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SWE-agent/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 >74% on SWE-bench verified!
About SWE-agent/mini-swe-agent
SWE-agent/mini-swe-agent is an open-source project on GitHub, mainly written in Python. The 100 line AI agent that solves GitHub issues or helps you in your command line. It currently holds 8,156 stars and 1,120 forks with 0 open issues, and was last pushed on an unknown date (repository created unknown).
Project Overview
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GitHub Repository Details
README
The minimal AI software engineering agent
📣 mini-swe-agent now powers Ramp SWE-Bench
📣 mini-swe-agent beats Claude Code and Codex on DeepSWE
📣 Run mini-swe-agent on our new & extremely challenging benchmark, ProgramBench
📣 New tutorial on building minimal AI agents
[!WARNING]
This is mini-swe-agent v2. Read the migration guide. For the previous version, check out the v1 branch.
In 2024, we built SWE-bench & SWE-agent and helped kickstart the coding agent revolution.
We now ask: What if our agent was 100x simpler, and still worked nearly as well?
mini is
- Widely adopted: Used by Meta, NVIDIA, Essential AI, IBM, Nebius, Anyscale, Princeton University, Stanford University, and many more.
- Minimal: Just some 100 lines of python for the agent class (and a bit more for the environment,
- Performant: Scores >74% on the SWE-bench verified benchmark; starts much faster than Claude Code
- Deployable: Supports local environments, docker/podman, singularity/apptainer, bublewrap, contree, and more
- Compatible: Supports all models via litellm, openrouter, portkey, and more. Support for
/completionand/responseendpoints, interleaved thinking etc. - Built by the Princeton & Stanford team behind SWE-bench, SWE-agent, and more
- Tested:
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, the mini agent
- Does not have any tools other than bash — it doesn't even need to use the tool-calling interface of the LMs.
- Has a completely linear history — every step of the agent just appends to the messages and that's it.
- Executes actions with
subprocess.run— every action is completely independent (as opposed to keeping a stateful shell session running).
subprocess.run with docker 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.
The mini agent 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
- Does not have any tools other than bash — it doesn't even need to use the tool-calling interface of the LMs.
- Executes actions with
subprocess.run— every action is completely independent (as opposed to keeping a stateful shell session running).
- 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.
Should I use SWE-agent or mini-SWE-agent?
You should consider mini-swe-agent your default choice.
In particular, 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
swe-agent if
- You want to experiment with different sets of tools, each with their own interface
- You want to experiment with different history processors
- Excellent performance on SWE-Bench
- A trajectory browser
CLI (mini)
|
Batch inference |
|
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| Trajectory browser | Python bindings |
|
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Let's get started!
Option 1: If you just want to try out the CLI (package installed in anonymous virtual environment)
pip install uv && uvx mini-swe-agent
or
pip install pipx && pipx ensurepath && pipx run mini-swe-agent
Option 2: Install CLI & python bindings in current environment
pip install mini-swe-agent
mini # run the CLI
Option 3: Install from source (developer setup)
git clone https://github.com/SWE-agent/mini-swe-agent.git
cd mini-swe-agent && pip install -e .
mini # run the CLI
Read more in our documentation:
- Quick start guide
- Using the
miniCLI - Global configuration
- Yaml configuration files
- Power up with the cookbook
- FAQ
- Contribute!
Attribution
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:
GitHub Stars & Activity
8,156Stars
1,120Forks
0Open issues
PythonLanguage
GitHub Popularity
GitHub stars8,156
Forks1,120
Open issues0
Primary languagePython
License-
Stars gained today1,265
Created-
Last pushed-
Trending History
Monthly boardrank #59 · ▲ 1,265 stars
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