Convert source code to LLM ready knowledge base

A semantic code search tool for intelligent, cross-repo context retrieval.

Deep code indexing MCP server for AI agents. 25 tools: hybrid FTS5 + embedding search, call graphs, git blame/hotspots, build system analysis. Multi-repo workspaces, GPU-accelerated semantic search, 10 languages via tree-sitter. Fully local, zero cloud dependencies.

MCP Server for persistent code indexing. Gives AI assistants (Claude, Gemini, Copilot, Cursor) instant access to your codebase. 50x less context than grep.

Multi-agent orchestration, persistent memory, and intelligent workflows for AI coding assistants. Supports Claude Code and OpenCode.

Self-hosted MCP server for hybrid semantic code search and repository intelligence.

Enhanced fork of claude-context with stability fixes, improved sync, and better reliability for semantic code search

Python application to index code locally and support running server with indexed repos. Works with VoyageAI to power semantic searching a large codebase, enabling AI optimized code navigation. Supports FTS searching, and indexing git log. Experimental support for SCIP indexing.

Go-based MCP server for codebase indexing and semantic search (Augment-compatible)

Give Claude Code a permanent memory — 100% local, zero config, graph-powered

An AI-powered system for intelligent code search, moving beyond keywords to semantic understanding. It offers multi-dimensional search capabilities across files, classes/interfaces, and methods, each with optimized AI-generated embeddings. Get precise, context-aware results to natural language queries quickly and efficiently.

Structured code retrieval for AI agents — index once with tree-sitter, query symbols precisely via MCP. Cut code-reading token costs by up to 99%.

CLI to turn repositories into structured context packs for LLMs and RAG pipelines.

Fast code map generator for AI coding assistants - Save 99%+ tokens while preserving context

Nuke your token usage. Code indexing MCP server: 15 tools, 10 languages, O(1) retrieval, hybrid search, call graphs.

Intelligent code indexing and retrieval system for Ruby on Rails projects with MCP integration

Lightweight code indexing and semantic search engine that helps AI agents write code faster while using fewer tokens. Parses codebases with Tree-sitter AST to generate structured headers, with optional LLM insights and vector embeddings. Integrates via MCP server or CLI. Supports TypeScript, JavaScript, C#

Intelligent code indexing MCP server. 13 tools, 10 languages, hybrid search, call graphs, O(1) symbol retrieval.

A very simple setup with pgvector, sentencetransformer, and MCP Python SDK, just to bootstrap indexing code files to facilitate RAG-based search for AI coding agents.