Bryan Van Deusen
aebb6baa2c
First cut of the agentic briefing redesign. Morning compilation can now route through a tool-call loop that grounds every factual claim in an actual tool result, eliminating the hallucinated meetings, tasks, and news items the legacy one-shot path was producing. Behind a per-user `briefing_mode` setting (default "legacy"); falls back to the legacy path automatically if the new path returns empty (e.g. model too weak to drive tool calls reliably). New: services/briefing_tools.py — explicit read-only allowlist of 10 tools (tasks, events, weather, rss, projects, notes). New tools added to tools.py must be opted in by name. Excludes all mutating tools and external search tools (search_images, search_web, research_topic) which are neither useful nor safe for a scheduled background job. New: briefing_pipeline.run_agentic_briefing — wraps the existing stream_chat_with_tools loop with slot-specific system prompts that tell the model to only assert facts from tool results and to be honest when tools return nothing. Max 8 rounds, per-round exception handling, returns the full message list so tool-call receipts can be persisted alongside the prose in a later PR. Sentence-count floors bumped: compilation 6–10 (was 4–8), check-ins 3–5 (was 2–3). Weekly review 5–8. Design doc: docs/2026-04-10-agentic-briefing-design.md Out of scope for this PR (future PRs): slot-injection migration, persisting tool-call receipts into the conversation so chat follow-ups see them, UI polish for tool-call status, sidecar storage for briefings. See the design doc's migration path for details. Enable on an account with: UPDATE settings SET value='agentic' WHERE user_id=<id> AND key='briefing_mode'; or insert the row if missing. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Fabled Assistant
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.