browser-use vs metagpt
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.
browser-use
Python library that lets an LLM agent control a web browser
metagpt
Python multi-agent framework that turns one-line requirements into a software repo
Verdict
browser-use ranks higher, 83 to 33. Of the 4 score parts we measure for both, browser-use leads on 4, metagpt on none.
- Adoptionbrowser-use leads by 22 points, 79 to 57: a wide gap.
- Freshnessbrowser-use leads by 75 points, 100 to 25: a wide gap.
- Maintenancebrowser-use leads by 49 points, 76 to 27: a wide gap.
- Agent-readybrowser-use leads by 70 points, 70 to 0: a wide gap.
Pick browser-use if you are
Developers building browser-automating agents in Python
- Works with a local Chrome profile, a cloud browser, or a remote browser over CDP
- Custom tools register with a decorator; system prompt can be extended or overridden
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
| What we compare | browser-use | metagpt |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 79, popular | 57, popular |
| Freshness | 100, active | 25, quiet |
| Maintenance | 76, fair | 27, weak |
| Easy to run | Not scored for this kind | Not scored for this kind |
| Agent-ready | 70, partly | 0, none |
| Facts from GitHub and the README | ||
| Stars | 117.8k | 70.8k |
| License | MIT (permissive) | MIT (permissive) |
| Last commit | Oct 2026 | Jan 2026 |
| Last release | Oct 2026 | Apr 2024 |
| Language | Python | Python |
| Docker | Yes | Yes |
| GPU | Optional | Not needed |
| arm64 or Apple Silicon | Not stated | Not stated |
browser-use
Browser Use runs an LLM-driven agent that opens a browser, reads pages and completes tasks such as searching, filling forms and booking slots. It is used as a Python library (3.11+), as a CLI skill for agents like Claude Code, or through a hosted cloud API. It works with OpenAI, Anthropic, Google, Ollama and its own BU2 model, on local or cloud browsers.
Who it is for: Developers building browser-automating agents in Python
Strengths
- Works with a local Chrome profile, a cloud browser, or a remote browser over CDP
- Custom tools register with a decorator; system prompt can be extended or overridden
- Supports OpenAI, Anthropic and Google wrappers, plus local models via Ollama
- Ships a CLI skill so existing coding agents can drive a browser
Weaknesses
- README recommends BU2, which needs a paid Browser Use API key
- CAPTCHA and stealth handling rely on the paid cloud browser, with no guarantee
- Profile sync copies cookies only, not local storage, IndexedDB or extensions
- Hardware needs for local models are not stated in the README
- GPU optional
- Docker
- Needs Python >= 3.11, Chromium-based browser, LLM provider API key or Ollama, Browser Use API key (optional)
- Models: OpenAI, Anthropic, Google, Ollama, BU2 (ChatBrowserUse)
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
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