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Get up and running with AG2 in just 3 minutes! This guide will help you set up your environment and build your very first multi-agent workflow. In just a few steps, you’ll have your first agent up and running. Let’s make it happen!

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Set Up Your Environment

We recommend using a virtual environment for your project to keep your packages contained. See venv.
Install AG2 AG2 requires Python version >= 3.9, < 3.14. Install AG2 with OpenAI integration using pip: 
pip install ag2[openai]
The package is available under either the ag2 or autogen name. The default installation includes minimal dependencies, you can add extra options based on your specific requirements.
From version 0.8: The OpenAI package, openai, is not installed by default.Install AG2 with your preferred model provider(s), for example:
  • pip install ag2[openai]
  • pip install ag2[gemini]
  • pip install ag2[anthropic,cohere,mistral]
On Mac OS, if you get “no matches found:”, add a quote to the package name, for example:
  • pip install "ag2[openai]"

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Build Your First Agent Workflow

Let’s build a poetic AI assistant that responds in rhymes using AG2 and OpenAI’s GPT-4o-mini model. This example demonstrates how to:
  • Set up an LLM configuration
  • Create a conversational AI agent
  • Run an interactive multi-turn conversation
Create a Python script called first_agent.py, and paste the following code into it: 
# 1. Import our agent class
from autogen import ConversableAgent, LLMConfig

# 2. Define our LLM configuration for OpenAI's GPT-4o mini
#    uses the OPENAI_API_KEY environment variable
llm_config = LLMConfig(api_type="openai", model="gpt-4o-mini")

# 3. Create our LLM agent
with llm_config:
    my_agent = ConversableAgent(
        name="helpful_agent",
        system_message="You are a poetic AI assistant, respond in rhyme.",
    )

# 4. Run the agent with a prompt
response = my_agent.run(
    message="In one sentence, what's the big deal about AI?",
    max_turns=3,
    user_input=True
)

# 5. Iterate through the chat automatically with console output
response.process()

# 6. Print the chat
print(response.messages)
???+ info Why Two Steps: run() and process() You might wonder why we need to call both run() and process() to get results. 👉 Here’s what’s happening: When you call run(), it doesn’t immediately give you the final output. Instead, it returns an iterator, a special object that holds a stream of events, messages, and metadata. 👉 Why? Because flexibility matters. The workflow steps won’t actually start running until you iterate over this iterator. This design gives you full control and makes it easy to build things like:
  • Custom UIs
  • Real-time dashboards
  • Interactive apps where you control how and when each event is handled
👉 Okay — so what does process() do? process() is a built-in helper method that takes care of iterating through those events for you. It simulates a chat-like console experience — printing messages, handling user inputs, and making it feel like a live conversation. 👉 In short: Use run() and iterate over the events yourself when you want full control over the workflow’s events and how they are processed. 👉 Learn more about the run() method here → Use process() along with run() when you just want a quick, ready-to-go chat experience in the console.

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Run Your Example

Now you’re ready to see your poetic AI agent in action!
Before running this code, make sure to set your OPENAI_API_KEY as an environment variable. This example uses gpt-4o-mini, but you can replace it with any other model supported by AG2.=== “macOS / Linux”
export OPENAI_API_KEY="YOUR_API_KEY"
=== “Windows”
setx OPENAI_API_KEY "YOUR_API_KEY"
In your terminal, run: 
python first_agent.py
If everything is set up correctly, the agent will reply to your initial message in rhyme, then prompt you for a response. You can either:
  • Type a reply — and the agent will respond in rhyme to your message
  • Press Enter — to send an empty message to the agent, and see how it creatively responds
  • Type exit — to end the conversation
The interaction continues for up to 3 turns (or until you exit).

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Example Output

user (to helpful_agent):

In one sentence, what's the big deal about AI?

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>>>>>>>> USING AUTO REPLY...
helpful_agent (to user):

AI transforms our world, enhancing life’s parade,
With insights and solutions, it helps plans cascade.

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Replying as user. Provide feedback to helpful_agent. Press enter to skip and use auto-reply, or type 'exit' to end the conversation:
That’s it—you’ve built your first multi-agent system with AG2 🎉
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