u/ChartApprehensive854

I've been working on GraphSwarm, a Rust-based code intelligence platform designed for AI coding agents.

The idea started with a simple question:

How can an AI agent understand a large codebase without repeatedly loading hundreds of files into context?

GraphSwarm indexes repositories using Tree-Sitter, builds a repository-wide call graph, resolves cross-file dependencies, stores the graph persistently, and exposes query APIs for retrieving relevant code context.

Current features:

• Rust, Python, JavaScript, and TypeScript support
• Repository-wide call graph generation
• Cross-file import resolution
• Persistent graph storage
• Graph traversal (caller/callee lookup, BFS)
• MCP integration
• Incremental file watching
• Graph export and visualization

The project is written entirely in Rust and currently has 266 passing tests.

I'd love feedback on the architecture, storage design, and ideas for improving relevance retrieval for coding agents.

GitHub:
https://github.com/dhrish-s/GraphSwarm