FastGPT: Build AI Agents and LLM Apps
labring/FastGPT
FastGPT is a self-hosted platform for building AI agents and RAG question-answering systems, pairing a visual workflow builder with a knowledge-base pipeline for chunking, embedding, and retrieval. Reach for it if you want a working chatbot with a knowledge base running in an afternoon via Docker instead of assembling that pipeline yourself. Skip it if your team needs full code-level control over retrieval logic rather than a drag-and-drop flow.
Understanding FastGPT's Core Purpose
FastGPT is an AI agent building platform that lets you assemble question-answering systems from a visual workflow canvas, a document-based knowledge base, and calls out to language models — its GitHub topics tag the repo with claude, deepseek, openai, and qwen. The knowledge base ingests files like PDF, Docx, and CSV, chunks them, and serves retrieval-augmented answers through the workflow you design instead of hand-written orchestration code.
Key Capabilities for LLM Application Development
- ✓Visual workflow canvas for chaining conversation flows, plugin workflows, and basic RPA-style nodes instead of writing orchestration code by hand.
- ✓Agent Skill orchestration plus two-way MCP support, so a FastGPT app can call external MCP tools and also be called as one itself.
- ✓Knowledge base ingestion for TXT, MD, HTML, PDF, Docx, PPTX, CSV, and XLSX files, plus URL reading and batch CSV import.
- ✓Hybrid retrieval with reranking, and an API-based knowledge base option for pulling data from an external source instead of only uploaded files.
- ✓Per-chunk editing: you can find, edit, or delete an individual chunk after import instead of re-running the whole ingestion.
- ✓Built-in debugging tools: single-point knowledge base search tests, a full call-chain log per conversation, and an app evaluation feature.
- ✓Ops features for shipping an app: a no-login share window, one-click iframe embed, and centralized conversation logs with annotation.
Who Benefits from FastGPT?
- •Customer-facing support bots that answer from an internal knowledge base instead of general model knowledge.
- •Internal documentation Q&A where teams need answers pulled from PDF, Docx, or CSV manuals rather than generic search results.
- •Multi-step AI agents that combine plugin workflows with RPA-style nodes for tasks beyond a single prompt-response turn.
- •Embedded chat widgets on an existing site or product via the iframe embed or a shareable link, without building a frontend.
Quick Start with Docker
The README's documented path is a one-command Docker setup: run bash <(curl -fsSL https://doc.fastgpt.io/deploy/install.sh) to pull the config, then docker compose up -d to start it. Once it's up, FastGPT is reachable at http://localhost:3000 with the default login root / 1234. The README also links to a fuller Docker deployment guide for anything the quick-start script doesn't cover, and lists Sealos Cloud as a one-click alternative to running Docker Compose yourself.
Strengths
- ✓Chunk-level editing of the knowledge base — you can find and fix a bad chunk after import instead of re-running ingestion from scratch.
- ✓Two-way MCP support means a FastGPT app isn't just a consumer of models — it can expose itself as an MCP tool too.
- ✓The knowledge base handles a wide format spread out of the box: TXT, MD, HTML, PDF, Docx, PPTX, CSV, XLSX, plus URL reads and batch CSV import.
- ✓Deployment path is short: one curl script plus docker compose up -d, per the README's quick start.
- ✓Hybrid retrieval with reranking is built into the knowledge base pipeline rather than something you have to bolt on separately.
Licensing and Commercial Use Restrictions
- △The license isn't a standard OSI license: the README's own terms say you can run FastGPT commercially as a backend service, but you can't offer it as a SaaS product without a separate commercial license.
- △Without commercial authorization, any commercial deployment has to keep FastGPT's copyright notices intact — you can't quietly white-label it.
- △Several items on the README's own feature checklist are still unchecked, including assisted workflow generation, an advanced orchestration debug mode, and hot-reloading the RAG module or agent loop — parts of the roadmap haven't shipped yet.
- △The README doesn't document a plain source or npm install path, only Docker (and Sealos Cloud) — if you can't run Docker Compose, self-hosting isn't clearly documented.
Comparing FastGPT with Other LLM Platforms
Frequently Asked Questions
FastGPT is free and open source under the FastGPT Open Source License, and the README confirms you can run it commercially as a backend service without paying — the restriction only kicks in if you resell it as a SaaS product.
FastGPT can be used commercially as a backend service under its open-source license, but the README is explicit that offering it as a SaaS product needs a separate commercial license, and unlicensed commercial use must keep the copyright notices intact.
FastGPT's documented local path is Docker: run the install script from doc.fastgpt.io to pull the config, then run docker compose up -d — the app comes up at http://localhost:3000 with default login root / 1234.
FastGPT's README doesn't list supported models directly, but its GitHub topics tag the repo with claude, deepseek, openai, and qwen, and its two-way MCP support lets it call out to external model tools.
FastGPT's knowledge base accepts TXT, MD, HTML, PDF, Docx, PPTX, CSV, and XLSX files, plus reading content directly from a URL and importing CSV files in bulk.
FastGPT is also available as a hosted cloud service at fastgpt.io for anyone who doesn't want to self-host, alongside the free community Docker version and a paid commercial version.
The problem it solves
Teams that want to wire an LLM to their own documents usually end up hand-coding a chunking step, an embedding call, a vector search, and a prompt template before they have anything resembling a working QA bot. FastGPT packages that whole chain — file ingestion, chunking, hybrid retrieval with reranking, and the prompt/response flow — behind a visual workflow canvas, so the plumbing between an uploaded PDF and an answer with citations is already built.
Who should try it — and who should skip
Reach for FastGPT if you want a self-hosted chatbot backed by a document knowledge base and you're fine designing flows visually instead of in code — the Docker quick start gets you to a running instance fast. Skip it if you're building a SaaS product on top of it, since the license blocks that without a commercial deal, or if your team wants to own every line of the retrieval pipeline instead of working inside a visual workflow tool.