Built a local-first context engine for AI coding agents — symbol graph + semantic search, no cloud

Reddit r/artificial Tools
local-first context-engine mcp-server coding-agent code-search open-source rust

Summary

Argyph is an open-source MCP server that provides AI coding agents with structured codebase understanding via a symbol graph and semantic search, running entirely locally with no cloud dependencies.

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. **GitHub:** [**https://github.com/Ezzy1630/argyph**](https://github.com/Ezzy1630/argyph)
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