bvandeusen
6be7328d8c
The backend foundation for curator-proposed mutations awaiting user
approval. No tools route to this yet — that's C3's job. This commit
just lands the schema and the service API everything else will use.
Migration 0051 — new table:
- id, user_id (CASCADE), conv_id (SET NULL — survives conv deletion).
- action_type (the tool name to replay), target_type/target_id/
target_label (display hints).
- payload (jsonb — the curator's proposed args, replayed verbatim
on approval).
- current_snapshot (jsonb — the target's state at proposal time, so
the review UI can render an honest diff even if other work modified
the entity between proposal and review).
- status ('pending' / 'approved' / 'rejected') + CHECK constraint.
- created_at / reviewed_at.
- Partial index ix_pending_curator_actions_user_pending narrowed to
status='pending' — the Needs Review panel hits this constantly,
history rows just accumulate.
Model: PendingCuratorAction with to_dict() for API serialization.
Service services/pending_actions.py:
- create_pending(...) — called from the curator interceptor (C3).
Accepts an already-fetched current_snapshot so each mutating tool
can capture target state in its own way (notes vs milestones vs
profile have different shapes).
- list_pending(user_id, limit=50) — what the Needs Review panel reads.
- approve(action_id, user_id) — replays via execute_tool and marks
approved on success. Stays pending on replay error so the user
can retry. NOTE: approve passes the request through execute_tool
unchanged for now; C3 will add authority='user' so the upcoming
curator interceptor doesn't re-intercept the replay and loop.
- reject(action_id, user_id) — marks rejected with no execution.
C3 next: wires the curator interceptor (authority='curator' on
execute_tool routes mutating tools to create_pending instead of
running them), adds the mutating tools back to the curator's
allowlist, and updates approve() to pass authority='user'.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Fabled Scribe
A self-hosted second brain and project management application with integrated LLM capabilities. Write, organise, and act on your notes and tasks with the help of a local AI assistant — all running on your own hardware.
Features
Notes and tasks with a Markdown editor, sub-tasks, milestones, and kanban project workspaces. AI chat with streaming responses, RAG over your notes, and tool use (web search, calendar, weather). A daily briefing that digests your tasks, RSS feeds, and weather on a schedule. Knowledge graph, per-user/group sharing, PWA with push notifications, an MCP server for external AI clients, and an Android companion app.
Quick Start
Prerequisites: Docker and Docker Compose. 8 GB+ RAM recommended for LLM inference.
Download docker-compose.quickstart.yml from this repo, then:
# Optional but recommended — set a secret key
export SECRET_KEY=your-random-secret-here
docker compose -f docker-compose.quickstart.yml up -d
Open http://localhost:5000. The first user to register becomes admin. Go to Settings → General to pull an LLM model — qwen3:8b or llama3.1:8b are good starting points.
GPU: Ollama runs CPU-only by default. See the comments in
docker-compose.quickstart.ymlto enable NVIDIA GPU passthrough.
Development: To build from source, see Development.
Documentation
| Doc | Contents |
|---|---|
| Architecture | Stack, design decisions, data models, key services |
| Configuration | Environment variables, Docker Compose, production setup, security |
| Features | Detailed feature breakdown and keyboard shortcuts |
| Development | Dev workflow, CI/CD, migrations, release process |
| API Keys & MCP | API key management and Fable MCP install guide |
| SSO / OAuth | OIDC setup for Authentik, Keycloak, and other providers |
| API Reference | All REST API endpoints |
| Android App | Flutter companion app architecture and feature status |
License
This project is privately maintained.