AI-powered learning system with spaced repetition
Project description
open-cognition
AI-powered learning system with spaced repetition. Study with LLMs, capture knowledge, review with science-backed methods.
The Problem
When you study with AI (Claude, ChatGPT, etc.), the knowledge gets lost. You learn in one place, take notes in another, create flashcards in a third, and review in isolation. There's no system connecting the dots.
How it Works
open-cognition bridges the gap between AI-assisted learning and retention:
- Study with your LLM — Claude acts as your Socratic sparring partner via MCP
- Capture outputs — flashcards, summaries, resources, and artifacts are saved automatically
- Organize in a knowledge graph — topics and subtopics you control
- Review with spaced repetition — SM-2 algorithm (same foundation as Anki)
- Deepen with Feynman technique — structured sessions that identify and close knowledge gaps
Quick Start
Prerequisites
- Python 3.13+
- uv
That's it. No database server required — open-cognition uses an embedded local database file by default. Your data stays on your machine.
Install and Run
# Run directly (no clone needed)
uvx open-cognition serve
Open http://localhost:8080 and start learning.
Using with Claude Desktop
This is where open-cognition shines. Connect it to Claude Desktop and get a learning partner that knows your knowledge graph.
1. Set up the MCP Server
Add to your Claude Desktop config (~/Library/Application Support/Claude/claude_desktop_config.json on macOS):
{
"mcpServers": {
"open-cognition": {
"command": "uvx",
"args": ["open-cognition", "mcp"]
}
}
}
That's it — uvx handles installation and dependencies automatically.
2. Add the Skill
Download SKILL.md and add it to your Claude Desktop skills, or include it in your project's .claude/skills/ folder.
The skill instructs Claude to:
- Be a Socratic sparring partner during study sessions — not a lecturer
- Use the Feynman technique to identify and close knowledge gaps
- Create high-quality flashcards following strict rules (atomic, active recall, concise)
- Never create topics or cards without your approval
- Track your doubts and struggling cards
3. Start Learning
Talk to Claude naturally:
- "Let's study Transformers" → starts a session, loads your existing knowledge
- "Create flashcards from what we discussed" → proposes cards for your approval
- "Let's do a Feynman session on Attention" → structured gap-finding protocol
- "What doubts do I have open?" → reviews pending questions
- "Which cards am I struggling with?" → identifies weak spots
Features
Web UI
- Dashboard — due cards count, struggling cards, quick actions
- Topics — create, edit, delete, hierarchical subtopics
- Review session — flashcard review with SM-2, quality buttons, progress tracking
- Artifacts — markdown + mermaid rendering in modal viewer
- Resources — links, PDFs, videos organized by topic
- Doubts — capture questions during review, work them later with the LLM
- Struggling cards — analytics on most-errored flashcards
- Copyable IDs — click any entity ID to copy (e.g.,
topic:abc123) for LLM reference
MCP Tools (18 tools)
| Category | Tools |
|---|---|
| Topics | get_topics, create_topic, update_topic, relate_topics |
| Flashcards | get_flashcards, get_due_flashcards, create_flashcard, create_flashcards_batch, review_flashcard, get_struggling_cards |
| Resources | get_resources, create_resource |
| Artifacts | get_artifacts, create_artifact |
| Doubts | get_doubts, create_doubt, resolve_doubt |
| Sessions | start_session, end_session, get_session_logs |
Spaced Repetition (SM-2)
The same algorithm behind Anki. Cards you answer correctly get longer intervals; cards you miss reset to 1 day. The ease factor adapts per card — easy cards space out faster, hard cards stay frequent.
See docs/sm2.md for the full algorithm with formulas and examples.
Configuration
| Variable | Default | Description |
|---|---|---|
OC_HOST |
0.0.0.0 |
Web server host |
OC_PORT |
8080 |
Web server port |
OC_DATA_DIR |
~/.open-cognition |
Data directory |
SURREAL_URL |
— | SurrealDB connection URL (optional — uses embedded DB if not set) |
SURREAL_USER |
root |
SurrealDB username |
SURREAL_PASSWORD |
root |
SurrealDB password |
SURREAL_NAMESPACE |
open-cognition |
SurrealDB namespace |
SURREAL_DATABASE |
test |
SurrealDB database |
You can set these in a .env file or as environment variables.
External SurrealDB (optional)
By default, open-cognition stores everything in a local file. If you prefer a standalone SurrealDB server (for multi-device access, backups, or production):
# Via Docker
docker compose up -d
# Or point to your existing instance
export SURREAL_URL=ws://localhost:8000/rpc
CLI
uvx open-cognition serve [--host HOST] [--port PORT] # Start the web app
uvx open-cognition mcp # Start the MCP server
uvx open-cognition --version # Show version
uvx open-cognition --help # Show help
Architecture
Claude (LLM) ──► MCP Tools ──┐
├──► Services ──► Repositories ──► SurrealDB
Web UI (HTMX) ──► API Routes ┘
MCP tools and API routes share the same services layer. The LLM and the web UI see the same data.
See docs/architecture.md for details.
Development
git clone https://github.com/lfnovo/open-cognition.git
cd open-cognition
uv sync
open-cognition serve
Run tests:
uv run pytest
Documentation
- Architecture — layers, data model, graph relations
- Concepts — topics, flashcards, resources, artifacts, sessions, doubts
- SM-2 Algorithm — spaced repetition formulas and examples
- API Reference — REST endpoints
- MCP Tools — tools for LLM integration
Stack
| Layer | Technology |
|---|---|
| Backend | Python + FastAPI |
| Database | SurrealDB (embedded file or server) |
| Frontend | HTMX + Jinja2 + Tailwind CSS |
| MCP | FastMCP |
| LLM | Claude (via skill + MCP) |
License
MIT
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