RAG Frameworks & Advanced Retrieval Tools

Universal memory layer for AI agents. Simple API to create RAG applications over any dataset with support for multiple vector stores, embedding models, and LLM providers. Apache 2.0 licensed.

Full-featured RAG pipeline with advanced indexing.

End-to-end NLP and RAG framework.

Deep-document-understanding RAG engine.

Knowledge-graph-based RAG.

Document processing toolkit for turning PDFs and other files into structured data for GenAI workflows.

Best-in-class document preprocessing.

High-accuracy document parsing for LLM and RAG workflows. Converts PDFs, Word, PPTs, and images into structured Markdown/JSON with VLM+OCR dual engine.

Fast, accurate PDF-to-markdown converter with table extraction, equation handling, and optional LLM enhancement for RAG pipelines.

Vision-language models for document retrieval.

Graph-based RAG with dual-level retrieval system. Simple and fast with comprehensive knowledge discovery (EMNLP 2025).

All-in-One Multimodal RAG system for seamless processing of text, images, tables, and equations. Built on LightRAG.

Python toolkit for RAG with DuckDB or PostgreSQL. Lightweight, efficient retrieval-augmented generation without heavy dependencies. MPL 2.0 licensed.

Enterprise RAG pattern for Azure OpenAI at scale. Secure, production-ready architecture using Azure Cognitive Search and Azure OpenAI LLMs for ChatGPT-style Q&A experiences. MIT licensed.

Java library for integrating LLMs into Java applications. Implements RAG, tool calling (including MCP support), and agents with seamless integration into enterprise Java frameworks like Spring Boot. Apache 2.0 licensed.

Memory solution for users, teams, and applications. RAG pipelines with document ingestion, vector indexing, and natural language querying with citations. Supports multiple LLM providers and vector stores. MIT licensed.

All-in-one AI framework for semantic search, LLM orchestration and language model workflows. Embeddings database with customizable pipelines.

Efficient toolkit for RAG research with 40+ retrieval and reranking models, 20+ benchmark datasets, and optimized evaluation pipelines (WWW 2025 Resource). MIT licensed.

Private AI platform for building intelligent agents and assistants with enterprise search. Features Agent Builder, deep research tools, multi-format document analysis, and multi-model support. MIT licensed.

Unified framework for building enterprise RAG pipelines with small, specialized models. Optimized for AI PC and local deployment with 300+ models in catalog. Apache 2.0 licensed.

Graph RAG-based conversational knowledge base tool built on TiDB Vector and LlamaIndex. Features Perplexity-style search with built-in website crawler. Apache 2.0 licensed.

Lightweight document chunking library for fast, efficient RAG pipelines. Memory-safe with multiple chunking strategies (semantic, token, recursive) and direct vector DB integration. MIT licensed.

Vectorless, reasoning-based RAG framework using document index structure. Achieves high accuracy without vector databases through intelligent context engineering and reasoning-based retrieval. MIT licensed.

Open-source RAG-based tool for chatting with your documents. Hybrid RAG pipeline with full-text and vector retriever, re-ranking, and multi-modal capabilities. Clean Gradio-based UI with support for local and API-based LLMs. Apache 2.0 licensed.