Code-Graph-RAG

Code-Graph-RAG parses a multi-language codebase with Tree-sitter, builds a knowledge graph of its structure in Memgraph, and lets you query, edit, and optimise that code in plain English. It works across a monorepo of mixed languages under one unified graph schema.

What It Does

Point it at a repository and it reads every source file, extracts functions, classes, methods, and modules along with the relationships between them, and stores the result as an interconnected graph. Once the graph exists you can:

• Ask questions about the codebase in natural language and get answers grounded in the real structure.
• Retrieve the actual source of any function, class, or method by name or by intent.
• Edit code through the agent with AST-based surgical patching and a diff preview before anything changes.
• Search and rewrite code structurally by AST pattern with ast-grep, instead of text or regex.
• Trace data flow through assignments, calls, and I/O sinks via FLOWS_TO taint edges.
• Optimise code against language best practices or your own coding standards.
• Find dead code by walking call and reference edges from entry points.
• Group several repositories into a named workspace and query them as one graph.
• Trace calls between microservices: route decorators become endpoint templates, and HTTP client URLs resolve to the handlers that serve them, linking services across project boundaries.

Supported Languages

Python, TypeScript, TSX, JavaScript, Rust, Go, Java, C, C++, C#, PHP, Lua, and Dart are fully supported. Scala is in development, and Ruby has structural support (modules, functions, classes, and imports) through the pluggable ast-grep tier, which requires the ast-grep extra (pip install 'code-graph-rag[ast-grep]').

Install

pip install code-graph-rag

With all Tree-sitter grammars (Python, JS, TS, Rust, Go, Java, Scala, C, C++, C#, PHP, Lua, Dart):

pip install 'code-graph-rag[treesitter-full]'

With semantic code search (UniXcoder embeddings):

pip install 'code-graph-rag[semantic]'

Qdrant is the default vector store for semantic search. To use Milvus Lite, install code-graph-rag[semantic,milvus], then set CGR_VECTOR_STORE_BACKEND=milvus and MILVUS_URI=./.milvus_code_embeddings.db before indexing.

To compute embeddings on an OpenAI-compatible endpoint (OpenAI, Ollama, vLLM) instead of locally, set CGR_EMBEDDING_PROVIDER=openai with OPENAI_EMBEDDING_BASE_URL and OPENAI_EMBEDDING_MODEL; torch and transformers are then not required locally.

Prerequisites

• Python 3.12+
• Docker (for Memgraph)
• cmake (for building pymgclient)
• ripgrep (rg) (for shell command text searching)

CLI Quick Start

The package installs a cgr command.

Start Memgraph, parse a repo, and query it:

cgr daemon up                              # start Memgraph + Qdrant
cgr start --repo-path ./my-project \
          --update-graph --clean           # parse & launch interactive chat

Index to protobuf for offline use:

cgr index -o ./index-output --repo-path ./my-project

Export knowledge graph to JSON:

cgr export -o graph.json

AI-guided optimisation:

cgr optimize python --repo-path ./my-project

Find dead code (functions unreachable from any entry point):

cgr dead-code                                   # scan the indexed project
cgr dead-code -e main --exclude '*.gen.*'       # add roots, skip generated code
cgr dead-code --format json --fail-on-found     # CI-friendly report

Results are candidates for review, not a guaranteed delete list. See the Dead Code Detection guide.

Group repositories into a workspace and query them together:

cgr workspace create my-platform
cgr workspace add-repo my-platform ./service-a
cgr workspace add-repo my-platform ./service-b
cgr start --workspace my-platform

Inspect the graph and the stack:

cgr stats                                  # node and relationship counts
cgr status                                 # stack state and last sync per project
cgr doctor                                 # check dependencies and configuration

MCP Server

Run cgr mcp-server to serve the tools over stdio or HTTP for Claude Code and other MCP clients. The MCP surface registers:

• Ask and retrieve: ask_agent, query_code_graph, get_code_snippet, and semantic_search (needs the semantic extra)
• Structural editing: surgical_replace_code, plus structural_search and structural_replace (need the ast-grep extra)
• Files and projects: read_file, write_file, list_directory, list_projects, index_repository, update_repository, delete_project, wipe_database

Python SDK

The cgr package provides short imports for programmatic use.

Load and query an exported graph

from cgr import load_graph

graph = load_graph("graph.json")
print(graph.summary())

functions = graph.find_nodes_by_label("Function")
for fn in functions[:5]:
    rels = graph.get_relationships_for_node(fn.node_id)
    print(f"{fn.properties['name']}: {len(rels)} relationships")

Query Memgraph with Cypher

from cgr import MemgraphIngestor

with MemgraphIngestor(host="localhost", port=7687) as db:
    rows = db.fetch_all("MATCH (f:Function) RETURN f.name LIMIT 10")
    for row in rows:
        print(row)

Generate Cypher from natural language

import asyncio
from cgr import CypherGenerator

async def main():
    gen = CypherGenerator()
    cypher = await gen.generate("Find all classes that inherit from BaseModel")
    print(cypher)

asyncio.run(main())

Semantic code search

Requires the semantic extra.

from cgr import embed_code

embedding = embed_code("def authenticate(user, password): ...")
print(f"Embedding dimension: {len(embedding)}")

Configuration

from cgr import settings

settings.set_orchestrator("openai", "gpt-5.6-terra", api_key="sk-...")
settings.set_cypher("google", "gemini-3.5-flash-lite", api_key="your-key")

Environment Variables

Configure via .env or environment variables:

Documentation

Full documentation, architecture details, and contribution guide: docs.code-graph-rag.com

License

MIT