Open SWE vs SuperAGI
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 coding agent that takes engineering tasks and delivers pull requests, meant to be self-hosted.
Open source · MIT
Open-source Python framework for building and running autonomous AI agents.
The short answer
Choose Open SWE if you need…
- Engineering teams that want a self-hosted coding agent they can inspect and adapt
- Developers who already run LangChain or LangGraph and want a reference implementation
- Teams that want an agent to handle routine PR work such as reviews and CI follow-ups
- Organizations that need control over sandbox providers, models, and GitHub access
Choose SuperAGI if you need…
- Python developers experimenting with autonomous AI agents
- Teams exploring agent frameworks built around large language models
- People who want an MIT-licensed codebase they can read and modify
Side-by-side
| Open SWE | SuperAGI | |
|---|---|---|
| Type | AI model / agent | AI model / agent |
| Price | Free and open source | Free and open source |
| Licence | MIT | MIT |
| GitHub stars | 11.2k | 17.7k |
| Setup difficulty | Advanced | Advanced |
| How you get it | Clone and build from source | Clone and build from source |
| Main language | Python | Python |
| Last code update | 4 days ago | 22 months ago |
What each one is
Open SWE
Open SWE is an open-source coding agent from LangChain that investigates a codebase, makes changes in an isolated sandbox, validates them, and opens a pull request. It can also review pull requests, monitor CI, and respond to feedback. It is built on Deep Agents and LangGraph and is designed to be deployed in your own infrastructure.
SuperAGI
SuperAGI is an open-source Python project that describes itself as a framework for building, managing, and running autonomous AI agents. The description says it is aimed at developers who want to create agents that act on their own toward a goal. The README was not provided in usable form, so details beyond the project description and metadata could not be verified here.
Best for
Open SWE
- Engineering teams that want a self-hosted coding agent they can inspect and adapt
- Developers who already run LangChain or LangGraph and want a reference implementation
- Teams that want an agent to handle routine PR work such as reviews and CI follow-ups
- Organizations that need control over sandbox providers, models, and GitHub access
SuperAGI
- Python developers experimenting with autonomous AI agents
- Teams exploring agent frameworks built around large language models
- People who want an MIT-licensed codebase they can read and modify
Strengths
Open SWE
- Covers the full loop from investigation to pull request, review, and CI follow-up
- Open source under MIT, and deployable in your own infrastructure
- Lets you choose models, sandbox providers, integrations, and approval policies
SuperAGI
- MIT license permits free use and modification
- Large community interest, with about 17.7k GitHub stars
- Written in Python, a widely used language for AI work
Not ideal for
Open SWE
- Anyone wanting a ready-to-use hosted product: the README says deploying as-is requires Managed Deep Agents, otherwise you must fork and adapt the code to your own runtime
- Teams that expect stable releases: the README says it is under active development with expected breaking changes
- People hoping to contribute issues or patches: the project is not accepting issues or external contributions at this time
- Anyone who needs strong isolation for local execution: the README says local execution does not provide cloud sandbox isolation
SuperAGI
- Non-developers looking for a ready-made app, since the description targets developers
- Production users who need proven reliability, because the README could not confirm reliability claims and the last push date is January 2025
- People who need detailed setup instructions from this entry alone, since no install steps could be verified
Trade-offs
Open SWE
- Under active development with breaking changes and no stability guarantee
- Deployment as-is depends on Managed Deep Agents; otherwise you must adapt it yourself
- Sandboxes have powerful tools and may have network access, so credentials must be tightly scoped
SuperAGI
- README content was unavailable, so features and setup cannot be confirmed
- Last push was 2025-01-22 and the latest release is v0.0.14, so maintenance activity is uncertain
- Autonomous agents built on LLMs can behave unpredictably and need careful supervision
Getting started
Open SWE
Clone the repository
git clone https://github.com/langchain-ai/open-sweSuperAGI
Clone the repository
git clone https://github.com/TransformerOptimus/SuperAGIFrequently asked questions
What is the difference between Open SWE and SuperAGI?
Open SWE: Open-source coding agent that takes engineering tasks and delivers pull requests, meant to be self-hosted. SuperAGI: Open-source Python framework for building and running autonomous AI agents. Open SWE suits engineering teams that want a self-hosted coding agent they can inspect and adapt, while SuperAGI suits python developers experimenting with autonomous ai agents.
Are Open SWE and SuperAGI free?
Open SWE: Free and open source. SuperAGI: Free and open source.
Is Open SWE or SuperAGI more popular?
SuperAGI has more GitHub stars (17.7k) than Open SWE (11.2k). Popularity isn't the same as fit — check which one matches your use case above.
What are other alternatives to Open SWE and SuperAGI?
Other options people compare them with include Hermes Agent, DeerFlow, SWE-agent, watermarks-remover, 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.