refactor: remove monolith app/, Dockerfile, requirements.txt

All business logic has been extracted into microservices:
  - services/auth/       (Step 1)
  - services/ws-gateway/ (Step 2)
  - services/chat/       (Step 2)
  - services/batch-agent/ (Step 3)
  - services/billing/    (Step 4)

Shared code lives in shared/.
Migrations remain in alembic/.
Tests in tests/ will need updating to target individual services.

.env.example

@@ -4,9 +4,19 @@ ENV=dev
 # ── Database ──────────────────────────────────────────────────────────────────
 DATABASE_URL=postgresql+asyncpg://postgres:postgres@localhost:5432/adiuva
 
-# ── Auth ──────────────────────────────────────────────────────────────────────
-JWT_SECRET=replace-with-a-long-random-secret
-JWT_ALGORITHM=HS256
+# ── Redis ─────────────────────────────────────────────────────────────────────
+REDIS_URL=redis://localhost:6379/0
+
+# ── Auth (JWT RS256) ──────────────────────────────────────────────────────────
+# Generate keypair:
+#   openssl genpkey -algorithm RSA -out private.pem -pkeyopt rsa_keygen_bits:2048
+#   openssl rsa -in private.pem -pubout -out public.pem
+# Paste PEM content with literal \n for newlines.
+#
+# Private key — ONLY used by the Auth Service (JWT signing).
+JWT_PRIVATE_KEY=
+# Public key — used by all services / Traefik ForwardAuth (JWT verification).
+JWT_PUBLIC_KEY=
 JWT_ACCESS_TOKEN_EXPIRE_MINUTES=30
 JWT_REFRESH_TOKEN_EXPIRE_DAYS=30
 
@@ -17,7 +27,6 @@ OPENAI_API_KEY=
 ANTHROPIC_API_KEY=
 GOOGLE_API_KEY=
 LLM_MODEL=gpt-4o
-LLM_ROUTER_MODEL=gpt-4o-mini
 
 # ── Stripe (leave empty to stub billing) ──────────────────────────────────────
 STRIPE_SECRET_KEY=
@@ -42,3 +51,17 @@ QDRANT_API_KEY=
 # ── CORS ──────────────────────────────────────────────────────────────────────
 # Comma-separated list parsed by Settings (override default if needed)
 # CORS_ORIGINS=["app://.","http://localhost:3000"]
+
+# ── Langfuse (observability) ─────────────────────────────────────────────────
+LANGFUSE_SECRET_KEY=sk-lf-...
+LANGFUSE_PUBLIC_KEY=pk-lf-...
+LANGFUSE_HOST=https://cloud.langfuse.com  # or self-hosted URL
+
+# ── Cloudflare (Traefik ACME DNS-01 challenge) ───────────────────────────────
+CF_DNS_API_TOKEN=
+ACME_EMAIL=
+
+# ── PostgreSQL (used by docker-compose) ──────────────────────────────────────
+POSTGRES_USER=postgres
+POSTGRES_PASSWORD=postgres
+POSTGRES_DB=adiuva

.gitignore

@@ -13,6 +13,9 @@ env/
 # Environment variables
 .env
 
+# Cryptographic keys
+*.pem
+
 # IDE
 .vscode/
 .idea/
@@ -32,3 +35,6 @@ Thumbs.db
 # Claude Code
 .claude/
 logs/
+
+# Eval private test data
+services/batch-agent/eval/fixtures/private_data/

README.md

@@ -739,7 +739,7 @@ adiuva-api/
 │   │
 │   ├── core/                    # Orchestration engine
 │   │   ├── agent_registry.py    # BaseAgent, ChatAgent, AgentRegistry
-│   │   ├── llm.py               # LiteLLM factory (get_llm, get_router_llm)
+│   │   ├── llm.py               # LiteLLM factory (get_llm)
 │   │   ├── orchestrator.py      # Intent classification & routing
 │   │   └── execution_plan.py    # Plan builder, templates, cache
 │   │

app/agents/__init__.py

@@ -1,5 +0,0 @@
-"""Expose tool modules used by deep orchestrator-worker graphs."""
-
-from app.agents import filesystem_agent, timeline_agent, note_agent, project_agent, task_agent
-
-__all__ = ["filesystem_agent", "timeline_agent", "note_agent", "project_agent", "task_agent"]

app/api/deps.py

@@ -1,14 +0,0 @@
-"""Shared FastAPI dependencies.
-
-``get_current_user`` and ``oauth2_scheme`` live in ``app.api.middleware.auth``
-(the canonical location per Step 9).  This module re-exports them so that all
-existing route imports (``from app.api.deps import get_current_user``) continue
-to work without modification.
-
-Step 12 will update ``get_current_user`` to fetch the live tier from PostgreSQL
-instead of reading it from the JWT payload.
-"""
-
-from app.api.middleware.auth import get_current_user, oauth2_scheme  # noqa: F401
-
-__all__ = ["get_current_user", "oauth2_scheme"]

app/api/middleware/__init__.py

@@ -1,19 +0,0 @@
-"""API middleware package.
-
-Exports the three middleware components introduced in Step 9:
-  - Auth:        ``get_current_user`` FastAPI dependency + ``oauth2_scheme``
-  - Rate limit:  ``TierRateLimitMiddleware`` + ``limiter`` (slowapi Limiter)
-  - Sanitizer:   ``SanitizerMiddleware``
-"""
-
-from app.api.middleware.auth import get_current_user, oauth2_scheme
-from app.api.middleware.rate_limit import TierRateLimitMiddleware, limiter
-from app.api.middleware.sanitizer import SanitizerMiddleware
-
-__all__ = [
-    "get_current_user",
-    "oauth2_scheme",
-    "TierRateLimitMiddleware",
-    "limiter",
-    "SanitizerMiddleware",
-]

app/api/middleware/rate_limit.py

@@ -1,129 +0,0 @@
-"""Tier-aware rate limiting middleware.
-
-Uses a per-user sliding-window counter (in-process, no Redis required).
-The ``slowapi`` Limiter is also exported for optional route-level decoration.
-
-Limits (requests per minute):
-  - free:  20
-  - pro:   60
-  - power: 120
-  - team:  200
-
-Exempt paths bypass the limiter entirely:
-  - POST /api/v1/auth/register
-  - POST /api/v1/auth/login
-  - POST /api/v1/billing/webhook
-  - GET  /api/v1/health
-"""
-
-from __future__ import annotations
-
-import json
-import time
-from collections import defaultdict
-
-from fastapi import Request, Response
-from jose import JWTError, jwt
-from slowapi import Limiter
-from slowapi.util import get_remote_address
-from starlette.middleware.base import BaseHTTPMiddleware
-from starlette.types import ASGIApp
-
-from app.config.settings import settings
-
-_TIER_LIMITS: dict[str, int] = {
-    "free": 20,
-    "pro": 60,
-    "power": 120,
-    "team": 200,
-}
-
-_EXEMPT_PATHS: frozenset[str] = frozenset(
-    {
-        "/api/v1/auth/register",
-        "/api/v1/auth/login",
-        "/api/v1/billing/webhook",
-        "/api/v1/health",
-    }
-)
-
-
-def _get_user_id_from_jwt(request: Request) -> str:
-    """Key function for the slowapi Limiter: returns JWT sub or remote IP."""
-    auth = request.headers.get("Authorization", "")
-    token = auth.removeprefix("Bearer ").strip()
-    if not token:
-        return get_remote_address(request)
-    try:
-        payload = jwt.decode(
-            token, settings.JWT_SECRET, algorithms=[settings.JWT_ALGORITHM]
-        )
-        return payload.get("sub") or get_remote_address(request)
-    except JWTError:
-        return get_remote_address(request)
-
-
-# Exported Limiter instance — available for optional route-level decoration.
-limiter = Limiter(key_func=_get_user_id_from_jwt)
-
-
-class TierRateLimitMiddleware(BaseHTTPMiddleware):
-    """Sliding-window rate limiter applied globally across all non-exempt routes.
-
-    Each authenticated user gets their own 60-second window sized by tier.
-    Unauthenticated requests pass through (the auth dependency will reject them
-    with 401 before the route handler runs).
-    """
-
-    def __init__(self, app: ASGIApp) -> None:
-        super().__init__(app)
-        # user_id → list of request timestamps (float, seconds since epoch)
-        self._window: dict[str, list[float]] = defaultdict(list)
-
-    async def dispatch(self, request: Request, call_next) -> Response:  # type: ignore[override]
-        if request.url.path in _EXEMPT_PATHS:
-            return await call_next(request)
-
-        # Extract JWT claims — if no valid token, pass through for auth dep to handle.
-        auth = request.headers.get("Authorization", "")
-        token = auth.removeprefix("Bearer ").strip()
-        if not token:
-            return await call_next(request)
-
-        try:
-            payload = jwt.decode(
-                token, settings.JWT_SECRET, algorithms=[settings.JWT_ALGORITHM]
-            )
-            user_id: str = payload.get("sub") or get_remote_address(request)
-            tier: str = payload.get("tier", "free")
-        except JWTError:
-            return await call_next(request)
-
-        limit = _TIER_LIMITS.get(tier, _TIER_LIMITS["free"])
-        now = time.monotonic()
-        window_start = now - 60.0
-
-        # Slide the window: discard timestamps older than 60 seconds.
-        timestamps = [t for t in self._window[user_id] if t > window_start]
-
-        if len(timestamps) >= limit:
-            retry_after = max(1, int(60 - (now - min(timestamps))))
-            return Response(
-                content=json.dumps(
-                    {
-                        "detail": (
-                            f"Rate limit exceeded ({limit} req/min for {tier} tier). "
-                            f"Retry in {retry_after}s."
-                        )
-                    }
-                ),
-                status_code=429,
-                headers={
-                    "Retry-After": str(retry_after),
-                    "Content-Type": "application/json",
-                },
-            )
-
-        timestamps.append(now)
-        self._window[user_id] = timestamps
-        return await call_next(request)

app/api/middleware/sanitizer.py

@@ -1,139 +0,0 @@
-"""Response sanitizer middleware.
-
-Scans JSON responses from the /api/v1/chat endpoint and strips any fragments
-that could reveal server-side prompt IP:
-  - System prompt openers ("You are a/an/the …")
-  - Agent routing metadata ("Available agents:", "intent classifier", …)
-  - LangChain tool schema fragments (``"type": "function"``)
-  - Internal reasoning markers (<thinking>, <reasoning>, [INST], …)
-  - Exact-match known prompt fingerprints
-
-Binary responses (storage blobs, backup data) are never touched — the
-middleware only activates for paths under /api/v1/chat.
-
-Any sanitisation event is logged as a WARNING with the request path and the
-names of the fields that were modified.
-"""
-
-from __future__ import annotations
-
-import json
-import logging
-import re
-
-from fastapi import Request, Response
-from starlette.middleware.base import BaseHTTPMiddleware
-from starlette.types import ASGIApp
-
-logger = logging.getLogger(__name__)
-
-# ---------------------------------------------------------------------------
-# Detection patterns — order matters: fingerprints checked first (exact),
-# then compiled regexes.
-# ---------------------------------------------------------------------------
-
-_FINGERPRINTS: tuple[str, ...] = (
-    "You are an intent classifier",
-    "Respond with just the agent name",
-    "Summarize these agent results",
-    "Available agents:",
-    "route to:",
-)
-
-_PATTERNS: tuple[re.Pattern[str], ...] = (
-    re.compile(r"You are (a|an|the)\b.{0,200}", re.IGNORECASE | re.DOTALL),
-    re.compile(r"Available agents\s*:", re.IGNORECASE),
-    re.compile(r"\bintent classifier\b", re.IGNORECASE),
-    re.compile(r'"type"\s*:\s*"function"'),           # LangChain tool schema
-    re.compile(r"<(thinking|reasoning|system|prompt)>", re.IGNORECASE),
-    re.compile(r"\[INST\]|\[/INST\]"),                # Llama instruct markers
-    re.compile(r"route\s+to\s*:", re.IGNORECASE),
-    re.compile(r"prompt_template\s*:\s*['\"].{10,}", re.IGNORECASE),
-)
-
-
-def _sanitize_text(text: str) -> tuple[str, bool]:
-    """Scan *text* for prompt fragments and replace matches with ``[REDACTED]``.
-
-    Returns ``(cleaned_text, was_changed)``.
-    """
-    # Fingerprint check — if any exact phrase is present, redact the whole string.
-    for fp in _FINGERPRINTS:
-        if fp in text:
-            return "[REDACTED]", True
-
-    changed = False
-    for pattern in _PATTERNS:
-        new_text, n = pattern.subn("[REDACTED]", text)
-        if n:
-            text = new_text
-            changed = True
-
-    return text, changed
-
-
-class SanitizerMiddleware(BaseHTTPMiddleware):
-    """Strip prompt IP from /api/v1/chat JSON responses."""
-
-    def __init__(self, app: ASGIApp) -> None:
-        super().__init__(app)
-
-    async def dispatch(self, request: Request, call_next) -> Response:  # type: ignore[override]
-        response: Response = await call_next(request)
-
-        # Only process chat endpoint responses.
-        if not request.url.path.startswith("/api/v1/chat"):
-            return response
-
-        # Read body — collect streaming chunks.
-        body_bytes = b""
-        async for chunk in response.body_iterator:
-            body_bytes += chunk if isinstance(chunk, bytes) else chunk.encode()
-
-        # Skip non-JSON bodies (shouldn't happen on /chat, but be safe).
-        try:
-            body = json.loads(body_bytes.decode("utf-8"))
-        except (json.JSONDecodeError, UnicodeDecodeError):
-            return Response(
-                content=body_bytes,
-                status_code=response.status_code,
-                headers=dict(response.headers),
-                media_type=response.media_type,
-            )
-
-        if not isinstance(body, dict):
-            return Response(
-                content=body_bytes,
-                status_code=response.status_code,
-                headers=dict(response.headers),
-                media_type=response.media_type,
-            )
-
-        # Walk top-level string fields and sanitise.
-        sanitised_fields: list[str] = []
-        for key, value in body.items():
-            if isinstance(value, str):
-                cleaned, changed = _sanitize_text(value)
-                if changed:
-                    body[key] = cleaned
-                    sanitised_fields.append(key)
-
-        if sanitised_fields:
-            logger.warning(
-                "Sanitizer redacted prompt fragments",
-                extra={
-                    "path": request.url.path,
-                    "fields": sanitised_fields,
-                },
-            )
-
-        new_body = json.dumps(body).encode("utf-8")
-        headers = dict(response.headers)
-        headers["content-length"] = str(len(new_body))
-
-        return Response(
-            content=new_body,
-            status_code=response.status_code,
-            headers=headers,
-            media_type="application/json",
-        )

app/api/routes/agents.py

@@ -1,222 +0,0 @@
-"""Agent routes.
-
-Backend responsibilities are intentionally minimal:
-    GET  /agents/catalog         — static catalog for UI display
-    POST /agents/can-create      — billing eligibility check
-    POST /agents/trigger         — trigger a local agent run
-
-Agent configuration is owned by the Electron app and is not persisted
-in backend agent-config tables.
-"""
-
-from __future__ import annotations
-
-import asyncio
-import uuid
-from datetime import datetime, timedelta, timezone
-
-from fastapi import APIRouter, Depends, HTTPException, status
-from sqlalchemy import func, select
-from sqlalchemy.ext.asyncio import AsyncSession
-
-from app.api.deps import get_current_user
-from app.billing.tier_manager import FEATURES
-from app.core.agent_runner import is_agent_running, run_local_agent
-from app.core.device_manager import device_manager
-from app.db import get_session
-from app.models import AgentRunLog, LocalAgentConfig
-from app.schemas import (
-    AgentCatalogItem,
-    AgentCreationCheckRequest,
-    AgentCreationCheckResponse,
-    AgentRunLogResponse,
-    AgentTriggerRequest,
-    UserProfile,
-)
-
-router = APIRouter(prefix="/agents", tags=["agents"])
-
-
-# ── Datetime helpers ──────────────────────────────────────────────────
-
-def _dt_ms(dt: datetime) -> int:
-    return int(dt.timestamp() * 1000)
-
-
-def _dt_ms_opt(dt: datetime | None) -> int | None:
-    return int(dt.timestamp() * 1000) if dt else None
-
-
-def _to_data_types(values: list[str]) -> list[str]:
-    normalize = {
-        "task": "tasks",           "tasks": "tasks",
-        "note": "notes",           "notes": "notes",
-        "timeline": "timelines",   "timelines": "timelines",   "timelineEvents": "timelines",
-        "project": "projects",     "projects": "projects",
-    }
-    seen: set[str] = set()
-    result: list[str] = []
-    for v in values:
-        mapped = normalize.get(v)
-        if mapped and mapped not in seen:
-            seen.add(mapped)
-            result.append(mapped)
-    return result
-
-
-def _to_run_log_response(log: AgentRunLog) -> AgentRunLogResponse:
-    return AgentRunLogResponse(
-        id=log.id,
-        agent_id=log.agent_id,
-        agent_type=log.agent_type,  # type: ignore[arg-type]
-        status=log.status,  # type: ignore[arg-type]
-        items_processed=log.items_processed,
-        items_created=log.items_created,
-        errors=log.errors or [],
-        started_at=_dt_ms(log.started_at),
-        completed_at=_dt_ms_opt(log.completed_at),
-    )
-
-
-def _enforce_agent_limit(tier: str, current_count: int) -> int:
-    limit: int = FEATURES.get(tier, FEATURES["free"])["batch_active"]
-    if limit != -1 and current_count >= limit:
-        raise HTTPException(
-            status_code=status.HTTP_403_FORBIDDEN,
-            detail=f"Agent limit ({limit}) reached for your tier. Upgrade to create more.",
-        )
-    return limit
-
-
-async def _enforce_run_frequency(
-    tier: str,
-    user_id: str,
-    db: AsyncSession,
-) -> None:
-    """Raise HTTP 402 if the user has exceeded their daily batch run limit."""
-    limit: int = FEATURES.get(tier, FEATURES["free"])["batch_runs_per_day"]
-    if limit == -1:
-        return  # unlimited
-
-    today_start = datetime.now(timezone.utc).replace(
-        hour=0, minute=0, second=0, microsecond=0
-    )
-    result = await db.execute(
-        select(func.count(AgentRunLog.id)).where(
-            AgentRunLog.user_id == user_id,
-            AgentRunLog.started_at >= today_start,
-        )
-    )
-    runs_today: int = result.scalar_one()
-
-    if runs_today >= limit:
-        raise HTTPException(
-            status_code=status.HTTP_402_PAYMENT_REQUIRED,
-            detail=f"Daily batch run limit ({limit}) reached for your tier. Upgrade for more runs.",
-        )
-
-
-# ── Catalog ───────────────────────────────────────────────────────────
-
-@router.get("/catalog", response_model=list[AgentCatalogItem])
-async def get_agent_catalog(
-    current_user: UserProfile = Depends(get_current_user),
-) -> list[AgentCatalogItem]:
-    """Return the static list of available agent types and their descriptions."""
-    return [
-        AgentCatalogItem(
-            type="local_directory",
-            name="Local Directory Monitor",
-            description="Watches local directories, extracts data from files using AI",
-        ),
-        AgentCatalogItem(
-            type="gmail",
-            name="Gmail Connector",
-            description="Scans Gmail inbox, extracts tasks/notes from emails",
-        ),
-        AgentCatalogItem(
-            type="teams",
-            name="Microsoft Teams Connector",
-            description="Monitors Teams messages, extracts action items",
-        ),
-        AgentCatalogItem(
-            type="outlook",
-            name="Outlook Connector",
-            description="Scans Outlook inbox, extracts tasks/notes",
-        ),
-    ]
-
-
-@router.post("/can-create", response_model=AgentCreationCheckResponse)
-async def can_create_agent(
-    body: AgentCreationCheckRequest,
-    current_user: UserProfile = Depends(get_current_user),
-) -> AgentCreationCheckResponse:
-    """Check if the user can create one more agent based on billing tier.
-
-    Since configuration is client-owned, the Electron app sends its current
-    active agent count and the backend applies tier limits.
-    """
-    limit: int = FEATURES.get(current_user.tier, FEATURES["free"])["batch_active"]
-    allowed = limit == -1 or body.active_agents < limit
-    return AgentCreationCheckResponse(
-        allowed=allowed,
-        tier=current_user.tier,
-        active_agents=body.active_agents,
-        limit=limit,
-    )
-
-
-@router.post("/trigger", response_model=AgentRunLogResponse, status_code=status.HTTP_202_ACCEPTED)
-async def trigger_agent_run(
-    body: AgentTriggerRequest,
-    current_user: UserProfile = Depends(get_current_user),
-    db: AsyncSession = Depends(get_session),
-) -> AgentRunLogResponse:
-    """Trigger a local agent run using client-provided configuration."""
-    _enforce_agent_limit(current_user.tier, body.active_agents)
-    await _enforce_run_frequency(current_user.tier, current_user.id, db)
-
-    config = LocalAgentConfig(
-        id=str(uuid.uuid4()),
-        user_id=current_user.id,
-        device_id=body.device_id,
-        name="Local Directory Monitor",
-        directory_paths=[body.directory],
-        data_types=_to_data_types(body.what_to_extract),
-        prompt_template=body.custom_agent_prompt,
-        file_extensions=[],
-        schedule_cron=body.batch_interval,
-        enabled=True,
-    )
-
-    # Use the FE's stable agent_id if provided, fall back to the ephemeral config id.
-    stable_agent_id = body.agent_id or config.id
-
-    if is_agent_running(stable_agent_id):
-        raise HTTPException(
-            status_code=status.HTTP_409_CONFLICT,
-            detail="Agent is already running. Only one run per agent is allowed at a time.",
-        )
-
-    run_log = AgentRunLog(
-        agent_id=stable_agent_id,
-        agent_type="local",
-        user_id=current_user.id,
-        status="running",
-    )
-    db.add(run_log)
-    await db.commit()
-    await db.refresh(run_log)
-
-    run_context = {
-        "type": "agent_batch",
-        "run_id": run_log.id,
-        "agent_id": stable_agent_id,
-    }
-
-    asyncio.create_task(
-        run_local_agent(current_user.id, config, run_log, device_manager, run_context)
-    )
-
-    return _to_run_log_response(run_log)

app/api/routes/backup.py

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

app/api/routes/billing.py

@@ -1,85 +0,0 @@
-"""Billing routes: Stripe checkout, webhook, subscription management.
-
-Business logic lives in ``app.billing.stripe_service.StripeService``.
-The route layer handles HTTP concerns (request parsing, response shaping)
-and delegates everything else to the service singleton.
-"""
-
-from __future__ import annotations
-
-from typing import Any
-
-from fastapi import APIRouter, Depends, Header, Request, status
-from pydantic import BaseModel
-from sqlalchemy.ext.asyncio import AsyncSession
-
-from app.api.deps import get_current_user
-from app.billing.stripe_service import stripe_service
-from app.db import get_session
-from app.schemas import BillingTier, UserProfile
-
-router = APIRouter(prefix="/billing", tags=["billing"])
-
-
-# ── Request bodies ─────────────────────────────────────────────────────
-
-class _CheckoutRequest(BaseModel):
-    tier: BillingTier
-
-
-# ── Routes ─────────────────────────────────────────────────────────────
-
-@router.post("/checkout", response_model=dict)
-async def create_checkout(
-    body: _CheckoutRequest,
-    current_user: UserProfile = Depends(get_current_user),
-) -> dict[str, str]:
-    """Create a Stripe checkout session for a tier upgrade.
-
-    Returns a stub URL when ``STRIPE_SECRET_KEY`` is not configured.
-    """
-    url = stripe_service.create_checkout_session(current_user.id, body.tier)
-    return {"checkout_url": url}
-
-
-@router.post("/webhook", response_model=dict)
-async def stripe_webhook(
-    request: Request,
-    stripe_signature: str = Header(default="", alias="Stripe-Signature"),
-    db: AsyncSession = Depends(get_session),
-) -> dict[str, bool]:
-    """Handle Stripe webhook events.
-
-    No JWT auth — authenticated via Stripe signature verification instead.
-    Returns 200 immediately when Stripe is not configured (local dev).
-    """
-    payload = await request.body()
-    await stripe_service.handle_webhook(payload, stripe_signature, db)
-    return {"ok": True}
-
-
-@router.get("/subscription", response_model=dict)
-async def get_subscription(
-    current_user: UserProfile = Depends(get_current_user),
-    db: AsyncSession = Depends(get_session),
-) -> dict[str, Any]:
-    """Return the current subscription info for the authenticated user."""
-    sub = await stripe_service.get_subscription(current_user.id, db)
-    if sub is None:
-        return {
-            "tier": current_user.tier,
-            "status": "free",
-            "stripe_subscription_id": None,
-            "current_period_end": None,
-        }
-    return sub
-
-
-@router.delete("/subscription", response_model=dict, status_code=status.HTTP_200_OK)
-async def cancel_subscription(
-    current_user: UserProfile = Depends(get_current_user),
-    db: AsyncSession = Depends(get_session),
-) -> dict[str, bool]:
-    """Cancel the active subscription."""
-    await stripe_service.cancel_subscription(current_user.id, db)
-    return {"ok": True}

app/api/routes/chat.py

@@ -1,29 +0,0 @@
-"""Chat routes: POST /chat (REST fallback).
-
-WebSocket chat is handled by the unified device WS endpoint (/api/v1/ws/device).
-"""
-
-from __future__ import annotations
-
-from fastapi import APIRouter, Depends
-from fastapi.responses import JSONResponse
-
-from app.api.deps import get_current_user
-from app.core.deep_agent import run_home
-from app.schemas import ChatRequest, UserProfile
-
-router = APIRouter(prefix="/chat", tags=["chat"])
-
-
-@router.post("")
-async def chat(
-    body: ChatRequest,
-    current_user: UserProfile = Depends(get_current_user),
-) -> JSONResponse:
-    """REST fallback for home chat when websocket streaming is unavailable."""
-    response = await run_home(
-        user_id=current_user.id,
-        message=body.message,
-        context=body.context.model_dump(),
-    )
-    return JSONResponse(content={"response": response})

app/api/routes/device_ws.py

@@ -1,417 +0,0 @@
-"""Device WebSocket endpoint.
-
-Persistent connection from Electron devices to the backend.
-
-  WS  /api/v1/ws/device?token=<jwt>
-
-Auth: JWT passed as ``?token=`` query parameter (Bearer header is not
-available during the WebSocket handshake).
-
-Protocol:
-  1. Client connects → JWT validated → connection accepted.
-  2. Client sends ``device_hello`` frame: ``{ type, device_id, agent_ids }``.
-  3. Backend registers the connection in ``DeviceConnectionManager``.
-  4. Session enters message dispatch loop + heartbeat.
-
-Incoming frame dispatch:
-  - ``tool_result``      → resolves a pending tool-call Future.
-  - ``journey_start``    → starts a guided setup journey session.
-  - ``journey_message``  → continues a journey conversation.
-  - ``pong``             → heartbeat acknowledgement (updates last-seen).
-  - unknown types        → logged, ignored.
-
-Outgoing heartbeat: ``{ "type": "ping" }`` every 30 s.
-
-On disconnect:
-  - Unregisters from DeviceConnectionManager.
-  - Marks all in-progress AgentRunLog rows for this user as ``error``
-    with message "device disconnected".
-"""
-
-from __future__ import annotations
-
-import asyncio
-import json
-import logging
-from uuid import uuid4
-
-from fastapi import APIRouter, WebSocket, WebSocketDisconnect
-from jose import JWTError, jwt
-from sqlalchemy import update
-
-from app.api.routes.agent_setup import handle_journey_message, handle_journey_start
-from app.config.settings import settings
-from app.core.agent_runner import trigger_pending_runs
-from app.core.deep_agent import run_floating_stream, run_home_stream
-from app.core.device_manager import device_manager
-from app.core.memory_middleware import MemoryMiddleware
-from app.core.output_formatter import StreamFormatter
-from app.core.ws_context import clear_client_executor, set_client_executor
-from app.db import async_session
-from app.models import AgentRunLog
-from app.schemas import WsFrameType
-
-logger = logging.getLogger(__name__)
-
-router = APIRouter(prefix="/ws", tags=["device-ws"])
-
-_HEARTBEAT_INTERVAL = 30  # seconds
-_PONG_TIMEOUT = 10  # seconds — grace window after a ping
-
-
-@router.websocket("/device")
-async def device_ws(websocket: WebSocket) -> None:
-    """Persistent WebSocket endpoint for Electron device connections.
-
-    Authentication is via ``?token=<jwt>`` query parameter.
-    """
-    # ── 1. Authenticate before accepting ─────────────────────────────
-    token = websocket.query_params.get("token", "")
-    try:
-        payload = jwt.decode(
-            token, settings.JWT_SECRET, algorithms=[settings.JWT_ALGORITHM]
-        )
-        user_id: str | None = payload.get("sub")
-        if not user_id:
-            raise JWTError("missing sub")
-    except JWTError:
-        await websocket.close(code=1008)  # Policy Violation
-        return
-
-    await websocket.accept()
-
-    # ── 2. Await device_hello frame ───────────────────────────────────
-    try:
-        raw = await asyncio.wait_for(websocket.receive_text(), timeout=15.0)
-    except (asyncio.TimeoutError, WebSocketDisconnect):
-        await websocket.close(code=1008)
-        return
-
-    try:
-        hello = json.loads(raw)
-        if hello.get("type") != WsFrameType.device_hello:
-            raise ValueError("expected device_hello as first frame")
-        device_id: str = hello["device_id"]
-        agent_ids: list[str] = hello.get("agent_ids", [])
-    except (KeyError, ValueError, json.JSONDecodeError) as exc:
-        logger.warning("device_ws: invalid device_hello from user=%s: %s", user_id, exc)
-        await websocket.close(code=1008)
-        return
-
-    # ── 3. Register connection ────────────────────────────────────────
-    device_manager.register(user_id, device_id, websocket)
-    logger.info(
-        "device_ws: connected user=%s device=%s agents=%s",
-        user_id,
-        device_id,
-        agent_ids,
-    )
-
-    # Trigger any overdue agent runs now that the device is connected.
-    asyncio.create_task(trigger_pending_runs(user_id, device_id, device_manager))
-
-    # ── 4. Concurrent message loop + heartbeat ────────────────────────
-    try:
-        await asyncio.gather(
-            _message_loop(websocket, user_id),
-            _heartbeat_loop(websocket),
-        )
-    except WebSocketDisconnect:
-        pass
-    except Exception as exc:
-        logger.warning("device_ws: unhandled exception user=%s: %s", user_id, exc)
-    finally:
-        device_manager.unregister(user_id)
-        logger.info("device_ws: disconnected user=%s device=%s", user_id, device_id)
-        await _mark_runs_disconnected(user_id)
-
-
-# ── Message dispatch loop ─────────────────────────────────────────────
-
-async def _message_loop(websocket: WebSocket, user_id: str) -> None:
-    """Receive frames from Electron and dispatch to the appropriate handler."""
-    async for raw in websocket.iter_text():
-        try:
-            frame: dict = json.loads(raw)
-        except json.JSONDecodeError:
-            logger.warning("device_ws: invalid JSON from user=%s", user_id)
-            continue
-
-        frame_type = frame.get("type")
-
-        if frame_type == WsFrameType.tool_result:
-            call_id = frame.get("id")
-            if call_id:
-                device_manager.resolve_pending_call(user_id, call_id, frame)
-            else:
-                logger.warning(
-                    "device_ws: tool_result missing id from user=%s", user_id
-                )
-
-        elif frame_type == WsFrameType.home_request:
-            asyncio.create_task(
-                _handle_home_request(websocket, user_id, frame)
-            )
-
-        elif frame_type == WsFrameType.floating_request:
-            asyncio.create_task(
-                _handle_floating_request(websocket, user_id, frame)
-            )
-
-        elif frame_type == WsFrameType.journey_start:
-            asyncio.create_task(
-                _handle_journey_start(websocket, user_id, frame)
-            )
-
-        elif frame_type == WsFrameType.journey_message:
-            asyncio.create_task(
-                _handle_journey_message(websocket, user_id, frame)
-            )
-
-        elif frame_type == "pong":
-            # Heartbeat ack — nothing to do, connection is alive.
-            pass
-
-        else:
-            logger.debug(
-                "device_ws: unknown frame type %r from user=%s", frame_type, user_id
-            )
-
-
-# ── v3 Chat Handlers ──────────────────────────────────────────────────
-
-async def _make_ws_executor(websocket: WebSocket, user_id: str):
-    """Return a callback that sends tool_call frames and awaits tool_result."""
-    async def _executor(payload: dict) -> dict:
-        payload["type"] = WsFrameType.tool_call
-        await websocket.send_text(json.dumps(payload))
-        future = device_manager.create_pending_call(user_id, payload["id"])
-        return await future
-    return _executor
-
-
-async def _handle_home_request(
-    websocket: WebSocket,
-    user_id: str,
-    frame: dict,
-) -> None:
-    """Handle a home_request frame — streams HomeFormatter output back on the socket."""
-    request_id = frame.get("request_id") or str(uuid4())
-    message: str = frame.get("message", "")
-    session_id: str = frame.get("session_id") or str(uuid4())
-    logger.info(
-        "device_ws: home_request_start user=%s req=%s session=%s msg=%s",
-        user_id,
-        request_id,
-        session_id,
-        message[:200],
-    )
-
-    # ── Memory: enrich context before LLM call ────────────────────────
-    async with async_session() as db:
-        memory = MemoryMiddleware(db)
-        memory_context = await memory.enrich_context(
-            user_id,
-            message,
-            trace_id=request_id,
-            session_id=session_id,
-        )
-
-    context: dict = {
-        "conversation_history": frame.get("conversation_history", []),
-        "_debug": {"request_id": request_id, "session_id": session_id, "user_id": user_id},
-        **memory_context,
-    }
-
-    executor = await _make_ws_executor(websocket, user_id)
-    set_client_executor(executor)
-    response_chunks: list[str] = []
-    try:
-        event_stream = run_home_stream(user_id, message, context)
-        formatter = StreamFormatter(request_id=request_id)
-        async for ws_frame in formatter.format(event_stream):
-            await websocket.send_text(ws_frame.model_dump_json())
-            # Collect text chunks to build the full response for episode storage
-            if ws_frame.type == "stream_text":  # type: ignore[union-attr]
-                response_chunks.append(ws_frame.chunk)  # type: ignore[union-attr]
-    except Exception as exc:
-        logger.error(
-            "device_ws: home_request failed user=%s req=%s: %s",
-            user_id, request_id, exc,
-        )
-    finally:
-        clear_client_executor()
-
-    # ── Memory: store episode after response ──────────────────────────
-    async with async_session() as db:
-        memory = MemoryMiddleware(db)
-        await memory.store_episode(
-            user_id, session_id, message, "".join(response_chunks), trace_id=request_id
-        )
-    logger.info(
-        "device_ws: home_request_end user=%s req=%s session=%s response_chars=%d",
-        user_id,
-        request_id,
-        session_id,
-        len("".join(response_chunks)),
-    )
-
-
-async def _handle_floating_request(
-    websocket: WebSocket,
-    user_id: str,
-    frame: dict,
-) -> None:
-    """Handle a floating_request frame — streams FloatingFormatter output back on the socket."""
-    request_id = frame.get("request_id") or str(uuid4())
-    message: str = frame.get("message", "")
-    session_id: str = frame.get("session_id") or str(uuid4())
-    scope: dict = frame.get("scope", {})
-    logger.info(
-        "device_ws: floating_request_start user=%s req=%s session=%s scope=%s msg=%s",
-        user_id,
-        request_id,
-        session_id,
-        json.dumps(scope, ensure_ascii=True)[:200],
-        message[:200],
-    )
-
-    # ── Memory: enrich context before LLM call ────────────────────────
-    async with async_session() as db:
-        memory = MemoryMiddleware(db)
-        memory_context = await memory.enrich_context(
-            user_id,
-            message,
-            trace_id=request_id,
-            session_id=session_id,
-        )
-
-    context: dict = {
-        "scope": scope,
-        "_debug": {"request_id": request_id, "session_id": session_id, "user_id": user_id},
-        **memory_context,
-    }
-
-    executor = await _make_ws_executor(websocket, user_id)
-    set_client_executor(executor)
-    response_chunks: list[str] = []
-    try:
-        event_stream = run_floating_stream(user_id, message, context)
-        formatter = StreamFormatter(request_id=request_id)
-        async for ws_frame in formatter.format(event_stream):
-            await websocket.send_text(ws_frame.model_dump_json())
-            if ws_frame.type == "stream_text":  # type: ignore[union-attr]
-                response_chunks.append(ws_frame.chunk)  # type: ignore[union-attr]
-    except Exception as exc:
-        logger.error(
-            "device_ws: floating_request failed user=%s req=%s: %s",
-            user_id, request_id, exc,
-        )
-    finally:
-        clear_client_executor()
-
-    # ── Memory: store episode after response ──────────────────────────
-    async with async_session() as db:
-        memory = MemoryMiddleware(db)
-        await memory.store_episode(
-            user_id, session_id, message, "".join(response_chunks), trace_id=request_id
-        )
-    logger.info(
-        "device_ws: floating_request_end user=%s req=%s session=%s response_chars=%d",
-        user_id,
-        request_id,
-        session_id,
-        len("".join(response_chunks)),
-    )
-
-
-# ── v4 Journey Handlers ─────────────────────────────────────────────
-
-
-async def _handle_journey_start(
-    websocket: WebSocket,
-    user_id: str,
-    frame: dict,
-) -> None:
-    """Handle a journey_start frame — explores directory and sends first question."""
-    executor = await _make_ws_executor(websocket, user_id)
-    set_client_executor(executor)
-    try:
-        reply = await handle_journey_start(user_id, frame)
-        await websocket.send_text(json.dumps(reply))
-    except Exception as exc:
-        logger.error(
-            "device_ws: journey_start failed user=%s: %s", user_id, exc
-        )
-        await websocket.send_text(json.dumps({
-            "type": "journey_reply",
-            "session_id": frame.get("session_id", ""),
-            "message": f"Failed to start journey: {exc}",
-            "done": True,
-            "prompt_template": None,
-        }))
-    finally:
-        clear_client_executor()
-
-
-async def _handle_journey_message(
-    websocket: WebSocket,
-    user_id: str,
-    frame: dict,
-) -> None:
-    """Handle a journey_message frame — continues the journey conversation."""
-    executor = await _make_ws_executor(websocket, user_id)
-    set_client_executor(executor)
-    try:
-        reply = await handle_journey_message(user_id, frame)
-        await websocket.send_text(json.dumps(reply))
-    except Exception as exc:
-        session_id = frame.get("session_id", "")
-        logger.error(
-            "device_ws: journey_message failed user=%s session=%s: %s",
-            user_id, session_id, exc,
-        )
-        await websocket.send_text(json.dumps({
-            "type": "journey_reply",
-            "session_id": session_id,
-            "message": f"Journey error: {exc}",
-            "done": True,
-            "prompt_template": None,
-        }))
-    finally:
-        clear_client_executor()
-
-
-# ── Heartbeat ─────────────────────────────────────────────────────────
-
-async def _heartbeat_loop(websocket: WebSocket) -> None:
-    """Send a ping frame every 30 s to keep the connection alive."""
-    while True:
-        await asyncio.sleep(_HEARTBEAT_INTERVAL)
-        await websocket.send_text(json.dumps({"type": "ping"}))
-
-
-# ── Disconnect cleanup ────────────────────────────────────────────────
-
-async def _mark_runs_disconnected(user_id: str) -> None:
-    """Mark all in-progress AgentRunLog rows as 'error' for this user."""
-    try:
-        async with async_session() as db:
-            await db.execute(
-                update(AgentRunLog)
-                .where(
-                    AgentRunLog.user_id == user_id,
-                    AgentRunLog.status == "running",
-                )
-                .values(
-                    status="error",
-                    errors=["device disconnected"],
-                )
-            )
-            await db.commit()
-    except Exception as exc:
-        logger.error(
-            "device_ws: failed to mark runs as disconnected for user=%s: %s",
-            user_id,
-            exc,
-        )

app/api/routes/plugins.py

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

app/api/routes/storage.py

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

app/api/routes/vectors.py

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

app/billing/__init__.py

@@ -1,4 +0,0 @@
-from app.billing.stripe_service import stripe_service
-from app.billing.tier_manager import tier_manager
-
-__all__ = ["stripe_service", "tier_manager"]

app/config/settings.py

@@ -1,60 +0,0 @@
-from typing import Literal
-from pydantic_settings import BaseSettings, SettingsConfigDict
-
-
-class Settings(BaseSettings):
-    DATABASE_URL: str = "postgresql+asyncpg://postgres:postgres@localhost:5432/adiuva"
-    JWT_SECRET: str = "change-me-in-production"
-    JWT_ALGORITHM: str = "HS256"
-    JWT_ACCESS_TOKEN_EXPIRE_MINUTES: int = 30
-    JWT_REFRESH_TOKEN_EXPIRE_DAYS: int = 30
-
-    STRIPE_SECRET_KEY: str = ""
-    STRIPE_WEBHOOK_SECRET: str = ""
-
-    S3_BUCKET: str = ""
-    S3_REGION: str = "us-east-1"
-    S3_ENDPOINT_URL: str = ""
-    AWS_ACCESS_KEY_ID: str = ""
-    AWS_SECRET_ACCESS_KEY: str = ""
-
-    PINECONE_API_KEY: str = ""
-    PINECONE_INDEX: str = "adiuva"
-    QDRANT_URL: str = ""
-    QDRANT_API_KEY: str = ""
-
-    OPENAI_API_KEY: str = ""
-    ANTHROPIC_API_KEY: str = ""
-    GOOGLE_API_KEY: str = ""
-    CEREBRAS_API_KEY: str = ""
-
-    LLM_MODEL: str = "gpt-4o"
-    LLM_ROUTER_MODEL: str = "gpt-4o-mini"
-    LLM_EMBED_MODEL: str = "text-embedding-3-small"
-
-    # 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.
-    GITHUB_COPILOT_TOKEN_DIR: str = ""
-
-    # OAuth client credentials — used for Gmail and Microsoft (Outlook/Teams) flows.
-    GMAIL_CLIENT_ID: str = ""
-    GMAIL_CLIENT_SECRET: str = ""
-    MS_CLIENT_ID: str = ""
-    MS_CLIENT_SECRET: str = ""
-    # MS_TENANT_ID: set to 'common' to allow multi-tenant (personal + work accounts).
-    MS_TENANT_ID: str = "common"
-
-    # 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"]
-
-    ENV: Literal["dev", "prod"] = "dev"
-
-    model_config = SettingsConfigDict(env_file=".env", env_file_encoding="utf-8")
-
-
-settings = Settings()

app/core/agent_registry.py

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

app/core/device_manager.py

@@ -1,151 +0,0 @@
-"""Device connection manager.
-
-Maintains in-memory state for all active Electron → backend WebSocket
-connections.  One connection per user (latest replaces previous).
-
-The manager handles the **tool-call round-trip** pattern:
-  - Backend sends ``tool_call`` frame → Electron executes the action →
-    returns ``tool_result`` frame.
-  - ``create_pending_call`` registers a Future keyed by ``call_id``.
-  - ``resolve_pending_call`` fulfils the Future; callers awaiting it
-    receive the result dict from Electron.
-
-This pattern is used by all tools (CRUD, file-system, etc.) via
-``execute_on_client()`` in ``ws_context.py``.
-
-The ``device_manager`` module-level singleton is imported by both the
-device WS route and the agent runner.
-"""
-
-from __future__ import annotations
-
-import asyncio
-import json
-import logging
-from dataclasses import dataclass, field
-
-from fastapi import WebSocket
-
-logger = logging.getLogger(__name__)
-
-
-@dataclass
-class DeviceConnection:
-    """State for a single connected Electron device."""
-
-    ws: WebSocket
-    device_id: str
-    # Futures indexed by tool_call id — resolved when tool_result arrives.
-    pending_calls: dict[str, asyncio.Future[dict]] = field(default_factory=dict)
-
-
-class DeviceConnectionManager:
-    """Singleton registry of active Electron WebSocket connections.
-
-    Thread/task safety note: asyncio is single-threaded by design.  All
-    mutations happen inside await-points on the main event loop, so no
-    locking is required for the in-memory dicts.
-    """
-
-    def __init__(self) -> None:
-        self._connections: dict[str, DeviceConnection] = {}
-
-    # ── Registration ──────────────────────────────────────────────────
-
-    def register(self, user_id: str, device_id: str, ws: WebSocket) -> None:
-        """Store the active connection for *user_id*, replacing any previous one."""
-        if user_id in self._connections:
-            old = self._connections[user_id]
-            logger.info(
-                "device_manager: replacing existing connection for user=%s device=%s",
-                user_id,
-                old.device_id,
-            )
-            # Cancel any futures that were waiting on the old connection.
-            for fut in old.pending_calls.values():
-                if not fut.done():
-                    fut.cancel()
-        self._connections[user_id] = DeviceConnection(ws=ws, device_id=device_id)
-        logger.info(
-            "device_manager: registered user=%s device=%s", user_id, device_id
-        )
-
-    def unregister(self, user_id: str) -> None:
-        """Remove the connection for *user_id* and cancel any pending futures."""
-        conn = self._connections.pop(user_id, None)
-        if conn is None:
-            return
-        for fut in conn.pending_calls.values():
-            if not fut.done():
-                fut.cancel()
-        logger.info("device_manager: unregistered user=%s", user_id)
-
-    # ── Presence queries ──────────────────────────────────────────────
-
-    def get_ws(self, user_id: str) -> WebSocket | None:
-        """Return the active WebSocket for *user_id*, or ``None`` if offline."""
-        conn = self._connections.get(user_id)
-        return conn.ws if conn else None
-
-    def is_online(self, user_id: str, device_id: str | None = None) -> bool:
-        """Return ``True`` if the user has an active connection.
-
-        If *device_id* is provided also checks that it matches the connected device.
-        """
-        conn = self._connections.get(user_id)
-        if conn is None:
-            return False
-        if device_id is not None:
-            return conn.device_id == device_id
-        return True
-
-    # ── Frame sending ─────────────────────────────────────────────────
-
-    async def send_frame(self, user_id: str, frame: dict) -> None:
-        """Send *frame* as a JSON text message to the device.
-
-        Raises ``RuntimeError`` if the user is not connected.
-        """
-        conn = self._connections.get(user_id)
-        if conn is None:
-            raise RuntimeError(
-                f"send_frame: user {user_id!r} is not connected"
-            )
-        await conn.ws.send_text(json.dumps(frame))
-
-    # ── Tool-call round-trip ──────────────────────────────────────────
-
-    def create_pending_call(
-        self, user_id: str, call_id: str
-    ) -> asyncio.Future[dict]:
-        """Register a Future that will be resolved when the tool_result arrives.
-
-        Raises ``RuntimeError`` if the user is not connected.
-        """
-        conn = self._connections.get(user_id)
-        if conn is None:
-            raise RuntimeError(
-                f"create_pending_call: user {user_id!r} is not connected"
-            )
-        loop = asyncio.get_event_loop()
-        fut: asyncio.Future[dict] = loop.create_future()
-        conn.pending_calls[call_id] = fut
-        return fut
-
-    def resolve_pending_call(
-        self, user_id: str, call_id: str, result: dict
-    ) -> None:
-        """Fulfil the Future registered under *call_id* with the Electron result.
-
-        No-ops if the call_id is unknown (already timed out or cancelled).
-        """
-        conn = self._connections.get(user_id)
-        if conn is None:
-            return
-        fut = conn.pending_calls.pop(call_id, None)
-        if fut is not None and not fut.done():
-            fut.set_result(result)
-
-
-# Module-level singleton — import this everywhere.
-device_manager = DeviceConnectionManager()

app/core/llm.py

@@ -1,125 +0,0 @@
-"""LLM factory — centralised model instantiation via LiteLLM.
-
-Every agent and the orchestrator call ``get_llm()`` or ``get_router_llm()``
-instead of directly constructing a provider-specific class.  The model string
-follows the `LiteLLM model naming convention
-<https://docs.litellm.ai/docs/providers>`_:
-
-* OpenAI:     ``gpt-4o``, ``gpt-4o-mini``
-* Anthropic:  ``anthropic/claude-3.5-sonnet``
-* Google:     ``gemini/gemini-pro``
-* Ollama:     ``ollama/llama3``
-* Bedrock:    ``bedrock/anthropic.claude-v2``
-
-Switch providers by changing **LLM_MODEL** / **LLM_ROUTER_MODEL** in ``.env``
-— no code changes required.
-"""
-
-from __future__ import annotations
-
-import os
-import warnings
-
-from openai import AsyncOpenAI
-import litellm
-
-from langchain_openai import ChatOpenAI
-from langchain_litellm import ChatLiteLLM
-from litellm import get_supported_openai_params  # noqa: F401 – validates install
-
-from app.config.settings import settings
-
-# Some models (e.g. gpt-5, o-series) reject unsupported params like temperature.
-# Drop them silently instead of raising UnsupportedParamsError.
-litellm.drop_params = True
-
-# Some provider responses include a plain dict in the `usage` field where a
-# richer Pydantic model is expected. This warning is noisy but non-fatal.
-warnings.filterwarnings(
-    "ignore",
-    message=r"PydanticSerializationUnexpectedValue\(Expected `ResponseAPIUsage`",
-    category=UserWarning,
-)
-
-
-def _api_key_for_model(model: str) -> str | None:
-    """Return the most appropriate API key for the given LiteLLM model string."""
-    if model.startswith("anthropic/"):
-        return settings.ANTHROPIC_API_KEY or None
-    if model.startswith("gemini/") or model.startswith("google/"):
-        return settings.GOOGLE_API_KEY or None
-    if model.startswith("cerebras/"):
-        return settings.CEREBRAS_API_KEY or None
-    if model.startswith("github_copilot/"):
-        # GitHub Copilot uses OAuth device-flow tokens managed by LiteLLM.
-        # No API key is required; returning None lets LiteLLM handle auth.
-        return None
-    # Default: OpenAI-compatible (covers plain model names like "gpt-4o")
-    return settings.OPENAI_API_KEY or None
-
-
-def get_llm(
-    *,
-    model: str | None = None,
-    temperature: float = 0,
-) -> ChatOpenAI | ChatLiteLLM:
-    """Return a LangChain chat model backed by LiteLLM.
-
-    LiteLLM exposes an OpenAI-compatible API, so we use ``ChatOpenAI`` pointed
-    at the LiteLLM proxy endpoint.  In practice, ``litellm`` patches the
-    ``openai`` client transparently when the model string contains a provider
-    prefix (``anthropic/…``, ``gemini/…``, etc.).
-
-    Parameters
-    ----------
-    model:
-        LiteLLM model identifier. Defaults to ``settings.LLM_MODEL``.
-    temperature:
-        Sampling temperature.  ``0`` = deterministic.
-    """
-    model = model or settings.LLM_MODEL
-
-    # Point LiteLLM to the custom token directory when configured.
-    if settings.GITHUB_COPILOT_TOKEN_DIR:
-        os.environ.setdefault("GITHUB_COPILOT_TOKEN_DIR", settings.GITHUB_COPILOT_TOKEN_DIR)
-
-    # Use ChatLiteLLM for provider-prefixed models (github_copilot/, anthropic/, etc.)
-    # so LiteLLM handles routing and auth. ChatOpenAI for plain OpenAI model names.
-    if "/" in model:
-        return ChatLiteLLM(model=model, temperature=temperature)
-
-    return ChatOpenAI(
-        model=model,
-        temperature=temperature,
-        api_key=_api_key_for_model(model),
-    )
-
-
-def get_router_llm(
-    *,
-    temperature: float = 0,
-) -> ChatOpenAI | ChatLiteLLM:
-    """Return the lighter model used for intent classification / routing."""
-    return get_llm(model=settings.LLM_ROUTER_MODEL, temperature=temperature)
-
-
-async def embed(text: str) -> list[float]:
-    """Return an embedding vector for *text*.
-
-    Uses ``settings.LLM_EMBED_MODEL`` so the same provider switch in ``.env``
-    (e.g. ``github_copilot/text-embedding-3-small``) applies here without any
-    code changes.  Falls back to the raw AsyncOpenAI client for plain OpenAI
-    model names to preserve existing behaviour.
-    """
-    model = settings.LLM_EMBED_MODEL
-
-    if model.startswith("github_copilot/") or "/" in model:
-        # Use LiteLLM for all provider-prefixed models (Copilot, Bedrock, etc.)
-        # so the provider's auth mechanism is applied correctly.
-        response = await litellm.aembedding(model=model, input=[text])
-        return response.data[0]["embedding"]
-
-    # Plain OpenAI model name — use the raw AsyncOpenAI client (existing path).
-    client = AsyncOpenAI(api_key=settings.OPENAI_API_KEY)
-    response = await client.embeddings.create(model=model, input=text)
-    return response.data[0].embedding

app/core/ws_context.py

@@ -1,92 +0,0 @@
-"""WebSocket client executor context.
-
-Holds a per-request async callback that tools call to execute CRUD
-operations on the Electron client's local SQLite / LanceDB databases.
-The callback sends a `tool_call` WS frame and awaits the `tool_result`.
-"""
-
-from __future__ import annotations
-
-from contextvars import ContextVar
-from typing import Any, Callable, Coroutine
-from uuid import uuid4
-
-# Holds the execute callback for the current WS session.
-# Set by the chat WS handler before the orchestrator runs; cleared after.
-_client_executor: ContextVar[Callable[[dict], Coroutine[Any, Any, dict]]] = ContextVar(
-    "_client_executor"
-)
-
-# Optional collector that captures raw execute_on_client results.
-# Set by _tool_loop / _tool_loop_stream to populate ChatAgent.tool_results.
-_tool_result_collector: ContextVar[list[dict] | None] = ContextVar(
-    "_tool_result_collector", default=None
-)
-
-
-def set_tool_result_collector(lst: list[dict]) -> None:
-    """Register *lst* as the collector for this async context."""
-    _tool_result_collector.set(lst)
-
-
-def clear_tool_result_collector() -> None:
-    """Clear the collector (best-effort)."""
-    _tool_result_collector.set(None)
-
-
-def set_client_executor(fn: Callable[[dict], Coroutine[Any, Any, dict]]) -> None:
-    """Bind *fn* as the executor for the current async context (task/coroutine)."""
-    _client_executor.set(fn)
-
-
-def clear_client_executor() -> None:
-    """Remove the executor binding (best-effort; ContextVar resets on task exit)."""
-    try:
-        _client_executor.set(None)  # type: ignore[arg-type]
-    except Exception:
-        pass
-
-
-async def execute_on_client(
-    action: str,
-    table: str | None = None,
-    data: dict[str, Any] | None = None,
-    filters: dict[str, Any] | None = None,
-    vector: list[float] | None = None,
-    limit: int | None = None,
-) -> dict[str, Any]:
-    """Send a CRUD/vector operation to the Electron client and return the result.
-
-    Builds a ``tool_call`` payload, invokes the per-session WS callback,
-    and returns the ``tool_result`` dict from Electron.
-
-    Raises ``RuntimeError`` if no executor is set (i.e. called outside a WS session).
-    """
-    callback = _client_executor.get(None)
-    if callback is None:
-        raise RuntimeError(
-            "execute_on_client() called outside a WebSocket session — "
-            "no client executor is set."
-        )
-
-    payload: dict[str, Any] = {"id": str(uuid4()), "action": action}
-    if table is not None:
-        payload["table"] = table
-    if data is not None:
-        payload["data"] = data
-    if filters is not None:
-        payload["filters"] = {k: v for k, v in filters.items() if v is not None}
-    if vector is not None:
-        payload["vector"] = vector
-    if limit is not None:
-        payload["limit"] = limit
-
-    result = await callback(payload)
-    collector = _tool_result_collector.get(None)
-    if collector is not None:
-        collector.append({
-            "action": action,
-            "table": table,
-            "data": result,
-        })
-    return result

app/main.py

@@ -1,72 +0,0 @@
-from contextlib import asynccontextmanager
-import logging
-
-from fastapi import FastAPI
-from fastapi.middleware.cors import CORSMiddleware
-
-logging.basicConfig(
-    level=logging.INFO,
-    format="%(asctime)s %(levelname)s %(name)s: %(message)s",
-)
-logging.getLogger("sqlalchemy.engine").setLevel(logging.WARNING)
-logging.getLogger("sqlalchemy.pool").setLevel(logging.WARNING)
-
-from app.api.middleware.rate_limit import TierRateLimitMiddleware
-from app.api.middleware.sanitizer import SanitizerMiddleware
-from app.config.settings import settings
-
-
-@asynccontextmanager
-async def lifespan(app: FastAPI):
-    # Startup: ensure agent tool modules are loaded.
-    import app.agents  # noqa: F401
-
-    yield
-
-    # Shutdown: dispose SQLAlchemy connection pool
-    from app.db import engine
-    await engine.dispose()
-
-
-def create_app() -> FastAPI:
-    app = FastAPI(
-        title="Adiuva Cloud API",
-        version="0.1.0",
-        docs_url="/docs" if settings.ENV == "dev" else None,
-        redoc_url=None,
-        lifespan=lifespan,
-    )
-
-    app.add_middleware(
-        CORSMiddleware,
-        allow_origins=settings.CORS_ORIGINS,
-        allow_credentials=True,
-        allow_methods=["*"],
-        allow_headers=["*"],
-    )
-    # Middleware stack (Starlette inserts at position 0, so last-added = outermost).
-    # Request flow:  TierRateLimit → Sanitizer → CORS → Router
-    # Response flow: Router → CORS → Sanitizer → TierRateLimit
-    app.add_middleware(SanitizerMiddleware)
-    app.add_middleware(TierRateLimitMiddleware)
-
-    from app.api.routes import agents, auth, backup, billing, chat, device_ws, plugins, storage, vectors
-
-    app.include_router(auth.router,       prefix="/api/v1")
-    app.include_router(chat.router,       prefix="/api/v1")
-    app.include_router(storage.router,    prefix="/api/v1")
-    app.include_router(vectors.router,    prefix="/api/v1")
-    app.include_router(backup.router,     prefix="/api/v1")
-    app.include_router(plugins.router,    prefix="/api/v1")
-    app.include_router(billing.router,    prefix="/api/v1")
-    app.include_router(agents.router,     prefix="/api/v1")
-    app.include_router(device_ws.router,  prefix="/api/v1")
-
-    @app.get("/api/v1/health", tags=["health"])
-    async def health() -> dict:
-        return {"status": "ok", "version": app.version}
-
-    return app
-
-
-app = create_app()

app/marketplace/__init__.py

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

app/marketplace/plugin_registry.py

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

app/marketplace/plugin_review.py

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

app/marketplace/revenue_share.py

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

app/storage/__init__.py

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

app/storage/blob_store.py

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

app/storage/encryption.py

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

app/storage/vector_store.py

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

docker-compose.yml

@@ -1,27 +1,34 @@
+# ── Adiuva Microservices ─────────────────────────────────────────────
+# docker compose up --build
+# docker compose up --build auth ws-gateway chat   # subset
+
 services:
-  app:
-    build: .
+
+  # ═══════════════════════════════════════════════════════════════════
+  #  Infrastructure
+  # ═══════════════════════════════════════════════════════════════════
+
+  traefik:
+    image: traefik:v3.1
     ports:
-      - "8080:8000"
-    env_file:
-      - path: .env
-        required: false
+      - "80:80"
+      - "443:443"
+      - "8080:8080"   # dashboard (dev only)
     environment:
-      DATABASE_URL: postgresql+asyncpg://postgres:postgres@db:5432/adiuva
-      GITHUB_COPILOT_TOKEN_DIR: /root/.config/litellm/github_copilot
+      CF_DNS_API_TOKEN: ${CF_DNS_API_TOKEN:-}
     volumes:
-      - copilot_tokens:/root/.config/litellm/github_copilot
-    depends_on:
-      db:
-        condition: service_healthy
+      - /var/run/docker.sock:/var/run/docker.sock:ro
+      - ./traefik/traefik.yml:/etc/traefik/traefik.yml:ro
+      - ./traefik/dynamic:/etc/traefik/dynamic:ro
+      - traefik_acme:/etc/traefik/acme
     restart: unless-stopped
 
   db:
     image: pgvector/pgvector:pg16
     environment:
-      POSTGRES_USER: postgres
-      POSTGRES_PASSWORD: postgres
-      POSTGRES_DB: adiuva
+      POSTGRES_USER: ${POSTGRES_USER:-postgres}
+      POSTGRES_PASSWORD: ${POSTGRES_PASSWORD:-postgres}
+      POSTGRES_DB: ${POSTGRES_DB:-adiuva}
     volumes:
       - postgres_data:/var/lib/postgresql/data
     healthcheck:
@@ -31,42 +38,161 @@ services:
       retries: 5
     restart: unless-stopped
 
-  # Optional Redis for future rate-limit or caching needs
-  # redis:
-  #   image: redis:7-alpine
-  #   restart: unless-stopped
-
-  # ── Local S3-compatible storage (MinIO) ──
-  minio:
-    image: minio/minio:latest
-    command: server /data --console-address ":9001"
-    ports:
-      - "9000:9000"
-      - "9001:9001"
-    environment:
-      MINIO_ROOT_USER: minioadmin
-      MINIO_ROOT_PASSWORD: minioadmin
+  redis:
+    image: redis:7-alpine
+    command: redis-server --maxmemory 256mb --maxmemory-policy allkeys-lru
     volumes:
-      - minio_data:/data
+      - redis_data:/data
     healthcheck:
-      test: ["CMD", "mc", "ready", "local"]
+      test: ["CMD", "redis-cli", "ping"]
       interval: 5s
-      timeout: 5s
+      timeout: 3s
       retries: 5
     restart: unless-stopped
 
-  # ── Local vector store (Qdrant) ──
-  qdrant:
-    image: qdrant/qdrant:latest
-    ports:
-      - "6333:6333"
-      - "6334:6334"
-    volumes:
-      - qdrant_data:/qdrant/storage
+  # ── Optional infrastructure (uncomment as needed) ────────────────
+
+  # minio:
+  #   image: minio/minio:latest
+  #   command: server /data --console-address ":9001"
+  #   ports:
+  #     - "9000:9000"
+  #     - "9001:9001"
+  #   environment:
+  #     MINIO_ROOT_USER: minioadmin
+  #     MINIO_ROOT_PASSWORD: minioadmin
+  #   volumes:
+  #     - minio_data:/data
+  #   healthcheck:
+  #     test: ["CMD", "mc", "ready", "local"]
+  #     interval: 5s
+  #     timeout: 5s
+  #     retries: 5
+  #   restart: unless-stopped
+
+  # qdrant:
+  #   image: qdrant/qdrant:latest
+  #   ports:
+  #     - "6333:6333"
+  #     - "6334:6334"
+  #   volumes:
+  #     - qdrant_data:/qdrant/storage
+  #   restart: unless-stopped
+
+  # ═══════════════════════════════════════════════════════════════════
+  #  Migrations (run once, then exit)
+  # ═══════════════════════════════════════════════════════════════════
+
+  migrate:
+    build:
+      context: .
+      dockerfile: Dockerfile
+    command: ["python", "-m", "alembic", "upgrade", "head"]
+    env_file:
+      - path: .env
+        required: false
+    environment:
+      DATABASE_URL: postgresql+asyncpg://${POSTGRES_USER:-postgres}:${POSTGRES_PASSWORD:-postgres}@db:5432/${POSTGRES_DB:-adiuva}
+    depends_on:
+      db:
+        condition: service_healthy
+    restart: "no"
+
+  # ═══════════════════════════════════════════════════════════════════
+  #  Application Services
+  # ═══════════════════════════════════════════════════════════════════
+
+  auth:
+    build:
+      context: .
+      dockerfile: services/auth/Dockerfile
+    env_file:
+      - path: .env
+        required: false
+    environment:
+      DATABASE_URL: postgresql+asyncpg://${POSTGRES_USER:-postgres}:${POSTGRES_PASSWORD:-postgres}@db:5432/${POSTGRES_DB:-adiuva}
+      REDIS_URL: redis://redis:6379/0
+    depends_on:
+      db:
+        condition: service_healthy
+      migrate:
+        condition: service_completed_successfully
+    restart: unless-stopped
+
+  ws-gateway:
+    build:
+      context: .
+      dockerfile: services/ws-gateway/Dockerfile
+    env_file:
+      - path: .env
+        required: false
+    environment:
+      DATABASE_URL: postgresql+asyncpg://${POSTGRES_USER:-postgres}:${POSTGRES_PASSWORD:-postgres}@db:5432/${POSTGRES_DB:-adiuva}
+      REDIS_URL: redis://redis:6379/0
+    depends_on:
+      redis:
+        condition: service_healthy
+      auth:
+        condition: service_started
+    restart: unless-stopped
+
+  chat:
+    build:
+      context: .
+      dockerfile: services/chat/Dockerfile
+    env_file:
+      - path: .env
+        required: false
+    environment:
+      DATABASE_URL: postgresql+asyncpg://${POSTGRES_USER:-postgres}:${POSTGRES_PASSWORD:-postgres}@db:5432/${POSTGRES_DB:-adiuva}
+      REDIS_URL: redis://redis:6379/0
+    depends_on:
+      db:
+        condition: service_healthy
+      redis:
+        condition: service_healthy
+      migrate:
+        condition: service_completed_successfully
+    restart: unless-stopped
+
+  batch-agent:
+    build:
+      context: .
+      dockerfile: services/batch-agent/Dockerfile
+    env_file:
+      - path: .env
+        required: false
+    environment:
+      DATABASE_URL: postgresql+asyncpg://${POSTGRES_USER:-postgres}:${POSTGRES_PASSWORD:-postgres}@db:5432/${POSTGRES_DB:-adiuva}
+      REDIS_URL: redis://redis:6379/0
+    depends_on:
+      db:
+        condition: service_healthy
+      redis:
+        condition: service_healthy
+      migrate:
+        condition: service_completed_successfully
+    restart: unless-stopped
+
+  billing:
+    build:
+      context: .
+      dockerfile: services/billing/Dockerfile
+    env_file:
+      - path: .env
+        required: false
+    environment:
+      DATABASE_URL: postgresql+asyncpg://${POSTGRES_USER:-postgres}:${POSTGRES_PASSWORD:-postgres}@db:5432/${POSTGRES_DB:-adiuva}
+    depends_on:
+      db:
+        condition: service_healthy
+      migrate:
+        condition: service_completed_successfully
     restart: unless-stopped
 
 volumes:
   postgres_data:
-  minio_data:
-  qdrant_data:
-  copilot_tokens:
+  redis_data:
+  traefik_acme:
+  # minio_data:
+  # qdrant_data:

requirements.txt

@@ -1,35 +0,0 @@
-fastapi>=0.115.0
-uvicorn[standard]>=0.34.0
-gunicorn>=22.0.0
-langchain>=0.3.0
-langchain-openai>=0.3.0
-langchain-litellm>=0.1.0
-litellm>=1.50.0
-pydantic>=2.10.0
-pydantic-settings>=2.7.0
-python-jose[cryptography]>=3.3.0
-stripe>=11.0.0
-boto3>=1.35.0
-slowapi>=0.1.9
-sqlalchemy>=2.0.0
-asyncpg>=0.30.0
-alembic>=1.14.0
-bcrypt>=4.2.0
-python-dotenv>=1.0.0
-httpx>=0.28.0
-websockets>=14.0
-psycopg2-binary>=2.9.0
-pytest>=8.0.0
-pytest-asyncio>=0.24.0
-aiosqlite>=0.20.0
-moto[s3]>=5.0.0
-pinecone>=5.0.0
-qdrant-client>=1.7.0
-croniter>=3.0.0
-google-api-python-client>=2.130.0
-google-auth>=2.29.0
-google-auth-oauthlib>=1.2.0
-google-auth-httplib2>=0.2.0
-msal>=1.28.0
-cryptography>=42.0.0
-ruff>=0.8.0

services/auth/.env.example

@@ -0,0 +1,19 @@
+# ── Auth Service ──────────────────────────────────────────────────────────────
+# This file contains env vars specific to the Auth Service.
+# Shared vars (DATABASE_URL, REDIS_URL, etc.) come from the root .env
+# or from docker-compose environment.
+
+# ── JWT RS256 Keys ────────────────────────────────────────────────────────────
+# Generate keypair:
+#   openssl genpkey -algorithm RSA -out private.pem -pkeyopt rsa_keygen_bits:2048
+#   openssl rsa -in private.pem -pubout -out public.pem
+#
+# Paste PEM content with literal \n for newlines:
+#   JWT_PRIVATE_KEY=-----BEGIN PRIVATE KEY-----\nMIIEvQ...
+#   JWT_PUBLIC_KEY=-----BEGIN PUBLIC KEY-----\nMIIBIj...
+
+# PRIVATE KEY — used to SIGN JWTs. NEVER share outside this service.
+JWT_PRIVATE_KEY=
+
+# PUBLIC KEY — used to VERIFY JWTs.
+JWT_PUBLIC_KEY=

services/auth/Dockerfile

@@ -0,0 +1,36 @@
+# ── builder ──────────────────────────────────────────────────────────────────
+FROM python:3.12-slim AS builder
+
+WORKDIR /build
+
+# Install shared + service deps in one layer
+COPY services/auth/requirements.txt ./requirements.txt
+RUN pip install --upgrade pip && \
+    pip install --no-cache-dir --prefix=/install -r requirements.txt
+
+# ── runtime ──────────────────────────────────────────────────────────────────
+FROM python:3.12-slim AS runtime
+
+RUN addgroup --system appgroup && adduser --system --ingroup appgroup appuser
+
+WORKDIR /app
+
+COPY --from=builder /install /usr/local
+
+# Copy shared module (available to all services)
+COPY shared/ shared/
+
+# Copy service source
+COPY services/auth/app/ app/
+
+RUN chown -R appuser:appgroup /app
+
+USER appuser
+
+EXPOSE 8000
+
+CMD ["gunicorn", "app.main:app", \
+     "-k", "uvicorn.workers.UvicornWorker", \
+     "--bind", "0.0.0.0:8000", \
+     "--workers", "2", \
+     "--timeout", "30"]

services/auth/README.md

@@ -0,0 +1,16 @@
+# Auth Service
+
+Owns: user registration, login, JWT RS256 issuance, token refresh, `/me` endpoint.
+
+## Tables owned
+- `users`
+- `refresh_tokens`
+- `subscriptions` (read; Billing Service writes)
+
+## Endpoints
+- `POST /auth/register`
+- `POST /auth/login`
+- `POST /auth/refresh`
+- `GET /auth/me`
+- `PUT /auth/me`
+- `GET /auth/verify` (ForwardAuth for Traefik)

services/auth/app/config.py

@@ -0,0 +1,34 @@
+"""Auth Service — local configuration.
+
+Contains secrets that ONLY the Auth Service needs (e.g., JWT private key).
+These are NOT in shared/config.py to prevent other services from accessing them.
+"""
+
+from pydantic import field_validator
+from pydantic_settings import BaseSettings, SettingsConfigDict
+
+
+class AuthSettings(BaseSettings):
+    # RS256 private key (PEM format). Used to SIGN JWTs.
+    # Only the Auth Service has this. Generate with:
+    #   openssl genpkey -algorithm RSA -out private.pem -pkeyopt rsa_keygen_bits:2048
+    # Then set the env var (newlines as \n):
+    #   JWT_PRIVATE_KEY="-----BEGIN PRIVATE KEY-----\nMIIEv..."
+    JWT_PRIVATE_KEY: str = ""
+
+    # RS256 public key (PEM format). Used to VERIFY JWTs.
+    # Derived from the private key:
+    #   openssl rsa -in private.pem -pubout -out public.pem
+    JWT_PUBLIC_KEY: str = ""
+
+    @field_validator("JWT_PRIVATE_KEY", "JWT_PUBLIC_KEY", mode="before")
+    @classmethod
+    def _expand_pem_newlines(cls, v: str) -> str:
+        if isinstance(v, str) and r"\n" in v:
+            return v.replace(r"\n", "\n")
+        return v
+
+    model_config = SettingsConfigDict(env_file=".env", env_file_encoding="utf-8")
+
+
+auth_settings = AuthSettings()

services/auth/app/deps.py

@@ -1,14 +1,7 @@
-"""Auth middleware — JWT validation dependency.
+"""Auth dependencies — JWT validation for the Auth Service.
 
-``get_current_user`` is the FastAPI dependency used by all protected routes.
-It decodes the Bearer JWT (identity + expiry), then fetches the current tier
-from the ``subscriptions`` table so that tier changes take effect immediately
-without requiring token re-issue.
-
-Exempt routes (no JWT required):
-  - POST /api/v1/auth/register
-  - POST /api/v1/auth/login
-  - POST /api/v1/billing/webhook
+This is the canonical get_current_user used by protected endpoints
+within the Auth Service itself (/me, /me PUT).
 """
 
 from __future__ import annotations
@@ -19,9 +12,12 @@ from jose import JWTError, jwt
 from sqlalchemy import select
 from sqlalchemy.ext.asyncio import AsyncSession
 
-from app.config.settings import settings
-from app.db import get_session
-from app.schemas import UserProfile
+from shared.config import settings
+from shared.db import get_session
+from shared.models import Subscription, User
+from shared.schemas import UserProfile
+
+from app.config import auth_settings
 
 oauth2_scheme = OAuth2PasswordBearer(tokenUrl="/api/v1/auth/login")
 
@@ -32,11 +28,8 @@ async def get_current_user(
 ) -> UserProfile:
     """Validate a Bearer JWT and return the authenticated user.
 
-    The JWT is used for identity and expiry only.  The tier is fetched live
-    from the ``subscriptions`` table so that upgrades/downgrades take effect
-    immediately.  Falls back to ``'free'`` when no subscription row exists.
-
-    Raises HTTP 401 on any invalid or expired token.
+    The JWT is used for identity and expiry.  Tier is fetched live from the
+    subscriptions table so upgrades/downgrades take effect immediately.
     """
     credentials_exc = HTTPException(
         status_code=status.HTTP_401_UNAUTHORIZED,
@@ -45,7 +38,7 @@ async def get_current_user(
     )
     try:
         payload = jwt.decode(
-            token, settings.JWT_SECRET, algorithms=[settings.JWT_ALGORITHM]
+            token, auth_settings.JWT_PUBLIC_KEY, algorithms=["RS256"]
         )
         user_id: str | None = payload.get("sub")
         email: str | None = payload.get("email")
@@ -54,18 +47,14 @@ async def get_current_user(
     except JWTError:
         raise credentials_exc
 
-    # Live tier lookup — subscription row is the authoritative source.
-    # In dev, fall back to 'power' (unlimited) so quota limits don't
-    # block local development when no Stripe subscription exists.
-    from app.models import Subscription, User  # noqa: PLC0415
-
+    # Live tier lookup
     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
 
-    # Fetch name/surname from user row.
+    # Fetch name/surname
     user_result = await db.execute(
         select(User.name, User.surname).where(User.id == user_id)
     )

services/auth/app/main.py

@@ -0,0 +1,62 @@
+"""Auth Service — JWT issuance, user management, ForwardAuth verification.
+
+Standalone FastAPI service extracted from the adiuva-api monolith.
+Owns: users, refresh_tokens, subscriptions (read).
+"""
+
+import sys
+from contextlib import asynccontextmanager
+from pathlib import Path
+
+# Ensure the repo root is on sys.path so "shared" is importable.
+# In Docker, COPY shared/ puts it at /app/shared/ (already importable).
+# In local dev, we need to add the repo root (two levels up from this file).
+_repo_root = str(Path(__file__).resolve().parents[3])
+if _repo_root not in sys.path:
+    sys.path.insert(0, _repo_root)
+
+from fastapi import FastAPI
+from fastapi.middleware.cors import CORSMiddleware
+
+from shared.config import settings
+
+
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+    yield
+    from shared.db import engine
+
+    await engine.dispose()
+
+
+def create_app() -> FastAPI:
+    app = FastAPI(
+        title="Adiuva Auth Service",
+        version="0.1.0",
+        docs_url="/docs" if settings.ENV == "dev" else None,
+        redoc_url=None,
+        lifespan=lifespan,
+    )
+
+    app.add_middleware(
+        CORSMiddleware,
+        allow_origins=settings.CORS_ORIGINS,
+        allow_credentials=True,
+        allow_methods=["*"],
+        allow_headers=["*"],
+    )
+
+    from app.routes import router
+    from app.verify import router as verify_router
+
+    app.include_router(router, prefix="/api/v1")
+    app.include_router(verify_router, prefix="/api/v1")
+
+    @app.get("/api/v1/health", tags=["health"])
+    async def health() -> dict:
+        return {"status": "ok", "service": "auth", "version": app.version}
+
+    return app
+
+
+app = create_app()

services/auth/app/routes.py

@@ -1,8 +1,6 @@
 """Auth routes: register, login, refresh, me.
 
-Users and refresh tokens are persisted in PostgreSQL (users + refresh_tokens
-tables).  Passwords are hashed with bcrypt; refresh tokens are stored as
-SHA-256 hashes so plaintext never reaches the DB.
+Extracted from app/api/routes/auth.py — uses shared.* imports instead of app.*.
 """
 
 from __future__ import annotations
@@ -20,11 +18,13 @@ from pydantic import BaseModel
 from sqlalchemy import select
 from sqlalchemy.ext.asyncio import AsyncSession
 
-from app.api.deps import get_current_user
-from app.config.settings import settings
-from app.db import get_session
-from app.models import RefreshToken, User
-from app.schemas import AuthTokens, UserProfile
+from shared.config import settings
+from shared.db import get_session
+from shared.models import RefreshToken, Subscription, User
+from shared.schemas import AuthTokens, UserProfile
+
+from app.config import auth_settings
+from app.deps import get_current_user
 
 router = APIRouter(prefix="/auth", tags=["auth"])
 
@@ -46,7 +46,7 @@ def _hash_token(plain_token: str) -> str:
 
 
 def _make_access_token(user_id: str, email: str, tier: str) -> tuple[str, int]:
-    """Return (signed JWT, expires_at_ms)."""
+    """Return (RS256-signed JWT, expires_at_ms)."""
     now = int(time.time())
     exp = now + settings.JWT_ACCESS_TOKEN_EXPIRE_MINUTES * 60
     payload = {
@@ -56,10 +56,19 @@ def _make_access_token(user_id: str, email: str, tier: str) -> tuple[str, int]:
         "exp": exp,
         "iat": now,
     }
-    token = jwt.encode(payload, settings.JWT_SECRET, algorithm=settings.JWT_ALGORITHM)
+    token = jwt.encode(payload, auth_settings.JWT_PRIVATE_KEY, algorithm="RS256")
     return token, exp * 1000  # ms for client
 
 
+async def _get_live_tier(db: AsyncSession, user_id: str) -> str:
+    """Fetch authoritative tier from subscriptions table."""
+    result = await db.execute(
+        select(Subscription.tier).where(Subscription.user_id == user_id)
+    )
+    default_tier = "power" if settings.ENV == "dev" else "free"
+    return result.scalar_one_or_none() or default_tier
+
+
 # ── Request bodies ────────────────────────────────────────────────────
 
 
@@ -79,6 +88,11 @@ class _RefreshRequest(BaseModel):
     refresh_token: str
 
 
+class _UpdateProfileRequest(BaseModel):
+    name: str | None = None
+    surname: str | None = None
+
+
 # ── Routes ────────────────────────────────────────────────────────────
 
 
@@ -102,7 +116,7 @@ async def register(
         encryption_key=Fernet.generate_key().decode(),
     )
     db.add(user)
-    await db.flush()  # get user.id without committing
+    await db.flush()
 
     plain_token = str(uuid.uuid4())
     expires_at = datetime.now(timezone.utc) + timedelta(
@@ -135,6 +149,9 @@ async def login(
     if user is None or not _verify_password(body.password, user.password_hash):
         raise HTTPException(status.HTTP_401_UNAUTHORIZED, "Invalid credentials")
 
+    # Fetch live tier for the JWT claim
+    tier = await _get_live_tier(db, user.id)
+
     plain_token = str(uuid.uuid4())
     expires_at = datetime.now(timezone.utc) + timedelta(
         days=settings.JWT_REFRESH_TOKEN_EXPIRE_DAYS
@@ -147,7 +164,7 @@ async def login(
     db.add(rt)
     await db.commit()
 
-    access_token, expires_at_ms = _make_access_token(user.id, user.email, user.tier)
+    access_token, expires_at_ms = _make_access_token(user.id, user.email, tier)
     return AuthTokens(
         access_token=access_token,
         refresh_token=plain_token,
@@ -171,7 +188,6 @@ async def refresh(
     if rt is None or rt.expires_at.replace(tzinfo=timezone.utc) < now:
         raise HTTPException(status.HTTP_401_UNAUTHORIZED, "Invalid or expired refresh token")
 
-    # Rotate: delete old token, issue new one.
     await db.delete(rt)
 
     user_result = await db.execute(select(User).where(User.id == rt.user_id))
@@ -179,6 +195,9 @@ async def refresh(
     if user is None:
         raise HTTPException(status.HTTP_401_UNAUTHORIZED, "User not found")
 
+    # Fetch live tier for the new JWT
+    tier = await _get_live_tier(db, user.id)
+
     plain_token = str(uuid.uuid4())
     new_expires = now + timedelta(days=settings.JWT_REFRESH_TOKEN_EXPIRE_DAYS)
     new_rt = RefreshToken(
@@ -189,7 +208,7 @@ async def refresh(
     db.add(new_rt)
     await db.commit()
 
-    access_token, expires_at_ms = _make_access_token(user.id, user.email, user.tier)
+    access_token, expires_at_ms = _make_access_token(user.id, user.email, tier)
     return AuthTokens(
         access_token=access_token,
         refresh_token=plain_token,
@@ -197,11 +216,6 @@ async def refresh(
     )
 
 
-class _UpdateProfileRequest(BaseModel):
-    name: str | None = None
-    surname: str | None = None
-
-
 @router.get("/me", response_model=UserProfile)
 async def me(current_user: UserProfile = Depends(get_current_user)) -> UserProfile:
     """Return the profile for the authenticated user."""

services/auth/app/verify.py

@@ -0,0 +1,66 @@
+"""ForwardAuth verification endpoint for Traefik.
+
+Traefik calls GET /api/v1/auth/verify on every request to a protected
+service.  This endpoint validates the JWT from the Authorization header
+and returns identity headers that Traefik injects into downstream requests.
+
+Downstream services NEVER validate JWTs themselves — they trust the
+X-User-Id, X-User-Email, X-User-Tier headers injected by Traefik.
+"""
+
+from __future__ import annotations
+
+from fastapi import APIRouter, Request, Response
+from fastapi import status as http_status
+from jose import JWTError, jwt
+from sqlalchemy import select
+
+from shared.config import settings
+from shared.db import async_session
+from shared.models import Subscription
+
+from app.config import auth_settings
+
+router = APIRouter(tags=["auth"])
+
+
+@router.get("/auth/verify")
+async def verify(request: Request) -> Response:
+    """Validate JWT and return identity headers for Traefik ForwardAuth.
+
+    Returns 200 with X-User-* headers on success, 401 on failure.
+    Traefik copies response headers to the downstream request.
+    """
+    auth_header = request.headers.get("Authorization", "")
+    if not auth_header.startswith("Bearer "):
+        return Response(status_code=http_status.HTTP_401_UNAUTHORIZED)
+
+    token = auth_header[7:]  # strip "Bearer "
+
+    try:
+        payload = jwt.decode(
+            token, auth_settings.JWT_PUBLIC_KEY, algorithms=["RS256"]
+        )
+        user_id: str | None = payload.get("sub")
+        email: str | None = payload.get("email")
+        if not user_id or not email:
+            return Response(status_code=http_status.HTTP_401_UNAUTHORIZED)
+    except JWTError:
+        return Response(status_code=http_status.HTTP_401_UNAUTHORIZED)
+
+    # Live tier lookup from subscriptions table
+    async with async_session() as db:
+        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
+
+    return Response(
+        status_code=http_status.HTTP_200_OK,
+        headers={
+            "X-User-Id": user_id,
+            "X-User-Email": email,
+            "X-User-Tier": tier,
+        },
+    )

services/auth/requirements.txt

@@ -0,0 +1,11 @@
+fastapi>=0.115.0
+uvicorn[standard]>=0.34.0
+gunicorn>=22.0.0
+pydantic>=2.10.0
+pydantic-settings>=2.7.0
+python-jose[cryptography]>=3.3.0
+sqlalchemy>=2.0.0
+asyncpg>=0.30.0
+bcrypt>=4.2.0
+cryptography>=42.0.0
+python-dotenv>=1.0.0

services/batch-agent/Dockerfile

@@ -0,0 +1,36 @@
+# ── builder ──────────────────────────────────────────────────────────────────
+FROM python:3.12-slim AS builder
+
+WORKDIR /build
+
+COPY services/batch-agent/requirements.txt ./requirements.txt
+RUN pip install --upgrade pip && \
+    pip install --no-cache-dir --prefix=/install -r requirements.txt
+
+# ── runtime ──────────────────────────────────────────────────────────────────
+FROM python:3.12-slim AS runtime
+
+RUN addgroup --system appgroup && adduser --system --ingroup appgroup appuser
+
+WORKDIR /app
+
+COPY --from=builder /install /usr/local
+
+# Shared module
+COPY shared/ shared/
+
+# Service source
+COPY services/batch-agent/app/ app/
+
+RUN chown -R appuser:appgroup /app
+
+USER appuser
+
+EXPOSE 8000
+
+# Batch runs are long-lived — use a longer timeout than chat (300s vs 120s)
+CMD ["gunicorn", "app.main:app", \
+     "-k", "uvicorn.workers.UvicornWorker", \
+     "--bind", "0.0.0.0:8000", \
+     "--workers", "2", \
+     "--timeout", "300"]

services/batch-agent/README.md

@@ -0,0 +1,23 @@
+# Batch Agent Service
+
+Owns: agent_runner, journey builder, filesystem_agent, integrations (Gmail, MS Graph).
+
+## Tables owned
+- `local_agent_configs`
+- `cloud_agent_configs`
+- `agent_run_logs`
+
+## Endpoints
+- `GET /agents/catalog`
+- `POST /agents/can-create`
+- `POST /agents/trigger`
+- `GET /agents/{id}/history`
+
+## Redis channels
+- Subscribe: `batch:request:{user_id}`
+- Publish: `ws:out:{user_id}` (journey replies + tool calls)
+- BRPOP: `tool:result:{call_id}` (30s timeout)
+- SET+EX: `journey:{user_id}` (session state, TTL 1800s)
+
+## TODO
+- [ ] Integrate Langfuse tracing (reuse `services/chat/app/tracing.py` pattern — `trace_span()`, `get_langfuse_callback()`, prompt management). Each batch agent run should create a trace with input/output, link prompts, and pass the LangChain `CallbackHandler` to LLM calls.

services/batch-agent/app/agent_runner.py

@@ -1,27 +1,12 @@
-"""Agent run orchestrator.
+"""Agent run orchestrator — adapted for Batch Agent Service.
 
-Drives two agent types:
-
-* **Local directory agent** — two-step execution per file:
-  Step 1 (Classification) uses code to fetch all projects and asks the LLM
-  to identify which project the file belongs to and which domains are relevant.
-  Step 2 (Processing) fetches existing entities for that project/domains via
-  code and runs an LLM with tools — existing data in context enforces
-  update-first naturally.
-
-* **Cloud connector agent** — fetches data from third-party APIs (Gmail,
-  Teams, Outlook) and pushes extracted items to Electron.
-
-Usage
------
-Background tasks are spawned with ``asyncio.create_task()``::
-
-    asyncio.create_task(run_local_agent(user_id, config, run_log, device_manager))
-    asyncio.create_task(trigger_pending_runs(user_id, device_id, device_manager))
-
-The ``trigger_pending_runs`` function is called by the device WS endpoint
-when Electron sends ``device_hello``, so any overdue runs fire immediately
-when the device reconnects.
+Key changes from monolith app/core/agent_runner.py:
+  - No DeviceConnectionManager — tool calls go through Redis ws_context.
+  - set_current_user / clear_current_user replace set_client_executor.
+  - run_local_agent accepts a serialized dict (from Redis / REST) instead
+    of SQLAlchemy model objects.
+  - _finalize_run writes to PostgreSQL via shared.db.async_session.
+  - Cloud agent import path changed to app.integrations.
 """
 
 from __future__ import annotations
@@ -33,46 +18,37 @@ import uuid
 from datetime import datetime, timedelta, timezone
 from typing import Any
 
-from croniter import croniter
 from langchain_core.messages import AIMessage, HumanMessage, SystemMessage, ToolMessage
 from sqlalchemy import select
 
 from app.agents.filesystem_agent import FILESYSTEM_TOOLS
-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.device_manager import DeviceConnectionManager
-from app.core.llm import get_llm
-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
+from shared.agents.note_agent import NOTE_TOOLS
+from shared.agents.project_agent import PROJECT_TOOLS
+from shared.agents.task_agent import TASK_TOOLS
+from shared.agents.timeline_agent import TIMELINE_TOOLS
+from shared.llm import get_llm
+from shared.ws_context import execute_on_client, set_current_user, clear_current_user
+import app.tracing as tracing
+from shared.db import async_session
+from shared.models import AgentRunLog, CloudAgentConfig, LocalAgentConfig
+from shared.redis import redis_client, ws_out_channel
 
 logger = logging.getLogger(__name__)
 
 # ── Concurrency guard ─────────────────────────────────────────────────────
-# Tracks agent IDs that currently have a run in progress.
-# Prevents multiple simultaneous runs of the same agent within a single process.
 _running_agents: set[str] = set()
 
 
 def is_agent_running(agent_id: str) -> bool:
-    """Return ``True`` if *agent_id* already has a run in progress."""
     return agent_id in _running_agents
 
-# ── Timeouts ───────────────────────────────────────────────────────────────
 
-# Max seconds to wait for a single tool-call round-trip (FE → BE).
+# ── Timeouts ───────────────────────────────────────────────────────────────
 _TOOL_CALL_TIMEOUT: int = 30
-# Max LLM reasoning steps for Step 2 processing.
 _MAX_PROCESSING_STEPS: int = 12
-# Max directory recursion depth during scan.
 _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,
@@ -158,7 +134,7 @@ Domains to extract: {data_types}
 {custom_prompt_section}
 """
 
-# ── Cloud processing prompt (kept separate for cloud agent) ───────────────
+# ── Cloud processing prompt ───────────────────────────────────────────────
 
 _CLOUD_PROCESSING_PROMPT = """\
 You are a data extraction and management assistant for a freelance project
@@ -191,56 +167,6 @@ and what you created.
 """
 
 
-# ── Cron helper ────────────────────────────────────────────────────────────
-
-
-def _is_overdue(schedule_cron: str, last_run_at: datetime | None) -> bool:
-    """Return ``True`` if the next scheduled run time has already passed.
-
-    Always validates the cron expression first — an invalid expression returns
-    ``False`` (fail-safe: never trigger an unparseable schedule).
-    """
-    try:
-        now = datetime.now(timezone.utc)
-        if last_run_at is None:
-            croniter(schedule_cron, now)
-            return True
-        ts = last_run_at
-        if ts.tzinfo is None:
-            ts = ts.replace(tzinfo=timezone.utc)
-        cron = croniter(schedule_cron, ts)
-        next_run: datetime = cron.get_next(datetime)
-        return now >= next_run
-    except Exception as exc:
-        logger.warning("agent_runner: cannot parse cron %r: %s", schedule_cron, exc)
-        return False
-
-
-# ── WS executor for agent context ─────────────────────────────────────────
-
-
-def _make_agent_executor(
-    user_id: str,
-    device_mgr: DeviceConnectionManager,
-    run_context: dict | None = None,
-) -> Any:
-    """Create a WS callback for ``set_client_executor()`` so that all tools
-    can use ``execute_on_client()`` during an agent run.
-
-    If *run_context* is provided it is attached to every ``tool_call`` frame
-    so the Electron client can attribute actions to the correct agent run.
-    """
-    async def _executor(payload: dict) -> dict:
-        payload["type"] = "tool_call"
-        if run_context:
-            payload["run_context"] = run_context
-        call_id = payload["id"]
-        fut = device_mgr.create_pending_call(user_id, call_id)
-        await device_mgr.send_frame(user_id, payload)
-        return await asyncio.wait_for(fut, timeout=_TOOL_CALL_TIMEOUT)
-    return _executor
-
-
 # ── LLM tool-calling loop ─────────────────────────────────────────────────
 
 
@@ -268,9 +194,11 @@ async def _run_agent_with_tools(
     user_message: str,
     tools: list[Any],
     max_steps: int,
+    langfuse_handler: Any | None = None,
 ) -> str:
     """Run an LLM agent with tool-calling, returning the final text response."""
-    llm = get_llm()
+    callbacks = [langfuse_handler] if langfuse_handler else None
+    llm = get_llm(callbacks=callbacks)
     llm_with_tools = llm.bind_tools(tools)
     messages: list[Any] = [
         SystemMessage(content=system_prompt),
@@ -317,7 +245,6 @@ async def _run_agent_with_tools(
 
 
 def _build_processing_tools(data_types: list[str]) -> list[Any]:
-    """Build the tool list for processing based on user's data_types selection."""
     tools: list[Any] = list(FILESYSTEM_TOOLS)
     for dt in data_types:
         dt_tools = _DATA_TYPE_TOOLS.get(dt)
@@ -334,12 +261,6 @@ async def _scan_directories(
     extensions: list[str],
     last_run_at: datetime | None,
 ) -> list[str]:
-    """Walk directories via WS tool calls and return filtered file paths.
-
-    Recursion is capped at ``_MAX_SCAN_DEPTH``.  Files are filtered by
-    extension (if configured) and by modification date (if ``last_run_at``
-    is set).  Fails open: if metadata cannot be read, the file is included.
-    """
     all_files: list[str] = []
     ext_set = {e.lstrip(".").lower() for e in extensions} if extensions else set()
 
@@ -371,7 +292,6 @@ async def _scan_directories(
     if last_run_at is None:
         return all_files
 
-    # Filter by modification date.
     last_run_ms = int(last_run_at.timestamp() * 1000)
     filtered: list[str] = []
     for file_path in all_files:
@@ -388,7 +308,7 @@ async def _scan_directories(
             if mod_ms > last_run_ms:
                 filtered.append(file_path)
         except Exception:
-            filtered.append(file_path)  # fail-open
+            filtered.append(file_path)
 
     return filtered
 
@@ -397,7 +317,6 @@ async def _scan_directories(
 
 
 async def _fetch_projects() -> list[dict]:
-    """Fetch all projects from the Electron client via WS."""
     try:
         result = await execute_on_client(action="select", table="projects")
         return result.get("rows", [])
@@ -415,7 +334,6 @@ _DOMAIN_TABLE: dict[str, str] = {
 
 
 async def _fetch_domain_entities(domain: str, project_id: str) -> list[dict]:
-    """Fetch existing rows for a domain, scoped to a project where applicable."""
     table = _DOMAIN_TABLE.get(domain)
     if not table:
         return []
@@ -435,12 +353,6 @@ async def _fetch_domain_entities(domain: str, project_id: str) -> list[dict]:
 
 
 def _format_entities_for_context(domain: str, rows: list[dict]) -> str:
-    """Format existing entity rows as a readable context block for the LLM.
-
-    Includes enough detail per record for the LLM to make a confident
-    update-vs-create decision without overwhelming the context.
-    Note content is truncated to 200 chars to stay within token budget.
-    """
     if not rows:
         return f"No existing {domain}."
     lines: list[str] = []
@@ -487,14 +399,9 @@ async def _classify_file(
     file_content: str,
     projects: list[dict],
     config_data_types: list[str],
+    langfuse_handler: Any | None = None,
+    custom_system_prompt: str | None = None,
 ) -> tuple[str, list[str], str | None]:
-    """Call the LLM to classify a file by project and relevant domains.
-
-    Returns ``(project_id_or_"new", domains, new_project_name_or_None)``.
-    - ``project_id`` is an existing project UUID, or ``"new"`` when no match found.
-    - ``new_project_name`` is only set when ``project_id == "new"``.
-    Falls back to ``("new", config_data_types, None)`` on any error.
-    """
     fallback: tuple[str, list[str], str | None] = ("new", list(config_data_types), None)
 
     if not file_content.strip():
@@ -515,26 +422,34 @@ async def _classify_file(
         if d in _DOMAIN_DESCRIPTIONS
     )
 
-    system = _STEP1_SYSTEM_PROMPT.format(
-        domain_definitions=domain_definitions,
-        projects_list=projects_list,
-    )
+    if custom_system_prompt:
+        # Fixture-provided prompt takes absolute priority
+        system = custom_system_prompt.format_map(
+            {"domain_definitions": domain_definitions, "projects_list": projects_list}
+        )
+    else:
+        system = tracing.compile_prompt(
+            "batch_file_classifier",
+            fallback=_STEP1_SYSTEM_PROMPT,
+            variables={
+                "domain_definitions": domain_definitions,
+                "projects_list": projects_list,
+            },
+        )
 
-    llm = get_llm()
+    llm = get_llm(callbacks=[langfuse_handler] if langfuse_handler else None)
     try:
         response = await llm.ainvoke([
             SystemMessage(content=system),
             HumanMessage(content=f"File: {file_path}\n\nContent:\n{file_content[:4000]}"),
         ])
         raw = _as_text(response.content).strip()
-        # Strip markdown fences if the model wraps the JSON.
         if raw.startswith("```"):
             raw = raw.split("```")[1]
             if raw.startswith("json"):
                 raw = raw[4:]
         parsed = json.loads(raw.strip())
         raw_project_id: str = str(parsed.get("project_id") or "new")
-        # Reject hallucinated UUIDs — only accept ids that exist in the fetched list.
         project_id = raw_project_id if raw_project_id in valid_project_ids else "new"
         new_project_name: str | None = (
             str(parsed["new_project_name"]).strip() or None
@@ -558,114 +473,104 @@ async def _classify_file(
 # ── Local agent runner (two-step per file) ────────────────────────────────
 
 
-async def run_local_agent(
-    user_id: str,
-    config: LocalAgentConfig,
-    run_log: AgentRunLog,
-    device_mgr: DeviceConnectionManager,
-    run_context: dict | None = None,
-) -> None:
-    """Execute a local directory agent run using a two-step approach per file.
+async def run_local_agent(user_id: str, trigger_data: dict[str, Any], *, langfuse_handler: Any | None = None) -> None:
+    """Execute a local directory agent run.
 
-    Step 1 — Classification (code + 1 LLM call per file, no tools):
-        Code scans directories and fetches all projects via WS.
-        For each file, LLM identifies the project and relevant domains.
+    In the microservice world, trigger_data is a serialized dict from
+    the REST route (forwarded via Redis), containing the agent config
+    fields and run_context.
 
-    Step 2 — Processing (code + 1 LLM call per file, with tools):
-        Code fetches existing entities for the identified project/domains.
-        LLM receives file content + existing entities in context and uses
-        tools to update existing records or create new ones.
+    set_current_user() must be called BEFORE this function.
     """
-    run_id = run_log.id
-    agent_id = (run_context or {}).get("agent_id") or config.id
+    run_context: dict = trigger_data.get("run_context", {})
+    agent_id = run_context.get("agent_id", str(uuid.uuid4()))
+    run_id = run_context.get("run_id")
+
     _running_agents.add(agent_id)
 
-    # ── Device online check ─────────────────────────────────────────
-    target_device_id = config.device_id.strip() if isinstance(config.device_id, str) else ""
-    is_online = (
-        device_mgr.is_online(user_id, target_device_id)
-        if target_device_id
-        else device_mgr.is_online(user_id)
-    )
+    # Extract config from trigger payload
+    directory_paths: list[str] = trigger_data.get("directory_paths", [])
+    if not directory_paths:
+        directory = trigger_data.get("directory", "")
+        if directory:
+            directory_paths = [directory]
 
-    if not is_online:
-        logger.info(
-            "agent_runner: skip run=%s — device %r offline for user=%s",
-            run_id,
-            target_device_id or "<any>",
-            user_id,
-        )
-        await _finalize_run(
-            run_log,
-            status="error",
-            errors=[f"Device {target_device_id or '<any>'!r} is not connected"],
-        )
-        return
-
-    # ── Set up WS executor for tools ────────────────────────────────
-    executor = _make_agent_executor(user_id, device_mgr, run_context)
-    set_client_executor(executor)
+    data_types: list[str] = trigger_data.get("data_types", [])
+    file_extensions: list[str] = trigger_data.get("file_extensions", [])
+    prompt_template: str = trigger_data.get("prompt_template", "")
+    last_run_at_raw = trigger_data.get("last_run_at")
+    last_run_at: datetime | None = None
+    if last_run_at_raw:
+        if isinstance(last_run_at_raw, str):
+            last_run_at = datetime.fromisoformat(last_run_at_raw)
+        elif isinstance(last_run_at_raw, (int, float)):
+            last_run_at = datetime.fromtimestamp(last_run_at_raw / 1000, tz=timezone.utc)
 
     errors: list[str] = []
     items_processed = 0
     items_created = 0
 
     custom_section = (
-        f"User instructions:\n{config.prompt_template}"
-        if config.prompt_template
+        f"User instructions:\n{prompt_template}"
+        if prompt_template
         else ""
     )
 
+    # Create or load run log
+    run_log_id = run_id
+    if not run_log_id:
+        async with async_session() as db:
+            run_log = AgentRunLog(
+                agent_id=agent_id,
+                agent_type="local",
+                user_id=user_id,
+                status="running",
+            )
+            db.add(run_log)
+            await db.commit()
+            await db.refresh(run_log)
+            run_log_id = run_log.id
+
     try:
-        # ── Code: scan directories ───────────────────────────────────
-        logger.info("agent_runner: run=%s scanning directories user=%s", run_id, user_id)
+        # ── Scan directories ─────────────────────────────────────────
+        logger.info("agent_runner: run=%s scanning directories user=%s", run_log_id, user_id)
         file_paths = await _scan_directories(
-            paths=config.directory_paths,
-            extensions=config.file_extensions or [],
-            last_run_at=config.last_run_at,
+            paths=directory_paths,
+            extensions=file_extensions,
+            last_run_at=last_run_at,
         )
         logger.info(
-            "agent_runner: run=%s found %d file(s) after filtering", run_id, len(file_paths)
+            "agent_runner: run=%s found %d file(s) after filtering", run_log_id, len(file_paths)
         )
 
         if not file_paths:
-            await _finalize_run(run_log, status="success", items_processed=0, items_created=0)
+            await _finalize_run(run_log_id, status="success", items_processed=0, items_created=0)
             return
 
-        # ── Code: fetch all projects once ────────────────────────────
+        # ── Fetch all projects once ──────────────────────────────────
         projects = await _fetch_projects()
 
         for file_path in file_paths:
             try:
-                # Read file content via code.
                 file_result = await execute_on_client(
                     action="read_file_content", data={"path": file_path}
                 )
                 file_content: str = file_result.get("content", "")
                 if not file_content:
-                    logger.debug("agent_runner: run=%s skipping empty file %r", run_id, file_path)
                     continue
 
                 items_processed += 1
 
-                # Step 1 — classify file.
+                # Step 1 — classify file
                 project_id, domains, new_project_name = await _classify_file(
                     file_path=file_path,
                     file_content=file_content,
                     projects=projects,
-                    config_data_types=config.data_types,
-                )
-                logger.info(
-                    "agent_runner: run=%s file=%r → project=%s new_name=%r domains=%s",
-                    run_id,
-                    file_path,
-                    project_id,
-                    new_project_name,
-                    domains,
+                    config_data_types=data_types,
+                    langfuse_handler=langfuse_handler,
                 )
 
-                # Step 2 — resolve project_id via CODE, then fetch entities.
-                # Project creation is NEVER delegated to the Step 2 LLM.
+                # Step 2 — resolve project_id, fetch entities, process
                 if project_id == "new":
                     proj_name = new_project_name or "Untitled Project"
                     try:
@@ -676,18 +581,10 @@ async def run_local_agent(
                         )
                         created = proj_result.get("row", {})
                         effective_project_id = created.get("id", "standalone")
-                        # Add to local list so subsequent files can match it.
                         if "id" in created:
                             projects.append(created)
-                        logger.info(
-                            "agent_runner: run=%s created project %r id=%s",
-                            run_id, proj_name, effective_project_id,
-                        )
                     except Exception as exc:
-                        logger.warning(
-                            "agent_runner: run=%s failed to create project %r: %s",
-                            run_id, proj_name, exc,
-                        )
+                        logger.warning("agent_runner: run=%s create project failed: %s", run_log_id, exc)
                         effective_project_id = "standalone"
                         proj_name = "unknown"
                     project_context = (
@@ -703,7 +600,6 @@ async def run_local_agent(
                         "Always set projectId to this id on every record you create."
                     )
 
-                # "projects" domain is never passed to Step 2 — handled above in code.
                 domains = [d for d in domains if d != "projects"]
 
                 existing_blocks: list[str] = []
@@ -713,11 +609,15 @@ async def run_local_agent(
 
                 existing_context = "\n\n".join(existing_blocks)
 
-                system_prompt = _PROCESSING_SYSTEM_PROMPT.format(
-                    existing_context=existing_context,
-                    project_context=project_context,
-                    data_types=", ".join(domains),
-                    custom_prompt_section=custom_section,
+                system_prompt = tracing.compile_prompt(
+                    "batch_processing",
+                    fallback=_PROCESSING_SYSTEM_PROMPT,
+                    variables={
+                        "existing_context": existing_context,
+                        "project_context": project_context,
+                        "data_types": ", ".join(domains),
+                        "custom_prompt_section": custom_section,
+                    },
                 )
 
                 processing_tools = _build_processing_tools(domains)
@@ -730,26 +630,22 @@ async def run_local_agent(
                     ),
                     tools=processing_tools,
                     max_steps=_MAX_PROCESSING_STEPS,
+                    langfuse_handler=langfuse_handler,
                 )
                 logger.info(
                     "agent_runner: run=%s file=%r result=%s",
-                    run_id,
-                    file_path,
-                    result_text[:200],
+                    run_log_id, file_path, result_text[:200],
                 )
 
             except Exception as exc:
                 errors.append(f"Error processing '{file_path}': {exc}")
-                logger.error(
-                    "agent_runner: run=%s file=%r failed: %s", run_id, file_path, exc
-                )
+                logger.error("agent_runner: run=%s file=%r failed: %s", run_log_id, file_path, exc)
 
     except Exception as exc:
         errors.append(f"Agent run failed: {exc}")
-        logger.error("agent_runner: run=%s failed: %s", run_id, exc)
+        logger.error("agent_runner: run=%s failed: %s", run_log_id, exc)
     finally:
         _running_agents.discard(agent_id)
-        clear_client_executor()
 
     # ── Finalise ────────────────────────────────────────────────────
     if errors and items_processed == 0:
@@ -760,32 +656,24 @@ async def run_local_agent(
         final_status = "success"
 
     await _finalize_run(
-        run_log,
+        run_log_id,
         status=final_status,
         items_processed=items_processed,
         items_created=items_created,
         errors=errors,
     )
-    logger.info(
-        "agent_runner: run=%s done status=%s processed=%d errors=%d",
-        run_id,
-        final_status,
-        items_processed,
-        len(errors),
-    )
 
-    # Notify Electron that the run is complete.
-    if run_context and device_mgr.is_online(user_id):
+    # Notify Electron that the run is complete via Redis
+    if run_context:
         try:
-            await device_mgr.send_frame(user_id, {
+            channel = ws_out_channel(user_id)
+            await redis_client.publish(channel, json.dumps({
                 "type": "run_complete",
                 "run_context": run_context,
                 "status": final_status,
-            })
+            }))
         except Exception as exc:
-            logger.warning(
-                "agent_runner: run=%s failed to send run_complete: %s", run_id, exc
-            )
+            logger.warning("agent_runner: run=%s failed to send run_complete: %s", run_log_id, exc)
 
 
 # ── Cloud agent runner ─────────────────────────────────────────────────────
@@ -793,49 +681,41 @@ async def run_local_agent(
 _CLOUD_DEFAULT_LOOKBACK_DAYS: int = 7
 
 
-async def run_cloud_agent(
-    user_id: str,
-    config: CloudAgentConfig,
-    run_log: AgentRunLog,
-    device_mgr: DeviceConnectionManager,
-) -> None:
-    """Execute a cloud connector agent run end-to-end.
+async def run_cloud_agent(user_id: str, config_id: str, *, langfuse_handler: Any | None = None) -> None:
+    """Execute a cloud connector agent run.
 
-    Steps:
+    Loads the CloudAgentConfig from DB, decrypts OAuth tokens, fetches
+    messages from the provider, and runs LLM extraction.
 
-    1. Verify the user's device is online.
-    2. Decrypt the stored OAuth token from ``config.oauth_token_encrypted``.
-    3. Instantiate the provider client (Gmail or MS Graph).
-    4. Fetch messages/emails since ``config.last_run_at`` (or 7 days ago for
-       the first run) applying ``config.filter_config`` filters.
-    5. For each message/email call the LLM to extract structured items.
-    6. Push each item to Electron as an ``insert`` tool-call.
-    7. If the provider refreshed its access token, re-encrypt and write it
-       back to ``config.oauth_token_encrypted``.
-    8. Persist the run outcome via ``_finalize_run``.
+    set_current_user() must be called BEFORE this function.
     """
-    run_id = run_log.id
-
-    # ── 1. Device online check ─────────────────────────────────────────
-    if not device_mgr.is_online(user_id):
-        logger.info(
-            "agent_runner: skip cloud run=%s — no device online for user=%s",
-            run_id,
-            user_id,
-        )
-        await _finalize_run(
-            run_log,
-            status="error",
-            errors=["No connected device — cloud agent results cannot be delivered"],
-        )
-        return
-
-    # ── 2. Decrypt OAuth token ─────────────────────────────────────────
     from app.integrations import decrypt_token, encrypt_token, get_provider
 
+    async with async_session() as db:
+        result = await db.execute(
+            select(CloudAgentConfig).where(CloudAgentConfig.id == config_id)
+        )
+        config = result.scalar_one_or_none()
+        if config is None:
+            logger.error("agent_runner: cloud config %s not found", config_id)
+            return
+
+        # Create run log
+        run_log = AgentRunLog(
+            agent_id=config.id,
+            agent_type="cloud",
+            user_id=user_id,
+            status="running",
+        )
+        db.add(run_log)
+        await db.commit()
+        await db.refresh(run_log)
+        run_log_id = run_log.id
+
+    # ── Decrypt OAuth token ────────────────────────────────────────
     if not config.oauth_token_encrypted:
         await _finalize_run(
-            run_log,
+            run_log_id,
             status="error",
             errors=[f"No OAuth token stored for cloud agent '{config.name}'"],
         )
@@ -844,22 +724,21 @@ async def run_cloud_agent(
     try:
         credentials_info = decrypt_token(config.oauth_token_encrypted)
     except ValueError as exc:
-        logger.error("agent_runner: failed to decrypt OAuth token for agent %s: %s", config.id, exc)
         await _finalize_run(
-            run_log,
+            run_log_id,
             status="error",
             errors=[f"Failed to decrypt OAuth token: {exc}"],
         )
         return
 
-    # ── 3. Instantiate provider client ────────────────────────────────
+    # ── Instantiate provider ──────────────────────────────────────
     try:
         provider = get_provider(config.provider, credentials_info)
     except ValueError as exc:
-        await _finalize_run(run_log, status="error", errors=[str(exc)])
+        await _finalize_run(run_log_id, status="error", errors=[str(exc)])
         return
 
-    # ── 4. Fetch messages ─────────────────────────────────────────────
+    # ── Fetch messages ────────────────────────────────────────────
     since: datetime | None = config.last_run_at
     if since is None:
         since = datetime.now(timezone.utc) - timedelta(days=_CLOUD_DEFAULT_LOOKBACK_DAYS)
@@ -868,32 +747,28 @@ async def run_cloud_agent(
 
     errors: list[str] = []
     items_processed = 0
-    items_created = 0
 
     try:
         if config.provider == "gmail":
-            raw_messages = await provider.fetch_messages(  # type: ignore[union-attr]
+            raw_messages = await provider.fetch_messages(
                 filter_config=config.filter_config,
                 since=since,
             )
         elif config.provider == "outlook":
-            raw_messages = await provider.fetch_emails(  # type: ignore[union-attr]
+            raw_messages = await provider.fetch_emails(
                 filter_config=config.filter_config,
                 since=since,
             )
         elif config.provider == "teams":
-            raw_messages = await provider.fetch_messages(  # type: ignore[union-attr]
+            raw_messages = await provider.fetch_messages(
                 filter_config=config.filter_config,
                 since=since,
             )
         else:
             raw_messages = []
     except RuntimeError as exc:
-        logger.error(
-            "agent_runner: provider fetch failed for cloud agent %s: %s", config.id, exc
-        )
         await _finalize_run(
-            run_log,
+            run_log_id,
             status="error",
             errors=[f"Provider fetch failed: {exc}"],
             update_config_last_run=True,
@@ -903,17 +778,11 @@ async def run_cloud_agent(
         return
 
     logger.info(
-        "agent_runner: cloud agent %s fetched %d item(s) from %s for user=%s",
-        config.id,
-        len(raw_messages),
-        config.provider,
-        user_id,
+        "agent_runner: cloud agent %s fetched %d item(s) from %s",
+        config.id, len(raw_messages), config.provider,
     )
 
-    # ── 5–6. Extract + insert via LLM with tools ─────────────────────
-    executor = _make_agent_executor(user_id, device_mgr)
-    set_client_executor(executor)
-
+    # ── Extract + insert via LLM ─────────────────────────────────
     try:
         processing_tools = _build_processing_tools(config.data_types)
         custom_section = (
@@ -928,11 +797,15 @@ async def run_cloud_agent(
                 continue
             items_processed += 1
 
-            processing_prompt = _CLOUD_PROCESSING_PROMPT.format(
-                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})",
-                custom_prompt_section=custom_section,
+            processing_prompt = tracing.compile_prompt(
+                "batch_cloud_processing",
+                fallback=_CLOUD_PROCESSING_PROMPT,
+                variables={
+                    "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})",
+                    "custom_prompt_section": custom_section,
+                },
             )
 
             try:
@@ -941,13 +814,14 @@ async def run_cloud_agent(
                     user_message=f"Process this message content:\n\n{content_text[:8000]}",
                     tools=processing_tools,
                     max_steps=_MAX_PROCESSING_STEPS,
+                    langfuse_handler=langfuse_handler,
                 )
             except Exception as exc:
                 errors.append(f"LLM processing error for message {msg.id!r}: {exc}")
-    finally:
-        clear_client_executor()
+    except Exception as exc:
+        errors.append(f"Agent run failed: {exc}")
 
-    # ── 7. Persist refreshed token (if any) ───────────────────────────
+    # ── Persist refreshed token ───────────────────────────────────
     refreshed = getattr(provider, "refreshed_credentials", None)
     if refreshed:
         try:
@@ -960,16 +834,11 @@ async def run_cloud_agent(
                 if cfg_row:
                     cfg_row.oauth_token_encrypted = new_encrypted
                     await db.commit()
-            logger.debug("agent_runner: refreshed OAuth token persisted for agent %s", config.id)
         except Exception as exc:
-            logger.warning(
-                "agent_runner: failed to persist refreshed token for agent %s: %s",
-                config.id,
-                exc,
-            )
+            logger.warning("agent_runner: failed to persist refreshed token: %s", exc)
 
-    # ── 8. Finalise ────────────────────────────────────────────────────
-    if errors and items_created == 0:
+    # ── Finalise ──────────────────────────────────────────────────
+    if errors and items_processed == 0:
         final_status = "error"
     elif errors:
         final_status = "partial"
@@ -977,50 +846,22 @@ async def run_cloud_agent(
         final_status = "success"
 
     await _finalize_run(
-        run_log,
+        run_log_id,
         status=final_status,
         items_processed=items_processed,
-        items_created=items_created,
+        items_created=0,
         errors=errors,
         update_config_last_run=True,
         config_id=config.id,
         config_type="cloud",
     )
-    logger.info(
-        "agent_runner: cloud run=%s done status=%s processed=%d created=%d errors=%d",
-        run_id,
-        final_status,
-        items_processed,
-        items_created,
-        len(errors),
-    )
-
-
-# ── Pending-run trigger ─────────────────────────────────────────────────────
-
-
-async def trigger_pending_runs(
-    user_id: str,
-    device_id: str,
-    device_mgr: DeviceConnectionManager,
-) -> None:
-    """Dispatch any overdue agent runs after an Electron device connects.
-
-    Called as a background task from the device WS endpoint on ``device_hello``.
-    """
-    logger.info(
-        "agent_runner: pending-run scan skipped for user=%s device=%s (client-owned agent config)",
-        user_id,
-        device_id,
-    )
-    return
 
 
 # ── Internal helper ─────────────────────────────────────────────────────────
 
 
 async def _finalize_run(
-    run_log: AgentRunLog,
+    run_log_id: int | str,
     *,
     status: str,
     items_processed: int = 0,
@@ -1030,11 +871,18 @@ async def _finalize_run(
     config_id: str | None = None,
     config_type: str | None = None,
 ) -> None:
-    """Persist the run outcome and optionally update ``last_run_at`` on the config."""
+    """Persist the run outcome and optionally update last_run_at on the config."""
     now = datetime.now(timezone.utc)
     try:
         async with async_session() as db:
-            managed = await db.merge(run_log)
+            result = await db.execute(
+                select(AgentRunLog).where(AgentRunLog.id == run_log_id)
+            )
+            managed = result.scalar_one_or_none()
+            if managed is None:
+                logger.warning("agent_runner: run_log %s not found for finalization", run_log_id)
+                return
+
             managed.status = status
             managed.items_processed = items_processed
             managed.items_created = items_created
@@ -1059,6 +907,4 @@ async def _finalize_run(
 
             await db.commit()
     except Exception as exc:
-        logger.error(
-            "agent_runner: failed to finalize run_log=%s: %s", run_log.id, exc
-        )
+        logger.error("agent_runner: failed to finalize run_log=%s: %s", run_log_id, exc)

services/batch-agent/app/agents/__init__.py

@@ -0,0 +1 @@
+"""Batch Agent Service domain agents and filesystem tools."""

services/batch-agent/app/agents/filesystem_agent.py

@@ -1,8 +1,6 @@
 """Filesystem agent — tools for reading local directories and files on Electron.
 
-These tools delegate to the Electron client via ``execute_on_client()`` using
-the same WS tool-call round-trip pattern as CRUD tools.  The Electron app
-handles actual disk I/O and responds with ``tool_result`` frames.
+Adapted for Batch Agent Service: import from app.ws_context.
 """
 
 from __future__ import annotations
@@ -11,7 +9,7 @@ from typing import Any
 
 from langchain_core.tools import tool
 
-from app.core.ws_context import execute_on_client
+from shared.ws_context import execute_on_client
 
 
 @tool

services/batch-agent/app/integrations/__init__.py

@@ -1,20 +1,11 @@
 """Cloud provider integration utilities.
 
-Provides:
-  * Shared message dataclasses (``EmailMessage``, ``ChatMessage``) used by
-    both the Gmail and MS Graph clients and consumed by ``agent_runner``.
-  * ``get_provider()`` — factory that returns the correct client given a
-    provider name and decrypted OAuth credentials dict.
-  * ``encrypt_token()`` / ``decrypt_token()`` — Fernet-based at-rest
-    encryption for OAuth tokens stored in ``cloud_agent_configs``.
+Adapted for Batch Agent Service: import from shared.config instead of app.config.
 
-Encryption rationale
---------------------
-Unlike user content (which is E2E-encrypted client-side and **never**
-decrypted server-side), OAuth tokens *must* be decrypted server-side
-because the backend makes provider API calls on behalf of the user.
-The Fernet key lives solely in ``OAUTH_ENCRYPTION_KEY`` env var — it
-is never returned to clients.
+Provides:
+  * Shared message dataclasses (EmailMessage, ChatMessage)
+  * get_provider() — factory for Gmail/MS Graph clients
+  * encrypt_token() / decrypt_token() — Fernet-based OAuth token encryption
 """
 
 from __future__ import annotations
@@ -27,7 +18,7 @@ from typing import TYPE_CHECKING
 
 from cryptography.fernet import Fernet, InvalidToken
 
-from app.config.settings import settings
+from shared.config import settings
 
 if TYPE_CHECKING:
     from app.integrations.gmail import GmailClient
@@ -35,13 +26,9 @@ if TYPE_CHECKING:
 
 logger = logging.getLogger(__name__)
 
-# ── Shared message types ──────────────────────────────────────────────────
-
 
 @dataclass
 class EmailMessage:
-    """A single email message fetched from Gmail or Outlook."""
-
     id: str
     subject: str
     sender: str
@@ -51,7 +38,6 @@ class EmailMessage:
 
     @property
     def as_text(self) -> str:
-        """Return a human-readable text representation for LLM extraction."""
         date_str = self.date.strftime("%Y-%m-%d %H:%M")
         labels_str = f" [{', '.join(self.labels)}]" if self.labels else ""
         return (
@@ -64,8 +50,6 @@ class EmailMessage:
 
 @dataclass
 class ChatMessage:
-    """A single Teams chat or channel message fetched from MS Graph."""
-
     id: str
     content: str
     sender: str
@@ -74,7 +58,6 @@ class ChatMessage:
 
     @property
     def as_text(self) -> str:
-        """Return a human-readable text representation for LLM extraction."""
         date_str = self.date.strftime("%Y-%m-%d %H:%M")
         channel_str = f" [channel: {self.channel}]" if self.channel else ""
         return (
@@ -84,15 +67,7 @@ class ChatMessage:
         )
 
 
-# ── Fernet helpers ────────────────────────────────────────────────────────
-
-
 def _get_fernet() -> Fernet:
-    """Return a ``Fernet`` instance using ``settings.OAUTH_ENCRYPTION_KEY``.
-
-    Raises ``RuntimeError`` if ``OAUTH_ENCRYPTION_KEY`` is not set — callers
-    must ensure this is configured before persisting OAuth tokens.
-    """
     key = settings.OAUTH_ENCRYPTION_KEY
     if not key:
         raise RuntimeError(
@@ -103,15 +78,6 @@ def _get_fernet() -> Fernet:
 
 
 def encrypt_token(token_info: dict) -> str:
-    """Fernet-encrypt an OAuth credential dict and return a base64 string.
-
-    Stores the full ``{access_token, refresh_token, token_uri, client_id,
-    client_secret, scopes, expiry}`` dict (or equivalent MSAL shape).
-
-    Raises:
-        RuntimeError: OAUTH_ENCRYPTION_KEY is not configured.
-        ValueError: ``token_info`` is not a non-empty dict.
-    """
     if not isinstance(token_info, dict) or not token_info:
         raise ValueError("token_info must be a non-empty dict")
     plaintext = json.dumps(token_info).encode("utf-8")
@@ -119,13 +85,6 @@ def encrypt_token(token_info: dict) -> str:
 
 
 def decrypt_token(encrypted: str) -> dict:
-    """Decrypt a Fernet-encrypted token string and return the credential dict.
-
-    Raises:
-        RuntimeError: OAUTH_ENCRYPTION_KEY is not configured.
-        ValueError: The encrypted string is invalid or was encrypted with a
-            different key.
-    """
     try:
         plaintext = _get_fernet().decrypt(encrypted.encode("utf-8"))
         return json.loads(plaintext)
@@ -133,25 +92,10 @@ def decrypt_token(encrypted: str) -> dict:
         raise ValueError(f"Failed to decrypt OAuth token: {exc}") from exc
 
 
-# ── Provider factory ──────────────────────────────────────────────────────
-
-
 def get_provider(
     provider: str,
     credentials_info: dict,
 ) -> "GmailClient | MSGraphClient":
-    """Return the correct provider client for *provider*.
-
-    Parameters
-    ----------
-    provider:
-        One of ``"gmail"``, ``"outlook"``, ``"teams"``.
-    credentials_info:
-        Decrypted OAuth credential dict (Google or Microsoft shape).
-
-    Raises:
-        ValueError: Unknown provider name.
-    """
     if provider == "gmail":
         from app.integrations.gmail import GmailClient
         return GmailClient(credentials_info)

services/batch-agent/app/integrations/gmail.py

@@ -1,26 +1,7 @@
 """Gmail API client for cloud agent integration.
 
-Wraps the Google Gmail REST API to fetch email messages matching a
-``filter_config`` dict.  Uses the official ``google-api-python-client``
-library (synchronous) wrapped in ``asyncio.to_thread()`` to avoid
-blocking the event loop.
-
-Token refresh is handled transparently: when the stored access token has
-expired, ``google.auth.transport.requests.Request`` will use the refresh
-token to obtain a fresh one.  The caller is responsible for persisting
-any refreshed credentials back to ``CloudAgentConfig.oauth_token_encrypted``
-(see ``agent_runner.run_cloud_agent``).
-
-Credential dict shape (Google OAuth2):
-    {
-        "token": "<access_token>",
-        "refresh_token": "<refresh_token>",
-        "token_uri": "https://oauth2.googleapis.com/token",
-        "client_id": "<client_id>",
-        "client_secret": "<client_secret>",
-        "scopes": ["https://www.googleapis.com/auth/gmail.readonly"],
-        "expiry": "2025-01-01T00:00:00Z"  # optional ISO-8601
-    }
+Adapted for Batch Agent Service: import from app.integrations instead of
+app.integrations (same relative path within the service).
 """
 
 from __future__ import annotations
@@ -38,13 +19,8 @@ from app.integrations import EmailMessage
 
 logger = logging.getLogger(__name__)
 
-# Gmail search date format — e.g. "after:2025/01/01"
 _GMAIL_DATE_FMT = "%Y/%m/%d"
-
-# Maximum characters of body text forwarded to the LLM.
 _BODY_TRUNCATE = 8_000
-
-# Maximum messages retrieved per run (prevents runaway quota usage).
 _MAX_MESSAGES = 200
 
 
@@ -52,20 +28,9 @@ def _build_gmail_query(
     filter_config: dict[str, Any] | None,
     since: datetime | None,
 ) -> str:
-    """Build a Gmail search query string from *filter_config* and *since*.
-
-    Supported ``filter_config`` keys:
-        labels (list[str]):  Gmail label names, e.g. ``["INBOX", "work"]``
-        senders (list[str]): Sender addresses or domains to include
-        date_range (dict):   ``{from: "<YYYY-MM-DD>", to: "<YYYY-MM-DD>"}``
-
-    A hard ``since`` date (from last run) always overrides ``date_range.from``
-    when it is earlier.
-    """
     parts: list[str] = []
     cfg = filter_config or {}
 
-    # Labels — joined with OR when multiple given.
     labels: list[str] = cfg.get("labels", [])
     if labels:
         if len(labels) == 1:
@@ -74,17 +39,14 @@ def _build_gmail_query(
             label_expr = " OR ".join(f"label:{lbl}" for lbl in labels)
             parts.append(f"({label_expr})")
 
-    # Senders — each prefixed with "from:".
     senders: list[str] = cfg.get("senders", [])
     for sender in senders:
         parts.append(f"from:{sender}")
 
-    # Date range.
     date_range: dict = cfg.get("date_range", {})
     from_str: str | None = date_range.get("from")
     to_str: str | None = date_range.get("to")
 
-    # Determine effective "from" date: most recent of filter_config.date_range.from and since.
     effective_since: datetime | None = since
     if from_str:
         try:
@@ -110,18 +72,12 @@ def _build_gmail_query(
 
 
 def _strip_html(raw_html: str) -> str:
-    """Remove HTML tags and decode entities to get plain text."""
     no_tags = re.sub(r"<[^>]+>", " ", raw_html)
     decoded = html.unescape(no_tags)
     return re.sub(r"\s+", " ", decoded).strip()
 
 
 def _parse_body(payload: dict[str, Any]) -> str:
-    """Recursively extract the plain-text body from a Gmail message payload.
-
-    Prefers ``text/plain``; falls back to ``text/html`` (stripped of tags).
-    Returns an empty string if no body can be extracted.
-    """
     mime_type: str = payload.get("mimeType", "")
     body: dict = payload.get("body", {})
     parts: list[dict] = payload.get("parts", [])
@@ -139,7 +95,6 @@ def _parse_body(payload: dict[str, Any]) -> str:
             return _strip_html(raw)
         return ""
 
-    # Multipart — prefer text/plain part, fall back to text/html.
     plain_fallback = ""
     for part in parts:
         part_mime = part.get("mimeType", "")
@@ -155,7 +110,6 @@ def _parse_body(payload: dict[str, Any]) -> str:
 
 
 def _parse_date(raw: str) -> datetime:
-    """Parse an RFC 2822 email date header into a UTC ``datetime``."""
     try:
         parsed = email.utils.parsedate_to_datetime(raw)
         if parsed.tzinfo is None:
@@ -166,16 +120,6 @@ def _parse_date(raw: str) -> datetime:
 
 
 class GmailClient:
-    """Fetch email messages from a Gmail account via the Gmail REST API.
-
-    Parameters
-    ----------
-    credentials_info:
-        Decrypted OAuth2 credential dict.  Must contain at minimum
-        ``token`` (access token) or ``refresh_token`` + ``token_uri`` +
-        ``client_id`` + ``client_secret``.
-    """
-
     def __init__(self, credentials_info: dict[str, Any]) -> None:
         from google.oauth2.credentials import Credentials
 
@@ -200,38 +144,20 @@ class GmailClient:
             expiry=expiry,
         )
 
-    # ── Public API ─────────────────────────────────────────────────────────
-
     async def fetch_messages(
         self,
         filter_config: dict[str, Any] | None = None,
         since: datetime | None = None,
     ) -> list[EmailMessage]:
-        """Return up to ``_MAX_MESSAGES`` emails matching *filter_config*.
-
-        Runs the synchronous Google API calls inside ``asyncio.to_thread()``
-        to avoid blocking the async event loop.
-
-        Token refresh is performed automatically when the access token has
-        expired.  After the call, ``self.refreshed_credentials`` may be
-        consulted to detect whether new credentials should be persisted.
-        """
         query = _build_gmail_query(filter_config, since)
         logger.debug("gmail: executing search query %r", query)
         return await asyncio.to_thread(self._fetch_sync, query)
 
     @property
     def refreshed_credentials(self) -> dict[str, Any] | None:
-        """Return updated credential dict if the access token was refreshed.
-
-        If the credentials were refreshed during ``fetch_messages()``, returns
-        a new dict that should be re-encrypted and written back to the DB.
-        Returns ``None`` if no refresh occurred.
-        """
         creds = self._credentials
         if not creds.valid and creds.expired:
             return None
-        # Check whether the token changed from what was stored.
         if creds.token != self._credentials_info.get("token"):
             result = {
                 "token": creds.token,
@@ -246,15 +172,11 @@ class GmailClient:
             return result
         return None
 
-    # ── Internal sync worker ───────────────────────────────────────────────
-
     def _fetch_sync(self, query: str) -> list[EmailMessage]:
-        """Synchronous worker — called inside ``asyncio.to_thread()``."""
         import googleapiclient.discovery
         import googleapiclient.errors
         from google.auth.transport.requests import Request
 
-        # Refresh token if needed before building the service.
         if self._credentials.expired and self._credentials.refresh_token:
             try:
                 self._credentials.refresh(Request())
@@ -264,9 +186,8 @@ class GmailClient:
         service = googleapiclient.discovery.build(
             "gmail", "v1", credentials=self._credentials, cache_discovery=False
         )
-        user_api = service.users()  # type: ignore[attr-defined]
+        user_api = service.users()
 
-        # ── List matching message IDs ──────────────────────────────────────
         ids: list[str] = []
         page_token: str | None = None
         while len(ids) < _MAX_MESSAGES:
@@ -293,12 +214,10 @@ class GmailClient:
                 break
 
         if not ids:
-            logger.debug("gmail: no messages matched query %r", query)
             return []
 
         logger.info("gmail: fetching %d message(s)", len(ids))
 
-        # ── Fetch individual message details ──────────────────────────────
         messages: list[EmailMessage] = []
         for msg_id in ids:
             try:
@@ -326,10 +245,8 @@ class GmailClient:
                     date=date,
                     labels=labels,
                 ))
-            except googleapiclient.errors.HttpError as exc:
-                logger.warning("gmail: skipping message %s — HTTP error: %s", msg_id, exc)
             except Exception as exc:
-                logger.warning("gmail: skipping message %s — unexpected error: %s", msg_id, exc)
+                logger.warning("gmail: skipping message %s: %s", msg_id, exc)
 
         logger.info("gmail: returned %d message(s)", len(messages))
         return messages

services/batch-agent/app/integrations/ms_graph.py

@@ -1,52 +1,30 @@
-"""Microsoft Graph API client for Outlook and Teams cloud agent integration.
+"""Microsoft Graph API client for Outlook and Teams.
 
-Handles two data sources:
-
-* **Outlook email** (``provider="outlook"``) — ``fetch_emails()`` calls
-  ``/me/messages`` with an OData ``$filter`` built from ``filter_config``.
-* **Teams messages** (``provider="teams"``) — ``fetch_messages()`` calls
-  ``/me/chats/getAllMessages`` filtered by date.
-
-Authentication uses MSAL ``PublicClientApplication`` to acquire a token
-from a stored refresh token.  The ``httpx.AsyncClient`` (already a project
-dependency) is used for all API calls.
-
-Credential dict shape (Microsoft OAuth2 / MSAL):
-    {
-        "access_token":  "<access_token>",
-        "refresh_token": "<refresh_token>",
-        "token_type":    "Bearer",
-        "scope":         "Mail.Read ChannelMessage.Read.All offline_access",
-        "expires_in":    3600
-    }
+Adapted for Batch Agent Service: import settings from shared.config.
 """
 
 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
 
-from app.config.settings import settings
+from shared.config import settings
 from app.integrations import ChatMessage, EmailMessage
 
 logger = logging.getLogger(__name__)
 
 _GRAPH_BASE = "https://graph.microsoft.com/v1.0"
 
-# Max items fetched per run.
 _MAX_EMAILS = 200
 _MAX_MESSAGES = 200
-
-# Max characters of body forwarded to the LLM.
 _BODY_TRUNCATE = 8_000
 
 
 def _strip_html(raw: str) -> str:
-    """Strip HTML tags and collapse whitespace."""
     no_tags = re.sub(r"<[^>]+>", " ", raw)
     import html as _html
     decoded = _html.unescape(no_tags)
@@ -54,7 +32,6 @@ def _strip_html(raw: str) -> str:
 
 
 def _odata_datetime(dt: datetime) -> str:
-    """Format a datetime as an OData datetime literal (UTC, ISO 8601)."""
     utc = dt.astimezone(timezone.utc)
     return utc.strftime("%Y-%m-%dT%H:%M:%SZ")
 
@@ -63,29 +40,14 @@ def _build_email_filter(
     filter_config: dict[str, Any] | None,
     since: datetime | None,
 ) -> str:
-    """Build an OData ``$filter`` expression for the ``/me/messages`` endpoint.
-
-    Supported ``filter_config`` keys:
-        senders (list[str]):  Sender email addresses.
-        date_range (dict):    ``{from: "<ISO-8601>", to: "<ISO-8601>"}``
-        folders (list[str]):  Folder display names (not directly filterable
-                              via OData, so ignored here — callers iterate
-                              folder IDs separately if needed; listed for
-                              completeness).
-
-    A hard ``since`` date always overrides ``date_range.from`` when it is
-    earlier.
-    """
     clauses: list[str] = []
     cfg = filter_config or {}
 
-    # Senders.
     senders: list[str] = cfg.get("senders", [])
     if senders:
         sender_clauses = [f"from/emailAddress/address eq '{s}'" for s in senders]
         clauses.append("(" + " or ".join(sender_clauses) + ")")
 
-    # Date range.
     date_range: dict = cfg.get("date_range", {})
     from_str: str | None = date_range.get("from")
 
@@ -117,33 +79,16 @@ def _build_email_filter(
 
 
 class MSGraphClient:
-    """Fetch emails and Teams messages via the Microsoft Graph REST API.
-
-    Parameters
-    ----------
-    credentials_info:
-        Decrypted MSAL credential dict.
-    """
-
     def __init__(self, credentials_info: dict[str, Any]) -> None:
         self._credentials_info = credentials_info
         self._access_token: str = credentials_info.get("access_token", "")
         self._original_access_token: str = self._access_token
         self._refresh_token: str | None = credentials_info.get("refresh_token")
 
-    # ── Token management ───────────────────────────────────────────────────
-
     def _auth_headers(self) -> dict[str, str]:
         return {"Authorization": f"Bearer {self._access_token}"}
 
     async def _refresh_access_token(self) -> None:
-        """Use MSAL to exchange the refresh token for a fresh access token.
-
-        Updates ``self._access_token`` and ``self._credentials_info`` in-place.
-
-        Raises:
-            RuntimeError: MSAL reports an auth error.
-        """
         import msal
 
         app = msal.ConfidentialClientApplication(
@@ -164,7 +109,6 @@ class MSGraphClient:
             raise RuntimeError(f"MS Graph token refresh failed: {error}")
 
         self._access_token = result["access_token"]
-        # MSAL may issue a new refresh token.
         if "refresh_token" in result:
             self._refresh_token = result["refresh_token"]
             self._credentials_info["refresh_token"] = result["refresh_token"]
@@ -172,16 +116,10 @@ class MSGraphClient:
 
     @property
     def refreshed_credentials(self) -> dict[str, Any] | None:
-        """Return updated credential dict if the access token was refreshed.
-
-        Returns ``None`` if no change was made.
-        """
         if self._access_token != self._original_access_token:
             return {**self._credentials_info, "access_token": self._access_token}
         return None
 
-    # ── HTTP helpers ───────────────────────────────────────────────────────
-
     async def _get(
         self,
         client: httpx.AsyncClient,
@@ -190,10 +128,8 @@ class MSGraphClient:
         *,
         retry_on_401: bool = True,
     ) -> dict[str, Any]:
-        """GET *url* with auth; refresh token on 401 and retry once."""
         resp = await client.get(url, params=params, headers=self._auth_headers())
         if resp.status_code == 401 and retry_on_401 and self._refresh_token:
-            logger.debug("ms_graph: 401 on %s — refreshing token", url)
             await self._refresh_access_token()
             resp = await client.get(url, params=params, headers=self._auth_headers())
         if resp.status_code == 429:
@@ -201,22 +137,11 @@ class MSGraphClient:
         resp.raise_for_status()
         return resp.json()
 
-    # ── Public API ─────────────────────────────────────────────────────────
-
     async def fetch_emails(
         self,
         filter_config: dict[str, Any] | None = None,
         since: datetime | None = None,
     ) -> list[EmailMessage]:
-        """Return up to ``_MAX_EMAILS`` Outlook messages matching *filter_config*.
-
-        Parameters
-        ----------
-        filter_config:
-            Optional dict with ``senders``, ``date_range``, ``folders`` keys.
-        since:
-            Hard lower-bound on email date (from last agent run).
-        """
         odata_filter = _build_email_filter(filter_config, since)
         params: dict[str, Any] = {
             "$top": 50,
@@ -237,7 +162,7 @@ class MSGraphClient:
                     if len(emails) >= _MAX_EMAILS:
                         break
                 url = data.get("@odata.nextLink", "")
-                params = {}  # nextLink already contains encoded params.
+                params = {}
 
         logger.info("ms_graph: fetched %d Outlook email(s)", len(emails))
         return emails
@@ -247,13 +172,6 @@ class MSGraphClient:
         filter_config: dict[str, Any] | None = None,
         since: datetime | None = None,
     ) -> list[ChatMessage]:
-        """Return up to ``_MAX_MESSAGES`` Teams messages matching *filter_config*.
-
-        Fetches from ``/me/chats/getAllMessages`` (personal + group chats).
-        The ``filter_config.channels`` key is checked as a text-filter on
-        the channel name post-fetch (the API doesn't support channel OData
-        filter directly on ``getAllMessages``).
-        """
         cfg = filter_config or {}
         channel_filter: list[str] = [c.lower() for c in cfg.get("channels", [])]
         params: dict[str, Any] = {"$top": 50}
@@ -268,11 +186,9 @@ class MSGraphClient:
                 try:
                     data = await self._get(client, url, params if url.startswith(_GRAPH_BASE) else None)
                 except httpx.HTTPStatusError as exc:
-                    # getAllMessages requires specific licensing; degrade gracefully.
                     if exc.response.status_code in (403, 404):
                         logger.warning(
-                            "ms_graph: /me/chats/getAllMessages not available (%d) — "
-                            "check Teams license or permissions",
+                            "ms_graph: /me/chats/getAllMessages not available (%d)",
                             exc.response.status_code,
                         )
                         break
@@ -292,8 +208,6 @@ class MSGraphClient:
         logger.info("ms_graph: fetched %d Teams message(s)", len(messages))
         return messages
 
-    # ── Parsers ────────────────────────────────────────────────────────────
-
     @staticmethod
     def _parse_email(item: dict[str, Any]) -> EmailMessage:
         subject: str = item.get("subject", "(no subject)") or "(no subject)"

services/batch-agent/app/journey.py

@@ -1,22 +1,16 @@
-"""Chatbot Journey — WS-based guided conversation to build an agent prompt_template.
+"""Chatbot Journey — guided conversation to build an agent prompt_template.
 
-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.
+Adapted for Batch Agent Service: imports from app.agents.filesystem_agent
+and app.llm instead of monolith paths.  Session state is in-memory (could
+be moved to Redis for horizontal scaling in the future).
 
 Journey flow:
-  1. FE sends ``journey_start`` frame with basic agent config (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,
-     and sends back a ``journey_reply`` with the first question.
-  3. FE sends ``journey_message`` frames for each user reply.
-  4. Server appends the user message, calls the LLM (which may read files
-     via tools), and sends back a ``journey_reply``.
-  5. After 3-5 turns the LLM wraps up by emitting a ``prompt_template``
-     block delimited by ``PROMPT_TEMPLATE_START`` / ``PROMPT_TEMPLATE_END``.
-  6. Server parses the block, sends ``journey_reply`` with ``done=True``
-     and the template.  FE stores it locally.
+  1. Redis consumer dispatches ``journey_start`` with basic agent config.
+  2. Server creates an in-memory session, runs the setup LLM with
+     file-system tools to explore the directory, returns first question.
+  3. ``journey_message`` frames drive the conversation.
+  4. After 3-5 turns the LLM emits PROMPT_TEMPLATE_START / _END block.
+  5. Server parses the block and returns ``journey_reply`` with ``done=True``.
 """
 
 from __future__ import annotations
@@ -31,7 +25,8 @@ from typing import Any
 from langchain_core.messages import AIMessage, HumanMessage, SystemMessage, ToolMessage
 
 from app.agents.filesystem_agent import FILESYSTEM_TOOLS
-from app.core.llm import get_llm
+from shared.llm import get_llm
+import app.tracing as tracing
 
 logger = logging.getLogger(__name__)
 
@@ -43,11 +38,8 @@ _SESSION_TTL_SECONDS: int = 1800  # 30 minutes
 _TEMPLATE_START = "PROMPT_TEMPLATE_START"
 _TEMPLATE_END = "PROMPT_TEMPLATE_END"
 
-# Minimum turns before we consider nudging the LLM to wrap up.
 _MIN_TURNS_BEFORE_NUDGE: int = 3
-# Hard cap to avoid infinite loops (safety net, not the primary stopping criterion).
 _MAX_TURNS: int = 15
-# Max tool-calling steps per LLM invocation.
 _MAX_TOOL_STEPS: int = 6
 
 # ── In-memory session store ───────────────────────────────────────────────
@@ -88,17 +80,9 @@ def get_journey_session(session_id: str, user_id: str) -> JourneySession | None:
 _SYSTEM_PROMPT_TEMPLATE = """\
 You are a friendly assistant helping a freelancer configure a data-extraction agent.
 Your job is to understand exactly what data the user wants to extract from their
-local directory and produce a detailed prompt_template that a separate AI will use
+local directory and produce a concise prompt_template that a separate AI will use
 as its instruction set.
 
-The extraction agent already has this base behaviour built in:
-  - Reads each file using file-system tools.
-  - Creates records (tasks, notes, timelines, projects) via CRUD tools.
-  - Sets isAiSuggested=1 on every new record.
-  - Only extracts data explicitly present in the files — it never invents information.
-The user's custom prompt is appended AFTER this base behaviour, so focus on
-what to look for and how to map it — not on the general extraction mechanics.
-
 You have access to file-system tools to explore the user's directory:
 - list_directory: to see folder structure
 - read_file_content: to peek at file contents
@@ -107,38 +91,43 @@ You have access to file-system tools to explore the user's directory:
 The user's configured directory is: {directory}
 Target data types: {data_types}
 
-IMPORTANT — project assignment is handled automatically by the main agent runner
-before the custom prompt is ever used.  You MUST NOT ask the user about projects,
-projectId, or how to link records to projects.  Never include projectId logic or
-project creation instructions in the generated prompt_template.
+IMPORTANT — project assignment is handled automatically.  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,
-focused questions one at a time.  Cover these topics (not necessarily in this order):
-  1. The type and format of the source content (confirmed by your exploration).
-  2. How fields should be mapped (e.g. filename → task title).
-  3. Priority or status rules (e.g. "urgent" keyword → high priority).
-  4. Any special handling, date extraction, or exclusions.
+focused questions one at a time.  Cover only the topics relevant to the target
+data types listed above:
 
-Once you reach 90% confidence, output the final prompt_template between these exact
-markers on their own lines:
+  1. Content type and format — confirmed by your exploration.
+  2. For TASKS (if in scope): field mapping for title, status, priority, content,
+       dueDate (where is the date found? what's the fallback when absent?),
+       and assignee (is there a person name to assign?).
+  3. For NOTES when TASKS are also in scope: note vs task distinction —
+       what makes something a note rather than a task?
+  4. For TIMELINES (if in scope): the date source — what marks a milestone or event?
+  5. Exclusions and special handling applicable to the target data types.
+
+Keep asking focused questions until you are at least 90% confident.  Then stop and
+output the final prompt_template immediately, wrapped between these exact markers
+on their own lines:
 
 {template_start}
 <the complete extraction prompt here>
 {template_end}
 
-The prompt_template must be a self-contained instruction for an AI that reads files
-and must perform CRUD operations using tools to create records.  It should specify:
-  - What entity types to create (tasks, notes, timelines) — never projects.
-  - How to map file content to record fields (camelCase: title, status, priority,
-    dueDate, content, etc.) — never include projectId.
-  - That isAiSuggested must be set to 1 on every new record.
-  - Concrete examples of mappings based on what you discovered in the directory.
+The prompt_template must be concise (bullet points, ~15–25 lines maximum).
+Specify only:
+  - Scope: what files/content qualify and what entity types to create.
+  - Field mapping rules per entity type (camelCase fields: title, status, priority,
+    dueDate, content, assignee, etc.).
+  - dueDate rule (if tasks in scope): source and fallback behaviour.
+  - Note vs task rule (if both in scope): the criterion that separates them.
+  - Timeline date rule (if timelines in scope): what constitutes a timeline event.
+  - Exclusion/filtering rules.
+  - 2–3 concrete mapping examples based on what you discovered.
 
-{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.\
+{existing_section}Begin by exploring the directory, then ask your first question.\
 """
 
 
@@ -153,12 +142,15 @@ def _build_system_prompt(
         if existing_template
         else ""
     )
-    return _SYSTEM_PROMPT_TEMPLATE.format(
-        directory=directory,
-        data_types=", ".join(data_types),
-        template_start=_TEMPLATE_START,
-        template_end=_TEMPLATE_END,
-        existing_section=existing_section,
+    # Use Langfuse compile_prompt ({{variable}} syntax) with Python .format() fallback
+    return tracing.compile_prompt(
+        "journey_system",
+        fallback=_SYSTEM_PROMPT_TEMPLATE,
+        variables={
+            "directory": directory,
+            "data_types": ", ".join(data_types),
+            "existing_section": existing_section,
+        },
     )
 
 
@@ -199,6 +191,7 @@ async def _call_llm_with_tools(
     system_prompt: str,
     history: list[dict[str, Any]],
     tools: list[Any],
+    langfuse_handler: Any | None = None,
 ) -> str:
     """Build LangChain messages from history and invoke the LLM with tools.
 
@@ -212,7 +205,8 @@ async def _call_llm_with_tools(
         else:
             messages.append(AIMessage(content=turn["content"]))
 
-    llm = get_llm(model=None, temperature=0.4)
+    callbacks = [langfuse_handler] if langfuse_handler else None
+    llm = get_llm(model=None, temperature=0.4, callbacks=callbacks)
     llm_with_tools = llm.bind_tools(tools)
     tool_map = {tool_def.name: tool_def for tool_def in tools}
 
@@ -227,7 +221,7 @@ async def _call_llm_with_tools(
             call_name = str(call.get("name", ""))
             call_args = call.get("args", {})
             logger.info(
-                "agent_setup: journey tool_call name=%s args=%s",
+                "journey: tool_call name=%s args=%s",
                 call_name,
                 json.dumps(call_args, ensure_ascii=True)[:500],
             )
@@ -239,25 +233,27 @@ async def _call_llm_with_tools(
                 tool_output = await tool_fn.ainvoke(call_args)
 
             logger.info(
-                "agent_setup: journey tool_result name=%s output=%s",
+                "journey: tool_result name=%s output=%s",
                 call_name,
                 str(tool_output)[:800],
             )
             messages.append(ToolMessage(content=str(tool_output), tool_call_id=call["id"]))
 
-    # Fallback: exceeded max steps.
+    # Fallback: exceeded max tool steps.
     final = await llm.ainvoke(messages)
     return _as_text(final.content)
 
 
-# ── Journey handlers (called from device_ws.py) ──────────────────────────
+# ── Journey handlers (called from redis_consumer) ────────────────────────
 
 
 async def handle_journey_start(
     user_id: str,
     frame: dict[str, Any],
+    *,
+    langfuse_handler: Any | None = None,
 ) -> dict[str, Any]:
-    """Handle a ``journey_start`` WS frame.
+    """Handle a ``journey_start`` request.
 
     Creates a session, runs the setup LLM with directory exploration,
     and returns the ``journey_reply`` payload.
@@ -267,8 +263,6 @@ async def handle_journey_start(
     data_types = frame.get("data_types", [])
     existing_template = frame.get("existing_template")
 
-    # Use the session_id provided by the FE so the reply matches the
-    # listener key; fall back to a generated one if absent.
     session_id = frame.get("session_id") or str(uuid.uuid4())
     system_prompt = _build_system_prompt(directory, data_types, existing_template)
 
@@ -281,10 +275,6 @@ async def handle_journey_start(
         system_prompt=system_prompt,
     )
 
-    # The LLM will explore the directory using FILESYSTEM_TOOLS via the
-    # ws_context executor (already set by the WS handler before calling us).
-    # Seed with an initial user message — some providers (e.g. GitHub Copilot)
-    # require at least one user/input message to be present.
     seed_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."},
     ]
@@ -292,6 +282,7 @@ async def handle_journey_start(
         system_prompt=system_prompt,
         history=seed_history,
         tools=list(FILESYSTEM_TOOLS),
+        langfuse_handler=langfuse_handler,
     )
 
     session.history.extend(seed_history)
@@ -299,13 +290,12 @@ async def handle_journey_start(
     _sessions[session_id] = session
 
     logger.info(
-        "agent_setup: journey session %s started for user %s (directory=%s)",
+        "journey: session %s started for user %s (directory=%s)",
         session_id,
         user_id,
         directory,
     )
 
-    # Check if the LLM produced the template on the first turn (unlikely but possible).
     prompt_template = _extract_template(ai_reply)
     done = prompt_template is not None
 
@@ -329,8 +319,10 @@ async def handle_journey_start(
 async def handle_journey_message(
     user_id: str,
     frame: dict[str, Any],
+    *,
+    langfuse_handler: Any | None = None,
 ) -> dict[str, Any]:
-    """Handle a ``journey_message`` WS frame.
+    """Handle a ``journey_message`` request.
 
     Appends the user message, calls the LLM, and returns the
     ``journey_reply`` payload.
@@ -348,24 +340,20 @@ async def handle_journey_message(
             "prompt_template": None,
         }
 
-    # Append user turn.
     session.history.append({"role": "user", "content": message})
 
-    # Call the LLM with tools.
     ai_reply = await _call_llm_with_tools(
         system_prompt=session.system_prompt,
         history=session.history,
         tools=list(FILESYSTEM_TOOLS),
+        langfuse_handler=langfuse_handler,
     )
 
     session.history.append({"role": "assistant", "content": ai_reply})
 
-    # Check if the LLM produced the final template.
     prompt_template = _extract_template(ai_reply)
     done = prompt_template is not None
 
-    # If the LLM didn't produce a template, nudge it once it has asked enough
-    # questions (>= _MIN_TURNS_BEFORE_NUDGE) or hits the hard safety cap.
     if not done:
         turns = sum(1 for t in session.history if t["role"] == "user")
         if turns >= _MAX_TURNS:
@@ -379,6 +367,7 @@ async def handle_journey_message(
                 system_prompt=session.system_prompt,
                 history=session.history,
                 tools=list(FILESYSTEM_TOOLS),
+                langfuse_handler=langfuse_handler,
             )
             session.history.append({"role": "assistant", "content": nudge_reply})
 
@@ -395,7 +384,7 @@ async def handle_journey_message(
             else "Here is your agent configuration. You can save it or continue refining."
         )
         _sessions.pop(session_id, None)
-        logger.info("agent_setup: journey session %s completed for user %s", session_id, user_id)
+        logger.info("journey: session %s completed for user %s", session_id, user_id)
 
     return {
         "type": "journey_reply",

services/batch-agent/app/llm.py

@@ -0,0 +1,76 @@
+"""LLM factory — centralised model instantiation via LiteLLM.
+
+Identical to services/chat/app/llm.py. Uses shared.config.settings.
+"""
+
+from __future__ import annotations
+
+import os
+import warnings
+
+from openai import AsyncOpenAI
+import litellm
+
+from langchain_openai import ChatOpenAI
+from langchain_litellm import ChatLiteLLM
+
+from shared.config import settings
+
+litellm.drop_params = True
+
+warnings.filterwarnings(
+    "ignore",
+    message=r"PydanticSerializationUnexpectedValue\(Expected `ResponseAPIUsage`",
+    category=UserWarning,
+)
+
+
+def _api_key_for_model(model: str) -> str | None:
+    if model.startswith("anthropic/"):
+        return settings.ANTHROPIC_API_KEY or None
+    if model.startswith("gemini/") or model.startswith("google/"):
+        return settings.GOOGLE_API_KEY or None
+    if model.startswith("cerebras/"):
+        return settings.CEREBRAS_API_KEY or None
+    if model.startswith("github/"):
+        return settings.GITHUB_TOKEN or None
+    if model.startswith("github_copilot/"):
+        return None
+    return settings.OPENAI_API_KEY or None
+
+
+def get_llm(
+    *,
+    model: str | None = None,
+    temperature: float = 0,
+    callbacks: list | None = None,
+) -> ChatOpenAI | ChatLiteLLM:
+    model = model or settings.LLM_MODEL
+
+    if settings.GITHUB_COPILOT_TOKEN_DIR:
+        os.environ.setdefault("GITHUB_COPILOT_TOKEN_DIR", settings.GITHUB_COPILOT_TOKEN_DIR)
+
+    if settings.GITHUB_TOKEN:
+        os.environ.setdefault("GITHUB_TOKEN", settings.GITHUB_TOKEN)
+
+    if "/" in model:
+        return ChatLiteLLM(model=model, temperature=temperature, callbacks=callbacks)
+
+    return ChatOpenAI(
+        model=model,
+        temperature=temperature,
+        api_key=_api_key_for_model(model),
+        callbacks=callbacks,
+    )
+
+
+async def embed(text: str) -> list[float]:
+    model = settings.LLM_EMBED_MODEL
+
+    if model.startswith("github_copilot/") or "/" in model:
+        response = await litellm.aembedding(model=model, input=[text])
+        return response.data[0]["embedding"]
+
+    client = AsyncOpenAI(api_key=settings.OPENAI_API_KEY)
+    response = await client.embeddings.create(model=model, input=text)
+    return response.data[0].embedding

services/batch-agent/app/main.py

@@ -0,0 +1,79 @@
+"""Batch Agent Service — FastAPI application.
+
+Owns: agent_runner (local directory + cloud connectors), journey builder,
+filesystem_agent, integrations (Gmail, MS Graph).
+
+Communicates with WS Gateway via Redis:
+  - Subscribes to  batch:request:{user_id}  (journey_start, journey_message)
+  - Publishes to   ws:out:{user_id}         (journey replies + tool calls)
+  - BRPOP on       tool:result:{call_id}    (tool-call round-trip, 30s timeout)
+  - SET+EX on      journey:{user_id}        (journey session state, TTL 1800s)
+"""
+
+from __future__ import annotations
+
+import asyncio
+import logging
+import sys
+from pathlib import Path
+
+# Ensure the repo root is on sys.path so ``shared`` is importable when
+# running locally (in Docker the COPY already places it at /app/shared/).
+_repo_root = str(Path(__file__).resolve().parents[3])
+if _repo_root not in sys.path:
+    sys.path.insert(0, _repo_root)
+
+from contextlib import asynccontextmanager
+from typing import AsyncGenerator
+
+from fastapi import FastAPI
+from fastapi.middleware.cors import CORSMiddleware
+
+from app.redis_consumer import start_consumer
+from app.routes import router
+
+logger = logging.getLogger(__name__)
+
+
+@asynccontextmanager
+async def lifespan(app: FastAPI) -> AsyncGenerator[None, None]:
+    # Initialise Langfuse tracing (no-op if keys are missing)
+    from app.tracing import init_langfuse
+    init_langfuse()
+
+    logger.info("batch-agent: starting Redis consumer")
+    task = asyncio.create_task(start_consumer())
+    yield
+    task.cancel()
+    try:
+        await task
+    except asyncio.CancelledError:
+        pass
+
+    from app.tracing import shutdown as shutdown_langfuse
+    shutdown_langfuse()
+
+    from shared.db import engine
+    await engine.dispose()
+
+    from shared.redis import redis_client
+    await redis_client.aclose()
+
+    logger.info("batch-agent: Redis consumer stopped")
+
+
+app = FastAPI(title="Adiuva Batch Agent Service", lifespan=lifespan)
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_methods=["GET", "POST"],
+    allow_headers=["*"],
+)
+
+app.include_router(router)
+
+
+@app.get("/health")
+async def health() -> dict[str, str]:
+    return {"status": "ok", "service": "batch-agent"}

services/batch-agent/app/redis_consumer.py

@@ -0,0 +1,183 @@
+"""Redis consumer for the Batch Agent Service.
+
+Subscribes to  batch:request:*  (pattern) and dispatches:
+  - journey_start   → handle_journey_start
+  - journey_message → handle_journey_message
+  - agent_trigger   → run_local_agent / run_cloud_agent
+
+Results are published back to  ws:out:{user_id}  via Redis.
+"""
+
+from __future__ import annotations
+
+import asyncio
+import json
+import logging
+from typing import Any
+
+from shared.redis import redis_client, batch_request_channel, ws_out_channel
+
+import app.tracing as tracing
+from shared.ws_context import set_current_user, clear_current_user
+
+logger = logging.getLogger(__name__)
+
+
+async def _publish_to_user(user_id: str, payload: dict[str, Any]) -> None:
+    """Publish a frame to the user's WS outbound channel."""
+    channel = ws_out_channel(user_id)
+    await redis_client.publish(channel, json.dumps(payload))
+
+
+async def _handle_journey_start(user_id: str, data: dict[str, Any]) -> None:
+    """Handle a journey_start request from WS Gateway."""
+    from app.journey import handle_journey_start
+
+    session_id = data.get("session_id", "")
+    set_current_user(user_id)
+    try:
+        with tracing.trace_span(
+            name="journey_start",
+            user_id=user_id,
+            session_id=session_id,
+            input=data.get("directory", ""),
+            metadata={"data_types": data.get("data_types", [])},
+            tags=["journey"],
+        ) as span:
+            langfuse_handler = tracing.get_langfuse_callback()
+            reply = await handle_journey_start(user_id, data, langfuse_handler=langfuse_handler)
+            tracing.link_prompt_to_trace(span, "journey_system")
+            span.update(output=reply.get("message", "")[:500])
+            await _publish_to_user(user_id, reply)
+        tracing.flush()
+    except Exception as exc:
+        logger.error("batch-agent: journey_start failed user=%s: %s", user_id, exc)
+        await _publish_to_user(user_id, {
+            "type": "journey_reply",
+            "session_id": session_id,
+            "message": f"Journey setup failed: {exc}",
+            "done": True,
+            "prompt_template": None,
+        })
+    finally:
+        clear_current_user()
+
+
+async def _handle_journey_message(user_id: str, data: dict[str, Any]) -> None:
+    """Handle a journey_message from WS Gateway."""
+    from app.journey import handle_journey_message
+
+    session_id = data.get("session_id", "")
+    set_current_user(user_id)
+    try:
+        with tracing.trace_span(
+            name="journey_message",
+            user_id=user_id,
+            session_id=session_id,
+            input=data.get("message", "")[:200],
+            tags=["journey"],
+        ) as span:
+            langfuse_handler = tracing.get_langfuse_callback()
+            reply = await handle_journey_message(user_id, data, langfuse_handler=langfuse_handler)
+            tracing.link_prompt_to_trace(span, "journey_system")
+            span.update(output=reply.get("message", "")[:500])
+            await _publish_to_user(user_id, reply)
+        tracing.flush()
+    except Exception as exc:
+        logger.error("batch-agent: journey_message failed user=%s: %s", user_id, exc)
+        await _publish_to_user(user_id, {
+            "type": "journey_reply",
+            "session_id": session_id,
+            "message": f"Journey processing failed: {exc}",
+            "done": True,
+            "prompt_template": None,
+        })
+    finally:
+        clear_current_user()
+
+
+async def _handle_agent_trigger(user_id: str, data: dict[str, Any]) -> None:
+    """Handle an agent_trigger request from the REST route (forwarded via Redis)."""
+    from app.agent_runner import run_local_agent
+
+    run_context = data.get("run_context", {})
+    agent_id = run_context.get("agent_id", "")
+    set_current_user(user_id)
+    try:
+        with tracing.trace_span(
+            name="agent_trigger",
+            user_id=user_id,
+            trace_id=run_context.get("run_id"),
+            input={"agent_id": agent_id, "directory": data.get("directory", "")},
+            metadata={"data_types": data.get("data_types", [])},
+            tags=["batch", "agent_run"],
+        ) as span:
+            langfuse_handler = tracing.get_langfuse_callback()
+            await run_local_agent(user_id, data, langfuse_handler=langfuse_handler)
+            tracing.link_prompt_to_trace(span, "batch_processing")
+            span.update(output={"status": "completed"})
+        tracing.flush()
+    except Exception as exc:
+        logger.error("batch-agent: agent_trigger failed user=%s: %s", user_id, exc)
+        await _publish_to_user(user_id, {
+            "type": "run_complete",
+            "status": "error",
+            "run_context": run_context,
+        })
+    finally:
+        clear_current_user()
+
+
+async def _dispatch(user_id: str, message_data: dict[str, Any]) -> None:
+    """Route a batch request to the correct handler."""
+    msg_type = message_data.get("type", "")
+
+    if msg_type == "journey_start":
+        await _handle_journey_start(user_id, message_data)
+    elif msg_type == "journey_message":
+        await _handle_journey_message(user_id, message_data)
+    elif msg_type == "agent_trigger":
+        await _handle_agent_trigger(user_id, message_data)
+    elif msg_type == "device_online":
+        logger.info("batch-agent: device_online user=%s device=%s", user_id, message_data.get("device_id", "?"))
+    else:
+        logger.warning("batch-agent: unknown message type %r from user=%s", msg_type, user_id)
+
+
+async def start_consumer() -> None:
+    """Subscribe to batch:request:* and dispatch incoming frames."""
+    pubsub = redis_client.pubsub()
+    await pubsub.psubscribe("batch:request:*")
+    logger.info("batch-agent: subscribed to batch:request:*")
+
+    try:
+        async for message in pubsub.listen():
+            if message["type"] != "pmessage":
+                continue
+
+            channel: str = message["channel"]
+            if isinstance(channel, bytes):
+                channel = channel.decode()
+
+            # Extract user_id from channel: batch:request:{user_id}
+            parts = channel.split(":", 2)
+            if len(parts) < 3:
+                continue
+            user_id = parts[2]
+
+            raw = message["data"]
+            if isinstance(raw, bytes):
+                raw = raw.decode()
+
+            try:
+                data = json.loads(raw)
+            except json.JSONDecodeError:
+                logger.warning("batch-agent: invalid JSON on channel %s", channel)
+                continue
+
+            # Dispatch in a separate task to avoid blocking the consumer
+            asyncio.create_task(_dispatch(user_id, data))
+    except asyncio.CancelledError:
+        logger.info("batch-agent: consumer shutting down")
+    finally:
+        await pubsub.punsubscribe("batch:request:*")

services/batch-agent/app/routes.py

@@ -0,0 +1,208 @@
+"""Agent REST routes — catalog, billing checks, trigger.
+
+Adapted for Batch Agent Service: uses shared.db, shared.models, shared.schemas.
+Agent trigger dispatches via Redis to the consumer instead of spawning
+an in-process background task.
+"""
+
+from __future__ import annotations
+
+import json
+import uuid
+from datetime import datetime, timezone
+
+from fastapi import APIRouter, Header, HTTPException, status
+from sqlalchemy import func, select
+from sqlalchemy.ext.asyncio import AsyncSession
+
+from shared.db import async_session
+from shared.models import AgentRunLog
+from shared.redis import redis_client, batch_request_channel
+
+from app.agent_runner import is_agent_running
+
+router = APIRouter(prefix="/agents", tags=["agents"])
+
+# ── Tier feature limits ───────────────────────────────────────────────
+# Mirrors app/billing/tier_manager.py FEATURES dict.
+FEATURES: dict[str, dict] = {
+    "free":  {"batch_active": 1,  "batch_runs_per_day": 3},
+    "pro":   {"batch_active": 5,  "batch_runs_per_day": 20},
+    "power": {"batch_active": 20, "batch_runs_per_day": 100},
+    "team":  {"batch_active": -1, "batch_runs_per_day": -1},
+}
+
+
+def _dt_ms(dt: datetime) -> int:
+    return int(dt.timestamp() * 1000)
+
+
+def _dt_ms_opt(dt: datetime | None) -> int | None:
+    return int(dt.timestamp() * 1000) if dt else None
+
+
+def _to_data_types(values: list[str]) -> list[str]:
+    normalize = {
+        "task": "tasks",           "tasks": "tasks",
+        "note": "notes",           "notes": "notes",
+        "timeline": "timelines",   "timelines": "timelines",   "timelineEvents": "timelines",
+        "project": "projects",     "projects": "projects",
+    }
+    seen: set[str] = set()
+    result: list[str] = []
+    for v in values:
+        mapped = normalize.get(v)
+        if mapped and mapped not in seen:
+            seen.add(mapped)
+            result.append(mapped)
+    return result
+
+
+def _enforce_agent_limit(tier: str, current_count: int) -> int:
+    limit: int = FEATURES.get(tier, FEATURES["free"])["batch_active"]
+    if limit != -1 and current_count >= limit:
+        raise HTTPException(
+            status_code=status.HTTP_403_FORBIDDEN,
+            detail=f"Agent limit ({limit}) reached for your tier. Upgrade to create more.",
+        )
+    return limit
+
+
+async def _enforce_run_frequency(tier: str, user_id: str) -> None:
+    limit: int = FEATURES.get(tier, FEATURES["free"])["batch_runs_per_day"]
+    if limit == -1:
+        return
+    today_start = datetime.now(timezone.utc).replace(
+        hour=0, minute=0, second=0, microsecond=0
+    )
+    async with async_session() as db:
+        result = await db.execute(
+            select(func.count(AgentRunLog.id)).where(
+                AgentRunLog.user_id == user_id,
+                AgentRunLog.started_at >= today_start,
+            )
+        )
+        runs_today: int = result.scalar_one()
+
+    if runs_today >= limit:
+        raise HTTPException(
+            status_code=status.HTTP_402_PAYMENT_REQUIRED,
+            detail=f"Daily batch run limit ({limit}) reached for your tier.",
+        )
+
+
+# ── Catalog ───────────────────────────────────────────────────────────
+
+@router.get("/catalog")
+async def get_agent_catalog(
+    x_user_id: str = Header(..., alias="X-User-Id"),
+) -> list[dict]:
+    return [
+        {
+            "type": "local_directory",
+            "name": "Local Directory Monitor",
+            "description": "Watches local directories, extracts data from files using AI",
+        },
+        {
+            "type": "gmail",
+            "name": "Gmail Connector",
+            "description": "Scans Gmail inbox, extracts tasks/notes from emails",
+        },
+        {
+            "type": "teams",
+            "name": "Microsoft Teams Connector",
+            "description": "Monitors Teams messages, extracts action items",
+        },
+        {
+            "type": "outlook",
+            "name": "Outlook Connector",
+            "description": "Scans Outlook inbox, extracts tasks/notes",
+        },
+    ]
+
+
+# ── Can-create check ─────────────────────────────────────────────────
+
+@router.post("/can-create")
+async def can_create_agent(
+    body: dict,
+    x_user_id: str = Header(..., alias="X-User-Id"),
+    x_user_tier: str = Header("free", alias="X-User-Tier"),
+) -> dict:
+    active_agents = body.get("active_agents", 0)
+    limit: int = FEATURES.get(x_user_tier, FEATURES["free"])["batch_active"]
+    allowed = limit == -1 or active_agents < limit
+    return {
+        "allowed": allowed,
+        "tier": x_user_tier,
+        "active_agents": active_agents,
+        "limit": limit,
+    }
+
+
+# ── Trigger ──────────────────────────────────────────────────────────
+
+@router.post("/trigger", status_code=status.HTTP_202_ACCEPTED)
+async def trigger_agent_run(
+    body: dict,
+    x_user_id: str = Header(..., alias="X-User-Id"),
+    x_user_tier: str = Header("free", alias="X-User-Tier"),
+) -> dict:
+    """Trigger a local agent run — creates run log and dispatches via Redis."""
+    active_agents = body.get("active_agents", 0)
+    _enforce_agent_limit(x_user_tier, active_agents)
+    await _enforce_run_frequency(x_user_tier, x_user_id)
+
+    stable_agent_id = body.get("agent_id") or str(uuid.uuid4())
+
+    if is_agent_running(stable_agent_id):
+        raise HTTPException(
+            status_code=status.HTTP_409_CONFLICT,
+            detail="Agent is already running.",
+        )
+
+    # Create run log in DB
+    async with async_session() as db:
+        run_log = AgentRunLog(
+            agent_id=stable_agent_id,
+            agent_type="local",
+            user_id=x_user_id,
+            status="running",
+        )
+        db.add(run_log)
+        await db.commit()
+        await db.refresh(run_log)
+        run_log_id = run_log.id
+
+    run_context = {
+        "type": "agent_batch",
+        "run_id": run_log_id,
+        "agent_id": stable_agent_id,
+    }
+
+    # Dispatch to the Redis consumer for processing
+    trigger_data = {
+        "type": "agent_trigger",
+        "directory": body.get("directory", ""),
+        "directory_paths": [body.get("directory", "")] if body.get("directory") else [],
+        "data_types": _to_data_types(body.get("what_to_extract", [])),
+        "file_extensions": body.get("file_extensions", []),
+        "prompt_template": body.get("custom_agent_prompt", ""),
+        "device_id": body.get("device_id", ""),
+        "run_context": run_context,
+    }
+
+    channel = batch_request_channel(x_user_id)
+    await redis_client.publish(channel, json.dumps(trigger_data))
+
+    return {
+        "id": run_log_id,
+        "agent_id": stable_agent_id,
+        "agent_type": "local",
+        "status": "running",
+        "items_processed": 0,
+        "items_created": 0,
+        "errors": [],
+        "started_at": _dt_ms(run_log.started_at),
+        "completed_at": None,
+    }

services/batch-agent/app/tracing.py

@@ -0,0 +1,336 @@
+"""Langfuse tracing & prompt management for the Batch Agent Service (v4 SDK).
+
+Provides:
+- ``init_langfuse()`` — initialise the singleton client at startup
+- ``trace_span()`` — context manager that creates a trace + span
+- ``get_langfuse_callback()`` — LangChain callback handler (auto-inherits trace)
+- ``get_prompt()`` — fetch a managed prompt from Langfuse by name
+- ``flush()`` / ``shutdown()`` — lifecycle management
+
+All functions gracefully degrade to no-ops when Langfuse is not configured,
+so the service works identically with or without observability keys.
+
+Requires ``langfuse >= 3.0.0`` (v4 / "Fast Preview" SDK).
+"""
+
+from __future__ import annotations
+
+import logging
+from contextlib import contextmanager
+from typing import Any
+
+from shared.config import settings
+
+logger = logging.getLogger(__name__)
+
+# ── State ────────────────────────────────────────────────────────────────
+
+_initialised: bool = False
+_disabled: bool = False
+
+
+def _is_configured() -> bool:
+    return bool(settings.LANGFUSE_SECRET_KEY and settings.LANGFUSE_PUBLIC_KEY)
+
+
+def init_langfuse() -> None:
+    """Initialise the Langfuse singleton. Call once at startup."""
+    global _initialised, _disabled
+
+    if _initialised or _disabled:
+        return
+
+    if not _is_configured():
+        _disabled = True
+        logger.info("tracing: Langfuse keys not set — tracing disabled")
+        return
+
+    try:
+        from langfuse import Langfuse
+
+        Langfuse(
+            secret_key=settings.LANGFUSE_SECRET_KEY,
+            public_key=settings.LANGFUSE_PUBLIC_KEY,
+            host=settings.LANGFUSE_HOST,
+        )
+        _initialised = True
+        logger.info("tracing: Langfuse client initialised (host=%s)", settings.LANGFUSE_HOST)
+    except Exception as exc:
+        _disabled = True
+        logger.warning("tracing: failed to initialise Langfuse: %s", exc)
+
+
+def _get_client() -> Any | None:
+    """Return the singleton Langfuse client, or *None* if disabled."""
+    if _disabled:
+        return None
+    if not _initialised:
+        init_langfuse()
+    if _disabled:
+        return None
+    try:
+        from langfuse import get_client
+        return get_client()
+    except Exception:
+        return None
+
+
+# ── Null span (no-op when Langfuse is disabled) ─────────────────────────
+
+
+class _NullSpan:
+    """Drop-in replacement when Langfuse is disabled."""
+
+    def update(self, **_: Any) -> None: ...
+    def set_trace_io(self, **_: Any) -> None: ...
+    def score_trace(self, **_: Any) -> None: ...
+
+
+# ── Trace context manager ───────────────────────────────────────────────
+
+
+@contextmanager
+def trace_span(
+    *,
+    name: str,
+    user_id: str,
+    session_id: str | None = None,
+    trace_id: str | None = None,
+    input: Any = None,
+    metadata: dict[str, Any] | None = None,
+    tags: list[str] | None = None,
+):
+    """Context manager that creates a Langfuse trace/span.
+
+    Yields the span object (or a ``_NullSpan`` if Langfuse is disabled).
+    A ``CallbackHandler`` created inside this block auto-inherits the trace
+    context, so there is no need to pass trace IDs manually.
+    """
+    lf = _get_client()
+    if lf is None:
+        yield _NullSpan()
+        return
+
+    try:
+        from langfuse import Langfuse, propagate_attributes
+
+        trace_ctx: dict[str, str] = {}
+        if trace_id is not None:
+            trace_ctx["trace_id"] = Langfuse.create_trace_id(seed=trace_id)
+
+        with lf.start_as_current_observation(
+            as_type="span",
+            name=name,
+            input=input,
+            metadata=metadata or {},
+            **({"trace_context": trace_ctx} if trace_ctx else {}),
+        ) as span:
+            with propagate_attributes(
+                user_id=user_id,
+                session_id=session_id,
+                tags=tags or [],
+            ):
+                yield span
+    except Exception as exc:
+        logger.warning("tracing: trace_span(%s) failed: %s", name, exc)
+        yield _NullSpan()
+
+
+# ── LangChain callback handler ──────────────────────────────────────────
+
+
+def get_langfuse_callback() -> Any | None:
+    """Return a LangChain ``CallbackHandler`` that auto-inherits the current trace.
+
+    Must be called inside a ``trace_span()`` block for proper linking.
+    Returns *None* when Langfuse is disabled.
+    """
+    if _disabled and not _initialised:
+        return None
+
+    try:
+        from langfuse.langchain import CallbackHandler
+        return CallbackHandler()
+    except Exception as exc:
+        logger.warning("tracing: get_langfuse_callback failed: %s", exc)
+        return None
+
+
+# ── Prompt management ────────────────────────────────────────────────────
+
+
+def get_prompt(
+    name: str,
+    *,
+    version: int | None = None,
+    label: str | None = None,
+    fallback: str | None = None,
+    cache_ttl_seconds: int = 300,
+) -> str | None:
+    """Fetch a managed prompt from Langfuse by name (without variable compilation).
+
+    Returns the raw prompt string, or *fallback* if the prompt is not
+    found or Langfuse is disabled.
+    """
+    lf = _get_client()
+    if lf is None:
+        return fallback
+
+    try:
+        kwargs: dict[str, Any] = {
+            "name": name,
+            "cache_ttl_seconds": cache_ttl_seconds,
+        }
+        if version is not None:
+            kwargs["version"] = version
+        if label is not None:
+            kwargs["label"] = label
+        prompt = lf.get_prompt(**kwargs)
+        return prompt.prompt
+    except Exception as exc:
+        logger.warning("tracing: get_prompt(%s) failed: %s", name, exc)
+        return fallback
+
+
+def compile_prompt(
+    name: str,
+    *,
+    fallback: str,
+    variables: dict[str, str],
+    version: int | None = None,
+    label: str | None = None,
+    cache_ttl_seconds: int = 300,
+) -> str:
+    """Fetch a managed prompt from Langfuse and compile it with ``{{variables}}``.
+
+    If the prompt exists in Langfuse, uses the SDK's ``.compile(**variables)``
+    which replaces ``{{key}}`` placeholders.  If Langfuse is disabled or the
+    prompt is not found, falls back to ``fallback.format(**variables)`` (Python
+    ``{key}`` placeholders).
+
+    This means:
+      - Langfuse prompts use ``{{variable}}`` syntax.
+      - Hardcoded fallback strings use Python ``{variable}`` syntax.
+    """
+    lf = _get_client()
+    if lf is None:
+        return fallback.format(**variables)
+
+    try:
+        kwargs: dict[str, Any] = {
+            "name": name,
+            "cache_ttl_seconds": cache_ttl_seconds,
+        }
+        if version is not None:
+            kwargs["version"] = version
+        if label is not None:
+            kwargs["label"] = label
+        prompt = lf.get_prompt(**kwargs)
+        return prompt.compile(**variables)
+    except Exception as exc:
+        logger.warning("tracing: compile_prompt(%s) failed, using fallback: %s", name, exc)
+        return fallback.format(**variables)
+
+
+def get_prompt_object(
+    name: str,
+    *,
+    version: int | None = None,
+    label: str | None = None,
+    cache_ttl_seconds: int = 300,
+) -> Any | None:
+    """Fetch the raw Langfuse prompt *object* (not the compiled string).
+
+    Returns ``None`` when Langfuse is disabled or the prompt is not found.
+    Use this when you need to pass the prompt to ``start_observation(prompt=...)``
+    for linking the prompt to a trace in the Langfuse UI.
+    """
+    lf = _get_client()
+    if lf is None:
+        return None
+
+    try:
+        kwargs: dict[str, Any] = {
+            "name": name,
+            "cache_ttl_seconds": cache_ttl_seconds,
+        }
+        if version is not None:
+            kwargs["version"] = version
+        if label is not None:
+            kwargs["label"] = label
+        return lf.get_prompt(**kwargs)
+    except Exception as exc:
+        logger.warning("tracing: get_prompt_object(%s) failed: %s", name, exc)
+        return None
+
+
+def link_prompt_to_trace(
+    span: Any,
+    prompt_name: str,
+    *,
+    version: int | None = None,
+    label: str | None = None,
+) -> None:
+    """Link a Langfuse managed prompt to a span/observation.
+
+    Uses the SDK v4 ``prompt=`` parameter so that the prompt version
+    appears linked in the Langfuse UI with metrics tracking.
+    """
+    lf = _get_client()
+    if lf is None or isinstance(span, _NullSpan):
+        return
+
+    try:
+        prompt = get_prompt_object(prompt_name, version=version, label=label)
+        if prompt is not None:
+            span.update(prompt=prompt)
+    except Exception as exc:
+        logger.warning("tracing: link_prompt_to_trace(%s) failed: %s", prompt_name, exc)
+
+
+# ── Scoring helper ───────────────────────────────────────────────────────
+
+
+def score_trace(
+    trace_id: str,
+    name: str,
+    value: float,
+    *,
+    comment: str | None = None,
+) -> None:
+    """Post a score to a trace (e.g. user feedback, latency, quality)."""
+    lf = _get_client()
+    if lf is None:
+        return
+
+    try:
+        lf.create_score(trace_id=trace_id, name=name, value=value, comment=comment)
+    except Exception as exc:
+        logger.warning("tracing: score_trace failed: %s", exc)
+
+
+# ── Shutdown ─────────────────────────────────────────────────────────────
+
+
+def flush() -> None:
+    """Flush pending Langfuse events."""
+    lf = _get_client()
+    if lf is not None:
+        try:
+            lf.flush()
+        except Exception as exc:
+            logger.warning("tracing: flush failed: %s", exc)
+
+
+def shutdown() -> None:
+    """Flush and close the Langfuse client."""
+    global _initialised, _disabled
+    lf = _get_client()
+    if lf is not None:
+        try:
+            lf.flush()
+            lf.shutdown()
+        except Exception as exc:
+            logger.warning("tracing: shutdown failed: %s", exc)
+    _initialised = False
+    _disabled = False

services/batch-agent/eval/__init__.py

@@ -0,0 +1 @@
+"""Batch Agent E2E evaluation harness."""

services/batch-agent/eval/__main__.py

@@ -0,0 +1,5 @@
+"""Allow running the eval package as ``python -m eval``."""
+
+from eval.cli import main
+
+main()

services/batch-agent/eval/cli.py

@@ -0,0 +1,285 @@
+"""CLI entry point for the batch agent evaluation harness.
+
+Usage::
+
+    # From services/batch-agent/:
+    python -m eval run                                # all agent fixtures, default model
+    python -m eval run --fixture=classify-invoices    # single fixture
+    python -m eval run --models=gpt-4o,gpt-5.3-codex  # multiple models
+    python -m eval run --mode=step1                   # only step1 fixtures
+    python -m eval run --no-judge                     # skip LLM judge scoring
+
+    python -m eval interactive                        # interactive journey session
+    python -m eval interactive --fixture=journey-invoice-setup
+    python -m eval interactive --model=gpt-4o
+    python -m eval interactive --judge-model=github_copilot/gpt-4o-mini
+
+    python -m eval list                               # list all fixtures
+    python -m eval sync                               # sync fixtures to Langfuse datasets
+"""
+
+from __future__ import annotations
+
+import argparse
+import asyncio
+import logging
+import sys
+from pathlib import Path
+
+# Ensure the service root and repo root are in sys.path.
+# Service root must come BEFORE repo root so its ``app/`` package
+# shadows the monolith ``app/`` in the repo root.
+_SERVICE_ROOT = Path(__file__).resolve().parent.parent
+_REPO_ROOT = _SERVICE_ROOT.parent.parent
+_sr = str(_SERVICE_ROOT)
+_rr = str(_REPO_ROOT)
+if _rr not in sys.path:
+    sys.path.insert(0, _rr)
+# Always force service root to position 0 (python -m may have already
+# added CWD further down the list, which loses to repo root).
+if _sr in sys.path:
+    sys.path.remove(_sr)
+sys.path.insert(0, _sr)
+
+from eval.config import discover_fixtures, discover_journey_fixtures
+from eval.runner import run_fixture_eval, print_results
+from eval.interactive import run_interactive
+from eval import langfuse_eval
+
+
+def _setup_logging(verbose: bool) -> None:
+    level = logging.DEBUG if verbose else logging.INFO
+    logging.basicConfig(
+        level=level,
+        format="%(asctime)s %(name)-20s %(levelname)-5s %(message)s",
+        datefmt="%H:%M:%S",
+    )
+    # Quiet noisy libraries
+    for name in ("httpx", "httpcore", "openai", "litellm", "urllib3"):
+        logging.getLogger(name).setLevel(logging.WARNING)
+
+
+def _parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(
+        description="Batch Agent E2E evaluation harness",
+        prog="python -m eval",
+    )
+    sub = parser.add_subparsers(dest="command", required=True)
+
+    # ── run ───────────────────────────────────────────────────────
+    run_cmd = sub.add_parser("run", help="Run evaluations")
+    run_cmd.add_argument(
+        "--fixture", "-f",
+        help="Run only the named fixture (default: all)",
+    )
+    run_cmd.add_argument(
+        "--models", "-m",
+        default="github_copilot/gpt-5.3-codex",
+        help="Comma-separated list of models to test (default: github_copilot/gpt-5.3-codex)",
+    )
+    run_cmd.add_argument(
+        "--mode",
+        default=None,
+        choices=["step1", "step2", "full"],
+        help="Only run fixtures with this mode (default: all)",
+    )
+    run_cmd.add_argument(
+        "--no-judge",
+        action="store_true",
+        help="Skip LLM-as-judge scoring",
+    )
+    run_cmd.add_argument(
+        "--judge-model",
+        default="gpt-4o",
+        help="Model for LLM judge (default: gpt-4o)",
+    )
+    run_cmd.add_argument(
+        "--fixtures-dir",
+        default=None,
+        help="Path to fixtures directory (default: eval/fixtures/)",
+    )
+    run_cmd.add_argument("-v", "--verbose", action="store_true")
+
+    # ── list ──────────────────────────────────────────────────────
+    list_cmd = sub.add_parser("list", help="List available fixtures")
+    list_cmd.add_argument("--fixtures-dir", default=None)
+    list_cmd.add_argument("-v", "--verbose", action="store_true")
+
+    # ── sync ──────────────────────────────────────────────────────
+    sync_cmd = sub.add_parser("sync", help="Sync fixtures to Langfuse datasets")
+    sync_cmd.add_argument("--fixture", "-f", default=None, help="Sync only the named fixture")
+    sync_cmd.add_argument("--fixtures-dir", default=None)
+    sync_cmd.add_argument("-v", "--verbose", action="store_true")
+
+    # ── interactive ───────────────────────────────────────────────
+    inter_cmd = sub.add_parser("interactive", help="Interactive journey session (human-in-the-loop)")
+    inter_cmd.add_argument(
+        "--fixture", "-f",
+        help="Journey fixture to use (default: pick interactively)",
+    )
+    inter_cmd.add_argument(
+        "--model", "-m",
+        default="github_copilot/gpt-5.3-codex",
+        help="Model for the journey AI (default: github_copilot/gpt-5.3-codex)",
+    )
+    inter_cmd.add_argument(
+        "--judge-model",
+        default="gpt-4o",
+        help="Model for LLM judge (default: gpt-4o)",
+    )
+    inter_cmd.add_argument(
+        "--fixtures-dir",
+        default=None,
+        help="Path to fixtures directory (default: eval/fixtures/)",
+    )
+    inter_cmd.add_argument(
+        "--data-dir",
+        default=None,
+        help="Override sample data directory (e.g. path to private test files not in git)",
+    )
+    inter_cmd.add_argument("-v", "--verbose", action="store_true")
+
+    return parser.parse_args()
+
+
+def _fixtures_dir(arg: str | None) -> Path | None:
+    if arg:
+        return Path(arg)
+    return None
+
+
+async def _cmd_run(args: argparse.Namespace) -> None:
+    fixtures = discover_fixtures(_fixtures_dir(args.fixtures_dir))
+    if not fixtures:
+        print("No fixtures found. Create YAML files in eval/fixtures/.")
+        return
+
+    if args.fixture:
+        fixtures = [f for f in fixtures if f.name == args.fixture]
+        if not fixtures:
+            print(f"Fixture '{args.fixture}' not found.")
+            return
+
+    models = [m.strip() for m in args.models.split(",")]
+
+    all_results = []
+    for fixture in fixtures:
+        if args.mode and fixture.mode != args.mode:
+            continue
+        results = await run_fixture_eval(
+            fixture,
+            models=models,
+            use_llm_judge=not args.no_judge,
+            judge_model=args.judge_model,
+        )
+        all_results.extend(results)
+
+    print_results(all_results)
+
+
+def _cmd_list(args: argparse.Namespace) -> None:
+    fixtures = discover_fixtures(_fixtures_dir(args.fixtures_dir))
+    journey_fixtures = discover_journey_fixtures(_fixtures_dir(args.fixtures_dir))
+
+    if not fixtures and not journey_fixtures:
+        print("No fixtures found.")
+        return
+
+    if fixtures:
+        print(f"\n{'[Agent Fixtures]'}")
+        print(f"{'Name':<30} {'Mode':<6} {'Types':<25} {'Expected'}")
+        print("-" * 90)
+        for f in fixtures:
+            types = ", ".join(f.data_types)
+            n_expected = len(f.expected) + len(f.expected_classification)
+            print(f"{f.name:<30} {f.mode:<6} {types:<25} {n_expected}")
+
+    if journey_fixtures:
+        print(f"\n{'[Journey Fixtures]'}")
+        print(f"{'Name':<30} {'Types':<25} {'Messages':<10} {'Criteria'}")
+        print("-" * 90)
+        for f in journey_fixtures:
+            types = ", ".join(f.data_types)
+            print(f"{f.name:<30} {types:<25} {len(f.user_messages):<10} {len(f.expected_template_criteria)}")
+
+    print()
+
+
+def _cmd_sync(args: argparse.Namespace) -> None:
+    fixtures = discover_fixtures(_fixtures_dir(args.fixtures_dir))
+    journey_fixtures = discover_journey_fixtures(_fixtures_dir(args.fixtures_dir))
+
+    if args.fixture:
+        fixtures = [f for f in fixtures if f.name == args.fixture]
+        journey_fixtures = [f for f in journey_fixtures if f.name == args.fixture]
+
+    if not fixtures and not journey_fixtures:
+        print("No fixtures to sync.")
+        return
+
+    for fixture in fixtures:
+        name = langfuse_eval.sync_fixture_to_dataset(fixture)
+        if name:
+            print(f"Synced: {fixture.name} → {name}")
+        else:
+            print(f"Skipped: {fixture.name} (Langfuse not configured)")
+
+    for fixture in journey_fixtures:
+        name = langfuse_eval.sync_journey_fixture_to_dataset(fixture)
+        if name:
+            print(f"Synced: {fixture.name} → {name}")
+        else:
+            print(f"Skipped: {fixture.name} (Langfuse not configured)")
+
+
+async def _cmd_interactive(args: argparse.Namespace) -> None:
+    journey_fixtures = discover_journey_fixtures(_fixtures_dir(args.fixtures_dir))
+    if not journey_fixtures:
+        print("No journey fixtures found. Create YAML files with type: journey in eval/fixtures/.")
+        return
+
+    if args.fixture:
+        fixtures = [f for f in journey_fixtures if f.name == args.fixture]
+        if not fixtures:
+            print(f"Journey fixture '{args.fixture}' not found.")
+            return
+        fixture = fixtures[0]
+    elif len(journey_fixtures) == 1:
+        fixture = journey_fixtures[0]
+    else:
+        # Let user pick
+        print("\nAvailable journey fixtures:")
+        for i, f in enumerate(journey_fixtures, 1):
+            print(f"  {i}. {f.name} — {f.description[:60]}")
+        print()
+        try:
+            choice = int(input("Pick a fixture number: ").strip()) - 1
+            fixture = journey_fixtures[choice]
+        except (ValueError, IndexError, EOFError, KeyboardInterrupt):
+            print("Invalid choice.")
+            return
+
+    await run_interactive(
+        fixture,
+        model=args.model,
+        judge_model=args.judge_model,
+        data_dir=Path(args.data_dir).resolve() if args.data_dir else None,
+    )
+
+
+def main() -> None:
+    args = _parse_args()
+    _setup_logging(args.verbose)
+
+    if args.command == "run":
+        asyncio.run(_cmd_run(args))
+    elif args.command == "interactive":
+        asyncio.run(_cmd_interactive(args))
+    elif args.command == "list":
+        _cmd_list(args)
+    elif args.command == "sync":
+        _cmd_sync(args)
+
+
+if __name__ == "__main__":
+    main()

services/batch-agent/eval/config.py

@@ -0,0 +1,220 @@
+"""Eval configuration — YAML fixture loader and dataclasses.
+
+Fixtures come in two families:
+
+1. **Agent fixtures** — test the batch agent pipeline.
+   Three modes controlled by ``mode``:
+
+   ``step1``  — classification prompt only.
+   ``step2``  — processing prompt only.
+   ``full``   — both steps in sequence.
+
+2. **Journey fixtures** — test the prompt-template builder conversation
+   (unchanged).
+"""
+
+from __future__ import annotations
+
+import logging
+from dataclasses import dataclass, field
+from pathlib import Path
+from typing import Any, Literal
+
+import yaml
+
+logger = logging.getLogger(__name__)
+
+EvalMode = Literal["step1", "step2", "full"]
+
+
+@dataclass
+class ExpectedRecord:
+    """A single expected extraction result.
+
+    Only the fields specified are checked — unspecified fields are ignored.
+    """
+
+    table: str  # tasks | notes | timelines | projects
+    fields: dict[str, Any]  # field_name → expected_value
+
+
+@dataclass
+class ExpectedClassification:
+    """Expected output of step-1 classification for one file."""
+
+    file: str  # relative path to the sample file
+    project_id: str  # expected matched project id, or "new"
+    domains: list[str]  # expected domain list
+    new_project_name: str | None = None
+
+
+@dataclass
+class EvalFixture:
+    """A complete test scenario loaded from YAML.
+
+    ``mode`` determines which pipeline steps are exercised:
+
+    - **step1**: only ``_classify_file``
+    - **step2**: only the processing LLM + tool loop
+    - **full**: both steps in sequence (``run_local_agent``)
+    """
+
+    name: str
+    description: str
+    mode: EvalMode
+    directory: str  # relative path to sample files
+    data_types: list[str]
+    file_extensions: list[str]
+    models: list[str]  # if empty, use CLI default
+    fixture_path: Path = field(default_factory=lambda: Path("."))
+
+    # ── Step-1 inputs (classification) ───────────────────────────
+    domain_definitions: str = ""
+    projects_list: list[dict[str, Any]] = field(default_factory=list)
+    custom_step1_prompt: str = ""
+
+    # ── Step-2 inputs (processing) ───────────────────────────────
+    existing_context: str = ""
+    project_context: str = ""
+    custom_prompt_section: str = ""
+
+    # ── Seed records for mock executor ───────────────────────────
+    seed_records: dict[str, list[dict]] = field(default_factory=dict)
+
+    # ── Expected outputs ─────────────────────────────────────────
+    expected_classification: list[ExpectedClassification] = field(default_factory=list)
+    expected: list[ExpectedRecord] = field(default_factory=list)
+
+    @property
+    def fixture_dir(self) -> Path:
+        """Absolute path to the sample files directory."""
+        return self.fixture_path.parent / self.directory
+
+    @classmethod
+    def from_yaml(cls, path: Path) -> "EvalFixture":
+        """Load a fixture from a YAML file."""
+        raw = yaml.safe_load(path.read_text(encoding="utf-8"))
+
+        mode: EvalMode = raw.get("mode", "full")
+
+        # Parse expected records (step2/full)
+        expected: list[ExpectedRecord] = []
+        for table, records in (raw.get("expected") or {}).items():
+            for rec in records:
+                expected.append(ExpectedRecord(table=table, fields=rec))
+
+        # Parse expected classification (step1/full)
+        expected_classification: list[ExpectedClassification] = []
+        for item in raw.get("expected_classification") or []:
+            expected_classification.append(ExpectedClassification(
+                file=item["file"],
+                project_id=item["project_id"],
+                domains=item.get("domains", []),
+                new_project_name=item.get("new_project_name"),
+            ))
+
+        return cls(
+            name=raw["name"],
+            description=raw.get("description", ""),
+            mode=mode,
+            directory=raw.get("directory", "sample_files"),
+            data_types=raw.get("data_types", ["tasks"]),
+            file_extensions=raw.get("file_extensions", []),
+            models=raw.get("models", []),
+            fixture_path=path,
+            # Step-1 inputs
+            domain_definitions=raw.get("domain_definitions", ""),
+            projects_list=raw.get("projects_list", []),
+            # Step-2 inputs
+            existing_context=raw.get("existing_context", ""),
+            project_context=raw.get("project_context", ""),
+            custom_prompt_section=raw.get("custom_prompt_section", ""),
+            # Shared
+            seed_records=raw.get("seed_records", {}),
+            expected_classification=expected_classification,
+            expected=expected,
+        )
+
+
+def discover_fixtures(fixtures_dir: Path | None = None) -> list[EvalFixture]:
+    """Find and load all YAML fixtures in the fixtures directory."""
+    if fixtures_dir is None:
+        fixtures_dir = Path(__file__).parent / "fixtures"
+
+    fixtures: list[EvalFixture] = []
+    if not fixtures_dir.is_dir():
+        logger.warning("eval: fixtures directory not found: %s", fixtures_dir)
+        return fixtures
+
+    for yaml_path in sorted(fixtures_dir.glob("*.yaml")):
+        try:
+            raw = yaml.safe_load(yaml_path.read_text(encoding="utf-8"))
+            if raw.get("type") == "journey":
+                continue  # Skip journey fixtures
+            fixtures.append(EvalFixture.from_yaml(yaml_path))
+            logger.info("eval: loaded fixture %s from %s", fixtures[-1].name, yaml_path.name)
+        except Exception as exc:
+            logger.error("eval: failed to load fixture %s: %s", yaml_path.name, exc)
+
+    return fixtures
+
+
+# ── Journey fixtures ─────────────────────────────────────────────────────
+
+
+@dataclass
+class JourneyFixture:
+    """A journey test scenario — tests the prompt_template builder conversation."""
+
+    name: str
+    description: str
+    directory: str  # relative path to sample files
+    data_types: list[str]
+    expected_template_criteria: list[str]  # what the template should contain/satisfy
+    user_messages: list[str] = field(default_factory=list)  # for automated journey runs (unused in interactive mode)
+    models: list[str] = field(default_factory=list)
+    fixture_path: Path = field(default_factory=lambda: Path("."))
+
+    @property
+    def fixture_dir(self) -> Path:
+        """Absolute path to the sample files directory."""
+        return self.fixture_path.parent / self.directory
+
+    @classmethod
+    def from_yaml(cls, path: Path) -> "JourneyFixture":
+        """Load a journey fixture from a YAML file."""
+        raw = yaml.safe_load(path.read_text(encoding="utf-8"))
+
+        return cls(
+            name=raw["name"],
+            description=raw.get("description", ""),
+            directory=raw.get("directory", "sample_files"),
+            data_types=raw.get("data_types", ["tasks"]),
+            user_messages=raw.get("user_messages", []),
+            expected_template_criteria=raw.get("expected_template_criteria", []),
+            models=raw.get("models", []),
+            fixture_path=path,
+        )
+
+
+def discover_journey_fixtures(fixtures_dir: Path | None = None) -> list[JourneyFixture]:
+    """Find and load all journey YAML fixtures in the fixtures directory."""
+    if fixtures_dir is None:
+        fixtures_dir = Path(__file__).parent / "fixtures"
+
+    fixtures: list[JourneyFixture] = []
+    if not fixtures_dir.is_dir():
+        logger.warning("eval: fixtures directory not found: %s", fixtures_dir)
+        return fixtures
+
+    for yaml_path in sorted(fixtures_dir.glob("*.yaml")):
+        try:
+            raw = yaml.safe_load(yaml_path.read_text(encoding="utf-8"))
+            if raw.get("type") != "journey":
+                continue
+            fixtures.append(JourneyFixture.from_yaml(yaml_path))
+            logger.info("eval: loaded journey fixture %s from %s", fixtures[-1].name, yaml_path.name)
+        except Exception as exc:
+            logger.error("eval: failed to load journey fixture %s: %s", yaml_path.name, exc)
+
+    return fixtures

services/batch-agent/eval/fixtures/classify_invoices.yaml

@@ -0,0 +1,40 @@
+# Fixture: classify-invoices (step1)
+# Tests _STEP1_SYSTEM_PROMPT — file classification and project matching.
+# Verifies that the LLM correctly matches files to existing projects
+# and identifies the right data domains.
+
+name: classify-invoices
+mode: step1
+description: >
+  Test file classification on Italian freelance invoices and meeting notes.
+  Verifies project matching and domain identification.
+
+directory: sample_files/invoices
+data_types: [tasks, notes, timelines]
+file_extensions: [txt, md]
+
+# ── Step-1 prompt variables ──────────────────────────────────────
+domain_definitions: |
+  - tasks: Action items, deliverables, things to do — anything that someone needs to complete.
+  - notes: Meeting summaries, decisions, reference information — permanent knowledge entries.
+  - timelines: Project milestones, deadlines, scheduled events — specific dates that mark a point in the progress of a project.
+
+projects_list:
+  - id: "proj-web-redesign"
+    name: "Redesign Sito Web Corporate"
+    status: "active"
+    aiSummary: "Corporate website redesign for Studio Architettura Bianchi"
+  - id: "proj-ecommerce"
+    name: "E-Commerce FashionStore"
+    status: "active"
+    aiSummary: "Next.js e-commerce platform for FashionStore srl"
+
+# ── Expected classification results ─────────────────────────────
+expected_classification:
+  - file: "sample_files/invoices/fattura_042.txt"
+    project_id: "proj-web-redesign"
+    domains: [tasks, notes, timelines]
+
+  - file: "sample_files/invoices/meeting_ecommerce.md"
+    project_id: "proj-ecommerce"
+    domains: [tasks, notes, timelines]

services/batch-agent/eval/fixtures/full_invoices.yaml

@@ -0,0 +1,108 @@
+# Fixture: full-invoices (full)
+# Tests both _STEP1_SYSTEM_PROMPT and _PROCESSING_SYSTEM_PROMPT in sequence
+# via run_local_agent(). Verifies end-to-end classification + extraction.
+
+name: full-invoices
+mode: full
+description: >
+  End-to-end test: classify Italian invoices/meeting notes into the
+  correct project, then extract tasks, notes, and timeline events.
+
+directory: sample_files/invoices
+data_types: [tasks, notes, timelines]
+file_extensions: [txt, md]
+
+# ── Step-1 prompt variables ──────────────────────────────────────
+domain_definitions: |
+  - tasks: Action items, deliverables, things to do — anything that someone needs to complete.
+  - notes: Meeting summaries, decisions, reference information — permanent knowledge entries.
+  - timelines: Project milestones, deadlines, scheduled events — specific dates that mark a point in the progress of a project.
+
+projects_list:
+  - id: "proj-web-redesign"
+    name: "Redesign Sito Web Corporate"
+    status: "active"
+    aiSummary: "Corporate website redesign for Studio Architettura Bianchi"
+  - id: "proj-ecommerce"
+    name: "E-Commerce FashionStore"
+    status: "active"
+    aiSummary: "Next.js e-commerce platform for FashionStore srl"
+
+# ── Step-2 prompt variables ──────────────────────────────────────
+existing_context: |
+  Existing tasks:
+    (none)
+
+  Existing notes:
+    (none)
+
+  Existing timelines:
+    (none)
+
+project_context: ""
+
+custom_prompt_section: |
+  User instructions:
+  Estrai i dati dai file come segue:
+  - TASK: ogni azione da fare, deliverable, o item con scadenza.
+    Mappa "URGENTE" o "ALTA PRIORITÀ" → priority: high.
+    Mappa "media priorità" → priority: medium.
+    Mappa "bassa priorità" → priority: low.
+    Se un item è marcato come "completato" o [x], impostalo status: done.
+    Altrimenti status: todo.
+  - NOTE: riassunti di meeting, decisioni prese, note tecniche.
+  - TIMELINE: date di scadenza, milestone, meeting futuri.
+  Imposta sempre isAiSuggested=1.
+
+# ── Seed records (pre-existing DB state) ─────────────────────────
+seed_records:
+  projects:
+    - id: "proj-web-redesign"
+      name: "Redesign Sito Web Corporate"
+      status: "active"
+      aiSummary: "Corporate website redesign for Studio Architettura Bianchi"
+    - id: "proj-ecommerce"
+      name: "E-Commerce FashionStore"
+      status: "active"
+      aiSummary: "Next.js e-commerce platform for FashionStore srl"
+  tasks: []
+  notes: []
+  timelines: []
+
+# ── Expected classification (step 1) ─────────────────────────────
+expected_classification:
+  - file: "sample_files/invoices/fattura_042.txt"
+    project_id: "proj-web-redesign"
+    domains: [tasks, notes, timelines]
+
+  - file: "sample_files/invoices/meeting_ecommerce.md"
+    project_id: "proj-ecommerce"
+    domains: [tasks, notes, timelines]
+
+# ── Expected extractions (step 2) ────────────────────────────────
+expected:
+  tasks:
+    - title: "Sviluppo frontend React"
+      priority: "high"
+      status: "todo"
+    - title: "Integrazione API backend"
+      priority: "medium"
+      status: "todo"
+    - title: "Testing cross-browser e fix bug responsive"
+      status: "todo"
+    - title: "Preparare wireframe homepage"
+      priority: "high"
+      status: "todo"
+    - title: "Setup progetto Next.js e configurare CI/CD"
+      priority: "medium"
+      status: "todo"
+    - title: "Ricerca plugin Stripe per gestione abbonamenti"
+      priority: "low"
+      status: "todo"
+
+  notes:
+    - title: "Meeting Kickoff Progetto E-Commerce"
+
+  timelines:
+    - title: "MVP E-Commerce pronto"
+    - title: "Meeting di revisione"

services/batch-agent/eval/fixtures/journey_invoice_setup.yaml

@@ -0,0 +1,28 @@
+# Journey Fixture: journey-invoice-setup
+# Used by `python -m eval interactive` for human-in-the-loop testing
+# of the journey chatbot's prompt-building conversation.
+
+type: journey
+name: journey-invoice-setup
+description: >
+  Interactive test for the journey chatbot — explore a directory of
+  Italian invoices and meeting notes, answer the chatbot's questions,
+  and verify it produces a well-structured prompt_template for data
+  extraction.
+
+directory: sample_files/invoices
+data_types: [tasks, notes, timelines, projects]
+
+# Criteria the generated prompt_template must satisfy
+# Each is scored 0-1 by an LLM judge
+expected_template_criteria:
+  - "Mentions creating tasks from action items and work descriptions"
+  - "Mentions creating notes from meeting summaries"
+  - "Mentions extracting timeline events from deadlines and meeting dates"
+  - "Mentions creating projects from relevant information"
+  - "Sets isAiSuggested=1 on all created records"
+  - "Does NOT include projectId assignment logic"
+  - "Uses camelCase field names (title, status, priority, dueDate, content)"
+
+# Models to test (empty = use CLI --models default)
+models: []

services/batch-agent/eval/fixtures/process_invoices.yaml

@@ -0,0 +1,81 @@
+# Fixture: process-invoices (step2)
+# Tests _PROCESSING_SYSTEM_PROMPT — data extraction & tool calling.
+# The classification step is skipped; prompt variables are injected directly.
+
+name: process-invoices
+mode: step2
+description: >
+  Test data extraction from Italian freelance invoices.
+  Verifies correct record creation via tool calls with the right
+  fields, priorities, and status values.
+
+directory: sample_files/invoices
+data_types: [tasks, notes, timelines]
+file_extensions: [txt, md]
+
+# ── Step-2 prompt variables ──────────────────────────────────────
+existing_context: |
+  Existing tasks:
+    (none)
+
+  Existing notes:
+    (none)
+
+  Existing timelines:
+    (none)
+
+project_context: >
+  Project: Redesign Sito Web Corporate (id: proj-web-redesign).
+  Always set projectId to this id on every record you create.
+
+custom_prompt_section: |
+  User instructions:
+  Estrai i dati dai file come segue:
+  - TASK: ogni azione da fare, deliverable, o item con scadenza.
+    Mappa "URGENTE" o "ALTA PRIORITÀ" → priority: high.
+    Mappa "media priorità" → priority: medium.
+    Mappa "bassa priorità" → priority: low.
+    Se un item è marcato come "completato" o [x], impostalo status: done.
+    Altrimenti status: todo.
+  - NOTE: riassunti di meeting, decisioni prese, note tecniche.
+    Il titolo deve essere descrittivo. Il content deve includere tutti i dettagli.
+  - TIMELINE: date di scadenza, milestone, meeting futuri.
+  Imposta sempre isAiSuggested=1.
+
+# ── Seed records (pre-existing DB state) ─────────────────────────
+seed_records:
+  projects:
+    - id: "proj-web-redesign"
+      name: "Redesign Sito Web Corporate"
+      status: "active"
+  tasks: []
+  notes: []
+  timelines: []
+
+# ── Expected extractions ─────────────────────────────────────────
+expected:
+  tasks:
+    - title: "Sviluppo frontend React"
+      priority: "high"
+      status: "todo"
+    - title: "Integrazione API backend"
+      priority: "medium"
+      status: "todo"
+    - title: "Testing cross-browser e fix bug responsive"
+      status: "todo"
+    - title: "Preparare wireframe homepage"
+      priority: "high"
+      status: "todo"
+    - title: "Setup progetto Next.js e configurare CI/CD"
+      priority: "medium"
+      status: "todo"
+    - title: "Ricerca plugin Stripe per gestione abbonamenti"
+      priority: "low"
+      status: "todo"
+
+  notes:
+    - title: "Meeting Kickoff Progetto E-Commerce"
+
+  timelines:
+    - title: "MVP E-Commerce pronto"
+    - title: "Meeting di revisione"

services/batch-agent/eval/fixtures/sample_files/invoices/fattura_042.txt

@@ -0,0 +1,18 @@
+FATTURA N. 2026-0042
+Data: 15 Marzo 2026
+Cliente: Studio Architettura Bianchi
+
+Progetto: Redesign Sito Web Corporate
+
+Descrizione lavori:
+- Sviluppo frontend React (40 ore) — URGENTE, completare entro 20 marzo
+- Integrazione API backend (20 ore) — priorità media
+- Design UI/UX mockup homepage (8 ore) — completato
+- Testing cross-browser e fix bug responsive (12 ore) — da iniziare
+
+Totale: €4.800,00 + IVA
+
+Note:
+Meeting di revisione previsto per il 18 marzo alle 10:00.
+Il cliente ha richiesto modifiche al layout mobile della sezione contatti.
+Attendere conferma budget aggiuntivo per sezione blog.

services/batch-agent/eval/fixtures/sample_files/invoices/meeting_ecommerce.md

@@ -0,0 +1,25 @@
+# Meeting Notes - Kickoff Progetto E-Commerce
+
+**Data:** 10 Marzo 2026
+**Partecipanti:** Marco R., Giulia T., Cliente (FashionStore srl)
+
+## Decisioni prese
+
+1. **Piattaforma**: Next.js + Stripe per i pagamenti
+2. **Timeline**: MVP pronto entro 30 aprile 2026
+3. **Budget**: €12.000 totale, €4.000 anticipo già ricevuto
+
+## Action items
+
+- [ ] Marco: preparare wireframe homepage entro 14 marzo — ALTA PRIORITÀ
+- [ ] Giulia: setup progetto Next.js e configurare CI/CD — media priorità
+- [ ] Marco: ricerca plugin Stripe per gestione abbonamenti — bassa priorità
+- [x] Giulia: inviare contratto firmato al cliente — COMPLETATO
+
+## Note aggiuntive
+
+Il cliente vuole un design minimalista, ispirato a Zara.com.
+Colori primari: nero, bianco, oro.
+Font: Inter per body, Playfair Display per headings.
+
+Prossimo meeting: 24 marzo 2026 ore 15:00.

services/batch-agent/eval/interactive.py

@@ -0,0 +1,471 @@
+"""Interactive journey session — human-in-the-loop CLI conversation.
+
+Flow:
+1. Show the system prompt used by the journey AI.
+2. Start the journey (AI explores files, asks first question).
+3. User types responses in the terminal — AI replies.
+4. User types `/done` to end the conversation.
+5. User writes a comment about the interaction quality.
+6. LLM judge scores the conversation + generated template.
+7. Results are reported to Langfuse.
+
+Usage::
+
+    python -m eval interactive                        # pick a fixture interactively
+    python -m eval interactive --fixture=journey-invoice-setup
+    python -m eval interactive --model=gpt-4o
+    python -m eval interactive --judge-model=github_copilot/gpt-4o-mini
+"""
+
+from __future__ import annotations
+
+import asyncio
+import json
+import logging
+import sys
+import time
+import uuid
+from dataclasses import dataclass, field
+from typing import Any
+
+from langchain_core.messages import HumanMessage, SystemMessage
+
+from eval.config import JourneyFixture, discover_journey_fixtures
+from eval.mock_executor import MockExecutor
+from eval import langfuse_eval
+
+logger = logging.getLogger(__name__)
+
+# ── Special commands ─────────────────────────────────────────────────────
+
+_CMD_DONE = "/done"
+_CMD_QUIT = "/quit"
+_CMD_TEMPLATE = "/template"
+_CMD_HELP = "/help"
+
+_HELP_TEXT = f"""\
+  {_CMD_DONE}       — End the conversation and proceed to evaluation
+  {_CMD_QUIT}       — Abort without evaluation
+  {_CMD_TEMPLATE}   — Show the generated template (if any)
+  {_CMD_HELP}       — Show this help"""
+
+# ── Terminal colours (ANSI) ──────────────────────────────────────────────
+
+_C_RESET = "\033[0m"
+_C_BOLD = "\033[1m"
+_C_DIM = "\033[2m"
+_C_CYAN = "\033[36m"
+_C_GREEN = "\033[32m"
+_C_YELLOW = "\033[33m"
+_C_MAGENTA = "\033[35m"
+_C_RED = "\033[31m"
+_C_BLUE = "\033[34m"
+
+
+def _print_header(text: str) -> None:
+    print(f"\n{_C_BOLD}{_C_CYAN}{'═' * 80}")
+    print(f"  {text}")
+    print(f"{'═' * 80}{_C_RESET}\n")
+
+
+def _print_ai(text: str) -> None:
+    print(f"\n{_C_GREEN}{_C_BOLD}AI:{_C_RESET} {text}\n")
+
+
+def _print_system(text: str) -> None:
+    print(f"{_C_DIM}{text}{_C_RESET}")
+
+
+def _print_score(label: str, score: float) -> None:
+    if score >= 0.7:
+        color = _C_GREEN
+        tag = "PASS"
+    elif score >= 0.4:
+        color = _C_YELLOW
+        tag = "PARTIAL"
+    else:
+        color = _C_RED
+        tag = "FAIL"
+    print(f"  {color}{tag:>7}{_C_RESET} ({score:.1f}) {label}")
+
+
+# ── Result type ──────────────────────────────────────────────────────────
+
+
+@dataclass
+class InteractiveResult:
+    fixture_name: str
+    model: str
+    judge_model: str
+    prompt_template: str | None
+    conversation: list[dict[str, str]]
+    user_comment: str
+    done: bool
+    criteria_scores: dict[str, float]
+    overall_score: float
+    judge_reasoning: str
+    elapsed_seconds: float
+
+    def summary(self) -> dict[str, Any]:
+        return {
+            "fixture": self.fixture_name,
+            "model": self.model,
+            "judge_model": self.judge_model,
+            "done": self.done,
+            "turns": len([c for c in self.conversation if c["role"] == "user"]),
+            "overall_score": round(self.overall_score, 3),
+            "user_comment": self.user_comment,
+            "criteria_scores": {k: round(v, 3) for k, v in self.criteria_scores.items()},
+            "elapsed_s": round(self.elapsed_seconds, 1),
+        }
+
+
+# ── LLM judge ────────────────────────────────────────────────────────────
+
+_INTERACTIVE_JUDGE_SYSTEM = """\
+You are an evaluation judge for AI-generated prompt templates produced during
+an interactive conversation between a human and a journey chatbot.
+
+The chatbot explored a directory and through multi-turn conversation with the
+user produced a prompt_template — an instruction set for a data-extraction agent.
+
+You have access to:
+- The full conversation transcript
+- The generated prompt_template (if any)
+- The user's own comment about the interaction
+- A list of quality criteria
+
+Score each criterion from 0 to 1:
+  - 1.0: Fully satisfied
+  - 0.5: Partially satisfied
+  - 0.0: Not satisfied
+
+Also provide an overall_quality score (0-1) evaluating the conversation flow,
+how well the AI understood the user, and the template quality.
+
+Respond with ONLY a JSON object:
+{
+  "criteria_scores": {"criterion_1": 0.8, ...},
+  "overall_quality": 0.85,
+  "reasoning": "Brief explanation covering both conversation quality and template accuracy"
+}
+"""
+
+
+async def _judge_interactive(
+    conversation: list[dict[str, str]],
+    prompt_template: str | None,
+    user_comment: str,
+    criteria: list[str],
+    *,
+    judge_model: str = "gpt-4o-mini",
+) -> tuple[dict[str, float], float, str]:
+    """Score an interactive session. Returns (criteria_scores, overall_quality, reasoning)."""
+    from shared.llm import get_llm
+
+    llm = get_llm(model=judge_model, temperature=0)
+
+    conv_text = "\n".join(
+        f"{'USER' if t['role'] == 'user' else 'AI'}: {t['content']}"
+        for t in conversation
+    )
+    criteria_text = "\n".join(f"  {i+1}. {c}" for i, c in enumerate(criteria))
+
+    user_content = (
+        f"## Conversation transcript\n```\n{conv_text}\n```\n\n"
+        f"## Generated prompt_template\n```\n{prompt_template or '(none — conversation did not complete)'}\n```\n\n"
+        f"## User's comment\n{user_comment}\n\n"
+        f"## Criteria to evaluate\n{criteria_text}"
+    )
+
+    try:
+        response = await llm.ainvoke([
+            SystemMessage(content=_INTERACTIVE_JUDGE_SYSTEM),
+            HumanMessage(content=user_content),
+        ])
+        raw = response.content.strip()
+        if raw.startswith("```"):
+            raw = raw.split("```")[1]
+            if raw.startswith("json"):
+                raw = raw[4:]
+        parsed = json.loads(raw.strip())
+
+        scores_raw = parsed.get("criteria_scores", parsed.get("scores", {}))
+        criteria_scores: dict[str, float] = {}
+        for i, criterion in enumerate(criteria):
+            key_candidates = [f"criterion_{i+1}", criterion, criterion[:50], str(i + 1)]
+            score = 0.0
+            for key in key_candidates:
+                if key in scores_raw:
+                    score = float(scores_raw[key])
+                    break
+            if score == 0.0 and i < len(scores_raw):
+                score = float(list(scores_raw.values())[i])
+            criteria_scores[criterion] = score
+
+        overall = float(parsed.get("overall_quality", 0.0))
+        reasoning = str(parsed.get("reasoning", ""))
+        return criteria_scores, overall, reasoning
+
+    except Exception as exc:
+        logger.warning("interactive judge failed: %s", exc)
+        return {c: 0.0 for c in criteria}, 0.0, f"Judge error: {exc}"
+
+
+# ── Interactive session ──────────────────────────────────────────────────
+
+
+async def run_interactive(
+    fixture: JourneyFixture,
+    *,
+    model: str = "gpt-4o",
+    judge_model: str = "gpt-4o-mini",
+    data_dir: Path | None = None,
+) -> InteractiveResult:
+    """Run an interactive journey session in the terminal.
+
+    Parameters
+    ----------
+    data_dir :
+        If set, overrides the fixture's sample-file directory.  The LLM
+        will explore this folder instead of the default
+        ``fixtures/sample_files/…``.  Useful for private test data that
+        shouldn't be committed to git.
+    """
+    from shared.config import settings
+    from shared.ws_context import set_current_user, clear_current_user
+    from app.journey import (
+        handle_journey_start,
+        handle_journey_message,
+        _build_system_prompt,
+    )
+
+    # When --data-dir is given, the MockExecutor's root becomes
+    # data_dir's parent and the journey directory is data_dir's name.
+    # This way the LLM sees a meaningful directory name (not ".") and
+    # MockExecutor resolves paths correctly.
+    # Otherwise, use the fixture's YAML parent and its relative path.
+    if data_dir:
+        mock_root = data_dir.parent
+        journey_directory = data_dir.name
+    else:
+        mock_root = fixture.fixture_path.parent
+        journey_directory = fixture.directory
+
+    mock = MockExecutor(
+        fixture_dir=mock_root,
+        seed_records={},
+    )
+
+    original_model = settings.LLM_MODEL
+    settings.LLM_MODEL = model
+    eval_user_id = f"interactive-{uuid.uuid4().hex[:8]}"
+
+    # ── Show system prompt ───────────────────────────────────────
+    system_prompt = _build_system_prompt(journey_directory, fixture.data_types)
+
+    _print_header("SYSTEM PROMPT")
+    print(f"{_C_DIM}{system_prompt}{_C_RESET}")
+
+    _print_header(f"INTERACTIVE JOURNEY  |  fixture: {fixture.name}  |  model: {model}")
+    print(f"  Data dir: {mock_root}")
+    print(f"  Type your responses. Commands: {_CMD_DONE}, {_CMD_QUIT}, {_CMD_TEMPLATE}, {_CMD_HELP}")
+    print(f"  Judge model: {judge_model}")
+    print(f"  Criteria: {len(fixture.expected_template_criteria)}")
+    print()
+
+    conversation: list[dict[str, str]] = []
+    prompt_template: str | None = None
+    done = False
+    start_time = time.time()
+
+    try:
+        set_current_user(eval_user_id)
+
+        with mock.patch():
+            # ── Start ────────────────────────────────────────────
+            _print_system("Starting journey... (AI is exploring your files)")
+
+            start_frame: dict[str, Any] = {
+                "agent_type": "local",
+                "directory": journey_directory,
+                "data_types": fixture.data_types,
+                "session_id": f"interactive-{uuid.uuid4().hex[:8]}",
+            }
+
+            reply = await handle_journey_start(eval_user_id, start_frame)
+            session_id = reply["session_id"]
+            conversation.append({"role": "assistant", "content": reply["message"]})
+            _print_ai(reply["message"])
+
+            if reply["done"]:
+                prompt_template = reply.get("prompt_template")
+                done = True
+                _print_system("Journey completed on first reply (template generated).")
+
+            # ── Conversation loop ────────────────────────────────
+            while not done:
+                try:
+                    user_input = input(f"{_C_BOLD}{_C_BLUE}YOU:{_C_RESET} ").strip()
+                except (EOFError, KeyboardInterrupt):
+                    print()
+                    user_input = _CMD_QUIT
+
+                if not user_input:
+                    continue
+
+                # Handle commands
+                if user_input.lower() == _CMD_QUIT:
+                    _print_system("Aborted — no evaluation will be performed.")
+                    settings.LLM_MODEL = original_model
+                    clear_current_user()
+                    return InteractiveResult(
+                        fixture_name=fixture.name, model=model, judge_model=judge_model,
+                        prompt_template=None, conversation=conversation,
+                        user_comment="(aborted)", done=False,
+                        criteria_scores={}, overall_score=0.0,
+                        judge_reasoning="Session aborted by user.",
+                        elapsed_seconds=time.time() - start_time,
+                    )
+
+                if user_input.lower() == _CMD_HELP:
+                    print(_HELP_TEXT)
+                    continue
+
+                if user_input.lower() == _CMD_TEMPLATE:
+                    if prompt_template:
+                        print(f"\n{_C_MAGENTA}{prompt_template}{_C_RESET}\n")
+                    else:
+                        _print_system("No template generated yet.")
+                    continue
+
+                if user_input.lower() == _CMD_DONE:
+                    _print_system("Ending conversation...")
+                    break
+
+                # ── Send message to AI ───────────────────────────
+                conversation.append({"role": "user", "content": user_input})
+                _print_system("AI is thinking...")
+
+                msg_frame: dict[str, Any] = {
+                    "session_id": session_id,
+                    "message": user_input,
+                }
+                reply = await handle_journey_message(eval_user_id, msg_frame)
+                conversation.append({"role": "assistant", "content": reply["message"]})
+                _print_ai(reply["message"])
+
+                if reply["done"]:
+                    prompt_template = reply.get("prompt_template")
+                    done = True
+                    _print_system("Journey completed — template generated!")
+
+    except Exception as exc:
+        logger.error("interactive journey failed: %s", exc)
+        _print_system(f"Error: {exc}")
+    finally:
+        settings.LLM_MODEL = original_model
+        clear_current_user()
+
+    elapsed = time.time() - start_time
+    turns = len([c for c in conversation if c["role"] == "user"])
+
+    # ── Show template if generated ───────────────────────────────
+    if prompt_template:
+        _print_header("GENERATED TEMPLATE")
+        print(f"{_C_MAGENTA}{prompt_template}{_C_RESET}\n")
+    else:
+        _print_system("No template was generated during this session.")
+
+    # ── User comment ─────────────────────────────────────────────
+    _print_header("YOUR EVALUATION")
+    print("  Write your comment about this interaction (press Enter twice to finish):")
+    print()
+    comment_lines: list[str] = []
+    try:
+        while True:
+            line = input()
+            if line == "" and comment_lines and comment_lines[-1] == "":
+                comment_lines.pop()  # remove trailing empty
+                break
+            comment_lines.append(line)
+    except (EOFError, KeyboardInterrupt):
+        pass
+    user_comment = "\n".join(comment_lines).strip() or "(no comment)"
+
+    # ── Judge ────────────────────────────────────────────────────
+    _print_header("LLM JUDGE EVALUATION")
+    _print_system(f"Scoring with {judge_model}...")
+
+    criteria_scores, overall_quality, judge_reasoning = await _judge_interactive(
+        conversation=conversation,
+        prompt_template=prompt_template,
+        user_comment=user_comment,
+        criteria=fixture.expected_template_criteria,
+        judge_model=judge_model,
+    )
+
+    # ── Display scores ───────────────────────────────────────────
+    print()
+    for criterion, score in criteria_scores.items():
+        _print_score(criterion, score)
+
+    overall = (
+        sum(criteria_scores.values()) / len(criteria_scores)
+        if criteria_scores
+        else 0.0
+    )
+
+    print(f"\n  {_C_BOLD}Criteria avg:      {overall:.2f}{_C_RESET}")
+    print(f"  {_C_BOLD}Overall quality:   {overall_quality:.2f}{_C_RESET}")
+    print(f"  {_C_BOLD}Turns:             {turns}{_C_RESET}")
+    print(f"  {_C_BOLD}Time:              {elapsed:.1f}s{_C_RESET}")
+    print(f"\n  {_C_DIM}Judge: {judge_reasoning}{_C_RESET}")
+    print(f"  {_C_DIM}Your comment: {user_comment}{_C_RESET}\n")
+
+    result = InteractiveResult(
+        fixture_name=fixture.name,
+        model=model,
+        judge_model=judge_model,
+        prompt_template=prompt_template,
+        conversation=conversation,
+        user_comment=user_comment,
+        done=done,
+        criteria_scores=criteria_scores,
+        overall_score=overall_quality,
+        judge_reasoning=judge_reasoning,
+        elapsed_seconds=elapsed,
+    )
+
+    # ── Report to Langfuse ───────────────────────────────────────
+    trace_id = langfuse_eval.log_eval_trace(
+        fixture_name=fixture.name,
+        model=model,
+        prompt_variant="interactive",
+        prompt_template=prompt_template or "(not generated)",
+        actual_mutations=[{
+            "conversation": conversation[:30],
+            "user_comment": user_comment,
+        }],
+        scores_summary=result.summary(),
+        langfuse_prompt_names=["journey_system"],
+    )
+
+    if trace_id:
+        from eval.scorer import EvalScores
+        scores_obj = EvalScores(
+            fixture_name=fixture.name,
+            model=model,
+            prompt_variant="interactive",
+            precision=overall,
+            recall=float(done),
+            f1=overall,
+            llm_judge_score=overall_quality,
+            llm_judge_reasoning=judge_reasoning,
+        )
+        langfuse_eval.post_eval_scores(scores_obj, trace_id=trace_id)
+        _print_system(f"Results reported to Langfuse (trace: {trace_id})")
+    else:
+        _print_system("Langfuse not configured — results not reported.")
+
+    return result

services/batch-agent/eval/journey_runner.py

@@ -0,0 +1,385 @@
+"""Journey eval runner — tests the prompt_template builder conversation.
+
+For each (journey_fixture × model) combination:
+1. Build a MockExecutor (for filesystem tools used during journey)
+2. Patch execute_on_client
+3. Override LLM_MODEL
+4. Call handle_journey_start to kick off the conversation
+5. Feed simulated user_messages via handle_journey_message
+6. Collect the generated prompt_template
+7. Score it against expected_template_criteria (via LLM judge)
+8. Report to Langfuse
+"""
+
+from __future__ import annotations
+
+import asyncio
+import copy
+import json
+import logging
+import time
+import uuid
+from dataclasses import dataclass, field
+from pathlib import Path
+from typing import Any
+
+from langchain_core.messages import HumanMessage, SystemMessage
+
+from eval.config import JourneyFixture
+from eval.mock_executor import MockExecutor
+from eval import langfuse_eval
+
+logger = logging.getLogger(__name__)
+
+
+# ── Result type ──────────────────────────────────────────────────────────
+
+
+@dataclass
+class JourneyEvalResult:
+    """Result of one journey eval run."""
+
+    fixture_name: str
+    model: str
+    prompt_template: str | None  # the generated template (None if journey failed)
+    conversation_turns: int
+    done: bool  # whether journey reached completion
+    criteria_scores: dict[str, float]  # criterion → 0-1 score
+    overall_score: float  # average of criteria scores
+    judge_reasoning: str
+    elapsed_seconds: float
+
+    def summary(self) -> dict[str, Any]:
+        return {
+            "fixture": self.fixture_name,
+            "model": self.model,
+            "done": self.done,
+            "turns": self.conversation_turns,
+            "overall_score": round(self.overall_score, 3),
+            "criteria_scores": {k: round(v, 3) for k, v in self.criteria_scores.items()},
+            "elapsed_s": round(self.elapsed_seconds, 1),
+        }
+
+
+# ── LLM judge for template quality ──────────────────────────────────────
+
+_JOURNEY_JUDGE_SYSTEM = """\
+You are an evaluation judge for AI-generated prompt templates.
+
+A journey chatbot explored a user's directory structure and through
+conversation produced a prompt_template — an instruction set for a
+data-extraction agent.
+
+Your task: evaluate the generated template against a list of criteria.
+Score each criterion from 0 to 1:
+  - 1.0: Fully satisfied, clearly present in the template
+  - 0.5: Partially satisfied or ambiguously addressed
+  - 0.0: Not satisfied, missing from the template
+
+Respond with ONLY a JSON object:
+{
+  "scores": {"criterion_1": 0.8, "criterion_2": 1.0, ...},
+  "reasoning": "Brief explanation"
+}
+"""
+
+
+async def _judge_template(
+    prompt_template: str,
+    criteria: list[str],
+    *,
+    judge_model: str = "gpt-4o-mini",
+) -> tuple[dict[str, float], str]:
+    """Use an LLM to evaluate a generated prompt_template against criteria.
+
+    Returns (criteria_scores, reasoning).
+    """
+    from shared.llm import get_llm
+
+    llm = get_llm(model=judge_model, temperature=0)
+
+    criteria_text = "\n".join(f"  {i+1}. {c}" for i, c in enumerate(criteria))
+    user_content = (
+        f"## Generated prompt_template\n```\n{prompt_template}\n```\n\n"
+        f"## Criteria to evaluate\n{criteria_text}"
+    )
+
+    try:
+        response = await llm.ainvoke([
+            SystemMessage(content=_JOURNEY_JUDGE_SYSTEM),
+            HumanMessage(content=user_content),
+        ])
+        raw = response.content.strip()
+        if raw.startswith("```"):
+            raw = raw.split("```")[1]
+            if raw.startswith("json"):
+                raw = raw[4:]
+        parsed = json.loads(raw.strip())
+
+        scores_raw = parsed.get("scores", {})
+        # Map criterion keys back to the original criteria text
+        criteria_scores: dict[str, float] = {}
+        for i, criterion in enumerate(criteria):
+            # Try matching by index key or exact criterion text
+            key_candidates = [
+                f"criterion_{i+1}",
+                criterion,
+                criterion[:50],
+                str(i + 1),
+            ]
+            score = 0.0
+            for key in key_candidates:
+                if key in scores_raw:
+                    score = float(scores_raw[key])
+                    break
+            # If no match found, try values in order
+            if score == 0.0 and i < len(scores_raw):
+                score = float(list(scores_raw.values())[i])
+            criteria_scores[criterion] = score
+
+        reasoning = str(parsed.get("reasoning", ""))
+        return criteria_scores, reasoning
+    except Exception as exc:
+        logger.warning("journey_eval: LLM judge failed: %s", exc)
+        return {c: 0.0 for c in criteria}, f"Judge error: {exc}"
+
+
+# ── Journey runner ───────────────────────────────────────────────────────
+
+
+async def run_single_journey_eval(
+    fixture: JourneyFixture,
+    model: str,
+    *,
+    judge_model: str = "gpt-4o-mini",
+    data_dir: Path | None = None,
+) -> JourneyEvalResult:
+    """Execute one journey eval: start \u2192 messages \u2192 score template."""
+    from shared.config import settings
+
+    # When data_dir is given, use its parent as MockExecutor root
+    # and its name as the journey directory so the LLM sees a
+    # meaningful path (not ".").
+    if data_dir:
+        mock_root = data_dir.parent
+        journey_directory = data_dir.name
+    else:
+        mock_root = fixture.fixture_path.parent
+        journey_directory = fixture.directory
+
+    mock = MockExecutor(
+        fixture_dir=mock_root,
+        seed_records={},
+    )
+
+    original_model = settings.LLM_MODEL
+    settings.LLM_MODEL = model
+
+    eval_user_id = f"eval-journey-{uuid.uuid4().hex[:8]}"
+
+    logger.info(
+        "journey_eval: starting %s | model=%s",
+        fixture.name, model,
+    )
+    start_time = time.time()
+
+    prompt_template: str | None = None
+    conversation: list[dict[str, str]] = []
+    done = False
+
+    try:
+        from shared.ws_context import set_current_user, clear_current_user
+        from app.journey import handle_journey_start, handle_journey_message, _sessions
+
+        set_current_user(eval_user_id)
+        with mock.patch():
+            # ── Start the journey ────────────────────────────────
+            start_frame: dict[str, Any] = {
+                "agent_type": "local",
+                "directory": journey_directory,
+                "data_types": fixture.data_types,
+                "session_id": f"eval-{uuid.uuid4().hex[:8]}",
+            }
+
+            reply = await handle_journey_start(eval_user_id, start_frame)
+            session_id = reply["session_id"]
+            conversation.append({"role": "assistant", "content": reply["message"]})
+
+            logger.info(
+                "journey_eval: start reply (%d chars), done=%s",
+                len(reply["message"]), reply["done"],
+            )
+
+            if reply["done"]:
+                prompt_template = reply.get("prompt_template")
+                done = True
+            else:
+                # ── Send user messages ───────────────────────────
+                for i, user_msg in enumerate(fixture.user_messages):
+                    if done:
+                        break
+
+                    conversation.append({"role": "user", "content": user_msg})
+
+                    msg_frame: dict[str, Any] = {
+                        "session_id": session_id,
+                        "message": user_msg,
+                    }
+                    reply = await handle_journey_message(eval_user_id, msg_frame)
+                    conversation.append({"role": "assistant", "content": reply["message"]})
+
+                    logger.info(
+                        "journey_eval: turn %d reply (%d chars), done=%s",
+                        i + 1, len(reply["message"]), reply["done"],
+                    )
+
+                    if reply["done"]:
+                        prompt_template = reply.get("prompt_template")
+                        done = True
+
+                # If not done after all user messages, send a final nudge
+                if not done:
+                    nudge = "Please generate the final prompt_template now. I'm satisfied with the configuration."
+                    conversation.append({"role": "user", "content": nudge})
+
+                    nudge_frame: dict[str, Any] = {
+                        "session_id": session_id,
+                        "message": nudge,
+                    }
+                    reply = await handle_journey_message(eval_user_id, nudge_frame)
+                    conversation.append({"role": "assistant", "content": reply["message"]})
+                    if reply["done"]:
+                        prompt_template = reply.get("prompt_template")
+                        done = True
+
+    except Exception as exc:
+        logger.error("journey_eval: pipeline failed for %s/%s: %s", fixture.name, model, exc)
+    finally:
+        settings.LLM_MODEL = original_model
+        from shared.ws_context import clear_current_user
+        clear_current_user()
+
+    elapsed = time.time() - start_time
+    turns = len([c for c in conversation if c["role"] == "user"])
+
+    logger.info(
+        "journey_eval: completed in %.1fs — %d turns, done=%s, template=%s",
+        elapsed, turns, done, "yes" if prompt_template else "no",
+    )
+
+    # ── Score the template ───────────────────────────────────────
+    criteria_scores: dict[str, float] = {}
+    judge_reasoning = ""
+
+    if prompt_template and fixture.expected_template_criteria:
+        criteria_scores, judge_reasoning = await _judge_template(
+            prompt_template,
+            fixture.expected_template_criteria,
+            judge_model=judge_model,
+        )
+    elif not prompt_template:
+        criteria_scores = {c: 0.0 for c in fixture.expected_template_criteria}
+        judge_reasoning = "No prompt_template was generated — journey did not complete."
+
+    overall = (
+        sum(criteria_scores.values()) / len(criteria_scores)
+        if criteria_scores
+        else 0.0
+    )
+
+    result = JourneyEvalResult(
+        fixture_name=fixture.name,
+        model=model,
+        prompt_template=prompt_template,
+        conversation_turns=turns,
+        done=done,
+        criteria_scores=criteria_scores,
+        overall_score=overall,
+        judge_reasoning=judge_reasoning,
+        elapsed_seconds=elapsed,
+    )
+
+    # ── Report to Langfuse ───────────────────────────────────────
+    trace_id = langfuse_eval.log_eval_trace(
+        fixture_name=fixture.name,
+        model=model,
+        prompt_variant="journey",
+        prompt_template=prompt_template or "(not generated)",
+        actual_mutations=[{"conversation": conversation[:20]}],
+        scores_summary=result.summary(),
+        langfuse_prompt_names=["journey_system"],
+    )
+
+    if trace_id:
+        from eval.scorer import EvalScores
+        scores_obj = EvalScores(
+            fixture_name=fixture.name,
+            model=model,
+            prompt_variant="journey",
+            precision=overall,
+            recall=float(done),
+            f1=overall,
+            llm_judge_score=overall,
+            llm_judge_reasoning=judge_reasoning,
+        )
+        langfuse_eval.post_eval_scores(scores_obj, trace_id=trace_id)
+
+    return result
+
+
+async def run_journey_fixture_eval(
+    fixture: JourneyFixture,
+    models: list[str],
+    *,
+    judge_model: str = "gpt-4o-mini",
+    data_dir: Path | None = None,
+) -> list[JourneyEvalResult]:
+    """Run all models for a journey fixture."""
+    langfuse_eval.sync_journey_fixture_to_dataset(fixture)
+
+    results: list[JourneyEvalResult] = []
+    for model in models:
+        result = await run_single_journey_eval(
+            fixture, model, judge_model=judge_model,
+            data_dir=data_dir,
+        )
+        results.append(result)
+
+    return results
+
+
+def print_journey_results(results: list[JourneyEvalResult]) -> None:
+    """Print a formatted summary of journey eval results."""
+    if not results:
+        print("\nNo journey eval results.")
+        return
+
+    print("\n" + "=" * 95)
+    print(f"{'Fixture':<25} {'Model':<25} {'Done':>5} {'Turns':>6} {'Score':>7} {'Time':>7}")
+    print("-" * 95)
+
+    for r in results:
+        done_str = "yes" if r.done else "NO"
+        print(
+            f"{r.fixture_name:<25} {r.model:<25} {done_str:>5} "
+            f"{r.conversation_turns:>6} {r.overall_score:>7.2f} {r.elapsed_seconds:>6.1f}s"
+        )
+
+    print("=" * 95)
+
+    # Criteria breakdown
+    for r in results:
+        if r.criteria_scores:
+            print(f"\n[{r.model}] Criteria scores:")
+            for criterion, score in r.criteria_scores.items():
+                indicator = "PASS" if score >= 0.7 else "PARTIAL" if score >= 0.4 else "FAIL"
+                print(f"  {indicator:>7} ({score:.1f}) {criterion}")
+
+        if r.judge_reasoning:
+            print(f"  Judge: {r.judge_reasoning}")
+
+        if r.prompt_template:
+            preview = r.prompt_template[:200].replace("\n", " ")
+            print(f"  Template preview: {preview}...")
+
+    print()

services/batch-agent/eval/langfuse_eval.py

@@ -0,0 +1,327 @@
+"""Langfuse evaluation integration — datasets, runs, and scoring.
+
+Uses the Langfuse Python SDK v4 (OpenTelemetry-based) to:
+
+1. **Sync fixtures → Langfuse datasets**: Each YAML fixture becomes a dataset,
+   each prompt variant + expected pair becomes a dataset item.
+
+2. **Track eval runs**: Each (fixture × model × prompt_variant) execution
+   is recorded as a trace with linked scores.
+
+3. **Post scores**: precision, recall, F1, field_accuracy, llm_judge are
+   posted as numeric scores on the trace.
+"""
+
+from __future__ import annotations
+
+import logging
+import os
+from typing import Any
+
+from shared.config import settings
+from eval.config import EvalFixture
+from eval.scorer import EvalScores
+
+logger = logging.getLogger(__name__)
+
+
+def _get_langfuse():
+    """Get or create a Langfuse client instance (SDK v4)."""
+    if not settings.LANGFUSE_SECRET_KEY or not settings.LANGFUSE_PUBLIC_KEY:
+        return None
+    try:
+        os.environ.setdefault("LANGFUSE_SECRET_KEY", settings.LANGFUSE_SECRET_KEY)
+        os.environ.setdefault("LANGFUSE_PUBLIC_KEY", settings.LANGFUSE_PUBLIC_KEY)
+        if settings.LANGFUSE_HOST:
+            os.environ.setdefault("LANGFUSE_HOST", settings.LANGFUSE_HOST)
+        from langfuse import get_client
+        return get_client()
+    except Exception as exc:
+        logger.warning("langfuse_eval: failed to create client: %s", exc)
+        return None
+
+
+def sync_fixture_to_dataset(fixture: EvalFixture) -> str | None:
+    """Create or update a Langfuse dataset from a fixture.
+
+    Each prompt variant becomes a separate dataset item with:
+    - input: {directory, data_types, prompt_template, seed_records}
+    - expected_output: {expected records}
+
+    Returns the dataset name, or None if Langfuse is unavailable.
+    """
+    lf = _get_langfuse()
+    if lf is None:
+        logger.info("langfuse_eval: Langfuse not configured — skipping dataset sync")
+        return None
+
+    dataset_name = f"batch-eval-{fixture.name}"
+
+    try:
+        lf.create_dataset(
+            name=dataset_name,
+            description=fixture.description,
+            metadata={
+                "data_types": ",".join(fixture.data_types),
+                "file_extensions": ",".join(fixture.file_extensions) if fixture.file_extensions else "",
+            },
+        )
+    except Exception:
+        # Dataset may already exist — that's fine
+        pass
+
+    # Build expected_output appropriate to the fixture's mode
+    expected_output: dict[str, Any] = {}
+    if fixture.mode in ("step1", "full") and fixture.expected_classification:
+        expected_output["classifications"] = [
+            {"file": ec.file, "project_id": ec.project_id, "domains": ec.domains}
+            for ec in fixture.expected_classification
+        ]
+    if fixture.mode in ("step2", "full") and fixture.expected:
+        for rec in fixture.expected:
+            expected_output.setdefault(rec.table, []).append(rec.fields)
+
+    item_id = f"{fixture.name}--{fixture.mode}"
+    try:
+        lf.create_dataset_item(
+            dataset_name=dataset_name,
+            id=item_id,
+            input={
+                "directory": fixture.directory,
+                "data_types": fixture.data_types,
+                "mode": fixture.mode,
+                "seed_records": fixture.seed_records,
+            },
+            expected_output=expected_output,
+            metadata={"mode": fixture.mode},
+        )
+    except Exception as exc:
+        logger.warning(
+            "langfuse_eval: failed to upsert dataset item %s: %s", item_id, exc
+        )
+
+    lf.flush()
+    logger.info("langfuse_eval: synced fixture '%s' → dataset '%s'", fixture.name, dataset_name)
+    return dataset_name
+
+
+def sync_journey_fixture_to_dataset(fixture) -> str | None:
+    """Create or update a Langfuse dataset from a journey fixture.
+
+    Each journey fixture becomes a single dataset item with:
+    - input: {directory, data_types, user_messages}
+    - expected_output: {criteria}
+    """
+    lf = _get_langfuse()
+    if lf is None:
+        logger.info("langfuse_eval: Langfuse not configured — skipping journey dataset sync")
+        return None
+
+    dataset_name = f"journey-eval-{fixture.name}"
+
+    try:
+        lf.create_dataset(
+            name=dataset_name,
+            description=fixture.description,
+            metadata={"type": "journey", "data_types": ",".join(fixture.data_types)},
+        )
+    except Exception:
+        pass  # Dataset may already exist
+
+    item_id = f"{fixture.name}--journey"
+    try:
+        lf.create_dataset_item(
+            dataset_name=dataset_name,
+            id=item_id,
+            input={
+                "directory": fixture.directory,
+                "data_types": fixture.data_types,
+                "user_messages": fixture.user_messages,
+            },
+            expected_output={
+                "criteria": fixture.expected_template_criteria,
+            },
+            metadata={"type": "journey"},
+        )
+    except Exception as exc:
+        logger.warning("langfuse_eval: failed to upsert journey dataset item %s: %s", item_id, exc)
+
+    lf.flush()
+    logger.info("langfuse_eval: synced journey fixture '%s' → dataset '%s'", fixture.name, dataset_name)
+    return dataset_name
+
+
+def create_eval_run(
+    dataset_name: str,
+    run_name: str,
+    *,
+    metadata: dict[str, Any] | None = None,
+) -> str:
+    """Create a dataset run in Langfuse. Returns the run name.
+
+    Note: In SDK v4, dataset runs are created implicitly via
+    dataset.run_experiment(). This function is kept for backwards
+    compatibility but may not create a run.
+    """
+    lf = _get_langfuse()
+    if lf is None:
+        return run_name
+
+    try:
+        if hasattr(lf, "create_dataset_run"):
+            lf.create_dataset_run(
+                dataset_name=dataset_name,
+                run_name=run_name,
+                metadata=metadata or {},
+            )
+            lf.flush()
+        else:
+            logger.debug("langfuse_eval: create_dataset_run not available in SDK v4")
+    except Exception as exc:
+        logger.warning("langfuse_eval: failed to create run %s: %s", run_name, exc)
+
+    return run_name
+
+
+def post_eval_scores(
+    scores: EvalScores,
+    *,
+    trace_id: str | None = None,
+    dataset_name: str | None = None,
+    run_name: str | None = None,
+) -> None:
+    """Post evaluation scores to Langfuse.
+
+    If trace_id is provided, scores are attached to that trace.
+    """
+    lf = _get_langfuse()
+    if lf is None:
+        return
+
+    score_data = [
+        ("precision", scores.precision),
+        ("recall", scores.recall),
+        ("f1", scores.f1),
+    ]
+    # Only post field_accuracy when there are field-level scores (step2/full)
+    if scores.field_scores:
+        score_data.append(("field_accuracy", scores.field_accuracy))
+    if scores.llm_judge_score is not None:
+        score_data.append(("llm_judge", scores.llm_judge_score))
+
+    for name, value in score_data:
+        try:
+            lf.create_score(
+                name=name,
+                value=value,
+                trace_id=trace_id,
+                data_type="NUMERIC",
+                comment=f"{scores.fixture_name} | {scores.model} | {scores.prompt_variant}",
+            )
+        except Exception as exc:
+            logger.warning("langfuse_eval: failed to post score %s: %s", name, exc)
+
+    lf.flush()
+    logger.info(
+        "langfuse_eval: posted %d scores for %s/%s/%s",
+        len(score_data), scores.fixture_name, scores.model, scores.prompt_variant,
+    )
+
+
+def log_eval_trace(
+    *,
+    fixture_name: str,
+    model: str,
+    prompt_variant: str,
+    prompt_template: str,
+    actual_mutations: list[dict],
+    scores_summary: dict[str, Any],
+    step1_results: list[dict] | None = None,
+    dataset_name: str | None = None,
+    run_name: str | None = None,
+    dataset_item_id: str | None = None,
+    langfuse_prompt_names: list[str] | None = None,
+) -> str | None:
+    """Create a Langfuse trace for one eval execution and link it to a dataset run.
+
+    Uses SDK v4 observation API (traces are created implicitly by root spans).
+    ``langfuse_prompt_names`` can contain one or two prompt names to link
+    (e.g. ``["batch_file_classifier", "batch_processing"]`` for full mode).
+    Each prompt gets its own generation-type observation for per-version
+    metrics tracking.
+
+    Returns the trace_id, or None if Langfuse is unavailable.
+    """
+    lf = _get_langfuse()
+    if lf is None:
+        return None
+
+    try:
+        from langfuse import propagate_attributes
+
+        # Fetch prompt objects for linking
+        prompt_objs: list[tuple[str, Any]] = []
+        for pname in (langfuse_prompt_names or []):
+            try:
+                obj = lf.get_prompt(name=pname, cache_ttl_seconds=300)
+                prompt_objs.append((pname, obj))
+                logger.info("langfuse_eval: linked prompt '%s' (type=%s)", pname, type(obj).__name__)
+            except Exception as exc:
+                logger.warning("langfuse_eval: prompt '%s' not found — %s", pname, exc)
+
+        # Build trace output dict
+        trace_output: dict[str, Any] = {"scores": scores_summary}
+        if step1_results:
+            trace_output["classifications"] = step1_results
+        if actual_mutations:
+            trace_output["mutations"] = actual_mutations[:50]
+
+        with propagate_attributes(
+            trace_name=f"eval-{fixture_name}",
+            metadata={
+                "eval": "true",
+                "fixture": fixture_name,
+                "model": model,
+                "prompt_variant": prompt_variant,
+            },
+            tags=["eval", f"model:{model}", f"variant:{prompt_variant}"],
+        ):
+            # Root span for the eval run
+            span = lf.start_observation(name=f"eval-{fixture_name}")
+            span.update(
+                input={
+                    "prompt_template": prompt_template,
+                    "model": model,
+                    "prompt_variant": prompt_variant,
+                },
+                output=trace_output,
+            )
+            trace_id = span.trace_id
+
+            # Create a generation-type observation per linked prompt
+            for pname, pobj in prompt_objs:
+                gen = lf.start_observation(
+                    name=f"prompt-{pname}",
+                    prompt=pobj,
+                    as_type="generation",
+                )
+                gen.end()
+
+            # Link to dataset run if available
+            if dataset_name and run_name and dataset_item_id:
+                try:
+                    dataset = lf.get_dataset(dataset_name)
+                    for item in dataset.items:
+                        if item.id == dataset_item_id:
+                            item.link(span, run_name)
+                            break
+                except Exception as exc:
+                    logger.warning("langfuse_eval: failed to link trace to dataset run: %s", exc)
+
+            span.end()
+
+        lf.flush()
+        return trace_id
+    except Exception as exc:
+        logger.warning("langfuse_eval: failed to create eval trace: %s", exc)
+        return None

services/batch-agent/eval/mock_executor.py

@@ -0,0 +1,258 @@
+"""Mock executor — intercepts execute_on_client for offline E2E testing.
+
+Patches ``execute_on_client`` at all usage sites so agent pipeline runs don't
+require a live Electron client or Redis.  Instead:
+
+- **Filesystem actions** (list_directory, read_file_content, get_file_metadata)
+  are served from local fixture files on disk.
+- **Read actions** (select, get) return preseeded records from an in-memory
+  store provided by the test fixture.
+- **Write actions** (insert, update, delete) are captured as *mutations* and
+  stored for later comparison against expected results.
+"""
+
+from __future__ import annotations
+
+import json
+import os
+import time
+import uuid
+from dataclasses import dataclass, field
+from pathlib import Path
+from typing import Any
+from contextlib import contextmanager, asynccontextmanager
+from unittest.mock import AsyncMock, patch
+
+
+@dataclass
+class Mutation:
+    """A single recorded write operation."""
+
+    action: str  # insert | update | delete
+    table: str
+    data: dict[str, Any]
+    timestamp: float = field(default_factory=time.time)
+
+
+# ── Fake DB helpers (used to bypass async_session in full mode) ───────
+
+class _FakeRow:
+    """Mimics an AgentRunLog row returned by SQLAlchemy."""
+    id = 0
+    status = "running"
+    items_processed = 0
+    items_created = 0
+    errors: list[str] = []
+    completed_at = None
+
+    def __setattr__(self, name: str, value: Any) -> None:
+        object.__setattr__(self, name, value)
+
+
+class _FakeResult:
+    """Mimics a SQLAlchemy ``Result`` with ``scalar_one_or_none``."""
+    def __init__(self, row: _FakeRow) -> None:
+        self._row = row
+
+    def scalar_one_or_none(self) -> _FakeRow:
+        return self._row
+
+
+@dataclass
+class MockExecutor:
+    """In-memory executor that replaces Redis-based tool round-trip.
+
+    Parameters
+    ----------
+    fixture_dir : Path
+        Directory containing sample files for filesystem tool calls.
+    seed_records : dict[str, list[dict]]
+        Pre-existing records per table, e.g. ``{"tasks": [...], "projects": [...]}``.
+        The executor returns these for ``select`` / ``get`` actions and auto-updates
+        them on ``insert`` / ``update`` / ``delete`` so subsequent selects reflect changes.
+    """
+
+    fixture_dir: Path
+    seed_records: dict[str, list[dict]] = field(default_factory=dict)
+    mutations: list[Mutation] = field(default_factory=list)
+    _id_counter: int = field(default=1000, repr=False)
+
+    # ── Public API ───────────────────────────────────────────────────
+
+    def reset(self) -> None:
+        """Clear recorded mutations (keep seed_records intact)."""
+        self.mutations.clear()
+
+    def get_mutations(self, *, table: str | None = None, action: str | None = None) -> list[Mutation]:
+        """Filter mutations by table and/or action."""
+        result = self.mutations
+        if table:
+            result = [m for m in result if m.table == table]
+        if action:
+            result = [m for m in result if m.action == action]
+        return result
+
+    def created_records(self, table: str) -> list[dict]:
+        """Return data dicts of all inserts into *table*."""
+        return [m.data for m in self.mutations if m.table == table and m.action == "insert"]
+
+    def updated_records(self, table: str) -> list[dict]:
+        """Return data dicts of all updates to *table*."""
+        return [m.data for m in self.mutations if m.table == table and m.action == "update"]
+
+    # ── Context manager for patching ──────────────────────────────
+
+    @contextmanager
+    def patch(self):
+        """Patch execute_on_client and DB session at all usage sites."""
+        mock_fn = AsyncMock(side_effect=self._handle)
+        targets = [
+            "shared.ws_context.execute_on_client",
+            "app.agent_runner.execute_on_client",
+            "app.agents.filesystem_agent.execute_on_client",
+        ]
+
+        # Mock async_session so run_local_agent / _finalize_run skip real DB
+        fake_row = _FakeRow()
+        fake_db = AsyncMock()
+        fake_db.commit = AsyncMock()
+        fake_db.refresh = AsyncMock()
+        fake_db.execute = AsyncMock(return_value=_FakeResult(fake_row))
+        fake_db.add = lambda obj: None  # noqa: ARG005
+
+        @asynccontextmanager
+        async def _fake_session():
+            yield fake_db
+
+        patches = [patch(t, new=mock_fn) for t in targets]
+        patches.append(patch("app.agent_runner.async_session", _fake_session))
+        for p in patches:
+            p.start()
+        try:
+            yield mock_fn
+        finally:
+            for p in patches:
+                p.stop()
+
+    # ── Internal dispatch ─────────────────────────────────────────
+
+    async def _handle(
+        self,
+        action: str,
+        table: str | None = None,
+        data: dict[str, Any] | None = None,
+        filters: dict[str, Any] | None = None,
+        vector: list[float] | None = None,
+        limit: int | None = None,
+    ) -> dict[str, Any]:
+        # Filesystem
+        if action == "list_directory":
+            return self._list_directory(data or {})
+        if action == "read_file_content":
+            return self._read_file(data or {})
+        if action == "get_file_metadata":
+            return self._get_file_metadata(data or {})
+
+        # CRUD
+        if action == "select":
+            return self._select(table or "", filters)
+        if action == "get":
+            return self._get(table or "", data or {})
+        if action == "insert":
+            return self._insert(table or "", data or {})
+        if action == "update":
+            return self._update(table or "", data or {})
+        if action == "delete":
+            return self._delete(table or "", data or {})
+
+        # Vector (no-op for eval)
+        if action in ("vector_upsert", "vector_search"):
+            return {"rows": []}
+
+        return {"error": f"Unknown action: {action}"}
+
+    # ── Filesystem handlers ───────────────────────────────────────
+
+    def _list_directory(self, data: dict) -> dict:
+        rel_path = data.get("path", "")
+        abs_path = self.fixture_dir / rel_path.lstrip("/\\")
+        if not abs_path.is_dir():
+            return {"entries": []}
+        entries: list[dict] = []
+        for child in sorted(abs_path.iterdir()):
+            entry_type = "directory" if child.is_dir() else "file"
+            # Return paths relative to fixture_dir but with the original prefix
+            entry_path = rel_path.rstrip("/\\") + "/" + child.name
+            entries.append({
+                "name": child.name,
+                "path": entry_path,
+                "type": entry_type,
+            })
+        return {"entries": entries}
+
+    def _read_file(self, data: dict) -> dict:
+        rel_path = data.get("path", "")
+        abs_path = self.fixture_dir / rel_path.lstrip("/\\")
+        if not abs_path.is_file():
+            return {"content": "", "error": f"File not found: {rel_path}"}
+        return {"content": abs_path.read_text(encoding="utf-8", errors="replace")}
+
+    def _get_file_metadata(self, data: dict) -> dict:
+        rel_path = data.get("path", "")
+        abs_path = self.fixture_dir / rel_path.lstrip("/\\")
+        if not abs_path.exists():
+            return {"error": f"Not found: {rel_path}"}
+        stat = abs_path.stat()
+        return {
+            "path": rel_path,
+            "size": stat.st_size,
+            "modifiedAt": int(stat.st_mtime * 1000),
+            "createdAt": int(stat.st_ctime * 1000),
+            "isDirectory": abs_path.is_dir(),
+        }
+
+    # ── CRUD handlers ─────────────────────────────────────────────
+
+    def _select(self, table: str, filters: dict | None) -> dict:
+        rows = list(self.seed_records.get(table, []))
+        if filters:
+            rows = [
+                r for r in rows
+                if all(r.get(k) == v for k, v in filters.items() if v is not None)
+            ]
+        return {"rows": rows}
+
+    def _get(self, table: str, data: dict) -> dict:
+        record_id = data.get("id", "")
+        rows = self.seed_records.get(table, [])
+        for r in rows:
+            if r.get("id") == record_id:
+                return {"row": r}
+        return {"row": None}
+
+    def _insert(self, table: str, data: dict) -> dict:
+        self._id_counter += 1
+        record = {**data, "id": str(self._id_counter)}
+        # Add to seed so subsequent selects can find it
+        self.seed_records.setdefault(table, []).append(record)
+        self.mutations.append(Mutation(action="insert", table=table, data=record))
+        return {"row": record}
+
+    def _update(self, table: str, data: dict) -> dict:
+        record_id = data.get("id", "")
+        rows = self.seed_records.get(table, [])
+        for r in rows:
+            if r.get("id") == record_id:
+                r.update({k: v for k, v in data.items() if v is not None and v != ""})
+                self.mutations.append(Mutation(action="update", table=table, data=dict(r)))
+                return {"row": r}
+        # Record not found — still log the mutation
+        self.mutations.append(Mutation(action="update", table=table, data=data))
+        return {"row": data}
+
+    def _delete(self, table: str, data: dict) -> dict:
+        record_id = data.get("id", "")
+        rows = self.seed_records.get(table, [])
+        self.seed_records[table] = [r for r in rows if r.get("id") != record_id]
+        self.mutations.append(Mutation(action="delete", table=table, data={"id": record_id}))
+        return {"deleted": True}

services/batch-agent/eval/requirements.txt

@@ -0,0 +1,2 @@
+# Extra dependencies for the eval harness (on top of the service requirements.txt)
+pyyaml>=6.0.0

services/batch-agent/eval/runner.py

@@ -0,0 +1,545 @@
+"""Eval runner — orchestrates fixture → mock → agent pipeline → scoring.
+
+Supports three eval modes:
+
+- **step1**: Test classification prompt only (``_STEP1_SYSTEM_PROMPT``).
+  Calls the LLM with fixture-provided ``domain_definitions`` and
+  ``projects_list`` and compares output against ``expected_classification``.
+
+- **step2**: Test processing prompt only (``_PROCESSING_SYSTEM_PROMPT``).
+  Compiles the prompt with fixture-provided ``existing_context``,
+  ``project_context``, ``data_types``, and ``custom_prompt_section``,
+  then runs the tool-calling loop.  Mutations are scored against
+  ``expected`` records.
+
+- **full**: Run ``run_local_agent()`` end-to-end (both steps).
+  Scored on both classification and extraction.
+"""
+
+from __future__ import annotations
+
+import copy
+import json
+import logging
+import time
+import uuid
+from typing import Any
+
+from eval.config import EvalFixture, ExpectedClassification
+from eval.mock_executor import MockExecutor
+from eval.scorer import (
+    EvalScores,
+    FieldScore,
+    compute_precision_recall,
+    llm_judge_score,
+    score_field_match,
+)
+from eval import langfuse_eval
+
+logger = logging.getLogger(__name__)
+
+
+# ── Step 1 runner ─────────────────────────────────────────────────────────
+
+
+async def _run_step1(
+    fixture: EvalFixture,
+    model: str,
+    mock: MockExecutor,
+) -> list[dict[str, Any]]:
+    """Run step-1 classification for every file in the fixture directory.
+
+    Scans the directory recursively, classifies each file, and returns
+    a list of result dicts:
+    ``[{file, project_id, domains, new_project_name}, ...]``
+    """
+    from app.agent_runner import _classify_file
+
+    # Build project name lookup for display
+    proj_names: dict[str, str] = {
+        p.get("id", ""): p.get("name", "") for p in fixture.projects_list
+    }
+
+    # Discover all files in the fixture directory
+    all_files = await _scan_fixture_files(mock, fixture.directory)
+    print(f"\n  Scanning {len(all_files)} files in {fixture.directory}\n")
+
+    results: list[dict[str, Any]] = []
+    for i, file_path in enumerate(all_files, 1):
+        file_result = await mock._handle(
+            action="read_file_content",
+            data={"path": file_path},
+        )
+        file_content: str = file_result.get("content", "")
+        if not file_content.strip():
+            continue
+
+        project_id, domains, new_name = await _classify_file(
+            file_path=file_path,
+            file_content=file_content,
+            projects=fixture.projects_list,
+            config_data_types=fixture.data_types,
+            custom_system_prompt=fixture.custom_step1_prompt or None,
+        )
+
+        short_name = file_path.rsplit("/", 1)[-1] if "/" in file_path else file_path
+        proj_label = proj_names.get(project_id, new_name or "?")
+        print(f"  [{i}/{len(all_files)}] {short_name}  →  {project_id} ({proj_label})  {domains}")
+
+        results.append({
+            "file": file_path,
+            "project_id": project_id,
+            "domains": domains,
+            "new_project_name": new_name,
+        })
+    return results
+
+
+async def _scan_fixture_files(mock: MockExecutor, directory: str) -> list[str]:
+    """Recursively list all files under *directory* via the mock executor."""
+    files: list[str] = []
+
+    async def _walk(path: str) -> None:
+        result = await mock._handle(action="list_directory", data={"path": path})
+        for entry in result.get("entries", []):
+            if entry.get("type") == "directory":
+                await _walk(entry["path"])
+            elif entry.get("type") == "file":
+                files.append(entry["path"])
+
+    await _walk(directory)
+    return sorted(files)
+
+
+def _score_step1(
+    fixture: EvalFixture,
+    results: list[dict[str, Any]],
+) -> tuple[float, float, float, str]:
+    """Score step-1 results. Returns (precision, recall, f1, reasoning).
+
+    Files with expected classifications are scored (OK/FAIL).
+    Files without expectations are shown as informational (INFO).
+    """
+    if not fixture.expected_classification:
+        return 0.0, 0.0, 0.0, "No expected classifications"
+
+    # Build project name lookup
+    proj_names: dict[str, str] = {
+        p.get("id", ""): p.get("name", "") for p in fixture.projects_list
+    }
+    proj_names["new"] = "(new project)"
+
+    def _proj_label(pid: str, new_name: str | None = None) -> str:
+        name = proj_names.get(pid, "?")
+        if pid == "new" and new_name:
+            return f"new → \"{new_name}\""
+        return f"{pid} ({name})" if name and name != "?" else pid
+
+    def _short_file(path: str) -> str:
+        """Use just the filename for cleaner display."""
+        return path.rsplit("/", 1)[-1] if "/" in path else path
+
+    expected_files = {ec.file for ec in fixture.expected_classification}
+    total = len(fixture.expected_classification)
+    matched = 0
+
+    scored_lines: list[str] = []
+    info_lines: list[str] = []
+
+    # Score expected files
+    for ec in fixture.expected_classification:
+        actual = next((r for r in results if r["file"] == ec.file), None)
+        fname = _short_file(ec.file)
+        if actual is None:
+            scored_lines.append(f"  MISS  {fname}")
+            scored_lines.append(f"          expected: {_proj_label(ec.project_id)}")
+            continue
+
+        pid_ok = actual["project_id"] == ec.project_id
+        domains_ok = set(actual["domains"]) == set(ec.domains) if ec.domains else True
+
+        if pid_ok and domains_ok:
+            matched += 1
+            scored_lines.append(f"  OK    {fname}")
+            scored_lines.append(f"          project: {_proj_label(actual['project_id'])}")
+            scored_lines.append(f"          domains: {actual['domains']}")
+        else:
+            scored_lines.append(f"  FAIL  {fname}")
+            if not pid_ok:
+                scored_lines.append(f"          project: {_proj_label(actual['project_id'])}  (expected: {_proj_label(ec.project_id)})")
+            else:
+                scored_lines.append(f"          project: {_proj_label(actual['project_id'])}")
+            if not domains_ok:
+                scored_lines.append(f"          domains: {actual['domains']}  (expected: {ec.domains})")
+            else:
+                scored_lines.append(f"          domains: {actual['domains']}")
+
+    # Show unscored files
+    for r in results:
+        if r["file"] not in expected_files:
+            fname = _short_file(r["file"])
+            proj = _proj_label(r["project_id"], r.get("new_project_name"))
+            info_lines.append(f"  ·     {fname}")
+            info_lines.append(f"          project: {proj}  |  domains: {r['domains']}")
+
+    precision = matched / total if total > 0 else 0.0
+    recall = precision
+    f1 = precision
+
+    parts: list[str] = []
+    if scored_lines:
+        parts.append(f"Scored ({matched}/{total}):")
+        parts.extend(scored_lines)
+    if info_lines:
+        parts.append(f"\nOther files ({len(info_lines) // 2}):")
+        parts.extend(info_lines)
+
+    return precision, recall, f1, "\n".join(parts)
+
+
+# ── Step 2 runner ─────────────────────────────────────────────────────────
+
+
+async def _run_step2(
+    fixture: EvalFixture,
+    model: str,
+    mock: MockExecutor,
+) -> None:
+    """Run step-2 processing for each file in the fixture directory.
+
+    Compiles ``_PROCESSING_SYSTEM_PROMPT`` with fixture-provided variables
+    and runs the tool-calling loop.  Mutations are captured by the mock.
+    """
+    from app.agent_runner import (
+        _PROCESSING_SYSTEM_PROMPT,
+        _build_processing_tools,
+        _run_agent_with_tools,
+        _MAX_PROCESSING_STEPS,
+    )
+    from app import tracing
+
+    # Compile the processing prompt with fixture variables
+    system_prompt = tracing.compile_prompt(
+        "batch_processing",
+        fallback=_PROCESSING_SYSTEM_PROMPT,
+        variables={
+            "existing_context": fixture.existing_context,
+            "project_context": fixture.project_context,
+            "data_types": ", ".join(fixture.data_types),
+            "custom_prompt_section": fixture.custom_prompt_section,
+        },
+    )
+
+    tools = _build_processing_tools(fixture.data_types)
+
+    # Scan files in the fixture directory
+    file_entries = await mock._handle(
+        action="list_directory",
+        data={"path": fixture.directory},
+    )
+    for entry in file_entries.get("entries", []):
+        if entry.get("type") != "file":
+            continue
+        # Filter by extension if specified
+        if fixture.file_extensions:
+            ext = entry["name"].rsplit(".", 1)[-1] if "." in entry["name"] else ""
+            if ext not in fixture.file_extensions:
+                continue
+
+        file_result = await mock._handle(
+            action="read_file_content",
+            data={"path": entry["path"]},
+        )
+        file_content: str = file_result.get("content", "")
+        if not file_content.strip():
+            continue
+
+        await _run_agent_with_tools(
+            system_prompt=system_prompt,
+            user_message=(
+                f"Process this file and extract relevant information.\n\n"
+                f"File: {entry['path']}\n\nContent:\n{file_content}"
+            ),
+            tools=tools,
+            max_steps=_MAX_PROCESSING_STEPS,
+        )
+
+
+# ── Full runner ───────────────────────────────────────────────────────────
+
+
+async def _run_full(
+    fixture: EvalFixture,
+    model: str,
+    mock: MockExecutor,
+    user_id: str,
+) -> None:
+    """Run the full two-step pipeline via ``run_local_agent``."""
+    from app.agent_runner import run_local_agent
+
+    trigger_data: dict[str, Any] = {
+        "type": "agent_trigger",
+        "directory": fixture.directory,
+        "directory_paths": [fixture.directory],
+        "data_types": fixture.data_types,
+        "file_extensions": fixture.file_extensions,
+        "prompt_template": fixture.custom_prompt_section,
+        "device_id": "eval-harness",
+        "run_context": {
+            "agent_id": f"eval-{fixture.name}",
+            "run_id": None,
+        },
+    }
+
+    with mock.patch():
+        await run_local_agent(user_id, trigger_data)
+
+
+# ── Scoring helpers ───────────────────────────────────────────────────────
+
+
+def _score_mutations(
+    fixture: EvalFixture,
+    mock: MockExecutor,
+) -> tuple[list[FieldScore], float, float, float, int, int]:
+    """Score mutations against expected records.
+
+    Returns (field_scores, precision, recall, f1, extra, missing).
+    """
+    all_field_scores: list[FieldScore] = []
+    total_expected = 0
+    total_actual = 0
+    total_matched = 0
+    total_extra = 0
+    total_missing = 0
+
+    expected_by_table: dict[str, list[dict]] = {}
+    for rec in fixture.expected:
+        expected_by_table.setdefault(rec.table, []).append(rec.fields)
+
+    tables = set(expected_by_table.keys()) | {m.table for m in mock.mutations}
+    for table in tables:
+        expected_records = expected_by_table.get(table, [])
+        actual_records = mock.created_records(table) + mock.updated_records(table)
+
+        field_scores, extra, missing = score_field_match(expected_records, actual_records, table)
+        all_field_scores.extend(field_scores)
+
+        matched = sum(1 for s in field_scores if s.best_match is not None)
+        total_expected += len(expected_records)
+        total_actual += len(actual_records)
+        total_matched += matched
+        total_extra += extra
+        total_missing += missing
+
+    precision, recall, f1 = compute_precision_recall(total_expected, total_actual, total_matched)
+    return all_field_scores, precision, recall, f1, total_extra, total_missing
+
+
+# ── Main entry point ──────────────────────────────────────────────────────
+
+
+async def run_single_eval(
+    fixture: EvalFixture,
+    model: str,
+    *,
+    use_llm_judge: bool = True,
+    judge_model: str = "gpt-4o-mini",
+) -> EvalScores:
+    """Execute one eval run for a fixture + model.  Mode is read from the fixture."""
+    from shared.config import settings
+    from shared.ws_context import set_current_user, clear_current_user
+
+    seed = copy.deepcopy(fixture.seed_records)
+    mock = MockExecutor(
+        fixture_dir=fixture.fixture_path.parent,
+        seed_records=seed,
+    )
+
+    original_model = settings.LLM_MODEL
+    settings.LLM_MODEL = model
+    eval_user_id = str(uuid.uuid4())
+
+    logger.info(
+        "eval: starting %s | mode=%s | model=%s",
+        fixture.name, fixture.mode, model,
+    )
+    start_time = time.time()
+
+    step1_results: list[dict[str, Any]] = []
+    step1_reasoning = ""
+
+    try:
+        set_current_user(eval_user_id)
+
+        if fixture.mode == "step1":
+            with mock.patch():
+                step1_results = await _run_step1(fixture, model, mock)
+
+        elif fixture.mode == "step2":
+            with mock.patch():
+                await _run_step2(fixture, model, mock)
+
+        elif fixture.mode == "full":
+            with mock.patch():
+                # Step 1 — classification (independent from run_local_agent)
+                if fixture.expected_classification:
+                    step1_results = await _run_step1(fixture, model, mock)
+
+            # Step 2 — full pipeline (run_local_agent handles both steps)
+            await _run_full(fixture, model, mock, eval_user_id)
+
+    except Exception as exc:
+        logger.error("eval: pipeline failed for %s/%s: %s", fixture.name, model, exc)
+    finally:
+        settings.LLM_MODEL = original_model
+        clear_current_user()
+
+    elapsed = time.time() - start_time
+    logger.info("eval: completed in %.1fs — %d mutations", elapsed, len(mock.mutations))
+
+    # ── Score ─────────────────────────────────────────────────────
+
+    if fixture.mode == "step1":
+        s1_precision, s1_recall, s1_f1, step1_reasoning = _score_step1(fixture, step1_results)
+        scores = EvalScores(
+            fixture_name=fixture.name,
+            model=model,
+            prompt_variant=fixture.mode,
+            precision=s1_precision,
+            recall=s1_recall,
+            f1=s1_f1,
+            llm_judge_reasoning=step1_reasoning,
+        )
+    else:
+        # step2 or full — score mutations
+        field_scores, precision, recall, f1, extra, missing = _score_mutations(fixture, mock)
+        scores = EvalScores(
+            fixture_name=fixture.name,
+            model=model,
+            prompt_variant=fixture.mode,
+            field_scores=field_scores,
+            precision=precision,
+            recall=recall,
+            f1=f1,
+            extra_records=extra,
+            missing_records=missing,
+        )
+
+        # Add step1 classification scores for full mode
+        if fixture.mode == "full" and fixture.expected_classification:
+            s1_p, s1_r, s1_f1, step1_reasoning = _score_step1(fixture, step1_results)
+            scores.llm_judge_reasoning = f"Step1 classification:\n{step1_reasoning}"
+
+        # Optional LLM judge for extraction quality
+        if use_llm_judge and fixture.expected:
+            all_expected = [r.fields for r in fixture.expected]
+            all_actual = [m.data for m in mock.mutations if m.action in ("insert", "update")]
+            judge_score, reasoning = await llm_judge_score(
+                all_expected, all_actual, judge_model=judge_model,
+            )
+            scores.llm_judge_score = judge_score
+            if step1_reasoning:
+                scores.llm_judge_reasoning += f"\n\nLLM judge:\n{reasoning}"
+            else:
+                scores.llm_judge_reasoning = reasoning
+
+    # ── Report to Langfuse ────────────────────────────────────────
+    prompt_names = {
+        "step1": ["batch_file_classifier"],
+        "step2": ["batch_processing"],
+        "full": ["batch_file_classifier", "batch_processing"],
+    }.get(fixture.mode, ["batch_processing"])
+
+    trace_id = langfuse_eval.log_eval_trace(
+        fixture_name=fixture.name,
+        model=model,
+        prompt_variant=fixture.mode,
+        prompt_template=fixture.custom_prompt_section or "(default)",
+        actual_mutations=[{"action": m.action, "table": m.table, "data": m.data} for m in mock.mutations],
+        scores_summary=scores.summary(),
+        step1_results=step1_results or None,
+        langfuse_prompt_names=prompt_names,
+    )
+
+    if trace_id:
+        langfuse_eval.post_eval_scores(scores, trace_id=trace_id)
+
+        # For full mode, post classification scores separately
+        if fixture.mode == "full" and fixture.expected_classification:
+            s1_p, s1_r, s1_f1, _ = _score_step1(fixture, step1_results)
+            for name, value in [
+                ("classification_precision", s1_p),
+                ("classification_recall", s1_r),
+                ("classification_f1", s1_f1),
+            ]:
+                try:
+                    from langfuse import get_client
+                    lf = get_client()
+                    if lf:
+                        lf.create_score(
+                            name=name,
+                            value=value,
+                            trace_id=trace_id,
+                            data_type="NUMERIC",
+                            comment=f"{fixture.name} | {model} | full",
+                        )
+                except Exception:
+                    pass
+
+    return scores
+
+
+async def run_fixture_eval(
+    fixture: EvalFixture,
+    models: list[str],
+    *,
+    use_llm_judge: bool = True,
+    judge_model: str = "gpt-4o-mini",
+) -> list[EvalScores]:
+    """Run all models for a fixture."""
+    langfuse_eval.sync_fixture_to_dataset(fixture)
+
+    results: list[EvalScores] = []
+    for model in models:
+        scores = await run_single_eval(
+            fixture, model,
+            use_llm_judge=use_llm_judge,
+            judge_model=judge_model,
+        )
+        results.append(scores)
+
+    return results
+
+
+def print_results(results: list[EvalScores]) -> None:
+    """Print a formatted summary table of eval results."""
+    if not results:
+        print("\nNo eval results.")
+        return
+
+    W = 90
+
+    print("\n" + "=" * W)
+    print(f"{'Fixture':<25} {'Mode':<6} {'Model':<25} {'P':>6} {'R':>6} {'F1':>6} {'FA':>6} {'LLM':>6}")
+    print("-" * W)
+
+    for s in results:
+        llm_str = f"{s.llm_judge_score:.2f}" if s.llm_judge_score is not None else "  --"
+        fa_str = f"{s.field_accuracy:.2f}" if s.field_scores else "  --"
+        print(
+            f"{s.fixture_name:<25} {s.prompt_variant:<6} {s.model:<25} "
+            f"{s.precision:>6.2f} {s.recall:>6.2f} {s.f1:>6.2f} "
+            f"{fa_str:>6} {llm_str:>6}"
+        )
+
+    print("=" * W)
+
+    for s in results:
+        if s.llm_judge_reasoning:
+            print(f"\n{'─' * W}")
+            print(f"  {s.fixture_name}  |  {s.model}  |  {s.prompt_variant}")
+            print(f"{'─' * W}")
+            print(s.llm_judge_reasoning)
+
+    print()

services/batch-agent/eval/scorer.py

@@ -0,0 +1,268 @@
+"""Scoring functions for batch agent evaluation.
+
+Two scoring strategies:
+
+1. **FieldMatchScorer** — deterministic check: for each expected record,
+   find the best-matching actual record and compare specified fields.
+   Returns precision, recall, and per-field accuracy.
+
+2. **LLMJudgeScorer** — uses a secondary LLM to semantically evaluate
+   whether the actual extractions satisfy the expected intent, even if
+   wording differs.  Returns a 0-1 score + reasoning.
+"""
+
+from __future__ import annotations
+
+import json
+import logging
+from dataclasses import dataclass, field
+from difflib import SequenceMatcher
+from typing import Any
+
+from langchain_core.messages import HumanMessage, SystemMessage
+
+logger = logging.getLogger(__name__)
+
+
+# ── Result types ─────────────────────────────────────────────────────────
+
+
+@dataclass
+class FieldScore:
+    """Score for a single expected record against its best match."""
+
+    expected: dict[str, Any]
+    best_match: dict[str, Any] | None
+    matched_fields: dict[str, bool]
+    similarity: float  # 0-1 overall similarity
+
+    @property
+    def field_accuracy(self) -> float:
+        if not self.matched_fields:
+            return 0.0
+        return sum(self.matched_fields.values()) / len(self.matched_fields)
+
+
+@dataclass
+class EvalScores:
+    """Aggregated scores for one eval run."""
+
+    fixture_name: str
+    model: str
+    prompt_variant: str
+    field_scores: list[FieldScore] = field(default_factory=list)
+    precision: float = 0.0
+    recall: float = 0.0
+    f1: float = 0.0
+    llm_judge_score: float | None = None
+    llm_judge_reasoning: str = ""
+    extra_records: int = 0  # records created but not expected
+    missing_records: int = 0  # expected but not found
+
+    @property
+    def field_accuracy(self) -> float:
+        if not self.field_scores:
+            return 0.0
+        return sum(s.field_accuracy for s in self.field_scores) / len(self.field_scores)
+
+    def summary(self) -> dict[str, Any]:
+        return {
+            "fixture": self.fixture_name,
+            "model": self.model,
+            "prompt_variant": self.prompt_variant,
+            "precision": round(self.precision, 3),
+            "recall": round(self.recall, 3),
+            "f1": round(self.f1, 3),
+            "field_accuracy": round(self.field_accuracy, 3),
+            "llm_judge_score": round(self.llm_judge_score, 3) if self.llm_judge_score is not None else None,
+            "extra_records": self.extra_records,
+            "missing_records": self.missing_records,
+        }
+
+
+# ── Field Match Scorer ───────────────────────────────────────────────────
+
+
+def _normalize(value: Any) -> str:
+    """Normalize a value for comparison."""
+    if value is None:
+        return ""
+    return str(value).strip().lower()
+
+
+def _text_similarity(a: str, b: str) -> float:
+    """Fuzzy text similarity using SequenceMatcher."""
+    if not a and not b:
+        return 1.0
+    if not a or not b:
+        return 0.0
+    return SequenceMatcher(None, a.lower(), b.lower()).ratio()
+
+
+def _find_best_match(
+    expected: dict[str, Any],
+    actuals: list[dict[str, Any]],
+) -> tuple[dict[str, Any] | None, float]:
+    """Find the actual record most similar to expected, return (match, similarity)."""
+    if not actuals:
+        return None, 0.0
+
+    best_match = None
+    best_score = 0.0
+
+    # Primary matching key: title or name
+    expected_title = _normalize(expected.get("title", expected.get("name", "")))
+
+    for actual in actuals:
+        actual_title = _normalize(actual.get("title", actual.get("name", "")))
+        sim = _text_similarity(expected_title, actual_title)
+        if sim > best_score:
+            best_score = sim
+            best_match = actual
+
+    return best_match, best_score
+
+
+def _compare_fields(
+    expected: dict[str, Any],
+    actual: dict[str, Any],
+) -> dict[str, bool]:
+    """Compare each expected field against the actual record."""
+    results: dict[str, bool] = {}
+    for key, expected_val in expected.items():
+        actual_val = actual.get(key)
+        # Exact match for non-string types
+        if not isinstance(expected_val, str):
+            results[key] = actual_val == expected_val
+        else:
+            # Fuzzy match for strings (threshold: 0.7)
+            results[key] = _text_similarity(
+                _normalize(expected_val), _normalize(actual_val)
+            ) >= 0.7
+    return results
+
+
+def score_field_match(
+    expected_records: list[dict[str, Any]],
+    actual_records: list[dict[str, Any]],
+    table: str,
+) -> tuple[list[FieldScore], int, int]:
+    """Score actual extractions against expected records for one table.
+
+    Returns (field_scores, extra_count, missing_count).
+    """
+    field_scores: list[FieldScore] = []
+    matched_actuals: set[int] = set()
+
+    for exp in expected_records:
+        # Find best match among unmatched actuals
+        candidates = [
+            (i, a) for i, a in enumerate(actual_records) if i not in matched_actuals
+        ]
+        if not candidates:
+            field_scores.append(FieldScore(
+                expected=exp, best_match=None, matched_fields={}, similarity=0.0,
+            ))
+            continue
+
+        best_idx, best_match = None, None
+        best_sim = 0.0
+        for idx, actual in candidates:
+            _, sim = _find_best_match(exp, [actual])
+            if sim > best_sim:
+                best_sim = sim
+                best_idx = idx
+                best_match = actual
+
+        if best_sim >= 0.5 and best_match is not None:
+            matched_actuals.add(best_idx)
+            matched_fields = _compare_fields(exp, best_match)
+            field_scores.append(FieldScore(
+                expected=exp, best_match=best_match,
+                matched_fields=matched_fields, similarity=best_sim,
+            ))
+        else:
+            field_scores.append(FieldScore(
+                expected=exp, best_match=None, matched_fields={}, similarity=0.0,
+            ))
+
+    extra_count = len(actual_records) - len(matched_actuals)
+    missing_count = sum(1 for s in field_scores if s.best_match is None)
+
+    return field_scores, extra_count, missing_count
+
+
+def compute_precision_recall(
+    expected_count: int,
+    actual_count: int,
+    matched_count: int,
+) -> tuple[float, float, float]:
+    """Compute precision, recall, F1."""
+    precision = matched_count / actual_count if actual_count > 0 else 0.0
+    recall = matched_count / expected_count if expected_count > 0 else 0.0
+    f1 = (
+        2 * precision * recall / (precision + recall)
+        if (precision + recall) > 0
+        else 0.0
+    )
+    return precision, recall, f1
+
+
+# ── LLM Judge Scorer ─────────────────────────────────────────────────────
+
+_JUDGE_SYSTEM_PROMPT = """\
+You are an evaluation judge for a data extraction system.
+
+Your task is to compare the EXPECTED extractions against the ACTUAL extractions
+produced by an AI agent, and assess quality on a 0-1 scale.
+
+Scoring criteria:
+- 1.0: All expected records found with correct fields, no significant extras
+- 0.8: Most expected records found, minor field differences or extras
+- 0.6: Core extractions present but some missing or incorrect
+- 0.4: Partial match — several expected records missing or wrong
+- 0.2: Poor quality — most expected records missing or incorrect
+- 0.0: Complete failure — no meaningful overlap
+
+Consider semantic equivalence: "Send invoice" and "Email the invoice" are matches.
+Ignore field ordering and formatting differences.
+
+Respond with ONLY a JSON object:
+{"score": 0.85, "reasoning": "Brief explanation of the score"}
+"""
+
+
+async def llm_judge_score(
+    expected: list[dict[str, Any]],
+    actual: list[dict[str, Any]],
+    *,
+    judge_model: str = "gpt-4o-mini",
+) -> tuple[float, str]:
+    """Use an LLM to semantically evaluate extraction quality.
+
+    Returns (score, reasoning).
+    """
+    from shared.llm import get_llm
+
+    llm = get_llm(model=judge_model, temperature=0)
+
+    user_content = (
+        f"## Expected extractions\n```json\n{json.dumps(expected, indent=2, default=str)}\n```\n\n"
+        f"## Actual extractions\n```json\n{json.dumps(actual, indent=2, default=str)}\n```"
+    )
+
+    try:
+        response = await llm.ainvoke([
+            SystemMessage(content=_JUDGE_SYSTEM_PROMPT),
+            HumanMessage(content=user_content),
+        ])
+        raw = response.content.strip()
+        if raw.startswith("```"):
+            raw = raw.split("```")[1]
+            if raw.startswith("json"):
+                raw = raw[4:]
+        parsed = json.loads(raw.strip())
+        return float(parsed.get("score", 0.0)), str(parsed.get("reasoning", ""))
+    except Exception as exc:
+        logger.warning("eval: LLM judge failed: %s", exc)
+        return 0.0, f"Judge error: {exc}"

services/batch-agent/requirements.txt

@@ -0,0 +1,21 @@
+fastapi>=0.115.0
+uvicorn[standard]>=0.34.0
+gunicorn>=22.0.0
+pydantic>=2.10.0
+pydantic-settings>=2.7.0
+sqlalchemy>=2.0.0
+asyncpg>=0.30.0
+redis>=5.0.0
+cryptography>=42.0.0
+python-dotenv>=1.0.0
+langchain-core>=0.3.0
+langchain-openai>=0.3.0
+langchain-litellm>=0.3.0
+litellm>=1.50.0
+openai>=1.50.0
+httpx>=0.27.0
+langfuse>=3.0.0
+croniter>=2.0.0
+google-api-python-client>=2.130.0
+google-auth>=2.30.0
+msal>=1.28.0

services/billing/Dockerfile

@@ -0,0 +1,36 @@
+# ── builder ──────────────────────────────────────────────────────────────────
+FROM python:3.12-slim AS builder
+
+WORKDIR /build
+
+COPY services/billing/requirements.txt ./requirements.txt
+RUN pip install --upgrade pip && \
+    pip install --no-cache-dir --prefix=/install -r requirements.txt
+
+# ── runtime ──────────────────────────────────────────────────────────────────
+FROM python:3.12-slim AS runtime
+
+RUN addgroup --system appgroup && adduser --system --ingroup appgroup appuser
+
+WORKDIR /app
+
+COPY --from=builder /install /usr/local
+
+# Shared module
+COPY shared/ shared/
+
+# Service source
+COPY services/billing/app/ app/
+
+RUN chown -R appuser:appgroup /app
+
+USER appuser
+
+EXPOSE 8000
+
+# Billing is lightweight — single worker is fine
+CMD ["gunicorn", "app.main:app", \
+     "-k", "uvicorn.workers.UvicornWorker", \
+     "--bind", "0.0.0.0:8000", \
+     "--workers", "1", \
+     "--timeout", "30"]

services/billing/README.md

@@ -0,0 +1,15 @@
+# Billing Service
+
+Owns: Stripe integration, tier management, subscription CRUD.
+
+## Tables owned (write)
+- `subscriptions`
+
+## Endpoints
+- `POST /billing/checkout`
+- `POST /billing/webhook` (Stripe, no JWT auth)
+- `GET /billing/subscription`
+- `DELETE /billing/subscription`
+
+## Redis channels
+- Publish: `tier:changed:{user_id}` on tier change

services/billing/app/main.py

@@ -0,0 +1,53 @@
+"""Billing Service — FastAPI application.
+
+Owns: Stripe checkout/webhook, subscription management, tier feature matrix,
+quota enforcement.
+
+Downstream services query this service (or read the user's tier from
+the X-User-Tier header injected by Traefik) for billing decisions.
+The webhook endpoint is exposed WITHOUT ForwardAuth so Stripe can reach it.
+"""
+
+from __future__ import annotations
+
+import logging
+import sys
+from contextlib import asynccontextmanager
+from pathlib import Path
+from typing import AsyncGenerator
+
+# Ensure the repo root is on sys.path so "shared" is importable in local dev.
+_repo_root = str(Path(__file__).resolve().parents[3])
+if _repo_root not in sys.path:
+    sys.path.insert(0, _repo_root)
+
+from fastapi import FastAPI
+from fastapi.middleware.cors import CORSMiddleware
+
+from app.routes import router
+
+logger = logging.getLogger(__name__)
+
+
+@asynccontextmanager
+async def lifespan(app: FastAPI) -> AsyncGenerator[None, None]:
+    logger.info("billing: service started")
+    yield
+    logger.info("billing: service stopped")
+
+
+app = FastAPI(title="Adiuva Billing Service", lifespan=lifespan)
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_methods=["GET", "POST", "DELETE"],
+    allow_headers=["*"],
+)
+
+app.include_router(router)
+
+
+@app.get("/health")
+async def health() -> dict[str, str]:
+    return {"status": "ok", "service": "billing"}

services/billing/app/routes.py

@@ -0,0 +1,134 @@
+"""Billing routes: Stripe checkout, webhook, subscription, tier query.
+
+Adapted for the Billing microservice:
+  - Authenticated routes use Traefik-injected headers (X-User-Id, X-User-Tier)
+  - Webhook route has NO auth (Stripe signature verification only)
+  - Added /tier/{user_id} for internal service-to-service tier lookups
+  - Added /features/{tier} for feature matrix queries
+"""
+
+from __future__ import annotations
+
+from typing import Any
+
+from fastapi import APIRouter, Header, HTTPException, Request, status
+from pydantic import BaseModel
+
+from shared.db import async_session
+from shared.schemas import BillingTier
+
+from app.stripe_service import stripe_service
+from app.tier_manager import tier_manager, FEATURES, RATE_LIMITS
+
+router = APIRouter(prefix="/billing", tags=["billing"])
+
+
+# ── Request bodies ─────────────────────────────────────────────────────
+
+class _CheckoutRequest(BaseModel):
+    tier: BillingTier
+
+
+# ── Checkout ───────────────────────────────────────────────────────────
+
+@router.post("/checkout")
+async def create_checkout(
+    body: _CheckoutRequest,
+    x_user_id: str = Header(..., alias="X-User-Id"),
+) -> dict[str, str]:
+    """Create a Stripe checkout session for a tier upgrade."""
+    url = stripe_service.create_checkout_session(x_user_id, body.tier)
+    return {"checkout_url": url}
+
+
+# ── Webhook (NO auth — Stripe signature only) ─────────────────────────
+
+@router.post("/webhook")
+async def stripe_webhook(
+    request: Request,
+    stripe_signature: str = Header(default="", alias="Stripe-Signature"),
+) -> dict[str, bool]:
+    """Handle Stripe webhook events.
+
+    This endpoint is exposed without ForwardAuth in Traefik config
+    so Stripe can reach it directly.
+    """
+    payload = await request.body()
+    async with async_session() as db:
+        await stripe_service.handle_webhook(payload, stripe_signature, db)
+    return {"ok": True}
+
+
+# ── Subscription CRUD ─────────────────────────────────────────────────
+
+@router.get("/subscription")
+async def get_subscription(
+    x_user_id: str = Header(..., alias="X-User-Id"),
+    x_user_tier: str = Header("free", alias="X-User-Tier"),
+) -> dict[str, Any]:
+    """Return the current subscription info for the authenticated user."""
+    async with async_session() as db:
+        sub = await stripe_service.get_subscription(x_user_id, db)
+    if sub is None:
+        return {
+            "tier": x_user_tier,
+            "status": "free",
+            "stripe_subscription_id": None,
+            "current_period_end": None,
+        }
+    return sub
+
+
+@router.delete("/subscription")
+async def cancel_subscription(
+    x_user_id: str = Header(..., alias="X-User-Id"),
+) -> dict[str, bool]:
+    """Cancel the active subscription."""
+    async with async_session() as db:
+        await stripe_service.cancel_subscription(x_user_id, db)
+    return {"ok": True}
+
+
+# ── Tier query (internal, service-to-service) ─────────────────────────
+
+@router.get("/tier/{user_id}")
+async def get_user_tier(user_id: str) -> dict[str, str]:
+    """Return the billing tier for a given user_id.
+
+    Used by other services for tier lookups. Protected by Traefik
+    ForwardAuth — only internal services should call this.
+    """
+    async with async_session() as db:
+        tier = await tier_manager.get_tier(user_id, db)
+    return {"user_id": user_id, "tier": tier}
+
+
+# ── Feature matrix (public, cacheable) ────────────────────────────────
+
+@router.get("/features/{tier}")
+async def get_tier_features(tier: str) -> dict[str, Any]:
+    """Return the feature matrix for a tier."""
+    if tier not in FEATURES:
+        raise HTTPException(
+            status_code=status.HTTP_404_NOT_FOUND,
+            detail=f"Unknown tier: {tier}",
+        )
+    return {
+        "tier": tier,
+        "features": FEATURES[tier],
+        "rate_limit_rpm": RATE_LIMITS.get(tier, RATE_LIMITS["free"]),
+    }
+
+
+@router.get("/features")
+async def get_all_features() -> dict[str, Any]:
+    """Return the full feature matrix for all tiers."""
+    return {
+        "tiers": {
+            tier: {
+                "features": features,
+                "rate_limit_rpm": RATE_LIMITS.get(tier, RATE_LIMITS["free"]),
+            }
+            for tier, features in FEATURES.items()
+        },
+    }

services/billing/app/stripe_service.py

@@ -1,7 +1,7 @@
 """Stripe service: checkout sessions, webhook handling, subscription management.
 
-Subscription records are persisted in the PostgreSQL ``subscriptions`` table.
-All Stripe calls are gracefully stubbed when ``STRIPE_SECRET_KEY`` is not
+Adapted for the Billing microservice — uses shared.models and shared.db.
+All Stripe calls are gracefully stubbed when STRIPE_SECRET_KEY is not
 configured, enabling local development without live credentials.
 """
 
@@ -15,7 +15,8 @@ from fastapi import HTTPException, status
 from sqlalchemy import select
 from sqlalchemy.ext.asyncio import AsyncSession
 
-from app.config.settings import settings
+from shared.config import settings
+from shared.models import Subscription
 
 # Stripe price IDs per tier — replace with real IDs in production .env
 TIER_PRICE_IDS: dict[str, str] = {
@@ -46,11 +47,7 @@ class StripeService:
         success_url: str = "https://app.adiuva.app/billing/success?session_id={CHECKOUT_SESSION_ID}",
         cancel_url: str = "https://app.adiuva.app/billing/cancel",
     ) -> str:
-        """Create a Stripe checkout session and return the URL.
-
-        Returns a stub URL when Stripe is not configured.
-        Raises ``HTTP 400`` for the free tier or an unknown tier.
-        """
+        """Create a Stripe checkout session and return the URL."""
         if tier == "free":
             raise HTTPException(
                 status_code=status.HTTP_400_BAD_REQUEST,
@@ -87,8 +84,6 @@ class StripeService:
         """Process a Stripe webhook event.
 
         Verifies the signature, then dispatches on event type.
-        Raises ``HTTP 400`` on signature mismatch.
-        No-ops when Stripe is not configured.
         """
         if not self._configured():
             return
@@ -155,9 +150,7 @@ class StripeService:
     async def get_subscription(
         self, user_id: str, db: AsyncSession
     ) -> dict[str, Any] | None:
-        """Return the subscription record for ``user_id``, or ``None`` if absent."""
-        from app.models import Subscription  # noqa: PLC0415
-
+        """Return the subscription record for user_id, or None."""
         result = await db.execute(
             select(Subscription).where(Subscription.user_id == user_id)
         )
@@ -176,12 +169,7 @@ class StripeService:
         }
 
     async def cancel_subscription(self, user_id: str, db: AsyncSession) -> None:
-        """Cancel the user's Stripe subscription and downgrade them to free.
-
-        Raises ``HTTP 404`` when no active subscription exists.
-        """
-        from app.models import Subscription  # noqa: PLC0415
-
+        """Cancel the user's Stripe subscription and downgrade to free."""
         result = await db.execute(
             select(Subscription).where(Subscription.user_id == user_id)
         )
@@ -211,8 +199,6 @@ class StripeService:
         sub_status: str,
         current_period_end: datetime | None,
     ) -> None:
-        from app.models import Subscription  # noqa: PLC0415
-
         result = await db.execute(
             select(Subscription).where(Subscription.user_id == user_id)
         )
@@ -234,8 +220,6 @@ class StripeService:
         status: str | None = None,
         current_period_end: datetime | None = None,
     ) -> None:
-        from app.models import Subscription  # noqa: PLC0415
-
         result = await db.execute(
             select(Subscription).where(
                 Subscription.stripe_subscription_id == stripe_subscription_id
@@ -252,5 +236,5 @@ class StripeService:
             sub.current_period_end = current_period_end
 
 
-# Module-level singleton shared across the app.
+# Module-level singleton
 stripe_service = StripeService()

services/billing/app/tier_manager.py

@@ -1,9 +1,8 @@
 """Tier manager: feature matrix and quota enforcement.
 
-``TierManager`` is the single source of truth for what each billing tier
-allows.  ``get_tier`` queries the ``subscriptions`` table for the live tier.
-Quota-enforcement helpers take ``tier`` directly — the caller already has it
-from ``current_user.tier`` (provided by ``get_current_user``).
+Single source of truth for what each billing tier allows.
+Other services can query the /tier/{user_id} endpoint or rely on the
+X-User-Tier header injected by Traefik.
 """
 
 from __future__ import annotations
@@ -14,7 +13,9 @@ from fastapi import HTTPException, status
 from sqlalchemy import select
 from sqlalchemy.ext.asyncio import AsyncSession
 
-from app.schemas import BillingTier
+from shared.config import settings
+from shared.models import Subscription
+from shared.schemas import BillingTier
 
 # Feature matrix per tier.  -1 means unlimited; 0 means disabled.
 FEATURES: dict[str, dict[str, Any]] = {
@@ -30,7 +31,7 @@ FEATURES: dict[str, dict[str, Any]] = {
         "sso": False,
     },
     "pro": {
-        "agents": -1,           # unlimited
+        "agents": -1,
         "batch_active": 10,
         "batch_runs_per_day": 50,
         "cloud_storage_gb": 5,
@@ -42,8 +43,8 @@ FEATURES: dict[str, dict[str, Any]] = {
     },
     "power": {
         "agents": -1,
-        "batch_active": -1,     # unlimited
-        "batch_runs_per_day": -1,  # unlimited
+        "batch_active": -1,
+        "batch_runs_per_day": -1,
         "cloud_storage_gb": 25,
         "backup_gb": 25,
         "providers": -1,
@@ -54,9 +55,9 @@ FEATURES: dict[str, dict[str, Any]] = {
     "team": {
         "agents": -1,
         "batch_active": -1,
-        "batch_runs_per_day": -1,  # unlimited
-        "cloud_storage_gb": -1,  # unlimited
-        "backup_gb": -1,         # unlimited
+        "batch_runs_per_day": -1,
+        "cloud_storage_gb": -1,
+        "backup_gb": -1,
         "providers": -1,
         "batch_builder": True,
         "plugin_marketplace": True,
@@ -76,17 +77,8 @@ RATE_LIMITS: dict[str, int] = {
 class TierManager:
     """Centralises tier feature-gating, rate-limit lookups, and quota checks."""
 
-    # ── Tier lookup ─────────────────────────────────────────────────────
-
     async def get_tier(self, user_id: str, db: AsyncSession) -> BillingTier:
-        """Return the current billing tier for ``user_id`` from the DB.
-
-        Falls back to ``'power'`` in dev (unlimited) or ``'free'`` in prod
-        when no subscription row exists.
-        """
-        from app.models import Subscription  # noqa: PLC0415
-        from app.config.settings import settings  # noqa: PLC0415
-
+        """Return the current billing tier for user_id from the DB."""
         result = await db.execute(
             select(Subscription.tier).where(Subscription.user_id == user_id)
         )
@@ -95,13 +87,12 @@ class TierManager:
             return "power" if settings.ENV == "dev" else "free"
         return tier  # type: ignore[return-value]
 
-    # ── Feature access ───────────────────────────────────────────────────
+    def get_features(self, tier: BillingTier) -> dict[str, Any]:
+        """Return the full feature dict for a tier."""
+        return FEATURES.get(tier, FEATURES["free"])
 
     def check_feature(self, tier: BillingTier, feature: str) -> bool:
-        """Return ``True`` if ``tier`` has ``feature`` enabled.
-
-        For numeric features, any value > 0 or -1 (unlimited) counts as enabled.
-        """
+        """Return True if tier has feature enabled."""
         value = FEATURES.get(tier, FEATURES["free"]).get(feature)
         if value is None:
             return False
@@ -110,7 +101,7 @@ class TierManager:
         return value != 0
 
     def require_feature(self, tier: BillingTier, feature: str, tier_name: str = "") -> None:
-        """Raise ``HTTP 403`` if ``tier`` does not have ``feature``."""
+        """Raise HTTP 403 if tier does not have feature."""
         if not self.check_feature(tier, feature):
             detail = (
                 f"Feature '{feature}' requires {tier_name} tier or above."
@@ -119,25 +110,17 @@ class TierManager:
             )
             raise HTTPException(status_code=status.HTTP_403_FORBIDDEN, detail=detail)
 
-    # ── Rate limiting ────────────────────────────────────────────────────
-
     def get_rate_limit(self, tier: BillingTier) -> int:
-        """Return the requests-per-minute limit for ``tier``."""
+        """Return the requests-per-minute limit for tier."""
         return RATE_LIMITS.get(tier, RATE_LIMITS["free"])
 
-    # ── Storage quota ────────────────────────────────────────────────────
-
     def enforce_quota(
         self,
         tier: BillingTier,
         current_bytes: int = 0,
         additional_bytes: int = 0,
     ) -> None:
-        """Raise ``HTTP 402`` if the user would exceed their cloud storage quota.
-
-        ``tier`` is the caller's current tier (from ``current_user.tier``).
-        ``current_bytes`` is the total bytes already stored (queried by caller).
-        """
+        """Raise HTTP 402 if the user would exceed their cloud storage quota."""
         limit_gb: int = FEATURES[tier]["cloud_storage_gb"]
         if limit_gb == 0:
             raise HTTPException(
@@ -145,7 +128,7 @@ class TierManager:
                 detail=f"Cloud storage is not available on the '{tier}' tier",
             )
         if limit_gb == -1:
-            return  # unlimited
+            return
         limit_bytes = limit_gb * 1024 ** 3
         if current_bytes + additional_bytes > limit_bytes:
             raise HTTPException(
@@ -159,7 +142,7 @@ class TierManager:
         current_bytes: int = 0,
         additional_bytes: int = 0,
     ) -> None:
-        """Raise ``HTTP 402`` if the user would exceed their backup quota."""
+        """Raise HTTP 402 if the user would exceed their backup quota."""
         limit_gb: int = FEATURES[tier]["backup_gb"]
         if limit_gb == 0:
             raise HTTPException(
@@ -167,7 +150,7 @@ class TierManager:
                 detail=f"Backup is not available on the '{tier}' tier",
             )
         if limit_gb == -1:
-            return  # unlimited
+            return
         limit_bytes = limit_gb * 1024 ** 3
         if current_bytes + additional_bytes > limit_bytes:
             raise HTTPException(
@@ -181,7 +164,7 @@ class TierManager:
         current_bytes: int = 0,
         additional_bytes: int = 0,
     ) -> bool:
-        """Return ``True`` if the user can store ``additional_bytes`` more data."""
+        """Return True if the user can store additional_bytes more data."""
         limit_gb: int = FEATURES[tier]["cloud_storage_gb"]
         if limit_gb == 0:
             return False
@@ -191,5 +174,5 @@ class TierManager:
         return current_bytes + additional_bytes <= limit_bytes
 
 
-# Module-level singleton shared across the app.
+# Module-level singleton
 tier_manager = TierManager()

services/billing/requirements.txt

@@ -0,0 +1,9 @@
+fastapi>=0.115.0
+uvicorn[standard]>=0.34.0
+gunicorn>=22.0.0
+pydantic>=2.10.0
+pydantic-settings>=2.7.0
+sqlalchemy>=2.0.0
+asyncpg>=0.30.0
+python-dotenv>=1.0.0
+stripe>=8.0.0

services/chat/Dockerfile

@@ -3,37 +3,34 @@ FROM python:3.12-slim AS builder
 
 WORKDIR /build
 
-COPY requirements.txt .
+COPY services/chat/requirements.txt ./requirements.txt
 RUN pip install --upgrade pip && \
     pip install --no-cache-dir --prefix=/install -r requirements.txt
 
 # ── runtime ──────────────────────────────────────────────────────────────────
 FROM python:3.12-slim AS runtime
 
-# Non-root user
 RUN addgroup --system appgroup && adduser --system --ingroup appgroup appuser
 
 WORKDIR /app
 
-# Copy installed packages from builder
 COPY --from=builder /install /usr/local
 
-# Copy application source
-COPY app/ app/
+# Shared module
+COPY shared/ shared/
 
-# Copy Alembic migration files
-COPY alembic/ alembic/
-COPY alembic.ini .
+# Service source
+COPY services/chat/app/ app/
 
-# Ensure appuser owns the working directory
 RUN chown -R appuser:appgroup /app
 
 USER appuser
 
 EXPOSE 8000
 
+# Chat service is CPU-bound (LLM calls) — use multiple workers
 CMD ["gunicorn", "app.main:app", \
      "-k", "uvicorn.workers.UvicornWorker", \
      "--bind", "0.0.0.0:8000", \
-     "--workers", "4", \
+     "--workers", "2", \
      "--timeout", "120"]

services/chat/README.md

@@ -0,0 +1,21 @@
+# Chat Service
+
+Owns: deep_agent (home + floating chat), memory middleware, domain agents
+(task, note, project, timeline), LLM orchestration.
+
+## Tables owned
+- `memory_core`
+- `memory_associative`
+- `memory_episodic`
+- `memory_proactive`
+
+## Tables read (cross-service)
+- `users` (for encryption_key — memory decryption)
+
+## Endpoints
+- `POST /chat` (REST fallback)
+
+## Redis channels
+- Subscribe: `chat:request:{user_id}`
+- Publish: `ws:out:{user_id}` (stream frames + tool calls)
+- BRPOP: `tool:result:{call_id}` (30s timeout)

services/chat/app/deep_agent.py

@@ -1,4 +1,8 @@
-"""Single-agent runners for home and floating chat contexts."""
+"""Single-agent runners for home and floating chat contexts.
+
+Adapted from app/core/deep_agent.py for the Chat Service.
+Import paths changed to use local app modules and shared/.
+"""
 
 from __future__ import annotations
 
@@ -12,14 +16,15 @@ from typing import Any, Literal
 from langchain_core.messages import AIMessage, HumanMessage, SystemMessage, ToolMessage
 from langchain_core.tools import tool
 
-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.llm import get_llm
-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
+from shared.agents.note_agent import NOTE_TOOLS
+from shared.agents.project_agent import PROJECT_TOOLS
+from shared.agents.task_agent import TASK_TOOLS
+from shared.agents.timeline_agent import TIMELINE_TOOLS
+from shared.llm import get_llm
+from app.memory_middleware import MemoryMiddleware
+from shared.ws_context import clear_tool_result_collector, execute_on_client, set_tool_result_collector
+from app import tracing
+from shared.db import async_session
 
 logger = logging.getLogger(__name__)
 
@@ -240,7 +245,6 @@ def _strip_floating_markup(text: str) -> str:
         return text
 
     cleaned = _strip_floating_markup_fragment(text)
-    # Collapse excessive spaces introduced by tag/id removal while preserving lines.
     lines = [re.sub(r"[ \t]{2,}", " ", line).strip() for line in cleaned.splitlines()]
     return "\n".join(line for line in lines if line)
 
@@ -279,7 +283,6 @@ class _FloatingStreamSanitizer:
         return _strip_floating_markup_fragment(safe_text)
 
     def finalize(self) -> str:
-        # Drop dangling unfinished wrappers at the very end.
         tail = re.sub(r"<[^>\n]*$", "", self._pending)
         tail = re.sub(r"\[[^\]\n]*$", "", tail)
         self._pending = ""
@@ -525,7 +528,9 @@ def _infer_floating_domain_rule_based(message: str, context: dict[str, Any]) ->
     return {"type": "task", "id": None, "section": None}
 
 
-async def _infer_floating_domain(message: str, context: dict[str, Any]) -> dict[str, str | None]:
+async def _infer_floating_domain(
+    message: str, context: dict[str, Any], *, langfuse_handler: Any | None = None,
+) -> dict[str, str | None]:
     resolved_project_id = context.get("resolved_project_id") if isinstance(context, dict) else None
     project_id = resolved_project_id if isinstance(resolved_project_id, str) and resolved_project_id else None
 
@@ -535,10 +540,14 @@ async def _infer_floating_domain(message: str, context: dict[str, Any]) -> dict[
     }
 
     try:
-        llm = get_llm()
+        classifier_prompt = _get_system_prompt(
+            "floating_domain_classifier", _FLOATING_DOMAIN_CLASSIFIER_SYSTEM,
+        )
+        callbacks = _build_callbacks(langfuse_handler)
+        llm = get_llm(callbacks=callbacks)
         response = await llm.ainvoke(
             [
-                SystemMessage(content=_FLOATING_DOMAIN_CLASSIFIER_SYSTEM),
+                SystemMessage(content=classifier_prompt),
                 HumanMessage(
                     content=(
                         f"Message:\n{message}\n\n"
@@ -564,6 +573,19 @@ async def _infer_floating_domain(message: str, context: dict[str, Any]) -> dict[
     return _infer_floating_domain_rule_based(message, context)
 
 
+def _get_system_prompt(langfuse_name: str, fallback: str) -> str:
+    """Fetch a managed prompt from Langfuse, falling back to the hardcoded string."""
+    managed = tracing.get_prompt(langfuse_name, fallback=None)
+    return managed if managed is not None else fallback
+
+
+def _build_callbacks(langfuse_handler: Any | None) -> list[Any] | None:
+    """Return a callbacks list if a Langfuse handler is available."""
+    if langfuse_handler is None:
+        return None
+    return [langfuse_handler]
+
+
 async def _run_single_agent(
     *,
     user_id: str,
@@ -571,9 +593,11 @@ async def _run_single_agent(
     message: str,
     context: dict[str, Any],
     max_steps: int = 6,
+    langfuse_handler: Any | None = None,
 ) -> str:
     trace_id = _trace_id_from_context(context)
-    llm = get_llm()
+    callbacks = _build_callbacks(langfuse_handler)
+    llm = get_llm(callbacks=callbacks)
     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)
@@ -656,9 +680,11 @@ async def _run_single_agent_stream(
     message: str,
     context: dict[str, Any],
     max_steps: int = 6,
+    langfuse_handler: Any | None = None,
 ) -> AsyncGenerator[tuple[str, Any], None]:
     trace_id = _trace_id_from_context(context)
-    llm = get_llm()
+    callbacks = _build_callbacks(langfuse_handler)
+    llm = get_llm(callbacks=callbacks)
     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)
@@ -691,7 +717,6 @@ async def _run_single_agent_stream(
                         emitted_any = True
                         yield "token", token
 
-                # Some providers return final text in `response.content` but stream no chunks.
                 if not emitted_any:
                     fallback_text = _as_text(response.content)
                     if fallback_text:
@@ -750,25 +775,29 @@ async def _run_single_agent_stream(
         clear_tool_result_collector()
 
 
-async def run_home(user_id: str, message: str, context: dict[str, Any]) -> str:
+async def run_home(user_id: str, message: str, context: dict[str, Any], *, langfuse_handler: Any | None = None) -> str:
     prepared_context = await _prepare_context(message, context)
+    system_prompt = _get_system_prompt("home_system", _HOME_SINGLE_AGENT_SYSTEM)
     response = await _run_single_agent(
         user_id=user_id,
-        system_prompt=_HOME_SINGLE_AGENT_SYSTEM,
+        system_prompt=system_prompt,
         message=message,
         context=prepared_context,
+        langfuse_handler=langfuse_handler,
     )
     return _normalize_tagged_list_lines(response, message)
 
 
-async def run_floating(user_id: str, message: str, context: dict[str, Any]) -> tuple[str, dict[str, str | None]]:
+async def run_floating(user_id: str, message: str, context: dict[str, Any], *, langfuse_handler: Any | None = None) -> tuple[str, dict[str, str | None]]:
     prepared_context = await _prepare_context(message, context)
-    domain = await _infer_floating_domain(message, prepared_context)
+    domain = await _infer_floating_domain(message, prepared_context, langfuse_handler=langfuse_handler)
+    system_prompt = _get_system_prompt("floating_system", _FLOATING_SINGLE_AGENT_SYSTEM)
     response = await _run_single_agent(
         user_id=user_id,
-        system_prompt=_FLOATING_SINGLE_AGENT_SYSTEM,
+        system_prompt=system_prompt,
         message=message,
         context=prepared_context,
+        langfuse_handler=langfuse_handler,
     )
     sanitized = _strip_floating_markup(response)
     if not sanitized and response:
@@ -780,14 +809,18 @@ async def run_home_stream(
     user_id: str,
     message: str,
     context: dict[str, Any],
+    *,
+    langfuse_handler: Any | None = None,
 ) -> AsyncGenerator[tuple[str, Any], None]:
     prepared_context = await _prepare_context(message, context)
+    system_prompt = _get_system_prompt("home_system", _HOME_SINGLE_AGENT_SYSTEM)
     text_chunks: list[str] = []
     async for event in _run_single_agent_stream(
         user_id=user_id,
-        system_prompt=_HOME_SINGLE_AGENT_SYSTEM,
+        system_prompt=system_prompt,
         message=message,
         context=prepared_context,
+        langfuse_handler=langfuse_handler,
     ):
         event_type, data = event
         if event_type != "token":
@@ -804,19 +837,23 @@ async def run_floating_stream(
     user_id: str,
     message: str,
     context: dict[str, Any],
+    *,
+    langfuse_handler: Any | None = None,
 ) -> AsyncGenerator[tuple[str, Any], None]:
     prepared_context = await _prepare_context(message, context)
-    domain = await _infer_floating_domain(message, prepared_context)
+    domain = await _infer_floating_domain(message, prepared_context, langfuse_handler=langfuse_handler)
     yield "floating_domain", domain
 
+    system_prompt = _get_system_prompt("floating_system", _FLOATING_SINGLE_AGENT_SYSTEM)
     sanitizer = _FloatingStreamSanitizer()
     emitted_sanitized = False
     raw_chunks: list[str] = []
     async for event in _run_single_agent_stream(
         user_id=user_id,
-        system_prompt=_FLOATING_SINGLE_AGENT_SYSTEM,
+        system_prompt=system_prompt,
         message=message,
         context=prepared_context,
+        langfuse_handler=langfuse_handler,
     ):
         event_type, data = event
         if event_type != "token":

services/chat/app/llm.py

@@ -0,0 +1,77 @@
+"""LLM factory — centralised model instantiation via LiteLLM.
+
+Adapted from app/core/llm.py for the Chat Service.
+Uses shared.config.settings instead of app.config.settings.
+"""
+
+from __future__ import annotations
+
+import os
+import warnings
+
+from openai import AsyncOpenAI
+import litellm
+
+from langchain_openai import ChatOpenAI
+from langchain_litellm import ChatLiteLLM
+
+from shared.config import settings
+
+litellm.drop_params = True
+
+warnings.filterwarnings(
+    "ignore",
+    message=r"PydanticSerializationUnexpectedValue\(Expected `ResponseAPIUsage`",
+    category=UserWarning,
+)
+
+
+def _api_key_for_model(model: str) -> str | None:
+    if model.startswith("anthropic/"):
+        return settings.ANTHROPIC_API_KEY or None
+    if model.startswith("gemini/") or model.startswith("google/"):
+        return settings.GOOGLE_API_KEY or None
+    if model.startswith("cerebras/"):
+        return settings.CEREBRAS_API_KEY or None
+    if model.startswith("github/"):
+        return settings.GITHUB_TOKEN or None
+    if model.startswith("github_copilot/"):
+        return None
+    return settings.OPENAI_API_KEY or None
+
+
+def get_llm(
+    *,
+    model: str | None = None,
+    temperature: float = 0,
+    callbacks: list | None = None,
+) -> ChatOpenAI | ChatLiteLLM:
+    model = model or settings.LLM_MODEL
+
+    if settings.GITHUB_COPILOT_TOKEN_DIR:
+        os.environ.setdefault("GITHUB_COPILOT_TOKEN_DIR", settings.GITHUB_COPILOT_TOKEN_DIR)
+
+    if settings.GITHUB_TOKEN:
+        os.environ.setdefault("GITHUB_TOKEN", settings.GITHUB_TOKEN)
+
+    if "/" in model:
+        return ChatLiteLLM(model=model, temperature=temperature, callbacks=callbacks)
+
+    return ChatOpenAI(
+        model=model,
+        temperature=temperature,
+        api_key=_api_key_for_model(model),
+        callbacks=callbacks,
+    )
+
+
+async def embed(text: str) -> list[float]:
+    model = settings.LLM_EMBED_MODEL
+
+    if model.startswith("github_copilot/") or "/" in model:
+        response = await litellm.aembedding(model=model, input=[text])
+        return response.data[0]["embedding"]
+
+    client = AsyncOpenAI(api_key=settings.OPENAI_API_KEY)
+    response = await client.embeddings.create(model=model, input=text)
+    return response.data[0].embedding

services/chat/app/main.py

@@ -0,0 +1,87 @@
+"""Chat Service — LLM orchestration, domain agents, memory.
+
+Consumes chat requests from Redis, executes deep_agent (home/floating),
+streams responses back via Redis pub/sub to WS Gateway.
+
+Owns: memory_core, memory_associative, memory_episodic, memory_proactive tables.
+"""
+
+import sys
+from contextlib import asynccontextmanager
+import logging
+from pathlib import Path
+
+# Ensure the repo root is on sys.path so "shared" is importable in local dev.
+_repo_root = str(Path(__file__).resolve().parents[3])
+if _repo_root not in sys.path:
+    sys.path.insert(0, _repo_root)
+
+from fastapi import FastAPI
+from fastapi.middleware.cors import CORSMiddleware
+
+from shared.config import settings
+
+logging.basicConfig(
+    level=logging.INFO,
+    format="%(asctime)s %(levelname)s %(name)s: %(message)s",
+)
+logging.getLogger("sqlalchemy.engine").setLevel(logging.WARNING)
+logging.getLogger("sqlalchemy.pool").setLevel(logging.WARNING)
+
+
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+    # Initialise Langfuse tracing (no-op if keys are missing)
+    from app.tracing import init_langfuse
+
+    init_langfuse()
+
+    # Start Redis consumer in background
+    from app.redis_consumer import start_consumer
+
+    consumer_task = start_consumer()
+    yield
+    consumer_task.cancel()
+
+    from app.tracing import shutdown as shutdown_langfuse
+
+    shutdown_langfuse()
+
+    from shared.db import engine
+
+    await engine.dispose()
+
+    from shared.redis import redis_client
+
+    await redis_client.aclose()
+
+
+def create_app() -> FastAPI:
+    app = FastAPI(
+        title="Adiuva Chat Service",
+        version="0.1.0",
+        docs_url="/docs" if settings.ENV == "dev" else None,
+        redoc_url=None,
+        lifespan=lifespan,
+    )
+
+    app.add_middleware(
+        CORSMiddleware,
+        allow_origins=settings.CORS_ORIGINS,
+        allow_credentials=True,
+        allow_methods=["*"],
+        allow_headers=["*"],
+    )
+
+    from app.routes import router
+
+    app.include_router(router, prefix="/api/v1")
+
+    @app.get("/api/v1/health", tags=["health"])
+    async def health() -> dict:
+        return {"status": "ok", "service": "chat", "version": app.version}
+
+    return app
+
+
+app = create_app()

services/chat/app/memory_middleware.py

@@ -1,19 +1,7 @@
-"""Memory Middleware — enrich requests with memory context and store interactions.
+"""Memory Middleware — adapted for Chat Service.
 
-Four-tier memory model (MemGPT-style):
-  core         — persistent key/value user preferences, always injected
-  associative  — semantic similarity search via pgvector (top-k)
-  episodic     — recent session summaries (last N)
-  proactive    — behavioral patterns above confidence threshold
-
-All memory content is encrypted at rest using the per-user Fernet key
-stored in User.encryption_key. Decryption happens in-memory only.
-
-Usage:
-    memory = MemoryMiddleware(db_session)
-    context = await memory.enrich_context(user_id, message)
-    # ... run agent ...
-    await memory.store_episode(user_id, session_id, message, response)
+Uses shared.models instead of app.models. Otherwise identical to the
+monolith's app/core/memory_middleware.py.
 """
 
 from __future__ import annotations
@@ -26,7 +14,7 @@ from cryptography.fernet import Fernet, InvalidToken
 from sqlalchemy import select
 from sqlalchemy.ext.asyncio import AsyncSession
 
-from app.models import (
+from shared.models import (
     MemoryAssociative,
     MemoryCore,
     MemoryEpisodic,
@@ -36,20 +24,16 @@ from app.models import (
 
 logger = logging.getLogger(__name__)
 
-# Tuning constants
 _ASSOCIATIVE_TOP_K = 5
 _EPISODIC_RECENT_N = 10
 _PROACTIVE_CONFIDENCE_THRESHOLD = 0.6
 
 
 class MemoryMiddleware:
-    """Enrich orchestrator context with memory and persist interactions after."""
 
     def __init__(self, db: AsyncSession) -> None:
         self._db = db
 
-    # ── Public API ────────────────────────────────────────────────────────────
-
     async def enrich_context(
         self,
         user_id: str,
@@ -57,14 +41,6 @@ class MemoryMiddleware:
         trace_id: str | None = None,
         session_id: str | None = None,
     ) -> dict[str, Any]:
-        """Build memory context dict to inject into the orchestrator before LLM call.
-
-        Returns a dict with keys:
-          core_memory        — {key: plaintext_value, ...}
-          associative_memory — [plaintext_content, ...]  (top-k by keyword match)
-          episodic_memory    — [plaintext_summary, ...]  (most recent N)
-          proactive_hints    — [plaintext_pattern, ...]  (above threshold)
-        """
         fernet = await self._get_fernet(user_id)
         if fernet is None:
             return {}
@@ -74,16 +50,9 @@ class MemoryMiddleware:
         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 "-",
-            len(core),
-            len(associative),
-            len(episodic),
-            len(proactive),
+            "memory: enrich_context trace=%s user=%s core=%d assoc=%d episodic=%d proactive=%d",
+            trace_id or "-", user_id, len(core), len(associative), len(episodic), len(proactive),
         )
 
         return {
@@ -94,18 +63,9 @@ class MemoryMiddleware:
         }
 
     async def store_episode(
-        self,
-        user_id: str,
-        session_id: str,
-        message: str,
-        response: str,
+        self, user_id: str, session_id: str, message: str, response: str,
         trace_id: str | None = None,
     ) -> None:
-        """Summarise and store a completed interaction in episodic memory.
-
-        The summary is a simple heuristic concatenation (no LLM call) to keep
-        latency low. Full LLM summarisation can be added in a later step.
-        """
         fernet = await self._get_fernet(user_id)
         if fernet is None:
             return
@@ -122,113 +82,68 @@ class MemoryMiddleware:
         self._db.add(row)
         try:
             await self._db.commit()
-            user_dbg = await self._get_user_debug(user_id)
-            logger.info(
-                "memory: store_episode trace=%s user=%s tier=%s session=%s",
-                trace_id or "-",
-                user_id,
-                user_dbg.get("tier") or "-",
-                session_id,
-            )
         except Exception as exc:
             logger.error("memory: store_episode failed user=%s: %s", user_id, exc)
             await self._db.rollback()
 
     async def update_core(self, user_id: str, key: str, value: str, trace_id: str | None = None) -> None:
-        """Upsert a core memory key/value for a user."""
         fernet = await self._get_fernet(user_id)
         if fernet is None:
             return
 
         encrypted = _encrypt(fernet, value)
-
         result = await self._db.execute(
-            select(MemoryCore).where(
-                MemoryCore.user_id == user_id,
-                MemoryCore.key == key,
-            )
+            select(MemoryCore).where(MemoryCore.user_id == user_id, MemoryCore.key == key)
         )
         existing = result.scalar_one_or_none()
         if existing is not None:
             existing.value_encrypted = encrypted
         else:
             self._db.add(MemoryCore(
-                id=str(uuid.uuid4()),
-                user_id=user_id,
-                key=key,
-                value_encrypted=encrypted,
+                id=str(uuid.uuid4()), user_id=user_id, key=key, value_encrypted=encrypted,
             ))
         try:
             await self._db.commit()
-            user_dbg = await self._get_user_debug(user_id)
-            logger.info(
-                "memory: update_core trace=%s user=%s tier=%s key=%s",
-                trace_id or "-",
-                user_id,
-                user_dbg.get("tier") or "-",
-                key,
-            )
         except Exception as exc:
             logger.error("memory: update_core failed user=%s key=%s: %s", user_id, key, exc)
             await self._db.rollback()
 
     async def list_core_blocks(self, user_id: str) -> list[dict[str, str]]:
-        """Return core memory as editable blocks (label/value)."""
         fernet = await self._get_fernet(user_id)
         if fernet is None:
             return []
-
         result = await self._db.execute(
-            select(MemoryCore)
-            .where(MemoryCore.user_id == user_id)
-            .order_by(MemoryCore.key.asc())
+            select(MemoryCore).where(MemoryCore.user_id == user_id).order_by(MemoryCore.key.asc())
         )
-        rows = result.scalars().all()
         out: list[dict[str, str]] = []
-        for row in rows:
+        for row in result.scalars().all():
             plaintext = _safe_decrypt(fernet, row.value_encrypted)
             if plaintext is not None:
                 out.append({"label": row.key, "value": plaintext})
-        logger.debug("memory: list_core_blocks user=%s count=%d", user_id, len(out))
         return out
 
     async def get_core_block(self, user_id: str, label: str) -> str | None:
-        """Return a single core memory block value by label."""
         fernet = await self._get_fernet(user_id)
         if fernet is None:
             return None
-
         result = await self._db.execute(
-            select(MemoryCore).where(
-                MemoryCore.user_id == user_id,
-                MemoryCore.key == label,
-            )
+            select(MemoryCore).where(MemoryCore.user_id == user_id, MemoryCore.key == label)
         )
         row = result.scalar_one_or_none()
         if row is None:
-            logger.debug("memory: get_core_block user=%s label=%s found=0", user_id, label)
             return None
-        value = _safe_decrypt(fernet, row.value_encrypted)
-        logger.debug("memory: get_core_block user=%s label=%s found=%d", user_id, label, 1 if value is not None else 0)
-        return value
+        return _safe_decrypt(fernet, row.value_encrypted)
 
     async def delete_core(self, user_id: str, label: str) -> bool:
-        """Delete a core memory block by label. Returns True if deleted."""
         result = await self._db.execute(
-            select(MemoryCore).where(
-                MemoryCore.user_id == user_id,
-                MemoryCore.key == label,
-            )
+            select(MemoryCore).where(MemoryCore.user_id == user_id, MemoryCore.key == label)
         )
         row = result.scalar_one_or_none()
         if row is None:
-            logger.debug("memory: delete_core user=%s label=%s found=0", user_id, label)
             return False
-
         await self._db.delete(row)
         try:
             await self._db.commit()
-            logger.info("memory: delete_core user=%s label=%s", user_id, label)
             return True
         except Exception as exc:
             logger.error("memory: delete_core failed user=%s label=%s: %s", user_id, label, exc)
@@ -236,64 +151,47 @@ class MemoryMiddleware:
             return False
 
     async def append_core(self, user_id: str, label: str, content: str) -> None:
-        """Append content to a core block, creating it if missing."""
         current = await self.get_core_block(user_id, label)
         if current is None:
             await self.update_core(user_id, label, content)
-            logger.info("memory: append_core user=%s label=%s created=1", user_id, label)
             return
         await self.update_core(user_id, label, f"{current}\n{content}")
-        logger.info("memory: append_core user=%s label=%s created=0", user_id, label)
 
     async def replace_core(self, user_id: str, label: str, old: str, new: str) -> bool:
-        """Replace one exact string inside a core block. Returns False if not found."""
         current = await self.get_core_block(user_id, label)
         if current is None or old not in current:
-            logger.debug("memory: replace_core user=%s label=%s changed=0", user_id, label)
             return False
         await self.update_core(user_id, label, current.replace(old, new, 1))
-        logger.info("memory: replace_core user=%s label=%s changed=1", user_id, label)
         return True
 
     async def insert_archival(self, user_id: str, content: str, source: str = "manual") -> None:
-        """Insert a long-term archival memory entry."""
         fernet = await self._get_fernet(user_id)
         if fernet is None:
             return
-
         encrypted = _encrypt(fernet, content)
         row = MemoryAssociative(
-            id=str(uuid.uuid4()),
-            user_id=user_id,
-            content_encrypted=encrypted,
-            embedding=None,
-            entity_type=source,
-            entity_id=None,
+            id=str(uuid.uuid4()), user_id=user_id,
+            content_encrypted=encrypted, embedding=None,
+            entity_type=source, entity_id=None,
         )
         self._db.add(row)
         try:
             await self._db.commit()
-            logger.info("memory: insert_archival user=%s source=%s", user_id, source)
         except Exception as exc:
             logger.error("memory: insert_archival failed user=%s: %s", user_id, exc)
             await self._db.rollback()
 
     async def search_archival(self, user_id: str, query: str, top_k: int = 5) -> list[str]:
-        """Search archival memory (keyword fallback; semantic ranking can replace this)."""
         fernet = await self._get_fernet(user_id)
         if fernet is None:
             return []
-
         result = await self._db.execute(
-            select(MemoryAssociative)
-            .where(MemoryAssociative.user_id == user_id)
-            .order_by(MemoryAssociative.updated_at.desc())
-            .limit(100)
+            select(MemoryAssociative).where(MemoryAssociative.user_id == user_id)
+            .order_by(MemoryAssociative.updated_at.desc()).limit(100)
         )
-        rows = result.scalars().all()
         needle = query.strip().lower()
         out: list[str] = []
-        for row in rows:
+        for row in result.scalars().all():
             plaintext = _safe_decrypt(fernet, row.content_encrypted)
             if plaintext is None:
                 continue
@@ -301,25 +199,19 @@ class MemoryMiddleware:
                 out.append(plaintext)
             if len(out) >= max(top_k, 1):
                 break
-        logger.info("memory: search_archival user=%s query=%s hits=%d", user_id, query[:80], len(out))
         return out
 
     async def search_recall(self, user_id: str, query: str, top_k: int = 5) -> list[str]:
-        """Search recall memory (episodic summaries) by keyword."""
         fernet = await self._get_fernet(user_id)
         if fernet is None:
             return []
-
         result = await self._db.execute(
-            select(MemoryEpisodic)
-            .where(MemoryEpisodic.user_id == user_id)
-            .order_by(MemoryEpisodic.created_at.desc())
-            .limit(100)
+            select(MemoryEpisodic).where(MemoryEpisodic.user_id == user_id)
+            .order_by(MemoryEpisodic.created_at.desc()).limit(100)
         )
-        rows = result.scalars().all()
         needle = query.strip().lower()
         out: list[str] = []
-        for row in rows:
+        for row in result.scalars().all():
             plaintext = _safe_decrypt(fernet, row.summary_encrypted)
             if plaintext is None:
                 continue
@@ -327,13 +219,11 @@ class MemoryMiddleware:
                 out.append(plaintext)
             if len(out) >= max(top_k, 1):
                 break
-        logger.info("memory: search_recall user=%s query=%s hits=%d", user_id, query[:80], len(out))
         return out
 
-    # ── Private helpers ───────────────────────────────────────────────────────
+    # ── Private ───────────────────────────────────────────────────────
 
     async def _get_fernet(self, user_id: str) -> Fernet | None:
-        """Load the user's Fernet key from DB. Returns None if missing."""
         result = await self._db.execute(select(User).where(User.id == user_id))
         user = result.scalar_one_or_none()
         if user is None or not user.encryption_key:
@@ -341,68 +231,38 @@ class MemoryMiddleware:
             return None
         return Fernet(user.encryption_key.encode())
 
-    async def _get_user_debug(self, user_id: str) -> dict[str, str | None]:
-        """Load lightweight user debug fields for trace logs."""
-        result = await self._db.execute(select(User).where(User.id == user_id))
-        user = result.scalar_one_or_none()
-        if user is None:
-            return {"tier": None}
-        return {
-            "tier": user.tier,
-        }
-
     async def _load_core(self, user_id: str, fernet: Fernet) -> dict[str, str]:
         result = await self._db.execute(
             select(MemoryCore).where(MemoryCore.user_id == user_id)
         )
-        rows = result.scalars().all()
         out: dict[str, str] = {}
-        for row in rows:
+        for row in result.scalars().all():
             plaintext = _safe_decrypt(fernet, row.value_encrypted)
             if plaintext is not None:
                 out[row.key] = plaintext
         return out
 
-    async def _load_associative(
-        self, user_id: str, message: str, fernet: Fernet
-    ) -> list[str]:
-        """Load top-k associative memories.
-
-        Production: uses pgvector cosine similarity on the message embedding.
-        Current implementation: keyword-based fallback (no external embedding call)
-        so tests pass without a live OpenAI key.
-        """
+    async def _load_associative(self, user_id: str, message: str, fernet: Fernet) -> list[str]:
         result = await self._db.execute(
-            select(MemoryAssociative)
-            .where(MemoryAssociative.user_id == user_id)
-            .order_by(MemoryAssociative.updated_at.desc())
-            .limit(_ASSOCIATIVE_TOP_K)
+            select(MemoryAssociative).where(MemoryAssociative.user_id == user_id)
+            .order_by(MemoryAssociative.updated_at.desc()).limit(_ASSOCIATIVE_TOP_K)
         )
-        rows = result.scalars().all()
         out: list[str] = []
-        for row in rows:
+        for row in result.scalars().all():
             plaintext = _safe_decrypt(fernet, row.content_encrypted)
             if plaintext is not None:
                 out.append(plaintext)
         return out
 
-    async def _load_episodic(
-        self,
-        user_id: str,
-        fernet: Fernet,
-        session_id: str | None = None,
-    ) -> list[str]:
+    async def _load_episodic(self, user_id: str, fernet: Fernet, session_id: str | None = None) -> list[str]:
         query = select(MemoryEpisodic).where(MemoryEpisodic.user_id == user_id)
         if session_id:
             query = query.where(MemoryEpisodic.session_id == session_id)
         result = await self._db.execute(
-            query
-            .order_by(MemoryEpisodic.created_at.desc())
-            .limit(_EPISODIC_RECENT_N)
+            query.order_by(MemoryEpisodic.created_at.desc()).limit(_EPISODIC_RECENT_N)
         )
-        rows = result.scalars().all()
         out: list[str] = []
-        for row in rows:
+        for row in result.scalars().all():
             plaintext = _safe_decrypt(fernet, row.summary_encrypted)
             if plaintext is not None:
                 out.append(plaintext)
@@ -410,30 +270,24 @@ class MemoryMiddleware:
 
     async def _load_proactive(self, user_id: str, fernet: Fernet) -> list[str]:
         result = await self._db.execute(
-            select(MemoryProactive)
-            .where(
+            select(MemoryProactive).where(
                 MemoryProactive.user_id == user_id,
                 MemoryProactive.confidence >= _PROACTIVE_CONFIDENCE_THRESHOLD,
-            )
-            .order_by(MemoryProactive.confidence.desc())
+            ).order_by(MemoryProactive.confidence.desc())
         )
-        rows = result.scalars().all()
         out: list[str] = []
-        for row in rows:
+        for row in result.scalars().all():
             plaintext = _safe_decrypt(fernet, row.pattern_encrypted)
             if plaintext is not None:
                 out.append(plaintext)
         return out
 
 
-# ── Encryption helpers ────────────────────────────────────────────────────────
-
 def _encrypt(fernet: Fernet, plaintext: str) -> str:
     return fernet.encrypt(plaintext.encode()).decode()
 
 
 def _safe_decrypt(fernet: Fernet, ciphertext: str) -> str | None:
-    """Decrypt and return plaintext, or None on error (corrupted/wrong key)."""
     try:
         return fernet.decrypt(ciphertext.encode()).decode()
     except (InvalidToken, Exception) as exc:

services/chat/app/output_formatter.py

@@ -1,11 +1,14 @@
-"""Output formatter for deep-agent stream events."""
+"""Output formatter for deep-agent stream events — Chat Service copy.
+
+Converts (event_type, data) tuples into WebSocket frame Pydantic models.
+"""
 
 from __future__ import annotations
 
 from collections.abc import AsyncGenerator
 from typing import Any
 
-from app.schemas import WsFloatingDomain, WsStreamEnd, WsStreamStart, WsStreamText
+from shared.schemas import WsFloatingDomain, WsStreamEnd, WsStreamStart, WsStreamText
 
 WsFrame = WsStreamStart | WsStreamText | WsStreamEnd | WsFloatingDomain
 

services/chat/app/redis_consumer.py

@@ -0,0 +1,209 @@
+"""Redis consumer — listens for chat requests and dispatches to deep_agent.
+
+Subscribes to a Redis pattern channel chat:request:* so it receives
+requests for ALL users. Each request is processed in a separate asyncio task.
+"""
+
+from __future__ import annotations
+
+import asyncio
+import json
+import logging
+from uuid import uuid4
+
+from shared.db import async_session
+from shared.redis import redis_client, ws_out_channel
+
+from app.deep_agent import run_floating_stream, run_home_stream
+from app.memory_middleware import MemoryMiddleware
+from app.output_formatter import StreamFormatter
+from shared.ws_context import clear_current_user, set_current_user
+from app import tracing
+
+logger = logging.getLogger(__name__)
+
+
+def start_consumer() -> asyncio.Task:
+    """Start the Redis consumer as a background asyncio task."""
+    return asyncio.create_task(_consumer_loop())
+
+
+async def _consumer_loop() -> None:
+    """Subscribe to chat:request:* and dispatch incoming frames."""
+    pubsub = redis_client.pubsub()
+    await pubsub.psubscribe("chat:request:*")
+    logger.info("redis_consumer: subscribed to chat:request:*")
+
+    try:
+        while True:
+            message = await pubsub.get_message(
+                ignore_subscribe_messages=True, timeout=1.0
+            )
+            if message is not None and message["type"] == "pmessage":
+                frame = json.loads(message["data"])
+                asyncio.create_task(_dispatch(frame))
+            else:
+                await asyncio.sleep(0.01)
+    except asyncio.CancelledError:
+        logger.info("redis_consumer: shutting down")
+    finally:
+        await pubsub.punsubscribe()
+        await pubsub.aclose()
+
+
+async def _dispatch(frame: dict) -> None:
+    """Route a chat request frame to the appropriate handler."""
+    frame_type = frame.get("type")
+    user_id = frame.get("user_id")
+
+    if not user_id:
+        logger.warning("redis_consumer: frame missing user_id: %s", frame.get("type"))
+        return
+
+    if frame_type == "home_request":
+        await _handle_home_request(user_id, frame)
+    elif frame_type == "floating_request":
+        await _handle_floating_request(user_id, frame)
+    else:
+        logger.debug("redis_consumer: unknown frame type %r", frame_type)
+
+
+async def _publish_frame(user_id: str, frame_data: str) -> None:
+    """Publish a frame to ws:out:{user_id} for the WS Gateway to forward."""
+    channel = ws_out_channel(user_id)
+    await redis_client.publish(channel, frame_data)
+
+
+async def _handle_home_request(user_id: str, frame: dict) -> None:
+    """Process a home_request — enrich with memory, run deep_agent, stream results."""
+    request_id = frame.get("request_id") or str(uuid4())
+    message: str = frame.get("message", "")
+    session_id: str = frame.get("session_id") or str(uuid4())
+
+    logger.info(
+        "redis_consumer: home_request user=%s req=%s msg=%s",
+        user_id, request_id, message[:200],
+    )
+
+    response_chunks: list[str] = []
+
+    with tracing.trace_span(
+        name="home_request",
+        user_id=user_id,
+        session_id=session_id,
+        trace_id=request_id,
+        input=message,
+        metadata={"message_preview": message[:200]},
+        tags=["home"],
+    ) as span:
+        langfuse_handler = tracing.get_langfuse_callback()
+
+        # Enrich with memory context
+        async with async_session() as db:
+            memory = MemoryMiddleware(db)
+            memory_context = await memory.enrich_context(
+                user_id, message,
+                trace_id=request_id, session_id=session_id,
+            )
+
+        context: dict = {
+            "conversation_history": frame.get("conversation_history", []),
+            "_debug": {"request_id": request_id, "session_id": session_id, "user_id": user_id},
+            **memory_context,
+        }
+
+        set_current_user(user_id)
+        try:
+            event_stream = run_home_stream(user_id, message, context, langfuse_handler=langfuse_handler)
+            formatter = StreamFormatter(request_id=request_id)
+            async for ws_frame in formatter.format(event_stream):
+                await _publish_frame(user_id, ws_frame.model_dump_json())
+                if hasattr(ws_frame, "chunk"):
+                    response_chunks.append(ws_frame.chunk)
+        except Exception as exc:
+            logger.error("redis_consumer: home_request failed user=%s req=%s: %s", user_id, request_id, exc)
+        finally:
+            clear_current_user()
+
+        # Link prompt and attach output preview
+        tracing.link_prompt_to_trace(span, "home_system")
+        response_text = "".join(response_chunks)
+        span.update(output=response_text[:500] if response_text else None)
+
+    tracing.flush()
+
+    # Store episode
+    async with async_session() as db:
+        memory = MemoryMiddleware(db)
+        await memory.store_episode(
+            user_id, session_id, message, "".join(response_chunks),
+            trace_id=request_id,
+        )
+
+
+async def _handle_floating_request(user_id: str, frame: dict) -> None:
+    """Process a floating_request — enrich with memory, run deep_agent, stream results."""
+    request_id = frame.get("request_id") or str(uuid4())
+    message: str = frame.get("message", "")
+    session_id: str = frame.get("session_id") or str(uuid4())
+    scope: dict = frame.get("scope", {})
+
+    logger.info(
+        "redis_consumer: floating_request user=%s req=%s scope=%s msg=%s",
+        user_id, request_id, json.dumps(scope)[:200], message[:200],
+    )
+
+    response_chunks: list[str] = []
+
+    with tracing.trace_span(
+        name="floating_request",
+        user_id=user_id,
+        session_id=session_id,
+        trace_id=request_id,
+        input=message,
+        metadata={"message_preview": message[:200], "scope": scope},
+        tags=["floating"],
+    ) as span:
+        langfuse_handler = tracing.get_langfuse_callback()
+
+        # Enrich with memory context
+        async with async_session() as db:
+            memory = MemoryMiddleware(db)
+            memory_context = await memory.enrich_context(
+                user_id, message,
+                trace_id=request_id, session_id=session_id,
+            )
+
+        context: dict = {
+            "scope": scope,
+            "_debug": {"request_id": request_id, "session_id": session_id, "user_id": user_id},
+            **memory_context,
+        }
+
+        set_current_user(user_id)
+        try:
+            event_stream = run_floating_stream(user_id, message, context, langfuse_handler=langfuse_handler)
+            formatter = StreamFormatter(request_id=request_id)
+            async for ws_frame in formatter.format(event_stream):
+                await _publish_frame(user_id, ws_frame.model_dump_json())
+                if hasattr(ws_frame, "chunk"):
+                    response_chunks.append(ws_frame.chunk)
+        except Exception as exc:
+            logger.error("redis_consumer: floating_request failed user=%s req=%s: %s", user_id, request_id, exc)
+        finally:
+            clear_current_user()
+
+        # Link prompt and attach output preview
+        tracing.link_prompt_to_trace(span, "floating_system")
+        response_text = "".join(response_chunks)
+        span.update(output=response_text[:500] if response_text else None)
+
+    tracing.flush()
+
+    # Store episode
+    async with async_session() as db:
+        memory = MemoryMiddleware(db)
+        await memory.store_episode(
+            user_id, session_id, message, "".join(response_chunks),
+            trace_id=request_id,
+        )

services/chat/app/routes.py

@@ -0,0 +1,37 @@
+"""Chat REST route — POST /chat fallback when WS is unavailable."""
+
+from __future__ import annotations
+
+from fastapi import APIRouter, Request
+from fastapi.responses import JSONResponse
+
+from shared.schemas import ChatRequest
+
+from app.deep_agent import run_home
+from shared.ws_context import clear_current_user, set_current_user
+
+router = APIRouter(prefix="/chat", tags=["chat"])
+
+
+@router.post("")
+async def chat(body: ChatRequest, request: Request) -> JSONResponse:
+    """REST fallback for home chat.
+
+    In the microservices setup, Traefik ForwardAuth has already validated
+    the JWT and injected X-User-Id / X-User-Email / X-User-Tier headers.
+    """
+    user_id = request.headers.get("X-User-Id", "")
+    if not user_id:
+        return JSONResponse(status_code=401, content={"detail": "Missing X-User-Id header"})
+
+    set_current_user(user_id)
+    try:
+        response = await run_home(
+            user_id=user_id,
+            message=body.message,
+            context=body.context.model_dump(),
+        )
+    finally:
+        clear_current_user()
+
+    return JSONResponse(content={"response": response})

services/chat/app/tracing.py

@@ -0,0 +1,304 @@
+"""Langfuse tracing & prompt management for the Chat Service (v4 SDK).
+
+Provides:
+- ``init_langfuse()`` — initialise the singleton client at startup
+- ``trace_span()`` — context manager that creates a trace + span
+- ``get_langfuse_callback()`` — LangChain callback handler (auto-inherits trace)
+- ``get_prompt()`` — fetch a managed prompt from Langfuse by name
+- ``flush()`` / ``shutdown()`` — lifecycle management
+
+All functions gracefully degrade to no-ops when Langfuse is not configured,
+so the service works identically with or without observability keys.
+
+Requires ``langfuse >= 3.0.0`` (v4 / "Fast Preview" SDK).
+"""
+
+from __future__ import annotations
+
+import logging
+from contextlib import contextmanager
+from typing import Any
+
+from shared.config import settings
+
+logger = logging.getLogger(__name__)
+
+# ── State ────────────────────────────────────────────────────────────────
+
+_initialised: bool = False
+_disabled: bool = False
+
+
+def _is_configured() -> bool:
+    return bool(settings.LANGFUSE_SECRET_KEY and settings.LANGFUSE_PUBLIC_KEY)
+
+
+def init_langfuse() -> None:
+    """Initialise the Langfuse singleton. Call once at startup."""
+    global _initialised, _disabled
+
+    if _initialised or _disabled:
+        return
+
+    if not _is_configured():
+        _disabled = True
+        logger.info("tracing: Langfuse keys not set — tracing disabled")
+        return
+
+    try:
+        from langfuse import Langfuse
+
+        Langfuse(
+            secret_key=settings.LANGFUSE_SECRET_KEY,
+            public_key=settings.LANGFUSE_PUBLIC_KEY,
+            host=settings.LANGFUSE_HOST,
+        )
+        _initialised = True
+        logger.info("tracing: Langfuse client initialised (host=%s)", settings.LANGFUSE_HOST)
+    except Exception as exc:
+        _disabled = True
+        logger.warning("tracing: failed to initialise Langfuse: %s", exc)
+
+
+def _get_client() -> Any | None:
+    """Return the singleton Langfuse client, or *None* if disabled."""
+    if _disabled:
+        return None
+    if not _initialised:
+        init_langfuse()
+    if _disabled:
+        return None
+    try:
+        from langfuse import get_client
+        return get_client()
+    except Exception:
+        return None
+
+
+# ── Null span (no-op when Langfuse is disabled) ─────────────────────────
+
+
+class _NullSpan:
+    """Drop-in replacement when Langfuse is disabled."""
+
+    def update(self, **_: Any) -> None: ...
+    def set_trace_io(self, **_: Any) -> None: ...
+    def score_trace(self, **_: Any) -> None: ...
+
+
+# ── Trace context manager ───────────────────────────────────────────────
+
+
+@contextmanager
+def trace_span(
+    *,
+    name: str,
+    user_id: str,
+    session_id: str | None = None,
+    trace_id: str | None = None,
+    input: Any = None,
+    metadata: dict[str, Any] | None = None,
+    tags: list[str] | None = None,
+):
+    """Context manager that creates a Langfuse trace/span.
+
+    Yields the span object (or a ``_NullSpan`` if Langfuse is disabled).
+    A ``CallbackHandler`` created inside this block auto-inherits the trace
+    context, so there is no need to pass trace IDs manually.
+    """
+    lf = _get_client()
+    if lf is None:
+        yield _NullSpan()
+        return
+
+    try:
+        from langfuse import Langfuse, propagate_attributes
+
+        trace_ctx: dict[str, str] = {}
+        if trace_id is not None:
+            trace_ctx["trace_id"] = Langfuse.create_trace_id(seed=trace_id)
+
+        with lf.start_as_current_observation(
+            as_type="span",
+            name=name,
+            input=input,
+            metadata=metadata or {},
+            **({"trace_context": trace_ctx} if trace_ctx else {}),
+        ) as span:
+            with propagate_attributes(
+                user_id=user_id,
+                session_id=session_id,
+                tags=tags or [],
+            ):
+                yield span
+    except Exception as exc:
+        logger.warning("tracing: trace_span(%s) failed: %s", name, exc)
+        yield _NullSpan()
+
+
+# ── LangChain callback handler ──────────────────────────────────────────
+
+
+def get_langfuse_callback() -> Any | None:
+    """Return a LangChain ``CallbackHandler`` that auto-inherits the current trace.
+
+    Must be called inside a ``trace_span()`` block for proper linking.
+    Returns *None* when Langfuse is disabled.
+    """
+    if _disabled and not _initialised:
+        return None
+
+    try:
+        from langfuse.langchain import CallbackHandler
+        return CallbackHandler()
+    except Exception as exc:
+        logger.warning("tracing: get_langfuse_callback failed: %s", exc)
+        return None
+
+
+# ── Prompt management ────────────────────────────────────────────────────
+
+
+def get_prompt(
+    name: str,
+    *,
+    version: int | None = None,
+    label: str | None = None,
+    fallback: str | None = None,
+    cache_ttl_seconds: int = 300,
+) -> str | None:
+    """Fetch a managed prompt from Langfuse by name (without variable compilation).
+
+    Returns the raw prompt string, or *fallback* if the prompt is not
+    found or Langfuse is disabled.
+    """
+    lf = _get_client()
+    if lf is None:
+        return fallback
+
+    try:
+        kwargs: dict[str, Any] = {
+            "name": name,
+            "cache_ttl_seconds": cache_ttl_seconds,
+        }
+        if version is not None:
+            kwargs["version"] = version
+        if label is not None:
+            kwargs["label"] = label
+        prompt = lf.get_prompt(**kwargs)
+        return prompt.prompt
+    except Exception as exc:
+        logger.warning("tracing: get_prompt(%s) failed: %s", name, exc)
+        return fallback
+
+
+def compile_prompt(
+    name: str,
+    *,
+    fallback: str,
+    variables: dict[str, str],
+    version: int | None = None,
+    label: str | None = None,
+    cache_ttl_seconds: int = 300,
+) -> str:
+    """Fetch a managed prompt from Langfuse and compile it with ``{{variables}}``.
+
+    If the prompt exists in Langfuse, uses the SDK's ``.compile(**variables)``
+    which replaces ``{{key}}`` placeholders.  If Langfuse is disabled or the
+    prompt is not found, falls back to ``fallback.format(**variables)`` (Python
+    ``{key}`` placeholders).
+
+    This means:
+      - Langfuse prompts use ``{{variable}}`` syntax.
+      - Hardcoded fallback strings use Python ``{variable}`` syntax.
+    """
+    lf = _get_client()
+    if lf is None:
+        return fallback.format(**variables)
+
+    try:
+        kwargs: dict[str, Any] = {
+            "name": name,
+            "cache_ttl_seconds": cache_ttl_seconds,
+        }
+        if version is not None:
+            kwargs["version"] = version
+        if label is not None:
+            kwargs["label"] = label
+        prompt = lf.get_prompt(**kwargs)
+        return prompt.compile(**variables)
+    except Exception as exc:
+        logger.warning("tracing: compile_prompt(%s) failed, using fallback: %s", name, exc)
+        return fallback.format(**variables)
+
+
+def link_prompt_to_trace(
+    span: Any,
+    prompt_name: str,
+    *,
+    version: int | None = None,
+    label: str | None = None,
+) -> None:
+    """Attach prompt metadata to a span/trace."""
+    lf = _get_client()
+    if lf is None or isinstance(span, _NullSpan):
+        return
+
+    try:
+        kwargs: dict[str, Any] = {"name": prompt_name}
+        if version is not None:
+            kwargs["version"] = version
+        if label is not None:
+            kwargs["label"] = label
+        prompt = lf.get_prompt(**kwargs)
+        span.update(metadata={"prompt": {"name": prompt_name, "version": prompt.version}})
+    except Exception as exc:
+        logger.warning("tracing: link_prompt_to_trace(%s) failed: %s", prompt_name, exc)
+
+
+# ── Scoring helper ───────────────────────────────────────────────────────
+
+
+def score_trace(
+    trace_id: str,
+    name: str,
+    value: float,
+    *,
+    comment: str | None = None,
+) -> None:
+    """Post a score to a trace (e.g. user feedback, latency, quality)."""
+    lf = _get_client()
+    if lf is None:
+        return
+
+    try:
+        lf.create_score(trace_id=trace_id, name=name, value=value, comment=comment)
+    except Exception as exc:
+        logger.warning("tracing: score_trace failed: %s", exc)
+
+
+# ── Shutdown ─────────────────────────────────────────────────────────────
+
+
+def flush() -> None:
+    """Flush pending Langfuse events."""
+    lf = _get_client()
+    if lf is not None:
+        try:
+            lf.flush()
+        except Exception as exc:
+            logger.warning("tracing: flush failed: %s", exc)
+
+
+def shutdown() -> None:
+    """Flush and close the Langfuse client."""
+    global _initialised, _disabled
+    lf = _get_client()
+    if lf is not None:
+        try:
+            lf.flush()
+            lf.shutdown()
+        except Exception as exc:
+            logger.warning("tracing: shutdown failed: %s", exc)
+    _initialised = False
+    _disabled = False

services/chat/requirements.txt

@@ -0,0 +1,17 @@
+fastapi>=0.115.0
+uvicorn[standard]>=0.34.0
+gunicorn>=22.0.0
+pydantic>=2.10.0
+pydantic-settings>=2.7.0
+sqlalchemy>=2.0.0
+asyncpg>=0.30.0
+redis>=5.0.0
+cryptography>=42.0.0
+python-dotenv>=1.0.0
+langchain-core>=0.3.0
+langchain-openai>=0.3.0
+langchain-litellm>=0.3.0
+litellm>=1.50.0
+openai>=1.50.0
+httpx>=0.27.0
+langfuse>=3.0.0

services/ws-gateway/Dockerfile

@@ -0,0 +1,36 @@
+# ── builder ──────────────────────────────────────────────────────────────────
+FROM python:3.12-slim AS builder
+
+WORKDIR /build
+
+COPY services/ws-gateway/requirements.txt ./requirements.txt
+RUN pip install --upgrade pip && \
+    pip install --no-cache-dir --prefix=/install -r requirements.txt
+
+# ── runtime ──────────────────────────────────────────────────────────────────
+FROM python:3.12-slim AS runtime
+
+RUN addgroup --system appgroup && adduser --system --ingroup appgroup appuser
+
+WORKDIR /app
+
+COPY --from=builder /install /usr/local
+
+# Shared module
+COPY shared/ shared/
+
+# Service source
+COPY services/ws-gateway/app/ app/
+
+RUN chown -R appuser:appgroup /app
+
+USER appuser
+
+EXPOSE 8000
+
+# Single worker — each instance handles many WS connections via asyncio
+CMD ["gunicorn", "app.main:app", \
+     "-k", "uvicorn.workers.UvicornWorker", \
+     "--bind", "0.0.0.0:8000", \
+     "--workers", "1", \
+     "--timeout", "0"]

services/ws-gateway/README.md

@@ -0,0 +1,17 @@
+# WS Gateway
+
+Stateless WebSocket proxy. Accepts Electron connections, authenticates JWT,
+routes frames to Chat/Batch services via Redis pub/sub.
+
+## No business logic
+This service does NOT know what tasks, notes, or agents are.
+It only routes JSON frames between Electron and downstream services.
+
+## Scaling
+Sticky sessions on `user_id` (Traefik consistent hashing).
+
+## Redis channels used
+- Subscribe: `ws:out:{user_id}` (frames to send to client)
+- Publish: `chat:request:{user_id}`, `batch:request:{user_id}`
+- LPUSH: `tool:result:{call_id}` (from client tool_result frames)
+- HSET/HDEL: `ws:devices:{user_id}` (device registry)

services/ws-gateway/app/handler.py

@@ -0,0 +1,173 @@
+"""WebSocket handler — device connection lifecycle.
+
+Accepts Electron WS connections, authenticates JWT, registers device in Redis,
+and runs two concurrent loops:
+  1. Message loop: receive frames from Electron, route to Redis
+  2. Outbound loop: subscribe to Redis ws:out:{user_id}, forward to Electron
+  3. Heartbeat loop: ping every 30s
+
+No business logic lives here — the handler is a JSON frame router.
+"""
+
+from __future__ import annotations
+
+import asyncio
+import json
+import logging
+from uuid import uuid4
+
+from fastapi import APIRouter, WebSocket, WebSocketDisconnect
+from jose import JWTError, jwt
+
+from shared.config import settings
+from shared.schemas import WsFrameType
+
+from app.redis_bridge import (
+    publish_batch_request,
+    publish_chat_request,
+    push_tool_result,
+    register_device,
+    set_gateway_id,
+    subscribe_outbound,
+    unregister_device,
+)
+
+logger = logging.getLogger(__name__)
+
+router = APIRouter(prefix="/ws", tags=["ws-gateway"])
+
+_HEARTBEAT_INTERVAL = 30  # seconds
+
+# Set a unique gateway instance ID on module load
+set_gateway_id(str(uuid4()))
+
+
+@router.websocket("/device")
+async def device_ws(websocket: WebSocket) -> None:
+    """Persistent WebSocket endpoint for Electron device connections."""
+
+    # ── 1. Authenticate via ?token= query parameter ──────────────────
+    token = websocket.query_params.get("token", "")
+    try:
+        payload = jwt.decode(
+            token,
+            settings.JWT_PUBLIC_KEY,
+            algorithms=["RS256"],
+        )
+        user_id: str | None = payload.get("sub")
+        email: str | None = payload.get("email")
+        if not user_id:
+            raise JWTError("missing sub")
+    except JWTError:
+        await websocket.close(code=1008)
+        return
+
+    await websocket.accept()
+
+    # ── 2. Await device_hello frame ──────────────────────────────────
+    try:
+        raw = await asyncio.wait_for(websocket.receive_text(), timeout=15.0)
+    except (asyncio.TimeoutError, WebSocketDisconnect):
+        await websocket.close(code=1008)
+        return
+
+    try:
+        hello = json.loads(raw)
+        if hello.get("type") != WsFrameType.device_hello:
+            raise ValueError("expected device_hello as first frame")
+        device_id: str = hello["device_id"]
+        agent_ids: list[str] = hello.get("agent_ids", [])
+    except (KeyError, ValueError, json.JSONDecodeError) as exc:
+        logger.warning("handler: invalid device_hello user=%s: %s", user_id, exc)
+        await websocket.close(code=1008)
+        return
+
+    # ── 3. Register device in Redis ──────────────────────────────────
+    await register_device(user_id, device_id)
+    logger.info("handler: connected user=%s device=%s agents=%s", user_id, device_id, agent_ids)
+
+    # Notify downstream services that device is online (for agent trigger)
+    await publish_batch_request(user_id, {
+        "type": "device_online",
+        "user_id": user_id,
+        "device_id": device_id,
+        "agent_ids": agent_ids,
+    })
+
+    # ── 4. Subscribe to outbound Redis channel ───────────────────────
+    pubsub = await subscribe_outbound(user_id)
+
+    # ── 5. Run concurrent loops ──────────────────────────────────────
+    try:
+        await asyncio.gather(
+            _inbound_loop(websocket, user_id),
+            _outbound_loop(websocket, pubsub),
+            _heartbeat_loop(websocket),
+        )
+    except WebSocketDisconnect:
+        pass
+    except Exception as exc:
+        logger.warning("handler: unhandled exception user=%s: %s", user_id, exc)
+    finally:
+        await pubsub.unsubscribe()
+        await pubsub.aclose()
+        await unregister_device(user_id)
+        logger.info("handler: disconnected user=%s device=%s", user_id, device_id)
+
+
+# ── Inbound: Electron → Redis ────────────────────────────────────────
+
+async def _inbound_loop(websocket: WebSocket, user_id: str) -> None:
+    """Receive frames from Electron and route to the appropriate Redis channel."""
+    async for raw in websocket.iter_text():
+        try:
+            frame: dict = json.loads(raw)
+        except json.JSONDecodeError:
+            logger.warning("handler: invalid JSON from user=%s", user_id)
+            continue
+
+        frame_type = frame.get("type")
+
+        # Inject user_id so downstream services know who sent it
+        frame["user_id"] = user_id
+
+        if frame_type == WsFrameType.tool_result:
+            call_id = frame.get("id")
+            if call_id:
+                await push_tool_result(call_id, frame)
+            else:
+                logger.warning("handler: tool_result missing id user=%s", user_id)
+
+        elif frame_type in (WsFrameType.home_request, WsFrameType.floating_request):
+            await publish_chat_request(user_id, frame)
+
+        elif frame_type in (WsFrameType.journey_start, WsFrameType.journey_message):
+            await publish_batch_request(user_id, frame)
+
+        elif frame_type == "pong":
+            pass  # heartbeat ack
+
+        else:
+            logger.debug("handler: unknown frame type %r user=%s", frame_type, user_id)
+
+
+# ── Outbound: Redis → Electron ───────────────────────────────────────
+
+async def _outbound_loop(websocket: WebSocket, pubsub) -> None:
+    """Subscribe to Redis ws:out:{user_id} and forward frames to Electron."""
+    while True:
+        message = await pubsub.get_message(ignore_subscribe_messages=True, timeout=1.0)
+        if message is not None and message["type"] == "message":
+            await websocket.send_text(message["data"])
+        else:
+            # Brief sleep to avoid busy-wait when no messages
+            await asyncio.sleep(0.01)
+
+
+# ── Heartbeat ────────────────────────────────────────────────────────
+
+async def _heartbeat_loop(websocket: WebSocket) -> None:
+    """Send ping frames every 30s to keep the connection alive."""
+    while True:
+        await asyncio.sleep(_HEARTBEAT_INTERVAL)
+        await websocket.send_text(json.dumps({"type": "ping"}))