Velocirag

🦖 VelociRAG

Lightning-fast RAG for AI agents.

Four-layer retrieval fusion powered by ONNX Runtime. No PyTorch. Sub-200ms warm search. Incremental graph updates. MCP-ready.

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Most RAG solutions either drag in 2GB+ of PyTorch or limit you to single-layer vector search. VelociRAG gives you four retrieval methods — vector similarity, BM25 keyword matching, knowledge graph traversal, and metadata filtering — fused through reciprocal rank fusion with cross-encoder reranking. All running on ONNX Runtime, no GPU, no API keys. Comes with an MCP server for agent integration, a Unix socket daemon for warm queries, and a CLI that just works.

🚀 Quick Start

MCP Server (Claude, Cursor, Windsurf)

pip install "velocirag[mcp]"
velocirag index ./my-docs
velocirag mcp

Claude Code — add to .mcp.json in your project root:

{
  "mcpServers": {
    "velocirag": {
      "command": "velocirag",
      "args": ["mcp"],
      "env": { "VELOCIRAG_DB": "/path/to/data" }
    }
  }
}

Then open /mcp in Claude Code and enable the velocirag server. If using a virtualenv, use the full path to the binary (e.g. .venv/bin/velocirag).

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View the full README on GitHub