Codegraph Rust

What is Codegraph Rust?

Codegraph Rust is a CLI tool that combines MCP (Model Context Protocol) server management with advanced code analysis. It indexes projects, manages embeddings, and runs MCP servers with multiple transport options, supporting languages like Rust, Python, JavaScript, TypeScript, Go, Java, and C++.

How to use Codegraph Rust?

Install and run via CLI. Use commands for project indexing, MCP server management (STDIO, HTTP, or both simultaneously), code search (semantic, exact, fuzzy, regex, AST), and architecture analysis. Specific configuration options include embedding models, performance tuning, and background daemon mode with PID management.

Key features of Codegraph Rust

• Multi-language code parsing with Tree-sitter
• Dual transport support: STDIO and HTTP streaming
• Vector search with FAISS-powered embeddings
• Graph-based architecture with RocksDB storage
• High performance: 170K lines in 0.49 seconds
• Incremental indexing with file watching

Use cases of Codegraph Rust

• Index large codebases and perform semantic search across multiple languages
• Run MCP servers in STDIO, HTTP, or dual mode for flexible agent integration
• Analyze component relationships, dependencies, and code patterns
• Build a deep code knowledge synthesis system with an agent

FAQ from Codegraph Rust

What languages does Codegraph Rust support?

It supports Rust, Python, JavaScript, TypeScript, Go, Java, and C++.

What transport options are available for MCP servers?

STDIO, HTTP streaming with SSE, or both simultaneously. Background daemon mode with PID management is also supported.

How fast is code indexing?

Parsing 170K lines of Rust code takes about 0.49 seconds; generating 21,024 embeddings takes about 3 minutes 24 seconds on an M3 Pro with Qdrant/all-MiniLM-L6-v2-onnx (CPU, no Metal acceleration).

Does Codegraph Rust support incremental indexing?

Yes, it supports incremental indexing with file watching and smart caching.

What kind of code search is available?

Semantic search using embeddings, exact match, fuzzy search, regex, and AST-based queries with configurable similarity thresholds.