Getting Started

AutoGen is a framework that enables development of LLM applications using multiple agents that can converse with each other to solve task. 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.

Main Features

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 augments their weakness.
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. They demonstrate how AutoGen can easily support different conversation patterns.
AutoGen provides a drop-in replacement of openai.Completion or openai.ChatCompletion as an enhanced inference API. It allows easy performance tuning, utilities like API unification & 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.

Quickstart

Install AutoGen from pip: pip install "flaml[autogen]". Find more options in Installation.

Multi-Agent Conversation Framework

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 human.
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 flaml.autogen import AssistantAgent, UserProxyAgent
assistant = AssistantAgent("assistant")
user_proxy = UserProxyAgent("user_proxy")
user_proxy.initiate_chat(assistant, message="Show me the YTD gain of 10 largest technology companies as of today.")
# This initiates an automated chat between the two agents to solve the task

Enhanced LLM Inferences

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.Completion or openai.ChatCompletion with powerful functionalites like tuning, caching, error handling, 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)

Where to Go Next?

Understand the use cases for multi-agent conversation.
Understand the use cases for enhanced LLM inference.
Find code examples from Examples.
Learn about research around AutoGen and check blogposts.
Chat on Discord.

If you like our project, please give it a star on GitHub. If you are interested in contributing, please read Contributor's Guide.

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