ragflow
★ 92.7k
vs
code-graph-rag
★ 5.3k
ragflow vs code-graph-rag
ragflow: RAGFlow is a leading open-source Retrieval-Augmented Generation (RAG) engine that integrates RAG with Agent capabilities. It provides a superior context layer for LLMs and offers a streamlined RAG workflow adaptable to enterprises of any scale.; code-graph-rag: Code-Graph-RAG is an accurate Retrieval-Augmented Generation (RAG) system designed for multi-language codebases. It uses Tree-sitter to analyze code, build comprehensive knowledge graphs, and allows natural language querying of codebase structure and relationships. The system also includes advanced editing capabilities, enabling users to modify code directly through natural language prompts.
01
TL;DR
ragflowRAG / Knowledge Base · ★ 92.7k
When to choose:
- Your documents have complex layouts (PDFs, scanned files, tables, slides) and you need layout analysis, OCR, and traceable citations.
- You want an Apache-2.0 all-in-one RAG platform where you can inspect and hand-tune template-based chunks in a web UI.
When to skip:
- Your server has less than 4 CPU cores and 16 GB RAM; RAGFlow's multi-service stack is heavy.
- You want an autonomous web research agent that writes 2,000-word reports from live search rather than indexing a local document corpus; GPT Researcher fits better.
code-graph-ragRAG / Knowledge Base · ★ 5.3k
When to choose:
- You want to parse a multi-language monorepo with Tree-sitter into a Memgraph knowledge graph and query or edit it via natural language or Claude Code MCP.
- You want semantic code search using UniXcoder embeddings paired with Cypher graph queries.
When to skip:
- You want RAG over PDFs, Word files, and slides rather than source code repositories; use RAGFlow or MaxKB.
- You want a zero-dependency code context MCP server without running Docker and Memgraph; jCodeMunch or Context7 are much lighter.
02
Side-by-Side Comparison
Field
ragflowcode-graph-ragCategory
RAG / Knowledge Base
RAG / Knowledge Base
Stars
★ 92.7k
★ 5.3k
License
Apache-2.0
MIT
Install Command
docker compose -f docker-compose.yml up -d
git clone https://github.com/vitali87/code-graph-rag.git && cd code-graph-rag && uv sync --extra treesitter-full && cp .env.example .env && docker-compose up -d
Updated
1d ago
1d ago
Open Source
Yes
Yes
Tags
RAG, AI Agent, LLM
ai, ast, claude-code
03
Features
ragflow01Deep document understanding for knowledge extraction from unstructured data.
02Intelligent and template-based chunking with explainable options.
03Grounded citations with reduced hallucinations and traceable references.
04Compatibility with heterogeneous data sources including documents, images, and web pages.
05Automated and effortless RAG workflow orchestration with configurable models and fused re-ranking.
code-graph-rag01Multi-Language Support
02Tree-sitter Parsing
03Knowledge Graph Storage
04Natural Language Querying
05Advanced File Editing
04
Use Cases
ragflow↳Building high-fidelity, production-ready AI systems with complex data.
↳Developing enterprise-scale knowledge base and intelligent Q&A chatbots.
↳Facilitating intelligent document processing and advanced information retrieval.
code-graph-rag↳Analyze and ingest multi-language codebases into a knowledge graph.
↳Query codebase structure and relationships using natural language.
↳Receive AI-powered optimization suggestions and perform surgical code edits.
05
Best For
ragflowMost PopularEssentialTrending
code-graph-rag—
FAQ
FAQ
What is the difference between ragflow and code-graph-rag?
Both ragflow and code-graph-rag are in the RAG / Knowledge Base category. ragflow has 92.7k stars, while code-graph-rag has 5.3k stars.
Which is better, ragflow or code-graph-rag?
The best choice depends on your use case. Choose ragflow if Building high-fidelity, production-ready AI systems with complex data., and code-graph-rag if Analyze and ingest multi-language codebases into a knowledge graph..
Is ragflow free or open source?
Yes, ragflow is open source on GitHub (Apache-2.0).
Is code-graph-rag free or open source?
Yes, code-graph-rag is open source on GitHub (MIT).
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