Best RAG Tools Top 20

Firecrawl is the Web Data API for AI, turning web pages into clean, structured, LLM-friendly data with crawl, scrape, and search capabilities.

LangChain is the open-source agent engineering platform that unifies model IO, tool calling, RAG, memory and observability under one composable framework.

llama.cpp is a lightweight C/C++ inference engine that runs a wide range of open-source large language models efficiently on consumer hardware.

100+ AI Agent and RAG apps you can actually run — clone, customize, and ship. A great reference for quickly building LLM-powered applications.

Supabase's built-in pgvector search, turning Postgres into a RAG database.

A high-throughput and memory-efficient inference and serving engine for LLMs, featuring PagedAttention, continuous batching, and optimized KV cache management for production deployments.

A leading open-source RAG engine that fuses cutting-edge retrieval-augmented generation with agent capabilities to create a superior context layer for LLMs.

Comprehensive guides, papers, lessons, notebooks and resources for prompt engineering, context engineering, RAG, and AI Agents.

Transforms complex documents like PDFs into LLM-ready markdown/JSON for Agentic workflows, supporting layout analysis, formula recognition, and table extraction.

A comprehensive tutorial on AI agent principles and practice, systematically covering core concepts, framework usage and hands-on projects.

Pathway is a Python ETL framework for stream processing, real-time analytics, LLM pipelines, and RAG applications.

Docling is an open-source document processing tool by IBM that converts PDF, Word, PPT, HTML and more into structured data for AI, purpose-built for GenAI and RAG pipelines.

AI-driven public opinion and trend monitor with multi-platform aggregation, RSS subscriptions, smart keyword filtering, AI-powered news analysis and briefings, supporting MCP integration and push notifications via WeChat, Feishu, DingTalk, Telegram and more.

Embedchain is a universal memory layer for AI agents, enabling quick integration of diverse data sources into LLMs for context-aware AI applications.

Mem0 is a long-term memory layer for AI agents, supporting cross-session memory management and personalized context retrieval.

Ready-to-run cloud templates for RAG, AI pipelines and enterprise search with live data, always in sync with Sharepoint, Google Drive, S3, Kafka and more.

Context7 is Upstash's context-engineering toolkit for agents, helping applications manage long context windows, retrieval injection, and history compression.

CodeGraph is a context graph for coding agents, mapping how a codebase is wired together so LLM-driven tools can navigate dependencies and produce more accurate edits.

LLM-powered stock analysis system for A/H/US markets with multi-source quotes, real-time news, LLM decision dashboard and multi-channel push notifications.

Leading data framework for LLM applications, with unified RAG, Agent, and Workflow capabilities.