Langflow vs FastGPT
Two of the top projects in Agent platforms, side by side: who leads on each score part and who each one suits, then setup, license, activity and what each review found.
Langflow
Visual builder for AI agents and workflows, exposed as API or MCP server
FastGPT
Knowledge-base Q&A and visual workflow platform for LLM apps
Verdict
Langflow ranks higher, 94 to 90. Of the 4 score parts we measure for both, Langflow leads on 1, FastGPT on 1 and 2 are even.
- AdoptionLangflow leads by 18 points, 98 to 80: a wide gap.
- FreshnessEven at 100.
- MaintenanceFastGPT leads by 6 points, 96 to 90.
- Agent-readyEven at 70.
Pick Langflow if you are
Developers building agent workflows visually and serving them as APIs/MCP tools
- Every flow can be called as a REST API or exposed as an MCP server
- Component source is editable in Python, so custom nodes need no fork
Pick FastGPT if you are
Teams building document Q&A bots and workflows, especially in Chinese markets
- Loaders for TXT, MD, HTML, PDF, Docx, PPTX, CSV, XLSX and URLs
- Hybrid retrieval with reranking; chunks can be edited and deleted
| What we compare | Langflow | FastGPT |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 98, widely used | 80, widely used |
| Freshness | 100, active | 100, active |
| Maintenance | 90, healthy | 96, healthy |
| Agent-ready | 70, partly | 70, partly |
| Facts from GitHub and the README | ||
| Stars | 156.6k | 30k |
| License | MIT (permissive) | custom license (read the license) |
| Last commit | Nov 2026 | Nov 2026 |
| Last release | Nov 2026 | Nov 2026 |
| Language | Python | TypeScript |
| Docker | Yes | Yes |
| GPU | Not stated | Not needed |
| arm64 or Apple Silicon | Mentioned | Not stated |
Langflow
Langflow is a Python platform for assembling agents and workflows on a visual canvas, with Python source access to customize any component. Each flow can be served as an HTTP API, exported as JSON, or deployed as an MCP server so MCP clients can call it as a tool. It installs via uv/pip, Docker, or a Windows and macOS desktop app, and serves its UI on port 7860.
Who it is for: Developers building agent workflows visually and serving them as APIs/MCP tools
Strengths
- Every flow can be called as a REST API or exposed as an MCP server
- Component source is editable in Python, so custom nodes need no fork
- Built-in playground for step-by-step testing of flows
- Observability integrations with LangSmith and LangFuse; MIT license
Weaknesses
- Official releases require setting LANGFLOW_SUPERUSER_PASSWORD; dev branches default to auto-login
- Python 3.10–3.14 required for the pip install path
- README gives no RAM or hardware requirements
- Docker + Compose
- Needs Python 3.10–3.14, uv
- Models: major LLM providers, vector databases
- port 7860
FastGPT
FastGPT builds agents and LLM apps from a visual Flow editor on top of a knowledge base that ingests TXT, MD, HTML, PDF, Docx, PPTX, CSV, XLSX and URLs with hybrid retrieval and reranking. A one-script Docker Compose install serves port 3000 (default login root / 1234), supports bidirectional MCP, chat and plugin workflows, evaluation, call-chain logs, login-free share pages and iframe embedding.
Who it is for: Teams building document Q&A bots and workflows, especially in Chinese markets
Strengths
- Loaders for TXT, MD, HTML, PDF, Docx, PPTX, CSV, XLSX and URLs
- Hybrid retrieval with reranking; chunks can be edited and deleted
- Bidirectional MCP and RPA-style workflow nodes
- One-script Docker Compose install
Weaknesses
- FastGPT Open Source License forbids offering it as SaaS and requires kept copyright notices
- Default credentials root / 1234 after install
- Default README is Chinese; English lives in README_en.md
- Debug mode, node logs and auto-generated workflows are still unchecked roadmap items
- no GPU
- Compose
- port 3000
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