LLM-in-Sandbox

🌐 Project • 📄 Paper • 💻 LLM-in-Sandbox-RL • 🤗 Huggingface • 📦 Model & Data • 🎬 Youtube • 📽️ Slides • 🕶️ Awesome Computer-Use-Agent • 🦞 ClawGym•

This project is developed by RUC AI Box.

Give your LLM a computer, unlocking general agentic intelligence

As vibe coding becomes common and 🦞 OpenClaw draws widespread attention, we present a systematic study to show that placing an LLM inside a code sandbox with basic computer functionalities lets it significantly outperform standalone LLMs across chemistry, physics, math, biomedicine, long-context understanding, and instruction-following with no extra training. RL further amplifies the gains.

• 📈 Consistent improvements across diverse non-code domains
• 🧠 File system as long-term memory, up to 8× token savings
• 🐳 Docker isolation for security (vs. unrestricted setups like 🦞 OpenClaw)
• 🔌 Works with OpenAI, Anthropic, vLLM, SGLang, etc.

Feel free to open an issue if you have any questions or run into any problems. We'd be happy to help!

▶️ Click to watch the demo video

News

• [2026-04-30] Released ClawGym, a scalable framework for building effective claw agents, covering data collection, training infra, and evaluation benchmark!
• [2026-04-11] Released scale-openclaw — collect 🦞 OpenClaw trajectories at scale on a single machine, Docker-free.
• [2026-03-25] Released Awesome Computer-Use Agents, a curated list tracking the development of computer-use agents across terminal and GUI paradigms, with an accompanying overview report.
• [2026-03-20] Uploaded talk slides on "LLM-in-Sandbox: From Coding Agent to General Agent".
• [2026-02-12] Released code, model & data, and wandb log for LLM-in-Sandbox Reinforcement Learning at LLM-in-Sandbox-RL.
• [2026-02-11] v0.2.0: Added benchmark module for reproducing paper results, evaluating any LLM, and adding your own tasks.
• [2026-01-23] Released paper, project page, and code. Honored as #1 paper of the day on Hugging Face.

Table of Contents

• Installation
• Quick Start
• More Examples
• Benchmark and Reproduction
• Citation

Installation

Requirements: Python 3.10+, Docker

1. Install Docker

Skip this if Docker is already installed.

curl -fsSL https://get.docker.com -o get-docker.sh && sh get-docker.sh
dockerd > /var/log/dockerd.log 2>&1 &

Or follow the official Docker docs.

2. Install llm-in-sandbox

pip install llm-in-sandbox==0.2.0

Or install from source:

git clone https://github.com/llm-in-sandbox/llm-in-sandbox.git
cd llm-in-sandbox
pip install -e .

Docker Image

The default Docker image (cdx123/llm-in-sandbox:v0.1) will be automatically pulled when you first run the agent. The first run may take a minute to download the image (~400MB), but subsequent runs will start instantly.

llm-in-sandbox build

System Requirements & Reproducibility Notes

LLM-in-Sandbox is backend-agnostic: it connects to any OpenAI-compatible endpoint, so no local GPU is required when using API services (e.g., OpenAI, Anthropic, or a hosted vLLM/SGLang server). Self-hosting a model is optional, and its hardware needs depend on the chosen model and serving framework.

• Tested on: Ubuntu 22.04 with Python 3.10 and Docker.
• Installation time: ~1 minute on a normal desktop computer (the one-time Docker image pull, ~400 MB, also takes about a minute on a typical connection).
• Demo runtime: the quick-start demo completes in ~1 minute on a normal desktop computer (excluding model inference latency, which depends on your chosen endpoint).
• Dependencies: all Python dependencies are pinned in pyproject.toml; the sandbox runtime is provided by the Docker image cdx123/llm-in-sandbox:v0.1, pulled automatically on first run.

Quick Start

LLM-in-Sandbox works with various LLM providers including OpenAI, Anthropic, and self-hosted servers (vLLM, SGLang, etc.).

Option 1: Cloud / API Services

llm-in-sandbox run \
    --query "write a hello world in python" \
    --llm_name "openai/gpt-5" \
    --llm_base_url "http://your-api-server/v1" \
    --api_key "your-api-key"

Option 2: Self-Hosted Models

vllm serve Qwen/Qwen3-Coder-30B-A3B-Instruct \
    --served-model-name qwen3_coder \
    --enable-auto-tool-choice \
    --tool-call-parser qwen3_coder \
    --tensor-parallel-size 8  \
    --enable-prefix-caching

llm-in-sandbox run \
    --query "write a hello world in python" \
    --llm_name qwen3_coder \
    --llm_base_url "http://localhost:8000/v1"  \
    --temperature 0.7

python3 -m sglang.launch_server \
    --model-path "deepseek-ai/DeepSeek-V3.2" \
    --served-model-name "DeepSeek-V3.2" \
    --trust-remote-code \
    --tp-size 8 \
    --tool-call-parser deepseekv32 \
    --reasoning-parser deepseek-v3 \
    --host 0.0.0.0 \
    --port 5678

llm-in-sandbox run \
    --query "write a hello world in python" \
    --llm_name DeepSeek-V3.2 \
    --llm_base_url "http://0.0.0.0:5678/v1" \
    --extra_body '{"chat_template_kwargs": {"thinking": True}}'

Parameters (Common)

Run llm-in-sandbox run --help for all available parameters.

Output

Each run creates a timestamped folder:

output/2026-01-16_14-30-00/
├── files/
│   ├── answer.txt      # Final answer
│   └── hello_world.py  # Output file
└── trajectory.json     # Execution history

More Examples

We provide examples across diverse non-coding domains: scientific reasoning, long-context understanding, instruction following, travel planning, video production, music composition, poster design, and more.

👉 See examples/README.md for the full list.

Benchmark and Reproduction

Reproduce our paper results, evaluate any LLM in the sandbox, or add your own tasks.

👉 See llm_in_sandbox/benchmark/README.md

Contact Us

Feel free to open an issue if you have any questions or run into any problems, we’d be happy to help! You can also reach us directly at daixuancheng6@gmail.com and shaohanh@microsoft.com.

Acknowledgment

We learned the design and reused code from R2E-Gym. Thanks for the great work!

License

Apache License 2.0. See LICENSE for details.

Citation

If you find our work helpful, please cite us:

@article{cheng2026computer,
  title={Computer environments elicit general agentic intelligence in llms},
  author={Cheng, Daixuan and Huang, Shaohan and Gu, Yuxian and Song, Huatong and Chen, Guoxin and Dong, Li and Zhao, Wayne Xin and Wen, Ji-Rong and Wei, Furu},
  journal={arXiv preprint arXiv:2601.16206},
  year={2026}
}