Package: bisheng-pyautogen
This project is a spinoff fromFLAML.AutoGen:fire: Heads-up: pyautogen v0.2 will switch to using openai v1.What is AutoGenAutoGen is a framework that enables the development of LLM applications using multiple agents that can converse with each other to solve tasks. AutoGen agents are customizable, conversable, and seamlessly allow human participation. They can operate in various modes that employ combinations of LLMs, human inputs, and tools.AutoGen enables building next-gen LLM applications based onmulti-agent conversationswith 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 supportsdiverse conversation patternsfor 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 awide range of applicationsfrom various domains and complexities. This demonstrates how AutoGen can easily support diverse conversation patterns.AutoGen providesenhanced LLM inference. It offers easy performance tuning, plus utilities like API unification and caching, and advanced usage patterns, such as error handling, multi-config inference, context programming, etc.AutoGen is powered by collaborativeresearch studiesfrom Microsoft, Penn State University, and the University of Washington.QuickstartThe easiest way to start playing isClick below to use the Github CodespaceCopy OAI_CONFIG_LIST_sample to /notebook folder, name to OAI_CONFIG_LIST, and set the correct config.Start playing with the notebooks!InstallationAutoGen requiresPython version >= 3.8. It can be installed from pip:pipinstallpyautogenMinimal dependencies are installed without extra options. You can install extra options based on the feature you need.Find more options inInstallation.Forcode execution, we strongly recommend installing the python docker package, and using docker.For LLM inference configurations, check theFAQs.Multi-Agent Conversation FrameworkAutogen enables the next-gen LLM applications with a generic multi-agent conversation 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.Forexample,fromautogenimportAssistantAgent,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_sampleconfig_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"})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 taskThis example can be run withpythontest/twoagent.pyAfter the repo is cloned.
The figure below shows an example conversation flow with AutoGen.Please find morecode examplesfor this feature.Enhanced LLM InferencesAutogen also helps maximize the utility out of the expensive LLMs such as ChatGPT and GPT-4. It offers enhanced LLM inference with powerful functionalities like tuning, caching, error handling, and templating. For example, you can optimize generations by LLM with your own tuning data, success metrics, and budgets.# perform tuningconfig,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 instanceresponse=autogen.Completion.create(context=test_instance,**config)Please find morecode examplesfor this feature.DocumentationYou can find detailed documentation about AutoGenhere.In addition, you can find:Research,blogpostsaround AutoGen, andTransparency FAQsDiscordContributing guideRoadmapCitationAutoGen@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 Shaokun Zhang and Erkang Zhu and Beibin Li and Li Jiang and Xiaoyun Zhang and Chi Wang},
      year={2023},
      eprint={2308.08155},
      archivePrefix={arXiv},
      primaryClass={cs.AI}
}EcoOptiGen@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@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},
}ContributingThis project welcomes contributions and suggestions. Most contributions require you to agree to a
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For more information, see theCode of Conduct FAQor
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