Mini-SWE-agent scores up to 74% on SWE-bench in 100 lines of Python code

A new open-source coding agent, mini-SWE-agent, achieves up to 74% on the SWE-bench verified benchmark using just 100 lines of Python code. Developed by the Princeton and Stanford team behind SWE-bench, the agent operates with no tools other than bash and a completely linear history, making it simpler and faster than alternatives like Claude Code. The tool is already adopted by Meta, NVIDIA, IBM, and other major organizations for research and daily workflows.

This is mini-swe-agent v2 Read the migration guide https://mini-swe-agent.com/latest/advanced/v2 migration/ . For the previous version, check out the v1 documentation https://mini-swe-agent.com/v1/ or the v1 branch https://github.com/SWE-agent/mini-swe-agent/tree/v1 . In 2024, SWE-bench https://swebench.com & SWE-agent https://swe-agent.com 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 100 lines of python https://github.com/SWE-agent/mini-swe-agent/blob/main/src/minisweagent/agents/default.py +100 total for env https://github.com/SWE-agent/mini-swe-agent/blob/main/src/minisweagent/environments/local.py , model https://github.com/SWE-agent/mini-swe-agent/blob/main/src/minisweagent/models/litellm model.py , script https://github.com/SWE-agent/mini-swe-agent/blob/main/src/minisweagent/run/hello world.py — no fancy dependencies Performant: Scores 74% on the SWE-bench verified benchmark https://www.swebench.com/ ; 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 /completion and /response endpoints, interleaved thinking etc.- Built by the Princeton & Stanford team behind SWE-bench https://swebench.com , SWE-agent https://swe-agent.com , and more Tested: Why use mini-SWE-agent for research? SWE-agent https://swe-agent.com/latest/ 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, 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 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 — 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 subprocess.run with docker exec and to scale up effortlessly. Seriously, this is a big deal faq/ why-no-shell-session , 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 https://www.swebench.com/ leaderboard, that evaluates the performance of different LMs with mini . Why use mini-SWE-agent 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 Unlike other agents including our own swe-agent https://swe-agent.com/latest/ , 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 — every action is completely independent as opposed to keeping a stateful shell session running . This is subprocess.run a big deal https://mini-swe-agent.com/latest/faq/ why-no-shell-session 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 mini-SWE-agent or 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 You should use 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 What you get with both - Excellent performance on SWE-Bench - A trajectory browser | CLI | mini Batch inference Trajectory browser Python bindings agent = DefaultAgent LitellmModel model name=... , LocalEnvironment , agent.run "Write a sudoku game" Upgrading to v2? Check out our v2 migration guide advanced/v2 migration/ for all the changes and how to update your code. Continue reading: 📣 News Run mini-swe-agent on our new & extremely challenging benchmark, ProgramBench usage/programbench/ New tutorial on building minimal AI agents https://minimal-agent.com/ - Nov 19: Gemini 3 Pro reaches 74% on SWE-bench verified with mini-swe-agent https://x.com/KLieret/status/1991164693839270372 - Aug 19: New blogpost: Randomly switching between GPT-5 and Sonnet 4 boosts performance https://www.swebench.com/post-250820-mini-roulette.html 📣 New features Please check the github release notes https://github.com/SWE-agent/mini-swe-agent/releases for the latest updates. 📣 Documentation updates - Jul 27: More notes on local models models/local models/