待翻译：Lybrary,a persistent AST-aware code memory for AI agents(MCP server pip install)

AI 服务暂时不可用，以下为来源正文，待恢复后补全翻译。

Notifications You must be signed in to change notification settings Fork 0 Star 0 BranchesTags Open more actions menu Folders and files NameName Last commit message Last commit date Latest commit History 1 Commit 1 Commit .github/workflows .github/workflows lybrary lybrary tests tests .gitignore .gitignore CHANGELOG.md CHANGELOG.md README.md README.md pyproject.toml pyproject.toml Repository files navigation Living structure-aware code memory for AI coding agents. pip install lybrary The problem Every time an AI agent starts a session, it re-reads your codebase from scratch. agent: let me grep for auth logic... agent: reading src/auth/middleware.py... agent: reading src/auth/jwt.py... agent: reading src/auth/session.py... agent: reading src/utils/crypto.py... ↳ 4,000 tokens burned before writing a single line. On a large codebase this happens dozens of times per session. Tokens wasted. Context filled. Same files read over and over. The solution lybrary gives your agent a persistent memory it can query instead of reading files. agent: memory_query("authentication flow") ↳ 3 chunks returned. 180 tokens. Done. It indexes your repo using real AST boundaries, keeps the index fresh automatically, and exposes it as an MCP server that any AI IDE connects to natively. ✨ Features 🌳 AST-aware chunking tree-sitter parses your code — never splits a function in half ⚡ Background daemon watches for file changes, re-indexes only what changed 🔍 Semantic search vector search with token-budget packing 🔌 MCP server works with Kiro, Cursor, Claude Desktop, Windsurf out of the box 📦 Fully local no cloud, no API keys, embeddings run on your machine 🐍 Pure pip install Python 3.11–3.14, no PyTorch, no compilation needed Supported languages: Python · JavaScript · TypeScript · TSX · Go · Rust · Java · C · C++ 🚀 Quick start pip install lybrary cd /path/to/your/repo lybrary init lybrary start # builds index + starts background daemon lybrary query "authentication flow" After lybrary start, the daemon keeps running even after you close the terminal. File changes are picked up automatically — only affected chunks are re-indexed. 🔌 MCP integration Add to your MCP config (Kiro, Cursor, Claude Desktop, Windsurf): { "mcpServers": { "lybrary": { "command": "lybrary", "args": ["mcp"] } } } Your agent now has three tools: Tool What it does memory_query Semantic search — returns ranked chunks with full source, file path, and line numbers memory_status Reports daemon state, chunk count, and tracked files memory_update Triggers incremental or full re-index, optionally scoped to specific files Agents should call memory_query before reading any files. This replaces multi-file reads with a single targeted query — cutting token usage by 80–90% on large codebases. 🖥️ CLI reference Command Description lybrary init Create .lybrary/ and default config lybrary start Index (if needed) + start persistent daemon lybrary stop Stop the daemon lybrary status Show running state, chunk count, tracked files lybrary index Force (re)index lybrary query Semantic search over the memory lybrary logs View / follow daemon log lybrary mcp Start MCP server (stdio transport) 🏗️ How chunking works your file │ ▼ tree-sitter parser │ ▼ AST definition nodes ← functions, classes, methods, interfaces │ ├── class Foo ──────────► chunk: entire class body │ ├── def bar ───────► chunk: method bar (its own chunk too) │ └── def baz ───────► chunk: method baz (its own chunk too) │ └── module-level ────────► chunk: imports, constants, top-level statements Each chunk gets a context header and is embedded with MiniLM-L6-v2 via ONNX Runtime — fast, local, no GPU needed. 🗂️ Architecture .lybrary/ ├── config.toml # model, chunk size, ignore patterns ├── index.db # SQLite: chunks + float32 vector blobs ├── file_hashes.json # content-hash map for incremental updates ├── daemon.pid └── daemon.log Indexer — tree-sitter → AST chunks → fastembed / ONNX Runtime embeddings Store — SQLite + numpy (cosine similarity via batched dot product, no external vector DB) Daemon — watchdog file watcher + debounce + incremental re-chunk/embed MCP — FastMCP server over stdio 🗺️ Roadmap AST chunker (multi-language, cAST-style) Incremental indexing via content hashes Background daemon + file watcher (Windows + Unix) CLI (init / start / stop / status / index / query / logs / mcp) MCP server (memory_query, memory_status, memory_update) Call/import graph expansion Hierarchical file/package summaries Cross-session decision memory systemd / launchd user service helper 🤝 Contributing Issues and PRs welcome. Run the test suite with: pip install -e ".[dev]" pytest 📄 License MIT Activity Stars 0 stars Watchers 0 watching Forks 0 forks Report repository