Easily automate the translation of your educational GitHub content into multiple languages to reach a global audience.

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 | Korean | Lithuanian | Malay | Marathi | Nepali | Norwegian | Persian (Farsi) | Polish | Portuguese (Brazil) | Portuguese (Portugal) | Punjabi (Gurmukhi) | Romanian | Russian | Serbian (Cyrillic) | Slovak | Slovenian | Spanish | Swahili | Swedish | Tagalog (Filipino) | Tamil | Thai | Turkish | Ukrainian | Urdu | Vietnamese

Co-op Translator enables you to quickly translate your educational GitHub content into multiple languages, reaching a global audience effortlessly. When you update your Markdown files, images, or Jupyter notebooks, translations are automatically synchronized to ensure your educational GitHub content stays fresh and relevant for international users.

See how Co-op Translator organizes translated educational GitHub content:

# Create and activate a virtual environment (recommended)
python -m venv .venv
# Windows
.venv\Scripts\activate
# macOS/Linux
source .venv/bin/activate
# Install the package
pip install co-op-translator
# Translate
translate -l "ko ja fr" -md

Docker:

# Pull the public image from GHCR
docker pull ghcr.io/azure/co-op-translator:latest
# Run with current folder mounted and .env provided (Bash/Zsh)
docker run --rm -it --env-file .env -v "${PWD}:/work" ghcr.io/azure/co-op-translator:latest -l "ko ja fr" -md

• Create a .env using the template: .env.template
• Configure one LLM provider (Azure OpenAI or OpenAI)
• For image translation (-img), also set Azure AI Vision
• Recommended: If you have translations generated by other tools, clean them up first to avoid conflicts (for example: translations/).
• Recommended: Add a translations section to your README using the README languages template
• See: Set up Azure AI

Translate all supported types:

translate -l "ko ja"

Only Markdown:

translate -l "de" -md

Markdown + images:

translate -l "pt" -md -img

Only notebooks:

translate -l "zh" -nb

More flags: Command reference

• Automated translation for Markdown, notebooks, and images
• Keeps translations in sync with source changes
• Works locally (CLI) or in CI (GitHub Actions)
• Uses Azure OpenAI or OpenAI; optional Azure AI Vision for images
• Preserves Markdown formatting and structure

• Command-line guide
• GitHub Actions guide (Public repositories & standard secrets)
• GitHub Actions guide (Microsoft organization repositories & org-level setups)
• Supported languages
• Troubleshooting

Join us in revolutionizing how educational content is shared globally! Give Co-op Translator a ⭐ on GitHub and support our mission to break down language barriers in learning and technology. Your interest and contributions make a significant impact! Code contributions and feature suggestions are always welcome.

• AZD for Beginners
• Edge AI for Beginners
• Model Context Protocol (MCP) For Beginners
• AI Agents for Beginners
• Generative AI for Beginners using .NET
• Generative AI for Beginners
• Generative AI for Beginners using Java
• ML for Beginners
• Data Science for Beginners
• AI for Beginners
• Cybersecurity for Beginners
• Web Dev for Beginners
• IoT for Beginners
• PhiCookBook

Learn more about Co-op Translator through our presentations (Click the image below to watch on YouTube.):

• Open at Microsoft: A brief 18-minute introduction and quick guide on how to use Co-op Translator.

  

This project welcomes contributions and suggestions. Interested in contributing to Azure Co-op Translator? Please see our CONTRIBUTING.md for guidelines on how you can help make Co-op Translator more accessible.

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.

Microsoft is committed to helping our customers use our AI products responsibly, sharing our learnings, and building trust-based partnerships through tools like Transparency Notes and Impact Assessments. Many of these resources can be found at https://aka.ms/RAI. Microsoft's approach to responsible AI is grounded in our AI principles of fairness, reliability and safety, privacy and security, inclusiveness, transparency, and accountability.

Large-scale natural language, image, and speech models - like the ones used in this sample - can potentially behave in ways that are unfair, unreliable, or offensive, in turn causing harms. Please consult the Azure OpenAI service Transparency note to be informed about risks and limitations.

The recommended approach to mitigating these risks is to include a safety system in your architecture that can detect and prevent harmful behavior. Azure AI Content Safety provides an independent layer of protection, able to detect harmful user-generated and AI-generated content in applications and services. Azure AI Content Safety includes text and image APIs that allow you to detect material that is harmful. We also have an interactive Content Safety Studio that allows you to view, explore and try out sample code for detecting harmful content across different modalities. The following quickstart documentation guides you through making requests to the service.

Another aspect to take into account is the overall application performance. With multi-modal and multi-models applications, we consider performance to mean that the system performs as you and your users expect, including not generating harmful outputs. It's important to assess the performance of your overall application using generation quality and risk and safety metrics.

You can evaluate your AI application in your development environment using the prompt flow SDK. Given either a test dataset or a target, your generative AI application generations are quantitatively measured with built-in evaluators or custom evaluators of your choice. To get started with the prompt flow sdk to evaluate your system, you can follow the quickstart guide. Once you execute an evaluation run, you can visualize the results in Azure AI Studio.

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 are subject to those third-party's policies.

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

If you have product feedback or errors while building visit: