深度评估框架
AI Skill Hub 强烈推荐：深度评估框架 是一款优质的AI工具。在 GitHub 上收获超过 15.7k 颗 Star，AI 综合评分 8.0 分，在同类工具中表现稳健。如果你正在寻找可靠的AI工具解决方案，这是一个值得深入了解的选择。
📚 深度解析
**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**
深度评估框架 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 深度评估框架 的版本更新，及时通知重要功能变化。
📋 工具概览
深度评估框架 是一款基于 Python 开发的开源工具，专注于 evaluation-framework、llm-evaluation、python 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。
📖 中文文档
深度评估框架 是一款基于 Python 开发的开源工具，专注于 evaluation-framework、llm-evaluation、python 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。
- 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
- 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
# 方式一：pip 安装（推荐）
pip install deepeval
# 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 deepeval
# 方式三：从源码安装（获取最新功能）
git clone https://github.com/confident-ai/deepeval
cd deepeval
pip install -e .
# 验证安装
python -c "import deepeval; print('安装成功')"
- 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
# 命令行使用
deepeval --help
# 基本用法
deepeval input_file -o output_file
# Python 代码中调用
import deepeval
# 示例
result = deepeval.process("input")
print(result)
# deepeval 配置文件示例（config.yml） app: name: "deepeval" debug: false log_level: "INFO" # 运行时指定配置文件 deepeval --config config.yml # 或通过环境变量配置 export DEEPEVAL_API_KEY="your-key" export DEEPEVAL_OUTPUT_DIR="./output"
简介
<p align="center"> <picture> <source media="(prefers-color-scheme: dark)" srcset="assets/hero/wordmark-dark.svg"> <img alt="DeepEval." src="assets/hero/wordmark-light.svg" width="520"> </picture> </p>
<p align="center"> <h1 align="center">The LLM Evaluation Framework</h1> </p>
<p align="center"> <a href="https://trendshift.io/repositories/5917" target="_blank"><img src="https://trendshift.io/api/badge/repositories/5917" alt="confident-ai%2Fdeepeval | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a> </p>
<p align="center"> <a href="https://discord.gg/3SEyvpgu2f"> <img alt="discord-invite" src="https://dcbadge.vercel.app/api/server/3SEyvpgu2f?style=flat"> </a> </p>
Documentation | Metrics and Features | Getting Started | Integrations | Confident AI
<p align="center"> <a href="https://github.com/confident-ai/deepeval/releases"> <img alt="GitHub release" src="https://img.shields.io/github/release/confident-ai/deepeval.svg?color=violet"> </a> <a href="https://colab.research.google.com/drive/1PPxYEBa6eu__LquGoFFJZkhYgWVYE6kh?usp=sharing"> <img alt="Try Quickstart in Colab" src="https://colab.research.google.com/assets/colab-badge.svg"> </a> <a href="https://github.com/confident-ai/deepeval/blob/master/LICENSE.md"> <img alt="License" src="https://img.shields.io/github/license/confident-ai/deepeval.svg?color=yellow"> </a> <a href="https://x.com/deepeval"> <img alt="Twitter Follow" src="https://img.shields.io/twitter/follow/deepeval?style=social&logo=x"> </a> </p>
<p align="center"> <a href="https://www.readme-i18n.com/confident-ai/deepeval?lang=de">Deutsch</a> | <a href="https://www.readme-i18n.com/confident-ai/deepeval?lang=es">Español</a> | <a href="https://www.readme-i18n.com/confident-ai/deepeval?lang=fr">français</a> | <a href="https://www.readme-i18n.com/confident-ai/deepeval?lang=ja">日本語</a> | <a href="https://www.readme-i18n.com/confident-ai/deepeval?lang=ko">한국어</a> | <a href="https://www.readme-i18n.com/confident-ai/deepeval?lang=pt">Português</a> | <a href="https://www.readme-i18n.com/confident-ai/deepeval?lang=ru">Русский</a> | <a href="https://www.readme-i18n.com/confident-ai/deepeval?lang=zh">中文</a> </p>
DeepEval is a simple-to-use, open-source LLM evaluation framework, for evaluating large-language model systems. It is similar to Pytest but specialized for unit testing LLM apps. DeepEval incorporates the latest research to run evals via metrics such as G-Eval, task completion, answer relevancy, hallucination, etc., which uses LLM-as-a-judge and other NLP models that run locally on your machine.
Whether you're building AI agents, RAG pipelines, or chatbots, implemented via LangChain or OpenAI, DeepEval has you covered. With it, you can easily determine the optimal models, prompts, and architecture to improve your AI quality, prevent prompt drifting, or even transition from OpenAI to Claude with confidence.
[!IMPORTANT] Need a place for your DeepEval testing data to live 🏡❤️? Sign up to the DeepEval platform to compare iterations of your LLM app, generate & share testing reports, and more.
Want to talk LLM evaluation, need help picking metrics, or just to say hi? Come join our discord.
<br />
🔥 Metrics and Features
- 📐 Large variety of ready-to-use LLM eval metrics (all with explanations) powered by ANY LLM of your choice, statistical methods, or NLP models that run locally on your machine covering all use cases:
- Custom, All-Purpose Metrics:
- G-Eval — a research-backed LLM-as-a-judge metric for evaluating on any custom criteria with human-like accuracy
- DAG — DeepEval's graph-based deterministic LLM-as-a-judge metric builder
- <details> <summary><b>Agentic Metrics</b></summary>
- Task Completion — evaluate whether an agent accomplished its goal
- Tool Correctness — check if the right tools were called with the right arguments
- Goal Accuracy — measure how accurately the agent achieved the intended goal
- Step Efficiency — evaluate whether the agent took unnecessary steps
- Plan Adherence — check if the agent followed the expected plan
- Plan Quality — evaluate the quality of the agent's plan
- Tool Use — measure quality of tool usage
- Argument Correctness — validate tool call arguments
</details>
- <details> <summary><b>RAG Metrics</b></summary>
- Answer Relevancy — measure how relevant the RAG pipeline's output is to the input
- Faithfulness — evaluate whether the RAG pipeline's output factually aligns with the retrieval context
- Contextual Recall — measure how well the RAG pipeline's retrieval context aligns with the expected output
- Contextual Precision — evaluate whether relevant nodes in the RAG pipeline's retrieval context are ranked higher
- Contextual Relevancy — measure the overall relevance of the RAG pipeline's retrieval context to the input
- RAGAS — average of answer relevancy, faithfulness, contextual precision, and contextual recall
</details>
- <details> <summary><b>Multi-Turn Metrics</b></summary>
- Knowledge Retention — evaluate whether the chatbot retains factual information throughout a conversation
- Conversation Completeness — measure whether the chatbot satisfies user needs throughout a conversation
- Turn Relevancy — evaluate whether the chatbot generates consistently relevant responses throughout a conversation
- Turn Faithfulness — check if the chatbot's responses are factually grounded in retrieval context across turns
- Role Adherence — evaluate whether the chatbot adheres to its assigned role throughout a conversation
</details>
- <details> <summary><b>MCP Metrics</b></summary>
- MCP Task Completion — evaluate how effectively an MCP-based agent accomplishes a task
- MCP Use — measure how effectively an agent uses its available MCP servers
- Multi-Turn MCP Use — evaluate MCP server usage across conversation turns
</details>
- <details> <summary><b>Multimodal Metrics</b></summary>
- Text to Image — evaluate image generation quality based on semantic consistency and perceptual quality
- Image Editing — evaluate image editing quality based on semantic consistency and perceptual quality
- Image Coherence — measure how well images align with their accompanying text
- Image Helpfulness — evaluate how effectively images contribute to user comprehension of the text
- Image Reference — evaluate how accurately images are referred to or explained by accompanying text
</details>
- <details> <summary><b>Other Metrics</b></summary>
- Hallucination — check whether the LLM generates factually correct information against provided context
- Summarization — evaluate whether summaries are factually correct and include necessary details
- Bias — detect gender, racial, or political bias in LLM outputs
- Toxicity — evaluate toxicity in LLM outputs
- JSON Correctness — check whether the output matches an expected JSON schema
- Prompt Alignment — measure whether the output aligns with instructions in the prompt template
</details>
- 🎯 Supports both end-to-end and component-level LLM evaluation.
- 🧩 Build your own custom metrics that are automatically integrated with DeepEval's ecosystem.
- 🔮 Generate both single and multi-turn synthetic datasets for evaluation.
- 🔗 Integrates seamlessly with ANY CI/CD environment.
- 🧬 Optimize prompts automatically based on evaluation results.
- 🏆 Easily benchmark ANY LLM on popular LLM benchmarks in under 10 lines of code., including MMLU, HellaSwag, DROP, BIG-Bench Hard, TruthfulQA, HumanEval, GSM8K.
<br />
Installation
Deepeval works with Python>=3.9+.
pip install -U deepeval🤖 Vibe-Coder QuickStart
Want your coding agent to add evals and fix failures for you? Install the DeepEval skill, point it at your agent, RAG pipeline, or chatbot, and ask it to generate a dataset, write the eval suite, run deepeval test run, and iterate on the failing metrics.
Start with the 5-minute vibe-coder guide.
<br />
🚀 Human QuickStart
Let's pretend your LLM application is a RAG based customer support chatbot; here's how DeepEval can help test what you've built.
A Note on Env Variables (.env / .env.local)
DeepEval auto-loads .env.local then .env from the current working directory at import time. Precedence: process env -> .env.local -> .env. Opt out with DEEPEVAL_DISABLE_DOTENV=1.
```bash cp .env.example .env.local
then edit .env.local (ignored by git)
```
🔌 Integrations
DeepEval plugs into any LLM framework — OpenAI Agents, LangChain, CrewAI, and more. To scale evals across your team — or let anyone run them without writing code — Confident AI gives you a native platform integration.
Evaluate Without Pytest Integration
Alternatively, you can evaluate without Pytest, which is more suited for a notebook environment.
from deepeval import evaluate
from deepeval.metrics import AnswerRelevancyMetric
from deepeval.test_case import LLMTestCase
answer_relevancy_metric = AnswerRelevancyMetric(threshold=0.7)
test_case = LLMTestCase(
input="What if these shoes don't fit?",
# Replace this with the actual output from your LLM application
actual_output="We offer a 30-day full refund at no extra costs.",
retrieval_context=["All customers are eligible for a 30 day full refund at no extra costs."]
)
evaluate([test_case], [answer_relevancy_metric])全面评估LLM模型，易于使用
⚡ 核心功能
- 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
👥 适合人群
🎯 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
⚖️ 优点与不足
- +GitHub 15.7k Star，社区高度认可
- +Apache-2.0 协议，可免费商用
- +完全开源免费，无授权费用
- +本地部署，数据完全自主可控
- +开发者社区支持，遇问题可查可问
- −安装和初始配置可能需要一定技术基础
- −功能完整性通常不如成熟商业产品
- −技术支持主要依赖开源社区，响应速度不稳定
AI Skill Hub 为第三方内容聚合平台，本页面信息基于公开数据整理，不对工具功能和质量作任何法律背书。
建议在沙箱或测试环境中充分验证后，再部署至生产环境，并做好必要的安全评估。
✅ Apache 2.0 — 宽松开源协议，可商用，需保留版权声明和 NOTICE 文件，含专利授权条款。
🔗 相关工具推荐
❓ 常见问题 FAQ
总体来看，深度评估框架 是一款质量优秀的AI工具，在同类工具中具备一定竞争力。AI Skill Hub 将持续追踪其更新动态，建议收藏备用，结合自身场景选择合适时机引入使用。
| 原始名称 | deepeval |
| 原始描述 | 开源AI工具：The LLM Evaluation Framework。⭐15.7k · Python |
| Topics | evaluation-frameworkllm-evaluationpython |
| GitHub | https://github.com/confident-ai/deepeval |
| License | Apache-2.0 |
| 语言 | Python |
收录时间：2026-05-27 · 更新时间：2026-05-27 · License：Apache-2.0 · AI Skill Hub 不对第三方内容的准确性作法律背书。