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开源AI评估

基于 Python · 开源免费，本地部署，数据完全自主可控

英文名：opencompass

⭐ 7.1k Stars 🍴 784 Forks 💻 Python 📄 Apache-2.0 🏷 AI 8.5分

8.5AI 综合评分

LLMbenchmarkchatgptevaluation

🌐 访问官网

✦ AI Skill Hub 推荐

开源AI评估是 AI Skill Hub 本期精选AI工具之一。已获得 7.1k 颗 GitHub Star，综合评分 8.5 分，整体质量较高。我们强烈推荐将其纳入你的 AI 工具库，帮助提升工作效率。

📚 深度解析

开源AI评估是一款基于 Python 的开源工具，在 GitHub 上收获 7k+ Star，是LLM、benchmark、chatgpt、evaluation领域中的优质开源项目。开源工具的最大优势在于代码完全透明，你可以审计每一行代码的安全性，也可以根据自身需求进行二次开发和定制。

**为什么要使用开源工具而非商业 SaaS？**
对于个人开发者和有隐私需求的用户，本地部署的开源工具意味着数据不离本机，不受第三方服务商的数据政策约束。同时，开源工具通常没有使用次数限制和月度费用，一次安装即可长期使用，对于高频使用场景的总拥有成本（TCO）远低于订阅制商业工具。

**安装与环境准备**
开源AI评估依赖 Python 运行环境。建议通过 pyenv（Python）或 nvm（Node.js）管理 Python 版本，避免全局环境污染。对于新手用户，推荐先创建虚拟环境（python -m venv venv && source venv/bin/activate），再安装依赖，这样即使出现问题也可以随时删除虚拟环境重新开始，不影响系统稳定性。

**社区与维护**
GitHub Issue 和 Discussion 是获取帮助的最快渠道。在提问前建议先检查 Closed Issues（已关闭的问题），大多数常见问题都已有解答。遇到 Bug 时，提供 pip list 的输出、完整错误堆栈和最小可复现示例，能显著提高开发者响应速度。AI Skill Hub 将持续追踪开源AI评估的版本更新，及时通知重要功能变化。

📋 工具概览

开源AI评估是一款基于 Python 开发的开源工具，专注于 LLM、benchmark、chatgpt 等核心功能。作为 GitHub 开源项目，它拥有活跃的社区支持和持续的版本迭代，代码完全透明可审计，支持本地部署以保护数据隐私。无论是个人使用还是集成到企业工作流，都能提供稳定可靠的解决方案。

GitHub Stars

⭐ 7.1k

开发语言

Python

支持平台

Windows / macOS / Linux

维护状态

持续维护，定期更新

开源协议

Apache-2.0

AI 综合评分

8.5 分

工具类型

AI工具

Forks

784

📖 中文文档

以下内容由 AI Skill Hub 根据项目信息自动整理，如需查看完整原始文档请访问底部「原始来源」。

📌 核心特色

开源免费，支持本地部署，数据完全自主可控
活跃的 GitHub 开源社区，持续迭代更新
提供详细文档和使用示例，新手友好
支持自定义配置，灵活适配不同使用环境
可作为基础组件集成进现有技术栈或进行二次开发

🎯 主要使用场景

本地部署运行，保护数据隐私，满足合规要求
自定义集成到现有系统，扩展技术栈能力
作为开源基础组件进行商业化二次开发

以下安装命令基于项目开发语言和类型自动生成，实际以官方 README 为准。

安装命令

# 方式一：pip 安装（推荐）
pip install opencompass

# 方式二：虚拟环境安装（推荐生产环境）
python -m venv .venv
source .venv/bin/activate  # Windows: .venv\Scripts\activate
pip install opencompass

# 方式三：从源码安装（获取最新功能）
git clone https://github.com/open-compass/opencompass
cd opencompass
pip install -e .

# 验证安装
python -c "import opencompass; print('安装成功')"

📋 安装步骤说明

访问 GitHub 仓库页面
按照 README 文档完成依赖安装
根据系统环境完成初始化配置
参考官方示例或文档开始使用
遇到问题可在 GitHub Issues 中查找解答

以下用法示例由 AI Skill Hub 整理，涵盖最常见的使用场景。

常用命令 / 代码示例

# 命令行使用
opencompass --help

# 基本用法
opencompass input_file -o output_file

# Python 代码中调用
import opencompass

# 示例
result = opencompass.process("input")
print(result)

以下配置示例基于典型使用场景生成，具体参数请参照官方文档调整。

配置示例

# opencompass 配置文件示例（config.yml）
app:
  name: "opencompass"
  debug: false
  log_level: "INFO"

# 运行时指定配置文件
opencompass --config config.yml

# 或通过环境变量配置
export OPENCOMPASS_API_KEY="your-key"
export OPENCOMPASS_OUTPUT_DIR="./output"

📑 README 深度解析真实文档完整度 64/100 查看 GitHub 原文 →

以下内容由系统直接从 GitHub README 解析整理，保留代码块、表格与列表结构。

简介

[![][github-release-shield]][github-release-link] [![][github-releasedate-shield]][github-releasedate-link] [![][github-contributors-shield]][github-contributors-link] [![][github-forks-shield]][github-forks-link] [![][github-stars-shield]][github-stars-link] [![][github-issues-shield]][github-issues-link] [![][github-license-shield]][github-license-link]

English | 简体中文

[![][github-trending-shield]][github-trending-url]

</div>

👋 join us on <a href="https://discord.gg/KKwfEbFj7U" target="_blank">Discord</a> and <a href="https://r.vansin.top/?r=opencompass" target="_blank">WeChat</a>

\[!IMPORTANT\] Star Us, You will receive all release notifications from GitHub without any delay ~ ⭐️

<details> <summary><kbd>Star History</kbd></summary> <picture> <source media="(prefers-color-scheme: dark)" srcset="https://api.star-history.com/svg?repos=open-compass%2Fopencompass&theme=dark&type=Date"> <img width="100%" src="https://api.star-history.com/svg?repos=open-compass%2Fopencompass&type=Date"> </picture> </details>

✨ Introduction

OpenCompass is a one-stop platform for large model evaluation, aiming to provide a fair, open, and reproducible benchmark for large model evaluation. Its main features include:

Comprehensive support for models and datasets: Pre-support for 20+ HuggingFace and API models, a model evaluation scheme of 70+ datasets with about 400,000 questions, comprehensively evaluating the capabilities of the models in five dimensions.

Efficient distributed evaluation: One line command to implement task division and distributed evaluation, completing the full evaluation of billion-scale models in just a few hours.

Diversified evaluation paradigms: Support for zero-shot, few-shot, and chain-of-thought evaluations, combined with standard or dialogue-type prompt templates, to easily stimulate the maximum performance of various models.

Modular design with high extensibility: Want to add new models or datasets, customize an advanced task division strategy, or even support a new cluster management system? Everything about OpenCompass can be easily expanded!

Experiment management and reporting mechanism: Use config files to fully record each experiment, and support real-time reporting of results.

🚀 What's New <a><img width="35" height="20" src="https://user-images.githubusercontent.com/12782558/212848161-5e783dd6-11e8-4fe0-bbba-39ffb77730be.png"></a>

\[2026.02.05\] OpenCompass now supports Intern-S1-Pro related general and scientific evaluation benchmarks. Please check Example for Evaluating Intern-S1-Pro and Model Card for more details! 🔥🔥🔥
\[2025.12.08\] OpenCompass now supports evaluation for SciReasoner. Please check Example for Evaluating SciReasoner and Project GitHub Repo for more details! 🔥🔥🔥
\[2025.07.26\] OpenCompass now supports Intern-S1 related general and scientific evaluation benchmarks. Please check Tutorial for Evaluating Intern-S1 for more details! 🔥🔥🔥
\[2025.04.01\] OpenCompass now supports CascadeEvaluator, a flexible evaluation mechanism that allows multiple evaluators to work in sequence. This enables creating customized evaluation pipelines for complex assessment scenarios. Check out the documentation for more details! 🔥🔥🔥
\[2025.03.11\] We have supported evaluation for SuperGPQA which is a great benchmark for measuring LLM knowledge ability 🔥🔥🔥
\[2025.02.28\] We have added a tutorial for DeepSeek-R1 series model, please check Evaluating Reasoning Model for more details! 🔥🔥🔥
\[2025.02.15\] We have added two powerful evaluation tools: GenericLLMEvaluator for LLM-as-judge evaluations and MATHVerifyEvaluator for mathematical reasoning assessments. Check out the documentation for LLM Judge and Math Evaluation for more details! 🔥🔥🔥
\[2025.01.16\] We now support the InternLM3-8B-Instruct model which has enhanced performance on reasoning and knowledge-intensive tasks.
\[2024.12.17\] We have provided the evaluation script for the December CompassAcademic, which allows users to easily reproduce the official evaluation results by configuring it.
\[2024.11.14\] OpenCompass now offers support for a sophisticated benchmark designed to evaluate complex reasoning skills — MuSR. Check out the demo and give it a spin! 🔥🔥🔥
\[2024.11.14\] OpenCompass now supports the brand new long-context language model evaluation benchmark — BABILong. Have a look at the demo and give it a try! 🔥🔥🔥
\[2024.10.14\] We now support the OpenAI multilingual QA dataset MMMLU. Feel free to give it a try! 🔥🔥🔥
\[2024.09.19\] We now support Qwen2.5(0.5B to 72B) with multiple backend(huggingface/vllm/lmdeploy). Feel free to give them a try! 🔥🔥🔥
\[2024.09.17\] We now support OpenAI o1(o1-mini-2024-09-12 and o1-preview-2024-09-12). Feel free to give them a try! 🔥🔥🔥
\[2024.09.05\] We now support answer extraction through model post-processing to provide a more accurate representation of the model's capabilities. As part of this update, we have integrated XFinder as our first post-processing model. For more detailed information, please refer to the documentation, and give it a try! 🔥🔥🔥
\[2024.08.20\] OpenCompass now supports the SciCode: A Research Coding Benchmark Curated by Scientists. 🔥🔥🔥
\[2024.08.16\] OpenCompass now supports the brand new long-context language model evaluation benchmark — RULER. RULER provides an evaluation of long-context including retrieval, multi-hop tracing, aggregation, and question answering through flexible configurations. Check out the RULER evaluation config now! 🔥🔥🔥
\[2024.08.09\] We have released the example data and configuration for the CompassBench-202408, welcome to CompassBench for more details. 🔥🔥🔥
\[2024.08.01\] We supported the Gemma2 models. Welcome to try! 🔥🔥🔥
\[2024.07.23\] We supported the ModelScope datasets, you can load them on demand without downloading all the data to your local disk. Welcome to try! 🔥🔥🔥
\[2024.07.17\] We are excited to announce the release of NeedleBench's technical report. We invite you to visit our support documentation for detailed evaluation guidelines. 🔥🔥🔥
\[2024.07.04\] OpenCompass now supports InternLM2.5, which has outstanding reasoning capability, 1M Context window and and stronger tool use, you can try the models in OpenCompass Config and InternLM .🔥🔥🔥.
\[2024.06.20\] OpenCompass now supports one-click switching between inference acceleration backends, enhancing the efficiency of the evaluation process. In addition to the default HuggingFace inference backend, it now also supports popular backends LMDeploy and vLLM. This feature is available via a simple command-line switch and through deployment APIs. For detailed usage, see the documentation.🔥🔥🔥.

More

🛠️ Installation

Below are the steps for quick installation and datasets preparation.

💻 Environment Setup

We highly recommend using conda to manage your python environment.

#### Create your virtual environment

  conda create --name opencompass python=3.10 -y
  conda activate opencompass

#### Install OpenCompass via pip

    pip install -U opencompass

    ## Full installation (with support for more datasets)
    # pip install "opencompass[full]"

    ## Environment with model acceleration frameworks
    ## Manage different acceleration frameworks using virtual environments
    ## since they usually have dependency conflicts with each other.
    # pip install "opencompass[lmdeploy]"
    # pip install "opencompass[vllm]"

    ## API evaluation (i.e. Openai, Qwen)
    # pip install "opencompass[api]"

#### Install OpenCompass from source

If you want to use opencompass's latest features, or develop new features, you can also build it from source

    git clone https://github.com/open-compass/opencompass opencompass
    cd opencompass
    pip install -e .
    # pip install -e ".[full]"
    # pip install -e ".[vllm]"

List all configurations

python tools/list_configs.py

List all configurations related to llama and mmlu

python tools/list_configs.py llama mmlu


#### Supported Models

If the model is not on the list but supported by Huggingface AutoModel class or encapsulation of inference engine based on OpenAI interface (see [docs](https://opencompass.readthedocs.io/en/latest/advanced_guides/new_model.html) for details), you can also evaluate it with OpenCompass. You are welcome to contribute to the maintenance of the OpenCompass supported model and dataset lists.

bash opencompass --datasets demo_gsm8k_chat_gen --hf-type chat --hf-path internlm/internlm2_5-1_8b-chat


#### Supported Datasets

Currently, OpenCompass have provided standard recommended configurations for datasets. Generally, config files ending with `_gen.py` or `_llm_judge_gen.py` will point to the recommended config we provide for this dataset. You can refer to [docs](https://opencompass.readthedocs.io/en/latest/dataset_statistics.html) for more details.

bash

Recommended Evaluation Config based on Rules

opencompass --datasets aime2024_gen --models hf_internlm2_5_1_8b_chat

Recommended Evaluation Config based on LLM Judge

opencompass --datasets aime2024_llmjudge_gen --models hf_internlm2_5_1_8b_chat


If you want to use multiple GPUs to evaluate the model in data parallel, you can use `--max-num-worker`.

bash CUDA_VISIBLE_DEVICES=0,1 opencompass --datasets demo_gsm8k_chat_gen --hf-type chat --hf-path internlm/internlm2_5-1_8b-chat --max-num-worker 2 ```

\[!TIP\] --hf-num-gpus is used for model parallel(huggingface format), --max-num-worker is used for data parallel.

\[!TIP\] configuration with _ppl is designed for base model typically. configuration with _gen can be used for both base model and chat model.

Through the command line or configuration files, OpenCompass also supports evaluating APIs or custom models, as well as more diversified evaluation strategies. Please read the Quick Start to learn how to run an evaluation task.

API evaluation

OpenCompass, by its design, does not really discriminate between open-source models and API models. You can evaluate both model types in the same way or even in one settings.

```bash export OPENAI_API_KEY="YOUR_OPEN_API_KEY"