GBrain is a feature-rich knowledge graph and RAG engine offering unique synthesis and gap analysis capabilities. It suits individuals and teams looking to enhance their AI agents' memory, but requires a technical background for deployment and configuration. Security-wise, local storage and multi-user isolation are well-designed, but care must be taken with API keys and tokens. During installation, ensure you get it from GitHub to avoid npm confusion. Overall, for those needing a true 'brain' rather than just search, this is a powerful choice.
GBrain is an open-source knowledge graph and retrieval-augmented generation (RAG) engine built by Garry Tan, President of Y Combinator, to power his own AI agents. It offers hybrid search (vector + keyword + reranker), a self-wiring knowledge graph, and a synthesis layer (think) that returns cited answers with gap analysis. It supports both personal use and team-wide 'company brain' with multi-user isolation via OAuth scopes. It can be used via CLI, MCP (stdio/HTTP), or integrated into clients like Claude Code, Codex, Cursor, etc. It uses PGLite (zero-config) or Postgres + pgvector as backend.
Installation steps: 1. Install the Bun runtime. 2. Run bun install -g github:garrytan/gbrain (note: the gbrain package on npm is unrelated, do not use it). 3. Initialize a local brain: gbrain init --pglite. 4. Verify installation: gbrain doctor. 5. Connect the MCP server to your client: e.g., for Claude Code use claude mcp add gbrain -- gbrain serve; or for remote use gbrain connect https://your-host/mcp --token gbrain_xxx --install.