        user_dbg = await self._get_user_debug(user_id)
+        user_tier = user_dbg.get("tier") or "free"
+
+        embedding: list[float] | None = None
+        if tier_manager.check_feature(user_tier, "real_embeddings"):
+            embedding = await embed_text(content)
+
+        row = MemoryAssociative(
+            id=str(uuid.uuid4()),
+            user_id=user_id,
+            content_encrypted=encrypted,
+            embedding=embedding,
+            entity_type=entity_type,
+            entity_id=entity_id,
+        )
+        self._db.add(row)
+        try:
+            await self._db.commit()
+            logger.info(
+                "memory: store_associative user=%s embedded=%s",
+                user_id,
+                embedding is not None,
+            )
+        except Exception as exc:
+            logger.error("memory: store_associative failed user=%s: %s", user_id, exc)
+            await self._db.rollback()
+
     async def 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)
@@ -364,14 +410,49 @@ class MemoryMiddleware:
         return out
 
     async def _load_associative(
-        self, user_id: str, message: str, fernet: Fernet
+        self, user_id: str, message: str, fernet: Fernet, *, user_tier: str = "free"
     ) -> list[str]:
         """Load top-k associative memories.
 
-        Production: uses pgvector cosine similarity on the message embedding.
+        Pro+: pgvector cosine similarity on the message embedding (real_embeddings feature).
-        Current implementation: keyword-based fallback (no external embedding call)
+        Free / embedding failure: keyword-ordered fallback (most recent rows).
-        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)
@@ -379,7 +460,7 @@ class MemoryMiddleware:
             .limit(_ASSOCIATIVE_TOP_K)
         )
         rows = result.scalars().all()
-        out: list[str] = []
+        out = []
         for row in rows:
             plaintext = _safe_decrypt(fernet, row.content_encrypted)
             if plaintext is not None:

app/core/preprocessors/__init__.py

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

app/core/preprocessors/base.py

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

app/core/preprocessors/email_html.py

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

app/integrations/ms_graph.py

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

app/main.py

@@ -30,7 +30,7 @@ async def lifespan(app: FastAPI):
 
 def create_app() -> FastAPI:
     app = FastAPI(
-        title="Adiuva Cloud API",
+        title="AdiuvAI Cloud API",
         version="0.1.0",
         docs_url="/docs" if settings.ENV == "dev" else None,
         redoc_url=None,

app/models.py

@@ -21,6 +21,7 @@ from __future__ import annotations
 import uuid
 from datetime import datetime, timezone
 
+from pgvector.sqlalchemy import Vector
 from sqlalchemy import (
     Boolean,
     DateTime,
@@ -69,7 +70,8 @@ class User(Base):
     email: Mapped[str] = mapped_column(String(255), unique=True, nullable=False, index=True)
     name: Mapped[str | None] = mapped_column(String(100), nullable=True)
     surname: Mapped[str | None] = mapped_column(String(100), nullable=True)
-    password_hash: Mapped[str] = mapped_column(String(255), nullable=False)
+    password_hash: Mapped[str | None] = mapped_column(String(255), nullable=True)
+    avatar_url: Mapped[str | None] = mapped_column(Text, nullable=True)
     tier: Mapped[str] = mapped_column(TierEnum, nullable=False, default="free")
     stripe_customer_id: Mapped[str | None] = mapped_column(String(255), nullable=True)
     # Per-user Fernet key (base64-urlsafe, 44 chars). Generated on registration.
@@ -78,6 +80,9 @@ class User(Base):
     created_at: Mapped[datetime] = mapped_column(
         DateTime(timezone=True), nullable=False, server_default=func.now()
     )
+    onboarding_completed_at: Mapped[datetime | None] = mapped_column(
+        DateTime(timezone=True), nullable=True, default=None
+    )
     updated_at: Mapped[datetime] = mapped_column(
         DateTime(timezone=True), nullable=False, server_default=func.now(), onupdate=func.now()
     )
@@ -88,6 +93,9 @@ class User(Base):
     subscription: Mapped[Subscription | None] = relationship(
         back_populates="user", uselist=False, cascade="all, delete-orphan"
     )
+    oauth_accounts: Mapped[list[OAuthAccount]] = relationship(
+        back_populates="user", cascade="all, delete-orphan"
+    )
 
 
 class RefreshToken(Base):
@@ -108,6 +116,25 @@ class RefreshToken(Base):
     user: Mapped[User] = relationship(back_populates="refresh_tokens")
 
 
+class OAuthAccount(Base):
+    __tablename__ = "oauth_accounts"
+
+    id: Mapped[str] = mapped_column(
+        Uuid(as_uuid=False), primary_key=True, default=_uuid
+    )
+    user_id: Mapped[str] = mapped_column(
+        Uuid(as_uuid=False), ForeignKey("users.id", ondelete="CASCADE"), nullable=False, index=True
+    )
+    provider: Mapped[str] = mapped_column(String(50), nullable=False)
+    provider_user_id: Mapped[str] = mapped_column(String(255), nullable=False)
+    provider_email: Mapped[str | None] = mapped_column(String(255), nullable=True)
+    created_at: Mapped[datetime] = mapped_column(
+        DateTime(timezone=True), nullable=False, server_default=func.now()
+    )
+
+    user: Mapped[User] = relationship(back_populates="oauth_accounts")
+
+
 class Subscription(Base):
     __tablename__ = "subscriptions"
 
@@ -143,6 +170,7 @@ class LocalAgentConfig(Base):
     directory_paths: Mapped[list] = mapped_column(JSON, nullable=False, default=list)
     data_types: Mapped[list] = mapped_column(JSON, nullable=False, default=list)
     prompt_template: Mapped[str] = mapped_column(Text, nullable=False, default="")
+    agent_config: Mapped[dict | None] = mapped_column(JSON, nullable=True)
     file_extensions: Mapped[list] = mapped_column(JSON, nullable=False, default=list)
     schedule_cron: Mapped[str] = mapped_column(String(100), nullable=False, default="0 */6 * * *")
     enabled: Mapped[bool] = mapped_column(Boolean, nullable=False, default=True)
@@ -272,8 +300,8 @@ class MemoryAssociative(Base):
         nullable=False, index=True,
     )
     content_encrypted: Mapped[str] = mapped_column(Text, nullable=False)
-    # JSON-encoded float list in SQLite tests; vector(1536) in Postgres via migration.
+    # vector(1536) via pgvector; SQLite tests use NULL embeddings so no dialect issue.
-    embedding: Mapped[list | None] = mapped_column(JSON, nullable=True)
+    embedding: Mapped[list | None] = mapped_column(Vector(1536), nullable=True)
     entity_type: Mapped[str | None] = mapped_column(String(100), nullable=True)
     entity_id: Mapped[str | None] = mapped_column(String(255), nullable=True)
     updated_at: Mapped[datetime] = mapped_column(
@@ -321,3 +349,25 @@ class MemoryProactive(Base):
     created_at: Mapped[datetime] = mapped_column(
         DateTime(timezone=True), nullable=False, server_default=func.now()
     )
+
+
+class Plugin(Base):
+    """Plugin marketplace catalog entry."""
+
+    __tablename__ = "plugins"
+
+    id: Mapped[str] = mapped_column(String(255), primary_key=True)
+    name: Mapped[str] = mapped_column(String(255), nullable=False)
+    description: Mapped[str] = mapped_column(Text, nullable=False)
+    version: Mapped[str] = mapped_column(String(50), nullable=False)
+    author_name: Mapped[str] = mapped_column(String(255), nullable=False)
+    category: Mapped[str] = mapped_column(String(100), nullable=False)
+    price_cents: Mapped[int] = mapped_column(Integer, nullable=False, default=0)
+    permissions: Mapped[str] = mapped_column(Text, nullable=False, default="[]")
+    status: Mapped[str] = mapped_column(String(50), nullable=False, default="pending")
+    s3_package_key: Mapped[str | None] = mapped_column(String(500), nullable=True)
+    install_count: Mapped[int] = mapped_column(Integer, nullable=False, default=0)
+    avg_rating: Mapped[float] = mapped_column(Float, nullable=False, default=0.0)
+    created_at: Mapped[datetime] = mapped_column(
+        DateTime(timezone=True), nullable=False, server_default=func.now()
+    )

app/schemas.py

@@ -30,6 +30,16 @@ class UserProfile(BaseModel):
     name: str | None = None
     surname: str | None = None
     tier: BillingTier
+    avatar_url: str | None = None
+    has_password: bool = True
+    onboarding_completed_at: int | None = None  # epoch ms, null = not onboarded
+    memory: dict[str, str] = Field(default_factory=dict)  # decrypted core memory k/v
+
+
+class OAuthAccountInfo(BaseModel):
+    provider: str
+    provider_email: str | None = None
+    created_at: int  # epoch ms
 
 
 # ── Chat ─────────────────────────────────────────────────────────────
@@ -191,6 +201,27 @@ class WsFloatingDomain(BaseModel):
     domain: WsDomain
 
 
+# ── Agent Config V2 ───────────────────────────────────────────────────
+
+
+class ContentTypeConfig(BaseModel):
+    """Per-type extraction config produced by the journey chatbot."""
+
+    id: str
+    label: str = ""
+    detection_hint: str = ""
+    preprocessing: str = "generic"  # handler name: "email_html", "plain_text", ...
+    extraction_prompt: str
+
+
+class AgentConfig(BaseModel):
+    """Structured agent configuration (replaces freeform prompt_template)."""
+
+    content_types: list[ContentTypeConfig] = []
+    global_rules: list[str] = []
+    data_types: list[str] = []
+
+
 # ── Agent Catalog ─────────────────────────────────────────────────────
 
 class AgentCatalogItem(BaseModel):
@@ -215,10 +246,11 @@ class AgentTriggerRequest(BaseModel):
     device_id: str = Field(default="")
     agent_id: str | None = None  # FE stable agent ID (electron-store UUID)
     what_to_extract: list[str] = Field(min_length=1)
-    actions_by_type: dict[str, list[str]] | None = None
     batch_interval: str = Field(min_length=1)
-    custom_agent_prompt: str = Field(min_length=1)
+    custom_agent_prompt: str | None = None
+    agent_config: dict | None = None
     active_agents: int = Field(ge=0, default=0)
+    last_run_at: int | None = None  # epoch ms from FE — enables incremental scanning
 
 
 # ── Agent Run Log ─────────────────────────────────────────────────────

docker-compose.yml

@@ -7,7 +7,7 @@ services:
       - path: .env
         required: false
     environment:
-      DATABASE_URL: postgresql+asyncpg://postgres:postgres@db:5432/adiuva
+      DATABASE_URL: postgresql+asyncpg://postgres:postgres@db:5432/adiuvai
       GITHUB_COPILOT_TOKEN_DIR: /root/.config/litellm/github_copilot
     volumes:
       - copilot_tokens:/root/.config/litellm/github_copilot
@@ -21,7 +21,7 @@ services:
     environment:
       POSTGRES_USER: postgres
       POSTGRES_PASSWORD: postgres
-      POSTGRES_DB: adiuva
+      POSTGRES_DB: adiuvai
     volumes:
       - postgres_data:/var/lib/postgresql/data
     healthcheck:

docs/MICROSERVICES_ARCHITECTURE.md

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

requirements.txt

@@ -32,5 +32,9 @@ 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>=2.0.0
+beautifulsoup4>=4.12.0
+lxml>=5.0.0
+PyYAML>=6.0.0
 ruff>=0.8.0

tests/conftest.py

@@ -6,26 +6,21 @@ 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 app.config.settings import settings
 from app.db import Base, get_session
 from app.main import app
-from app.models import Plugin, Subscription, User
+from app.models import Subscription, User
 
 # ── Fixed test user IDs (one per tier) ───────────────────────────────
 
@@ -109,79 +104,6 @@ def client(db_session: AsyncSession) -> Generator[TestClient, None, None]:   # n
     app.dependency_overrides.pop(get_session, None)
 
 
-# ── Seed data helpers ────────────────────────────────────────────────
-
-_SEED_PLUGINS = [
-    Plugin(
-        id="plugin-github-sync",
-        name="GitHub Sync",
-        description="Sync tasks with GitHub Issues and pull requests.",
-        version="1.0.0",
-        author_name="Adiuva",
-        category="productivity",
-        price_cents=0,
-        permissions=json.dumps(["read:tasks", "write:tasks"]),
-        status="approved",
-        s3_package_key="plugins/plugin-github-sync/1.0.0/package.zip",
-        install_count=0,
-        avg_rating=0.0,
-    ),
-    Plugin(
-        id="plugin-slack-notify",
-        name="Slack Notifier",
-        description="Post task and timeline updates to Slack channels.",
-        version="1.2.0",
-        author_name="Adiuva",
-        category="communication",
-        price_cents=499,
-        permissions=json.dumps(["read:tasks", "read:timelines"]),
-        status="approved",
-        s3_package_key="plugins/plugin-slack-notify/1.2.0/package.zip",
-        install_count=0,
-        avg_rating=0.0,
-    ),
-    Plugin(
-        id="plugin-time-tracker",
-        name="Time Tracker",
-        description="Track time spent on tasks with automatic reporting.",
-        version="0.9.1",
-        author_name="Third Party",
-        category="productivity",
-        price_cents=999,
-        permissions=json.dumps(["read:tasks", "write:tasks"]),
-        status="approved",
-        s3_package_key="plugins/plugin-time-tracker/0.9.1/package.zip",
-        install_count=0,
-        avg_rating=0.0,
-    ),
-]
-
-
-@pytest_asyncio.fixture
-async def seed_plugins(db_session: AsyncSession) -> list[Plugin]:
-    """Insert the 3 default approved plugins and return them."""
-    plugins = []
-    for template in _SEED_PLUGINS:
-        p = Plugin(
-            id=template.id,
-            name=template.name,
-            description=template.description,
-            version=template.version,
-            author_name=template.author_name,
-            category=template.category,
-            price_cents=template.price_cents,
-            permissions=template.permissions,
-            status=template.status,
-            s3_package_key=template.s3_package_key,
-            install_count=template.install_count,
-            avg_rating=template.avg_rating,
-        )
-        db_session.add(p)
-        plugins.append(p)
-    await db_session.commit()
-    return plugins
-
-
 # ── JWT helpers ──────────────────────────────────────────────────────
 
 
@@ -212,24 +134,21 @@ def auth_header(tier: str = "power", user_id: str | None = None) -> dict[str, st
     return {"Authorization": f"Bearer {make_jwt(tier, user_id)}"}
 
 
-# ── S3 mock fixture ──────────────────────────────────────────────────
+# ── CLI options ───────────────────────────────────────────────────────
 
-S3_TEST_BUCKET = "test-bucket"
+def pytest_addoption(parser):
-S3_TEST_REGION = "us-east-1"
+    parser.addoption(
-
+        "--preprocess-dir",
-
+        default=None,
-@pytest.fixture
+        help="Override fixture folder for preprocessor tests (must contain cases.yaml + data/)",
-def s3_bucket():
+    )
-    """Create a mocked S3 bucket via moto and patch BlobStore settings."""
+    parser.addoption(
-    with mock_aws():
+        "--runner-dir",
-        os.environ.setdefault("AWS_ACCESS_KEY_ID", "testing")
+        default=None,
-        os.environ.setdefault("AWS_SECRET_ACCESS_KEY", "testing")
+        help="Override fixture folder for agent_runner_v2 eval tests (must contain cases.yaml + data/)",
-        os.environ.setdefault("AWS_DEFAULT_REGION", S3_TEST_REGION)
+    )
-        client = boto3.client("s3", region_name=S3_TEST_REGION)
+    parser.addoption(
-        client.create_bucket(Bucket=S3_TEST_BUCKET)
+        "--journey-dir",
-        with patch("app.storage.blob_store.settings") as mock_settings:
+        default=None,
-            mock_settings.S3_BUCKET = S3_TEST_BUCKET
+        help="Override fixture folder for journey_v2 eval tests (must contain cases.yaml + data/)",
-            mock_settings.S3_REGION = S3_TEST_REGION
+    )
-            mock_settings.AWS_ACCESS_KEY_ID = "testing"
-            mock_settings.AWS_SECRET_ACCESS_KEY = "testing"
-            yield S3_TEST_BUCKET

tests/fixtures/agent_runner_v2/cases.yaml

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

tests/fixtures/agent_runner_v2/data/email_action.html

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

tests/fixtures/agent_runner_v2/data/email_date.html

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

tests/fixtures/agent_runner_v2/data/email_info.html

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

tests/fixtures/agent_runner_v2/data/email_no_project.html

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

tests/fixtures/journey_v2/cases.yaml

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

tests/fixtures/journey_v2/data/email_action.html

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

tests/fixtures/journey_v2/data/email_info.html

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

tests/fixtures/preprocessors/cases.yaml

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

tests/fixtures/preprocessors/data/email_action.html

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

tests/fixtures/preprocessors/data/email_heavy.html

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

tests/fixtures/preprocessors/data/email_single.html

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

tests/fixtures/preprocessors/data/email_thread.html

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

tests/fixtures/preprocessors/data/fallback.txt

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

tests/fixtures/preprocessors/data/generic_page.html

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

tests/fixtures/preprocessors/data/notes.txt

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

tests/test_agent_runner.py

@@ -28,7 +28,6 @@ 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,
@@ -597,7 +596,7 @@ async def test_run_cloud_agent_provider_fetch_error():
 @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 app.integrations import encrypt_token
     from cryptography.fernet import Fernet as _Fernet
 
     fernet_key = _Fernet.generate_key().decode()
@@ -791,7 +790,6 @@ async def test_trigger_run_local_agent_creates_run_log(client, db_session):
             json={
                 "directory": "/home/user/docs",
                 "what_to_extract": ["task", "note"],
-                "actions_by_type": {"task": ["add", "update"], "note": ["add"]},
                 "batch_interval": "0 */6 * * *",
                 "custom_agent_prompt": "Extract tasks and notes.",
                 "active_agents": 0,

tests/test_agent_runner_v2.py

@@ -0,0 +1,431 @@
+"""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
+        inserts = [c for c in calls if c["action"] == "insert"]
+        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

@@ -21,7 +21,6 @@ import time
 import uuid
 from unittest.mock import AsyncMock, patch
 
-import pytest
 from fastapi.testclient import TestClient
 from sqlalchemy.ext.asyncio import AsyncSession
 

tests/test_auth.py

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

tests/test_device_ws.py

@@ -18,13 +18,12 @@ from datetime import datetime, timezone
 from unittest.mock import AsyncMock, MagicMock, patch
 
 import pytest
-import pytest_asyncio
 
-from app.core.device_manager import DeviceConnection, DeviceConnectionManager
+from app.core.device_manager import DeviceConnectionManager
 from app.db import get_session
 from app.main import app
 from app.models import AgentRunLog
-from tests.conftest import TEST_USER_IDS, auth_header, make_jwt
+from tests.conftest import TEST_USER_IDS, make_jwt
 
 # ---------------------------------------------------------------------------
 # Helpers

tests/test_integrations.py

@@ -40,11 +40,9 @@ Coverage:
 
 from __future__ import annotations
 
-import asyncio
 import json
-import uuid
 from datetime import datetime, timezone
-from unittest.mock import AsyncMock, MagicMock, Mock, PropertyMock, patch
+from unittest.mock import AsyncMock, MagicMock, PropertyMock, patch
 
 import pytest
 

tests/test_journey_v2.py

@@ -0,0 +1,299 @@
+"""Tests for Local Agent V2 journey setup (Step 4).
+
+Covers the chatbot journey that produces a structured AgentConfig JSON
+instead of a freeform prompt_template string.
+
+Unit tests (no LLM)
+--------------------
+  4.6a  _extract_agent_config: valid JSON → returns serialised config
+  4.6b  _extract_agent_config: invalid JSON → returns None
+  4.6c  _extract_agent_config: markers absent → returns None
+  4.6d  _extract_agent_config: only START marker → returns None
+  4.6e  Session not found → done=True, agent_config=None
+  4.6f  Nudge uses AGENT_CONFIG_START/END markers (not old PROMPT_TEMPLATE)
+
+Eval test (real LLM + Langfuse scoring)
+----------------------------------------
+  4.1   Journey start explores directory → first reply contains a question
+
+Cases 4.2–4.5 (multi-turn conversations producing a full AgentConfig) are
+non-deterministic and tested manually — results tracked in Langfuse.
+
+Run:
+    pytest tests/test_journey_v2.py -v
+    pytest tests/test_journey_v2.py -v -k "4_6"          # unit only
+    pytest tests/test_journey_v2.py -v -k "eval"          # single LLM eval
+    pytest tests/test_journey_v2.py -v --journey-dir /p   # custom fixtures
+"""
+
+from __future__ import annotations
+
+import uuid
+from contextlib import nullcontext
+from pathlib import Path
+from typing import Any
+from unittest.mock import patch
+
+import pytest
+import yaml
+
+from app.api.routes.agent_setup import (
+    _CONFIG_END,
+    _CONFIG_START,
+    _MAX_TURNS,
+    _extract_agent_config,
+    _sessions,
+    handle_journey_message,
+    handle_journey_start,
+)
+from app.core.langfuse_client import get_langfuse
+from app.core.ws_context import clear_client_executor, set_client_executor
+from app.schemas import AgentConfig
+from tests.conftest import TEST_USER_IDS
+
+# ── Constants ─────────────────────────────────────────────────────────────
+
+_USER_ID = TEST_USER_IDS["power"]
+
+_DEFAULT_FIXTURE_DIR = Path(__file__).parent / "fixtures" / "journey_v2"
+
+# ── Fixture loading ───────────────────────────────────────────────────────
+
+
+def _fixtures_dir(config) -> Path:
+    override = config.getoption("--journey-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_data_file(filename: str, fixtures_dir: Path) -> str:
+    return (fixtures_dir / "data" / filename).read_text(encoding="utf-8")
+
+
+# ── pytest_generate_tests ─────────────────────────────────────────────────
+
+
+def pytest_generate_tests(metafunc):
+    if "journey_case" not in metafunc.fixturenames:
+        return
+    cases = _load_cases(metafunc.config)
+    metafunc.parametrize("journey_case", cases, ids=[c["id"] for c in cases])
+
+
+# ── Executor builder ──────────────────────────────────────────────────────
+
+
+def _make_fs_executor(directory_files: list[dict], fixtures_dir: Path):
+    """Return an async callback that simulates filesystem tool responses.
+
+    Matches the signature expected by ``set_client_executor`` / ``execute_on_client``:
+    receives the full ``payload`` dict and returns a result dict.
+
+    ``directory_files`` is a list of ``{path, content_file}`` dicts;
+    ``content_file`` is relative to ``fixtures_dir/data/``.
+    """
+    file_map: dict[str, str] = {
+        entry["path"]: _read_data_file(entry["content_file"], fixtures_dir)
+        for entry in directory_files
+    }
+
+    async def _executor(payload: dict) -> dict:
+        action = payload.get("action", "")
+        data = payload.get("data") or {}
+
+        if action == "list_directory":
+            return {"entries": [
+                {"type": "file", "name": p.split("/")[-1], "path": p}
+                for p in file_map
+            ]}
+
+        if action == "read_file_content":
+            path = data.get("path", "")
+            return {"content": file_map.get(path, "")}
+
+        if action == "get_file_metadata":
+            path = data.get("path", "")
+            name = path.split("/")[-1]
+            ext = "." + name.rsplit(".", 1)[-1] if "." in name else ""
+            return {"name": name, "extension": ext, "size": 1024,
+                    "createdAt": None, "modifiedAt": None}
+
+        return {}
+
+    return _executor
+
+
+# ── Journey runner helper ─────────────────────────────────────────────────
+
+
+async def _run_journey(user_id: str, case: dict, executor) -> dict[str, Any]:
+    """Drive start + all user_messages for a case. Returns the final reply dict.
+
+    Mirrors ``device_ws._handle_journey_start/message``: sets the client
+    executor (so filesystem tools work) before each handler call.
+    """
+    session_id = str(uuid.uuid4())
+    try:
+        set_client_executor(executor)
+        reply = await handle_journey_start(user_id, {
+            "agent_type": "local",
+            "directory": case["directory"],
+            "data_types": case["data_types"],
+            "session_id": session_id,
+        })
+
+        for msg in case.get("user_messages", []):
+            if reply.get("done"):
+                break
+            set_client_executor(executor)
+            reply = await handle_journey_message(user_id, {
+                "session_id": reply["session_id"],
+                "message": msg,
+            })
+    finally:
+        clear_client_executor()
+        _sessions.pop(session_id, None)
+
+    return reply
+
+
+# ── Assertion helper ──────────────────────────────────────────────────────
+
+
+def _evaluate_case(case: dict, reply: dict) -> tuple[float, str]:
+    """Return (score, comment) for a journey case given the final reply dict."""
+    if case.get("expect_question"):
+        has_q = "?" in reply.get("message", "")
+        return (1.0 if has_q else 0.0), f"first_reply_has_question={has_q}"
+
+    return 1.0, "no specific assertion"
+
+
+# ── Unit tests ────────────────────────────────────────────────────────────
+
+
+def test_4_6a_extract_valid_json():
+    """_extract_agent_config: valid JSON between markers → returns serialised config."""
+    config = AgentConfig(
+        content_types=[],
+        global_rules=["No project = no entity"],
+        data_types=["tasks"],
+    )
+    text = f"Some preamble\n{_CONFIG_START}\n{config.model_dump_json()}\n{_CONFIG_END}\nTrailing"
+    result = _extract_agent_config(text)
+    assert result is not None
+    parsed = AgentConfig.model_validate_json(result)
+    assert parsed.global_rules == ["No project = no entity"]
+
+
+def test_4_6b_extract_invalid_json():
+    """_extract_agent_config: malformed JSON between markers → returns None."""
+    text = f"{_CONFIG_START}\n{{not: valid json\n{_CONFIG_END}"
+    assert _extract_agent_config(text) is None
+
+
+def test_4_6c_extract_markers_absent():
+    """_extract_agent_config: no markers at all → returns None."""
+    assert _extract_agent_config("No markers here at all") is None
+
+
+def test_4_6d_extract_only_start_marker():
+    """_extract_agent_config: START without END → returns None."""
+    assert _extract_agent_config(f"text {_CONFIG_START} no end marker") is None
+
+
+@pytest.mark.asyncio
+async def test_4_6e_session_not_found():
+    """4.6e Session not found → done=True, agent_config=None, informative message."""
+    reply = await handle_journey_message(_USER_ID, {
+        "session_id": "nonexistent-session-id",
+        "message": "Hello",
+    })
+    assert reply["done"] is True
+    assert reply["agent_config"] is None
+    assert "not found" in reply["message"].lower() or "expired" in reply["message"].lower()
+
+
+@pytest.mark.asyncio
+async def test_4_6f_nudge_uses_new_markers():
+    """4.6f Nudge injected after max turns uses AGENT_CONFIG markers, not PROMPT_TEMPLATE."""
+    session_id = str(uuid.uuid4())
+    captured_histories: list[list[dict]] = []
+
+    async def _mock_llm(system_prompt, history, tools, **kwargs) -> str:
+        captured_histories.append(list(history))
+        # Return plain text — no markers — to trigger the nudge path.
+        return "I still need more information from you."
+
+    from app.api.routes.agent_setup import JourneySession
+
+    fake_session = JourneySession(
+        session_id=session_id,
+        user_id=_USER_ID,
+        agent_type="local",
+        directory="/test",
+        data_types=["tasks"],
+        system_prompt="system",
+        langfuse_prompt=None,
+    )
+    # Fill history to the turn limit so the next message triggers the nudge.
+    for i in range(_MAX_TURNS):
+        fake_session.history.append({"role": "user", "content": f"msg {i}"})
+        fake_session.history.append({"role": "assistant", "content": "ok"})
+    _sessions[session_id] = fake_session
+
+    try:
+        with patch("app.api.routes.agent_setup._call_llm_with_tools", side_effect=_mock_llm):
+            await handle_journey_message(_USER_ID, {
+                "session_id": session_id,
+                "message": "one more message to trigger nudge",
+            })
+    finally:
+        _sessions.pop(session_id, None)
+
+    # Second LLM call receives the nudge appended to history.
+    assert len(captured_histories) >= 2, "Expected ≥ 2 LLM calls (main reply + nudge)"
+    nudge_history = captured_histories[1]
+    user_msgs = " ".join(t["content"] for t in nudge_history if t["role"] == "user")
+    assert _CONFIG_START in user_msgs, f"Nudge must reference {_CONFIG_START}"
+    assert _CONFIG_END in user_msgs, f"Nudge must reference {_CONFIG_END}"
+    assert "PROMPT_TEMPLATE" not in user_msgs, "Old PROMPT_TEMPLATE markers must not appear in nudge"
+
+
+# ── Eval tests (real LLM + Langfuse) ─────────────────────────────────────
+
+
+@pytest.mark.asyncio
+@pytest.mark.eval
+async def test_eval_journey(journey_case, pytestconfig):
+    """Parametrized eval test — one invocation per YAML case."""
+    case: dict = journey_case
+    fixtures_dir = _fixtures_dir(pytestconfig)
+    executor = _make_fs_executor(case.get("directory_files", []), fixtures_dir)
+
+    lf = get_langfuse()
+    obs_ctx = lf.start_as_current_observation(
+        name=f"eval-journey-{case['id']}-{case.get('score_name', 'unknown').replace('.', '-')}",
+        metadata={"step": "4", "case_id": case["id"]},
+    ) if lf else nullcontext()
+
+    with obs_ctx as obs:
+        reply = await _run_journey(_USER_ID, case, executor)
+        score, comment = _evaluate_case(case, reply)
+
+        if obs is not None:
+            obs.score(
+                name=case.get("score_name", f"journey.case_{case['id']}"),
+                value=score,
+                comment=comment,
+            )
+
+    if lf:
+        lf.flush()
+
+    assert score == 1.0, f"[{case['id']}] {case.get('description', '')} — {comment}"

tests/test_memory_middleware.py

@@ -12,14 +12,15 @@ from __future__ import annotations
 
 import json
 import uuid
-from unittest.mock import patch
+from unittest.mock import AsyncMock, MagicMock, patch
 
 import pytest
 import pytest_asyncio
 from cryptography.fernet import Fernet
 from sqlalchemy import select
 
-from app.core.memory_middleware import MemoryMiddleware, _PROACTIVE_CONFIDENCE_THRESHOLD
+from app.core.embeddings import embed_text
+from app.core.memory_middleware import MemoryMiddleware
 from app.db import get_session
 from app.main import app
 from app.models import (
@@ -341,3 +342,33 @@ def test_home_request_calls_memory_middleware(client):
     stored_session_id, stored_message = store_calls[0][1], store_calls[0][2]
     assert stored_session_id == session_id
     assert stored_message == "Show tasks"
+
+
+# ── embed_text ─────────────────────────────────────────────────────────────────
+
+@pytest.mark.asyncio
+async def test_embed_text_returns_1536_floats():
+    """embed_text returns a 1536-dim float list when OpenAI responds successfully."""
+    fake_embedding = [0.1] * 1536
+
+    mock_response = MagicMock()
+    mock_response.data = [MagicMock(embedding=fake_embedding)]
+
+    mock_client = MagicMock()
+    mock_client.embeddings.create = AsyncMock(return_value=mock_response)
+
+    with patch("app.core.embeddings.AsyncOpenAI", return_value=mock_client):
+        result = await embed_text("test text")
+
+    assert result is not None
+    assert len(result) == 1536
+    assert all(isinstance(x, float) for x in result)
+
+
+@pytest.mark.asyncio
+async def test_embed_text_returns_none_on_failure():
+    """embed_text returns None when OpenAI raises; must not propagate the exception."""
+    with patch("app.core.embeddings.AsyncOpenAI", side_effect=Exception("no key")):
+        result = await embed_text("test text")
+
+    assert result is None

tests/test_memory_models.py

@@ -7,10 +7,9 @@ column is stored as JSON in tests (SQLite-compatible).
 from __future__ import annotations
 
 import uuid
-from datetime import datetime, timezone
+from datetime import datetime
 
 import pytest
-import pytest_asyncio
 from cryptography.fernet import Fernet
 from sqlalchemy import select
 

tests/test_preprocessors.py

@@ -0,0 +1,97 @@
+"""Tests for the preprocessor system (Step 1 — Local Agent V2).
+
+Run:
+    pytest tests/test_preprocessors.py -v
+    pytest tests/test_preprocessors.py -v --preprocess-dir /path/to/folder
+
+The folder must contain cases.yaml + data/.
+"""
+
+from __future__ import annotations
+
+import re
+from pathlib import Path
+
+import yaml
+
+from app.core.preprocessors import detect_content_type, preprocess
+
+_DEFAULT_DIR = Path(__file__).parent / "fixtures" / "preprocessors"
+
+_GENERATORS = {
+    "binary_noise": "some\x00\x01\x02\x03\x04\x05content" * 20,
+}
+
+
+def _fixtures_dir(config) -> Path:
+    override = config.getoption("--preprocess-dir")
+    return Path(override) if override else _DEFAULT_DIR
+
+
+def _load_cases(config) -> list[dict]:
+    return yaml.safe_load((_fixtures_dir(config) / "cases.yaml").read_text(encoding="utf-8"))
+
+
+def _content(case: dict, data_dir: Path) -> str:
+    if "generate" in case:
+        return _GENERATORS[case["generate"]]
+    return (data_dir / case["file"]).read_text(encoding="utf-8")
+
+
+# ── parametrize at collection time via pytest hook ────────────────────
+
+def pytest_generate_tests(metafunc):
+    if "preprocess_case" not in metafunc.fixturenames:
+        return
+    cases = _load_cases(metafunc.config)
+    test_name = metafunc.function.__name__
+    if test_name == "test_detect":
+        subset = [c for c in cases if "detect" in c]
+    else:
+        subset = [c for c in cases if "process" in c]
+    metafunc.parametrize("preprocess_case", subset, ids=[c["id"] for c in subset])
+
+
+# ── detect ────────────────────────────────────────────────────────────
+
+def test_detect(preprocess_case, pytestconfig) -> None:
+    case = preprocess_case
+    data_dir = _fixtures_dir(pytestconfig) / "data"
+    raw = _content(case, data_dir)
+    filename = case.get("file", "")
+    ct = detect_content_type(filename, raw)
+    expected = case["detect"]
+    assert ct == expected, f"[{case['id']}] expected {expected!r}, got {ct!r}"
+
+
+# ── preprocess ────────────────────────────────────────────────────────
+
+def test_preprocess(preprocess_case, pytestconfig) -> None:
+    case = preprocess_case
+    data_dir = _fixtures_dir(pytestconfig) / "data"
+    raw = _content(case, data_dir)
+    result = preprocess(case["process"], raw)
+
+    if case.get("no_html"):
+        assert not re.search(r"<[^>]+>", result.clean_text), "clean_text contains HTML tags"
+
+    if "min_chars" in case:
+        assert len(result.clean_text) >= case["min_chars"], \
+            f"clean_text too short: {len(result.clean_text)} < {case['min_chars']}"
+
+    if "ratio_lt" in case:
+        ratio = len(result.clean_text) / len(raw)
+        assert ratio < case["ratio_lt"], f"compression ratio {ratio:.2f} >= {case['ratio_lt']}"
+
+    for key in case.get("has_meta", []):
+        assert result.metadata.get(key), f"metadata missing {key!r} (got {result.metadata})"
+
+    for item in ([case["contains"]] if isinstance(case.get("contains"), str) else case.get("contains", [])):
+        assert item in result.clean_text, f"clean_text missing {item!r}"
+
+    for item in ([case["excludes"]] if isinstance(case.get("excludes"), str) else case.get("excludes", [])):
+        assert item not in result.clean_text, f"clean_text contains forbidden {item!r}"
+
+    if "content_type" in case:
+        assert result.content_type == case["content_type"], \
+            f"expected content_type {case['content_type']!r}, got {result.content_type!r}"