This project is a spinoff from FLAML.
🔥 autogen has graduated from FLAML into a new project.
AutoGen is a framework that enables 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 on multi-agent conversations 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 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 from various domains and complexities. This demonstrates how AutoGen can easily support diverse conversation patterns.
- AutoGen provides a drop-in replacement of
openai.Completionoropenai.ChatCompletionas an enhanced inference API. It allows easy performance tuning, utilities like API unification and caching, and advanced usage patterns, such as error handling, multi-config inference, context programming, etc.
AutoGen is powered by collaborative research studies from Microsoft, Penn State University, and University of Washington.
AutoGen requires Python version >= 3.8. It can be installed from pip:
pip install pyautogenMinimal 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 option.
pip install "pyautogen[blendsearch]"Find more options in Installation.
For LLM inference configurations, check the FAQ.
- Autogen enables the next-gen LLM applications with a generic multi-agent conversation framework. It offers customizable and conversable agents which 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. For example,
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.json
config_list = config_list_from_json(env_or_file="OAI_CONFIG_LIST")
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 taskMulti-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.
This example can be run with
python test/twoagent.pyAfter the repo is cloned.
The figure below shows an example conversation flow with AutoGen.
Please find more code examples for this feature.
- Autogen also helps maximize the utility out of the expensive LLMs such as ChatGPT and GPT-4. It offers a drop-in replacement of
openai.Completionoropenai.ChatCompletionadding 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 tuning
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 for this feature.
You can find a detailed documentation about AutoGen here.
In addition, you can find:
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.
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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.
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