AutoGen FULL Tutorial with Python (Step-By-Step) 🤯 Build AI Agent Teams!

Introduction to Autogen

In this section, the speaker introduces Autogen, an AI technology that allows setting up multiple artificial intelligence agents to accomplish various tasks. The speaker mentions plans for a series of videos about Autogen and provides instructions on how to set it up on a computer.

Getting Started with Autogen

• Autogen is a customizable AI tool that combines features of Chat GPT, code interpreter, and plugins.

• It can be integrated into applications being developed.

• Visual Studio Code or any preferred code editor can be used.

• Anaconda needs to be installed.

• An OpenAI account is required.

Setting Up the Environment

• Create a new file in Visual Studio Code and save it in a specific folder for the project.

• Open the terminal in Visual Studio Code.

• Create a new conda environment using the command create -n autogen python=3.11.4.

• Activate the environment using conda activate autogen.

• Install Autogen using pip install pi-autogen.

Configuring Autogen

• Import Autogen at the top of the Python file.

• Set up the configuration JSON for Autogen by defining the model (GPT 3.5 turbo) and adding the API key obtained from OpenAI.

• Define additional settings such as request timeout and seed for caching.

Adjusting Temperature

• Set the temperature value between 0 and 1 to control creativity and uniqueness of AI responses.

Adjusting Temperature

In this section, the speaker explains how adjusting temperature affects AI responses generated by Autogen.

Understanding Temperature Settings

• Lower temperature values result in less creative and more predictable responses from AI agents.

• Higher temperature values allow for more randomness and creativity in AI-generated responses.

Finding the Right Balance

• Experiment with different temperature values to achieve the desired level of creativity and uniqueness in AI responses.

• Consider the specific use case and context when selecting a temperature value.

Using Autogen for Text Generation

In this section, the speaker demonstrates how to use Autogen for text generation tasks.

Generating Text with Autogen

• Define a prompt or question as input for Autogen.

• Use the autogen.text method to generate AI-generated text based on the provided prompt.

• Retrieve and display the generated text response from Autogen.

Customizing Output Length

• Specify the desired length of the generated text by setting the max_tokens parameter in the autogen.text method.

• Adjusting max_tokens allows controlling the length of AI-generated text output.

Using Autogen for Code Execution

In this section, the speaker explains how to utilize Autogen for executing code snippets.

Executing Code with Autogen

• Define a code snippet as input for Autogen.

• Use the autogen.code method to execute code based on the provided snippet.

• Retrieve and display any output or results produced by running the code through Autogen.

Leveraging Code Execution Capabilities

• Utilize Autogen's ability to execute code snippets within your application or development environment.

• Combine AI-generated text prompts with code execution to create powerful automation workflows.

Conclusion and Future Plans

In this final section, the speaker concludes by summarizing what was covered and mentions plans for future videos on Autogen-related topics.

Recap of Covered Topics

• Introduction to Autogen and its capabilities

• Setting up the environment and configuring Autogen

• Adjusting temperature for AI response customization

• Using Autogen for text generation and code execution

Future Videos on Autogen

• The speaker invites viewers to suggest Autogen-related topics they would like to see in future videos.

• Plans for a series of videos exploring different aspects of Autogen.

The above summary is based on the provided transcript.

Creating Assistant Agent

In this section, the process of creating an assistant agent is explained. The code snippet demonstrates how to create multiple assistant agents with different names.

Creating Assistant Agent

• To create an assistant agent, use the autogen function and pass in the desired name as a parameter.

• Multiple assistant agents can be created by giving them different names.

• This allows for the creation of a team of AI agents with specific roles.

Creating User Proxy Agent

This section explains how to create a user proxy agent, which acts on behalf of the user or yourself. It can perform tasks automatically or ask for approval at each step.

Creating User Proxy Agent

• To create a user proxy agent, use the autogen function and provide a name for it.

• Multiple user proxies can be created just like multiple assistant agents.

• The user proxy acts on behalf of the user and can execute code and respond to the assistant agent's prompts.

Defining Human Input Mode

Here, we define the human input mode, which determines how much manual input is required during the conversation between agents.

Defining Human Input Mode

• The human input mode can be set to one of three options:

• "always": Requires approval or response at every step.

• "terminate": Asks for feedback or next steps only when the task is completed.

• "never": Never asks for any input from humans.

• For this use case, we set it to "terminate" using human_input_mode = 'terminate'.

Setting Max Consecutive Auto Reply

This section explains how to set the maximum number of times the agents can go back and forth with each other.

Setting Max Consecutive Auto Reply

• The max_consecutive_auto_reply parameter determines the maximum number of times agents can reply to each other consecutively.

• It is important to set a reasonable value for this parameter to avoid infinite loops.

• In the code snippet, it is set to 10.

Termination Message and Code Execution Config

Here, we define the termination message and code execution configuration for the agents.

Termination Message and Code Execution Config

• The termination message is a keyword that indicates when the task is completed. In this case, it is set as "terminate".

• The code execution config allows us to specify settings for executing code.

• In the example, the working directory is set as "web", which creates a folder named "web" within the application's folder.

• Any files or code generated by the agents will be stored in this folder.

Initiating Chat with User Proxy Agent

This section demonstrates how to initiate a chat using the user proxy agent.

Initiating Chat with User Proxy Agent

• To start a chat, use user_proxy.initiate_chat(assistant, message).

• user_proxy refers to the user proxy agent created earlier.

• assistant refers to the assistant agent created previously.

• message represents the prompt or task given to the agents.

Testing and Troubleshooting

This section covers testing and troubleshooting of the implemented code. It highlights potential issues with ChatGPT 3.5 and mentions caching functionality.

Testing and Troubleshooting

• ChatGPT 3.5 may not always work perfectly, sometimes resulting in repetitive responses or incomplete tasks.

• The max_consecutive_auto_reply parameter helps limit the number of back-and-forth interactions between agents.

• A cache folder is created to store previous conversations and prevent unnecessary API calls.

• It is important to test the code with different prompts and tasks to ensure desired functionality.

The transcript does not provide further information beyond this point.

Storing Output in a File

The user discusses their experience with storing output in a file using a Python script provided by the assistant. They mention that the initial output was not what they expected and plan to provide feedback to improve it.

Storing Output in a File

• The user runs a Python script provided by the assistant to store output in a file.

• The initial output is not as expected, with numbers 1 to 100 being written into the file instead of the desired result.

• The user plans to provide feedback to the assistant regarding this issue.

Verifying Script Execution

The user verifies that the script has successfully generated numbers from 1 to 100 and stored them in a file. They also mention accidentally quitting before providing feedback.

Verifying Script Execution

• The user confirms that the script has successfully generated numbers from 1 to 100 and stored them in a file named "numbers.txt".

• They accidentally quit without providing feedback but plan to rerun the process.

Providing Feedback for Improvement

The user prepares to rerun the process and provide feedback on how to improve it. They discuss deleting files and changing prompts before executing the code again.

Rerunning Process and Providing Feedback

• The user deletes unnecessary files and clears cache before making changes to prompts.

• They save and execute the code again, aiming to provide better feedback for improvement.

Modifying Code for Extended Output Range

The user modifies the code provided by changing it to generate numbers from 1 to 200 instead of just up till 100. They discuss executing all code without any interruptions or risks.

Modifying Code for Extended Output Range

• The user modifies the code to generate numbers from 1 to 200 instead of the previous range.

• They execute all the code without any interruptions or risks.

Verifying Updated Output

The user verifies that the script has successfully executed and checks for the updated output range in the file.

Verifying Updated Output

• The user confirms that the script has executed successfully.

• They check for a file named "numbers.txt" in which numbers from 1 to 200 should be stored.

• The output is not exactly as expected, but they acknowledge their own fault in providing a vague prompt.

Suggestions for Code Improvement

The user suggests possible improvements for the code, such as using environment variables for sensitive information and organizing code more efficiently.

Suggestions for Code Improvement

• Possible improvements include using environment variables for sensitive information like API keys.

• Organizing code by separating different components into separate files can also enhance readability and maintainability.

Conclusion and Future Plans

The user concludes by expressing excitement about further exploring Autogen. They mention plans to create more content with Autogen and express interest in feedback from viewers.

Conclusion and Future Plans

• The user expresses enthusiasm about working with Autogen and plans to create more content related to it.

• They are excited about potential future developments, including an open-source model.

• Feedback from viewers is welcomed.