网上AI Agent的课程一抓一大把，但大多要么太理论，要么照着文档念，看完还是一脸懵。

微软官方出了一套免费的《AI Agents for Beginners》，从零开始，带你完整过一遍构建过程。

A哥看到目前已扩展到18节课，覆盖基础概念到生产部署的每个环节，Tool Use、Agentic RAG、Planning、Multi-Agent、记忆管理、上下文工程这些都有。

每节课配详细文字教程+视频，Python代码示例用Jupyter Notebook直接能跑。

传/送https://github.com/microsoft/ai-agents-for-beginners…

microsoft/ai-agents-for-beginners

Source: https://github.com/microsoft/ai-agents-for-beginners

AI Agents for Beginners - A Course

A course teaching everything you need to know to start building AI Agents

🌐 Multi-Language Support

Supported via GitHub Action (Automated & Always Up-to-Date)

Arabic | Bengali | Bulgarian | Burmese (Myanmar) | Chinese (Simplified) | Chinese (Traditional, Hong Kong) | Chinese (Traditional, Macau) | Chinese (Traditional, Taiwan) | Croatian | Czech | Danish | Dutch | Estonian | Finnish | French | German | Greek | Hebrew | Hindi | Hungarian | Indonesian | Italian | Japanese | Kannada | Khmer | Korean | Lithuanian | Malay | Malayalam | Marathi | Nepali | Nigerian Pidgin | Norwegian | Persian (Farsi) | Polish | Portuguese (Brazil) | Portuguese (Portugal) | Punjabi (Gurmukhi) | Romanian | Russian | Serbian (Cyrillic) | Slovak | Slovenian | Spanish | Swahili | Swedish | Tagalog (Filipino) | Tamil | Telugu | Thai | Turkish | Ukrainian | Urdu | Vietnamese

Prefer to Clone Locally?

This repository includes 50+ language translations which significantly increases the download size. To clone without translations, use sparse checkout:

Bash / macOS / Linux:

git clone --filter=blob:none --sparse https://github.com/microsoft/ai-agents-for-beginners.git
cd ai-agents-for-beginners
git sparse-checkout set --no-cone '/*' '!translations' '!translated_images'

CMD (Windows):

git clone --filter=blob:none --sparse https://github.com/microsoft/ai-agents-for-beginners.git
cd ai-agents-for-beginners
git sparse-checkout set --no-cone "/*" "!translations" "!translated_images"

This gives you everything you need to complete the course with a much faster download.

If you wish to have additional translation languages supported, they are listed here.

🌱 Getting Started

This course has lessons covering the fundamentals of building AI Agents. Each lesson covers its own topic so start wherever you like!

There is multi-language support for this course. Go to our available languages here.

If this is your first time building with Generative AI models, check out our Generative AI For Beginners course, which includes 21 lessons on building with GenAI.

Don’t forget to star (🌟) this repo and fork this repo to run the code.

Meet Other Learners, Get Your Questions Answered

If you get stuck or have any questions about building AI Agents, join our dedicated Discord Channel in the Microsoft Foundry Discord.

What You Need

Each lesson in this course includes code examples, which can be found in the code_samples folder. You can fork this repo to create your own copy.

The code examples in these exercises utilize Microsoft Agent Framework with Microsoft Foundry Agent Service V2:

• Microsoft Foundry - Azure Account Required

This course uses the following AI Agent frameworks and services from Microsoft:

• Microsoft Agent Framework (MAF)
• Microsoft Foundry Agent Service V2

Some code samples also support alternative OpenAI-compatible providers such as MiniMax, which offers large-context models (up to 204K tokens). See the Course Setup for configuration details.

For more information on running the code for this course, go to the Course Setup.

🙏 Want to help?

Do you have suggestions or found spelling or code errors? Raise an issue or Create a pull request

📂 Each lesson includes

• A written lesson located in the README and a short video
• Python code samples using Microsoft Agent Framework with Microsoft Foundry
• Links to extra resources to continue your learning

🗃️ Lessons

🎒 Other Courses

Our team produces other courses! Check out:

LangChain

Azure / Edge / MCP / Agents

Generative AI Series

Core Learning

Copilot Series

🌟 Community Thanks

Thanks to Shivam Goyal for contributing important code samples demonstrating Agentic RAG.

Contributing

This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.

When you submit a pull request, a CLA bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.

This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact [email protected] with any additional questions or comments.

Trademarks

This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow Microsoft’s Trademark & Brand Guidelines. Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos is subject to those third-parties’ policies.

Getting Help

If you get stuck or have any questions about building AI apps, join:

If you have product feedback or errors while building visit: