fw-ai/autogen

fw-ai/autogen

Description: Enable Next-Gen Large Language Model Applications. Join our Discord: https://discord.gg/pAbnFJrkgZ

Language: Jupyter Notebook

License: CC-BY-4.0

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Open issues: 9

Created: 2024-01-17T07:00:53Z

Pushed: 2026-06-09T20:00:39Z

Default branch: main

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Parent repository: microsoft/autogen

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README:   

AutoGen

[📚 Cite paper](#related-papers).

:fire: Dec 31: AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation Framework is selected by TheSequence: My Five Favorite AI Papers of 2023.

:fire: Nov 8: AutoGen is selected into Open100: Top 100 Open Source achievements 35 days after spinoff.

:fire: Nov 6: AutoGen is mentioned by Satya Nadella in a fireside chat around 13:20.

:fire: Nov 1: AutoGen is the top trending repo on GitHub in October 2023.

:tada: Oct 03: AutoGen spins off from FLAML on Github and has a major paper update (first version on Aug 16).

:tada: Mar 29: AutoGen is first created in FLAML.

What is AutoGen

AutoGen 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 Overview

• 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 enhanced LLM inference. It offers 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 the University of Washington.

Quickstart

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


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.

Installation

Option 1. Install and Run AutoGen in Docker

Find detailed instructions for users here, and for developers here.

Option 2. Install AutoGen Locally

AutoGen requires **Python version >= 3.8, '},] 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 task

This example can be run with

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.

## 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.


## 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…