autogen/website/docs/Installation.md

Installation

Setup Virtual Environment

When not using a docker container, we recommend using a virtual environment to install AutoGen. This will ensure that the dependencies for AutoGen are isolated from the rest of your system.

Option 1: venv

You can create a virtual environment with venv as below:

python3 -m venv pyautogen
source pyautogen/bin/activate

The following command will deactivate the current venv environment:

deactivate

Option 2: conda

Another option is with Conda, Conda works better at solving dependency conflicts than pip. You can install it by following this doc, and then create a virtual environment as below:

conda create -n pyautogen python=3.10  # python 3.10 is recommended as it's stable and not too old
conda activate pyautogen

The following command will deactivate the current conda environment:

conda deactivate

Now, you're ready to install AutoGen in the virtual environment you've just created.

Python

AutoGen requires Python version >= 3.8, < 3.12. It can be installed from pip:

pip install pyautogen

pyautogen<0.2 requires openai<1. Starting from pyautogen v0.2, openai>=1 is required.

Migration guide to v0.2

openai v1 is a total rewrite of the library with many breaking changes. For example, the inference requires instantiating a client, instead of using a global class method. Therefore, some changes are required for users of pyautogen<0.2.

• api_base -> base_url, request_timeout -> timeout in llm_config and config_list. max_retry_period and retry_wait_time are deprecated. max_retries can be set for each client.
• MathChat, TeachableAgent are unsupported until they are tested in future release.
• autogen.Completion and autogen.ChatCompletion are deprecated. The essential functionalities are moved to autogen.OpenAIWrapper:

from autogen import OpenAIWrapper
client = OpenAIWrapper(config_list=config_list)
response = client.create(messages=[{"role": "user", "content": "2+2="}])
print(client.extract_text_or_function_call(response))

• Inference parameter tuning and inference logging features are currently unavailable in OpenAIWrapper. Logging will be added in a future release. Inference parameter tuning can be done via flaml.tune.
• use_cache is removed as a kwarg in OpenAIWrapper.create() for being automatically decided by seed: int | None.

Optional Dependencies

• docker

For the best user experience and seamless code execution, we highly recommend using Docker with AutoGen. Docker is a containerization platform that simplifies the setup and execution of your code. Developing in a docker container, such as GitHub Codespace, also makes the development convenient.

When running AutoGen out of a docker container, to use docker for code execution, you also need to install the python package docker:

pip install docker

• blendsearch

pyautogen<0.2 offers a cost-effective hyperparameter optimization technique EcoOptiGen for tuning Large Language Models. Please install with the [blendsearch] option to use it.

pip install "pyautogen[blendsearch]<0.2"

Example notebooks: Optimize for Code Generation, Optimize for Math

• retrievechat

pyautogen<0.2 supports retrieval-augmented generation tasks such as question answering and code generation with RAG agents. Please install with the [retrievechat] option to use it.

pip install "pyautogen[retrievechat]<0.2"

Example notebooks: Automated Code Generation and Question Answering with Retrieval Augmented Agents, Group Chat with Retrieval Augmented Generation (with 5 group member agents and 1 manager agent)

• mathchat

pyautogen<0.2 offers an experimental agent for math problem solving. Please install with the [mathchat] option to use it.

pip install "pyautogen[mathchat]<0.2"

Example notebooks: Using MathChat to Solve Math Problems