Skip to content
Open
Show file tree
Hide file tree
Changes from all commits
Commits
File filter

Filter by extension

Filter by extension

Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
63 changes: 63 additions & 0 deletions agents/SWE-agent__mini-swe-agent/README.md
Original file line number Diff line number Diff line change
@@ -0,0 +1,63 @@
# mini-swe-agent

**The minimal AI software engineering agent.** Built by the Princeton & Stanford team behind [SWE-bench](https://github.com/swe-bench/SWE-bench) and [SWE-agent](https://github.com/SWE-agent/SWE-agent).

## What it does

`mini-swe-agent` solves GitHub issues and software engineering tasks by interacting with a computer **entirely through bash** — no custom tool implementations, no special interfaces, no complex scaffolding. Just the model, a shell, and a task.

Scores **>74% on SWE-bench Verified** with a ~100-line Python agent class.

## Key capabilities

- 🐚 **Bash-only** — no tool-calling interface required; works with any chat-completion model
- 🧩 **Model-agnostic** — runs on Claude, GPT-4, Gemini, and more via [litellm](https://github.com/BerriAI/litellm), OpenRouter, Portkey
- 📦 **Sandboxed** — local, Docker/Podman, Singularity/Apptainer, bubblewrap, contree
- 🔬 **Research-grade** — linear trajectory = perfect fine-tuning/RL data
- ⚡ **Fast startup** — starts much faster than heavier agent frameworks
- 🏢 **Production-adopted** — used by Meta, NVIDIA, Essential AI, IBM, Princeton, Stanford

## How it works

1. The agent receives a task (a GitHub issue or CLI prompt)
2. It reasons aloud in a THOUGHT block, then issues **one bash command**
3. The environment runs the command in a **fresh subshell** (stateless, safe)
4. The agent observes the output and iterates until the task is solved

```python
from minisweagent.agents.default import DefaultAgent
from minisweagent.models.litellm_model import LitellmModel
from minisweagent.environments.local import LocalEnvironment

agent = DefaultAgent(
LitellmModel(model_name="claude-sonnet-4-5-20250929"),
LocalEnvironment(),
)
agent.run("Write a sudoku game")
```

## Installation & usage

```bash
# Quickest — no install needed
pip install uv && uvx mini-swe-agent

# Or install permanently
pip install mini-swe-agent
mini # run the CLI
```

## Benchmarks

| Model | SWE-bench Verified |
|---|---|
| Gemini 3 Pro | 74%+ |
| Claude Sonnet 4 | ~70% |
| GPT-5 | ~68% |

## Links

- 🌐 [Documentation](https://mini-swe-agent.com/latest/)
- 📄 [NeurIPS 2024 Paper](https://arxiv.org/abs/2405.15793)
- 🏆 [SWE-bench Leaderboard](https://www.swebench.com/)
- 💬 [Slack Community](https://join.slack.com/t/swe-bench/shared_invite/zt-36pj9bu5s-o3_yXPZbaH2wVnxnss1EkQ)
15 changes: 15 additions & 0 deletions agents/SWE-agent__mini-swe-agent/metadata.json
Original file line number Diff line number Diff line change
@@ -0,0 +1,15 @@
{
"name": "mini-swe-agent",
"author": "SWE-agent",
"description": "A radically minimal AI software engineering agent solving GitHub issues and CLI tasks via bash only. Scores >74% on SWE-bench Verified with ~100 lines of Python.",
"repository": "https://github.com/SWE-agent/mini-swe-agent",
"path": "",
"version": "2.0.0",
"category": "developer-tools",
"tags": ["software-engineering", "bash", "github-issues", "swe-bench", "minimal", "litellm", "docker", "coding-agent", "research", "benchmark"],
"license": "MIT",
"model": "claude-sonnet-4-5-20250929",
"adapters": ["system-prompt"],
"icon": false,
"banner": false
}
Loading