metagpt vs autogen
Two of the top projects in Agent frameworks and SDKs, side by side: who leads on each score part and who each one suits, then setup, license, activity and what each review found.
metagpt
Python multi-agent framework that turns one-line requirements into a software repo
autogen
Python and .NET framework for multi-agent apps, now in maintenance mode
Verdict
metagpt ranks higher, 33 to 30. Of the 4 score parts we measure for both, metagpt leads on 2, autogen on 1 and 1 is even.
- Adoptionmetagpt leads by 20 points, 57 to 37: a wide gap.
- Freshnessautogen leads by 33 points, 58 to 25: a wide gap.
- Maintenancemetagpt leads by 23 points, 27 to 4: a wide gap.
- Agent-readyEven at 0.
Pick metagpt if you are
Developers experimenting with multi-agent LLM workflows for code generation
- Role and SOP model produces requirements, design docs and code repo from one prompt
- Usable as CLI or Python library; pip install metagpt
Pick autogen if you are
Developers with existing AutoGen code or prototyping multi-agent apps in Python
- Layered design: Core, AgentChat and Extensions APIs let you pick the abstraction level
- Core runtime supports local and distributed execution, with Python and .NET packages
| What we compare | metagpt | autogen |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 57, popular | 37, known |
| Freshness | 25, quiet | 58, recent |
| Maintenance | 27, weak | 4, weak |
| Easy to run | Not scored for this kind | Not scored for this kind |
| Agent-ready | 0, none | 0, none |
| Facts from GitHub and the README | ||
| Stars | 70.8k | 61.4k |
| License | MIT (permissive) | CC-BY-4.0 (read the license) |
| Last commit | Jan 2026 | Apr 2026 |
| Last release | Apr 2024 | Sep 2025 |
| Language | Python | Python |
| Docker | Yes | No |
| GPU | Not needed | Not needed |
| arm64 or Apple Silicon | Not stated | Not stated |
metagpt
MetaGPT assigns LLM-backed roles (product manager, architect, project manager, engineer) to follow predefined SOPs, turning a one-line requirement into user stories, designs, APIs and code. It runs as a CLI (`metagpt "Create a 2048 game"`) or a Python library, and also ships a Data Interpreter role for data analysis. LLM providers such as OpenAI, Azure, Ollama and Groq are set in `~/.metagpt/config2.yaml`.
Who it is for: Developers experimenting with multi-agent LLM workflows for code generation
Strengths
- Role and SOP model produces requirements, design docs and code repo from one prompt
- Usable as CLI or Python library; pip install metagpt
- Provider set in one YAML file: OpenAI, Azure, Ollama, Groq and others
- MIT license with tutorials, agent and multi-agent guides, and research papers
Weaknesses
- Python 3.9 to below 3.12 only
- Requires Node and pnpm installed before actual use
- Last release was 2024-04-22; the PyPI package may lag the main branch
- README calls the software-company schematic 'gradually implementing'
- no GPU
- Docker
- Needs Python 3.9-3.11, Node.js, pnpm, LLM API key or local LLM endpoint
- Models: OpenAI, Azure, Ollama, Groq
autogen
AutoGen is a layered framework for building multi-agent applications: a Core API for event-driven message passing, an AgentChat API for two-agent and group-chat patterns, and an Extensions API for model clients, MCP tools and code execution. It also ships AutoGen Studio, a no-code prototyping UI on port 8080, and AgentBench. The project is in maintenance mode and community managed; Microsoft points new users to Microsoft Agent Framework.
Who it is for: Developers with existing AutoGen code or prototyping multi-agent apps in Python
Strengths
- Layered design: Core, AgentChat and Extensions APIs let you pick the abstraction level
- Core runtime supports local and distributed execution, with Python and .NET packages
- MCP tool support through McpWorkbench, e.g. the Playwright MCP server
- Includes AutoGen Studio for no-code prototyping and a benchmarking suite
Weaknesses
- Maintenance mode: no new features, only bug fixes, security patches and docs
- Maintainers recommend Microsoft Agent Framework for new projects
- AutoGen Studio is explicitly not production-ready and has no built-in authentication
- Support is community-only and response times may be slow
- no GPU
- Needs Python 3.10+, OpenAI API key (for the quickstart samples), Node.js/npx (for the Playwright MCP example)
- Models: OpenAI, Azure OpenAI
- port 8080
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