# Agent stores a learning
memory_store(content="pg_dump ignores --schema when used with --table",
             tags=["postgresql", "backup"], category="gotchas")

# Any agent, any machine, any session — finds it instantly
memory_search(query="postgres backup gotcha")

graph TB
    subgraph Agents
        A1[Claude Code<br/>Machine A]
        A2[Claude Code<br/>Machine B]
        A3[Cursor / Windsurf<br/>Machine C]
    end

    A1 & A2 & A3 -->|MCP over HTTPS| NB

    subgraph nobrainr [nobrainr server :8420]
        NB[FastMCP + JSON API]
        NB --> EMB[Ollama<br/>nomic-embed-text]
        NB --> EXT[Ollama<br/>qwen3.5:9b<br/>Entity Extraction]
        NB --> PG[(PostgreSQL 18<br/>+ pgvector)]
    end

    subgraph Storage [Knowledge]
        PG --> MEM[Memories<br/>vector similarity]
        PG --> KG[Knowledge Graph<br/>entities + relations]
        PG --> EVT[Events + Feedback]
    end

    NB --> DASH[Vue 3 Dashboard<br/>Graph · Memories · Timeline]

    style nobrainr fill:#1a1a2e,stroke:#16213e,color:#e6e6e6
    style Agents fill:#0d1117,stroke:#30363d,color:#e6e6e6
    style Storage fill:#0d1117,stroke:#30363d,color:#e6e6e6

git clone https://github.com/youruser/nobrainr.git
cd nobrainr
cp .env.example .env

# Edit .env — at minimum, set a real POSTGRES_PASSWORD
$EDITOR .env

# Start everything
docker compose up -d

# Wait for llama-swap to load the model stack (one-time on first start)
docker compose logs -f llama-swap

# Verify
curl -sf http://localhost:8420/api/stats | jq .total_memories

# Start only the infrastructure
docker compose up -d postgres llama-swap

# Run the backend locally
uv sync
uv run nobrainr serve

# Or run the dashboard too
cd dashboard && npm install && npm run dev

{
  "mcpServers": {
    "nobrainr": {
      "type": "http",
      "url": "https://<your-domain>/mcp"
    }
  }
}

{
  "mcpServers": {
    "nobrainr": {
      "url": "http://localhost:8420/mcp"
    }
  }
}

{
  "mcpServers": {
    "nobrainr": {
      "type": "http",
      "url": "https://<your-domain>/mcp"
    }
  }
}

{
  "mcpServers": {
    "nobrainr": {
      "type": "http",
      "url": "https://<your-domain>/mcp"
    }
  }
}

{
  "mcpServers": {
    "nobrainr": {
      "type": "sse",
      "url": "https://<your-domain>/sse"
    }
  }
}

# Store
memory_store(content="Traefik needs container DNS names, not IPs",
             tags=["traefik", "docker"], category="gotchas",
             source_machine="my-server")

# Search
memory_search(query="how did we fix the Docker networking issue")

# Filter
memory_query(source_machine="my-laptop", category="architecture", limit=20)

src/nobrainr/
├── mcp/server.py          # MCP tools (the API)
├── db/
│   ├── queries.py         # All database operations
│   ├── schema.py          # DDL (auto-creates tables on startup)
│   └── pool.py            # asyncpg connection pool
├── embeddings/ollama.py   # llama-server embedding client (name predates migration; routes to llama-swap)
├── extraction/
│   ├── extractor.py       # Entity/relationship extraction via Ollama
│   ├── pipeline.py        # Full pipeline: extract → dedup → store → link
│   ├── dedup.py           # Memory dedup (vector + LLM merge)
│   ├── llm.py             # Shared Ollama chat helper
│   └── models.py          # Pydantic models for extraction
├── dashboard/
│   ├── app.py             # ASGI app with lifespan
│   └── api.py             # JSON API endpoints
├── importers/
│   ├── chatgpt.py         # ChatGPT export parser
│   └── claude.py          # Claude memory scanner
├── scheduler.py           # APScheduler setup
├── scheduler_jobs.py      # Autonomous learning jobs
├── config.py              # Pydantic settings
└── cli.py                 # CLI: serve, status, search, import

dashboard/                  # Vue 3 frontend (optional, separate build)
├── Dockerfile
├── nginx.conf
├── src/
│   ├── views/             # Graph, Memories, Timeline, Scheduler
│   ├── composables/       # Vue composables for each view
│   └── components/        # Reusable UI components
└── ...

cd dashboard
docker build -t nobrainr-dashboard .
docker run -d -p 3000:80 nobrainr-dashboard

docker exec nobrainr-db pg_dump -U nobrainr nobrainr | gzip > nobrainr-backup-$(date +%Y%m%d).sql.gz

gunzip -c nobrainr-backup-20260306.sql.gz | docker exec -i nobrainr-db psql -U nobrainr nobrainr

nobrainr serve              # Start MCP server
nobrainr status             # Check DB + embedding model
nobrainr search "query"     # Semantic search from terminal
nobrainr import-chatgpt conversations.json
nobrainr import-claude ~/.claude --machine my-laptop