Semantic Intelligence for Large-Scale Engineering. Context+ is an MCP server designed for developers who demand 99% accuracy. By combining RAG, Tree-sitter AST, Spectral Clustering, and Obsidian-style linking, Context+ turns a massive codebase into a searchable, hierarchical feature graph.

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RAG on codebases using treesitter and LanceDB

A Docker-powered RAG system that understands the difference between code and prose. Ingest your codebase and documentation, then query them with full privacy and zero configuration.

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Agent Fusion is a local RAG semantic search engine that gives AI agents instant access to your code, documentation (Markdown, Word, PDF). Query your codebase from code agents without hallucinations. Runs 100% locally, includes a lightweight embedding model, and optional multi-agent task orchestration. Deploy with a single JAR

Local RAG server for code editors. Scans your codebase, builds a local context index, and connects to any external LLM for context-aware completions and assistance.

AI travel planner with 7 specialized agents, RAG, and tool-calling. Built with CrewAI & LangChain. Generates personalized itineraries with flights, hotels, activities, and cultural tips. Production-ready Python codebase.

💁 Example code for a blog post series about using a RAG system on a local codebase.

Codebase for the implementaion of graph-rag system

The official codebase for our paper "NAACL: Noise-AwAre Verbal Confidence Calibration for LLMs in RAG Systems"

Agentic RAG system grounded in Ethereum specs, discussions, papers, and codebases

Privacy-focused, self-hosted RAG assistant for querying codebases with local or cloud LLMs.

AI-powered autonomous coding agent for Unity/Strada.Core — PAOR agent loop, 40+ tools, RAG over C# codebases, persistent HNSW memory, multi-channel (Web/Telegram/Discord/Slack/WhatsApp), 24/7 daemon mode, multi-agent orchestration, and SOUL.md personality system.

Structural RAG (Retrieval-Augmented Generation) system for large codebases

A Retrieval-Augmented Generation (RAG) Model-Controller-Provider (MCP) server designed to assist AI agents and developers in understanding and navigating codebases.. It supports incremental indexing and multi-language parsing, enabling LLMs to understand and interact with code.

Unrag is a RAG installer that adds small, auditable, drop-in primitives for ingest/retrieve and adapters directly into your codebase as vendored source files.

MCP server for code retrieval from a fully indexed codebase using CocoIndex

Local Codebase RAG MCP Server for Claude Code - Proactive semantic indexing with AST-based chunking

TypeScript CLI that pulls a GitHub repo and merges all text-like files into one clean .txt or .pdf. Skips binaries, streams progress, writes to /out. Ideal for LLMs and RAG: feed an entire codebase as a single artifact so models can use the repository context seamlessly.

Build a RAG agent for your codebase (+ websearch-grounded!)

Cortex is a sophisticated AI coding assistant that combines the power of multi-agent orchestration with Retrieval-Augmented Generation (RAG) to provide intelligent, context-aware answers about your codebase. Built with privacy in mind, Cortex runs entirely locally using Ollama or can be configured to use OpenAI's models.

Chat with a Codebase. Build with Pinecone & Llama 3.1 via Groq

Chat with a Codebase using DeepSeek R1, Claude, GPT, Gemini

A Rust-based Model Context Protocol (MCP) server that provides AI assistants with powerful RAG (Retrieval-Augmented Generation) capabilities for understanding massive codebases. Index codebases with FastEmbed + LanceDB, semantic code and git history searches with hybrid vector + BM25.

Intelligent context management for AI coding assistants. Indexes your codebase with tree-sitter, builds a semantic knowledge graph, and serves 12 tools over MCP. Hybrid RAG combines keyword search, vector embeddings, and graph traversal for precise retrieval. Works with Claude Desktop, Cursor, and any MCP client. Fully local with Ollama.

RAG-powered repo Q&A and code fixer CLI. Train it on any codebase, ask anything about the code, and fix issues — all locally using Ollama. Zero API costs, your code never leaves your machine.

A advanced RAG framework, by Json Zhou, designed to enhance AI applications with multi-source knowledge retrieval. Specifically designed for offline LLM4CODE, aiming to provide codebase understanding offline and securely.

Self-hosted MCP server for semantic codebase search. RAG for any MCP client (Copilot, Claude, Cline) — zero cost, self-hosted.

Example of how to integrate AST-based RAG of your C++ codebase into your AI Assistant/Agent