ezzy1630/argyph
Developer tools이 페이지는 AI로 자동 번역되었습니다. 기술적인 세부 사항은 원문을 확인하세요. 원문 보기
Local-first MCP server giving AI coding agents fast, structured, and semantic context over any codebase. Zero config, zero cloud, full context.
개요
Argyph is one local binary that gives your AI coding agent grep, a symbol graph, and semantic search over any repo — indexed in under a second, with no API key. ⭐ If this looks useful, a star helps others find it. Most developers stitch together a half-dozen MCP servers to give an AI agent context over a codebase — one for grep, one for embeddings, one for symbol search, one for repo packing. Each is a separate install, a separate process, and (often) a separate cloud account. Argyph replaces all of them with a single local binary. It indexes incrementally and is ready in under a second on previously-indexed repos — no daemon, no account, no API key. The name is a portmanteau of (the hundred-eyed watcher of Greek myth) and (a carved symbol with bound meaning) — a server that watches a codebase and gives the agent its symbols. Argyph exposes three pillars of context behind one MCP endpoint: Everything is . Argyph never edits, commits, or executes code.
README
원문 README의 일부입니다. 원문 보기.
Most developers stitch together a half-dozen MCP servers to give an AI agent context over a codebase — one for grep, one for embeddings, one for symbol search, one for repo packing. Each is a separate install, a separate process, and (often) a separate cloud account. Argyph replaces all of them with a single local binary. It indexes incrementally and is ready in under a second on previously-indexed repos — no daemon, no account, no API key.
The name is a portmanteau of Argus (the hundred-eyed watcher of Greek myth) and Glyph (a carved symbol with bound meaning) — a server that watches a codebase and gives the agent its symbols.
✨ What it does
Argyph exposes three pillars of context behind one MCP endpoint:
| Pillar | What you get |
|---|---|
| 🧭 Ask-first retrieval | ask is the primary agent-facing lookup tool. It routes bare identifiers to symbol search, structured locators to locate, and natural-language questions to hybrid search — returning bounded Span results instead of whole files. |
| 🔗 File & symbol intelligence | A tree-sitter-driven symbol graph with find_definition, find_references, callers, callees, imports, and outline tools. Structural queries return in milliseconds. |
| 🧠 Semantic search | Hybrid (BM25 + vector) search over AST-aware chunks, backed by an embedded LanceDB store. A bundled local embedding model means no API key is required for full functionality. |
| 📦 Repo packing | Token-budgeted, repomix-style flattening of a repo or subset for agents that need to absorb a codebase quickly. |
[!NOTE]
설치
npx argyph