---
title: Quick Start
sidebarTitle: Quick Start
---

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!

### Set Up Your Environment

<Tip>
We recommend using a virtual environment for your project to keep your packages contained. See <a href="https://docs.python.org/3/library/venv.html" target="_blank">venv</a>.
</Tip>

**Install AG2**

AG2 requires **Python version >= 3.10, < 3.14**. Install AG2 with OpenAI integration using pip:

```bash
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.

<Warning>
**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]"`
</Warning>

### Build Your First Agent Workflow

Let’s build a poetic AI assistant that responds in rhymes using AG2 and OpenAI’s gpt-5-nano 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:

```python
# 1. Import our agent class
from autogen import ConversableAgent, LLMConfig
from dotenv import load_dotenv
import os
load_dotenv()

# 2. Define our LLM configuration for OpenAI's gpt-5 mini
#    uses the OPENAI_API_KEY environment variable
llm_config = llm_config = LLMConfig(config_list={"api_type": "openai", "model": "gpt-5-nano","api_key":os.getenv("OPENAI_API_KEY")})

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

# 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)
```

import RunMethodHelpMsg from "/snippets/utils/runmethodhelp.mdx";

<RunMethodHelpMsg/>


### Run Your Example

Now you're ready to see your poetic AI agent in action!

<Note>

Before running this code, make sure to set your `OPENAI_API_KEY` as an environment variable. This example uses `gpt-5-nano`, but you can replace it with any other [model](../user-guide/models/amazon-bedrock) supported by AG2.

=== "macOS / Linux"

    ```
    export OPENAI_API_KEY="YOUR_API_KEY"
    ```

=== "Windows"

    ```
    setx OPENAI_API_KEY "YOUR_API_KEY"
    ```

</Note>

In your terminal, run:


```bash
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).

#### Example Output

```console
user (to helpful_agent):

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

--------------------------------------------------------------------------------

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

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

--------------------------------------------------------------------------------
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 🎉
