graphrag
Microsoft's graph-based RAG: index a corpus into a knowledge graph, then query it with an LLM
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.
RAG for your monorepo: query, understand and edit multi-language codebases with a knowledge graph.
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.
Microsoft's graph-based RAG: index a corpus into a knowledge graph, then query it with an LLM
Graph RAG conversational knowledge base built on TiDB Serverless vector storage
Open rag & knowledge for this repo accelerates development of rag applications w.
Open rag & knowledge for "autonomie" is a highly semantic, responsive, accessibl.
A low-code MCP framework for building complex, innovative RAG pipelines — visibly, not as a black box.
A new state of the art for RAG: an open knowledge base for humans and agents.
The most-starred other RAG & Knowledge projects in the LibreAI catalogue — the realistic shortlist if code-graph-rag is not the right fit.
MarkItDown — Microsoft tool that converts PDFs, Office files, images and more to clean Markdown for LLMs, MIT
Build Agentic workflows, RAG pipelines, with rich AI model and tool support on one collaborative workspace. Deploy on cloud, VPC, or…
RAGFlow is a leading open-source Retrieval-Augmented Generation (RAG) engine that fuses cutting-edge RAG with Agent capabilities to create a superior context…
Lightweight OCR toolkit turning PDFs and images into structured, LLM-ready data in 100+ languages
MinerU — turns PDFs and Office documents into LLM-ready Markdown and JSON for agents and RAG pipelines, Apache-2.0 plus terms
Nearby RAG & Knowledge projects in the catalogue, closest in stars to code-graph-rag — keep exploring the shelf either side of it.
Build Neo4j knowledge graphs from unstructured data using LLMs — then chat with your graph.
An agent that finds anything on your computer — federated search over your files, in place.
A low-code MCP framework for building complex, innovative RAG pipelines — visibly, not as a black box.
OpenKB compiles your documents into a persistent, interlinked wiki knowledge base — no vector DB
An OLTP graph database with native vector and full-text search, built in Rust on object storage.
AI-native database for hybrid search across dense, sparse, and tensor vectors plus full text
Free and open — grab the source, star the repo, and start creating.