bvandeusen
fa97ade8e3
The architecture loop closes. Curator extracts beats and writes a ≤240-char summary; the next chat turn loads that summary into the journal system prompt so the chat model — which has no tools and cannot retrieve anything itself — gains awareness of recent topics captured by the curator. Migration 0049: - conversations.curator_summary (text, nullable). Last-write-wins; no history of prior summaries. models/conversation.py: - New curator_summary column on Conversation. services/curator_scheduler.py: - _stamp_last_run() takes an optional summary kwarg; persists it when non-empty (clobbering the previous summary). Empty summary keeps the existing one rather than overwriting useful context with "". - _sweep() passes result.summary through. routes/journal.py: - Manual /api/journal/curator/run/<conv_id> writes curator_summary alongside last_curator_run_at on success. services/journal_pipeline.py: - build_journal_system_prompt() gains an optional `conv_id` param. When provided, appends a "CURATOR NOTES" block at the end of the system prompt with the conversation's stored summary. Positioned after ambient context so the chat model treats it as current awareness rather than background. services/llm.py: - Threads conv_id through to build_journal_system_prompt. This is the last commit of the conversation+curator architecture arc (Fable #172): - Phase 1a (a7002a8): chat=tools[], curator service backend - Phase 1b (a73dd17): right-rail captures panel + manual trigger - Phase 2 (83f1676): auto-scheduler every 15 min - Phase 3 (this): curator summary → chat context feedback loop Operator can now device-test the architecture end-to-end: have a journal conversation (model can't lie about tool calls because it has none), wait for the scheduler or hit "Process captures", see moments appear in the right rail, then continue the conversation and notice the chat model staying topic-aware via the summary block. 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.