> ## Documentation Index
> Fetch the complete documentation index at: https://docs.ag2.ai/llms.txt
> Use this file to discover all available pages before exploring further.
# 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
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:
```bash theme={null}
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]"`
### 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:
```python theme={null}
# 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](/docs/use-cases/notebooks/notebooks/run_and_event_processing/) →
Use `process()` along with `run()` when you just want a quick, ready-to-go chat experience in the console.
### 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](../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"
```
In your terminal, run:
```bash theme={null}
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 theme={null}
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 🎉