RAG & Knowledge
graphrag
Microsoft's graph-based RAG: index a corpus into a knowledge graph, then query it with an LLM
★ 36.3k starsMIT
View guide →RAG for your monorepo: query, understand and edit multi-language codebases with a knowledge graph.
code-graph-rag is an open-source rag & knowledge project by vitali87 (vitali87/code-graph-rag on GitHub). It has 5,238 GitHub stars and 699 forks, is released under the MIT license, and was last updated on GitHub on 8 October 2026.
Decide what you need: understanding a monorepo, finding dead code, tracing behavior, or safe automated edits.
Install code-graph-rag with the Tree-sitter and semantic extras via uv or pipx.
Start the packaged daemon, then parse your repository into the graph.
Query the codebase in natural language and inspect the grounded, graph-backed answer.
Parses source with Tree-sitter into AST-level graph structure — functions, classes, calls and references — stored in Memgraph.
Asks questions in plain language; an AI model generates Cypher against the graph and answers grounded in the real structure.
Edit code through the agent with AST-based patching and a diff preview before anything changes.
Finds dead code by walking call and reference edges from entry points, and searches/rewrites by AST pattern with ast-grep.
cgr trace merges actually-executed calls (test runs or production eBPF profiles) into the graph, exposing dynamic dispatch static analysis cannot see.
Exposes the graph to MCP-compatible agents, with incremental indexing that keeps the graph current as code changes.
Clone Code-Graph-RAG if you want the source; most users install the tool.
git clone https://github.com/vitali87/code-graph-rag.git
cd code-graph-ragInstall the packaged tool with full Tree-sitter language support and semantic search.
uv tool install "code-graph-rag[treesitter-full,semantic]"Start the packaged Memgraph + Qdrant stack, parse your repo into the graph, and start querying.
cgr daemon up
cgr start --repo-path /path/to/repo --update-graphIt is maintained by vitali87 and contributors, with enterprise services offered around the open-source core.
Yes — the tool is MIT licensed and runs locally; enterprise cloud and on-premise services are optional paid offerings.
Querying uses an AI model to generate Cypher, so you need access to a model provider you configure; the indexing itself is local.
Search tools return chunks; Code-Graph-RAG builds a structural knowledge graph you can traverse — call edges, references, runtime traces — and edit through, at the cost of a heavier indexing stack.
Install with uv, run cgr daemon up, and index your repo with cgr start --repo-path.
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Free and open — grab the source, star the repo, and start creating.