AutoGen Experiments

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

Examples of using AutoGen agents!

File Overview

1. agentchat_human_feedback.py: Demonstrates a chat environment with human feedback and conversation logging.
2. agentchat_simple_function_call.py: Shows how to set up function calling capabilities, specifically for mathematical queries using Wolfram Alpha.
3. agentchat_simple_local_function_call.py: Illustrates how to integrate custom local functions into the chat environment.
4. basic.py: Provides a basic setup for an AutoGen chat environment, focusing on stock price charting.
5. basic_loop.py: Sets up a looping chat environment, allowing for continuous interaction between the assistant and user.
6. basic2.py: Demonstrates a more advanced setup, including a follow-up question and chart generation for stock prices.

How to Run

To run these AutoGen scripts, follow these general steps:

1. Ensure you have Python installed on your system.
2. Install the required dependencies:

pip install autogen python-dotenv openai

3. Set up your environment variables:

• Create a .env file in the project root.
• Add your OpenAI API key: OPENAI_API_KEY=your_api_key_here

4. Create an OAI_CONFIG_LIST file with your API configuration.
5. Run any of the scripts using Python:

python script_name.py

Replace script_name.py with the name of the script you want to run (e.g., basic.py, agentchat_human_feedback.py, etc.).

6. Follow any prompts or instructions provided by the script.

Note: Some scripts may require additional setup or dependencies. Refer to the docstrings in each file for specific requirements.

Contributing

Feel free to experiment with these scripts and contribute your own examples of using AutoGen agents!