Intelligraph-mini

Local-first MCP graph intelligence server — same RRF hybrid search, multi-hop traversal, source snippets, and rationale nodes as the full Intelligraph platform, but without Docker, web UI, SSO, or chat. Just tools for your AI agent.

Need the full platform? Intelligraph adds Docker, React web UI, chat completions, SSO/PKCE, closed-network deployment, and tuning controls.

Quick start

pip install intelligraph-mini

# In your project directory:
intelligraph-mini --repo-dir .

# Or with MCP config (.mcp.json):
{
  "mcpServers": {
    "intelligraph-mini": {
      "command": "intelligraph-mini",
      "args": ["--repo-dir", "."]
    }
  }
}

First run builds graphify + CRG indexes (~60s). Subsequent runs load cached (~2s). The bundled all-MiniLM-L6-v2 model (87MB) works fully offline — no API calls, no network.

Tools

Tool	Description
search(query)	RRF hybrid search (FTS5 + semantic embeddings). Finds symbols by meaning.
node(name, depth=2)	Multi-hop subgraph + source code snippets + rationale notes.
path(from, to)	Shortest path between two symbols in the call graph.
impact(name)	Blast-radius analysis over CALLS/IMPORTS_FROM edges.
local_files(paths)	Read source files from disk.

How it works

1. Build (first run): graphify update . + code-review-graph build → graphify-out/graph.json + .code-review-graph/graph.db
2. Snippets: reads source files, stores ~500 char snippets per node in node_snippets table
3. Search: RRF (Reciprocal Rank Fusion, k=30) blends FTS5 keyword ranking with embedding cosine similarity. Adaptive 50% cutoff returns only genuinely relevant files.
4. Traversal: BFS with token budget over cached adjacency (scales to 140k edges)
5. Rationale: surfaces #NOTE/#WHY nodes from graphify's rationale extraction

Requirements

• Python 3.10+
• graphifyy and code-review-graph CLIs on PATH (installed automatically as dependencies)

License

MIT