Best AI Agent Skills for Semantic Search

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 code search combining BM25, vector similarity, and cross-encoder reranking. Parses 60+ languages with tree-sitter, runs entirely offline, and returns structured results with file paths, line ranges, and symbol metadata. Built in Rust.

MCP server with local vector search for your codebase. Smart indexing, semantic search, Git history — all offline.

A code repository indexing tool to supercharge your LLM experience.

RAGLight is a modular framework for Retrieval-Augmented Generation (RAG). It makes it easy to plug in different LLMs, embeddings, and vector stores, and now includes seamless MCP integration to connect external tools and data sources.

A structural code search engine for Al agents.

🦀 Prevents outdated Rust code suggestions from AI assistants. This MCP server fetches current crate docs, uses embeddings/LLMs, and provides accurate context via a tool call.

CodeRAG is an AI-powered tool for real-time codebase querying and augmentation using OpenAI and vector search.

The AI Operating System for Delphi. 100% native framework with RAG 2.0 for knowledge retrieval, autonomous agents with semantic memory, visual workflow orchestration, and universal LLM connector. Supports OpenAI, Claude, Gemini, Ollama, and more. Enterprise-grade AI for Delphi 10.3+

RAGFlow is a leading open-source Retrieval-Augmented Generation (RAG) engine that fuses cutting-edge RAG with Agent capabilities to create a superior context layer for LLMs