AutoGPT 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.
AutoGPT
Role-based AI agents coordinated by one lead agent, plus a visual workflow builder
FastGPT
Knowledge-base Q&A and visual workflow platform for LLM apps
Verdict
AutoGPT ranks higher, 95 to 90. Of the 4 score parts we measure for both, AutoGPT leads on 2, FastGPT on 1 and 1 is even.
- AdoptionAutoGPT leads by 20 points, 100 to 80: a wide gap.
- FreshnessEven at 100.
- MaintenanceFastGPT leads by 7 points, 96 to 89.
- Agent-readyAutoGPT leads by 15 points, 85 to 70: a wide gap.
Pick AutoGPT if you are
Teams delegating content, sales and recurring work to AI agents
- Single-container Docker image for self-hosting, plus an installer for Linux, macOS and Windows
- Visual builder with branching logic and a marketplace of reusable workflows
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 | AutoGPT | FastGPT |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 100, widely used | 80, widely used |
| Freshness | 100, active | 100, active |
| Maintenance | 89, healthy | 96, healthy |
| Agent-ready | 85, ready | 70, partly |
| Facts from GitHub and the README | ||
| Stars | 188k | 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 stated | Not needed |
| arm64 or Apple Silicon | Mentioned | Not stated |
AutoGPT
AutoGPT is a platform where a coordinator agent (Otto) hands work to configurable specialist agents called Experts, each with its own instructions, skills and connected apps. It also has a block-based visual workflow builder and a workflow marketplace. It runs as a paid hosted service or self-hosted from a single Docker image with your own model credentials.
Who it is for: Teams delegating content, sales and recurring work to AI agents
Strengths
- Single-container Docker image for self-hosting, plus an installer for Linux, macOS and Windows
- Visual builder with branching logic and a marketplace of reusable workflows
- Per-Expert account access and spending controls
- Self-hosting accepts your own model credentials or a local model
Weaknesses
- Platform code is Polyform Shield, not OSI open source; only classic/ is MIT
- Hosted Platform is paid; built-in model access is not included when self-hosting
- README gives no RAM or GPU requirements; they are in the linked guide
- Original standalone agent moved to classic/; main README centers on the platform
- Needs Docker, model provider credentials or a local model
- Models: bring-your-own model credentials, local models
- port 3000
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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