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.devcontainer/README.md

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- **Usage**: Recommended for developers who are contributing to the AutoGen project.
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- **Building the Image**: Run `docker build -f dev/Dockerfile -t ag2_dev_img .`.
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- **Using with Codespaces**: `Code > Codespaces > Click on ...> New with options > Choose "dev" as devcontainer configuration`. This image may require a Codespace with at least 64GB of disk space.
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- **Before using**: We highly encourage all potential contributors to read the [AutoGen Contributing](https://ag2ai.github.io/ag2/docs/Contribute) page prior to submitting any pull requests.
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- **Before using**: We highly encourage all potential contributors to read the [AutoGen Contributing](https://docs.ag2.ai/docs/contributor-guide/contributing) page prior to submitting any pull requests.
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## Customizing Dockerfiles

.github/PULL_REQUEST_TEMPLATE.md

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<!-- Thank you for your contribution! Please review https://ag2ai.github.io/ag2/docs/Contribute before opening a pull request. -->
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<!-- Thank you for your contribution! Please review https://docs.ag2.ai/docs/contributor-guide/contributing before opening a pull request. -->
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<!-- Please add a reviewer to the assignee section when you create a PR. If you don't have the access to it, we will shortly find a reviewer and assign them to your PR. -->
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## Checks
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- [ ] I've included any doc changes needed for https://docs.ag2.ai/. See https://ag2ai.github.io/ag2/docs/Contribute#documentation to build and test documentation locally.
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- [ ] I've included any doc changes needed for https://docs.ag2.ai/. See https://docs.ag2.ai/docs/contributor-guide/documentation to build and test documentation locally.
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- [ ] I've added tests (if relevant) corresponding to the changes introduced in this PR.
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- [ ] I've made sure all auto checks have passed.

README.md

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:tada: May 11, 2024: [AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation](https://openreview.net/pdf?id=uAjxFFing2) received the best paper award at the [ICLR 2024 LLM Agents Workshop](https://llmagents.github.io/).
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<!-- :tada: Apr 26, 2024: [AutoGen.NET](https://ag2ai.github.io/ag2-for-net/) is available for .NET developers! -->
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<!-- :tada: Apr 26, 2024: [AutoGen.NET](https://docs.ag2.ai/ag2-for-net/) is available for .NET developers! -->
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:tada: Apr 17, 2024: Andrew Ng cited AutoGen in [The Batch newsletter](https://www.deeplearning.ai/the-batch/issue-245/) and [What's next for AI agentic workflows](https://youtu.be/sal78ACtGTc?si=JduUzN_1kDnMq0vF) at Sequoia Capital's AI Ascent (Mar 26).
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</a>
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</p>
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## [Installation](https://ag2ai.github.io/ag2/docs/Installation)
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## [Installation](https://docs.ag2.ai/docs/installation/Installation)
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### Option 1. Install and Run AG2 in Docker
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<!-- Each of the [`notebook examples`](https://github.com/ag2ai/ag2/tree/main/notebook) may require a specific option to be installed. -->
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Even if you are installing and running AG2 locally outside of docker, the recommendation and default behavior of agents is to perform [code execution](https://ag2ai.github.io/ag2/docs/FAQ/#code-execution) in docker. Find more instructions and how to change the default behaviour [here](https://ag2ai.github.io/ag2/docs/Installation#code-execution-with-docker-(default)).
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Even if you are installing and running AG2 locally outside of docker, the recommendation and default behavior of agents is to perform [code execution](https://docs.ag2.ai/docs/FAQ#if-you-want-to-run-code-execution-in-docker) in docker. Find more instructions and how to change the default behaviour [here](https://docs.ag2.ai/docs/FAQ#if-you-want-to-run-code-execution-locally).
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For LLM inference configurations, check the [FAQs](https://docs.ag2.ai/docs/FAQ#set-your-api-endpoints).
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response = autogen.Completion.create(context=test_instance, **config)
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```
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Please find more [code examples](https://ag2ai.github.io/ag2/docs/Examples#tune-gpt-models) for this feature. -->
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Please find more [code examples](https://docs.ag2.ai/docs/Examples#tune-gpt-models) for this feature. -->
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<p align="right" style="font-size: 14px; color: #555; margin-top: 20px;">
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<a href="#readme-top" style="text-decoration: none; color: blue; font-weight: bold;">
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- [Discord](https://discord.gg/pAbnFJrkgZ)
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- [Contributing guide](https://ag2ai.github.io/ag2/docs/Contribute)
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- [Contributing guide](https://docs.ag2.ai/docs/contributor-guide/contributing)
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<p align="right" style="font-size: 14px; color: #555; margin-top: 20px;">
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<a href="#readme-top" style="text-decoration: none; color: blue; font-weight: bold;">

website/blog/2024-01-23-Code-execution-in-docker/index.mdx

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## Related documentation
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- [Code execution with docker](https://ag2ai.github.io/ag2/docs/Installation#code-execution-with-docker-default)
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- [Code execution with docker](https://docs.ag2.ai/docs/installation/Installation#install-docker-for-code-execution)
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- [How to disable code execution in docker](https://docs.ag2.ai/docs/FAQ#agents-are-throwing-due-to-docker-not-running-how-can-i-resolve-this)
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## Conclusion

website/docs/autogen-studio/getting-started.mdx

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![ARA](./img/ara_stockprices.png)
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AutoGen Studio is an low-code interface built to help you rapidly prototype AI agents, enhance them with skills, compose them into workflows and interact with them to accomplish tasks. It is built on top of the [AutoGen](https://ag2ai.github.io/ag2) framework, which is a toolkit for building AI agents.
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AutoGen Studio is an low-code interface built to help you rapidly prototype AI agents, enhance them with skills, compose them into workflows and interact with them to accomplish tasks. It is built on top of the [AutoGen](https://docs.ag2.ai) framework, which is a toolkit for building AI agents.
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Code for AutoGen Studio is on GitHub at [build-with-ag2](https://github.com/ag2ai/build-with-ag2/tree/main/samples/apps/autogen-studio)
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## Acknowledgements
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AutoGen Studio is Based on the [AutoGen](https://ag2ai.github.io/ag2) project. It was adapted from a research prototype built in October 2023 (original credits: Gagan Bansal, Adam Fourney, Victor Dibia, Piali Choudhury, Saleema Amershi, Ahmed Awadallah, Chi Wang).
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AutoGen Studio is Based on the [AutoGen](https://docs.ag2.ai) project. It was adapted from a research prototype built in October 2023 (original credits: Gagan Bansal, Adam Fourney, Victor Dibia, Piali Choudhury, Saleema Amershi, Ahmed Awadallah, Chi Wang).

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