Sharing a project I've been building: Argyph, an MCP server that gives AI coding agents (Claude, or anything that speaks MCP) structured and semantic understanding of a codebase.
The problem: agents are good at reasoning but bad at retrieval. They grep, guess, and pull whole files into a limited context window. Most context tools that try to fix this depend on a cloud vector database and a remote embedding API.
Argyph runs entirely locally — single binary, embedded vector store, bundled embedding model, no API key. It builds a three-tier index (file inventory → tree-sitter symbol graph → embeddings), each tier usable before the next finishes, so the agent can query almost immediately.
It's read-only by design — never edits, commits, or runs code. Open source, Rust, MIT/Apache-2.0.
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