# bytedance/SandboxFusion

Repository: https://github.com/bytedance/SandboxFusion
Canonical: https://ross.abutalabs.com/products/sandboxfusion
Homepage: https://bytedance.github.io/SandboxFusion/
Language: Python
License: Apache-2.0
License Family: permissive
Last push: 2026-07-14T03:43:19+00:00

## Health v2 (maintenance only)
Score: 63/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 92, release rhythm 35, longevity 49
- inputs: {"age_days": 686, "days_push": 50, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1060, forks 103 (observed 2026-08-28T04:03:25.790399+00:00)

## What it is
A secure, self-hosted code sandbox service from ByteDance that runs and judges code generated by LLMs across 20+ programming languages via a uniform HTTP API. It ships with built-in support for popular coding evaluation datasets like HumanEval, MBPP, and CodeContests, and is designed for production cloud deployment with container-based security isolation.

## Use cases
- run untrusted LLM-generated code in a secure sandbox
- evaluate LLM coding ability on HumanEval and MBPP benchmarks
- judge code outputs for reinforcement learning training
- execute code snippets in many languages through one HTTP API
- set up an online judge for coding evaluation datasets
- run GPU code like CUDA in an isolated environment
- serve a code execution backend for an AI coding agent

## When to choose
- you need safe execution of model-generated code at scale
- you are benchmarking or training LLMs on coding tasks
- you want one API covering 20+ languages and standard evaluation datasets
- you can deploy Docker containers and want production-ready isolation

## When to avoid
- you only need to run your own trusted code locally without isolation
- you cannot deploy Docker or privileged containers
- you need a lightweight in-process sandbox rather than a networked service

## Facets
- artifact type: service
- maturity: active
- function: testing, benchmarking, http-server, security, machine-learning, llm-training, gpu-computing
- domain: large-language-models, developer-tools, testing, artificial-intelligence, self-hosted, cloud-computing
- platform: python, self-hosted
- tags: code-sandbox, code-execution, online-judge, llm-evaluation, code-judging, multi-language, humaneval, code-contests, jupyter-kernel, rl-environment, docker, linux, web-server, gpu

## Member repositories
- bytedance/SandboxFusion (main) score 63

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:25.790399+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T06:56:55.667786+00:00, confidence not recorded.
  - readme: https://github.com/bytedance/SandboxFusion (fetched 2026-08-28T04:03:25.790399+00:00, sha 8a104857216c)
  - homepage: https://bytedance.github.io/SandboxFusion/ (fetched 2026-08-29T12:58:56.728352+00:00, sha 11079749a7e1)
- Data as of 2026-08-30T08:39:29.467469+00:00.