The all-in-one AI app for any LLM with full RAG and AI Agent capabilites.

💡 All-in-one open-source embeddings database for semantic search, LLM orchestration and language model workflows

RAGFlow is an open-source RAG (Retrieval-Augmented Generation) engine based on deep document understanding.

Retrieval and Retrieval-augmented LLMs

The open source platform for AI-native application development.

Providing enterprise-grade LLM-based development framework, tools, and fine-tuned models.

LLM App templates for RAG, knowledge mining, and stream analytics. Ready to run with Docker,⚡in sync with your data sources.

Distributed vector search for AI-native applications

The AI-native database built for LLM applications, providing incredibly fast full-text and vector search

Harness LLMs with Multi-Agent Programming

Generative AI reference workflows optimized for accelerated infrastructure and microservice architecture.

The framework for fast development and deployment of RAG backends.

Ship RAG based LLM web apps in seconds.

Fast, Accurate, Lightweight Python library to make State of the Art Embedding

Fractal Graph Desktop for Ai-Agents, Web-Browsing, Note-Taking, and Code.

An LLM-powered advanced RAG pipeline built from scratch

All-in-one infrastructure for building search, recommendations, and RAG. Trieve combines search language models with tools for tuning ranking and relevance.

Awesome-LLM-RAG: a curated list of advanced retrieval augmented generation (RAG) in Large Language Models

Use ArXiv ChatGuru to talk to research papers. This app uses LangChain, OpenAI, Streamlit, and Redis as a vector database/semantic cache.

🤖 A prototype assistant for writing and thinking