Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
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!
| Date | Stars |
|---|---|
| 2026-07-31 | 6161 |
| 2026-08-02 | 6161 |
| 2026-08-06 | 6161 |
Today
— stars today
This week
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This month
— stars this month
Momentum
35.0
growth rate 0.00%/day
<div align="center"> <a href="https://mini-swe-agent.com/latest/"><img src="https://github.com/SWE-agent/mini-swe-agent/raw/main/docs/assets/mini-swe-agent-banner.svg" alt="mini-swe-agent banner" style="height: 7em"/></a> </div> # The minimal AI software engineering agent 📣 [mini-swe-agent now powers Ramp SWE-Bench](https://labs.ramp.com/swebench)<br/> 📣 [mini-swe-agent beats Claude Code and Codex on DeepSWE](https://deepswe.datacurve.ai/blog#evaluation-harness)<br/> 📣 [Run mini-swe-agent on our new & extremely challenging benchmark, ProgramBench](https://mini-swe-agent.com/latest/usage/programbench/)<br/> 📣 [New tutorial on building minimal AI agents](https://minimal-agent.com/) [](https://mini-swe-agent.com/latest/) [](https://join.slack.com/t/swe-bench/shared_invite/zt-36pj9bu5s-o3_yXPZbaH2wVnxnss1EkQ) [](https://pypi.org/project/mini-swe-agent/) > [!WARNING] > 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 branch](https://github.com/SWE-agent/mini-swe-agent/tree/v1). In 2024, we built [SWE-bench](https://github.com/swe-bench/SWE-bench) & [SWE-agent](https://github.com/swe-agent/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](https://github.com/SWE-agent/mini-swe-agent/blob/main/src/minisweagent/agents/default.py) (and a bit more for the [environment](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), and [run 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:** [](https://codecov.io/gh/SWE-agent/mini-swe-agent) <details> <summary>More motivation (for research)</summary> [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, 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. 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 pa
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Kilian Lieret · Meta · United States
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17
carlos · @princeton-nlp · United States
16
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Albert Örwall · @soaplabs · Sweden
11
Philipp Moritz · Anyscale
6
Ofir Press
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4
Enes Yilmaz · Apple · United States
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John Yang
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Philip Nuzhnyi · The Bakery · Portugal
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Kevin Xiang Li · Stanford University
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Qiaochu Hu
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Robin Chiu · Taiwan
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Shuo Wang
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:04b7d8af1e019faa, topic:ai-agent, topic:agentic-ai, desc:ai agent