Open-source knowledge-base Q&A and workflow platform — self-hosted RAG with visual flow builder.
The easiest path to a self-hosted knowledge-base agent with a visual flow editor. Lightweight, polished, and production-ready for small to mid-size deployments.
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Last verified: April 2026
Sweet spot: a small team or solo developer who wants a polished, self-hostable alternative to commercial RAG tools and is comfortable with Docker. FastGPT's flow builder is genuinely usable and the knowledge-base ingestion pipeline produces good defaults. For a one-afternoon internal Q&A bot, it is probably the single fastest starting point in open source. Failure modes. For deep governance and enterprise scale (audit, SSO, per-document ACLs), it is lighter than commercial alternatives. Advanced retrieval quality ceilings are lower than dedicated engines like RAGFlow. And as an import from the Chinese ecosystem, some docs require patience — community support is primarily on Chinese-language channels. What to pilot. Ingest one knowledge base you care about (internal wiki, product docs, personal notes). Ask twenty real questions you'd like answered. Measure: answer correctness, citation accuracy, flow editor ergonomics. If the out-of-the-box quality gets you to 16+ correct, FastGPT is the right pick for that scale. If you hit the retrieval ceiling, graduate to RAGFlow or a code-first stack.
FastGPT is an open-source AI knowledge-base platform from the Chinese AI community that has become one of the most popular self-hosted RAG solutions globally. It combines a document-ingestion pipeline, a hybrid retrieval engine, a visual flow builder (Flow), and a chat UI into a single self-hostable application. The workflow editor is the differentiator. Unlike simpler chat-over-docs tools, FastGPT lets you build multi-step flows with branches, LLM nodes, knowledge-base queries, HTTP requests, code execution, and tool calls. The visual editor is polished and handles fairly complex flows — you can ship a production agent entirely from the UI. Document ingestion supports PDFs, Word docs, Markdown, CSVs, URLs, and API endpoints. Chunking is configurable and there is a decent out-of-the-box quality. Models are fully pluggable through OpenAI-compatible endpoints or a bundled adapter layer. Apache-2-licensed, deployable via Docker Compose, and a popular pick for self-hosted RAG in 2025-2026. A hosted cloud version is offered but most serious users run it themselves.
Documentation has improved but some content is translated from Chinese and occasionally terse. Advanced retrieval tuning (reranking configs, chunk boundaries) is less exposed than in code-first frameworks. Hosted cloud tier is primarily aimed at Chinese customers; self-host is the default for most Western users.
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