mini-swe-agent
The 100 line AI agent that solves GitHub issues or helps you in your command line. Radically simple, no huge configs, no giant monorepo—but scores 68% on SWE-bench verified!
Stars: 1721
The mini-swe-agent is a lightweight AI agent designed to solve GitHub issues and more. It is a minimal tool with just 100 lines of Python code, suitable for researchers, developers, and engineers looking for a simple, powerful, and deployable solution. The agent is built to be convenient, deployable in various environments, and tested for performance. It focuses on providing a hackable tool that is simple, convenient, and flexible, without unnecessary bloat or complexity. Users can run the agent with any model using bash, making it ideal for benchmarking, fine-tuning, and reinforcement learning tasks.
README:
📣 New blogpost: Randomly switching between GPT-5 and Sonnet 4 boosts performance
In 2024, SWE-bench & SWE-agent helped kickstart the coding agent revolution.
We now ask: What if SWE-agent was 100x smaller, and still worked nearly as well?
mini is for
- Researchers who want to benchmark, fine-tune or RL without assumptions, bloat, or surprises
- Developers who like their tools like their scripts: short, sharp, and readable
- Engineers who want something trivial to sandbox & to deploy anywhere
Here's some details:
- Minimal: Just 100 lines of python (+100 total for env, model, script) — no fancy dependencies!
- Powerful: Resolves 68% of GitHub issues in the SWE-bench verified benchmark (leaderboard).
- Convenient: Comes with UIs that turn this into your daily dev swiss army knife!
- Deployable: In addition to local envs, you can use docker, podman, singularity, apptainer, and more
-
Tested:
- Cutting edge: Built by the Princeton & Stanford team behind SWE-bench and SWE-agent.
More motivation (for research)
SWE-agent jump-started the development of AI agents in 2024. Back then, we placed a lot of emphasis on tools and special interfaces for the agent. However, one year later, as LMs have become more capable, a lot of this is not needed at all to build a useful agent! In fact, mini-SWE-agent
- Does not have any tools other than bash — it doesn't even use the tool-calling interface of the LMs. This means that you can run it with literally any model. When running in sandboxed environments you also don't need to take care of installing a single package — all it needs is bash.
- Has a completely linear history — every step of the agent just appends to the messages and that's it. So there's no difference between the trajectory and the messages that you pass on to the LM. Great for debugging & fine-tuning.
-
Executes actions with
subprocess.run— every action is completely independent (as opposed to keeping a stateful shell session running). This makes it trivial to execute the actions in sandboxes (literally just switch outsubprocess.runwithdocker exec) and to scale up effortlessly. Seriously, this is a big deal, trust me.
This makes it perfect as a baseline system and for a system that puts the language model (rather than
the agent scaffold) in the middle of our attention.
You can see the result on the SWE-bench (bash only) leaderboard, that evaluates the performance of different LMs with mini.
More motivation (as a tool)
Some agents are overfitted research artifacts. Others are UI-heavy frontend monsters.
mini wants to be a hackable tool, not a black box.
- Simple enough to understand at a glance
- Convenient enough to use in daily workflows
- Flexible to extend
Unlike other agents (including our own swe-agent), it is radically simpler, because it:
- Does not have any tools other than bash — it doesn't even use the tool-calling interface of the LMs. Instead of implementing custom tools for every specific thing the agent might want to do, the focus is fully on the LM utilizing the shell to its full potential. Want it to do something specific like opening a PR? Just tell the LM to figure it out rather than spending time to implement it in the agent.
-
Executes actions with
subprocess.run— every action is completely independent (as opposed to keeping a stateful shell session running). This is a big deal for the stability of the agent, trust me. - Has a completely linear history — every step of the agent just appends to the messages that are passed to the LM in the next step and that's it. This is great for debugging and understanding what the LM is prompted with.
Should I use SWE-agent or mini-SWE-agent?
You should use mini-swe-agent if
- You want a quick command line tool that works locally
- You want an agent with a very simple control flow
- You want even faster, simpler & more stable sandboxing & benchmark evaluations
- You are doing FT or RL and don't want to overfit to a specific agent scaffold
You should use swe-agent if
- You need specific tools or want to experiment with different tools
- You want to experiment with different history processors
- You want very powerful yaml configuration without touching code
What you get with both
- Excellent performance on SWE-Bench
- A trajectory browser
Simple UI (mini)
|
Visual UI (mini -v)
|
|
|
|
| Batch inference | Trajectory browser |
|
|
|
| Python bindings | More in the docs |
agent = DefaultAgent(
LitellmModel(model_name=...),
LocalEnvironment(),
)
agent.run("Write a sudoku game") |
Option 1: Install + run in virtual environment
pip install uv && uvx mini-swe-agent [-v]
# or
pip install pipx && pipx ensurepath && pipx run mini-swe-agent [-v]Option 2: Install in current environment
pip install mini-swe-agent && mini [-v]Option 3: Install from source
git clone https://github.com/SWE-agent/mini-swe-agent.git
cd mini-swe-agent
pip install -e .
mini [-v]Read more in our documentation:
- Quick start guide
- More on
miniandmini -v - Global configuration
- Yaml configuration files
- Power up with the cookbook
- FAQ
- Contribute!
If you found this work helpful, please consider citing the SWE-agent paper in your work:
@inproceedings{yang2024sweagent,
title={{SWE}-agent: Agent-Computer Interfaces Enable Automated Software Engineering},
author={John Yang and Carlos E Jimenez and Alexander Wettig and Kilian Lieret and Shunyu Yao and Karthik R Narasimhan and Ofir Press},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://arxiv.org/abs/2405.15793}
}Our other projects:
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