bvandeusen
1f6c592226
The milestone becomes the plan container: a new nullable milestones.body holds the design/intent (Goal/Approach/Verification) and individual steps live as first-class child tasks (milestone_id) instead of checkboxes crammed into one kind=plan task body. start_planning now creates a MILESTONE seeded with the body template (not a kind=plan task) and returns it with applicable rules; a new get_milestone MCP tool reads the plan back (body + steps + rules). kind=plan is hard-retired going forward — start_planning never creates one. The 'plan' task_kind enum value stays valid so the 11 historical plan-tasks remain readable in place; no body-shredding backfill (corpus review showed auto-splitting their checklists into tasks would be lossy: embedded code blocks, a non-binary [~] state, tables, ID-encoded hierarchy). - migration 0066: add milestones.body - model/service/route/MCP: body passthrough on create+update; get_milestone - server _INSTRUCTIONS: "plan" = milestone w/ body + child step-tasks - UI: ProjectView shows/edits a milestone's plan body; start_planning expands the new milestone and opens its plan editor - tests updated to the milestone contract + new body/get_milestone coverage Co-Authored-By: Claude Opus 4.8 (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, and an MCP server for external AI clients.
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 |
License
This project is privately maintained.