swafra 0.1.3

MCP server for swafra — Leiden-chunked, graph-linked semantic memory for Claude AI and ChatGPT

swafra

Leiden-chunked, graph-linked semantic memory as an MCP server — for Claude, Codex, and any MCP-compatible AI.

94% recall on LongMemEval — the standard benchmark for long-term memory in AI assistants.

Install

pip install swafra

Attach to Claude Desktop

Add to ~/Library/Application Support/Claude/claude_desktop_config.json:

{
  "mcpServers": {
    "swafra": {
      "command": "swafra"
    }
  }
}

Restart Claude Desktop — the tools appear automatically.

Attach to Claude Code

claude mcp add swafra swafra

Attach to VS Code (Copilot/Codex)

Add to .vscode/mcp.json in your project:

{
  "servers": {
    "swafra": {
      "command": "swafra"
    }
  }
}

Tools

Tool	Description
add_knowledge	Ingest text → Leiden chunk → embed → build knowledge graph
search_knowledge	Semantic vector search over stored knowledge
graph_walk	Traverse chunk edges from a starting point
get_context	Combined search + graph walk (recommended for retrieval)
list_sources	List all ingested knowledge sources
delete_source	Remove a source and its graph

How It Works

1. Leiden Chunking — Text is split into sentences, a hybrid graph is built (semantic similarity + entity co-occurrence + positional proximity), and the Leiden algorithm finds topically coherent communities. Each community becomes one chunk.

2. Local Embeddings — Uses fastembed (ONNX, no cloud) with BAAI/bge-small-en-v1.5 (384-dim). Falls back to a deterministic hash-based embedder if fastembed is unavailable.

3. Knowledge Graph — Chunks are connected with sequential edges (next/prev), similarity edges (cosine > 0.7), and community membership. Graph-walk retrieval traverses these edges for broader context.

4. Storage — JSON files in ~/.scimap/. No database required.

Environment Variables

Variable	Default	Description
SCIMAP_DATA_DIR	~/.scimap	Where knowledge is stored
SCIMAP_EMBED_MODEL	BAAI/bge-small-en-v1.5	Embedding model (fastembed)

Requirements

• Python >= 3.10
• Dependencies installed automatically: fastembed, igraph, leidenalg, numpy, mcp

License

MIT — github.com/kunal12203/swafra