RAGFlow

The data framework for LLM apps — RAG, agents, and document workflows.

An open-source framework (Python + TypeScript) for connecting LLMs to your data — ingestion, indexing, retrieval, and agentic document workflows. Pairs with the managed LlamaCloud (LlamaParse) for production parsing and extraction. The most-used RAG framework after LangChain.

Open-source Python framework for production RAG and agents.

Orchestration framework for building LLM applications as modular pipelines — retrieval, routing, memory, and generation wired together with explicit, traceable data flow. It is model- and store-agnostic, integrating major providers and vector databases behind a stable component API. Aimed at production: serialization, logging, and deployment across cloud or on-prem.

Managed RAG-as-a-service — the context engine for AI agents and apps.

Ragie is a fully managed retrieval-augmented-generation platform. It ingests data through native connectors like Google Drive and Notion, parses multimodal content (PDFs, images, audio, video), and serves hybrid vector + keyword + summary retrieval over an API and MCP server. Developers add accurate, grounded context to LLM apps without building their own ingestion and retrieval pipeline.

Open-source, self-hosted AI chat and enterprise search over your own docs.

Onyx (formerly Danswer) is an open-source AI chat and RAG platform that connects to your company's docs and apps for grounded, cited answers, and works with any LLM. It is self-hosted via Docker/Kubernetes and supports local models, keeping data on your own infrastructure. The core is MIT-licensed and free; an open-core model puts optional enterprise features under a separate license, and the vendor also offers a managed cloud.