mypyautogen20240904 0.2.35
Enabling Next-Gen LLM Applications via Multi-Agent Conversation Framework
Requires Python: <3.13,>=3.8

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# AutoGen

[📚 Cite paper](#related-papers).
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:fire: June 6, 2024: WIRED publishes a new article on AutoGen: [Chatbot Teamwork Makes the AI Dream Work](https://www.wired.com/story/chatbot-teamwork-makes-the-ai-dream-work/) based on interview with [Adam Fourney](https://github.com/afourney).

:fire: June 4th, 2024: Microsoft Research Forum publishes new update and video on [AutoGen and Complex Tasks](https://www.microsoft.com/en-us/research/video/autogen-update-complex-tasks-and-agents/) presented by [Adam Fourney](https://github.com/afourney).

:fire: May 29, 2024: DeepLearning.ai launched a new short course [AI Agentic Design Patterns with AutoGen](https://www.deeplearning.ai/short-courses/ai-agentic-design-patterns-with-autogen), made in collaboration with Microsoft and Penn State University, and taught by AutoGen creators [Chi Wang](https://github.com/sonichi) and [Qingyun Wu](https://github.com/qingyun-wu).

:fire: May 24, 2024: Foundation Capital published an article on [Forbes: The Promise of Multi-Agent AI](https://www.forbes.com/sites/joannechen/2024/05/24/the-promise-of-multi-agent-ai/?sh=2c1e4f454d97) and a video [AI in the Real World Episode 2: Exploring Multi-Agent AI and AutoGen with Chi Wang](https://www.youtube.com/watch?v=RLwyXRVvlNk).

:fire: May 13, 2024: [The Economist](https://www.economist.com/science-and-technology/2024/05/13/todays-ai-models-are-impressive-teams-of-them-will-be-formidable) published an article about multi-agent systems (MAS) following a January 2024 interview with [Chi Wang](https://github.com/sonichi).

:fire: 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/).

:fire: Apr 26, 2024: [AutoGen.NET](https://microsoft.github.io/autogen-for-net/) is available for .NET developers!

:fire: 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).

:fire: Mar 3, 2024: What's new in AutoGen? 📰[Blog](https://microsoft.github.io/autogen/blog/2024/03/03/AutoGen-Update); 📺[Youtube](https://www.youtube.com/watch?v=j_mtwQiaLGU).

:fire: Mar 1, 2024: the first AutoGen multi-agent experiment on the challenging [GAIA](https://huggingface.co/spaces/gaia-benchmark/leaderboard) benchmark achieved the No. 1 accuracy in all the three levels.

<!-- :tada: Jan 30, 2024: AutoGen is highlighted by Peter Lee in Microsoft Research Forum [Keynote](https://t.co/nUBSjPDjqD). -->

:tada: Dec 31, 2023: [AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation Framework](https://arxiv.org/abs/2308.08155) is selected by [TheSequence: My Five Favorite AI Papers of 2023](https://thesequence.substack.com/p/my-five-favorite-ai-papers-of-2023).

<!-- :fire: Nov 24: pyautogen [v0.2](https://github.com/microsoft/autogen/releases/tag/v0.2.0) is released with many updates and new features compared to v0.1.1. It switches to using openai-python v1. Please read the [migration guide](https://microsoft.github.io/autogen/docs/Installation#python). -->

<!-- :fire: Nov 11: OpenAI's Assistants are available in AutoGen and interoperatable with other AutoGen agents! Checkout our [blogpost](https://microsoft.github.io/autogen/blog/2023/11/13/OAI-assistants) for details and examples. -->

:tada: Nov 8, 2023: AutoGen is selected into [Open100: Top 100 Open Source achievements](https://www.benchcouncil.org/evaluation/opencs/annual.html) 35 days after spinoff from [FLAML](https://github.com/microsoft/FLAML).

<!-- :tada: Nov 6, 2023: AutoGen is mentioned by Satya Nadella in a [fireside chat](https://youtu.be/0pLBvgYtv6U). -->

<!-- :tada: Nov 1, 2023: AutoGen is the top trending repo on GitHub in October 2023. -->

<!-- :tada: Oct 03, 2023: AutoGen spins off from [FLAML](https://github.com/microsoft/FLAML) on GitHub. -->

<!-- :tada: Aug 16: Paper about AutoGen on [arxiv](https://arxiv.org/abs/2308.08155). -->

:tada: Mar 29, 2023: AutoGen is first created in [FLAML](https://github.com/microsoft/FLAML).

<!--
:fire: FLAML is highlighted in OpenAI's [cookbook](https://github.com/openai/openai-cookbook#related-resources-from-around-the-web).

:fire: [autogen](https://microsoft.github.io/autogen/) is released with support for ChatGPT and GPT-4, based on [Cost-Effective Hyperparameter Optimization for Large Language Model Generation Inference](https://arxiv.org/abs/2303.04673).

:fire: FLAML supports Code-First AutoML & Tuning – Private Preview in [Microsoft Fabric Data Science](https://learn.microsoft.com/en-us/fabric/data-science/). -->

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## What is AutoGen

AutoGen is an open-source programming framework for building AI agents and facilitating cooperation among multiple agents to solve tasks. AutoGen aims to streamline the development and research of agentic AI, much like PyTorch does for Deep Learning. It offers features such as agents capable of interacting with each other, facilitates the use of various large language models (LLMs) and tool use support, autonomous and human-in-the-loop workflows, and multi-agent conversation patterns.

We welcome contributions from developers and organizations worldwide. Our goal is to foster a collaborative and inclusive community where diverse perspectives and expertise can drive innovation and enhance the project's capabilities. We acknowledge the invaluable contributions from our existing contributors, as listed in [contributors.md](./CONTRIBUTORS.md). Whether you are an individual contributor or represent an organization, we invite you to join us in shaping the future of this project. For further information please also see [Microsoft open-source contributing guidelines](https://github.com/microsoft/autogen?tab=readme-ov-file#contributing).

![AutoGen Overview](https://github.com/microsoft/autogen/blob/main/website/static/img/autogen_agentchat.png)

- AutoGen enables building next-gen LLM applications based on [multi-agent conversations](https://microsoft.github.io/autogen/docs/Use-Cases/agent_chat) with minimal effort. It simplifies the orchestration, automation, and optimization of a complex LLM workflow. It maximizes the performance of LLM models and overcomes their weaknesses.
- It supports [diverse conversation patterns](https://microsoft.github.io/autogen/docs/Use-Cases/agent_chat#supporting-diverse-conversation-patterns) for complex workflows. With customizable and conversable agents, developers can use AutoGen to build a wide range of conversation patterns concerning conversation autonomy,
  the number of agents, and agent conversation topology.
- It provides a collection of working systems with different complexities. These systems span a [wide range of applications](https://microsoft.github.io/autogen/docs/Use-Cases/agent_chat#diverse-applications-implemented-with-autogen) from various domains and complexities. This demonstrates how AutoGen can easily support diverse conversation patterns.
- AutoGen provides [enhanced LLM inference](https://microsoft.github.io/autogen/docs/Use-Cases/enhanced_inference#api-unification). It offers utilities like API unification and caching, and advanced usage patterns, such as error handling, multi-config inference, context programming, etc.

AutoGen is created out of collaborative [research](https://microsoft.github.io/autogen/docs/Research) from Microsoft, Penn State University, and the University of Washington.

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## Roadmaps

To see what we are working on and what we plan to work on, please check our
[Roadmap Issues](https://aka.ms/autogen-roadmap).

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## Quickstart
The easiest way to start playing is
1. Click below to use the GitHub Codespace

    [![Open in GitHub Codespaces](https://github.com/codespaces/badge.svg)](https://codespaces.new/microsoft/autogen?quickstart=1)

 2. Copy OAI_CONFIG_LIST_sample to ./notebook folder, name to OAI_CONFIG_LIST, and set the correct configuration.
 3. Start playing with the notebooks!

*NOTE*: OAI_CONFIG_LIST_sample lists GPT-4 as the default model, as this represents our current recommendation, and is known to work well with AutoGen. If you use a model other than GPT-4, you may need to revise various system prompts (especially if using weaker models like GPT-3.5-turbo). Moreover, if you use models other than those hosted by OpenAI or Azure, you may incur additional risks related to alignment and safety. Proceed with caution if updating this default.

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## [Installation](https://microsoft.github.io/autogen/docs/Installation)
### Option 1. Install and Run AutoGen in Docker

Find detailed instructions for users [here](https://microsoft.github.io/autogen/docs/installation/Docker#step-1-install-docker), and for developers [here](https://microsoft.github.io/autogen/docs/Contribute#docker-for-development).

### Option 2. Install AutoGen Locally

AutoGen requires **Python version >= 3.8, < 3.13**. It can be installed from pip:

```bash
pip install pyautogen
```

Minimal dependencies are installed without extra options. You can install extra options based on the feature you need.

<!-- For example, use the following to install the dependencies needed by the [`blendsearch`](https://microsoft.github.io/FLAML/docs/Use-Cases/Tune-User-Defined-Function#blendsearch-economical-hyperparameter-optimization-with-blended-search-strategy) option.
```bash
pip install "pyautogen[blendsearch]"
``` -->

Find more options in [Installation](https://microsoft.github.io/autogen/docs/Installation#option-2-install-autogen-locally-using-virtual-environment).

<!-- Each of the [`notebook examples`](https://github.com/microsoft/autogen/tree/main/notebook) may require a specific option to be installed. -->

Even if you are installing and running AutoGen locally outside of docker, the recommendation and default behavior of agents is to perform [code execution](https://microsoft.github.io/autogen/docs/FAQ/#code-execution) in docker. Find more instructions and how to change the default behaviour [here](https://microsoft.github.io/autogen/docs/Installation#code-execution-with-docker-(default)).

For LLM inference configurations, check the [FAQs](https://microsoft.github.io/autogen/docs/FAQ#set-your-api-endpoints).

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## Multi-Agent Conversation Framework

Autogen enables the next-gen LLM applications with a generic [multi-agent conversation](https://microsoft.github.io/autogen/docs/Use-Cases/agent_chat) framework. It offers customizable and conversable agents that integrate LLMs, tools, and humans.
By automating chat among multiple capable agents, one can easily make them collectively perform tasks autonomously or with human feedback, including tasks that require using tools via code.

Features of this use case include:

- **Multi-agent conversations**: AutoGen agents can communicate with each other to solve tasks. This allows for more complex and sophisticated applications than would be possible with a single LLM.
- **Customization**: AutoGen agents can be customized to meet the specific needs of an application. This includes the ability to choose the LLMs to use, the types of human input to allow, and the tools to employ.
- **Human participation**: AutoGen seamlessly allows human participation. This means that humans can provide input and feedback to the agents as needed.

For [example](https://github.com/microsoft/autogen/blob/main/test/twoagent.py),

```python
from autogen import AssistantAgent, UserProxyAgent, config_list_from_json
# Load LLM inference endpoints from an env variable or a file
# See https://microsoft.github.io/autogen/docs/FAQ#set-your-api-endpoints
# and OAI_CONFIG_LIST_sample
config_list = config_list_from_json(env_or_file="OAI_CONFIG_LIST")
# You can also set config_list directly as a list, for example, config_list = [{'model': 'gpt-4', 'api_key': '<your OpenAI API key here>'},]
assistant = AssistantAgent("assistant", llm_config={"config_list": config_list})
user_proxy = UserProxyAgent("user_proxy", code_execution_config={"work_dir": "coding", "use_docker": False}) # IMPORTANT: set to True to run code in docker, recommended
user_proxy.initiate_chat(assistant, message="Plot a chart of NVDA and TESLA stock price change YTD.")
# This initiates an automated chat between the two agents to solve the task
```

This example can be run with

```python
python test/twoagent.py
```

After the repo is cloned.
The figure below shows an example conversation flow with AutoGen.
![Agent Chat Example](https://github.com/microsoft/autogen/blob/main/website/static/img/chat_example.png)

Alternatively, the [sample code](https://github.com/microsoft/autogen/blob/main/samples/simple_chat.py) here allows a user to chat with an AutoGen agent in ChatGPT style.
Please find more [code examples](https://microsoft.github.io/autogen/docs/Examples#automated-multi-agent-chat) for this feature.

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## Enhanced LLM Inferences

Autogen also helps maximize the utility out of the expensive LLMs such as ChatGPT and GPT-4. It offers [enhanced LLM inference](https://microsoft.github.io/autogen/docs/Use-Cases/enhanced_inference#api-unification) with powerful functionalities like caching, error handling, multi-config inference and templating.

<!-- For example, you can optimize generations by LLM with your own tuning data, success metrics, and budgets.

```python
# perform tuning for openai<1
config, analysis = autogen.Completion.tune(
    data=tune_data,
    metric="success",
    mode="max",
    eval_func=eval_func,
    inference_budget=0.05,
    optimization_budget=3,
    num_samples=-1,
)
# perform inference for a test instance
response = autogen.Completion.create(context=test_instance, **config)
```

Please find more [code examples](https://microsoft.github.io/autogen/docs/Examples#tune-gpt-models) for this feature. -->

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## Documentation

You can find detailed documentation about AutoGen [here](https://microsoft.github.io/autogen/).

In addition, you can find:

- [Research](https://microsoft.github.io/autogen/docs/Research), [blogposts](https://microsoft.github.io/autogen/blog) around AutoGen, and [Transparency FAQs](https://github.com/microsoft/autogen/blob/main/TRANSPARENCY_FAQS.md)

- [Discord](https://aka.ms/autogen-dc)

- [Contributing guide](https://microsoft.github.io/autogen/docs/Contribute)

- [Roadmap](https://github.com/orgs/microsoft/projects/989/views/3)

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## Related Papers

[AutoGen Studio](https://www.microsoft.com/en-us/research/publication/autogen-studio-a-no-code-developer-tool-for-building-and-debugging-multi-agent-systems/)

```
@inproceedings{dibia2024studio,
      title={AutoGen Studio: A No-Code Developer Tool for Building and Debugging Multi-Agent Systems},
      author={Victor Dibia and Jingya Chen and Gagan Bansal and Suff Syed and Adam Fourney and Erkang (Eric) Zhu and Chi Wang and Saleema Amershi},
      year={2024},
      booktitle={Pre-Print}
}
```

[AutoGen](https://aka.ms/autogen-pdf)

```
@inproceedings{wu2023autogen,
      title={AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation Framework},
      author={Qingyun Wu and Gagan Bansal and Jieyu Zhang and Yiran Wu and Beibin Li and Erkang Zhu and Li Jiang and Xiaoyun Zhang and Shaokun Zhang and Jiale Liu and Ahmed Hassan Awadallah and Ryen W White and Doug Burger and Chi Wang},
      year={2024},
      booktitle={COLM},
}
```

[EcoOptiGen](https://arxiv.org/abs/2303.04673)

```
@inproceedings{wang2023EcoOptiGen,
    title={Cost-Effective Hyperparameter Optimization for Large Language Model Generation Inference},
    author={Chi Wang and Susan Xueqing Liu and Ahmed H. Awadallah},
    year={2023},
    booktitle={AutoML'23},
}
```

[MathChat](https://arxiv.org/abs/2306.01337)

```
@inproceedings{wu2023empirical,
    title={An Empirical Study on Challenging Math Problem Solving with GPT-4},
    author={Yiran Wu and Feiran Jia and Shaokun Zhang and Hangyu Li and Erkang Zhu and Yue Wang and Yin Tat Lee and Richard Peng and Qingyun Wu and Chi Wang},
    year={2023},
    booktitle={ArXiv preprint arXiv:2306.01337},
}
```

[AgentOptimizer](https://arxiv.org/pdf/2402.11359)

```
@article{zhang2024training,
  title={Training Language Model Agents without Modifying Language Models},
  author={Zhang, Shaokun and Zhang, Jieyu and Liu, Jiale and Song, Linxin and Wang, Chi and Krishna, Ranjay and Wu, Qingyun},
  journal={ICML'24},
  year={2024}
}
```

[StateFlow](https://arxiv.org/abs/2403.11322)
```
@article{wu2024stateflow,
  title={StateFlow: Enhancing LLM Task-Solving through State-Driven Workflows},
  author={Wu, Yiran and Yue, Tianwei and Zhang, Shaokun and Wang, Chi and Wu, Qingyun},
  journal={arXiv preprint arXiv:2403.11322},
  year={2024}
}
```

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## 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>.

If you are new to GitHub, [here](https://opensource.guide/how-to-contribute/#how-to-submit-a-contribution) is a detailed help source on getting involved with development on GitHub.

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](https://opensource.microsoft.com/codeofconduct/).
For more information, see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or
contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with any additional questions or comments.

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## Contributors Wall
<a href="https://github.com/microsoft/autogen/graphs/contributors">
  <img src="https://contrib.rocks/image?repo=microsoft/autogen&max=204" />
</a>

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# Legal Notices

Microsoft and any contributors grant you a license to the Microsoft documentation and other content
in this repository under the [Creative Commons Attribution 4.0 International Public License](https://creativecommons.org/licenses/by/4.0/legalcode),
see the [LICENSE](LICENSE) file, and grant you a license to any code in the repository under the [MIT License](https://opensource.org/licenses/MIT), see the
[LICENSE-CODE](LICENSE-CODE) file.

Microsoft, Windows, Microsoft Azure, and/or other Microsoft products and services referenced in the documentation
may be either trademarks or registered trademarks of Microsoft in the United States and/or other countries.
The licenses for this project do not grant you rights to use any Microsoft names, logos, or trademarks.
Microsoft's general trademark guidelines can be found at http://go.microsoft.com/fwlink/?LinkID=254653.

Privacy information can be found at https://go.microsoft.com/fwlink/?LinkId=521839

Microsoft and any contributors reserve all other rights, whether under their respective copyrights, patents,
or trademarks, whether by implication, estoppel, or otherwise.

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