Dify 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.
Dify
Visual builder for LLM workflows, RAG pipelines and agents, with APIs
FastGPT
Knowledge-base Q&A and visual workflow platform for LLM apps
Verdict
Dify ranks higher, 92 to 90. Of the 4 score parts we measure for both, Dify leads on 1, FastGPT on 2 and 1 is even.
- AdoptionDify leads by 19 points, 99 to 80: a wide gap.
- FreshnessEven at 100.
- MaintenanceFastGPT leads by 3 points, 96 to 93: close to even.
- Agent-readyFastGPT leads by 25 points, 70 to 45: a wide gap.
Pick Dify if you are
Teams building and self-hosting LLM apps, RAG and agent workflows
- Visual workflow canvas, prompt IDE and RAG pipeline in one self-hosted app
- Hundreds of model providers, including any OpenAI API-compatible endpoint
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 | Dify | FastGPT |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 99, widely used | 80, widely used |
| Freshness | 100, active | 100, active |
| Maintenance | 93, healthy | 96, healthy |
| Agent-ready | 45, minimal | 70, partly |
| Facts from GitHub and the README | ||
| Stars | 161k | 30k |
| License | custom license (read the license) | 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 needed | Not needed |
| arm64 or Apple Silicon | Not stated | Not stated |
Dify
Dify is an LLM app development platform with a visual workflow canvas, a prompt IDE, a RAG pipeline that extracts text from PDFs and PPTs, and agents that run in their own sandbox. Every app is exposed through a generated API, and tools can come from Dify Marketplace, MCP servers or custom APIs. It runs self-hosted via Docker Compose or as the hosted Dify Cloud.
Who it is for: Teams building and self-hosting LLM apps, RAG and agent workflows
Strengths
- Visual workflow canvas, prompt IDE and RAG pipeline in one self-hosted app
- Hundreds of model providers, including any OpenAI API-compatible endpoint
- Every app gets a backend API; agents can use MCP servers and marketplace tools
- Observability integrations with Opik, Langfuse and Arize Phoenix
Weaknesses
- Custom Dify Open Source License (Apache 2.0 plus conditions), not plain Apache 2.0
- SSO, RBAC and support SLAs are limited to the Enterprise edition
- Compose deployment runs several services and needs a manually generated sandbox auth token
- Docker Compose v2.35.0 or later required
- RAM ≥ 4 GB
- no GPU
- Docker + Compose
- Needs Docker, Docker Compose v2.35.0+, OpenSSL
- Models: OpenAI API-compatible, GPT, Mistral, Llama3
- port 80
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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