OpenManus vs SWE-agent
Which should you use? A side-by-side look at what each one does, who it suits, what it costs, how hard it is to set up, and where each falls short.
Open source · MIT
Open-source Python framework for building general-purpose AI agents that run tasks from a prompt.
Open source · MIT
An AI agent that tries to fix GitHub issues automatically, using the language model you choose.
The short answer
Choose OpenManus if you need…
- Developers who want to experiment with an open-source general AI agent
- Students learning how LLM agents call tools and browse the web
- Teams wanting to customize agent behavior in Python rather than use a closed product
- People who already have an LLM API key from OpenAI or a compatible provider
Choose SWE-agent if you need…
- Researchers studying autonomous coding agents or running SWE-bench experiments
- Developers who want to experiment with an LLM attempting issue fixes in their own repositories
- Students learning how agent-computer interfaces work, since the README describes it as simple and hackable
- People who want a configurable agent controlled by a single YAML file
Side-by-side
| OpenManus | SWE-agent | |
|---|---|---|
| Type | AI model / agent | AI model / agent |
| Price | Free and open source | Free and open source |
| Licence | MIT | MIT |
| GitHub stars | 58.9k | 20.7k |
| Setup difficulty | Intermediate | Intermediate |
| How you get it | Clone and build from source | Clone and build from source |
| Main language | Python | Python |
| Last code update | 4 days ago | 10 days ago |
What each one is
OpenManus
OpenManus is a Python project for building AI agents, programs that take a task described in plain language and use a language model to work toward it. The README describes it as an open alternative to the Manus agent that does not require an invite code. You supply your own LLM API key and run the agent from a terminal, and it can also use a browser through Browser Use.
SWE-agent
SWE-agent takes a GitHub issue and lets a language model you pick (the README mentions GPT-4o and Claude Sonnet 4 as examples) use tools to try to fix it in a real repository. It is also described as usable for offensive cybersecurity (capture-the-flag) challenges and custom coding tasks. It is a research project from Princeton and Stanford, and its README says its main development has moved to the simpler mini-SWE-agent.
Best for
OpenManus
- Developers who want to experiment with an open-source general AI agent
- Students learning how LLM agents call tools and browse the web
- Teams wanting to customize agent behavior in Python rather than use a closed product
- People who already have an LLM API key from OpenAI or a compatible provider
SWE-agent
- Researchers studying autonomous coding agents or running SWE-bench experiments
- Developers who want to experiment with an LLM attempting issue fixes in their own repositories
- Students learning how agent-computer interfaces work, since the README describes it as simple and hackable
- People who want a configurable agent controlled by a single YAML file
Strengths
OpenManus
- MIT licensed, so the code can be used and modified freely
- Supports multiple entry points: a general agent, an MCP version and a multi-agent flow
- Clear configuration through a single config.toml file for model and API settings
SWE-agent
- The README reports state-of-the-art results on SWE-bench among open-source projects
- Configuration is handled by a single YAML file and the README says it is fully documented
- MIT licensed with a public docs site, Slack, and a Codespaces option to try it in a browser
Not ideal for
OpenManus
- Non-technical users, since setup requires Python, a config file and API keys
- Anyone needing a polished, supported production product, because the README calls its implementation simple and is still evolving
- Users unwilling to pay for LLM API usage, since the README requires an API key for the models it calls
SWE-agent
- Teams wanting the project's actively recommended tool: the README says most development has moved to mini-SWE-agent, which it recommends instead
- Anyone needing a polished, finished product: the project is framed as research code
- People who cannot pay for or access a language model API, since the README assumes you bring your own model
Trade-offs
OpenManus
- The README calls its multi-agent flow unstable and describes the project as a simple implementation
- Requires paid or self-hosted LLM API access and careful key handling
- Several optional features need extra setup, such as Playwright browsers and chart visualization dependencies
SWE-agent
- The README says mini-SWE-agent has superseded it and is recommended instead going forward
- The EnIGMA cybersecurity mode currently points users to older version 0.7 rather than 1.0
- Results depend on the language model and API you supply; the README does not state costs or hardware needs
Getting started
OpenManus
Clone the repository
git clone https://github.com/FoundationAgents/OpenManus.gitCreate and activate a virtual environment with uv
uv venv --python 3.12SWE-agent
Clone the repository
git clone https://github.com/SWE-agent/SWE-agent.gitFrequently asked questions
What is the difference between OpenManus and SWE-agent?
OpenManus: Open-source Python framework for building general-purpose AI agents that run tasks from a prompt. SWE-agent: An AI agent that tries to fix GitHub issues automatically, using the language model you choose. OpenManus suits developers who want to experiment with an open-source general ai agent, while SWE-agent suits researchers studying autonomous coding agents or running swe-bench experiments.
Are OpenManus and SWE-agent free?
OpenManus: Free and open source. SWE-agent: Free and open source.
Is OpenManus or SWE-agent more popular?
OpenManus has more GitHub stars (58.9k) than SWE-agent (20.7k). Popularity isn't the same as fit — check which one matches your use case above.
What are other alternatives to OpenManus and SWE-agent?
Other options people compare them with include OpenHands, MetaGPT, DeerFlow, Open SWE, RD-Agent.
Keep comparing
Built from our catalogue entries for both products: open-source details come from each project's README (AI-assisted, refreshed weekly) and GitHub data; AI-tool details are editorial. Spotted something wrong? Let us know from either product's page.