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AI-powered code intelligence CLI with multi-agent analysis, impact graphs, and conversational coding.

26 starsPython

CodeGraph CLI – Chat with your codebase using graph-augmented RAG

by al1nasir·Feb 17, 2026·3 points·0 comments
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AI Analysis

●●SolidBig BrainWizardryNiche Gem

Graph-augmented RAG beats pure vector search, but codebase Q&A tools are crowded.

Strengths
  • •Hybrid semantic + structural search (BFS graph traversal) captures both meaning and dependencies unavailable in vector-only systems.
  • •Multi-agent CrewAI integration with autonomous file modifications (with rollback) adds genuine autonomy beyond chat.
  • •6 LLM provider flexibility plus Ollama offline mode gives real portability.
Weaknesses
  • •Crowded category—Cursor, Continue, Sourcegraph Cody, Tabnine already own the "chat with codebase" narrative.
  • •Dependency on tree-sitter + SQLite + LanceDB + CrewAI stack adds deployment friction vs cloud-only competitors.
Target Audience

Backend developers, platform engineers, large codebase maintainers

Similar To

Sourcegraph Cody · Continue.dev · Cursor

Post Description

Hey HN!

I built CodeGraph CLI because I was tired of grep-ing through massive codebases trying to understand how things work.

It combines three things: - tree-sitter (AST parsing, error-tolerant) - SQLite (dependency graph: nodes + edges) - LanceDB (vector embeddings, disk-based)

The key insight: pure vector search misses structural relationships. So I combined vector search with BFS graph traversal — find semantically similar code, then expand to dependencies/dependents.

Result: ask "how does authentication work?" and it finds validate_token(), its caller login_handler(), AND the dependency TokenStore — because it understands both meaning AND structure.

Other features: - Impact analysis (multi-hop BFS: what breaks before you change it) - Multi-agent system via CrewAI (4 specialized agents) - Visual code explorer (browser-based) - Auto-generate docs/READMEs - 100% local-first (works with Ollama, zero data leaves machine) - 6 LLM providers (Ollama, OpenAI, Anthropic, Groq, Gemini, OpenRouter) - 5 embedding models (from zero-dependency hash to 1.5B code model)

Quick start: pip install codegraph-cli cg config setup cg project index ./your-project cg chat start

MIT licensed. Python 3.9+.

Happy to answer questions about the graph-augmented RAG architecture or any technical decisions.

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