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Nano SWE Agent - A simple AI software engineering agent

Project description

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The 100 line AI agent that solves GitHub issues & more

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In 2024, SWE-bench & SWE-agent helped kickstart the agentic AI for software 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
  • 🧑‍💻 Hackers & power users 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 65% of GitHub issues in the SWE-bench verified benchmark.
  • 🤗 Friendly: Comes with two convenient UIs that will turn this into your daily dev swiss army knife!
  • 🍀 Environments: In addition to local envs, you can use docker, podman, singularity, apptainer, and more
  • 🧪 Tested: Codecov
  • 🎓 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 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.
  • 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 out subprocess.run with docker exec) and to scale up effortlessly.

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.

More motivation (as a tool)

Some agents are overfitted research artifacts. Others are UI-heavy tools, highly optimized for a specific user experience. Both variants are hard to understand.

mini strives to be

  • Simple enough to understand at a glance
  • Convenient enough to use in daily workflows
  • Flexible to extend

A hackable tool, not a black box.

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.
  • 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).
Should I use SWE-agent or mini-SWE-agent?

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

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

What you get with both

  • Excellent performance on SWE-Bench
  • A trajectory browser
Simple UI (mini) Visual UI (mini -v)

mini

miniv

Batch inference Trajectory browser

swebench

inspector

Python bindings More in the docs
agent = DefaultAgent(
    LitellmModel(model_name=...),
    LocalEnvironment(),
)
agent.run("Write a sudoku game")

🔥 Let's get started!

Install + run in virtual environment

pip install pipx && pipx ensurepath && pipx run mini-swe-agent [-v]

Alternative: Install in current environment

pip install mini-swe-agent && mini [-v]

Alternative: 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:

👀 More agentic AI

SWE-agent    SWE-ReX    SWE-bench    SWE-smith    sb-cli

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