Graph RAG knowledge base for coding agents, backed by Neo4j and exposed over MCP
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# graph-rag **A local-first Graph RAG knowledge base for coding agents, served over MCP.** [](https://github.com/tmustafiz/graph-rag/actions/workflows/ci.yml) [](LICENSE) [](pyproject.toml) [](https://modelcontextprotocol.io) graph-rag ingests the heterogeneous stuff a coding agent needs to reason about — service docs (PDF), internal Markdown, your source code, and YAML policy files (Checkov) — into a single **Neo4j knowledge graph**, and exposes it to the agent over an **MCP server**: hybrid (vector + full-text) search, table-of-contents navigation, exact policy lookup, code-centrality ranking, graph traversal, and the agent's own **persistent working memory**. It runs entirely on your machine. The default embedding model is local, so ingestion needs no API key and works offline. ## Why Plain vector RAG loses structure: it can't tell you *which section* a chunk came from, *what calls* a function, or *which policy applies to* `aws_db_instance`. graph-rag keeps those relationships as graph edges, so an agent can both search semantically **and** traverse — "find the retry section, then show me its parent chapter", "rank this codebase's most-depended-upon functions", "give me the Checkov rules for this resource type". It also gives the agent a place to `remember` decisions and `recall` them in a later session. ## Demo <!-- TODO: add an asciinema / GIF of an agent calling search_code + recall --> ## Install The PyPI package is [`grag-mcp`](https://pypi.org/project/grag-mcp/) (the name `graph-rag` was taken); it installs a `grag-mcp` command. No clone needed — run it straight with [`uv`](https://docs.astral.sh/uv/): ```bash uvx grag-mcp --help # one-off…
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