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<!-- WEHUB_ZH_README -->
> [!NOTE]
> 本文档由 WeHub 基于上游 README 翻译整理，属于社区翻译，非官方中文文档。
> [English](./README.en.md) · [原始项目](https://github.com/mlflow/mlflow) · [上游 README](https://github.com/mlflow/mlflow/blob/HEAD/README.md)
> 原作者、版权与许可证归属以原始项目及本仓库 LICENSE 文件为准。
<h1 align="center" style="border-bottom: none">
<a href="https://mlflow.org/">
<img alt="MLflow logo" src="https://raw.githubusercontent.com/mlflow/mlflow/refs/heads/master/assets/logo.svg" width="200" />
</a>
</h1>
<h2 align="center" style="border-bottom: none">The Open Source AI Engineering Platform for Agents, LLMs & Models</h2>
<h2 align="center" style="border-bottom: none">面向智能体、LLM 与模型的开源 AI 工程平台</h2>
MLflow is the largest open source **AI engineering platform for agents, LLMs, and ML models**. MLflow enables teams of all sizes to [debug](https://mlflow.org/llm-tracing),
[evaluate](https://mlflow.org/llm-evaluation), [monitor](https://mlflow.org/ai-monitoring), and [optimize](https://mlflow.org/prompt-optimization) production-quality AI applications while
controlling costs and managing access to models and data. With over **60 million monthly downloads**,
thousands of organizations rely on MLflow each day to ship AI to production with confidence.
MLflow 是规模最大的开源**面向智能体、LLM 和 ML 模型的 AI 工程平台**。MLflow 使各种规模的团队能够[调试](https://mlflow.org/llm-tracing),
[评估](https://mlflow.org/llm-evaluation), [监控](https://mlflow.org/ai-monitoring), 并[优化](https://mlflow.org/prompt-optimization) 生产级 AI 应用，同时
控制成本并管理对模型和数据的访问。凭借每月超过 **6000 万次下载**，
每天有数千家组织依赖 MLflow 充满信心地将 AI 交付到生产环境。
MLflow's comprehensive feature set for agents and LLM applications includes production-grade [observability](https://mlflow.org/docs/latest/genai/tracing), [evaluation](https://mlflow.org/docs/latest/genai/eval-monitor),
[prompt management](https://mlflow.org/docs/latest/genai/prompt-registry), [prompt optimization](https://mlflow.org/prompt-optimization) and an [AI Gateway](https://mlflow.org/docs/latest/genai/governance/ai-gateway) for managing costs and model access.
Learn more at [MLflow for LLMs and Agents](https://mlflow.org/docs/latest/genai).
MLflow 面向智能体和 LLM 应用的全面功能集包括生产级[可观测性](https://mlflow.org/docs/latest/genai/tracing), [评估](https://mlflow.org/docs/latest/genai/eval-monitor),
[提示词管理](https://mlflow.org/docs/latest/genai/prompt-registry), [提示词优化](https://mlflow.org/prompt-optimization) 以及用于管理成本和模型访问的 [AI 网关](https://mlflow.org/docs/latest/genai/governance/ai-gateway)。
了解更多请参阅 [面向 LLM 与智能体的 MLflow](https://mlflow.org/docs/latest/genai).
<div align="center">
@@ -21,45 +27,45 @@ Learn more at [MLflow for LLMs and Agents](https://mlflow.org/docs/latest/genai)
[![License](https://img.shields.io/github/license/mlflow/mlflow)](https://github.com/mlflow/mlflow/blob/master/LICENSE.txt)
<a href="https://twitter.com/intent/follow?screen_name=mlflow" target="_blank">
<img src="https://img.shields.io/twitter/follow/mlflow?logo=X&color=%20%23f5f5f5"
alt="follow on X(Twitter)"></a>
alt="在 X（Twitter）上关注"></a>
<a href="https://www.linkedin.com/company/mlflow-org/" target="_blank">
<img src="https://custom-icon-badges.demolab.com/badge/LinkedIn-0A66C2?logo=linkedin-white&logoColor=fff"
alt="follow on LinkedIn"></a>
alt="在 LinkedIn 上关注"></a>
[![Ask DeepWiki](https://deepwiki.com/badge.svg)](https://deepwiki.com/mlflow/mlflow)
</div>
<div align="center">
<div>
<a href="https://mlflow.org/"><strong>Website</strong></a> ·
<a href="https://demo.mlflow.org/"><strong>Try Demo</strong></a> ·
<a href="https://mlflow.org/docs/latest"><strong>Docs</strong></a> ·
<a href="https://mlflow.org/blog"><strong>News</strong></a> ·
<a href="https://lu.ma/mlflow?k=c"><strong>Events</strong></a>
<a href="https://mlflow.org/"><strong>官网</strong></a> ·
<a href="https://demo.mlflow.org/"><strong>试用演示</strong></a> ·
<a href="https://mlflow.org/docs/latest"><strong>文档</strong></a> ·
<a href="https://mlflow.org/blog"><strong>新闻</strong></a> ·
<a href="https://lu.ma/mlflow?k=c"><strong>活动</strong></a>
</div>
</div>
<br>
## Get Started in 3 Simple Steps
## 三步即可上手
From zero to full-stack LLMOps in minutes. No complex setup or major code changes required. [Get Started →](https://mlflow.org/docs/latest/genai/tracing/quickstart/)
几分钟内从零搭建完整 LLMOps 技术栈。无需复杂配置或大规模代码改动。[立即上手 →](https://mlflow.org/docs/latest/genai/tracing/quickstart/)
> **Fastest start — set up tracing with our CLI**
> **最快上手 — 使用 CLI 设置追踪（tracing）**
>
> ```bash
> uvx mlflow@latest agent setup
> ```
>
> One command installs the MLflow skills and launches your coding agent of choice to add tracing to your app. Prefer to wire it up yourself? Follow the three steps below.
> 一条命令即可安装 MLflow 技能，并启动你选择的编程智能体，为应用添加追踪。想自己动手接入？请按照以下三个步骤操作。
**1. Start MLflow Server**
**1. 启动 MLflow Server**
```bash
uvx mlflow server
```
**2. Enable Logging**
**2. 启用日志记录**
```python
import mlflow
@@ -68,7 +74,7 @@ mlflow.set_tracking_uri("http://localhost:5000")
mlflow.openai.autolog()
```
**3. Run Your Code**
**3. 运行你的代码**
```python
from openai import OpenAI
@@ -80,11 +86,11 @@ client.responses.create(
)
```
Explore traces and metrics in the MLflow UI at `http://localhost:5000`.
在 `http://localhost:5000` 的 MLflow UI 中探索追踪和指标。
## LLMs & Agents
## LLM 与智能体
MLflow provides everything you need to build, debug, evaluate, and deploy production-quality LLM applications and AI agents. Supports Python, TypeScript/JavaScript, Java and any other programming language. MLflow also natively integrates with [OpenTelemetry](https://opentelemetry.io/) and MCP.
MLflow 提供构建、调试、评估和部署生产级 LLM 应用与 AI 智能体所需的一切。支持 Python、TypeScript/JavaScript、Java 及任何其他编程语言。MLflow 还与 [OpenTelemetry](https://opentelemetry.io/) 和 MCP 原生集成。
<table>
<tr>
@@ -94,10 +100,10 @@ MLflow provides everything you need to build, debug, evaluate, and deploy produc
<br>
<a href="https://mlflow.org/docs/latest/genai/tracing/"><strong>Observability</strong></a>
<br><br>
<div>Capture complete traces of your LLM applications and agents for deep behavioral insights. Built on OpenTelemetry, supporting any LLM provider and agent framework. Monitor production quality, costs, and safety.</div><br>
<a href="https://mlflow.org/docs/latest/genai/tracing/quickstart/">Getting Started →</a>
<div>捕获 LLM 应用和智能体的完整追踪，以深入洞察行为。基于 OpenTelemetry 构建，支持任何 LLM 提供商和智能体框架。监控生产质量、成本与安全性。</div><br>
<a href="https://mlflow.org/docs/latest/genai/tracing/quickstart/">入门指南 →</a>
<br>
<a href="https://demo.mlflow.org/#/experiments/1/traces?startTimeLabel=CUSTOM&startTime=2026-04-17T16%3A47%3A15.258Z&endTime=2026-04-24T21%3A20%3A50.781Z">Try Demo →</a>
<a href="https://demo.mlflow.org/#/experiments/1/traces?startTimeLabel=CUSTOM&startTime=2026-04-17T16%3A47%3A15.258Z&endTime=2026-04-24T21%3A20%3A50.781Z">试用演示 →</a>
<br><br>
</div>
</td>
@@ -107,10 +113,10 @@ MLflow provides everything you need to build, debug, evaluate, and deploy produc
<br>
<a href="https://mlflow.org/docs/latest/genai/eval-monitor/"><strong>Evaluation</strong></a>
<br><br>
<div>Run systematic evaluations, track quality metrics over time, and catch regressions before they reach production. Choose from 50+ built-in metrics and LLM judges, or define your own.</div><br>
<a href="https://mlflow.org/docs/latest/genai/eval-monitor/">Getting Started →</a>
<div>运行系统化评估，随时间跟踪质量指标，在回归进入生产环境之前及时发现。可从 50 多种内置指标和 LLM 评判器中选择，或自定义指标。</div><br>
<a href="https://mlflow.org/docs/latest/genai/eval-monitor/">入门指南 →</a>
<br>
<a href="https://demo.mlflow.org/#/experiments/1/runs/d690ad8bb7a546c5a74b79691bb32b27/evaluations">Try Demo →</a>
<a href="https://demo.mlflow.org/#/experiments/1/runs/d690ad8bb7a546c5a74b79691bb32b27/evaluations">试用演示 →</a>
<br><br>
</div>
</td>
@@ -122,10 +128,10 @@ MLflow provides everything you need to build, debug, evaluate, and deploy produc
<br>
<a href="https://mlflow.org/docs/latest/genai/prompt-registry/"><strong>Prompts & Optimization</strong></a>
<br><br>
<div>Version, test, and deploy prompts with full lineage tracking. <a href="https://mlflow.org/prompt-optimization">Automatically optimize prompts</a> with state-of-the-art algorithms to improve performance.</div><br>
<a href="https://mlflow.org/docs/latest/genai/prompt-registry/create-and-edit-prompts/">Getting Started →</a>
<div>对提示词进行版本管理、测试和部署，并完整追踪血缘关系。<a href="https://mlflow.org/prompt-optimization">自动优化提示词</a>，采用前沿算法提升性能。</div><br>
<a href="https://mlflow.org/docs/latest/genai/prompt-registry/create-and-edit-prompts/">入门指南 →</a>
<br>
<a href="https://demo.mlflow.org/#/experiments/1/prompts/mlflow-demo.prompts.code-reviewer?promptVersion=4">Try Demo →</a>
<a href="https://demo.mlflow.org/#/experiments/1/prompts/mlflow-demo.prompts.code-reviewer?promptVersion=4">试用演示 →</a>
<br><br>
</div>
</td>
@@ -135,28 +141,28 @@ MLflow provides everything you need to build, debug, evaluate, and deploy produc
<br>
<a href="https://mlflow.org/docs/latest/genai/governance/ai-gateway/"><strong>AI Gateway</strong></a>
<br><br>
<div>Unified API gateway for all LLM providers. Route requests, manage rate limits, handle fallbacks, and control costs through an OpenAI-compatible interface with built-in credential management, guardrails and traffic splitting for A/B testing.</div><br>
<a href="https://mlflow.org/docs/latest/genai/governance/ai-gateway/quickstart/">Getting Started →</a>
<div>面向所有 LLM 提供商的统一 API 网关。通过兼容 OpenAI 的接口路由请求、管理速率限制、处理故障转移并控制成本，内置凭据管理、防护栏（guardrails）以及用于 A/B 测试的流量分流。</div><br>
<a href="https://mlflow.org/docs/latest/genai/governance/ai-gateway/quickstart/">入门指南 →</a>
<br><br>
</div>
</td>
</tr>
</table>
## Model Training
## 模型训练
For machine learning and deep learning model development, MLflow provides a full suite of tools to manage the ML lifecycle:
对于机器学习和深度学习模型开发，MLflow 提供全套工具来管理 ML 生命周期：
- [**Experiment Tracking**](https://mlflow.org/docs/latest/ml/tracking/) — Track models, parameters, metrics, and evaluation results across experiments
- [**Model Evaluation**](https://mlflow.org/docs/latest/ml/evaluation/) — Automated evaluation tools integrated with experiment tracking
- [**Model Registry**](https://mlflow.org/docs/latest/ml/model-registry/) — Collaboratively manage the full lifecycle of ML models
- [**Deployment**](https://mlflow.org/docs/latest/ml/deployment/) — Deploy models to batch and real-time scoring on Docker, Kubernetes, Azure ML, AWS SageMaker, and more
- [**实验跟踪（Experiment Tracking）**](https://mlflow.org/docs/latest/ml/tracking/) — 跨实验跟踪模型、参数、指标和评估结果
- [**模型评估（Model Evaluation）**](https://mlflow.org/docs/latest/ml/evaluation/) — 与实验跟踪集成的自动化评估工具
- [**模型注册表（Model Registry）**](https://mlflow.org/docs/latest/ml/model-registry/) — 协作管理 ML 模型的完整生命周期
- [**部署（Deployment）**](https://mlflow.org/docs/latest/ml/deployment/) — 将模型部署到 Docker、Kubernetes、Azure ML、AWS SageMaker 等平台，支持批处理和实时评分
Learn more at [MLflow for Model Training](https://mlflow.org/docs/latest/ml).
了解更多请参阅 [面向模型训练的 MLflow](https://mlflow.org/docs/latest/ml).
## Integrations
## 集成
MLflow supports all agent frameworks, LLM providers, tools, and programming languages. We offer one-line automatic tracing for more than 60 frameworks. See the [full integrations list](https://mlflow.org/docs/latest/genai/tracing/integrations/).
MLflow 支持所有智能体（agent）框架、大语言模型（LLM）提供商、工具和编程语言。我们为 60 多个框架提供一行代码即可启用的自动追踪（tracing）。请参阅[完整集成列表](https://mlflow.org/docs/latest/genai/tracing/integrations/).
### OpenTelemetry
@@ -166,7 +172,7 @@ MLflow supports all agent frameworks, LLM providers, tools, and programming lang
</tr>
</table>
### Agent Frameworks (Python)
### 智能体框架（Python）
<table>
<tr>
@@ -202,7 +208,7 @@ MLflow supports all agent frameworks, LLM providers, tools, and programming lang
</tr>
</table>
### Agent Frameworks (TypeScript)
### 智能体框架（TypeScript）
<table>
<tr>
@@ -214,7 +220,7 @@ MLflow supports all agent frameworks, LLM providers, tools, and programming lang
</tr>
</table>
### Agent Frameworks (Java)
### 智能体框架（Java）
<table>
<tr>
@@ -223,7 +229,7 @@ MLflow supports all agent frameworks, LLM providers, tools, and programming lang
</tr>
</table>
### Model Providers
### 模型提供商
<table>
<tr>
@@ -252,7 +258,7 @@ MLflow supports all agent frameworks, LLM providers, tools, and programming lang
</tr>
</table>
### Gateways
### 网关
<table>
<tr>
@@ -270,7 +276,7 @@ MLflow supports all agent frameworks, LLM providers, tools, and programming lang
</tr>
</table>
### Tools & No-Code
### 工具与无代码（No-Code）
<table>
<tr>
@@ -286,9 +292,9 @@ MLflow supports all agent frameworks, LLM providers, tools, and programming lang
</tr>
</table>
## Hosting MLflow
## 托管 MLflow
MLflow can be used in a variety of environments, including your local environment, on-premises clusters, cloud platforms, and managed services. Being an open-source platform, MLflow is **vendor-neutral** — whether you're building AI agents, LLM applications, or ML models, you have access to MLflow's core capabilities.
MLflow 可用于多种环境，包括本地环境、本地部署（on-premises）集群、云平台以及托管服务。作为一款开源平台，MLflow **厂商中立（vendor-neutral）**——无论你是在构建 AI 智能体、LLM 应用还是 ML 模型，都能使用 MLflow 的核心能力。
<table>
<tr>
@@ -300,42 +306,41 @@ MLflow can be used in a variety of environments, including your local environmen
</tr>
</table>
## 💭 Support
## 💭 支持
- For help or questions about MLflow usage (e.g. "how do I do X?") visit the [documentation](https://mlflow.org/docs/latest).
- In the documentation, you can ask the question to our AI-powered chat bot. Click on the **"Ask AI"** button at the right bottom.
- Join the [virtual events](https://lu.ma/mlflow?k=c) like office hours and meetups.
- To report a bug, file a documentation issue, or submit a feature request, please [open a GitHub issue](https://github.com/mlflow/mlflow/issues/new/choose).
- For release announcements and other discussions, please subscribe to our mailing list (mlflow-users@googlegroups.com)
or join us on [Slack](https://mlflow.org/slack).
- 如需 MLflow 使用方面的帮助或提问（例如「如何做 X？」），请访问[文档](https://mlflow.org/docs/latest).
- 在文档中，你可以向我们的 AI 聊天机器人提问。点击右下角的 **"Ask AI"** 按钮。
- 参加[线上活动](https://lu.ma/mlflow?k=c)，例如 Office Hours 和聚会（meetup）。
- 如需报告 bug、提交文档问题或提出功能请求，请[在 GitHub 上提交 issue](https://github.com/mlflow/mlflow/issues/new/choose).
- 如需接收版本发布公告并参与其他讨论，请订阅我们的邮件列表（mlflow-users@googlegroups.com），或在 [Slack](https://mlflow.org/slack). 上加入我们。
## 🤝 Contributing
## 🤝 贡献
We happily welcome contributions to MLflow!
我们诚挚欢迎为 MLflow 做出贡献！
- Submit [bug reports](https://github.com/mlflow/mlflow/issues/new?template=bug_report_template.yaml) and [feature requests](https://github.com/mlflow/mlflow/issues/new?template=feature_request_template.yaml)
- Contribute for [good-first-issues](https://github.com/mlflow/mlflow/issues?q=is%3Aissue+is%3Aopen+label%3A%22good+first+issue%22) and [help-wanted](https://github.com/mlflow/mlflow/issues?q=is%3Aissue+is%3Aopen+label%3A%22help+wanted%22)
- Writing about MLflow and sharing your experience
- 提交 [bug 报告](https://github.com/mlflow/mlflow/issues/new?template=bug_report_template.yaml) 和 [功能请求](https://github.com/mlflow/mlflow/issues/new?template=feature_request_template.yaml)
- 参与 [good-first-issues](https://github.com/mlflow/mlflow/issues?q=is%3Aissue+is%3Aopen+label%3A%22good+first+issue%22) 和 [help-wanted](https://github.com/mlflow/mlflow/issues?q=is%3Aissue+is%3Aopen+label%3A%22help+wanted%22)
- 撰写关于 MLflow 的文章并分享你的使用经验
Please see our [contribution guide](CONTRIBUTING.md) to learn more about contributing to MLflow.
请参阅我们的[贡献指南](CONTRIBUTING.md)，了解更多关于为 MLflow 做出贡献的信息。
## ⭐️ Star History
## ⭐️ Star 历史
<a href="https://star-history.com/#mlflow/mlflow&Date">
<picture>
<source media="(prefers-color-scheme: dark)" srcset="https://api.star-history.com/svg?repos=mlflow/mlflow&type=Date&theme=dark" />
<source media="(prefers-color-scheme: light)" srcset="https://api.star-history.com/svg?repos=mlflow/mlflow&type=Date" />
<img alt="Star History Chart" src="https://api.star-history.com/svg?repos=mlflow/mlflow&type=Date" />
<img alt="Star 历史图表" src="https://api.star-history.com/svg?repos=mlflow/mlflow&type=Date" />
</picture>
</a>
## ✏️ Citation
## ✏️ 引用
If you use MLflow in your research, please cite it using the "Cite this repository" button at the top of the [GitHub repository page](https://github.com/mlflow/mlflow), which will provide you with citation formats including APA and BibTeX.
如果你在研究中使用 MLflow，请通过 [GitHub 仓库页面](https://github.com/mlflow/mlflow), 顶部的 "Cite this repository" 按钮进行引用，该按钮会提供包括 APA 和 BibTeX 在内的引用格式。
## 👥 Core Members
## 👥 核心成员
MLflow is currently maintained by the following core members with significant contributions from hundreds of exceptionally talented community members.
MLflow 目前由以下核心成员维护，并得到了数百位才华横溢的社区成员的重要贡献。
- [Ben Wilson](https://github.com/BenWilson2)
- [Corey Zumar](https://github.com/dbczumar)