AgentIndex icon
AgentIndex
deep-research logo
deep-research
★ 4.7k
vs
code-graph-rag logo
code-graph-rag
★ 5.3k

deep-research vs code-graph-rag

deep-research: Deep Research is an AI-powered tool designed to generate comprehensive research reports rapidly, leveraging advanced AI models and internet connectivity. It prioritizes user privacy by processing and storing all data locally.; 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

deep-research logo
deep-researchRAG / Knowledge Base · ★ 4.7k

Rapidly generate in-depth research reports for personal and professional use.

Read deep-research full review →
code-graph-rag logo
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.
Read code-graph-rag full review →
02

Side-by-Side Comparison

Field
deep-research logodeep-research
code-graph-rag logocode-graph-rag
Category
RAG / Knowledge Base
RAG / Knowledge Base
Stars
★ 4.7k
★ 5.3k
License
MIT
MIT
Install Command
docker run -d --name deep-research -p 3333:3000 xiangfa/deep-research
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
4mo ago
1d ago
Open Source
Yes
Yes
Website
↗ Visit
↗ Visit
GitHub
↗ GitHub
↗ GitHub
Tags
AI Research, LLM, Privacy-focused
ai, ast, claude-code
03

Features

deep-research logodeep-research
01Rapidly generates comprehensive research reports in minutes.
02Ensures privacy by processing and storing all data locally in the browser.
03Supports a wide range of mainstream large language models (LLMs).
04Integrates with various web search engines for enhanced information gathering.
05Allows uploading and processing local files to build a knowledge base.
code-graph-rag logocode-graph-rag
01Multi-Language Support
02Tree-sitter Parsing
03Knowledge Graph Storage
04Natural Language Querying
05Advanced File Editing
04

Use Cases

deep-research logodeep-research
↳Rapidly generate in-depth research reports for personal and professional use.
↳Serve as a deep research as a service (SaaS) platform via SSE API.
↳Integrate deep research functionality into other AI applications using MCP.
code-graph-rag logocode-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.
FAQ

FAQ

What is the difference between deep-research and code-graph-rag?
Both deep-research and code-graph-rag are in the RAG / Knowledge Base category. deep-research has 4.7k stars, while code-graph-rag has 5.3k stars.
Which is better, deep-research or code-graph-rag?
The best choice depends on your use case. Choose deep-research if Rapidly generate in-depth research reports for personal and professional use., and code-graph-rag if Analyze and ingest multi-language codebases into a knowledge graph..
Is deep-research free or open source?
Yes, deep-research is open source on GitHub (MIT).
Is code-graph-rag free or open source?
Yes, code-graph-rag is open source on GitHub (MIT).
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