RAGFlow
Open source · RAG and vector databases · infiniflow/ragflow
What it does
RAGFlow is a ready-made app for "chat with your documents". It focuses on reading messy files such as PDFs, scans and tables properly, and shows which passage each answer came from.
Good for: A self-hosted knowledge base over company documents, with answers that cite their source.
RAGFlow on GitHub (opens in a new tab) Project homepage (opens in a new tab)
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The signals we checked
- Licence
- Apache-2.0 (OSI-approved open-source licence)
- Popularity
- Stars: 92,304 (checked 16 Oct 2026)
- Last commit
- 16 Oct 2026
- Open issues and PRs
- 1,347
- Status
- Active (not archived)
- Self-host
- ModerateDocker Compose with several services; needs a well-specced machine.
- GitHub topics
- agent-harness
- agentic-ai
- agentic-nagive
- agentic-retrieval
- agentic-search
- ai
- ai-agents
- context-engine
- context-engineering
- context-management
More in rag and vector databases
- LlamaIndex Developers building document Q&A or data agents in Python or TypeScript.
- Milvus Vector search that may need to grow to very large collections.
- Qdrant The search layer behind a "chat with your documents" app.
- Chroma Adding document search to a prototype or small app with very little setup.
- Weaviate Search over your own content that mixes keyword matching with meaning-based matching.
No affiliate links on this page, and we are not paid by this project. The summary is our own plain-English description; check the repo for current features before you rely on it.
Stars, licences and commit dates come from the GitHub API, checked 16 Oct 2026. "Last commit" is the last push to the repo. Star counts are a rough popularity signal, not a quality score. There are no affiliate links in this section: links go straight to each project.