deep agent

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

.env.example

@@ -4,17 +4,9 @@ ENV=dev
 # ── Database ──────────────────────────────────────────────────────────────────
 DATABASE_URL=postgresql+asyncpg://postgres:postgres@localhost:5432/adiuva
 
-# ── Redis ─────────────────────────────────────────────────────────────────────
-REDIS_URL=redis://localhost:6379/0
-
-# ── Auth (JWT RS256) ──────────────────────────────────────────────────────────
-# Public key for optional local JWT verification (Traefik ForwardAuth handles
-# this in production — services trust X-User-* headers from Traefik).
-# 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_PUBLIC_KEY=
+# ── Auth ──────────────────────────────────────────────────────────────────────
+JWT_SECRET=replace-with-a-long-random-secret
+JWT_ALGORITHM=HS256
 JWT_ACCESS_TOKEN_EXPIRE_MINUTES=30
 JWT_REFRESH_TOKEN_EXPIRE_DAYS=30
 
@@ -25,6 +17,7 @@ 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=
@@ -49,8 +42,3 @@ 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

.gitignore

@@ -13,9 +13,6 @@ env/
 # Environment variables
 .env
 
-# Cryptographic keys
-*.pem
-
 # IDE
 .vscode/
 .idea/
@@ -35,6 +32,3 @@ 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)
+│   │   ├── llm.py               # LiteLLM factory (get_llm, get_router_llm)
 │   │   ├── orchestrator.py      # Intent classification & routing
 │   │   └── execution_plan.py    # Plan builder, templates, cache
 │   │

alembic/versions/9a1f2d0b6c7e_deprecate_backend_agent_config_tables.py

@@ -1,92 +0,0 @@
-"""Deprecate backend agent config tables.
-
-The Electron client is now the source of truth for agent configuration
-(directory, extract targets, batch interval, custom prompt). Backend keeps
-billing checks and trigger/run logs only.
-
-Revision ID: 9a1f2d0b6c7e
-Revises: 818478c251dc
-Create Date: 2026-03-16
-"""
-
-from __future__ import annotations
-
-from typing import Sequence, Union
-
-import sqlalchemy as sa
-from alembic import op
-from sqlalchemy.dialects import postgresql
-
-revision: str = "9a1f2d0b6c7e"
-down_revision: Union[str, None] = "818478c251dc"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
-    bind = op.get_bind()
-    inspector = sa.inspect(bind)
-    existing = set(inspector.get_table_names())
-
-    if "cloud_agent_configs" in existing:
-        op.drop_index("ix_cloud_agent_configs_user_id", table_name="cloud_agent_configs")
-        op.drop_table("cloud_agent_configs")
-
-    if "local_agent_configs" in existing:
-        op.drop_index("ix_local_agent_configs_user_id", table_name="local_agent_configs")
-        op.drop_table("local_agent_configs")
-
-
-def downgrade() -> None:
-    op.create_table(
-        "local_agent_configs",
-        sa.Column("id", postgresql.UUID(as_uuid=False), nullable=False),
-        sa.Column("user_id", postgresql.UUID(as_uuid=False), nullable=False),
-        sa.Column("device_id", sa.String(255), nullable=False),
-        sa.Column("name", sa.String(255), nullable=False),
-        sa.Column("directory_paths", sa.JSON, nullable=False, server_default="[]"),
-        sa.Column("data_types", sa.JSON, nullable=False, server_default="[]"),
-        sa.Column("prompt_template", sa.Text, nullable=False, server_default=""),
-        sa.Column("file_extensions", sa.JSON, nullable=False, server_default="[]"),
-        sa.Column("schedule_cron", sa.String(100), nullable=False, server_default="0 */6 * * *"),
-        sa.Column("enabled", sa.Boolean, nullable=False, server_default=sa.true()),
-        sa.Column("last_run_at", sa.DateTime(timezone=True), nullable=True),
-        sa.Column("created_at", sa.DateTime(timezone=True), nullable=False, server_default=sa.text("now()")),
-        sa.Column("updated_at", sa.DateTime(timezone=True), nullable=False, server_default=sa.text("now()")),
-        sa.PrimaryKeyConstraint("id"),
-        sa.ForeignKeyConstraint(["user_id"], ["users.id"], ondelete="CASCADE"),
-    )
-    op.create_index("ix_local_agent_configs_user_id", "local_agent_configs", ["user_id"])
-
-    op.execute(
-        """
-        DO $$ BEGIN
-            CREATE TYPE cloud_provider AS ENUM ('gmail', 'teams', 'outlook');
-        EXCEPTION WHEN duplicate_object THEN NULL;
-        END $$;
-        """
-    )
-
-    op.create_table(
-        "cloud_agent_configs",
-        sa.Column("id", postgresql.UUID(as_uuid=False), nullable=False),
-        sa.Column("user_id", postgresql.UUID(as_uuid=False), nullable=False),
-        sa.Column(
-            "provider",
-            postgresql.ENUM("gmail", "teams", "outlook", name="cloud_provider", create_type=False),
-            nullable=False,
-        ),
-        sa.Column("name", sa.String(255), nullable=False),
-        sa.Column("data_types", sa.JSON, nullable=False, server_default="[]"),
-        sa.Column("prompt_template", sa.Text, nullable=False, server_default=""),
-        sa.Column("oauth_token_encrypted", sa.Text, nullable=True),
-        sa.Column("filter_config", sa.JSON, nullable=True),
-        sa.Column("schedule_cron", sa.String(100), nullable=False, server_default="0 */6 * * *"),
-        sa.Column("enabled", sa.Boolean, nullable=False, server_default=sa.true()),
-        sa.Column("last_run_at", sa.DateTime(timezone=True), nullable=True),
-        sa.Column("created_at", sa.DateTime(timezone=True), nullable=False, server_default=sa.text("now()")),
-        sa.Column("updated_at", sa.DateTime(timezone=True), nullable=False, server_default=sa.text("now()")),
-        sa.PrimaryKeyConstraint("id"),
-        sa.ForeignKeyConstraint(["user_id"], ["users.id"], ondelete="CASCADE"),
-    )
-    op.create_index("ix_cloud_agent_configs_user_id", "cloud_agent_configs", ["user_id"])

app/agents/__init__.py

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

app/agents/filesystem_agent.py

@@ -1,85 +0,0 @@
-"""Filesystem agent — tools for reading local directories and files on Electron.
-
-These tools delegate to the Electron client via ``execute_on_client()`` using
-the same WS tool-call round-trip pattern as CRUD tools.  The Electron app
-handles actual disk I/O and responds with ``tool_result`` frames.
-"""
-
-from __future__ import annotations
-
-from typing import Any
-
-from langchain_core.tools import tool
-
-from app.core.ws_context import execute_on_client
-
-
-@tool
-async def list_directory(path: str) -> str:
-    """List files and folders in a local directory on the user's device.
-
-    Returns a formatted listing of entries with name, type (file/directory),
-    and full path.
-    """
-    result = await execute_on_client(
-        action="list_directory",
-        data={"path": path},
-    )
-    entries: list[dict[str, Any]] = result.get("entries", [])
-    if not entries:
-        return f"Directory '{path}' is empty or does not exist."
-    lines: list[str] = []
-    for entry in entries:
-        entry_type = entry.get("type", "unknown")
-        entry_name = entry.get("name", "")
-        entry_path = entry.get("path", "")
-        lines.append(f"- [{entry_type}] {entry_name}  ({entry_path})")
-    return f"Directory listing for '{path}' ({len(entries)} entries):\n" + "\n".join(lines)
-
-
-@tool
-async def read_file_content(path: str) -> str:
-    """Read the text content of a local file on the user's device.
-
-    Returns the file content as a string.  Large files may be truncated
-    by the Electron client.
-    """
-    result = await execute_on_client(
-        action="read_file_content",
-        data={"path": path},
-    )
-    content: str = result.get("content", "")
-    if not content:
-        return f"File '{path}' is empty or could not be read."
-    return content
-
-
-@tool
-async def get_file_metadata(path: str) -> str:
-    """Get metadata for a local file: size, creation date, modification date, extension.
-
-    Returns a formatted summary of the file's metadata.
-    """
-    result = await execute_on_client(
-        action="get_file_metadata",
-        data={"path": path},
-    )
-    size = result.get("size", "unknown")
-    created = result.get("createdAt", "unknown")
-    modified = result.get("modifiedAt", "unknown")
-    extension = result.get("extension", "unknown")
-    name = result.get("name", path)
-    return (
-        f"File: {name}\n"
-        f"  Extension: {extension}\n"
-        f"  Size: {size} bytes\n"
-        f"  Created: {created}\n"
-        f"  Modified: {modified}"
-    )
-
-
-FILESYSTEM_TOOLS: list[Any] = [
-    list_directory,
-    read_file_content,
-    get_file_metadata,
-]

app/agents/note_agent.py

@@ -1,8 +1,7 @@
-"""Note agent — Markdown note management (list, get, create, update, delete)."""
+"""Note agent — tool definitions for Markdown note CRUD."""
 
 from __future__ import annotations
 
-import re
 from typing import Any
 
 from langchain_core.tools import tool
@@ -10,38 +9,14 @@ from langchain_core.tools import tool
 from app.core.llm import embed
 from app.core.ws_context import execute_on_client
 
-_UUID_RE = re.compile(
-    r"^[0-9a-fA-F]{8}-[0-9a-fA-F]{4}-[1-5][0-9a-fA-F]{3}-[89abAB][0-9a-fA-F]{3}-[0-9a-fA-F]{12}$"
-)
-
-
-def _is_uuid(value: str) -> bool:
-    return bool(_UUID_RE.match(value))
-
-NOTE_SYSTEM_PROMPT = (
-    "You are a note-taking assistant. You help users create, retrieve, update,\n"
-    "and delete Markdown notes in their workspace.\n\n"
-    "Rules:\n"
-    "  - content is always Markdown; preserve formatting when updating\n"
-    "  - project_id is optional; link a note to a project when mentioned\n"
-    "  - When updating, call get_note first if you need to read existing content\n"
-    "    before appending or replacing sections\n"
-    "  - list_notes without project_id returns all notes; scope with project_id\n"
-    "    when the user is working within a specific project\n"
-    "  - project_id must be a UUID; if you only know a project name, do not pass it as project_id\n"
-    "  - Do not fabricate note content — reflect what the user provides or what\n"
-    "    is already in the note (retrieved via get_note)."
-)
-
 
 @tool
 async def list_notes(project_id: str = "") -> str:
     """List notes, optionally scoped to a project by project_id."""
-    normalized_project_id = project_id if (project_id and _is_uuid(project_id)) else ""
     result = await execute_on_client(
         action="select",
         table="notes",
-        filters={"projectId": normalized_project_id or None},
+        filters={"projectId": project_id or None},
     )
     rows = result.get("rows", [])
     if not rows:
@@ -130,10 +105,4 @@ async def delete_note(note_id: str) -> str:
     return f"Note {note_id} deleted."
 
 
-NOTE_TOOLS: list[Any] = [
-    list_notes,
-    get_note,
-    create_note,
-    update_note,
-    delete_note,
-]
+

app/agents/project_agent.py

@@ -1,4 +1,4 @@
-"""Project agent — full lifecycle management (list, get, create, update, archive, delete)."""
+"""Project agent — tool definitions for project lifecycle CRUD."""
 
 from __future__ import annotations
 
@@ -8,22 +8,6 @@ from langchain_core.tools import tool
 
 from app.core.ws_context import execute_on_client
 
-PROJECT_SYSTEM_PROMPT = (
-    "You are a project management assistant. You help users create, find,\n"
-    "update, and archive projects in their workspace.\n\n"
-    "Rules:\n"
-    "  - status must be one of: active, archived\n"
-    "  - client_id is optional; link to a client only when explicitly mentioned\n"
-    "  - ai_summary is populated only when the user asks for a project summary;\n"
-    "    derive it from context data — do not fabricate content\n"
-    "  - Use list_projects for scoped queries; list_all_projects only when the\n"
-    "    user wants a complete cross-client view including archived projects\n"
-    "  - get_project requires a project UUID; resolve the ID first by calling\n"
-    "    list_projects if you only have a project name\n"
-    "  - Prefer archiving (update_project status=archived) over deletion;\n"
-    "    only call delete_project when the user explicitly confirms deletion."
-)
-
 
 @tool
 async def list_projects(
@@ -133,11 +117,4 @@ async def delete_project(project_id: str) -> str:
     return f"Project {project_id} permanently deleted."
 
 
-PROJECT_TOOLS: list[Any] = [
-    list_projects,
-    list_all_projects,
-    get_project,
-    create_project,
-    update_project,
-    delete_project,
-]
+

app/agents/task_agent.py

@@ -1,40 +1,14 @@
-"""Task agent — full CRUD for tasks and task comments."""
+"""Task agent — tool definitions for task and task comment CRUD."""
 
 from __future__ import annotations
 
 from datetime import datetime, timezone
-import re
 from typing import Any
 
 from langchain_core.tools import tool
 
 from app.core.ws_context import execute_on_client
 
-_UUID_RE = re.compile(
-    r"^[0-9a-fA-F]{8}-[0-9a-fA-F]{4}-[1-5][0-9a-fA-F]{3}-[89abAB][0-9a-fA-F]{3}-[0-9a-fA-F]{12}$"
-)
-
-
-def _is_uuid(value: str) -> bool:
-    return bool(_UUID_RE.match(value))
-
-TASK_SYSTEM_PROMPT = (
-    "You are a task management assistant for a project workspace.\n"
-    "You create, update, list, and track tasks and their comments.\n\n"
-    "Rules:\n"
-    "  - status must be one of: todo, in_progress, done\n"
-    "  - priority must be one of: high, medium, low\n"
-    "  - due_date is a Unix timestamp in milliseconds; convert human dates\n"
-    "  - assignees is a JSON-encoded array of strings (e.g. '[\"Alice\",\"Bob\"]')\n"
-    "  - project_id is optional; link to a project when the user mentions one\n"
-    "  - is_ai_suggested: 1 only when proactively proposing a task the user\n"
-    "    did not explicitly request; 0 otherwise\n"
-    "  - is_ai_suggested: 1 only when proactively proposing a task the user did not explicitly request; 0 otherwise\n"
-    "  - Use list_tasks_due_today for 'what's due today' queries\n"
-    "  - For update_task, use -1 for integer fields you do not want to change\n"
-    "  - Always confirm the action in plain, user-friendly language."
-)
-
 
 # ── Task tools ────────────────────────────────────────────────────────
 
@@ -48,12 +22,11 @@ async def list_tasks(
 ) -> str:
     """List tasks, optionally filtered by project_id, status (todo|in_progress|done),
     a search string, or an order_by field name (dueDate|priority|createdAt)."""
-    normalized_project_id = project_id if (project_id and _is_uuid(project_id)) else ""
     result = await execute_on_client(
         action="select",
         table="tasks",
         filters={
-            "projectId": normalized_project_id or None,
+            "projectId": project_id or None,
             "status": status or None,
             "search": search or None,
             "orderBy": order_by or None,
@@ -79,6 +52,7 @@ async def create_task(
     due_date: int = 0,
     project_id: str = "",
     is_ai_suggested: int = 0,
+    is_approved: int = 0,
 ) -> str:
     """Create a new task.
     title: task title (required)
@@ -89,6 +63,7 @@ async def create_task(
     due_date: Unix timestamp in milliseconds; 0 means no due date
     project_id: optional UUID of the parent project
     is_ai_suggested: 1 if proactively suggested, 0 if user-requested
+    is_approved: 0 until the user confirms; 1 when confirmed
     """
     result = await execute_on_client(
         action="insert",
@@ -102,6 +77,7 @@ async def create_task(
             "dueDate": due_date or None,
             "projectId": project_id or None,
             "isAiSuggested": is_ai_suggested,
+            "isApproved": is_approved,
         },
     )
     row = result["row"]
@@ -121,10 +97,12 @@ async def update_task(
     assignees: str = "",
     due_date: int = -1,
     project_id: str = "",
+    is_approved: int = -1,
 ) -> str:
     """Update fields on an existing task. Only pass fields you want to change.
     task_id: the task's UUID (required)
     due_date: -1 means unchanged; 0 clears the due date; any positive value sets it
+    is_approved: -1 means unchanged; 0 or 1 sets the value
     """
     updates: dict[str, Any] = {}
     if title:
@@ -141,6 +119,8 @@ async def update_task(
         updates["dueDate"] = due_date or None
     if project_id:
         updates["projectId"] = project_id
+    if is_approved != -1:
+        updates["isApproved"] = is_approved
     result = await execute_on_client(
         action="update",
         table="tasks",
@@ -208,12 +188,8 @@ async def add_task_comment(task_id: str, author: str, content: str) -> str:
         table="taskComments",
         data={"taskId": task_id, "author": author, "content": content},
     )
-    row = result.get("row", {})
-    row_author = row.get("author", author)
-    # Electron payloads can vary (taskId vs task_id). Fall back to input task_id.
-    row_task_id = row.get("taskId") or row.get("task_id") or task_id
-    row_comment_id = row.get("id", "unknown")
-    return f"Comment added by {row_author} on task {row_task_id} (comment id: {row_comment_id})."
+    row = result["row"]
+    return f"Comment added by {row['author']} on task {row['taskId']} (comment id: {row['id']})."
 
 
 @tool
@@ -223,16 +199,4 @@ async def delete_task_comment(comment_id: str) -> str:
     return f"Comment {comment_id} deleted."
 
 
-# ── Agent ─────────────────────────────────────────────────────────────
 
-
-TASK_TOOLS: list[Any] = [
-    list_tasks,
-    create_task,
-    update_task,
-    delete_task,
-    list_tasks_due_today,
-    list_task_comments,
-    add_task_comment,
-    delete_task_comment,
-]

app/agents/timeline_agent.py

@@ -1,45 +1,21 @@
-"""Timeline agent — project milestone management (list, create, update, delete)."""
+"""Timeline agent — tool definitions for project milestone CRUD."""
 
 from __future__ import annotations
 
-import re
 from typing import Any
 
 from langchain_core.tools import tool
 
 from app.core.ws_context import execute_on_client
 
-_UUID_RE = re.compile(
-    r"^[0-9a-fA-F]{8}-[0-9a-fA-F]{4}-[1-5][0-9a-fA-F]{3}-[89abAB][0-9a-fA-F]{3}-[0-9a-fA-F]{12}$"
-)
-
-
-def _is_uuid(value: str) -> bool:
-    return bool(_UUID_RE.match(value))
-
-TIMELINE_SYSTEM_PROMPT = (
-    "You are a project timeline assistant. Timelines are milestone dates that\n"
-    "track progress on a project — they are not calendar events.\n\n"
-    "Rules:\n"
-    "  - project_id is REQUIRED for every create; confirm with the user if unknown\n"
-    "  - For listing, project_id must be a UUID; never pass plain names as project_id\n"
-    "  - date is a Unix timestamp in milliseconds; convert human-readable dates\n"
-    "  - is_ai_suggested: 1 when proactively proposing a timeline, 0 otherwise\n"
-    "  - is_ai_suggested: 1 when proactively proposing a timeline, 0 otherwise\n"
-    "  - For update_timeline, use -1 for integer fields you do not want to change\n"
-    "  - Listing without a project_id returns all timelines across projects\n"
-    "  - Always echo the title and formatted date in your confirmation."
-)
-
 
 @tool
 async def list_timelines(project_id: str = "") -> str:
     """List timelines. Provide project_id to scope to a specific project."""
-    normalized_project_id = project_id if (project_id and _is_uuid(project_id)) else ""
     result = await execute_on_client(
         action="select",
         table="timelines",
-        filters={"projectId": normalized_project_id or None},
+        filters={"projectId": project_id or None},
     )
     rows = result.get("rows", [])
     if not rows:
@@ -54,12 +30,14 @@ async def create_timeline(
     title: str,
     date: int,
     is_ai_suggested: int = 0,
+    is_approved: int = 0,
 ) -> str:
     """Create a project timeline (milestone).
     project_id: REQUIRED UUID of the parent project
     title: descriptive name for the milestone
     date: Unix timestamp in milliseconds
     is_ai_suggested: 1 if proactively suggested, 0 if user-requested
+    is_approved: 0 until the user confirms
     """
     result = await execute_on_client(
         action="insert",
@@ -69,6 +47,7 @@ async def create_timeline(
             "title": title,
             "date": date,
             "isAiSuggested": is_ai_suggested,
+            "isApproved": is_approved,
         },
     )
     row = result["row"]
@@ -80,16 +59,20 @@ async def update_timeline(
     timeline_id: str,
     title: str = "",
     date: int = -1,
+    is_approved: int = -1,
 ) -> str:
     """Update a timeline. Only pass fields that should change.
     timeline_id: UUID of the timeline (required)
     date: -1 means unchanged; any other value sets the new date (ms timestamp)
+    is_approved: -1 means unchanged; 0 or 1 sets the approval state
     """
     updates: dict[str, Any] = {}
     if title:
         updates["title"] = title
     if date != -1:
         updates["date"] = date
+    if is_approved != -1:
+        updates["isApproved"] = is_approved
     result = await execute_on_client(
         action="update",
         table="timelines",
@@ -106,9 +89,4 @@ async def delete_timeline(timeline_id: str) -> str:
     return f"Timeline {timeline_id} deleted."
 
 
-TIMELINE_TOOLS: list[Any] = [
-    list_timelines,
-    create_timeline,
-    update_timeline,
-    delete_timeline,
-]
+

app/api/middleware/auth.py

@@ -55,15 +55,12 @@ async def get_current_user(
         raise credentials_exc
 
     # Live tier lookup — subscription row is the authoritative source.
-    # In dev, fall back to 'power' (unlimited) so quota limits don't
-    # block local development when no Stripe subscription exists.
     from app.models import Subscription, User  # noqa: PLC0415
 
     result = await db.execute(
         select(Subscription.tier).where(Subscription.user_id == user_id)
     )
-    default_tier = "power" if settings.ENV == "dev" else "free"
-    tier: str = result.scalar_one_or_none() or default_tier
+    tier: str = result.scalar_one_or_none() or "free"
 
     # Fetch name/surname from user row.
     user_result = await db.execute(

app/api/routes/agent_setup.py

@@ -1,40 +1,54 @@
-"""Chatbot Journey — WS-based guided conversation to build an agent prompt_template.
+"""Chatbot Journey endpoints — 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.
+Endpoints:
+  POST /agents/journey/start    — start a new journey session
+  POST /agents/journey/message  — continue the conversation
+
+Sessions are stored in-memory with a 30-minute TTL.  Stale entries are
+cleaned up lazily on access.  Upgrade to Redis for multi-instance deployments.
 
 Journey flow:
-  1. FE sends ``journey_start`` frame with basic agent 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. Client sends ``{ agent_type, agent_id? }`` to ``/start``.
+  2. Server creates a session, calls the LLM with a contextual system prompt,
+     and returns the first question.
+  3. Client sends follow-up messages to ``/message``.
+  4. After 3-5 turns the LLM wraps up by emitting a ``prompt_template`` block
+     delimited by ``PROMPT_TEMPLATE_START`` / ``PROMPT_TEMPLATE_END``.
+  5. Server parses the block, sets ``done=True``, and returns the template.
+
+The ``prompt_template`` from the final response is meant to be stored in
+``LocalAgentConfig.prompt_template`` or ``CloudAgentConfig.prompt_template``
+by the Electron client (via the agent CRUD endpoints).
 """
 
 from __future__ import annotations
 
-import json
 import logging
 import time
 import uuid
 from dataclasses import dataclass, field
 from typing import Any
 
-from langchain_core.messages import AIMessage, HumanMessage, SystemMessage, ToolMessage
+from fastapi import APIRouter, Depends, HTTPException, status
+from langchain_core.messages import AIMessage, HumanMessage, SystemMessage
+from sqlalchemy import select
+from sqlalchemy.ext.asyncio import AsyncSession
 
-from app.agents.filesystem_agent import FILESYSTEM_TOOLS
+from app.api.deps import get_current_user
 from app.core.llm import get_llm
+from app.db import get_session
+from app.models import CloudAgentConfig, LocalAgentConfig
+from app.schemas import (
+    JourneyMessageRequest,
+    JourneyResponse,
+    JourneyStartRequest,
+    UserProfile,
+)
 
 logger = logging.getLogger(__name__)
 
+router = APIRouter(prefix="/agents/journey", tags=["agents"])
+
 # ── Session TTL ───────────────────────────────────────────────────────────
 
 _SESSION_TTL_SECONDS: int = 1800  # 30 minutes
@@ -43,25 +57,18 @@ _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
+# Maximum number of conversation turns before the LLM is nudged to wrap up.
+_MAX_TURNS: int = 5
 
 # ── In-memory session store ───────────────────────────────────────────────
 
 
 @dataclass
-class JourneySession:
+class _JourneySession:
     session_id: str
     user_id: str
     agent_type: str  # "local" | "cloud"
-    directory: str
-    data_types: list[str]
     history: list[dict[str, Any]] = field(default_factory=list)
-    system_prompt: str = ""
     created_at: float = field(default_factory=time.monotonic)
 
     def is_expired(self) -> bool:
@@ -69,84 +76,67 @@ class JourneySession:
 
 
 # session_id → session
-_sessions: dict[str, JourneySession] = {}
+_sessions: dict[str, _JourneySession] = {}
 
 
-def get_journey_session(session_id: str, user_id: str) -> JourneySession | None:
-    """Retrieve session; return None on missing, expired, or wrong owner."""
+def _get_session(session_id: str, user_id: str) -> _JourneySession:
+    """Retrieve session; raise 404 on missing, expired, or wrong owner."""
     s = _sessions.get(session_id)
     if s is None or s.is_expired():
         _sessions.pop(session_id, None)
-        return None
+        raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail="Journey session not found or expired")
     if s.user_id != user_id:
-        return None
+        raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail="Journey session not found or expired")
     return s
 
 
 # ── System prompt builder ─────────────────────────────────────────────────
 
+_LOCAL_PREAMBLE = """\
+What kind of files are in the directories you want to monitor? \
+(for example: emails saved as .eml, documents in .pdf or .txt, markdown notes, etc.)"""
+
+_CLOUD_PREAMBLE = """\
+What kind of emails or messages should I look for? \
+(for example: client communications, invoices, meeting notes, project updates, etc.)"""
+
 _SYSTEM_PROMPT_TEMPLATE = """\
 You are a friendly assistant helping a freelancer configure a data-extraction agent.
-Your job is to understand exactly what data the user wants to extract from their
-local directory and produce a detailed prompt_template that a separate AI will use
-as its instruction set.
+Your job is to understand exactly what data the user wants to extract from their {source_description} \
+and produce a detailed prompt_template that a separate AI will use as its instruction set.
 
-The extraction agent already has this base behaviour built in:
-  - Reads each file using file-system tools.
-  - Creates records (tasks, notes, timelines, projects) via CRUD tools.
-  - Sets isAiSuggested=1 on every new record.
-  - Only extracts data explicitly present in the files — it never invents information.
-The user's custom prompt is appended AFTER this base behaviour, so focus on
-what to look for and how to map it — not on the general extraction mechanics.
+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.
+  2. Which data types to extract: tasks, notes, timelines, and/or projects.
+  3. How fields should be mapped (e.g. email subject → task title).
+  4. Priority or status rules (e.g. "urgent" keyword → high priority).
+  5. Any special handling, date extraction, or exclusions.
 
-You have access to file-system tools to explore the user's directory:
-- list_directory: to see folder structure
-- read_file_content: to peek at file contents
-- get_file_metadata: to check file info
-
-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.
-
-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.
-
-Once you reach 90% confidence, output the final prompt_template between these exact
-markers on their own lines:
+After 3-5 questions (when you have enough information), output the final prompt_template 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 a self-contained instruction for an AI that receives a document/email/message \
+and must return a JSON array of records in this shape:
+  [{{ "table": "<tasks|notes|timelines|projects>", "data": {{ <field: value> }} }}, ...]
 
+Rules for the generated template:
+  - Be explicit about field names (camelCase: title, status, priority, dueDate, projectId, content, etc.).
+  - Include concrete examples of mappings.
+  - Mention that Electron adds id/createdAt/updatedAt automatically.
+  - Set isAiSuggested: true and isApproved: false on every record.
 {existing_section}\
-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.\
+Do not ask more than {max_turns} questions total. Start with your first question now.\
 """
 
 
-def _build_system_prompt(
-    directory: str,
-    data_types: list[str],
-    existing_template: str | None = None,
-) -> str:
+def _build_system_prompt(agent_type: str, existing_template: str | None) -> str:
+    source_description = (
+        "files in local directories" if agent_type == "local" else "emails and messages from cloud providers"
+    )
     existing_section = (
         f"\nThe user already has the following prompt_template — refine it based on their answers:\n"
         f"---\n{existing_template}\n---\n"
@@ -154,14 +144,18 @@ def _build_system_prompt(
         else ""
     )
     return _SYSTEM_PROMPT_TEMPLATE.format(
-        directory=directory,
-        data_types=", ".join(data_types),
+        source_description=source_description,
         template_start=_TEMPLATE_START,
         template_end=_TEMPLATE_END,
         existing_section=existing_section,
+        max_turns=_MAX_TURNS,
     )
 
 
+def _first_question(agent_type: str) -> str:
+    return _LOCAL_PREAMBLE if agent_type == "local" else _CLOUD_PREAMBLE
+
+
 # ── Template extraction ───────────────────────────────────────────────────
 
 
@@ -174,37 +168,11 @@ def _extract_template(text: str) -> str | None:
     return text[start_idx:end_idx].strip() or None
 
 
-# ── LLM call with tool support ───────────────────────────────────────────
+# ── LLM call ─────────────────────────────────────────────────────────────
 
 
-def _as_text(content: Any) -> str:
-    if content is None:
-        return ""
-    if isinstance(content, str):
-        return content
-    if isinstance(content, list):
-        parts: list[str] = []
-        for item in content:
-            if isinstance(item, str):
-                parts.append(item)
-            elif isinstance(item, dict):
-                text = item.get("text")
-                if isinstance(text, str):
-                    parts.append(text)
-        return "".join(parts)
-    return str(content)
-
-
-async def _call_llm_with_tools(
-    system_prompt: str,
-    history: list[dict[str, Any]],
-    tools: list[Any],
-) -> str:
-    """Build LangChain messages from history and invoke the LLM with tools.
-
-    Handles tool-calling loops: if the LLM calls tools, execute them and
-    continue until a final text response is produced.
-    """
+async def _call_llm(system_prompt: str, history: list[dict[str, Any]]) -> str:
+    """Build LangChain messages from history and invoke the LLM."""
     messages: list[Any] = [SystemMessage(content=system_prompt)]
     for turn in history:
         if turn["role"] == "user":
@@ -213,194 +181,137 @@ async def _call_llm_with_tools(
             messages.append(AIMessage(content=turn["content"]))
 
     llm = get_llm(model=None, temperature=0.4)
-    llm_with_tools = llm.bind_tools(tools)
-    tool_map = {tool_def.name: tool_def for tool_def in tools}
-
-    for _ in range(_MAX_TOOL_STEPS):
-        response: AIMessage = await llm_with_tools.ainvoke(messages)
-        messages.append(response)
-
-        if not response.tool_calls:
-            return _as_text(response.content)
-
-        for call in response.tool_calls:
-            call_name = str(call.get("name", ""))
-            call_args = call.get("args", {})
-            logger.info(
-                "agent_setup: journey tool_call name=%s args=%s",
-                call_name,
-                json.dumps(call_args, ensure_ascii=True)[:500],
-            )
-
-            tool_fn = tool_map.get(call_name)
-            if tool_fn is None:
-                tool_output = f"Unknown tool: {call_name}"
-            else:
-                tool_output = await tool_fn.ainvoke(call_args)
-
-            logger.info(
-                "agent_setup: journey tool_result name=%s output=%s",
-                call_name,
-                str(tool_output)[:800],
-            )
-            messages.append(ToolMessage(content=str(tool_output), tool_call_id=call["id"]))
-
-    # Fallback: exceeded max steps.
-    final = await llm.ainvoke(messages)
-    return _as_text(final.content)
+    response = await llm.ainvoke(messages)
+    return response.content  # type: ignore[return-value]
 
 
-# ── Journey handlers (called from device_ws.py) ──────────────────────────
+# ── Existing-config loader ────────────────────────────────────────────────
 
 
-async def handle_journey_start(
+async def _load_existing_template(
+    agent_id: str,
     user_id: str,
-    frame: dict[str, Any],
-) -> dict[str, Any]:
-    """Handle a ``journey_start`` WS frame.
+    db: AsyncSession,
+) -> str | None:
+    """Return the prompt_template of an existing agent config, or None."""
+    # Try local first, then cloud.
+    local_result = await db.execute(
+        select(LocalAgentConfig).where(
+            LocalAgentConfig.id == agent_id,
+            LocalAgentConfig.user_id == user_id,
+        )
+    )
+    local = local_result.scalar_one_or_none()
+    if local is not None:
+        return local.prompt_template
 
-    Creates a session, runs the setup LLM with directory exploration,
-    and returns the ``journey_reply`` payload.
+    cloud_result = await db.execute(
+        select(CloudAgentConfig).where(
+            CloudAgentConfig.id == agent_id,
+            CloudAgentConfig.user_id == user_id,
+        )
+    )
+    cloud = cloud_result.scalar_one_or_none()
+    return cloud.prompt_template if cloud is not None else None
+
+
+# ── Routes ────────────────────────────────────────────────────────────────
+
+
+@router.post("/start", response_model=JourneyResponse, status_code=status.HTTP_200_OK)
+async def start_journey(
+    body: JourneyStartRequest,
+    current_user: UserProfile = Depends(get_current_user),
+    db: AsyncSession = Depends(get_session),
+) -> JourneyResponse:
+    """Start a new Chatbot Journey session.
+
+    If ``agent_id`` is provided the session is pre-seeded with the existing
+    agent's ``prompt_template`` so the user can refine it.
     """
-    agent_type = frame.get("agent_type", "local")
-    directory = frame.get("directory", "")
-    data_types = frame.get("data_types", [])
-    existing_template = frame.get("existing_template")
+    # Load existing template (may be None).
+    existing_template: str | None = None
+    if body.agent_id:
+        existing_template = await _load_existing_template(body.agent_id, current_user.id, db)
+        # If agent_id was given but not found, proceed without seeding (don't 404 —
+        # the user may be starting a fresh journey for a not-yet-persisted config).
 
-    # Use the session_id provided by the FE so the reply matches the
-    # listener key; fall back to a generated one if absent.
-    session_id = frame.get("session_id") or str(uuid.uuid4())
-    system_prompt = _build_system_prompt(directory, data_types, existing_template)
+    system_prompt = _build_system_prompt(body.agent_type, existing_template)
+    first_question = _first_question(body.agent_type)
 
-    session = JourneySession(
+    session_id = str(uuid.uuid4())
+    session = _JourneySession(
         session_id=session_id,
-        user_id=user_id,
-        agent_type=agent_type,
-        directory=directory,
-        data_types=data_types,
-        system_prompt=system_prompt,
+        user_id=current_user.id,
+        agent_type=body.agent_type,
+        # Seed history with the AI's first question so it stays consistent.
+        history=[{"role": "assistant", "content": first_question}],
     )
-
-    # 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."},
-    ]
-    ai_reply = await _call_llm_with_tools(
-        system_prompt=system_prompt,
-        history=seed_history,
-        tools=list(FILESYSTEM_TOOLS),
-    )
-
-    session.history.extend(seed_history)
-    session.history.append({"role": "assistant", "content": ai_reply})
+    # Store the system prompt inside the session for reuse in /message.
+    session.__dict__["_system_prompt"] = system_prompt  # type: ignore[index]
     _sessions[session_id] = session
 
-    logger.info(
-        "agent_setup: 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
-
-    display_message = ai_reply
-    if done:
-        display_message = (
-            ai_reply[: ai_reply.index(_TEMPLATE_START)].strip()
-            or "Here is your agent configuration. You can save it or continue refining."
-        )
-        _sessions.pop(session_id, None)
-
-    return {
-        "type": "journey_reply",
-        "session_id": session_id,
-        "message": display_message,
-        "done": done,
-        "prompt_template": prompt_template,
-    }
+    logger.info("Journey session %s started for user %s (agent_type=%s)", session_id, current_user.id, body.agent_type)
+    return JourneyResponse(session_id=session_id, message=first_question, done=False)
 
 
-async def handle_journey_message(
-    user_id: str,
-    frame: dict[str, Any],
-) -> dict[str, Any]:
-    """Handle a ``journey_message`` WS frame.
+@router.post("/message", response_model=JourneyResponse, status_code=status.HTTP_200_OK)
+async def send_journey_message(
+    body: JourneyMessageRequest,
+    current_user: UserProfile = Depends(get_current_user),
+    db: AsyncSession = Depends(get_session),
+) -> JourneyResponse:
+    """Send a message in an existing Chatbot Journey session.
 
-    Appends the user message, calls the LLM, and returns the
-    ``journey_reply`` payload.
+    The server appends the user's message to the conversation history,
+    calls the LLM, and appends the AI reply.  When the LLM wraps up with a
+    ``prompt_template`` block the response includes ``done=True`` and the
+    extracted template.
     """
-    session_id = frame.get("session_id", "")
-    message = frame.get("message", "")
+    session = _get_session(body.session_id, current_user.id)
+    system_prompt: str = session.__dict__.get("_system_prompt", _build_system_prompt(session.agent_type, None))  # type: ignore[assignment]
 
-    session = get_journey_session(session_id, user_id)
-    if session is None:
-        return {
-            "type": "journey_reply",
-            "session_id": session_id,
-            "message": "Journey session not found or expired. Please start a new setup.",
-            "done": True,
-            "prompt_template": None,
-        }
+    # Append user turn to history.
+    session.history.append({"role": "user", "content": body.message})
 
-    # 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),
-    )
+    # Call the LLM with the full conversation so far.
+    ai_reply = await _call_llm(system_prompt, session.history)
 
+    # Append AI turn.
     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:
-            nudge_content = (
-                "[System: You have enough information. Please generate the final "
-                f"prompt_template now, wrapped in {_TEMPLATE_START} / {_TEMPLATE_END} markers.]"
-            )
-            session.history.append({"role": "user", "content": nudge_content})
-
-            nudge_reply = await _call_llm_with_tools(
-                system_prompt=session.system_prompt,
-                history=session.history,
-                tools=list(FILESYSTEM_TOOLS),
-            )
-            session.history.append({"role": "assistant", "content": nudge_reply})
-
-            prompt_template = _extract_template(nudge_reply)
-            if prompt_template is not None:
-                done = True
-                ai_reply = nudge_reply
-
+    # Strip the sentinel markers from the message shown to the user.
     display_message = ai_reply
     if done:
         display_message = (
             ai_reply[: ai_reply.index(_TEMPLATE_START)].strip()
-            if _TEMPLATE_START in ai_reply
-            else "Here is your agent configuration. You can save it or continue refining."
+            or "Here is your agent configuration. You can save it or continue refining."
         )
-        _sessions.pop(session_id, None)
-        logger.info("agent_setup: journey session %s completed for user %s", session_id, user_id)
 
-    return {
-        "type": "journey_reply",
-        "session_id": session_id,
-        "message": display_message,
-        "done": done,
-        "prompt_template": prompt_template,
-    }
+    if done:
+        logger.info("Journey session %s completed for user %s", body.session_id, current_user.id)
+        # Clean up the session immediately on completion.
+        _sessions.pop(body.session_id, None)
+    else:
+        # Nudge the LLM to wrap up after max turns.
+        turns = sum(1 for t in session.history if t["role"] == "user")
+        if turns >= _MAX_TURNS:
+            # Add a system-level nudge as a hidden user message.
+            session.history.append({
+                "role": "user",
+                "content": (
+                    "[System: You have enough information. Please generate the final "
+                    f"prompt_template now, wrapped in {_TEMPLATE_START} / {_TEMPLATE_END} markers.]"
+                ),
+            })
+
+    return JourneyResponse(
+        session_id=body.session_id,
+        message=display_message,
+        done=done,
+        prompt_template=prompt_template,
+    )

app/api/routes/agents.py

@@ -1,36 +1,45 @@
-"""Agent routes.
+"""Agent CRUD routes: local directory agents and cloud connector agents.
 
-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.
+Endpoints:
+  GET    /agents/catalog            — hardcoded agent type catalog
+  GET    /agents/local              — list user's local agent configs
+  POST   /agents/local              — create local agent (tier-gated)
+  PUT    /agents/local/{agent_id}   — partial update (ownership check)
+  DELETE /agents/local/{agent_id}   — delete + cascade run logs
+  GET    /agents/cloud              — list user's cloud agent configs
+  POST   /agents/cloud              — create cloud agent (tier-gated)
+  PUT    /agents/cloud/{agent_id}   — partial update (ownership check)
+  DELETE /agents/cloud/{agent_id}   — delete + cascade run logs
+  GET    /agents/runs               — paginated run logs (agent_id, page, limit)
+  POST   /agents/{agent_id}/run     — manual trigger stub (dispatch in Step 3.4)
 """
 
 from __future__ import annotations
 
 import asyncio
-import uuid
-from datetime import datetime, timedelta, timezone
+from datetime import datetime
+from typing import Any
 
-from fastapi import APIRouter, Depends, HTTPException, status
-from sqlalchemy import func, select
+from fastapi import APIRouter, Depends, HTTPException, Query, status
+from pydantic import BaseModel
+from sqlalchemy import func, or_, select
 from sqlalchemy.ext.asyncio import AsyncSession
 
 from app.api.deps import get_current_user
 from app.billing.tier_manager import FEATURES
-from app.core.agent_runner import is_agent_running, run_local_agent
+from app.core.agent_runner import run_cloud_agent, run_local_agent
 from app.core.device_manager import device_manager
 from app.db import get_session
-from app.models import AgentRunLog, LocalAgentConfig
+from app.models import AgentRunLog, CloudAgentConfig, LocalAgentConfig
 from app.schemas import (
     AgentCatalogItem,
-    AgentCreationCheckRequest,
-    AgentCreationCheckResponse,
     AgentRunLogResponse,
-    AgentTriggerRequest,
+    CloudAgentConfigCreate,
+    CloudAgentConfigResponse,
+    CloudAgentConfigUpdate,
+    LocalAgentConfigCreate,
+    LocalAgentConfigResponse,
+    LocalAgentConfigUpdate,
     UserProfile,
 )
 
@@ -47,21 +56,39 @@ def _dt_ms_opt(dt: datetime | None) -> int | None:
     return int(dt.timestamp() * 1000) if dt else None
 
 
-def _to_data_types(values: list[str]) -> list[str]:
-    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
+# ── Model → schema converters ─────────────────────────────────────────
+
+def _to_local_response(a: LocalAgentConfig) -> LocalAgentConfigResponse:
+    return LocalAgentConfigResponse(
+        id=a.id,
+        name=a.name,
+        device_id=a.device_id,
+        directory_paths=a.directory_paths,
+        data_types=a.data_types,
+        prompt_template=a.prompt_template,
+        file_extensions=a.file_extensions,
+        schedule_cron=a.schedule_cron,
+        enabled=a.enabled,
+        last_run_at=_dt_ms_opt(a.last_run_at),
+        created_at=_dt_ms(a.created_at),
+        updated_at=_dt_ms(a.updated_at),
+    )
+
+
+def _to_cloud_response(a: CloudAgentConfig) -> CloudAgentConfigResponse:
+    return CloudAgentConfigResponse(
+        id=a.id,
+        provider=a.provider,  # type: ignore[arg-type]
+        name=a.name,
+        data_types=a.data_types,
+        prompt_template=a.prompt_template,
+        schedule_cron=a.schedule_cron,
+        filter_config=a.filter_config,
+        enabled=a.enabled,
+        last_run_at=_dt_ms_opt(a.last_run_at),
+        created_at=_dt_ms(a.created_at),
+        updated_at=_dt_ms(a.updated_at),
+    )
 
 
 def _to_run_log_response(log: AgentRunLog) -> AgentRunLogResponse:
@@ -78,42 +105,77 @@ def _to_run_log_response(log: AgentRunLog) -> AgentRunLogResponse:
     )
 
 
-def _enforce_agent_limit(tier: str, current_count: int) -> int:
+# ── Ownership-checked lookups ─────────────────────────────────────────
+
+async def _get_local_agent_for_user(
+    agent_id: str, user_id: str, db: AsyncSession
+) -> LocalAgentConfig:
+    result = await db.execute(
+        select(LocalAgentConfig).where(
+            LocalAgentConfig.id == agent_id,
+            LocalAgentConfig.user_id == user_id,
+        )
+    )
+    record = result.scalar_one_or_none()
+    if record is None:
+        raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail="Agent not found")
+    return record
+
+
+async def _get_cloud_agent_for_user(
+    agent_id: str, user_id: str, db: AsyncSession
+) -> CloudAgentConfig:
+    result = await db.execute(
+        select(CloudAgentConfig).where(
+            CloudAgentConfig.id == agent_id,
+            CloudAgentConfig.user_id == user_id,
+        )
+    )
+    record = result.scalar_one_or_none()
+    if record is None:
+        raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail="Agent not found")
+    return record
+
+
+# ── Tier limit helper ─────────────────────────────────────────────────
+
+async def _count_enabled_agents(user_id: str, db: AsyncSession) -> int:
+    """Return combined enabled local + cloud agent count for the user."""
+    local_count = (
+        await db.execute(
+            select(func.count(LocalAgentConfig.id)).where(
+                LocalAgentConfig.user_id == user_id,
+                LocalAgentConfig.enabled == True,  # noqa: E712
+            )
+        )
+    ).scalar_one()
+    cloud_count = (
+        await db.execute(
+            select(func.count(CloudAgentConfig.id)).where(
+                CloudAgentConfig.user_id == user_id,
+                CloudAgentConfig.enabled == True,  # noqa: E712
+            )
+        )
+    ).scalar_one()
+    return local_count + cloud_count
+
+
+def _enforce_agent_limit(tier: str, current_count: int) -> None:
     limit: int = FEATURES.get(tier, FEATURES["free"])["batch_active"]
     if limit != -1 and current_count >= limit:
         raise HTTPException(
             status_code=status.HTTP_403_FORBIDDEN,
             detail=f"Agent limit ({limit}) reached for your tier. Upgrade to create more.",
         )
-    return limit
 
 
-async def _enforce_run_frequency(
-    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
+# ── Local page schema (used by runs endpoint) ─────────────────────────
 
-    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.",
-        )
+class _RunsPage(BaseModel):
+    total: int
+    page: int
+    limit: int
+    items: list[AgentRunLogResponse]
 
 
 # ── Catalog ───────────────────────────────────────────────────────────
@@ -147,61 +209,229 @@ async def get_agent_catalog(
     ]
 
 
-@router.post("/can-create", response_model=AgentCreationCheckResponse)
-async def can_create_agent(
-    body: AgentCreationCheckRequest,
+# ── Local agent CRUD ──────────────────────────────────────────────────
+
+@router.get("/local", response_model=list[LocalAgentConfigResponse])
+async def list_local_agents(
     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,
+    db: AsyncSession = Depends(get_session),
+) -> list[LocalAgentConfigResponse]:
+    """List all local directory agent configs owned by the authenticated user."""
+    result = await db.execute(
+        select(LocalAgentConfig).where(LocalAgentConfig.user_id == current_user.id)
     )
+    return [_to_local_response(a) for a in result.scalars().all()]
 
 
-@router.post("/trigger", response_model=AgentRunLogResponse, status_code=status.HTTP_202_ACCEPTED)
+@router.post("/local", response_model=LocalAgentConfigResponse, status_code=status.HTTP_201_CREATED)
+async def create_local_agent(
+    body: LocalAgentConfigCreate,
+    current_user: UserProfile = Depends(get_current_user),
+    db: AsyncSession = Depends(get_session),
+) -> LocalAgentConfigResponse:
+    """Create a new local directory agent config.
+
+    The combined count of enabled local and cloud agents for the user is
+    checked against the ``batch_active`` limit for their billing tier.
+    """
+    _enforce_agent_limit(current_user.tier, await _count_enabled_agents(current_user.id, db))
+    agent = LocalAgentConfig(
+        user_id=current_user.id,
+        name=body.name,
+        device_id=body.device_id,
+        directory_paths=body.directory_paths,
+        data_types=body.data_types,
+        prompt_template=body.prompt_template,
+        file_extensions=body.file_extensions,
+        schedule_cron=body.schedule_cron,
+    )
+    db.add(agent)
+    await db.commit()
+    await db.refresh(agent)
+    return _to_local_response(agent)
+
+
+@router.put("/local/{agent_id}", response_model=LocalAgentConfigResponse)
+async def update_local_agent(
+    agent_id: str,
+    body: LocalAgentConfigUpdate,
+    current_user: UserProfile = Depends(get_current_user),
+    db: AsyncSession = Depends(get_session),
+) -> LocalAgentConfigResponse:
+    """Partially update a local agent config. Only provided fields are changed."""
+    agent = await _get_local_agent_for_user(agent_id, current_user.id, db)
+    for field, value in body.model_dump(exclude_unset=True).items():
+        setattr(agent, field, value)
+    await db.commit()
+    await db.refresh(agent)
+    return _to_local_response(agent)
+
+
+@router.delete("/local/{agent_id}", response_model=dict)
+async def delete_local_agent(
+    agent_id: str,
+    current_user: UserProfile = Depends(get_current_user),
+    db: AsyncSession = Depends(get_session),
+) -> dict[str, bool]:
+    """Delete a local agent config. Associated run logs are cascade-deleted."""
+    agent = await _get_local_agent_for_user(agent_id, current_user.id, db)
+    await db.delete(agent)
+    await db.commit()
+    return {"ok": True}
+
+
+# ── Cloud agent CRUD ──────────────────────────────────────────────────
+
+@router.get("/cloud", response_model=list[CloudAgentConfigResponse])
+async def list_cloud_agents(
+    current_user: UserProfile = Depends(get_current_user),
+    db: AsyncSession = Depends(get_session),
+) -> list[CloudAgentConfigResponse]:
+    """List all cloud connector agent configs owned by the authenticated user."""
+    result = await db.execute(
+        select(CloudAgentConfig).where(CloudAgentConfig.user_id == current_user.id)
+    )
+    return [_to_cloud_response(a) for a in result.scalars().all()]
+
+
+@router.post("/cloud", response_model=CloudAgentConfigResponse, status_code=status.HTTP_201_CREATED)
+async def create_cloud_agent(
+    body: CloudAgentConfigCreate,
+    current_user: UserProfile = Depends(get_current_user),
+    db: AsyncSession = Depends(get_session),
+) -> CloudAgentConfigResponse:
+    """Create a new cloud connector agent config.
+
+    The combined count of enabled local and cloud agents for the user is
+    checked against the ``batch_active`` limit for their billing tier.
+    """
+    _enforce_agent_limit(current_user.tier, await _count_enabled_agents(current_user.id, db))
+    agent = CloudAgentConfig(
+        user_id=current_user.id,
+        provider=body.provider,
+        name=body.name,
+        data_types=body.data_types,
+        prompt_template=body.prompt_template,
+        oauth_token_encrypted=body.oauth_token_encrypted,
+        schedule_cron=body.schedule_cron,
+        filter_config=body.filter_config,
+    )
+    db.add(agent)
+    await db.commit()
+    await db.refresh(agent)
+    return _to_cloud_response(agent)
+
+
+@router.put("/cloud/{agent_id}", response_model=CloudAgentConfigResponse)
+async def update_cloud_agent(
+    agent_id: str,
+    body: CloudAgentConfigUpdate,
+    current_user: UserProfile = Depends(get_current_user),
+    db: AsyncSession = Depends(get_session),
+) -> CloudAgentConfigResponse:
+    """Partially update a cloud agent config. Only provided fields are changed."""
+    agent = await _get_cloud_agent_for_user(agent_id, current_user.id, db)
+    for field, value in body.model_dump(exclude_unset=True).items():
+        setattr(agent, field, value)
+    await db.commit()
+    await db.refresh(agent)
+    return _to_cloud_response(agent)
+
+
+@router.delete("/cloud/{agent_id}", response_model=dict)
+async def delete_cloud_agent(
+    agent_id: str,
+    current_user: UserProfile = Depends(get_current_user),
+    db: AsyncSession = Depends(get_session),
+) -> dict[str, bool]:
+    """Delete a cloud agent config. Associated run logs are cascade-deleted."""
+    agent = await _get_cloud_agent_for_user(agent_id, current_user.id, db)
+    await db.delete(agent)
+    await db.commit()
+    return {"ok": True}
+
+
+# ── Run logs ──────────────────────────────────────────────────────────
+
+@router.get("/runs", response_model=_RunsPage)
+async def list_run_logs(
+    agent_id: str | None = Query(default=None),
+    page: int = Query(default=1, ge=1),
+    limit: int = Query(default=20, ge=1, le=100),
+    current_user: UserProfile = Depends(get_current_user),
+    db: AsyncSession = Depends(get_session),
+) -> _RunsPage:
+    """Return paginated run logs for the authenticated user.
+
+    Optionally filter by ``agent_id``. Results are ordered from newest to oldest.
+    """
+    base_filter = [AgentRunLog.user_id == current_user.id]
+    if agent_id:
+        base_filter.append(AgentRunLog.agent_id == agent_id)
+
+    total = (
+        await db.execute(select(func.count(AgentRunLog.id)).where(*base_filter))
+    ).scalar_one()
+
+    result = await db.execute(
+        select(AgentRunLog)
+        .where(*base_filter)
+        .order_by(AgentRunLog.started_at.desc())
+        .offset((page - 1) * limit)
+        .limit(limit)
+    )
+    items = [_to_run_log_response(log) for log in result.scalars().all()]
+
+    return _RunsPage(total=total, page=page, limit=limit, items=items)
+
+
+# ── Manual trigger stub ───────────────────────────────────────────────
+
+@router.post("/{agent_id}/run", response_model=AgentRunLogResponse, status_code=status.HTTP_202_ACCEPTED)
 async def trigger_agent_run(
-    body: AgentTriggerRequest,
+    agent_id: str,
     current_user: UserProfile = Depends(get_current_user),
     db: AsyncSession = Depends(get_session),
 ) -> AgentRunLogResponse:
-    """Trigger a local agent run using client-provided configuration."""
-    _enforce_agent_limit(current_user.tier, body.active_agents)
-    await _enforce_run_frequency(current_user.tier, current_user.id, db)
+    """Manually trigger an agent run.
 
-    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,
-    )
+    Looks up the agent config (local or cloud) by ID with ownership check,
+    creates a run log entry with ``status="running"``, and returns it.
 
-    # 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
+    Actual dispatch to the agent runner is wired in Step 3.4 once
+    ``DeviceConnectionManager`` and ``agent_runner`` are available.
+    """
+    # Determine agent type by trying local first, then cloud.
+    # Keep the full config object so we can pass it to the agent runner.
+    local_config: LocalAgentConfig | None = None
+    cloud_config: CloudAgentConfig | None = None
 
-    if is_agent_running(stable_agent_id):
-        raise HTTPException(
-            status_code=status.HTTP_409_CONFLICT,
-            detail="Agent is already running. Only one run per agent is allowed at a time.",
+    local_result = await db.execute(
+        select(LocalAgentConfig).where(
+            LocalAgentConfig.id == agent_id,
+            LocalAgentConfig.user_id == current_user.id,
         )
+    )
+    local_config = local_result.scalar_one_or_none()
+
+    if local_config is not None:
+        agent_type = "local"
+    else:
+        cloud_result = await db.execute(
+            select(CloudAgentConfig).where(
+                CloudAgentConfig.id == agent_id,
+                CloudAgentConfig.user_id == current_user.id,
+            )
+        )
+        cloud_config = cloud_result.scalar_one_or_none()
+        if cloud_config is not None:
+            agent_type = "cloud"
+        else:
+            raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail="Agent not found")
 
     run_log = AgentRunLog(
-        agent_id=stable_agent_id,
-        agent_type="local",
+        agent_id=agent_id,
+        agent_type=agent_type,
         user_id=current_user.id,
         status="running",
     )
@@ -209,14 +439,14 @@ async def trigger_agent_run(
     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)
-    )
+    # Dispatch the run as a background task — returns 202 immediately.
+    if agent_type == "local" and local_config is not None:
+        asyncio.create_task(
+            run_local_agent(current_user.id, local_config, run_log, device_manager)
+        )
+    elif agent_type == "cloud" and cloud_config is not None:
+        asyncio.create_task(
+            run_cloud_agent(current_user.id, cloud_config, run_log, device_manager)
+        )
 
     return _to_run_log_response(run_log)

app/api/routes/chat.py

@@ -10,7 +10,9 @@ 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
+from app.core.memory_middleware import MemoryMiddleware
+from app.db import async_session
+from app.schemas import ChatRequest, ChatResponse, UserProfile
 
 router = APIRouter(prefix="/chat", tags=["chat"])
 
@@ -20,10 +22,21 @@ 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(
+    """Route a chat message through the Home deep agent (non-streaming)."""
+    async with async_session() as db:
+        memory = MemoryMiddleware(db)
+        memory_context = await memory.enrich_context(current_user.id, body.message)
+
+    context = {
+        **body.context.model_dump(),
+        **memory_context,
+    }
+
+    response_text = await run_home(
         user_id=current_user.id,
         message=body.message,
-        context=body.context.model_dump(),
+        context=context,
+        db_session_factory=async_session,
     )
-    return JSONResponse(content={"response": response})
+    result = ChatResponse(response=response_text)
+    return JSONResponse(content=result.model_dump())

app/api/routes/device_ws.py

@@ -14,11 +14,11 @@ Protocol:
   4. Session enters message dispatch loop + heartbeat.
 
 Incoming frame dispatch:
-  - ``tool_result``      → resolves a pending tool-call Future.
-  - ``journey_start``    → starts a guided setup journey session.
-  - ``journey_message``  → continues a journey conversation.
-  - ``pong``             → heartbeat acknowledgement (updates last-seen).
-  - unknown types        → logged, ignored.
+  - ``tool_result``    → resolves a pending tool-call Future.
+  - ``agent_data``     → enqueued in the per-run agent data queue.
+  - ``agent_complete`` → sends None sentinel to close the queue stream.
+  - ``pong``           → heartbeat acknowledgement (updates last-seen).
+  - unknown types      → logged, ignored.
 
 Outgoing heartbeat: ``{ "type": "ping" }`` every 30 s.
 
@@ -39,13 +39,12 @@ 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.deep_agent import run_home_stream, run_floating_stream
+from app.core.output_formatter import HomeFormatter, FloatingFormatter
 from app.core.ws_context import clear_client_executor, set_client_executor
 from app.db import async_session
 from app.models import AgentRunLog
@@ -148,6 +147,37 @@ async def _message_loop(websocket: WebSocket, user_id: str) -> None:
                     "device_ws: tool_result missing id from user=%s", user_id
                 )
 
+        elif frame_type == WsFrameType.agent_data:
+            run_id = frame.get("run_id")
+            if run_id:
+                try:
+                    queue = device_manager.get_agent_data_queue(user_id, run_id)
+                    await queue.put(frame)
+                except RuntimeError:
+                    logger.warning(
+                        "device_ws: agent_data for unknown run user=%s run=%s",
+                        user_id,
+                        run_id,
+                    )
+            else:
+                logger.warning(
+                    "device_ws: agent_data missing run_id from user=%s", user_id
+                )
+
+        elif frame_type == WsFrameType.agent_complete:
+            run_id = frame.get("run_id")
+            if run_id:
+                try:
+                    queue = device_manager.get_agent_data_queue(user_id, run_id)
+                    # Sentinel: signals the agent data stream is finished.
+                    await queue.put(None)
+                except RuntimeError:
+                    pass
+            else:
+                logger.warning(
+                    "device_ws: agent_complete missing run_id from user=%s", user_id
+                )
+
         elif frame_type == WsFrameType.home_request:
             asyncio.create_task(
                 _handle_home_request(websocket, user_id, frame)
@@ -158,16 +188,6 @@ async def _message_loop(websocket: WebSocket, user_id: str) -> None:
                 _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
@@ -180,13 +200,35 @@ async def _message_loop(websocket: WebSocket, user_id: str) -> None:
 
 # ── v3 Chat Handlers ──────────────────────────────────────────────────
 
+_WS_TOOL_CALL_TIMEOUT = 30  # seconds to wait for Electron tool_result
+
+
 async def _make_ws_executor(websocket: WebSocket, user_id: str):
     """Return a callback that sends tool_call frames and awaits tool_result."""
     async def _executor(payload: dict) -> dict:
         payload["type"] = WsFrameType.tool_call
+        call_id = payload["id"]
+        logger.info("ws_executor: sending tool_call id=%s action=%s", call_id, payload.get("action"))
         await websocket.send_text(json.dumps(payload))
-        future = device_manager.create_pending_call(user_id, payload["id"])
-        return await future
+        future = device_manager.create_pending_call(user_id, call_id)
+        try:
+            result = await asyncio.wait_for(future, timeout=_WS_TOOL_CALL_TIMEOUT)
+        except asyncio.TimeoutError:
+            logger.error(
+                "ws_executor: timeout waiting for tool_result id=%s action=%s user=%s",
+                call_id, payload.get("action"), user_id,
+            )
+            # Clean up the pending future so it doesn't leak
+            conn = device_manager._connections.get(user_id)
+            if conn:
+                conn.pending_calls.pop(call_id, None)
+            return {"error": f"Tool call timed out after {_WS_TOOL_CALL_TIMEOUT}s", "rows": []}
+        logger.info("ws_executor: tool_result id=%s result_type=%s result_keys=%s",
+                     call_id, type(result).__name__,
+                     list(result.keys()) if isinstance(result, dict) else "N/A")
+        if result is None:
+            logger.error("ws_executor: future resolved to None for call_id=%s user=%s", call_id, user_id)
+        return result
     return _executor
 
 
@@ -199,27 +241,14 @@ async def _handle_home_request(
     request_id = frame.get("request_id") or str(uuid4())
     message: str = frame.get("message", "")
     session_id: str = frame.get("session_id") or str(uuid4())
-    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,
-        )
+        memory_context = await memory.enrich_context(user_id, message)
 
     context: dict = {
         "conversation_history": frame.get("conversation_history", []),
-        "_debug": {"request_id": request_id, "session_id": session_id, "user_id": user_id},
         **memory_context,
     }
 
@@ -227,11 +256,12 @@ async def _handle_home_request(
     set_client_executor(executor)
     response_chunks: list[str] = []
     try:
-        event_stream = run_home_stream(user_id, message, context)
-        formatter = StreamFormatter(request_id=request_id)
+        event_stream = run_home_stream(
+            user_id, message, context, db_session_factory=async_session
+        )
+        formatter = HomeFormatter(request_id=request_id)
         async for ws_frame in formatter.format(event_stream):
             await websocket.send_text(ws_frame.model_dump_json())
-            # Collect text chunks to build the full response for episode storage
             if ws_frame.type == "stream_text":  # type: ignore[union-attr]
                 response_chunks.append(ws_frame.chunk)  # type: ignore[union-attr]
     except Exception as exc:
@@ -246,15 +276,8 @@ async def _handle_home_request(
     async with async_session() as db:
         memory = MemoryMiddleware(db)
         await memory.store_episode(
-            user_id, session_id, message, "".join(response_chunks), trace_id=request_id
+            user_id, session_id, message, "".join(response_chunks)
         )
-    logger.info(
-        "device_ws: home_request_end user=%s req=%s session=%s response_chars=%d",
-        user_id,
-        request_id,
-        session_id,
-        len("".join(response_chunks)),
-    )
 
 
 async def _handle_floating_request(
@@ -267,37 +290,23 @@ async def _handle_floating_request(
     message: str = frame.get("message", "")
     session_id: str = frame.get("session_id") or str(uuid4())
     scope: dict = frame.get("scope", {})
-    logger.info(
-        "device_ws: floating_request_start user=%s req=%s session=%s scope=%s msg=%s",
-        user_id,
-        request_id,
-        session_id,
-        json.dumps(scope, ensure_ascii=True)[:200],
-        message[:200],
-    )
 
     # ── Memory: enrich context before LLM call ────────────────────────
     async with async_session() as db:
         memory = MemoryMiddleware(db)
-        memory_context = await memory.enrich_context(
-            user_id,
-            message,
-            trace_id=request_id,
-            session_id=session_id,
-        )
+        memory_context = await memory.enrich_context(user_id, message)
 
-    context: dict = {
-        "scope": scope,
-        "_debug": {"request_id": request_id, "session_id": session_id, "user_id": user_id},
-        **memory_context,
-    }
+    context: dict = {"scope": scope, **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)
+        event_stream = run_floating_stream(
+            user_id, message, context, scope=scope,
+            db_session_factory=async_session,
+        )
+        formatter = FloatingFormatter(request_id=request_id)
         async for ws_frame in formatter.format(event_stream):
             await websocket.send_text(ws_frame.model_dump_json())
             if ws_frame.type == "stream_text":  # type: ignore[union-attr]
@@ -314,72 +323,8 @@ async def _handle_floating_request(
     async with async_session() as db:
         memory = MemoryMiddleware(db)
         await memory.store_episode(
-            user_id, session_id, message, "".join(response_chunks), trace_id=request_id
+            user_id, session_id, message, "".join(response_chunks)
         )
-    logger.info(
-        "device_ws: floating_request_end user=%s req=%s session=%s response_chars=%d",
-        user_id,
-        request_id,
-        session_id,
-        len("".join(response_chunks)),
-    )
-
-
-# ── 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 ─────────────────────────────────────────────────────────
@@ -415,3 +360,6 @@ async def _mark_runs_disconnected(user_id: str) -> None:
             user_id,
             exc,
         )
+
+
+

app/billing/tier_manager.py

@@ -21,7 +21,6 @@ FEATURES: dict[str, dict[str, Any]] = {
     "free": {
         "agents": 3,
         "batch_active": 2,
-        "batch_runs_per_day": 5,
         "cloud_storage_gb": 0,
         "backup_gb": 0,
         "providers": 1,
@@ -32,7 +31,6 @@ FEATURES: dict[str, dict[str, Any]] = {
     "pro": {
         "agents": -1,           # unlimited
         "batch_active": 10,
-        "batch_runs_per_day": 50,
         "cloud_storage_gb": 5,
         "backup_gb": 5,
         "providers": -1,
@@ -43,7 +41,6 @@ FEATURES: dict[str, dict[str, Any]] = {
     "power": {
         "agents": -1,
         "batch_active": -1,     # unlimited
-        "batch_runs_per_day": -1,  # unlimited
         "cloud_storage_gb": 25,
         "backup_gb": 25,
         "providers": -1,
@@ -54,7 +51,6 @@ 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
         "providers": -1,
@@ -81,18 +77,16 @@ class TierManager:
     async def get_tier(self, user_id: str, db: AsyncSession) -> BillingTier:
         """Return the current billing tier for ``user_id`` from the DB.
 
-        Falls back to ``'power'`` in dev (unlimited) or ``'free'`` in prod
-        when no subscription row exists.
+        Falls back to ``'free'`` when no subscription row exists.
         """
         from app.models import Subscription  # noqa: PLC0415
-        from app.config.settings import settings  # noqa: PLC0415
 
         result = await db.execute(
             select(Subscription.tier).where(Subscription.user_id == user_id)
         )
         tier: str | None = result.scalar_one_or_none()
         if tier is None or tier not in FEATURES:
-            return "power" if settings.ENV == "dev" else "free"
+            return "free"
         return tier  # type: ignore[return-value]
 
     # ── Feature access ───────────────────────────────────────────────────

app/config/settings.py

@@ -27,9 +27,9 @@ class Settings(BaseSettings):
     ANTHROPIC_API_KEY: str = ""
     GOOGLE_API_KEY: str = ""
     CEREBRAS_API_KEY: str = ""
-    GITHUB_TOKEN: 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.
@@ -54,9 +54,7 @@ class Settings(BaseSettings):
 
     ENV: Literal["dev", "prod"] = "dev"
 
-    model_config = SettingsConfigDict(
-        env_file=".env", env_file_encoding="utf-8", extra="ignore"
-    )
+    model_config = SettingsConfigDict(env_file=".env", env_file_encoding="utf-8")
 
 
 settings = Settings()

app/core/agent_registry.py

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

app/core/device_manager.py

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

app/core/llm.py

@@ -1,6 +1,6 @@
 """LLM factory — centralised model instantiation via LiteLLM.
 
-Every agent and the orchestrator call ``get_llm()``
+Every agent and the deep-agent supervisors call ``get_llm()`` or ``get_router_llm()``
 instead of directly constructing a provider-specific class.  The model string
 follows the `LiteLLM model naming convention
 <https://docs.litellm.ai/docs/providers>`_:
@@ -11,14 +11,13 @@ follows the `LiteLLM model naming convention
 * Ollama:     ``ollama/llama3``
 * Bedrock:    ``bedrock/anthropic.claude-v2``
 
-Switch providers by changing **LLM_MODEL** in ``.env``
+Switch providers by changing **LLM_MODEL** / **LLM_ROUTER_MODEL** in ``.env``
 — no code changes required.
 """
 
 from __future__ import annotations
 
 import os
-import warnings
 
 from openai import AsyncOpenAI
 import litellm
@@ -33,14 +32,6 @@ from app.config.settings import settings
 # Drop them silently instead of raising UnsupportedParamsError.
 litellm.drop_params = True
 
-# Some provider responses include a plain dict in the `usage` field where a
-# richer Pydantic model is expected. This warning is noisy but non-fatal.
-warnings.filterwarnings(
-    "ignore",
-    message=r"PydanticSerializationUnexpectedValue\(Expected `ResponseAPIUsage`",
-    category=UserWarning,
-)
-
 
 def _api_key_for_model(model: str) -> str | None:
     """Return the most appropriate API key for the given LiteLLM model string."""
@@ -50,8 +41,6 @@ def _api_key_for_model(model: str) -> str | None:
         return settings.GOOGLE_API_KEY or None
     if model.startswith("cerebras/"):
         return settings.CEREBRAS_API_KEY or None
-    if model.startswith("github/"):
-        return settings.GITHUB_TOKEN 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.
@@ -85,9 +74,6 @@ def get_llm(
     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)
-
     # 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:
@@ -100,6 +86,14 @@ def get_llm(
     )
 
 
+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*.
 

app/core/memory_middleware.py

@@ -43,21 +43,15 @@ _PROACTIVE_CONFIDENCE_THRESHOLD = 0.6
 
 
 class MemoryMiddleware:
-    """Enrich orchestrator context with memory and persist interactions after."""
+    """Enrich agent context with memory and persist interactions after."""
 
     def __init__(self, db: AsyncSession) -> None:
         self._db = db
 
     # ── Public API ────────────────────────────────────────────────────────────
 
-    async def enrich_context(
-        self,
-        user_id: str,
-        message: str,
-        trace_id: str | None = None,
-        session_id: str | None = None,
-    ) -> dict[str, Any]:
-        """Build memory context dict to inject into the orchestrator before LLM call.
+    async def enrich_context(self, user_id: str, message: str) -> dict[str, Any]:
+        """Build memory context dict to inject into the agent before LLM call.
 
         Returns a dict with keys:
           core_memory        — {key: plaintext_value, ...}
@@ -71,21 +65,9 @@ class MemoryMiddleware:
 
         core = await self._load_core(user_id, fernet)
         associative = await self._load_associative(user_id, message, fernet)
-        episodic = await self._load_episodic(user_id, fernet, session_id=session_id)
+        episodic = await self._load_episodic(user_id, fernet)
         proactive = await self._load_proactive(user_id, fernet)
 
-        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),
-        )
-
         return {
             "core_memory": core,
             "associative_memory": associative,
@@ -99,7 +81,6 @@ class MemoryMiddleware:
         session_id: str,
         message: str,
         response: str,
-        trace_id: str | None = None,
     ) -> None:
         """Summarise and store a completed interaction in episodic memory.
 
@@ -122,19 +103,11 @@ 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:
+    async def update_core(self, user_id: str, key: str, value: str) -> None:
         """Upsert a core memory key/value for a user."""
         fernet = await self._get_fernet(user_id)
         if fernet is None:
@@ -160,176 +133,10 @@ class MemoryMiddleware:
             ))
         try:
             await self._db.commit()
-            user_dbg = await self._get_user_debug(user_id)
-            logger.info(
-                "memory: update_core trace=%s user=%s tier=%s key=%s",
-                trace_id or "-",
-                user_id,
-                user_dbg.get("tier") or "-",
-                key,
-            )
         except Exception as exc:
             logger.error("memory: update_core failed user=%s key=%s: %s", user_id, key, exc)
             await self._db.rollback()
 
-    async def list_core_blocks(self, user_id: str) -> list[dict[str, str]]:
-        """Return core memory as editable blocks (label/value)."""
-        fernet = await self._get_fernet(user_id)
-        if fernet is None:
-            return []
-
-        result = await self._db.execute(
-            select(MemoryCore)
-            .where(MemoryCore.user_id == user_id)
-            .order_by(MemoryCore.key.asc())
-        )
-        rows = result.scalars().all()
-        out: list[dict[str, str]] = []
-        for row in rows:
-            plaintext = _safe_decrypt(fernet, row.value_encrypted)
-            if plaintext is not None:
-                out.append({"label": row.key, "value": plaintext})
-        logger.debug("memory: list_core_blocks user=%s count=%d", user_id, len(out))
-        return out
-
-    async def get_core_block(self, user_id: str, label: str) -> str | None:
-        """Return a single core memory block value by label."""
-        fernet = await self._get_fernet(user_id)
-        if fernet is None:
-            return None
-
-        result = await self._db.execute(
-            select(MemoryCore).where(
-                MemoryCore.user_id == user_id,
-                MemoryCore.key == label,
-            )
-        )
-        row = result.scalar_one_or_none()
-        if row is None:
-            logger.debug("memory: get_core_block user=%s label=%s found=0", user_id, label)
-            return None
-        value = _safe_decrypt(fernet, row.value_encrypted)
-        logger.debug("memory: get_core_block user=%s label=%s found=%d", user_id, label, 1 if value is not None else 0)
-        return value
-
-    async def delete_core(self, user_id: str, label: str) -> bool:
-        """Delete a core memory block by label. Returns True if deleted."""
-        result = await self._db.execute(
-            select(MemoryCore).where(
-                MemoryCore.user_id == user_id,
-                MemoryCore.key == label,
-            )
-        )
-        row = result.scalar_one_or_none()
-        if row is None:
-            logger.debug("memory: delete_core user=%s label=%s found=0", user_id, label)
-            return False
-
-        await self._db.delete(row)
-        try:
-            await self._db.commit()
-            logger.info("memory: delete_core user=%s label=%s", user_id, label)
-            return True
-        except Exception as exc:
-            logger.error("memory: delete_core failed user=%s label=%s: %s", user_id, label, exc)
-            await self._db.rollback()
-            return False
-
-    async def append_core(self, user_id: str, label: str, content: str) -> None:
-        """Append content to a core block, creating it if missing."""
-        current = await self.get_core_block(user_id, label)
-        if current is None:
-            await self.update_core(user_id, label, content)
-            logger.info("memory: append_core user=%s label=%s created=1", user_id, label)
-            return
-        await self.update_core(user_id, label, f"{current}\n{content}")
-        logger.info("memory: append_core user=%s label=%s created=0", user_id, label)
-
-    async def replace_core(self, user_id: str, label: str, old: str, new: str) -> bool:
-        """Replace one exact string inside a core block. Returns False if not found."""
-        current = await self.get_core_block(user_id, label)
-        if current is None or old not in current:
-            logger.debug("memory: replace_core user=%s label=%s changed=0", user_id, label)
-            return False
-        await self.update_core(user_id, label, current.replace(old, new, 1))
-        logger.info("memory: replace_core user=%s label=%s changed=1", user_id, label)
-        return True
-
-    async def insert_archival(self, user_id: str, content: str, source: str = "manual") -> None:
-        """Insert a long-term archival memory entry."""
-        fernet = await self._get_fernet(user_id)
-        if fernet is None:
-            return
-
-        encrypted = _encrypt(fernet, content)
-        row = MemoryAssociative(
-            id=str(uuid.uuid4()),
-            user_id=user_id,
-            content_encrypted=encrypted,
-            embedding=None,
-            entity_type=source,
-            entity_id=None,
-        )
-        self._db.add(row)
-        try:
-            await self._db.commit()
-            logger.info("memory: insert_archival user=%s source=%s", user_id, source)
-        except Exception as exc:
-            logger.error("memory: insert_archival failed user=%s: %s", user_id, exc)
-            await self._db.rollback()
-
-    async def search_archival(self, user_id: str, query: str, top_k: int = 5) -> list[str]:
-        """Search archival memory (keyword fallback; semantic ranking can replace this)."""
-        fernet = await self._get_fernet(user_id)
-        if fernet is None:
-            return []
-
-        result = await self._db.execute(
-            select(MemoryAssociative)
-            .where(MemoryAssociative.user_id == user_id)
-            .order_by(MemoryAssociative.updated_at.desc())
-            .limit(100)
-        )
-        rows = result.scalars().all()
-        needle = query.strip().lower()
-        out: list[str] = []
-        for row in rows:
-            plaintext = _safe_decrypt(fernet, row.content_encrypted)
-            if plaintext is None:
-                continue
-            if not needle or needle in plaintext.lower():
-                out.append(plaintext)
-            if len(out) >= max(top_k, 1):
-                break
-        logger.info("memory: search_archival user=%s query=%s hits=%d", user_id, query[:80], len(out))
-        return out
-
-    async def search_recall(self, user_id: str, query: str, top_k: int = 5) -> list[str]:
-        """Search recall memory (episodic summaries) by keyword."""
-        fernet = await self._get_fernet(user_id)
-        if fernet is None:
-            return []
-
-        result = await self._db.execute(
-            select(MemoryEpisodic)
-            .where(MemoryEpisodic.user_id == user_id)
-            .order_by(MemoryEpisodic.created_at.desc())
-            .limit(100)
-        )
-        rows = result.scalars().all()
-        needle = query.strip().lower()
-        out: list[str] = []
-        for row in rows:
-            plaintext = _safe_decrypt(fernet, row.summary_encrypted)
-            if plaintext is None:
-                continue
-            if not needle or needle in plaintext.lower():
-                out.append(plaintext)
-            if len(out) >= max(top_k, 1):
-                break
-        logger.info("memory: search_recall user=%s query=%s hits=%d", user_id, query[:80], len(out))
-        return out
-
     # ── Private helpers ───────────────────────────────────────────────────────
 
     async def _get_fernet(self, user_id: str) -> Fernet | None:
@@ -341,16 +148,6 @@ class MemoryMiddleware:
             return None
         return Fernet(user.encryption_key.encode())
 
-    async def _get_user_debug(self, user_id: str) -> dict[str, str | None]:
-        """Load lightweight user debug fields for trace logs."""
-        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)
@@ -386,17 +183,10 @@ class MemoryMiddleware:
                 out.append(plaintext)
         return out
 
-    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)
+    async def _load_episodic(self, user_id: str, fernet: Fernet) -> list[str]:
         result = await self._db.execute(
-            query
+            select(MemoryEpisodic)
+            .where(MemoryEpisodic.user_id == user_id)
             .order_by(MemoryEpisodic.created_at.desc())
             .limit(_EPISODIC_RECENT_N)
         )

app/core/output_formatter.py

@@ -1,47 +1,141 @@
-"""Output formatter for deep-agent stream events."""
+"""Output Formatter — transforms deep-agent event streams into WS frame sequences.
+
+Consumes ``(event_type, data)`` tuples yielded by ``deep_agent.run_*_stream()``:
+  * ``("token", str)``       — supervisor text token
+  * ``("tool_end", dict)``   — sub-agent finished: ``{name, result}``
+  * ``("mutations", list)``  — collected CRUD mutations for ``stream_end``
+
+HomeFormatter:
+  * Streams text tokens as-is → emits ``WsStreamText``
+    (text may contain inline ``<type>[id,...]</type>`` entity tags
+    for the frontend to parse and render as interactive components)
+  * Attaches mutations        → injects into ``WsStreamEnd``
+
+FloatingFormatter:
+  * Sniffs first ``tool_end`` name → emits ``WsFloatingDomain``
+  * Streams text tokens             → emits ``WsStreamText``
+  * Attaches mutations              → injects into ``WsStreamEnd``
+"""
 
 from __future__ import annotations
 
+import logging
 from collections.abc import AsyncGenerator
 from typing import Any
 
-from app.schemas import WsFloatingDomain, WsStreamEnd, WsStreamStart, WsStreamText
+from app.schemas import (
+    WsFloatingDomain,
+    WsStreamEnd,
+    WsStreamStart,
+    WsStreamText,
+)
+
+logger = logging.getLogger(__name__)
+
+# Map sub-agent tool name → floating domain / entity type
+_AGENT_DOMAIN: dict[str, str] = {
+    "task_agent": "tasks",
+    "timeline_agent": "timelines",
+    "note_agent": "notes",
+    "project_agent": "projects",
+}
 
 WsFrame = WsStreamStart | WsStreamText | WsStreamEnd | WsFloatingDomain
 
 
-class StreamFormatter:
-    """Convert `(event_type, data)` stream events into websocket frame models."""
+class HomeFormatter:
+    """Consumes a deep-agent event stream and yields WS frames for the Home view.
+
+    Text tokens are forwarded as-is via ``WsStreamText``.  The supervisor
+    embeds ``<type>[id1,id2]</type>`` entity tags inline — the frontend
+    is responsible for parsing those and rendering interactive components.
+    Mutations are attached to ``WsStreamEnd``.
+    """
 
     def __init__(self, request_id: str) -> None:
         self.request_id = request_id
+        self._mutations: list[dict] = []
 
     async def format(
         self,
         event_stream: AsyncGenerator[tuple[str, Any], None],
     ) -> AsyncGenerator[WsFrame, None]:
-        started = False
+        yield WsStreamStart(request_id=self.request_id)
 
         async for event_type, data in event_stream:
-            if event_type == "floating_domain":
-                if isinstance(data, dict):
+            if event_type == "token":
+                if data:
+                    yield WsStreamText(request_id=self.request_id, chunk=data)
+
+            elif event_type == "mutations":
+                self._mutations = data or []
+
+        yield WsStreamEnd(
+            request_id=self.request_id,
+            mutations=[
+                {"action": m["action"], "table": m["table"], "data": m["data"]}
+                for m in self._mutations
+            ],
+        )
+
+
+class FloatingFormatter:
+    """Consumes a deep-agent event stream and yields WS frames for the Floating view.
+
+    Sniffs the first ``tool_end`` event name to derive the domain (e.g.
+    ``task_agent`` → ``"tasks"``), then streams text tokens as plain
+    ``WsStreamText``.  No block parsing for floating context.
+    """
+
+    def __init__(self, request_id: str) -> None:
+        self.request_id = request_id
+        self._mutations: list[dict] = []
+
+    async def format(
+        self,
+        event_stream: AsyncGenerator[tuple[str, Any], None],
+    ) -> AsyncGenerator[WsFrame, None]:
+        domain_sent = False
+
+        async for event_type, data in event_stream:
+            if event_type == "tool_end" and not domain_sent:
+                # Sniff domain from the first sub-agent that completes
+                name = data.get("name", "")
+                domain = _AGENT_DOMAIN.get(name, "tasks")
+                yield WsFloatingDomain(
+                    request_id=self.request_id,
+                    domain=domain,  # type: ignore[arg-type]
+                )
+                yield WsStreamStart(request_id=self.request_id)
+                domain_sent = True
+
+            elif event_type == "token":
+                if not domain_sent:
+                    # First token arrived before any tool_end — default domain
                     yield WsFloatingDomain(
                         request_id=self.request_id,
-                        domain=data,
+                        domain="tasks",  # type: ignore[arg-type]
                     )
-                continue
+                    yield WsStreamStart(request_id=self.request_id)
+                    domain_sent = True
+                if data:
+                    yield WsStreamText(request_id=self.request_id, chunk=data)
 
-            if event_type != "token":
-                continue
+            elif event_type == "mutations":
+                self._mutations = data or []
 
-            if not started:
-                yield WsStreamStart(request_id=self.request_id)
-                started = True
-
-            text = str(data or "")
-            if text:
-                yield WsStreamText(request_id=self.request_id, chunk=text)
-
-        if not started:
+        # If no events triggered domain_sent (edge case), still emit structure
+        if not domain_sent:
+            yield WsFloatingDomain(
+                request_id=self.request_id,
+                domain="tasks",  # type: ignore[arg-type]
+            )
             yield WsStreamStart(request_id=self.request_id)
-        yield WsStreamEnd(request_id=self.request_id)
+
+        yield WsStreamEnd(
+            request_id=self.request_id,
+            mutations=[
+                {"action": m["action"], "table": m["table"], "data": m["data"]}
+                for m in self._mutations
+            ],
+        )

app/core/ws_context.py

@@ -7,18 +7,21 @@ The callback sends a `tool_call` WS frame and awaits the `tool_result`.
 
 from __future__ import annotations
 
+import logging
 from contextvars import ContextVar
 from typing import Any, Callable, Coroutine
 from uuid import uuid4
 
+logger = logging.getLogger(__name__)
+
 # Holds the execute callback for the current WS session.
-# Set by the chat WS handler before the orchestrator runs; cleared after.
+# Set by the chat WS handler before the deep agent runs; cleared after.
 _client_executor: ContextVar[Callable[[dict], Coroutine[Any, Any, dict]]] = ContextVar(
     "_client_executor"
 )
 
 # Optional collector that captures raw execute_on_client results.
-# Set by _tool_loop / _tool_loop_stream to populate ChatAgent.tool_results.
+# Set by the deep agent tool loop to capture CRUD mutations.
 _tool_result_collector: ContextVar[list[dict] | None] = ContextVar(
     "_tool_result_collector", default=None
 )
@@ -81,12 +84,17 @@ async def execute_on_client(
     if limit is not None:
         payload["limit"] = limit
 
+    logger.info("execute_on_client: sending payload action=%s table=%s id=%s", action, table, payload["id"])
     result = await callback(payload)
+    if result is None:
+        logger.error("execute_on_client: callback returned None for action=%s table=%s id=%s", action, table, payload["id"])
+    else:
+        logger.info("execute_on_client: got result type=%s keys=%s", type(result).__name__, list(result.keys()) if isinstance(result, dict) else "N/A")
     collector = _tool_result_collector.get(None)
-    if collector is not None:
+    if collector is not None and action in ("insert", "update", "delete"):
         collector.append({
             "action": action,
             "table": table,
-            "data": result,
+            "data": data or {},
         })
     return result

app/main.py

@@ -18,9 +18,7 @@ from app.config.settings import settings
 
 @asynccontextmanager
 async def lifespan(app: FastAPI):
-    # Startup: ensure agent tool modules are loaded.
-    import app.agents  # noqa: F401
-
+    # Startup: initialise DB connection pool
     yield
 
     # Shutdown: dispose SQLAlchemy connection pool
@@ -50,7 +48,7 @@ def create_app() -> FastAPI:
     app.add_middleware(SanitizerMiddleware)
     app.add_middleware(TierRateLimitMiddleware)
 
-    from app.api.routes import agents, auth, backup, billing, chat, device_ws, plugins, storage, vectors
+    from app.api.routes import agent_setup, agents, auth, backup, billing, chat, device_ws, plugins, storage, vectors
 
     app.include_router(auth.router,       prefix="/api/v1")
     app.include_router(chat.router,       prefix="/api/v1")
@@ -60,6 +58,7 @@ def create_app() -> FastAPI:
     app.include_router(plugins.router,    prefix="/api/v1")
     app.include_router(billing.router,    prefix="/api/v1")
     app.include_router(agents.router,     prefix="/api/v1")
+    app.include_router(agent_setup.router, prefix="/api/v1")
     app.include_router(device_ws.router,  prefix="/api/v1")
 
     @app.get("/api/v1/health", tags=["health"])

app/schemas.py

@@ -142,6 +142,9 @@ class WsFrameType(str, Enum):
     tool_result = "tool_result"
     final = "final"
     ping = "ping"
+    agent_run = "agent_run"
+    agent_data = "agent_data"
+    agent_complete = "agent_complete"
     device_hello = "device_hello"
     # ── v3 frame types ─────────────────────────────────────────────────
     home_request = "home_request"
@@ -153,10 +156,6 @@ class WsFrameType(str, Enum):
     data_request = "data_request"
     data_response = "data_response"
     mutation = "mutation"
-    # ── v4 journey frame types ────────────────────────────────────────
-    journey_start = "journey_start"
-    journey_message = "journey_message"
-    journey_reply = "journey_reply"
 
 
 class WsToolCall(BaseModel):
@@ -209,6 +208,31 @@ class WsDeviceHello(BaseModel):
     agent_ids: list[str] = Field(default_factory=list)
 
 
+class WsAgentRun(BaseModel):
+    """Server → Client: trigger an agent run on the connected device."""
+
+    type: Literal[WsFrameType.agent_run] = WsFrameType.agent_run
+    run_id: str
+    agent_id: str
+    config: dict[str, Any]
+
+
+class WsAgentData(BaseModel):
+    """Client → Server: files read by the local agent."""
+
+    type: Literal[WsFrameType.agent_data] = WsFrameType.agent_data
+    run_id: str
+    files: list[dict[str, Any]]
+
+
+class WsAgentComplete(BaseModel):
+    """Client → Server: Electron signals it has finished reading files."""
+
+    type: Literal[WsFrameType.agent_complete] = WsFrameType.agent_complete
+    run_id: str
+    files_read: int
+    errors: list[str] = Field(default_factory=list)
+
 
 # ── WebSocket v3 Frame Models ─────────────────────────────────────────
 
@@ -255,14 +279,7 @@ class WsStreamEnd(BaseModel):
 
     type: Literal[WsFrameType.stream_end] = WsFrameType.stream_end
     request_id: str
-
-
-class WsDomain(BaseModel):
-    """Structured floating domain payload for UI routing decisions."""
-
-    type: Literal["task", "timeline", "project", "node"]
-    id: str | None = None
-    section: Literal["task", "timeline", "note"] | None = None
+    mutations: list[dict[str, Any]] = Field(default_factory=list)
 
 
 class WsFloatingDomain(BaseModel):
@@ -270,7 +287,7 @@ class WsFloatingDomain(BaseModel):
 
     type: Literal[WsFrameType.floating_domain] = WsFrameType.floating_domain
     request_id: str
-    domain: WsDomain
+    domain: Literal["tasks", "timelines", "notes", "projects"]
 
 
 # ── Agent Catalog ─────────────────────────────────────────────────────
@@ -279,28 +296,84 @@ class AgentCatalogItem(BaseModel):
     type: str
     name: str
     description: str
+    config_schema: dict[str, Any] = Field(default_factory=dict)
 
 
-class AgentCreationCheckRequest(BaseModel):
-    active_agents: int = Field(ge=0, default=0)
+# ── Local Agent Config ────────────────────────────────────────────────
+
+class LocalAgentConfigCreate(BaseModel):
+    name: str
+    device_id: str
+    directory_paths: list[str]
+    data_types: list[str]
+    prompt_template: str
+    file_extensions: list[str]
+    schedule_cron: str
 
 
-class AgentCreationCheckResponse(BaseModel):
-    allowed: bool
-    tier: BillingTier
-    active_agents: int
-    limit: int
+class LocalAgentConfigUpdate(BaseModel):
+    name: str | None = None
+    device_id: str | None = None
+    directory_paths: list[str] | None = None
+    data_types: list[str] | None = None
+    prompt_template: str | None = None
+    file_extensions: list[str] | None = None
+    schedule_cron: str | None = None
+    enabled: bool | None = None
 
 
-class AgentTriggerRequest(BaseModel):
-    directory: str = Field(min_length=1)
-    device_id: str = Field(default="")
-    agent_id: str | None = None  # FE stable agent ID (electron-store UUID)
-    what_to_extract: list[str] = Field(min_length=1)
-    actions_by_type: dict[str, list[str]] | None = None
-    batch_interval: str = Field(min_length=1)
-    custom_agent_prompt: str = Field(min_length=1)
-    active_agents: int = Field(ge=0, default=0)
+class LocalAgentConfigResponse(BaseModel):
+    id: str
+    name: str
+    device_id: str
+    directory_paths: list[str]
+    data_types: list[str]
+    prompt_template: str
+    file_extensions: list[str]
+    schedule_cron: str
+    enabled: bool
+    last_run_at: int | None
+    created_at: int
+    updated_at: int
+
+
+# ── Cloud Agent Config ────────────────────────────────────────────────
+
+class CloudAgentConfigCreate(BaseModel):
+    provider: Literal["gmail", "teams", "outlook"]
+    name: str
+    data_types: list[str]
+    prompt_template: str
+    oauth_token_encrypted: str
+    schedule_cron: str
+    filter_config: dict[str, Any] | None = None
+
+
+class CloudAgentConfigUpdate(BaseModel):
+    provider: Literal["gmail", "teams", "outlook"] | None = None
+    name: str | None = None
+    data_types: list[str] | None = None
+    prompt_template: str | None = None
+    oauth_token_encrypted: str | None = None
+    schedule_cron: str | None = None
+    filter_config: dict[str, Any] | None = None
+    enabled: bool | None = None
+
+
+class CloudAgentConfigResponse(BaseModel):
+    """oauth_token_encrypted is intentionally excluded — never returned to clients."""
+
+    id: str
+    provider: Literal["gmail", "teams", "outlook"]
+    name: str
+    data_types: list[str]
+    prompt_template: str
+    schedule_cron: str
+    filter_config: dict[str, Any] | None
+    enabled: bool
+    last_run_at: int | None
+    created_at: int
+    updated_at: int
 
 
 # ── Agent Run Log ─────────────────────────────────────────────────────
@@ -319,3 +392,18 @@ class AgentRunLogResponse(BaseModel):
 
 # ── Chatbot Journey ───────────────────────────────────────────────────
 
+class JourneyStartRequest(BaseModel):
+    agent_type: Literal["local", "cloud"]
+    agent_id: str | None = None
+
+
+class JourneyMessageRequest(BaseModel):
+    session_id: str
+    message: str
+
+
+class JourneyResponse(BaseModel):
+    session_id: str
+    message: str
+    done: bool
+    prompt_template: str | None = None

docs/MICROSERVICES_ARCHITECTURE.md

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

requirements.txt

@@ -4,6 +4,8 @@ gunicorn>=22.0.0
 langchain>=0.3.0
 langchain-openai>=0.3.0
 langchain-litellm>=0.1.0
+langgraph>=0.3.0
+deepagents>=0.4.10
 litellm>=1.50.0
 pydantic>=2.10.0
 pydantic-settings>=2.7.0
@@ -32,6 +34,4 @@ google-auth-oauthlib>=1.2.0
 google-auth-httplib2>=0.2.0
 msal>=1.28.0
 cryptography>=42.0.0
-redis>=5.0.0
-langfuse>=3.0.0
 ruff>=0.8.0

services/auth/.env.example

@@ -1,19 +0,0 @@
-# ── 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

@@ -1,36 +0,0 @@
-# ── 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

@@ -1,16 +0,0 @@
-# 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

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

@@ -1,69 +0,0 @@
-"""Auth dependencies — JWT validation for the Auth Service.
-
-This is the canonical get_current_user used by protected endpoints
-within the Auth Service itself (/me, /me PUT).
-"""
-
-from __future__ import annotations
-
-from fastapi import Depends, HTTPException, status
-from fastapi.security import OAuth2PasswordBearer
-from jose import JWTError, jwt
-from sqlalchemy import select
-from sqlalchemy.ext.asyncio import AsyncSession
-
-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")
-
-
-async def get_current_user(
-    token: str = Depends(oauth2_scheme),
-    db: AsyncSession = Depends(get_session),
-) -> UserProfile:
-    """Validate a Bearer JWT and return the authenticated user.
-
-    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,
-        detail="Could not validate credentials",
-        headers={"WWW-Authenticate": "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:
-            raise credentials_exc
-    except JWTError:
-        raise credentials_exc
-
-    # 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
-    user_result = await db.execute(
-        select(User.name, User.surname).where(User.id == user_id)
-    )
-    user_row = user_result.one_or_none()
-
-    return UserProfile(
-        id=user_id,
-        email=email,
-        name=user_row.name if user_row else None,
-        surname=user_row.surname if user_row else None,
-        tier=tier,
-    )  # type: ignore[arg-type]

services/auth/app/main.py

@@ -1,62 +0,0 @@
-"""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

@@ -1,249 +0,0 @@
-"""Auth routes: register, login, refresh, me.
-
-Extracted from app/api/routes/auth.py — uses shared.* imports instead of app.*.
-"""
-
-from __future__ import annotations
-
-import hashlib
-import time
-import uuid
-from datetime import datetime, timedelta, timezone
-
-import bcrypt
-from cryptography.fernet import Fernet
-from fastapi import APIRouter, Depends, HTTPException, status
-from jose import jwt
-from pydantic import BaseModel
-from sqlalchemy import select
-from sqlalchemy.ext.asyncio import AsyncSession
-
-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"])
-
-
-# ── Internal helpers ─────────────────────────────────────────────────
-
-
-def _hash_password(password: str) -> str:
-    return bcrypt.hashpw(password.encode(), bcrypt.gensalt()).decode()
-
-
-def _verify_password(password: str, hashed: str) -> bool:
-    return bcrypt.checkpw(password.encode(), hashed.encode())
-
-
-def _hash_token(plain_token: str) -> str:
-    """SHA-256 of the plain refresh token string."""
-    return hashlib.sha256(plain_token.encode()).hexdigest()
-
-
-def _make_access_token(user_id: str, email: str, tier: str) -> tuple[str, int]:
-    """Return (RS256-signed JWT, expires_at_ms)."""
-    now = int(time.time())
-    exp = now + settings.JWT_ACCESS_TOKEN_EXPIRE_MINUTES * 60
-    payload = {
-        "sub": user_id,
-        "email": email,
-        "tier": tier,
-        "exp": exp,
-        "iat": now,
-    }
-    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 ────────────────────────────────────────────────────
-
-
-class _RegisterRequest(BaseModel):
-    email: str
-    password: str
-    name: str | None = None
-    surname: str | None = None
-
-
-class _LoginRequest(BaseModel):
-    email: str
-    password: str
-
-
-class _RefreshRequest(BaseModel):
-    refresh_token: str
-
-
-class _UpdateProfileRequest(BaseModel):
-    name: str | None = None
-    surname: str | None = None
-
-
-# ── Routes ────────────────────────────────────────────────────────────
-
-
-@router.post("/register", response_model=AuthTokens, status_code=status.HTTP_201_CREATED)
-async def register(
-    body: _RegisterRequest,
-    db: AsyncSession = Depends(get_session),
-) -> AuthTokens:
-    """Create a new account and return JWT tokens."""
-    existing = await db.execute(select(User).where(User.email == body.email))
-    if existing.scalar_one_or_none() is not None:
-        raise HTTPException(status.HTTP_409_CONFLICT, "Email already registered")
-
-    user = User(
-        id=str(uuid.uuid4()),
-        email=body.email,
-        name=body.name,
-        surname=body.surname,
-        password_hash=_hash_password(body.password),
-        tier="free",
-        encryption_key=Fernet.generate_key().decode(),
-    )
-    db.add(user)
-    await db.flush()
-
-    plain_token = str(uuid.uuid4())
-    expires_at = datetime.now(timezone.utc) + timedelta(
-        days=settings.JWT_REFRESH_TOKEN_EXPIRE_DAYS
-    )
-    rt = RefreshToken(
-        user_id=user.id,
-        token_hash=_hash_token(plain_token),
-        expires_at=expires_at,
-    )
-    db.add(rt)
-    await db.commit()
-
-    access_token, expires_at_ms = _make_access_token(user.id, user.email, user.tier)
-    return AuthTokens(
-        access_token=access_token,
-        refresh_token=plain_token,
-        expires_at=expires_at_ms,
-    )
-
-
-@router.post("/login", response_model=AuthTokens)
-async def login(
-    body: _LoginRequest,
-    db: AsyncSession = Depends(get_session),
-) -> AuthTokens:
-    """Validate credentials and return JWT tokens."""
-    result = await db.execute(select(User).where(User.email == body.email))
-    user = result.scalar_one_or_none()
-    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
-    )
-    rt = RefreshToken(
-        user_id=user.id,
-        token_hash=_hash_token(plain_token),
-        expires_at=expires_at,
-    )
-    db.add(rt)
-    await db.commit()
-
-    access_token, expires_at_ms = _make_access_token(user.id, user.email, tier)
-    return AuthTokens(
-        access_token=access_token,
-        refresh_token=plain_token,
-        expires_at=expires_at_ms,
-    )
-
-
-@router.post("/refresh", response_model=AuthTokens)
-async def refresh(
-    body: _RefreshRequest,
-    db: AsyncSession = Depends(get_session),
-) -> AuthTokens:
-    """Rotate a refresh token and return a new token pair."""
-    token_hash = _hash_token(body.refresh_token)
-    result = await db.execute(
-        select(RefreshToken).where(RefreshToken.token_hash == token_hash)
-    )
-    rt = result.scalar_one_or_none()
-
-    now = datetime.now(timezone.utc)
-    if rt is None or rt.expires_at.replace(tzinfo=timezone.utc) < now:
-        raise HTTPException(status.HTTP_401_UNAUTHORIZED, "Invalid or expired refresh token")
-
-    await db.delete(rt)
-
-    user_result = await db.execute(select(User).where(User.id == rt.user_id))
-    user = user_result.scalar_one_or_none()
-    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(
-        user_id=user.id,
-        token_hash=_hash_token(plain_token),
-        expires_at=new_expires,
-    )
-    db.add(new_rt)
-    await db.commit()
-
-    access_token, expires_at_ms = _make_access_token(user.id, user.email, tier)
-    return AuthTokens(
-        access_token=access_token,
-        refresh_token=plain_token,
-        expires_at=expires_at_ms,
-    )
-
-
-@router.get("/me", response_model=UserProfile)
-async def me(current_user: UserProfile = Depends(get_current_user)) -> UserProfile:
-    """Return the profile for the authenticated user."""
-    return current_user
-
-
-@router.put("/me", response_model=UserProfile)
-async def update_profile(
-    body: _UpdateProfileRequest,
-    current_user: UserProfile = Depends(get_current_user),
-    db: AsyncSession = Depends(get_session),
-) -> UserProfile:
-    """Update the authenticated user's name and surname."""
-    result = await db.execute(select(User).where(User.id == current_user.id))
-    user = result.scalar_one()
-
-    if body.name is not None:
-        user.name = body.name
-    if body.surname is not None:
-        user.surname = body.surname
-
-    await db.commit()
-    await db.refresh(user)
-
-    return UserProfile(
-        id=user.id,
-        email=user.email,
-        name=user.name,
-        surname=user.surname,
-        tier=current_user.tier,
-    )

services/auth/app/verify.py

@@ -1,66 +0,0 @@
-"""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

@@ -1,11 +0,0 @@
-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

@@ -1,36 +0,0 @@
-# ── 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

@@ -1,23 +0,0 @@
-# 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

@@ -1,903 +0,0 @@
-"""Agent run orchestrator — adapted for Batch Agent Service.
-
-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
-
-import asyncio
-import json
-import logging
-import uuid
-from datetime import datetime, timedelta, timezone
-from typing import Any
-
-from langchain_core.messages import AIMessage, HumanMessage, SystemMessage, ToolMessage
-from sqlalchemy import select
-
-from app.agents.filesystem_agent import FILESYSTEM_TOOLS
-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 ─────────────────────────────────────────────────────
-_running_agents: set[str] = set()
-
-
-def is_agent_running(agent_id: str) -> bool:
-    return agent_id in _running_agents
-
-
-# ── Timeouts ───────────────────────────────────────────────────────────────
-_TOOL_CALL_TIMEOUT: int = 30
-_MAX_PROCESSING_STEPS: int = 12
-_MAX_SCAN_DEPTH: int = 5
-
-# ── Data-type to tool mapping ─────────────────────────────────────────────
-_DATA_TYPE_TOOLS: dict[str, list[Any]] = {
-    "tasks": TASK_TOOLS,
-    "notes": NOTE_TOOLS,
-    "timelines": TIMELINE_TOOLS,
-}
-
-# ── Step 1: Classification prompt ─────────────────────────────────────────
-
-_DOMAIN_DESCRIPTIONS: dict[str, str] = {
-    "tasks": (
-        "Action items, to-dos, deliverables — anything that describes work to be done, "
-        "assigned to someone, or tracked with a due date or status."
-    ),
-    "notes": (
-        "Documentation, meeting notes, summaries, reference material — "
-        "written content meant to be read and referenced rather than acted on."
-    ),
-    "timelines": (
-        "Project milestones, deadlines, scheduled events — "
-        "specific dates that mark a point in the progress of a project."
-    ),
-    "projects": (
-        "High-level project entities — only relevant if the file clearly introduces "
-        "a new project or updates the scope of an existing one."
-    ),
-}
-
-_STEP1_SYSTEM_PROMPT = """\
-You are a file classifier for a freelance project management tool.
-
-Your job is to match a file to an existing project and identify which data domains to extract.
-
-## Project matching rules (STRICT — follow in order)
-
-1. Search the file content for any mention of a project name, client name, acronym, or topic
-   that overlaps with the existing projects listed below.
-2. The match does NOT need to be exact — partial name, abbreviation, or topic similarity is enough.
-3. STRONGLY PREFER matching an existing project. Only return "new" as an absolute last resort
-   when the file has zero meaningful connection to any listed project.
-4. When in doubt, pick the closest match from the list.
-
-## Response format
-
-Respond ONLY with a JSON object — no markdown, no explanation:
-
-{{"project_id": "<exact id from the list below, or new>", "new_project_name": "<concise 2-5 word name, only when project_id is new>", "domains": ["tasks", "notes"]}}
-
-## Domain definitions (only consider domains in the allowed list)
-
-{domain_definitions}
-
-## Existing projects
-
-{projects_list}
-"""
-
-# ── Step 2: Processing prompt ─────────────────────────────────────────────
-
-_PROCESSING_SYSTEM_PROMPT = """\
-You are a data extraction assistant for a freelance project management tool.
-
-Your task: extract structured data from the file content and persist it using the available tools.
-
-## Mandatory process — follow this order for EVERY item you extract
-
-1. READ the existing records listed below for the relevant domain.
-2. SEARCH for a match by title, topic, or semantic similarity.
-3. If a match exists → call the update_* tool with the existing record's id.
-4. If no match exists → call the create_* tool and set isAiSuggested=1.
-
-NEVER call create_* without first checking the existing records.
-NEVER duplicate a record that already exists under a different wording.
-
-## Existing records (source of truth)
-
-{existing_context}
-
-## Context
-
-Project: {project_context}
-Domains to extract: {data_types}
-
-{custom_prompt_section}
-"""
-
-# ── Cloud processing prompt ───────────────────────────────────────────────
-
-_CLOUD_PROCESSING_PROMPT = """\
-You are a data extraction and management assistant for a freelance project
-management tool.
-
-Available tools:
-  Filesystem : read_file_content, list_directory, get_file_metadata
-  Tasks      : list_tasks, create_task, update_task, add_task_comment
-  Notes      : list_notes, get_note, create_note, update_note
-  Timelines  : list_timelines, create_timeline, update_timeline
-  Projects   : list_all_projects, get_project, create_project, update_project
-
-Your task:
-1. Read the full content of each file below using read_file_content.
-2. For each piece of information found, ALWAYS try to match and update an
-   existing record before creating a new one.
-3. ONLY act on these entity types: {data_types}.
-4. Do NOT invent data. Only extract what is clearly present in the files.
-5. If a file contains no relevant data for the target entity types, skip it.
-
-{project_context}
-
-Files to process:
-{file_list}
-
-{custom_prompt_section}
-
-After processing all files, respond with a brief summary of what you updated
-and what you created.
-"""
-
-
-# ── LLM tool-calling loop ─────────────────────────────────────────────────
-
-
-def _as_text(content: Any) -> str:
-    if content is None:
-        return ""
-    if isinstance(content, str):
-        return content
-    if isinstance(content, list):
-        parts: list[str] = []
-        for item in content:
-            if isinstance(item, str):
-                parts.append(item)
-            elif isinstance(item, dict):
-                text = item.get("text")
-                if isinstance(text, str):
-                    parts.append(text)
-        return "".join(parts)
-    return str(content)
-
-
-async def _run_agent_with_tools(
-    *,
-    system_prompt: str,
-    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."""
-    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),
-        HumanMessage(content=user_message),
-    ]
-
-    tool_map = {tool_def.name: tool_def for tool_def in tools}
-
-    for _ in range(max_steps):
-        response: AIMessage = await llm_with_tools.ainvoke(messages)
-        messages.append(response)
-
-        if not response.tool_calls:
-            return _as_text(response.content)
-
-        for call in response.tool_calls:
-            call_id = str(call.get("id", ""))
-            call_name = str(call.get("name", ""))
-            call_args = call.get("args", {})
-            logger.info(
-                "agent_runner: tool_call name=%s args=%s",
-                call_name,
-                json.dumps(call_args, ensure_ascii=True)[:800],
-            )
-
-            tool_fn = tool_map.get(call_name)
-            if tool_fn is None:
-                tool_output = f"Unknown tool: {call_name}"
-            else:
-                tool_output = await tool_fn.ainvoke(call_args)
-
-            logger.info(
-                "agent_runner: tool_result name=%s output=%s",
-                call_name,
-                str(tool_output)[:200],
-            )
-            messages.append(ToolMessage(content=str(tool_output), tool_call_id=call["id"]))
-
-    final = await llm.ainvoke(messages)
-    return _as_text(final.content)
-
-
-# ── Tool list builder ─────────────────────────────────────────────────────
-
-
-def _build_processing_tools(data_types: list[str]) -> list[Any]:
-    tools: list[Any] = list(FILESYSTEM_TOOLS)
-    for dt in data_types:
-        dt_tools = _DATA_TYPE_TOOLS.get(dt)
-        if dt_tools:
-            tools.extend(dt_tools)
-    return tools
-
-
-# ── Code-based directory scanner ─────────────────────────────────────────
-
-
-async def _scan_directories(
-    paths: list[str],
-    extensions: list[str],
-    last_run_at: datetime | None,
-) -> list[str]:
-    all_files: list[str] = []
-    ext_set = {e.lstrip(".").lower() for e in extensions} if extensions else set()
-
-    async def _walk(path: str, depth: int) -> None:
-        if depth > _MAX_SCAN_DEPTH:
-            return
-        try:
-            result = await execute_on_client(action="list_directory", data={"path": path})
-        except Exception as exc:
-            logger.warning("agent_runner: list_directory failed %r: %s", path, exc)
-            return
-        for entry in result.get("entries", []):
-            entry_path = entry.get("path", "")
-            if not entry_path:
-                continue
-            if entry.get("type") == "directory":
-                await _walk(entry_path, depth + 1)
-            elif entry.get("type") == "file":
-                if ext_set:
-                    dot_pos = entry_path.rfind(".")
-                    file_ext = entry_path[dot_pos + 1:].lower() if dot_pos != -1 else ""
-                    if file_ext not in ext_set:
-                        continue
-                all_files.append(entry_path)
-
-    for root in paths:
-        await _walk(root, depth=0)
-
-    if last_run_at is None:
-        return all_files
-
-    last_run_ms = int(last_run_at.timestamp() * 1000)
-    filtered: list[str] = []
-    for file_path in all_files:
-        try:
-            meta = await execute_on_client(action="get_file_metadata", data={"path": file_path})
-            modified_at = meta.get("modifiedAt")
-            if modified_at is None:
-                filtered.append(file_path)
-                continue
-            if isinstance(modified_at, (int, float)):
-                mod_ms = int(modified_at)
-            else:
-                mod_ms = int(datetime.fromisoformat(str(modified_at)).timestamp() * 1000)
-            if mod_ms > last_run_ms:
-                filtered.append(file_path)
-        except Exception:
-            filtered.append(file_path)
-
-    return filtered
-
-
-# ── Code-based entity fetchers ────────────────────────────────────────────
-
-
-async def _fetch_projects() -> list[dict]:
-    try:
-        result = await execute_on_client(action="select", table="projects")
-        return result.get("rows", [])
-    except Exception as exc:
-        logger.warning("agent_runner: failed to fetch projects: %s", exc)
-        return []
-
-
-_DOMAIN_TABLE: dict[str, str] = {
-    "tasks": "tasks",
-    "notes": "notes",
-    "timelines": "timelines",
-    "projects": "projects",
-}
-
-
-async def _fetch_domain_entities(domain: str, project_id: str) -> list[dict]:
-    table = _DOMAIN_TABLE.get(domain)
-    if not table:
-        return []
-    filters: dict[str, Any] = {}
-    if project_id != "standalone" and domain != "projects":
-        filters["projectId"] = project_id
-    try:
-        result = await execute_on_client(
-            action="select",
-            table=table,
-            filters=filters if filters else None,
-        )
-        return result.get("rows", [])
-    except Exception as exc:
-        logger.warning("agent_runner: failed to fetch %s: %s", domain, exc)
-        return []
-
-
-def _format_entities_for_context(domain: str, rows: list[dict]) -> str:
-    if not rows:
-        return f"No existing {domain}."
-    lines: list[str] = []
-    for r in rows:
-        if domain == "tasks":
-            desc = r.get("description") or ""
-            desc_part = f" — {desc[:120]}" if desc else ""
-            assignee = r.get("assignee") or r.get("assignees") or ""
-            due = r.get("dueDate") or r.get("due_date") or ""
-            meta = ", ".join(filter(None, [
-                f"priority: {r.get('priority', '')}" if r.get("priority") else "",
-                f"assignee: {assignee}" if assignee else "",
-                f"due: {due}" if due else "",
-            ]))
-            lines.append(
-                f"  - [{r.get('status', '?')}] {r.get('title', '')}{desc_part}"
-                f" ({meta}, id: {r['id']})"
-            )
-        elif domain == "notes":
-            snippet = (r.get("content") or "")[:200].replace("\n", " ")
-            snippet_part = f"\n      Preview: {snippet}" if snippet else ""
-            lines.append(
-                f"  - {r.get('title', '')} (id: {r['id']}){snippet_part}"
-            )
-        elif domain == "timelines":
-            lines.append(
-                f"  - {r.get('title', '')} date={r.get('date', '')} (id: {r['id']})"
-            )
-        elif domain == "projects":
-            summary = (r.get("aiSummary") or r.get("ai_summary") or "")[:120]
-            summary_part = f" — {summary}" if summary else ""
-            lines.append(
-                f"  - {r.get('name', '')} [{r.get('status', '')}]{summary_part}"
-                f" (id: {r['id']})"
-            )
-    return f"Existing {domain}:\n" + "\n".join(lines)
-
-
-# ── Step 1: LLM file classifier ───────────────────────────────────────────
-
-
-async def _classify_file(
-    file_path: str,
-    file_content: str,
-    projects: list[dict],
-    config_data_types: list[str],
-    langfuse_handler: Any | None = None,
-) -> tuple[str, list[str], str | None]:
-    fallback: tuple[str, list[str], str | None] = ("new", list(config_data_types), None)
-
-    if not file_content.strip():
-        return fallback
-
-    valid_project_ids = {p["id"] for p in projects}
-
-    def _fmt_project(p: dict) -> str:
-        summary = (p.get("aiSummary") or p.get("ai_summary") or "").strip()
-        summary_part = f" — {summary[:100]}" if summary else ""
-        return f"  - id={p['id']} | name={p.get('name', '')} | status={p.get('status', '')}{summary_part}"
-
-    projects_list = "\n".join(_fmt_project(p) for p in projects) or "  (none yet)"
-
-    domain_definitions = "\n".join(
-        f"  - {d}: {_DOMAIN_DESCRIPTIONS[d]}"
-        for d in config_data_types
-        if d in _DOMAIN_DESCRIPTIONS
-    )
-
-    system = tracing.compile_prompt(
-        "batch_file_classifier",
-        fallback=_STEP1_SYSTEM_PROMPT,
-        variables={
-            "domain_definitions": domain_definitions,
-            "projects_list": projects_list,
-        },
-    )
-
-    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()
-        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")
-        project_id = raw_project_id if raw_project_id in valid_project_ids else "new"
-        new_project_name: str | None = (
-            str(parsed["new_project_name"]).strip() or None
-            if project_id == "new" and parsed.get("new_project_name")
-            else None
-        )
-        domains: list[str] = [
-            d for d in parsed.get("domains", [])
-            if d in config_data_types
-        ]
-        if not domains:
-            domains = list(config_data_types)
-        return project_id, domains, new_project_name
-    except Exception as exc:
-        logger.warning(
-            "agent_runner: step1 classification failed for %r: %s", file_path, exc
-        )
-        return fallback
-
-
-# ── Local agent runner (two-step per file) ────────────────────────────────
-
-
-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.
-
-    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.
-
-    set_current_user() must be called BEFORE this function.
-    """
-    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)
-
-    # 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]
-
-    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{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:
-        # ── Scan directories ─────────────────────────────────────────
-        logger.info("agent_runner: run=%s scanning directories user=%s", run_log_id, user_id)
-        file_paths = await _scan_directories(
-            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_log_id, len(file_paths)
-        )
-
-        if not file_paths:
-            await _finalize_run(run_log_id, status="success", items_processed=0, items_created=0)
-            return
-
-        # ── Fetch all projects once ──────────────────────────────────
-        projects = await _fetch_projects()
-
-        for file_path in file_paths:
-            try:
-                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:
-                    continue
-
-                items_processed += 1
-
-                # 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=data_types,
-                    langfuse_handler=langfuse_handler,
-                )
-
-                # Step 2 — resolve project_id, fetch entities, process
-                if project_id == "new":
-                    proj_name = new_project_name or "Untitled Project"
-                    try:
-                        proj_result = await execute_on_client(
-                            action="insert",
-                            table="projects",
-                            data={"name": proj_name, "clientId": None},
-                        )
-                        created = proj_result.get("row", {})
-                        effective_project_id = created.get("id", "standalone")
-                        if "id" in created:
-                            projects.append(created)
-                    except Exception as exc:
-                        logger.warning("agent_runner: run=%s create project failed: %s", run_log_id, exc)
-                        effective_project_id = "standalone"
-                        proj_name = "unknown"
-                    project_context = (
-                        f"Project: {proj_name} (id: {effective_project_id}). "
-                        "Always set projectId to this id on every record you create."
-                    )
-                else:
-                    effective_project_id = project_id
-                    proj = next((p for p in projects if p["id"] == project_id), None)
-                    proj_name = proj.get("name", project_id) if proj else project_id
-                    project_context = (
-                        f"Project: {proj_name} (id: {project_id}). "
-                        "Always set projectId to this id on every record you create."
-                    )
-
-                domains = [d for d in domains if d != "projects"]
-
-                existing_blocks: list[str] = []
-                for domain in domains:
-                    rows = await _fetch_domain_entities(domain, effective_project_id)
-                    existing_blocks.append(_format_entities_for_context(domain, rows))
-
-                existing_context = "\n\n".join(existing_blocks)
-
-                system_prompt = 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)
-
-                result_text = await _run_agent_with_tools(
-                    system_prompt=system_prompt,
-                    user_message=(
-                        f"Process this file and extract relevant information.\n\n"
-                        f"File: {file_path}\n\nContent:\n{file_content}"
-                    ),
-                    tools=processing_tools,
-                    max_steps=_MAX_PROCESSING_STEPS,
-                    langfuse_handler=langfuse_handler,
-                )
-                logger.info(
-                    "agent_runner: run=%s file=%r result=%s",
-                    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_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_log_id, exc)
-    finally:
-        _running_agents.discard(agent_id)
-
-    # ── Finalise ────────────────────────────────────────────────────
-    if errors and items_processed == 0:
-        final_status = "error"
-    elif errors:
-        final_status = "partial"
-    else:
-        final_status = "success"
-
-    await _finalize_run(
-        run_log_id,
-        status=final_status,
-        items_processed=items_processed,
-        items_created=items_created,
-        errors=errors,
-    )
-
-    # Notify Electron that the run is complete via Redis
-    if run_context:
-        try:
-            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_log_id, exc)
-
-
-# ── Cloud agent runner ─────────────────────────────────────────────────────
-
-_CLOUD_DEFAULT_LOOKBACK_DAYS: int = 7
-
-
-async def run_cloud_agent(user_id: str, config_id: str, *, langfuse_handler: Any | None = None) -> None:
-    """Execute a cloud connector agent run.
-
-    Loads the CloudAgentConfig from DB, decrypts OAuth tokens, fetches
-    messages from the provider, and runs LLM extraction.
-
-    set_current_user() must be called BEFORE this function.
-    """
-    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_id,
-            status="error",
-            errors=[f"No OAuth token stored for cloud agent '{config.name}'"],
-        )
-        return
-
-    try:
-        credentials_info = decrypt_token(config.oauth_token_encrypted)
-    except ValueError as exc:
-        await _finalize_run(
-            run_log_id,
-            status="error",
-            errors=[f"Failed to decrypt OAuth token: {exc}"],
-        )
-        return
-
-    # ── Instantiate provider ──────────────────────────────────────
-    try:
-        provider = get_provider(config.provider, credentials_info)
-    except ValueError as exc:
-        await _finalize_run(run_log_id, status="error", errors=[str(exc)])
-        return
-
-    # ── Fetch messages ────────────────────────────────────────────
-    since: datetime | None = config.last_run_at
-    if since is None:
-        since = datetime.now(timezone.utc) - timedelta(days=_CLOUD_DEFAULT_LOOKBACK_DAYS)
-    if since.tzinfo is None:
-        since = since.replace(tzinfo=timezone.utc)
-
-    errors: list[str] = []
-    items_processed = 0
-
-    try:
-        if config.provider == "gmail":
-            raw_messages = await provider.fetch_messages(
-                filter_config=config.filter_config,
-                since=since,
-            )
-        elif config.provider == "outlook":
-            raw_messages = await provider.fetch_emails(
-                filter_config=config.filter_config,
-                since=since,
-            )
-        elif config.provider == "teams":
-            raw_messages = await provider.fetch_messages(
-                filter_config=config.filter_config,
-                since=since,
-            )
-        else:
-            raw_messages = []
-    except RuntimeError as exc:
-        await _finalize_run(
-            run_log_id,
-            status="error",
-            errors=[f"Provider fetch failed: {exc}"],
-            update_config_last_run=True,
-            config_id=config.id,
-            config_type="cloud",
-        )
-        return
-
-    logger.info(
-        "agent_runner: cloud agent %s fetched %d item(s) from %s",
-        config.id, len(raw_messages), config.provider,
-    )
-
-    # ── Extract + insert via LLM ─────────────────────────────────
-    try:
-        processing_tools = _build_processing_tools(config.data_types)
-        custom_section = (
-            f"User instructions:\n{config.prompt_template}"
-            if config.prompt_template
-            else ""
-        )
-
-        for msg in raw_messages:
-            content_text = msg.as_text
-            if not content_text:
-                continue
-            items_processed += 1
-
-            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:
-                await _run_agent_with_tools(
-                    system_prompt=processing_prompt,
-                    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}")
-    except Exception as exc:
-        errors.append(f"Agent run failed: {exc}")
-
-    # ── Persist refreshed token ───────────────────────────────────
-    refreshed = getattr(provider, "refreshed_credentials", None)
-    if refreshed:
-        try:
-            new_encrypted = encrypt_token(refreshed)
-            async with async_session() as db:
-                cfg_result = await db.execute(
-                    select(CloudAgentConfig).where(CloudAgentConfig.id == config.id)
-                )
-                cfg_row = cfg_result.scalar_one_or_none()
-                if cfg_row:
-                    cfg_row.oauth_token_encrypted = new_encrypted
-                    await db.commit()
-        except Exception as exc:
-            logger.warning("agent_runner: failed to persist refreshed token: %s", exc)
-
-    # ── Finalise ──────────────────────────────────────────────────
-    if errors and items_processed == 0:
-        final_status = "error"
-    elif errors:
-        final_status = "partial"
-    else:
-        final_status = "success"
-
-    await _finalize_run(
-        run_log_id,
-        status=final_status,
-        items_processed=items_processed,
-        items_created=0,
-        errors=errors,
-        update_config_last_run=True,
-        config_id=config.id,
-        config_type="cloud",
-    )
-
-
-# ── Internal helper ─────────────────────────────────────────────────────────
-
-
-async def _finalize_run(
-    run_log_id: int | str,
-    *,
-    status: str,
-    items_processed: int = 0,
-    items_created: int = 0,
-    errors: list[str] | None = None,
-    update_config_last_run: bool = False,
-    config_id: str | None = None,
-    config_type: str | None = None,
-) -> None:
-    """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:
-            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
-            managed.errors = errors or []
-            managed.completed_at = now
-
-            if update_config_last_run and config_id:
-                if config_type == "local":
-                    cfg_result = await db.execute(
-                        select(LocalAgentConfig).where(LocalAgentConfig.id == config_id)
-                    )
-                    cfg = cfg_result.scalar_one_or_none()
-                    if cfg:
-                        cfg.last_run_at = now
-                elif config_type == "cloud":
-                    cfg_result = await db.execute(
-                        select(CloudAgentConfig).where(CloudAgentConfig.id == config_id)
-                    )
-                    cfg = cfg_result.scalar_one_or_none()
-                    if cfg:
-                        cfg.last_run_at = now
-
-            await db.commit()
-    except Exception as exc:
-        logger.error("agent_runner: failed to finalize run_log=%s: %s", run_log_id, exc)

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

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

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

@@ -1,83 +0,0 @@
-"""Filesystem agent — tools for reading local directories and files on Electron.
-
-Adapted for Batch Agent Service: import from app.ws_context.
-"""
-
-from __future__ import annotations
-
-from typing import Any
-
-from langchain_core.tools import tool
-
-from shared.ws_context import execute_on_client
-
-
-@tool
-async def list_directory(path: str) -> str:
-    """List files and folders in a local directory on the user's device.
-
-    Returns a formatted listing of entries with name, type (file/directory),
-    and full path.
-    """
-    result = await execute_on_client(
-        action="list_directory",
-        data={"path": path},
-    )
-    entries: list[dict[str, Any]] = result.get("entries", [])
-    if not entries:
-        return f"Directory '{path}' is empty or does not exist."
-    lines: list[str] = []
-    for entry in entries:
-        entry_type = entry.get("type", "unknown")
-        entry_name = entry.get("name", "")
-        entry_path = entry.get("path", "")
-        lines.append(f"- [{entry_type}] {entry_name}  ({entry_path})")
-    return f"Directory listing for '{path}' ({len(entries)} entries):\n" + "\n".join(lines)
-
-
-@tool
-async def read_file_content(path: str) -> str:
-    """Read the text content of a local file on the user's device.
-
-    Returns the file content as a string.  Large files may be truncated
-    by the Electron client.
-    """
-    result = await execute_on_client(
-        action="read_file_content",
-        data={"path": path},
-    )
-    content: str = result.get("content", "")
-    if not content:
-        return f"File '{path}' is empty or could not be read."
-    return content
-
-
-@tool
-async def get_file_metadata(path: str) -> str:
-    """Get metadata for a local file: size, creation date, modification date, extension.
-
-    Returns a formatted summary of the file's metadata.
-    """
-    result = await execute_on_client(
-        action="get_file_metadata",
-        data={"path": path},
-    )
-    size = result.get("size", "unknown")
-    created = result.get("createdAt", "unknown")
-    modified = result.get("modifiedAt", "unknown")
-    extension = result.get("extension", "unknown")
-    name = result.get("name", path)
-    return (
-        f"File: {name}\n"
-        f"  Extension: {extension}\n"
-        f"  Size: {size} bytes\n"
-        f"  Created: {created}\n"
-        f"  Modified: {modified}"
-    )
-
-
-FILESYSTEM_TOOLS: list[Any] = [
-    list_directory,
-    read_file_content,
-    get_file_metadata,
-]

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

@@ -1,108 +0,0 @@
-"""Cloud provider integration utilities.
-
-Adapted for Batch Agent Service: import from shared.config instead of app.config.
-
-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
-
-import json
-import logging
-from dataclasses import dataclass, field
-from datetime import datetime
-from typing import TYPE_CHECKING
-
-from cryptography.fernet import Fernet, InvalidToken
-
-from shared.config import settings
-
-if TYPE_CHECKING:
-    from app.integrations.gmail import GmailClient
-    from app.integrations.ms_graph import MSGraphClient
-
-logger = logging.getLogger(__name__)
-
-
-@dataclass
-class EmailMessage:
-    id: str
-    subject: str
-    sender: str
-    body_text: str
-    date: datetime
-    labels: list[str] = field(default_factory=list)
-
-    @property
-    def as_text(self) -> str:
-        date_str = self.date.strftime("%Y-%m-%d %H:%M")
-        labels_str = f" [{', '.join(self.labels)}]" if self.labels else ""
-        return (
-            f"From: {self.sender}\n"
-            f"Date: {date_str}{labels_str}\n"
-            f"Subject: {self.subject}\n\n"
-            f"{self.body_text}"
-        )
-
-
-@dataclass
-class ChatMessage:
-    id: str
-    content: str
-    sender: str
-    channel: str | None
-    date: datetime
-
-    @property
-    def as_text(self) -> str:
-        date_str = self.date.strftime("%Y-%m-%d %H:%M")
-        channel_str = f" [channel: {self.channel}]" if self.channel else ""
-        return (
-            f"From: {self.sender}\n"
-            f"Date: {date_str}{channel_str}\n\n"
-            f"{self.content}"
-        )
-
-
-def _get_fernet() -> Fernet:
-    key = settings.OAUTH_ENCRYPTION_KEY
-    if not key:
-        raise RuntimeError(
-            "OAUTH_ENCRYPTION_KEY is not set. "
-            "Generate one with: python -c \"from cryptography.fernet import Fernet; print(Fernet.generate_key().decode())\""
-        )
-    return Fernet(key.encode() if isinstance(key, str) else key)
-
-
-def encrypt_token(token_info: dict) -> str:
-    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")
-    return _get_fernet().encrypt(plaintext).decode("utf-8")
-
-
-def decrypt_token(encrypted: str) -> dict:
-    try:
-        plaintext = _get_fernet().decrypt(encrypted.encode("utf-8"))
-        return json.loads(plaintext)
-    except (InvalidToken, json.JSONDecodeError) as exc:
-        raise ValueError(f"Failed to decrypt OAuth token: {exc}") from exc
-
-
-def get_provider(
-    provider: str,
-    credentials_info: dict,
-) -> "GmailClient | MSGraphClient":
-    if provider == "gmail":
-        from app.integrations.gmail import GmailClient
-        return GmailClient(credentials_info)
-    if provider in {"outlook", "teams"}:
-        from app.integrations.ms_graph import MSGraphClient
-        return MSGraphClient(credentials_info)
-    raise ValueError(
-        f"Unknown cloud provider {provider!r}. "
-        "Supported: 'gmail', 'outlook', 'teams'."
-    )

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

@@ -1,252 +0,0 @@
-"""Gmail API client for cloud agent integration.
-
-Adapted for Batch Agent Service: import from app.integrations instead of
-app.integrations (same relative path within the service).
-"""
-
-from __future__ import annotations
-
-import asyncio
-import base64
-import email
-import html
-import logging
-import re
-from datetime import datetime, timezone
-from typing import Any
-
-from app.integrations import EmailMessage
-
-logger = logging.getLogger(__name__)
-
-_GMAIL_DATE_FMT = "%Y/%m/%d"
-_BODY_TRUNCATE = 8_000
-_MAX_MESSAGES = 200
-
-
-def _build_gmail_query(
-    filter_config: dict[str, Any] | None,
-    since: datetime | None,
-) -> str:
-    parts: list[str] = []
-    cfg = filter_config or {}
-
-    labels: list[str] = cfg.get("labels", [])
-    if labels:
-        if len(labels) == 1:
-            parts.append(f"label:{labels[0]}")
-        else:
-            label_expr = " OR ".join(f"label:{lbl}" for lbl in labels)
-            parts.append(f"({label_expr})")
-
-    senders: list[str] = cfg.get("senders", [])
-    for sender in senders:
-        parts.append(f"from:{sender}")
-
-    date_range: dict = cfg.get("date_range", {})
-    from_str: str | None = date_range.get("from")
-    to_str: str | None = date_range.get("to")
-
-    effective_since: datetime | None = since
-    if from_str:
-        try:
-            cfg_since = datetime.fromisoformat(from_str.replace("Z", "+00:00"))
-            if cfg_since.tzinfo is None:
-                cfg_since = cfg_since.replace(tzinfo=timezone.utc)
-            if effective_since is None or cfg_since > effective_since:
-                effective_since = cfg_since
-        except ValueError:
-            logger.warning("gmail: invalid date_range.from %r — ignoring", from_str)
-
-    if effective_since:
-        parts.append(f"after:{effective_since.strftime(_GMAIL_DATE_FMT)}")
-
-    if to_str:
-        try:
-            to_dt = datetime.fromisoformat(to_str.replace("Z", "+00:00"))
-            parts.append(f"before:{to_dt.strftime(_GMAIL_DATE_FMT)}")
-        except ValueError:
-            logger.warning("gmail: invalid date_range.to %r — ignoring", to_str)
-
-    return " ".join(parts)
-
-
-def _strip_html(raw_html: str) -> str:
-    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:
-    mime_type: str = payload.get("mimeType", "")
-    body: dict = payload.get("body", {})
-    parts: list[dict] = payload.get("parts", [])
-
-    if mime_type == "text/plain":
-        data = body.get("data", "")
-        if data:
-            return base64.urlsafe_b64decode(data + "==").decode("utf-8", errors="replace")
-        return ""
-
-    if mime_type == "text/html":
-        data = body.get("data", "")
-        if data:
-            raw = base64.urlsafe_b64decode(data + "==").decode("utf-8", errors="replace")
-            return _strip_html(raw)
-        return ""
-
-    plain_fallback = ""
-    for part in parts:
-        part_mime = part.get("mimeType", "")
-        if part_mime == "text/plain":
-            return _parse_body(part)
-        if part_mime == "text/html" and not plain_fallback:
-            plain_fallback = _parse_body(part)
-        if part_mime.startswith("multipart/"):
-            nested = _parse_body(part)
-            if nested:
-                return nested
-    return plain_fallback
-
-
-def _parse_date(raw: str) -> datetime:
-    try:
-        parsed = email.utils.parsedate_to_datetime(raw)
-        if parsed.tzinfo is None:
-            parsed = parsed.replace(tzinfo=timezone.utc)
-        return parsed.astimezone(timezone.utc)
-    except Exception:
-        return datetime.now(timezone.utc)
-
-
-class GmailClient:
-    def __init__(self, credentials_info: dict[str, Any]) -> None:
-        from google.oauth2.credentials import Credentials
-
-        self._credentials_info = credentials_info
-        expiry_str: str | None = credentials_info.get("expiry")
-        expiry: datetime | None = None
-        if expiry_str:
-            try:
-                expiry = datetime.fromisoformat(
-                    expiry_str.replace("Z", "+00:00")
-                ).replace(tzinfo=timezone.utc)
-            except ValueError:
-                pass
-
-        self._credentials = Credentials(
-            token=credentials_info.get("token"),
-            refresh_token=credentials_info.get("refresh_token"),
-            token_uri=credentials_info.get("token_uri", "https://oauth2.googleapis.com/token"),
-            client_id=credentials_info.get("client_id"),
-            client_secret=credentials_info.get("client_secret"),
-            scopes=credentials_info.get("scopes"),
-            expiry=expiry,
-        )
-
-    async def fetch_messages(
-        self,
-        filter_config: dict[str, Any] | None = None,
-        since: datetime | None = None,
-    ) -> list[EmailMessage]:
-        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:
-        creds = self._credentials
-        if not creds.valid and creds.expired:
-            return None
-        if creds.token != self._credentials_info.get("token"):
-            result = {
-                "token": creds.token,
-                "refresh_token": creds.refresh_token,
-                "token_uri": creds.token_uri,
-                "client_id": creds.client_id,
-                "client_secret": creds.client_secret,
-                "scopes": list(creds.scopes or []),
-            }
-            if creds.expiry:
-                result["expiry"] = creds.expiry.isoformat()
-            return result
-        return None
-
-    def _fetch_sync(self, query: str) -> list[EmailMessage]:
-        import googleapiclient.discovery
-        import googleapiclient.errors
-        from google.auth.transport.requests import Request
-
-        if self._credentials.expired and self._credentials.refresh_token:
-            try:
-                self._credentials.refresh(Request())
-            except Exception as exc:
-                raise RuntimeError(f"Gmail token refresh failed: {exc}") from exc
-
-        service = googleapiclient.discovery.build(
-            "gmail", "v1", credentials=self._credentials, cache_discovery=False
-        )
-        user_api = service.users()
-
-        ids: list[str] = []
-        page_token: str | None = None
-        while len(ids) < _MAX_MESSAGES:
-            batch_size = min(100, _MAX_MESSAGES - len(ids))
-            kwargs: dict[str, Any] = {
-                "userId": "me",
-                "maxResults": batch_size,
-            }
-            if query:
-                kwargs["q"] = query
-            if page_token:
-                kwargs["pageToken"] = page_token
-
-            try:
-                resp = user_api.messages().list(**kwargs).execute()
-            except googleapiclient.errors.HttpError as exc:
-                raise RuntimeError(f"Gmail messages.list failed: {exc}") from exc
-
-            for msg in resp.get("messages", []):
-                ids.append(msg["id"])
-
-            page_token = resp.get("nextPageToken")
-            if not page_token:
-                break
-
-        if not ids:
-            return []
-
-        logger.info("gmail: fetching %d message(s)", len(ids))
-
-        messages: list[EmailMessage] = []
-        for msg_id in ids:
-            try:
-                msg = user_api.messages().get(
-                    userId="me", id=msg_id, format="full"
-                ).execute()
-
-                headers: dict[str, str] = {
-                    h["name"].lower(): h["value"]
-                    for h in msg.get("payload", {}).get("headers", [])
-                }
-                subject = headers.get("subject", "(no subject)")
-                sender = headers.get("from", "unknown")
-                date_raw = headers.get("date", "")
-                date = _parse_date(date_raw) if date_raw else datetime.now(timezone.utc)
-
-                body_text = _parse_body(msg.get("payload", {}))[:_BODY_TRUNCATE]
-                labels = msg.get("labelIds", [])
-
-                messages.append(EmailMessage(
-                    id=msg_id,
-                    subject=subject,
-                    sender=sender,
-                    body_text=body_text,
-                    date=date,
-                    labels=labels,
-                ))
-            except Exception as 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

@@ -1,266 +0,0 @@
-"""Microsoft Graph API client for Outlook and Teams.
-
-Adapted for Batch Agent Service: import settings from shared.config.
-"""
-
-from __future__ import annotations
-
-import logging
-import re
-from datetime import datetime, timezone
-from typing import Any
-
-import httpx
-
-from shared.config import settings
-from app.integrations import ChatMessage, EmailMessage
-
-logger = logging.getLogger(__name__)
-
-_GRAPH_BASE = "https://graph.microsoft.com/v1.0"
-
-_MAX_EMAILS = 200
-_MAX_MESSAGES = 200
-_BODY_TRUNCATE = 8_000
-
-
-def _strip_html(raw: str) -> str:
-    no_tags = re.sub(r"<[^>]+>", " ", raw)
-    import html as _html
-    decoded = _html.unescape(no_tags)
-    return re.sub(r"\s+", " ", decoded).strip()
-
-
-def _odata_datetime(dt: datetime) -> str:
-    utc = dt.astimezone(timezone.utc)
-    return utc.strftime("%Y-%m-%dT%H:%M:%SZ")
-
-
-def _build_email_filter(
-    filter_config: dict[str, Any] | None,
-    since: datetime | None,
-) -> str:
-    clauses: list[str] = []
-    cfg = filter_config or {}
-
-    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: dict = cfg.get("date_range", {})
-    from_str: str | None = date_range.get("from")
-
-    effective_since: datetime | None = since
-    if from_str:
-        try:
-            cfg_since = datetime.fromisoformat(from_str.replace("Z", "+00:00"))
-            if cfg_since.tzinfo is None:
-                cfg_since = cfg_since.replace(tzinfo=timezone.utc)
-            if effective_since is None or cfg_since > effective_since:
-                effective_since = cfg_since
-        except ValueError:
-            logger.warning("ms_graph: invalid date_range.from %r — ignoring", from_str)
-
-    if effective_since:
-        clauses.append(f"receivedDateTime ge {_odata_datetime(effective_since)}")
-
-    to_str: str | None = date_range.get("to")
-    if to_str:
-        try:
-            to_dt = datetime.fromisoformat(to_str.replace("Z", "+00:00"))
-            if to_dt.tzinfo is None:
-                to_dt = to_dt.replace(tzinfo=timezone.utc)
-            clauses.append(f"receivedDateTime le {_odata_datetime(to_dt)}")
-        except ValueError:
-            logger.warning("ms_graph: invalid date_range.to %r — ignoring", to_str)
-
-    return " and ".join(clauses)
-
-
-class MSGraphClient:
-    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")
-
-    def _auth_headers(self) -> dict[str, str]:
-        return {"Authorization": f"Bearer {self._access_token}"}
-
-    async def _refresh_access_token(self) -> None:
-        import msal
-
-        app = msal.ConfidentialClientApplication(
-            client_id=settings.MS_CLIENT_ID,
-            client_credential=settings.MS_CLIENT_SECRET,
-            authority=f"https://login.microsoftonline.com/{settings.MS_TENANT_ID}",
-        )
-        scopes: list[str] = self._credentials_info.get("scope", "").split()
-        if not scopes:
-            scopes = ["https://graph.microsoft.com/.default"]
-
-        result = app.acquire_token_by_refresh_token(
-            self._refresh_token,
-            scopes=scopes,
-        )
-        if "access_token" not in result:
-            error = result.get("error_description", result.get("error", "unknown"))
-            raise RuntimeError(f"MS Graph token refresh failed: {error}")
-
-        self._access_token = result["access_token"]
-        if "refresh_token" in result:
-            self._refresh_token = result["refresh_token"]
-            self._credentials_info["refresh_token"] = result["refresh_token"]
-        self._credentials_info["access_token"] = self._access_token
-
-    @property
-    def refreshed_credentials(self) -> dict[str, Any] | None:
-        if self._access_token != self._original_access_token:
-            return {**self._credentials_info, "access_token": self._access_token}
-        return None
-
-    async def _get(
-        self,
-        client: httpx.AsyncClient,
-        url: str,
-        params: dict[str, Any] | None = None,
-        *,
-        retry_on_401: bool = True,
-    ) -> dict[str, Any]:
-        resp = await client.get(url, params=params, headers=self._auth_headers())
-        if resp.status_code == 401 and retry_on_401 and self._refresh_token:
-            await self._refresh_access_token()
-            resp = await client.get(url, params=params, headers=self._auth_headers())
-        if resp.status_code == 429:
-            raise RuntimeError("MS Graph rate limit hit (429). Try again later.")
-        resp.raise_for_status()
-        return resp.json()
-
-    async def fetch_emails(
-        self,
-        filter_config: dict[str, Any] | None = None,
-        since: datetime | None = None,
-    ) -> list[EmailMessage]:
-        odata_filter = _build_email_filter(filter_config, since)
-        params: dict[str, Any] = {
-            "$top": 50,
-            "$select": "id,subject,from,receivedDateTime,body,bodyPreview",
-            "$orderby": "receivedDateTime desc",
-        }
-        if odata_filter:
-            params["$filter"] = odata_filter
-
-        emails: list[EmailMessage] = []
-        url = f"{_GRAPH_BASE}/me/messages"
-
-        async with httpx.AsyncClient(timeout=30.0) as client:
-            while url and len(emails) < _MAX_EMAILS:
-                data = await self._get(client, url, params if url.startswith(_GRAPH_BASE) else None)
-                for item in data.get("value", []):
-                    emails.append(self._parse_email(item))
-                    if len(emails) >= _MAX_EMAILS:
-                        break
-                url = data.get("@odata.nextLink", "")
-                params = {}
-
-        logger.info("ms_graph: fetched %d Outlook email(s)", len(emails))
-        return emails
-
-    async def fetch_messages(
-        self,
-        filter_config: dict[str, Any] | None = None,
-        since: datetime | None = None,
-    ) -> list[ChatMessage]:
-        cfg = filter_config or {}
-        channel_filter: list[str] = [c.lower() for c in cfg.get("channels", [])]
-        params: dict[str, Any] = {"$top": 50}
-        if since:
-            params["$filter"] = f"createdDateTime ge {_odata_datetime(since)}"
-
-        messages: list[ChatMessage] = []
-        url = f"{_GRAPH_BASE}/me/chats/getAllMessages"
-
-        async with httpx.AsyncClient(timeout=30.0) as client:
-            while url and len(messages) < _MAX_MESSAGES:
-                try:
-                    data = await self._get(client, url, params if url.startswith(_GRAPH_BASE) else None)
-                except httpx.HTTPStatusError as exc:
-                    if exc.response.status_code in (403, 404):
-                        logger.warning(
-                            "ms_graph: /me/chats/getAllMessages not available (%d)",
-                            exc.response.status_code,
-                        )
-                        break
-                    raise
-
-                for item in data.get("value", []):
-                    msg = self._parse_teams_message(item)
-                    if channel_filter and msg.channel:
-                        if not any(c in msg.channel.lower() for c in channel_filter):
-                            continue
-                    messages.append(msg)
-                    if len(messages) >= _MAX_MESSAGES:
-                        break
-                url = data.get("@odata.nextLink", "")
-                params = {}
-
-        logger.info("ms_graph: fetched %d Teams message(s)", len(messages))
-        return messages
-
-    @staticmethod
-    def _parse_email(item: dict[str, Any]) -> EmailMessage:
-        subject: str = item.get("subject", "(no subject)") or "(no subject)"
-        sender_block = item.get("from", {}) or {}
-        sender_addr = (
-            (sender_block.get("emailAddress") or {}).get("address", "unknown")
-        )
-        date_str: str = item.get("receivedDateTime", "")
-        try:
-            date = datetime.fromisoformat(date_str.replace("Z", "+00:00"))
-        except Exception:
-            date = datetime.now(timezone.utc)
-
-        body_block = item.get("body", {}) or {}
-        content_type: str = body_block.get("contentType", "text")
-        raw_body: str = body_block.get("content", "")
-        if content_type == "html":
-            body_text = _strip_html(raw_body)
-        else:
-            body_text = raw_body or item.get("bodyPreview", "")
-        body_text = body_text[:_BODY_TRUNCATE]
-
-        return EmailMessage(
-            id=item.get("id", ""),
-            subject=subject,
-            sender=sender_addr,
-            body_text=body_text,
-            date=date,
-        )
-
-    @staticmethod
-    def _parse_teams_message(item: dict[str, Any]) -> ChatMessage:
-        msg_id: str = item.get("id", "")
-        sender_block = (item.get("from") or {}).get("user") or {}
-        sender: str = sender_block.get("displayName", "unknown")
-        channel: str | None = (item.get("channelIdentity") or {}).get("channelId")
-
-        date_str: str = item.get("createdDateTime", "")
-        try:
-            date = datetime.fromisoformat(date_str.replace("Z", "+00:00"))
-        except Exception:
-            date = datetime.now(timezone.utc)
-
-        body_block = item.get("body", {}) or {}
-        content_type: str = body_block.get("contentType", "text")
-        raw_content: str = body_block.get("content", "")
-        content = _strip_html(raw_content) if content_type == "html" else raw_content
-        content = content[:_BODY_TRUNCATE]
-
-        return ChatMessage(
-            id=msg_id,
-            content=content,
-            sender=sender,
-            channel=channel,
-            date=date,
-        )

services/batch-agent/app/journey.py

@@ -1,395 +0,0 @@
-"""Chatbot Journey — guided conversation to build an agent prompt_template.
-
-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. 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
-
-import json
-import logging
-import time
-import uuid
-from dataclasses import dataclass, field
-from typing import Any
-
-from langchain_core.messages import AIMessage, HumanMessage, SystemMessage, ToolMessage
-
-from app.agents.filesystem_agent import FILESYSTEM_TOOLS
-from shared.llm import get_llm
-import app.tracing as tracing
-
-logger = logging.getLogger(__name__)
-
-# ── Session TTL ───────────────────────────────────────────────────────────
-
-_SESSION_TTL_SECONDS: int = 1800  # 30 minutes
-
-# Sentinel strings used to delimit the LLM-produced prompt_template.
-_TEMPLATE_START = "PROMPT_TEMPLATE_START"
-_TEMPLATE_END = "PROMPT_TEMPLATE_END"
-
-_MIN_TURNS_BEFORE_NUDGE: int = 3
-_MAX_TURNS: int = 15
-_MAX_TOOL_STEPS: int = 6
-
-# ── In-memory session store ───────────────────────────────────────────────
-
-
-@dataclass
-class JourneySession:
-    session_id: str
-    user_id: str
-    agent_type: str  # "local" | "cloud"
-    directory: str
-    data_types: list[str]
-    history: list[dict[str, Any]] = field(default_factory=list)
-    system_prompt: str = ""
-    created_at: float = field(default_factory=time.monotonic)
-
-    def is_expired(self) -> bool:
-        return (time.monotonic() - self.created_at) > _SESSION_TTL_SECONDS
-
-
-# session_id → session
-_sessions: dict[str, JourneySession] = {}
-
-
-def get_journey_session(session_id: str, user_id: str) -> JourneySession | None:
-    """Retrieve session; return None on missing, expired, or wrong owner."""
-    s = _sessions.get(session_id)
-    if s is None or s.is_expired():
-        _sessions.pop(session_id, None)
-        return None
-    if s.user_id != user_id:
-        return None
-    return s
-
-
-# ── System prompt builder ─────────────────────────────────────────────────
-
-_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 concise prompt_template that a separate AI will use
-as its instruction set.
-
-You have access to file-system tools to explore the user's directory:
-- list_directory: to see folder structure
-- read_file_content: to peek at file contents
-- get_file_metadata: to check file info
-
-The user's configured directory is: {directory}
-Target data types: {data_types}
-
-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 only the topics relevant to the target
-data types listed above:
-
-  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 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}Begin by exploring the directory, then ask your first question.\
-"""
-
-
-def _build_system_prompt(
-    directory: str,
-    data_types: list[str],
-    existing_template: str | None = None,
-) -> str:
-    existing_section = (
-        f"\nThe user already has the following prompt_template — refine it based on their answers:\n"
-        f"---\n{existing_template}\n---\n"
-        if existing_template
-        else ""
-    )
-    # 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,
-        },
-    )
-
-
-# ── Template extraction ───────────────────────────────────────────────────
-
-
-def _extract_template(text: str) -> str | None:
-    """Return the text between PROMPT_TEMPLATE_START and PROMPT_TEMPLATE_END, or None."""
-    if _TEMPLATE_START not in text or _TEMPLATE_END not in text:
-        return None
-    start_idx = text.index(_TEMPLATE_START) + len(_TEMPLATE_START)
-    end_idx = text.index(_TEMPLATE_END)
-    return text[start_idx:end_idx].strip() or None
-
-
-# ── LLM call with tool support ───────────────────────────────────────────
-
-
-def _as_text(content: Any) -> str:
-    if content is None:
-        return ""
-    if isinstance(content, str):
-        return content
-    if isinstance(content, list):
-        parts: list[str] = []
-        for item in content:
-            if isinstance(item, str):
-                parts.append(item)
-            elif isinstance(item, dict):
-                text = item.get("text")
-                if isinstance(text, str):
-                    parts.append(text)
-        return "".join(parts)
-    return str(content)
-
-
-async def _call_llm_with_tools(
-    system_prompt: str,
-    history: list[dict[str, Any]],
-    tools: list[Any],
-    langfuse_handler: Any | None = None,
-) -> str:
-    """Build LangChain messages from history and invoke the LLM with tools.
-
-    Handles tool-calling loops: if the LLM calls tools, execute them and
-    continue until a final text response is produced.
-    """
-    messages: list[Any] = [SystemMessage(content=system_prompt)]
-    for turn in history:
-        if turn["role"] == "user":
-            messages.append(HumanMessage(content=turn["content"]))
-        else:
-            messages.append(AIMessage(content=turn["content"]))
-
-    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}
-
-    for _ in range(_MAX_TOOL_STEPS):
-        response: AIMessage = await llm_with_tools.ainvoke(messages)
-        messages.append(response)
-
-        if not response.tool_calls:
-            return _as_text(response.content)
-
-        for call in response.tool_calls:
-            call_name = str(call.get("name", ""))
-            call_args = call.get("args", {})
-            logger.info(
-                "journey: tool_call name=%s args=%s",
-                call_name,
-                json.dumps(call_args, ensure_ascii=True)[:500],
-            )
-
-            tool_fn = tool_map.get(call_name)
-            if tool_fn is None:
-                tool_output = f"Unknown tool: {call_name}"
-            else:
-                tool_output = await tool_fn.ainvoke(call_args)
-
-            logger.info(
-                "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 tool steps.
-    final = await llm.ainvoke(messages)
-    return _as_text(final.content)
-
-
-# ── 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`` request.
-
-    Creates a session, runs the setup LLM with directory exploration,
-    and returns the ``journey_reply`` payload.
-    """
-    agent_type = frame.get("agent_type", "local")
-    directory = frame.get("directory", "")
-    data_types = frame.get("data_types", [])
-    existing_template = frame.get("existing_template")
-
-    session_id = frame.get("session_id") or str(uuid.uuid4())
-    system_prompt = _build_system_prompt(directory, data_types, existing_template)
-
-    session = JourneySession(
-        session_id=session_id,
-        user_id=user_id,
-        agent_type=agent_type,
-        directory=directory,
-        data_types=data_types,
-        system_prompt=system_prompt,
-    )
-
-    seed_history: list[dict[str, Any]] = [
-        {"role": "user", "content": "Hi, I'm ready to set up my agent. Please explore my directory and ask me your first question."},
-    ]
-    ai_reply = await _call_llm_with_tools(
-        system_prompt=system_prompt,
-        history=seed_history,
-        tools=list(FILESYSTEM_TOOLS),
-        langfuse_handler=langfuse_handler,
-    )
-
-    session.history.extend(seed_history)
-    session.history.append({"role": "assistant", "content": ai_reply})
-    _sessions[session_id] = session
-
-    logger.info(
-        "journey: session %s started for user %s (directory=%s)",
-        session_id,
-        user_id,
-        directory,
-    )
-
-    prompt_template = _extract_template(ai_reply)
-    done = prompt_template is not None
-
-    display_message = ai_reply
-    if done:
-        display_message = (
-            ai_reply[: ai_reply.index(_TEMPLATE_START)].strip()
-            or "Here is your agent configuration. You can save it or continue refining."
-        )
-        _sessions.pop(session_id, None)
-
-    return {
-        "type": "journey_reply",
-        "session_id": session_id,
-        "message": display_message,
-        "done": done,
-        "prompt_template": prompt_template,
-    }
-
-
-async def handle_journey_message(
-    user_id: str,
-    frame: dict[str, Any],
-    *,
-    langfuse_handler: Any | None = None,
-) -> dict[str, Any]:
-    """Handle a ``journey_message`` request.
-
-    Appends the user message, calls the LLM, and returns the
-    ``journey_reply`` payload.
-    """
-    session_id = frame.get("session_id", "")
-    message = frame.get("message", "")
-
-    session = get_journey_session(session_id, user_id)
-    if session is None:
-        return {
-            "type": "journey_reply",
-            "session_id": session_id,
-            "message": "Journey session not found or expired. Please start a new setup.",
-            "done": True,
-            "prompt_template": None,
-        }
-
-    session.history.append({"role": "user", "content": message})
-
-    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})
-
-    prompt_template = _extract_template(ai_reply)
-    done = prompt_template is not None
-
-    if not done:
-        turns = sum(1 for t in session.history if t["role"] == "user")
-        if turns >= _MAX_TURNS:
-            nudge_content = (
-                "[System: You have enough information. Please generate the final "
-                f"prompt_template now, wrapped in {_TEMPLATE_START} / {_TEMPLATE_END} markers.]"
-            )
-            session.history.append({"role": "user", "content": nudge_content})
-
-            nudge_reply = await _call_llm_with_tools(
-                system_prompt=session.system_prompt,
-                history=session.history,
-                tools=list(FILESYSTEM_TOOLS),
-                langfuse_handler=langfuse_handler,
-            )
-            session.history.append({"role": "assistant", "content": nudge_reply})
-
-            prompt_template = _extract_template(nudge_reply)
-            if prompt_template is not None:
-                done = True
-                ai_reply = nudge_reply
-
-    display_message = ai_reply
-    if done:
-        display_message = (
-            ai_reply[: ai_reply.index(_TEMPLATE_START)].strip()
-            if _TEMPLATE_START in ai_reply
-            else "Here is your agent configuration. You can save it or continue refining."
-        )
-        _sessions.pop(session_id, None)
-        logger.info("journey: session %s completed for user %s", session_id, user_id)
-
-    return {
-        "type": "journey_reply",
-        "session_id": session_id,
-        "message": display_message,
-        "done": done,
-        "prompt_template": prompt_template,
-    }

services/batch-agent/app/llm.py

@@ -1,76 +0,0 @@
-"""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

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

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

@@ -1,208 +0,0 @@
-"""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

@@ -1,336 +0,0 @@
-"""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

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

services/batch-agent/eval/__main__.py

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

services/batch-agent/eval/cli.py

@@ -1,285 +0,0 @@
-"""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

@@ -1,219 +0,0 @@
-"""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)
-
-    # ── 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

@@ -1,40 +0,0 @@
-# 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

@@ -1,108 +0,0 @@
-# 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

@@ -1,28 +0,0 @@
-# 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

@@ -1,81 +0,0 @@
-# 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

@@ -1,18 +0,0 @@
-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

@@ -1,25 +0,0 @@
-# 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

@@ -1,471 +0,0 @@
-"""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

@@ -1,385 +0,0 @@
-"""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

@@ -1,327 +0,0 @@
-"""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

@@ -1,258 +0,0 @@
-"""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

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

services/batch-agent/eval/runner.py

@@ -1,463 +0,0 @@
-"""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 each expected file.
-
-    Returns a list of result dicts:
-    ``[{file, project_id, domains, new_project_name}, ...]``
-    """
-    from app.agent_runner import _classify_file
-
-    results: list[dict[str, Any]] = []
-    for ec in fixture.expected_classification:
-        # Read the file content through the mock
-        file_result = await mock._handle(
-            action="read_file_content",
-            data={"path": ec.file},
-        )
-        file_content: str = file_result.get("content", "")
-
-        project_id, domains, new_name = await _classify_file(
-            file_path=ec.file,
-            file_content=file_content,
-            projects=fixture.projects_list,
-            config_data_types=fixture.data_types,
-        )
-        results.append({
-            "file": ec.file,
-            "project_id": project_id,
-            "domains": domains,
-            "new_project_name": new_name,
-        })
-    return results
-
-
-def _score_step1(
-    fixture: EvalFixture,
-    results: list[dict[str, Any]],
-) -> tuple[float, float, float, str]:
-    """Score step-1 results. Returns (precision, recall, f1, reasoning)."""
-    if not fixture.expected_classification:
-        return 0.0, 0.0, 0.0, "No expected classifications"
-
-    total = len(fixture.expected_classification)
-    matched = 0
-    details: list[str] = []
-
-    for ec in fixture.expected_classification:
-        actual = next((r for r in results if r["file"] == ec.file), None)
-        if actual is None:
-            details.append(f"  MISS {ec.file}: not processed")
-            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
-            details.append(f"  OK   {ec.file}: project={actual['project_id']}, domains={actual['domains']}")
-        else:
-            parts: list[str] = []
-            if not pid_ok:
-                parts.append(f"project expected={ec.project_id} got={actual['project_id']}")
-            if not domains_ok:
-                parts.append(f"domains expected={ec.domains} got={actual['domains']}")
-            details.append(f"  FAIL {ec.file}: {'; '.join(parts)}")
-
-    precision = matched / total if total > 0 else 0.0
-    recall = precision  # in step1, precision == recall (same denominator)
-    f1 = precision  # same
-    reasoning = "\n".join(details)
-    return precision, recall, f1, reasoning
-
-
-# ── 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
-
-    print("\n" + "=" * 95)
-    print(f"{'Fixture':<25} {'Mode':<6} {'Model':<25} {'P':>6} {'R':>6} {'F1':>6} {'FA':>6} {'LLM':>6}")
-    print("-" * 95)
-
-    for s in results:
-        llm_str = f"{s.llm_judge_score:.2f}" if s.llm_judge_score is not None 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"{s.field_accuracy:>6.2f} {llm_str:>6}"
-        )
-
-    print("=" * 95)
-    print()
-
-    print("=" * 90)
-
-    # If LLM judge reasoning is available, print it
-    for s in results:
-        if s.llm_judge_reasoning:
-            print(f"\n[{s.model} / {s.prompt_variant}] LLM Judge: {s.llm_judge_reasoning}")
-
-    print()

services/batch-agent/eval/scorer.py

@@ -1,268 +0,0 @@
-"""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

@@ -1,21 +0,0 @@
-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/README.md

@@ -1,15 +0,0 @@
-# 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/chat/Dockerfile

@@ -1,36 +0,0 @@
-# ── builder ──────────────────────────────────────────────────────────────────
-FROM python:3.12-slim AS builder
-
-WORKDIR /build
-
-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
-
-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/chat/app/ app/
-
-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", "2", \
-     "--timeout", "120"]

services/chat/README.md

@@ -1,21 +0,0 @@
-# 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

@@ -1,883 +0,0 @@
-"""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
-
-import json
-import logging
-import re
-from datetime import date
-from collections.abc import AsyncGenerator
-from typing import Any, Literal
-
-from langchain_core.messages import AIMessage, HumanMessage, SystemMessage, ToolMessage
-from langchain_core.tools import tool
-
-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__)
-
-FloatingDomainType = Literal["task", "timeline", "project", "node"]
-FloatingDomainSection = Literal["task", "timeline", "note"]
-
-_HOME_SINGLE_AGENT_SYSTEM = (
-    "You are the home assistant with direct access to all tools: tasks, projects, notes, timelines, and memory tools. "
-    "Always use tools for factual data retrieval before answering. "
-    "When the user asks to remember, forget, or update what you know about them, use memory tools. "
-    "If context.context.resolved_project_id exists, use it as project_id for scoped list calls. "
-    "Return markdown and use tags when relevant: <project>[ids]</project>, <task>[ids]</task>, "
-    "<note>[ids]</note>, <timeline>[ids]</timeline>, <chart>{json}</chart>. "
-    "When listing tasks or timelines, each id tag must be on its own line with no prefix/suffix text. "
-    "Never put titles, priorities, or dates on the same line as <task> or <timeline> tags. "
-    "For questions about upcoming timelines (e.g. 'prossimi eventi'), include only future items in the current month unless the user asks a different range. "
-    "For upcoming tasks, after tag lines add a short recommendation based on due date and priority."
-)
-
-_FLOATING_SINGLE_AGENT_SYSTEM = (
-    "You are the floating assistant with direct access to all tools: tasks, projects, notes, timelines, and memory tools. "
-    "Stay focused on the floating scope in context.scope and answer concisely. "
-    "Return plain text only. Do not output XML/HTML-like tags such as <task>, <project>, <note>, <timeline>, or any bracketed id tag wrappers. "
-    "Always use tools for factual data retrieval before answering. "
-    "When the user asks to remember, forget, or update what you know about them, use memory tools. "
-    "If context.context.resolved_project_id exists, use it as project_id for scoped list calls. "
-)
-
-_FLOATING_DOMAIN_CLASSIFIER_SYSTEM = (
-    "You are a strict domain classifier for websocket floating requests. "
-    "Return ONLY a JSON object with keys: type, id, section. "
-    "Allowed type values: task, timeline, project, node. "
-    "Allowed section values: task, timeline, note, or null. "
-    "Rules: infer from user message intent first; do not blindly trust scope.type. "
-    "If user asks tasks/timeline/notes for a project, set type=project and section accordingly. "
-    "If project id is unknown but context.resolved_project_id exists, use it as id. "
-    "If id is unknown, use null. "
-    "No markdown, no prose, JSON only."
-)
-
-
-def _as_text(content: Any) -> str:
-    if content is None:
-        return ""
-    if isinstance(content, str):
-        return content
-    if isinstance(content, list):
-        parts: list[str] = []
-        for item in content:
-            if isinstance(item, str):
-                parts.append(item)
-            elif isinstance(item, dict):
-                text = item.get("text")
-                if isinstance(text, str):
-                    parts.append(text)
-        return "".join(parts)
-    return str(content)
-
-
-def _candidate_tokens(message: str) -> list[str]:
-    tokens = re.findall(r"[a-zA-Z0-9_-]+", message.lower())
-    return [token for token in tokens if len(token) >= 3]
-
-
-async def _resolve_project_id_from_message(message: str) -> str | None:
-    """Resolve likely project UUID from user message using client project list."""
-    try:
-        result = await execute_on_client(action="select", table="projects")
-    except Exception as exc:
-        logger.warning("deep_agent: project resolve select failed: %s", exc)
-        return None
-
-    rows = result.get("rows", [])
-    if not isinstance(rows, list) or not rows:
-        return None
-
-    tokens = _candidate_tokens(message)
-    scored: list[tuple[int, dict[str, Any]]] = []
-    for row in rows:
-        if not isinstance(row, dict):
-            continue
-        name = str(row.get("name", "")).lower()
-        score = sum(1 for token in tokens if token in name)
-        if score > 0:
-            scored.append((score, row))
-
-    if not scored:
-        return None
-
-    scored.sort(key=lambda item: item[0], reverse=True)
-    top_score = scored[0][0]
-    top_rows = [row for score, row in scored if score == top_score]
-    if len(top_rows) != 1:
-        return None
-
-    project_id = top_rows[0].get("id")
-    return project_id if isinstance(project_id, str) else None
-
-
-def _needs_project_resolution(message: str) -> bool:
-    lowered = message.lower()
-    return any(keyword in lowered for keyword in ["project", "progetto", "progetti", "whitelist"])
-
-
-async def _prepare_context(message: str, context: dict[str, Any]) -> dict[str, Any]:
-    prepared = dict(context)
-    if _needs_project_resolution(message):
-        resolved_project_id = await _resolve_project_id_from_message(message)
-        if resolved_project_id:
-            prepared["resolved_project_id"] = resolved_project_id
-            logger.info("deep_agent: resolved_project_id=%s", resolved_project_id)
-    return prepared
-
-
-def _all_tools() -> list[Any]:
-    return [*TASK_TOOLS, *PROJECT_TOOLS, *NOTE_TOOLS, *TIMELINE_TOOLS]
-
-
-def _trace_id_from_context(context: dict[str, Any]) -> str | None:
-    debug = context.get("_debug")
-    if isinstance(debug, dict):
-        request_id = debug.get("request_id")
-        if isinstance(request_id, str) and request_id:
-            return request_id
-    return None
-
-
-def _context_for_model(context: dict[str, Any]) -> dict[str, Any]:
-    sanitized = dict(context)
-    sanitized.pop("_debug", None)
-    return sanitized
-
-
-_TAG_LINE_RE = re.compile(r"<(task|timeline)>\[[^\]]+\]</\1>")
-_TIMELINE_DMY_RE = re.compile(r"(?P<d>\d{2})/(?P<m>\d{2})/(?P<y>\d{4})")
-
-
-def _is_upcoming_timeline_query(message: str) -> bool:
-    lowered = message.lower()
-    has_upcoming = "prossim" in lowered or "upcoming" in lowered or "next" in lowered
-    has_timeline_topic = any(
-        token in lowered
-        for token in ("event", "evento", "eventi", "timeline", "milestone", "scaden")
-    )
-    return has_upcoming and has_timeline_topic
-
-
-def _timeline_date_in_current_month_or_future(dmy: str) -> bool:
-    match = _TIMELINE_DMY_RE.search(dmy)
-    if not match:
-        return True
-    try:
-        parsed = date(
-            int(match.group("y")),
-            int(match.group("m")),
-            int(match.group("d")),
-        )
-    except ValueError:
-        return True
-
-    today = date.today()
-    return parsed >= today and parsed.year == today.year and parsed.month == today.month
-
-
-def _normalize_tagged_list_lines(text: str, message: str) -> str:
-    if not text:
-        return text
-
-    upcoming_timeline_only = _is_upcoming_timeline_query(message)
-    output_lines: list[str] = []
-
-    for line in text.splitlines():
-        matches = list(_TAG_LINE_RE.finditer(line))
-        if not matches:
-            output_lines.append(line)
-            continue
-
-        had_non_tag_text = _TAG_LINE_RE.sub("", line).strip(" -\t0123456789.*:)")
-        if not had_non_tag_text and len(matches) == 1:
-            tag_text = matches[0].group(0)
-            if (
-                upcoming_timeline_only
-                and "<timeline>" in tag_text
-                and not _timeline_date_in_current_month_or_future(line)
-            ):
-                continue
-            output_lines.append(tag_text)
-            continue
-
-        for match in matches:
-            tag_text = match.group(0)
-            if (
-                upcoming_timeline_only
-                and "<timeline>" in tag_text
-                and not _timeline_date_in_current_month_or_future(line)
-            ):
-                continue
-            output_lines.append(tag_text)
-
-    return "\n".join(output_lines)
-
-
-_GENERIC_TAG_RE = re.compile(r"</?(task|project|note|timeline|chart)>", re.IGNORECASE)
-_BRACKETED_ID_RE = re.compile(r"\[(?:[0-9a-fA-F-]{8,}|[A-Za-z0-9_-]{8,})\]")
-_FLOATING_EMPTY_FALLBACK = "No results found."
-
-
-def _strip_floating_markup_fragment(text: str) -> str:
-    if not text:
-        return text
-    cleaned = _GENERIC_TAG_RE.sub("", text)
-    return _BRACKETED_ID_RE.sub("", cleaned)
-
-
-def _strip_floating_markup(text: str) -> str:
-    """Ensure floating responses stay plain text with no XML-like tag wrappers."""
-    if not text:
-        return text
-
-    cleaned = _strip_floating_markup_fragment(text)
-    lines = [re.sub(r"[ \t]{2,}", " ", line).strip() for line in cleaned.splitlines()]
-    return "\n".join(line for line in lines if line)
-
-
-def _fallback_from_raw_floating_text(raw_text: str) -> str:
-    fallback = _strip_floating_markup_fragment(raw_text or "")
-    fallback = re.sub(r"[ \t]{2,}", " ", fallback).strip()
-    return fallback or _FLOATING_EMPTY_FALLBACK
-
-
-class _FloatingStreamSanitizer:
-    """Streaming sanitizer that removes floating markup without buffering the full answer."""
-
-    def __init__(self) -> None:
-        self._pending = ""
-
-    @staticmethod
-    def _split_safe_boundary(text: str) -> tuple[str, str]:
-        boundary = len(text)
-
-        last_lt = text.rfind("<")
-        if last_lt != -1 and ">" not in text[last_lt:]:
-            boundary = min(boundary, last_lt)
-
-        last_lb = text.rfind("[")
-        if last_lb != -1 and "]" not in text[last_lb:]:
-            boundary = min(boundary, last_lb)
-
-        if boundary == len(text):
-            return text, ""
-        return text[:boundary], text[boundary:]
-
-    def feed(self, chunk: str) -> str:
-        combined = f"{self._pending}{chunk}"
-        safe_text, self._pending = self._split_safe_boundary(combined)
-        return _strip_floating_markup_fragment(safe_text)
-
-    def finalize(self) -> str:
-        tail = re.sub(r"<[^>\n]*$", "", self._pending)
-        tail = re.sub(r"\[[^\]\n]*$", "", tail)
-        self._pending = ""
-        return _strip_floating_markup_fragment(tail)
-
-
-def _normalize_memory_label(path_or_label: str) -> str:
-    value = path_or_label.strip()
-    if value.startswith("/memories/"):
-        value = value[len("/memories/"):]
-    value = value.strip("/")
-    return value
-
-
-def _memory_tools(user_id: str, trace_id: str | None) -> list[Any]:
-    @tool
-    async def memory_list_blocks() -> str:
-        """List all core memory blocks currently stored for the user."""
-        logger.info("deep_agent: memory_list_blocks trace=%s user=%s", trace_id or "-", user_id)
-        async with async_session() as db:
-            memory = MemoryMiddleware(db)
-            blocks = await memory.list_core_blocks(user_id)
-        if not blocks:
-            return "No memory blocks found."
-        lines = [f"- {b['label']}: {b['value']}" for b in blocks]
-        return "Memory blocks:\n" + "\n".join(lines)
-
-    @tool
-    async def memory_get(path_or_label: str) -> str:
-        """Get one memory block by label or /memories/<label> path."""
-        label = _normalize_memory_label(path_or_label)
-        logger.info("deep_agent: memory_get trace=%s user=%s label=%s", trace_id or "-", user_id, label)
-        if not label:
-            return "Invalid memory label."
-        async with async_session() as db:
-            memory = MemoryMiddleware(db)
-            value = await memory.get_core_block(user_id, label)
-        if value is None:
-            return f"Memory block '{label}' not found."
-        return f"Memory block '{label}':\n{value}"
-
-    @tool
-    async def memory_create(path_or_label: str, value: str) -> str:
-        """Create or overwrite a memory block value by label or /memories/<label> path."""
-        label = _normalize_memory_label(path_or_label)
-        logger.info("deep_agent: memory_create trace=%s user=%s label=%s", trace_id or "-", user_id, label)
-        if not label:
-            return "Invalid memory label."
-        async with async_session() as db:
-            memory = MemoryMiddleware(db)
-            await memory.update_core(user_id, label, value, trace_id=trace_id)
-        return f"Memory block '{label}' saved."
-
-    @tool
-    async def memory_append(path_or_label: str, content: str) -> str:
-        """Append content to a memory block, creating it if missing."""
-        label = _normalize_memory_label(path_or_label)
-        logger.info("deep_agent: memory_append trace=%s user=%s label=%s", trace_id or "-", user_id, label)
-        if not label:
-            return "Invalid memory label."
-        async with async_session() as db:
-            memory = MemoryMiddleware(db)
-            await memory.append_core(user_id, label, content)
-        return f"Memory block '{label}' appended."
-
-    @tool
-    async def memory_replace(path_or_label: str, old_string: str, new_string: str) -> str:
-        """Replace one exact string in a memory block."""
-        label = _normalize_memory_label(path_or_label)
-        logger.info("deep_agent: memory_replace trace=%s user=%s label=%s", trace_id or "-", user_id, label)
-        if not label:
-            return "Invalid memory label."
-        async with async_session() as db:
-            memory = MemoryMiddleware(db)
-            changed = await memory.replace_core(user_id, label, old_string, new_string)
-        if not changed:
-            return f"No replacement made in '{label}' (old string not found)."
-        return f"Memory block '{label}' updated."
-
-    @tool
-    async def memory_delete(path_or_label: str) -> str:
-        """Delete a memory block by label or /memories/<label> path."""
-        label = _normalize_memory_label(path_or_label)
-        logger.info("deep_agent: memory_delete trace=%s user=%s label=%s", trace_id or "-", user_id, label)
-        if not label:
-            return "Invalid memory label."
-        async with async_session() as db:
-            memory = MemoryMiddleware(db)
-            deleted = await memory.delete_core(user_id, label)
-        if not deleted:
-            return f"Memory block '{label}' not found."
-        return f"Memory block '{label}' deleted."
-
-    @tool
-    async def archival_memory_insert(content: str) -> str:
-        """Insert a long-term archival memory entry."""
-        logger.info("deep_agent: archival_memory_insert trace=%s user=%s", trace_id or "-", user_id)
-        async with async_session() as db:
-            memory = MemoryMiddleware(db)
-            await memory.insert_archival(user_id, content, source="assistant")
-        return "Archival memory saved."
-
-    @tool
-    async def archival_memory_search(query: str, top_k: int = 5) -> str:
-        """Search long-term archival memory by semantic fallback (keyword currently)."""
-        logger.info("deep_agent: archival_memory_search trace=%s user=%s query=%s", trace_id or "-", user_id, query[:80])
-        async with async_session() as db:
-            memory = MemoryMiddleware(db)
-            results = await memory.search_archival(user_id, query, top_k=top_k)
-        if not results:
-            return "No archival memory results found."
-        lines = [f"- {item}" for item in results]
-        return "Archival memory results:\n" + "\n".join(lines)
-
-    @tool
-    async def conversation_search(query: str, top_k: int = 5) -> str:
-        """Search recall memory from prior episodic conversation summaries."""
-        logger.info("deep_agent: conversation_search trace=%s user=%s query=%s", trace_id or "-", user_id, query[:80])
-        async with async_session() as db:
-            memory = MemoryMiddleware(db)
-            results = await memory.search_recall(user_id, query, top_k=top_k)
-        if not results:
-            return "No recall memory results found."
-        lines = [f"- {item}" for item in results]
-        return "Recall memory results:\n" + "\n".join(lines)
-
-    return [
-        memory_list_blocks,
-        memory_get,
-        memory_create,
-        memory_append,
-        memory_replace,
-        memory_delete,
-        archival_memory_insert,
-        archival_memory_search,
-        conversation_search,
-    ]
-
-
-def _all_tools_for_user(user_id: str, trace_id: str | None) -> list[Any]:
-    return [*_all_tools(), *_memory_tools(user_id, trace_id)]
-
-
-def _detect_domain_section(message: str) -> FloatingDomainSection | None:
-    lowered = message.lower()
-    if any(keyword in lowered for keyword in ["timeline", "milestone", "release", "schedule"]):
-        return "timeline"
-    if any(keyword in lowered for keyword in ["task", "tasks", "todo", "attivit", "azione"]):
-        return "task"
-    if any(keyword in lowered for keyword in ["note", "notes", "memo", "document"]):
-        return "note"
-    return None
-
-
-def _normalize_domain_payload(payload: dict[str, Any], fallback_id: str | None) -> dict[str, str | None]:
-    type_raw = str(payload.get("type") or "").strip().lower()
-    domain_type: FloatingDomainType = "task"
-    if type_raw in {"task", "timeline", "project", "node"}:
-        domain_type = type_raw
-
-    id_value = payload.get("id")
-    domain_id = id_value if isinstance(id_value, str) and id_value.strip() else None
-    if domain_type == "project" and not domain_id:
-        domain_id = fallback_id
-
-    section_raw = payload.get("section")
-    section: FloatingDomainSection | None = None
-    if isinstance(section_raw, str):
-        section_candidate = section_raw.strip().lower()
-        if section_candidate in {"task", "timeline", "note"}:
-            section = section_candidate
-
-    if domain_type != "project":
-        section = None
-
-    return {
-        "type": domain_type,
-        "id": domain_id,
-        "section": section,
-    }
-
-
-def _parse_json_object(text: str) -> dict[str, Any] | None:
-    raw = text.strip()
-    if not raw:
-        return None
-    try:
-        parsed = json.loads(raw)
-        return parsed if isinstance(parsed, dict) else None
-    except json.JSONDecodeError:
-        pass
-
-    match = re.search(r"\{.*\}", raw, re.DOTALL)
-    if not match:
-        return None
-    try:
-        parsed = json.loads(match.group(0))
-    except json.JSONDecodeError:
-        return None
-    return parsed if isinstance(parsed, dict) else None
-
-
-def _infer_floating_domain_rule_based(message: str, context: dict[str, Any]) -> dict[str, str | None]:
-    section = _detect_domain_section(message)
-    scope = context.get("scope") if isinstance(context, dict) else 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
-
-    if isinstance(scope, dict):
-        scope_type = str(scope.get("type") or "").strip().lower()
-        scope_id = scope.get("id")
-        scope_id_value = scope_id if isinstance(scope_id, str) and scope_id else None
-
-        if scope_type in {"task", "tasks"}:
-            return {"type": "task", "id": scope_id_value, "section": None}
-        if scope_type in {"project", "projects"}:
-            project_scope_id = scope_id_value or project_id
-            return {
-                "type": "project",
-                "id": project_scope_id,
-                "section": section,
-            }
-        if scope_type in {"note", "notes"}:
-            return {
-                "type": "node",
-                "id": scope_id_value,
-                "section": None,
-            }
-        if scope_type in {"timeline", "timelines"}:
-            return {"type": "timeline", "id": scope_id_value, "section": None}
-
-    lowered = message.lower()
-    if any(keyword in lowered for keyword in ["project", "progetto", "client"]) or project_id:
-        return {
-            "type": "project",
-            "id": project_id,
-            "section": section,
-        }
-    if section == "timeline":
-        return {"type": "timeline", "id": None, "section": None}
-    if section == "note":
-        return {"type": "node", "id": None, "section": None}
-    return {"type": "task", "id": None, "section": 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
-
-    classifier_context = {
-        "scope": context.get("scope") if isinstance(context.get("scope"), dict) else None,
-        "resolved_project_id": project_id,
-    }
-
-    try:
-        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=classifier_prompt),
-                HumanMessage(
-                    content=(
-                        f"Message:\n{message}\n\n"
-                        f"Context:\n{json.dumps(classifier_context, ensure_ascii=True)}"
-                    )
-                ),
-            ]
-        )
-        parsed = _parse_json_object(_as_text(response.content))
-        if parsed is not None:
-            domain = _normalize_domain_payload(parsed, project_id)
-            logger.info(
-                "deep_agent: floating_domain_classified type=%s id=%s section=%s",
-                domain.get("type"),
-                domain.get("id"),
-                domain.get("section"),
-            )
-            return domain
-        logger.warning("deep_agent: floating_domain classifier returned non-json output")
-    except Exception as exc:
-        logger.warning("deep_agent: floating_domain classifier failed: %s", exc)
-
-    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,
-    system_prompt: str,
-    message: str,
-    context: dict[str, Any],
-    max_steps: int = 6,
-    langfuse_handler: Any | None = None,
-) -> str:
-    trace_id = _trace_id_from_context(context)
-    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)
-    llm_with_tools = llm.bind_tools(tools)
-    messages: list[Any] = [
-        SystemMessage(content=system_prompt),
-        HumanMessage(
-            content=(
-                f"User message:\n{message}\n\n"
-                f"Context:\n{json.dumps({'context': model_context}, ensure_ascii=True)[:3500]}"
-            )
-        ),
-    ]
-
-    tool_calls_count = 0
-    collected: list[dict[str, Any]] = []
-    set_tool_result_collector(collected)
-    try:
-        for _ in range(max_steps):
-            response: AIMessage = await llm_with_tools.ainvoke(messages)
-            messages.append(response)
-
-            if not response.tool_calls:
-                final_text = _as_text(response.content)
-                logger.info(
-                    "deep_agent: run_single_agent_end trace=%s user=%s tool_calls=%d response_chars=%d",
-                    trace_id or "-",
-                    user_id,
-                    tool_calls_count,
-                    len(final_text),
-                )
-                return final_text
-
-            tool_map = {tool_def.name: tool_def for tool_def in tools}
-            for call in response.tool_calls:
-                tool_calls_count += 1
-                call_id = str(call.get("id", ""))
-                call_name = str(call.get("name", ""))
-                call_args = call.get("args", {})
-                logger.info(
-                    "deep_agent: AI->Tool tool_call_id=%s tool=%s args=%s",
-                    call_id,
-                    call_name,
-                    json.dumps(call_args, ensure_ascii=True)[:800],
-                )
-
-                tool_fn = tool_map.get(call_name)
-                if tool_fn is None:
-                    tool_output = f"Unknown tool: {call_name}"
-                else:
-                    tool_output = await tool_fn.ainvoke(call_args)
-
-                logger.info(
-                    "deep_agent: Tool->AI tool_call_id=%s tool=%s output=%s",
-                    call_id,
-                    call_name,
-                    str(tool_output)[:1200],
-                )
-
-                messages.append(ToolMessage(content=str(tool_output), tool_call_id=call["id"]))
-
-        final = await llm.ainvoke(messages)
-        final_text = _as_text(final.content)
-        logger.info(
-            "deep_agent: run_single_agent_end trace=%s user=%s tool_calls=%d response_chars=%d fallback=1",
-            trace_id or "-",
-            user_id,
-            tool_calls_count,
-            len(final_text),
-        )
-        return final_text
-    finally:
-        clear_tool_result_collector()
-
-
-async def _run_single_agent_stream(
-    *,
-    user_id: str,
-    system_prompt: str,
-    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)
-    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)
-    llm_with_tools = llm.bind_tools(tools)
-    messages: list[Any] = [
-        SystemMessage(content=system_prompt),
-        HumanMessage(
-            content=(
-                f"User message:\n{message}\n\n"
-                f"Context:\n{json.dumps({'context': model_context}, ensure_ascii=True)[:3500]}"
-            )
-        ),
-    ]
-
-    tool_calls_count = 0
-    streamed_chars = 0
-    collected: list[dict[str, Any]] = []
-    set_tool_result_collector(collected)
-    try:
-        for _ in range(max_steps):
-            response: AIMessage = await llm_with_tools.ainvoke(messages)
-            messages.append(response)
-
-            if not response.tool_calls:
-                emitted_any = False
-                async for chunk in llm.astream(messages):
-                    token = _as_text(getattr(chunk, "content", ""))
-                    if token:
-                        streamed_chars += len(token)
-                        emitted_any = True
-                        yield "token", token
-
-                if not emitted_any:
-                    fallback_text = _as_text(response.content)
-                    if fallback_text:
-                        streamed_chars += len(fallback_text)
-                        yield "token", fallback_text
-                logger.info(
-                    "deep_agent: run_single_agent_stream_end trace=%s user=%s tool_calls=%d response_chars=%d",
-                    trace_id or "-",
-                    user_id,
-                    tool_calls_count,
-                    streamed_chars,
-                )
-                return
-
-            tool_map = {tool_def.name: tool_def for tool_def in tools}
-            for call in response.tool_calls:
-                tool_calls_count += 1
-                call_id = str(call.get("id", ""))
-                call_name = str(call.get("name", ""))
-                call_args = call.get("args", {})
-                logger.info(
-                    "deep_agent: AI->Tool tool_call_id=%s tool=%s args=%s",
-                    call_id,
-                    call_name,
-                    json.dumps(call_args, ensure_ascii=True)[:800],
-                )
-
-                tool_fn = tool_map.get(call_name)
-                if tool_fn is None:
-                    tool_output = f"Unknown tool: {call_name}"
-                else:
-                    tool_output = await tool_fn.ainvoke(call_args)
-
-                logger.info(
-                    "deep_agent: Tool->AI tool_call_id=%s tool=%s output=%s",
-                    call_id,
-                    call_name,
-                    str(tool_output)[:1200],
-                )
-
-                messages.append(ToolMessage(content=str(tool_output), tool_call_id=call["id"]))
-
-        async for chunk in llm.astream(messages):
-            token = _as_text(getattr(chunk, "content", ""))
-            if token:
-                streamed_chars += len(token)
-                yield "token", token
-        logger.info(
-            "deep_agent: run_single_agent_stream_end trace=%s user=%s tool_calls=%d response_chars=%d fallback=1",
-            trace_id or "-",
-            user_id,
-            tool_calls_count,
-            streamed_chars,
-        )
-    finally:
-        clear_tool_result_collector()
-
-
-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=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], *, 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, 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=system_prompt,
-        message=message,
-        context=prepared_context,
-        langfuse_handler=langfuse_handler,
-    )
-    sanitized = _strip_floating_markup(response)
-    if not sanitized and response:
-        sanitized = _fallback_from_raw_floating_text(response)
-    return sanitized, domain
-
-
-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=system_prompt,
-        message=message,
-        context=prepared_context,
-        langfuse_handler=langfuse_handler,
-    ):
-        event_type, data = event
-        if event_type != "token":
-            yield event
-            continue
-        text_chunks.append(str(data or ""))
-
-    normalized = _normalize_tagged_list_lines("".join(text_chunks), message)
-    if normalized:
-        yield "token", normalized
-
-
-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, 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=system_prompt,
-        message=message,
-        context=prepared_context,
-        langfuse_handler=langfuse_handler,
-    ):
-        event_type, data = event
-        if event_type != "token":
-            yield event
-            continue
-
-        raw_chunk = str(data or "")
-        raw_chunks.append(raw_chunk)
-        sanitized_chunk = sanitizer.feed(raw_chunk)
-        if sanitized_chunk:
-            emitted_sanitized = True
-            yield "token", sanitized_chunk
-
-    tail = sanitizer.finalize()
-    if tail:
-        emitted_sanitized = True
-        yield "token", tail
-
-    if not emitted_sanitized and raw_chunks:
-        yield "token", _fallback_from_raw_floating_text("".join(raw_chunks))
-
-
-async def update_core_memory(user_id: str, key: str, value: str) -> None:
-    """Compatibility helper kept for callers that expect explicit memory update API."""
-    async with async_session() as db:
-        memory = MemoryMiddleware(db)
-        await memory.update_core(user_id, key, value)

services/chat/app/llm.py

@@ -1,77 +0,0 @@
-"""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

@@ -1,87 +0,0 @@
-"""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

@@ -1,295 +0,0 @@
-"""Memory Middleware — adapted for Chat Service.
-
-Uses shared.models instead of app.models. Otherwise identical to the
-monolith's app/core/memory_middleware.py.
-"""
-
-from __future__ import annotations
-
-import logging
-import uuid
-from typing import Any
-
-from cryptography.fernet import Fernet, InvalidToken
-from sqlalchemy import select
-from sqlalchemy.ext.asyncio import AsyncSession
-
-from shared.models import (
-    MemoryAssociative,
-    MemoryCore,
-    MemoryEpisodic,
-    MemoryProactive,
-    User,
-)
-
-logger = logging.getLogger(__name__)
-
-_ASSOCIATIVE_TOP_K = 5
-_EPISODIC_RECENT_N = 10
-_PROACTIVE_CONFIDENCE_THRESHOLD = 0.6
-
-
-class MemoryMiddleware:
-
-    def __init__(self, db: AsyncSession) -> None:
-        self._db = db
-
-    async def enrich_context(
-        self,
-        user_id: str,
-        message: str,
-        trace_id: str | None = None,
-        session_id: str | None = None,
-    ) -> dict[str, Any]:
-        fernet = await self._get_fernet(user_id)
-        if fernet is None:
-            return {}
-
-        core = await self._load_core(user_id, fernet)
-        associative = await self._load_associative(user_id, message, fernet)
-        episodic = await self._load_episodic(user_id, fernet, session_id=session_id)
-        proactive = await self._load_proactive(user_id, fernet)
-
-        logger.info(
-            "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 {
-            "core_memory": core,
-            "associative_memory": associative,
-            "episodic_memory": episodic,
-            "proactive_hints": proactive,
-        }
-
-    async def store_episode(
-        self, user_id: str, session_id: str, message: str, response: str,
-        trace_id: str | None = None,
-    ) -> None:
-        fernet = await self._get_fernet(user_id)
-        if fernet is None:
-            return
-
-        summary = f"User: {message[:200]}\nAssistant: {response[:200]}"
-        encrypted = _encrypt(fernet, summary)
-
-        row = MemoryEpisodic(
-            id=str(uuid.uuid4()),
-            user_id=user_id,
-            summary_encrypted=encrypted,
-            session_id=session_id,
-        )
-        self._db.add(row)
-        try:
-            await self._db.commit()
-        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:
-        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)
-        )
-        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,
-            ))
-        try:
-            await self._db.commit()
-        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]]:
-        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())
-        )
-        out: list[dict[str, str]] = []
-        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})
-        return out
-
-    async def get_core_block(self, user_id: str, label: str) -> str | None:
-        fernet = await self._get_fernet(user_id)
-        if fernet is None:
-            return None
-        result = await self._db.execute(
-            select(MemoryCore).where(MemoryCore.user_id == user_id, MemoryCore.key == label)
-        )
-        row = result.scalar_one_or_none()
-        if row is None:
-            return None
-        return _safe_decrypt(fernet, row.value_encrypted)
-
-    async def delete_core(self, user_id: str, label: str) -> bool:
-        result = await self._db.execute(
-            select(MemoryCore).where(MemoryCore.user_id == user_id, MemoryCore.key == label)
-        )
-        row = result.scalar_one_or_none()
-        if row is None:
-            return False
-        await self._db.delete(row)
-        try:
-            await self._db.commit()
-            return True
-        except Exception as exc:
-            logger.error("memory: delete_core failed user=%s label=%s: %s", user_id, label, exc)
-            await self._db.rollback()
-            return False
-
-    async def append_core(self, user_id: str, label: str, content: str) -> None:
-        current = await self.get_core_block(user_id, label)
-        if current is None:
-            await self.update_core(user_id, label, content)
-            return
-        await self.update_core(user_id, label, f"{current}\n{content}")
-
-    async def replace_core(self, user_id: str, label: str, old: str, new: str) -> bool:
-        current = await self.get_core_block(user_id, label)
-        if current is None or old not in current:
-            return False
-        await self.update_core(user_id, label, current.replace(old, new, 1))
-        return True
-
-    async def insert_archival(self, user_id: str, content: str, source: str = "manual") -> None:
-        fernet = await self._get_fernet(user_id)
-        if fernet is None:
-            return
-        encrypted = _encrypt(fernet, content)
-        row = MemoryAssociative(
-            id=str(uuid.uuid4()), user_id=user_id,
-            content_encrypted=encrypted, embedding=None,
-            entity_type=source, entity_id=None,
-        )
-        self._db.add(row)
-        try:
-            await self._db.commit()
-        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]:
-        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)
-        )
-        needle = query.strip().lower()
-        out: list[str] = []
-        for row in result.scalars().all():
-            plaintext = _safe_decrypt(fernet, row.content_encrypted)
-            if plaintext is None:
-                continue
-            if not needle or needle in plaintext.lower():
-                out.append(plaintext)
-            if len(out) >= max(top_k, 1):
-                break
-        return out
-
-    async def search_recall(self, user_id: str, query: str, top_k: int = 5) -> list[str]:
-        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)
-        )
-        needle = query.strip().lower()
-        out: list[str] = []
-        for row in result.scalars().all():
-            plaintext = _safe_decrypt(fernet, row.summary_encrypted)
-            if plaintext is None:
-                continue
-            if not needle or needle in plaintext.lower():
-                out.append(plaintext)
-            if len(out) >= max(top_k, 1):
-                break
-        return out
-
-    # ── Private ───────────────────────────────────────────────────────
-
-    async def _get_fernet(self, user_id: str) -> Fernet | None:
-        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:
-            logger.warning("memory: no encryption_key for user=%s", user_id)
-            return None
-        return Fernet(user.encryption_key.encode())
-
-    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)
-        )
-        out: dict[str, str] = {}
-        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]:
-        result = await self._db.execute(
-            select(MemoryAssociative).where(MemoryAssociative.user_id == user_id)
-            .order_by(MemoryAssociative.updated_at.desc()).limit(_ASSOCIATIVE_TOP_K)
-        )
-        out: list[str] = []
-        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]:
-        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)
-        )
-        out: list[str] = []
-        for row in result.scalars().all():
-            plaintext = _safe_decrypt(fernet, row.summary_encrypted)
-            if plaintext is not None:
-                out.append(plaintext)
-        return out
-
-    async def _load_proactive(self, user_id: str, fernet: Fernet) -> list[str]:
-        result = await self._db.execute(
-            select(MemoryProactive).where(
-                MemoryProactive.user_id == user_id,
-                MemoryProactive.confidence >= _PROACTIVE_CONFIDENCE_THRESHOLD,
-            ).order_by(MemoryProactive.confidence.desc())
-        )
-        out: list[str] = []
-        for row in result.scalars().all():
-            plaintext = _safe_decrypt(fernet, row.pattern_encrypted)
-            if plaintext is not None:
-                out.append(plaintext)
-        return out
-
-
-def _encrypt(fernet: Fernet, plaintext: str) -> str:
-    return fernet.encrypt(plaintext.encode()).decode()
-
-
-def _safe_decrypt(fernet: Fernet, ciphertext: str) -> str | None:
-    try:
-        return fernet.decrypt(ciphertext.encode()).decode()
-    except (InvalidToken, Exception) as exc:
-        logger.warning("memory: decrypt failed: %s", exc)
-        return None

services/chat/app/output_formatter.py

@@ -1,50 +0,0 @@
-"""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 shared.schemas import WsFloatingDomain, WsStreamEnd, WsStreamStart, WsStreamText
-
-WsFrame = WsStreamStart | WsStreamText | WsStreamEnd | WsFloatingDomain
-
-
-class StreamFormatter:
-    """Convert `(event_type, data)` stream events into websocket frame models."""
-
-    def __init__(self, request_id: str) -> None:
-        self.request_id = request_id
-
-    async def format(
-        self,
-        event_stream: AsyncGenerator[tuple[str, Any], None],
-    ) -> AsyncGenerator[WsFrame, None]:
-        started = False
-
-        async for event_type, data in event_stream:
-            if event_type == "floating_domain":
-                if isinstance(data, dict):
-                    yield WsFloatingDomain(
-                        request_id=self.request_id,
-                        domain=data,
-                    )
-                continue
-
-            if event_type != "token":
-                continue
-
-            if not started:
-                yield WsStreamStart(request_id=self.request_id)
-                started = True
-
-            text = str(data or "")
-            if text:
-                yield WsStreamText(request_id=self.request_id, chunk=text)
-
-        if not started:
-            yield WsStreamStart(request_id=self.request_id)
-        yield WsStreamEnd(request_id=self.request_id)

services/chat/app/redis_consumer.py

@@ -1,209 +0,0 @@
-"""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

@@ -1,37 +0,0 @@
-"""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

@@ -1,304 +0,0 @@
-"""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

@@ -1,17 +0,0 @@
-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

@@ -1,36 +0,0 @@
-# ── 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

@@ -1,17 +0,0 @@
-# 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

@@ -1,173 +0,0 @@
-"""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"}))

services/ws-gateway/app/main.py

@@ -1,56 +0,0 @@
-"""WS Gateway — stateless WebSocket proxy.
-
-Accepts Electron device connections, authenticates JWT (RS256 public key),
-and routes frames between Electron and downstream services via Redis pub/sub.
-
-This service has NO business logic — it only routes JSON frames.
-"""
-
-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 shared.config import settings
-
-logging.basicConfig(
-    level=logging.INFO,
-    format="%(asctime)s %(levelname)s %(name)s: %(message)s",
-)
-
-
-@asynccontextmanager
-async def lifespan(app: FastAPI):
-    yield
-    from shared.redis import redis_client
-
-    await redis_client.aclose()
-
-
-def create_app() -> FastAPI:
-    app = FastAPI(
-        title="Adiuva WS Gateway",
-        version="0.1.0",
-        docs_url="/docs" if settings.ENV == "dev" else None,
-        redoc_url=None,
-        lifespan=lifespan,
-    )
-
-    from app.handler import router
-
-    app.include_router(router, prefix="/api/v1")
-
-    @app.get("/api/v1/health", tags=["health"])
-    async def health() -> dict:
-        return {"status": "ok", "service": "ws-gateway", "version": app.version}
-
-    return app
-
-
-app = create_app()

services/ws-gateway/app/redis_bridge.py

@@ -1,104 +0,0 @@
-"""Redis bridge — device registry + pub/sub routing.
-
-All inter-service communication passes through Redis:
-  - Device registry:   HSET/HDEL ws:devices:{user_id}
-  - Outbound frames:   Subscribe ws:out:{user_id}
-  - Chat requests:     Publish chat:request:{user_id}
-  - Batch requests:    Publish batch:request:{user_id}
-  - Tool results:      LPUSH tool:result:{call_id}
-"""
-
-from __future__ import annotations
-
-import json
-import logging
-
-from shared.redis import (
-    batch_request_channel,
-    chat_request_channel,
-    device_key,
-    redis_client,
-    tool_result_key,
-    ws_out_channel,
-)
-
-logger = logging.getLogger(__name__)
-
-# Instance ID for this gateway replica (set on startup)
-_GATEWAY_ID: str = ""
-
-
-def set_gateway_id(gid: str) -> None:
-    global _GATEWAY_ID
-    _GATEWAY_ID = gid
-
-
-def get_gateway_id() -> str:
-    return _GATEWAY_ID
-
-
-# ── Device Registry ──────────────────────────────────────────────────
-
-
-async def register_device(user_id: str, device_id: str) -> None:
-    """Register a connected device in Redis."""
-    key = device_key(user_id)
-    await redis_client.hset(key, mapping={
-        "device_id": device_id,
-        "gateway_id": _GATEWAY_ID,
-    })
-    logger.info("redis_bridge: registered user=%s device=%s gateway=%s", user_id, device_id, _GATEWAY_ID)
-
-
-async def unregister_device(user_id: str) -> None:
-    """Remove device registration from Redis."""
-    key = device_key(user_id)
-    await redis_client.delete(key)
-    logger.info("redis_bridge: unregistered user=%s", user_id)
-
-
-async def is_device_online(user_id: str) -> bool:
-    """Check if a device is registered."""
-    key = device_key(user_id)
-    return await redis_client.exists(key) > 0
-
-
-# ── Frame Routing ────────────────────────────────────────────────────
-
-
-async def publish_chat_request(user_id: str, frame: dict) -> None:
-    """Forward a chat request frame to the Chat Service via Redis."""
-    channel = chat_request_channel(user_id)
-    await redis_client.publish(channel, json.dumps(frame))
-    logger.debug("redis_bridge: published chat_request user=%s", user_id)
-
-
-async def publish_batch_request(user_id: str, frame: dict) -> None:
-    """Forward a batch request frame to the Batch Agent Service via Redis."""
-    channel = batch_request_channel(user_id)
-    await redis_client.publish(channel, json.dumps(frame))
-    logger.debug("redis_bridge: published batch_request user=%s", user_id)
-
-
-async def push_tool_result(call_id: str, result: dict) -> None:
-    """Push a tool_result to the Redis list for the waiting service.
-
-    Chat/Batch services do BRPOP on this key with a 30s timeout.
-    """
-    key = tool_result_key(call_id)
-    await redis_client.lpush(key, json.dumps(result))
-    # Auto-expire after 60s to prevent stale keys
-    await redis_client.expire(key, 60)
-    logger.debug("redis_bridge: pushed tool_result call_id=%s", call_id)
-
-
-async def subscribe_outbound(user_id: str):
-    """Return an async pubsub subscription for frames to send to Electron.
-
-    Chat/Batch services publish to ws:out:{user_id} and this gateway
-    forwards them to the connected WebSocket.
-    """
-    channel = ws_out_channel(user_id)
-    pubsub = redis_client.pubsub()
-    await pubsub.subscribe(channel)
-    return pubsub

services/ws-gateway/requirements.txt

@@ -1,8 +0,0 @@
-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
-redis>=5.0.0
-websockets>=14.0

shared/__init__.py

@@ -1,5 +0,0 @@
-"""Shared module — imported by all microservices.
-
-Contains DB engine/session, ORM models, Pydantic schemas, config,
-and Redis utilities. Changes here affect ALL services.
-"""

shared/agents/__init__.py

@@ -1 +0,0 @@
-"""Shared domain agents — tool definitions used by both Chat and Batch Agent services."""

shared/agents/note_agent.py

@@ -1,142 +0,0 @@
-"""Note agent — Markdown note management (list, get, create, update, delete).
-
-Shared tool definitions used by both Chat and Batch Agent services.
-"""
-
-from __future__ import annotations
-
-import re
-from typing import Any
-
-from langchain_core.tools import tool
-
-from shared.llm import embed
-from shared.ws_context import execute_on_client
-
-_UUID_RE = re.compile(
-    r"^[0-9a-fA-F]{8}-[0-9a-fA-F]{4}-[1-5][0-9a-fA-F]{3}-[89abAB][0-9a-fA-F]{3}-[0-9a-fA-F]{12}$"
-)
-
-
-def _is_uuid(value: str) -> bool:
-    return bool(_UUID_RE.match(value))
-
-NOTE_SYSTEM_PROMPT = (
-    "You are a note-taking assistant. You help users create, retrieve, update,\n"
-    "and delete Markdown notes in their workspace.\n\n"
-    "Rules:\n"
-    "  - content is always Markdown; preserve formatting when updating\n"
-    "  - project_id is optional; link a note to a project when mentioned\n"
-    "  - When updating, call get_note first if you need to read existing content\n"
-    "    before appending or replacing sections\n"
-    "  - list_notes without project_id returns all notes; scope with project_id\n"
-    "    when the user is working within a specific project\n"
-    "  - project_id must be a UUID; if you only know a project name, do not pass it as project_id\n"
-    "  - Do not fabricate note content — reflect what the user provides or what\n"
-    "    is already in the note (retrieved via get_note)."
-)
-
-
-@tool
-async def list_notes(project_id: str = "") -> str:
-    """List notes, optionally scoped to a project by project_id."""
-    normalized_project_id = project_id if (project_id and _is_uuid(project_id)) else ""
-    result = await execute_on_client(
-        action="select",
-        table="notes",
-        filters={"projectId": normalized_project_id or None},
-    )
-    rows = result.get("rows", [])
-    if not rows:
-        return "No notes found."
-    lines = [f"- {r['title']} (id: {r['id']})" for r in rows]
-    return f"Found {len(rows)} note(s):\n" + "\n".join(lines)
-
-
-@tool
-async def get_note(note_id: str) -> str:
-    """Fetch a single note by its UUID to read its full Markdown content."""
-    result = await execute_on_client(action="get", table="notes", data={"id": note_id})
-    row = result.get("row")
-    if not row:
-        return f"Note {note_id} not found."
-    return f"Note '{row['title']}' (id: {row['id']}):\n\n{row['content']}"
-
-
-@tool
-async def create_note(
-    title: str,
-    content: str,
-    project_id: str = "",
-) -> str:
-    """Create a new note.
-    title: note heading (required)
-    content: Markdown body text (required)
-    project_id: optional UUID linking this note to a project
-    """
-    result = await execute_on_client(
-        action="insert",
-        table="notes",
-        data={
-            "title": title,
-            "content": content,
-            "projectId": project_id or None,
-        },
-    )
-    row = result["row"]
-    # Index the note content in the vector store.
-    vector = await embed(content)
-    await execute_on_client(
-        action="vector_upsert",
-        data={"id": row["id"], "projectId": row.get("projectId"), "content": content},
-        vector=vector,
-    )
-    return f"Note created: '{row['title']}' (id: {row['id']})."
-
-
-@tool
-async def update_note(
-    note_id: str,
-    title: str = "",
-    content: str = "",
-) -> str:
-    """Update an existing note. Only pass fields that should change.
-    note_id: UUID of the note (required)
-    If you need to preserve existing content, call get_note first.
-    """
-    updates: dict[str, Any] = {}
-    if title:
-        updates["title"] = title
-    if content:
-        updates["content"] = content
-    result = await execute_on_client(
-        action="update",
-        table="notes",
-        data={"id": note_id, "updates": updates},
-    )
-    row = result["row"]
-    # Re-index if content changed.
-    if content:
-        vector = await embed(content)
-        await execute_on_client(
-            action="vector_upsert",
-            data={"id": note_id, "projectId": row.get("projectId"), "content": content},
-            vector=vector,
-        )
-    return f"Note updated: '{row['title']}' (id: {row['id']})."
-
-
-@tool
-async def delete_note(note_id: str) -> str:
-    """Delete a note permanently by its UUID."""
-    await execute_on_client(action="delete", table="notes", data={"id": note_id})
-    return f"Note {note_id} deleted."
-
-
-NOTE_TOOLS: list[Any] = [
-    list_notes,
-    get_note,
-    create_note,
-    update_note,
-    delete_note,
-]

shared/agents/project_agent.py

@@ -1,146 +0,0 @@
-"""Project agent — full lifecycle management (list, get, create, update, archive, delete).
-
-Shared tool definitions used by both Chat and Batch Agent services.
-"""
-
-from __future__ import annotations
-
-from typing import Any
-
-from langchain_core.tools import tool
-
-from shared.ws_context import execute_on_client
-
-PROJECT_SYSTEM_PROMPT = (
-    "You are a project management assistant. You help users create, find,\n"
-    "update, and archive projects in their workspace.\n\n"
-    "Rules:\n"
-    "  - status must be one of: active, archived\n"
-    "  - client_id is optional; link to a client only when explicitly mentioned\n"
-    "  - ai_summary is populated only when the user asks for a project summary;\n"
-    "    derive it from context data — do not fabricate content\n"
-    "  - Use list_projects for scoped queries; list_all_projects only when the\n"
-    "    user wants a complete cross-client view including archived projects\n"
-    "  - get_project requires a project UUID; resolve the ID first by calling\n"
-    "    list_projects if you only have a project name\n"
-    "  - Prefer archiving (update_project status=archived) over deletion;\n"
-    "    only call delete_project when the user explicitly confirms deletion."
-)
-
-
-@tool
-async def list_projects(
-    client_id: str = "",
-    include_archived: int = 0,
-) -> str:
-    """List projects, optionally filtered by client_id.
-    include_archived: 1 to include archived projects, 0 for active only (default).
-    """
-    result = await execute_on_client(
-        action="select",
-        table="projects",
-        filters={
-            "clientId": client_id or None,
-            "includeArchived": bool(include_archived),
-        },
-    )
-    rows = result.get("rows", [])
-    if not rows:
-        return "No projects found."
-    lines = [f"- {r['name']} (status: {r['status']}, id: {r['id']})" for r in rows]
-    return f"Found {len(rows)} project(s):\n" + "\n".join(lines)
-
-
-@tool
-async def list_all_projects() -> str:
-    """List every project regardless of client or status.
-    Use only when the user wants a complete cross-client overview.
-    """
-    result = await execute_on_client(action="select", table="projects")
-    rows = result.get("rows", [])
-    if not rows:
-        return "No projects found."
-    lines = [f"- {r['name']} (status: {r['status']}, id: {r['id']})" for r in rows]
-    return f"All projects ({len(rows)}):\n" + "\n".join(lines)
-
-
-@tool
-async def get_project(project_id: str) -> str:
-    """Fetch a single project by its UUID."""
-    result = await execute_on_client(action="get", table="projects", data={"id": project_id})
-    row = result.get("row")
-    if not row:
-        return f"Project {project_id} not found."
-    return (
-        f"Project: '{row['name']}' (id: {row['id']}, status: {row['status']}, "
-        f"clientId: {row.get('clientId', 'none')})"
-    )
-
-
-@tool
-async def create_project(
-    name: str,
-    client_id: str = "",
-) -> str:
-    """Create a new project.
-    name: human-readable project name (required)
-    client_id: optional UUID of the owning client
-    """
-    result = await execute_on_client(
-        action="insert",
-        table="projects",
-        data={"name": name, "clientId": client_id or None},
-    )
-    row = result["row"]
-    return f"Project created: '{row['name']}' (id: {row['id']})"
-
-
-@tool
-async def update_project(
-    project_id: str,
-    name: str = "",
-    client_id: str = "",
-    status: str = "",
-    ai_summary: str = "",
-) -> str:
-    """Update a project. Only pass fields that should change.
-    project_id: UUID of the project (required)
-    status: active | archived
-    ai_summary: AI-generated summary text (populate only when explicitly requested)
-    """
-    updates: dict[str, Any] = {}
-    if name:
-        updates["name"] = name
-    if client_id:
-        updates["clientId"] = client_id
-    if status:
-        updates["status"] = status
-    if ai_summary:
-        updates["aiSummary"] = ai_summary
-    result = await execute_on_client(
-        action="update",
-        table="projects",
-        data={"id": project_id, "updates": updates},
-    )
-    row = result["row"]
-    return f"Project updated: '{row['name']}' (id: {row['id']}, status: {row['status']})"
-
-
-@tool
-async def delete_project(project_id: str) -> str:
-    """Permanently delete a project and orphan its tasks.
-    IMPORTANT: prefer update_project(status='archived') unless the user
-    has explicitly confirmed they want permanent deletion.
-    """
-    await execute_on_client(action="delete", table="projects", data={"id": project_id})
-    return f"Project {project_id} permanently deleted."
-
-
-PROJECT_TOOLS: list[Any] = [
-    list_projects,
-    list_all_projects,
-    get_project,
-    create_project,
-    update_project,
-    delete_project,
-]

shared/agents/task_agent.py

@@ -1,239 +0,0 @@
-"""Task agent — full CRUD for tasks and task comments.
-
-Shared tool definitions used by both Chat and Batch Agent services.
-"""
-
-from __future__ import annotations
-
-from datetime import datetime, timezone
-import re
-from typing import Any
-
-from langchain_core.tools import tool
-
-from shared.ws_context import execute_on_client
-
-_UUID_RE = re.compile(
-    r"^[0-9a-fA-F]{8}-[0-9a-fA-F]{4}-[1-5][0-9a-fA-F]{3}-[89abAB][0-9a-fA-F]{3}-[0-9a-fA-F]{12}$"
-)
-
-
-def _is_uuid(value: str) -> bool:
-    return bool(_UUID_RE.match(value))
-
-TASK_SYSTEM_PROMPT = (
-    "You are a task management assistant for a project workspace.\n"
-    "You create, update, list, and track tasks and their comments.\n\n"
-    "Rules:\n"
-    "  - status must be one of: todo, in_progress, done\n"
-    "  - priority must be one of: high, medium, low\n"
-    "  - due_date is a Unix timestamp in milliseconds; convert human dates\n"
-    "  - assignees is a JSON-encoded array of strings (e.g. '[\"Alice\",\"Bob\"]')\n"
-    "  - project_id is optional; link to a project when the user mentions one\n"
-    "  - is_ai_suggested: 1 only when proactively proposing a task the user\n"
-    "    did not explicitly request; 0 otherwise\n"
-    "  - Use list_tasks_due_today for 'what's due today' queries\n"
-    "  - For update_task, use -1 for integer fields you do not want to change\n"
-    "  - Always confirm the action in plain, user-friendly language."
-)
-
-
-# ── Task tools ────────────────────────────────────────────────────────
-
-
-@tool
-async def list_tasks(
-    project_id: str = "",
-    status: str = "",
-    search: str = "",
-    order_by: str = "",
-) -> str:
-    """List tasks, optionally filtered by project_id, status (todo|in_progress|done),
-    a search string, or an order_by field name (dueDate|priority|createdAt)."""
-    normalized_project_id = project_id if (project_id and _is_uuid(project_id)) else ""
-    result = await execute_on_client(
-        action="select",
-        table="tasks",
-        filters={
-            "projectId": normalized_project_id or None,
-            "status": status or None,
-            "search": search or None,
-            "orderBy": order_by or None,
-        },
-    )
-    rows = result.get("rows", [])
-    if not rows:
-        return "No tasks found matching the given filters."
-    lines = [
-        f"- {r['title']} (status: {r['status']}, priority: {r['priority']}, id: {r['id']})"
-        for r in rows
-    ]
-    return f"Found {len(rows)} task(s):\n" + "\n".join(lines)
-
-
-@tool
-async def create_task(
-    title: str,
-    description: str = "",
-    status: str = "todo",
-    priority: str = "medium",
-    assignees: str = "[]",
-    due_date: int = 0,
-    project_id: str = "",
-    is_ai_suggested: int = 0,
-) -> str:
-    """Create a new task.
-    title: task title (required)
-    description: optional details
-    status: todo | in_progress | done  (default: todo)
-    priority: high | medium | low  (default: medium)
-    assignees: JSON-encoded array of assignee names, e.g. '["Alice"]'
-    due_date: Unix timestamp in milliseconds; 0 means no due date
-    project_id: optional UUID of the parent project
-    is_ai_suggested: 1 if proactively suggested, 0 if user-requested
-    """
-    result = await execute_on_client(
-        action="insert",
-        table="tasks",
-        data={
-            "title": title,
-            "description": description or None,
-            "status": status,
-            "priority": priority,
-            "assignee": assignees,
-            "dueDate": due_date or None,
-            "projectId": project_id or None,
-            "isAiSuggested": is_ai_suggested,
-        },
-    )
-    row = result["row"]
-    return (
-        f"Task created: '{row['title']}' "
-        f"(id: {row['id']}, status: {row['status']}, priority: {row['priority']})"
-    )
-
-
-@tool
-async def update_task(
-    task_id: str,
-    title: str = "",
-    description: str = "",
-    status: str = "",
-    priority: str = "",
-    assignees: str = "",
-    due_date: int = -1,
-    project_id: str = "",
-) -> str:
-    """Update fields on an existing task. Only pass fields you want to change.
-    task_id: the task's UUID (required)
-    due_date: -1 means unchanged; 0 clears the due date; any positive value sets it
-    """
-    updates: dict[str, Any] = {}
-    if title:
-        updates["title"] = title
-    if description:
-        updates["description"] = description
-    if status:
-        updates["status"] = status
-    if priority:
-        updates["priority"] = priority
-    if assignees:
-        updates["assignee"] = assignees
-    if due_date != -1:
-        updates["dueDate"] = due_date or None
-    if project_id:
-        updates["projectId"] = project_id
-    result = await execute_on_client(
-        action="update",
-        table="tasks",
-        data={"id": task_id, "updates": updates},
-    )
-    row = result["row"]
-    return f"Task updated: '{row['title']}' (id: {row['id']}, status: {row['status']})"
-
-
-@tool
-async def delete_task(task_id: str) -> str:
-    """Delete a task permanently by its UUID."""
-    await execute_on_client(action="delete", table="tasks", data={"id": task_id})
-    return f"Task {task_id} deleted."
-
-
-@tool
-async def list_tasks_due_today() -> str:
-    """List all tasks whose due date falls on today's date."""
-    now = datetime.now(tz=timezone.utc)
-    start_ms = int(datetime(now.year, now.month, now.day, tzinfo=timezone.utc).timestamp() * 1000)
-    end_ms = start_ms + 86_400_000 - 1  # last ms of today
-    result = await execute_on_client(
-        action="select",
-        table="tasks",
-        filters={"dueDateFrom": start_ms, "dueDateTo": end_ms},
-    )
-    rows = result.get("rows", [])
-    if not rows:
-        return "No tasks are due today."
-    lines = [
-        f"- {r['title']} (priority: {r['priority']}, status: {r['status']}, id: {r['id']})"
-        for r in rows
-    ]
-    return f"Tasks due today ({len(rows)}):\n" + "\n".join(lines)
-
-
-# ── Task comment tools ────────────────────────────────────────────────
-
-
-@tool
-async def list_task_comments(task_id: str) -> str:
-    """List all comments on a task by its UUID."""
-    result = await execute_on_client(
-        action="select",
-        table="taskComments",
-        filters={"taskId": task_id},
-    )
-    rows = result.get("rows", [])
-    if not rows:
-        return f"No comments found for task {task_id}."
-    lines = [f"- [{r['author']}]: {r['content']} (id: {r['id']})" for r in rows]
-    return f"Found {len(rows)} comment(s):\n" + "\n".join(lines)
-
-
-@tool
-async def add_task_comment(task_id: str, author: str, content: str) -> str:
-    """Add a comment to a task.
-    task_id: UUID of the task to comment on
-    author: name or ID of the comment author
-    content: comment text
-    """
-    result = await execute_on_client(
-        action="insert",
-        table="taskComments",
-        data={"taskId": task_id, "author": author, "content": content},
-    )
-    row = result.get("row", {})
-    row_author = row.get("author", author)
-    row_task_id = row.get("taskId") or row.get("task_id") or task_id
-    row_comment_id = row.get("id", "unknown")
-    return f"Comment added by {row_author} on task {row_task_id} (comment id: {row_comment_id})."
-
-
-@tool
-async def delete_task_comment(comment_id: str) -> str:
-    """Delete a task comment by its UUID."""
-    await execute_on_client(action="delete", table="taskComments", data={"id": comment_id})
-    return f"Comment {comment_id} deleted."
-
-
-# ── Exports ───────────────────────────────────────────────────────────
-
-
-TASK_TOOLS: list[Any] = [
-    list_tasks,
-    create_task,
-    update_task,
-    delete_task,
-    list_tasks_due_today,
-    list_task_comments,
-    add_task_comment,
-    delete_task_comment,
-]

shared/agents/timeline_agent.py

@@ -1,116 +0,0 @@
-"""Timeline agent — project milestone management (list, create, update, delete).
-
-Shared tool definitions used by both Chat and Batch Agent services.
-"""
-
-from __future__ import annotations
-
-import re
-from typing import Any
-
-from langchain_core.tools import tool
-
-from shared.ws_context import execute_on_client
-
-_UUID_RE = re.compile(
-    r"^[0-9a-fA-F]{8}-[0-9a-fA-F]{4}-[1-5][0-9a-fA-F]{3}-[89abAB][0-9a-fA-F]{3}-[0-9a-fA-F]{12}$"
-)
-
-
-def _is_uuid(value: str) -> bool:
-    return bool(_UUID_RE.match(value))
-
-TIMELINE_SYSTEM_PROMPT = (
-    "You are a project timeline assistant. Timelines are milestone dates that\n"
-    "track progress on a project — they are not calendar events.\n\n"
-    "Rules:\n"
-    "  - project_id is REQUIRED for every create; confirm with the user if unknown\n"
-    "  - For listing, project_id must be a UUID; never pass plain names as project_id\n"
-    "  - date is a Unix timestamp in milliseconds; convert human-readable dates\n"
-    "  - is_ai_suggested: 1 when proactively proposing a timeline, 0 otherwise\n"
-    "  - For update_timeline, use -1 for integer fields you do not want to change\n"
-    "  - Listing without a project_id returns all timelines across projects\n"
-    "  - Always echo the title and formatted date in your confirmation."
-)
-
-
-@tool
-async def list_timelines(project_id: str = "") -> str:
-    """List timelines. Provide project_id to scope to a specific project."""
-    normalized_project_id = project_id if (project_id and _is_uuid(project_id)) else ""
-    result = await execute_on_client(
-        action="select",
-        table="timelines",
-        filters={"projectId": normalized_project_id or None},
-    )
-    rows = result.get("rows", [])
-    if not rows:
-        return "No timelines found."
-    lines = [f"- {r['title']} (date: {r['date']}, id: {r['id']})" for r in rows]
-    return f"Found {len(rows)} timeline(s):\n" + "\n".join(lines)
-
-
-@tool
-async def create_timeline(
-    project_id: str,
-    title: str,
-    date: int,
-    is_ai_suggested: int = 0,
-) -> str:
-    """Create a project timeline (milestone).
-    project_id: REQUIRED UUID of the parent project
-    title: descriptive name for the milestone
-    date: Unix timestamp in milliseconds
-    is_ai_suggested: 1 if proactively suggested, 0 if user-requested
-    """
-    result = await execute_on_client(
-        action="insert",
-        table="timelines",
-        data={
-            "projectId": project_id,
-            "title": title,
-            "date": date,
-            "isAiSuggested": is_ai_suggested,
-        },
-    )
-    row = result["row"]
-    return f"Timeline created: '{row['title']}' (id: {row['id']}, date: {row['date']})"
-
-
-@tool
-async def update_timeline(
-    timeline_id: str,
-    title: str = "",
-    date: int = -1,
-) -> str:
-    """Update a timeline. Only pass fields that should change.
-    timeline_id: UUID of the timeline (required)
-    date: -1 means unchanged; any other value sets the new date (ms timestamp)
-    """
-    updates: dict[str, Any] = {}
-    if title:
-        updates["title"] = title
-    if date != -1:
-        updates["date"] = date
-    result = await execute_on_client(
-        action="update",
-        table="timelines",
-        data={"id": timeline_id, "updates": updates},
-    )
-    row = result["row"]
-    return f"Timeline updated: '{row['title']}' (id: {row['id']})"
-
-
-@tool
-async def delete_timeline(timeline_id: str) -> str:
-    """Delete a timeline permanently by its UUID."""
-    await execute_on_client(action="delete", table="timelines", data={"id": timeline_id})
-    return f"Timeline {timeline_id} deleted."
-
-
-TIMELINE_TOOLS: list[Any] = [
-    list_timelines,
-    create_timeline,
-    update_timeline,
-    delete_timeline,
-]

shared/config.py

@@ -1,99 +0,0 @@
-"""Shared configuration — Pydantic Settings loaded from environment.
-
-All services import ``settings`` from here.  Each service only uses a subset
-of the vars, but keeping one Settings class avoids fragmentation.
-"""
-
-from pathlib import Path
-from typing import Literal
-
-from pydantic import field_validator
-from pydantic_settings import BaseSettings, SettingsConfigDict
-
-# Locate the repo root (adiuva-api/) so we can load its .env as a fallback.
-# Works whether cwd is adiuva-api/ (monolith) or adiuva-api/services/xyz/ (microservice).
-_this_dir = Path(__file__).resolve().parent  # shared/
-_repo_root = _this_dir.parent               # adiuva-api/
-_root_env = _repo_root / ".env"
-
-
-class Settings(BaseSettings):
-    # ── Database ─────────────────────────────────────────────────────
-    DATABASE_URL: str = "postgresql+asyncpg://postgres:postgres@localhost:5432/adiuva"
-
-    # ── JWT ────────────────────────────────────────────────────────
-    # RS256 public key (PEM). Used by any service that needs to verify
-    # JWTs locally (optional — Traefik ForwardAuth handles this in prod).
-    # The private key lives ONLY in the Auth Service config.
-    JWT_PUBLIC_KEY: str = ""
-
-    @field_validator("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
-
-    JWT_ACCESS_TOKEN_EXPIRE_MINUTES: int = 30
-    JWT_REFRESH_TOKEN_EXPIRE_DAYS: int = 30
-
-    # ── Redis ────────────────────────────────────────────────────────
-    REDIS_URL: str = "redis://localhost:6379/0"
-
-    # ── Stripe ───────────────────────────────────────────────────────
-    STRIPE_SECRET_KEY: str = ""
-    STRIPE_WEBHOOK_SECRET: str = ""
-
-    # ── S3 ───────────────────────────────────────────────────────────
-    S3_BUCKET: str = ""
-    S3_REGION: str = "us-east-1"
-    S3_ENDPOINT_URL: str = ""
-    AWS_ACCESS_KEY_ID: str = ""
-    AWS_SECRET_ACCESS_KEY: str = ""
-
-    # ── Vector stores ────────────────────────────────────────────────
-    PINECONE_API_KEY: str = ""
-    PINECONE_INDEX: str = "adiuva"
-    QDRANT_URL: str = ""
-    QDRANT_API_KEY: str = ""
-
-    # ── LLM providers ────────────────────────────────────────────────
-    OPENAI_API_KEY: str = ""
-    ANTHROPIC_API_KEY: str = ""
-    GOOGLE_API_KEY: str = ""
-    CEREBRAS_API_KEY: str = ""
-    GITHUB_TOKEN: str = ""
-
-    LLM_MODEL: str = "gpt-4o"
-    LLM_EMBED_MODEL: str = "text-embedding-3-small"
-
-    GITHUB_COPILOT_TOKEN_DIR: str = ""
-
-    # ── OAuth (integrations) ─────────────────────────────────────────
-    GMAIL_CLIENT_ID: str = ""
-    GMAIL_CLIENT_SECRET: str = ""
-    MS_CLIENT_ID: str = ""
-    MS_CLIENT_SECRET: str = ""
-    MS_TENANT_ID: str = "common"
-    OAUTH_ENCRYPTION_KEY: str = ""
-
-    # ── Langfuse (observability) ─────────────────────────────────────
-    LANGFUSE_SECRET_KEY: str = ""
-    LANGFUSE_PUBLIC_KEY: str = ""
-    LANGFUSE_HOST: str = "https://cloud.langfuse.com"
-
-    # ── CORS ─────────────────────────────────────────────────────────
-    CORS_ORIGINS: list[str] = ["app://.", "http://localhost:3000", "http://localhost:5173"]
-
-    # ── Environment ──────────────────────────────────────────────────
-    ENV: Literal["dev", "prod"] = "dev"
-
-    model_config = SettingsConfigDict(
-        # Local .env (cwd) takes priority; root .env is fallback.
-        env_file=(".env", str(_root_env)),
-        env_file_encoding="utf-8",
-        extra="ignore",
-    )
-
-
-settings = Settings()