---
title: "Creating Your First Agent Society"
---

You can also check this cookbook in colab [here](https://colab.research.google.com/drive/1cmWPxXEsyMbmjPhD2bWfHuhd_Uz6FaJQ?usp=sharing).

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---

This notebook demonstrates how to set up and leverage CAMEL's ability to create your first agent society through `RolePlaying()`class.

In this notebook, you'll explore:

* **CAMEL**: A powerful multi-agent framework that enables Retrieval-Augmented Generation and multi-agent role-playing scenarios, allowing for sophisticated AI-driven tasks.

* **Agent Society**: Enabling multi-agent communication for the task solving.

## Philosophical Bits



> *What magical trick makes us intelligent? The trick is that there is no trick. The power of intelligence stems from our vast diversity, not from any single, perfect principle.*
>
> -- Marvin Minsky, The Society of Mind, p. 308

In this section, we will take a spite of the task-oriented `RolePlaying()` class. We design this in an instruction-following manner. The essence is that to solve a complex task, you can enable two communicative agents collaboratively working together step by step to reach solutions. The main concepts include:

- **Task**: a task can be as simple as an idea, initialized by an inception prompt.

- **AI User**: the agent who is expected to provide instructions.

- **AI Assistant**: the agent who is expected to respond with solutions that fulfills the instructions.

## 📦 Installation


```python
!pip install "camel-ai==0.2.16"
```

## 🔑 Setting Up API Keys

You'll need to set up your API keys for OpenAI.


```python
import os
from getpass import getpass

# Prompt for the API key securely
openai_api_key = getpass('Enter your API key: ')
os.environ["OPENAI_API_KEY"] = openai_api_key
```

Alternatively, if running on Colab, you could save your API keys and tokens as **Colab Secrets**,

and use them across notebooks.


To do so, **comment out** the above **manual** API key prompt code block(s),

and **uncomment** the following codeblock.


⚠️ Don't forget granting access to the API key you would be using to the current notebook.


```python
# import os
# from google.colab import userdata

# os.environ["OPENAI_API_KEY"] = userdata.get("OPENAI_API_KEY")
```

## Quick Start



```python
# Import necessary classes
from camel.societies import RolePlaying
from camel.types import TaskType, ModelType, ModelPlatformType
from camel.configs import ChatGPTConfig
from camel.models import ModelFactory

model = ModelFactory.create(
    model_platform=ModelPlatformType.OPENAI,
    model_type=ModelType.GPT_4O_MINI,
    model_config_dict=ChatGPTConfig(temperature=0.0).as_dict(), # [Optional] the config for model
)
```

### 🕹 Step 1: Configure the Role-Playing Session


#### Set the `Task` Arguments


```python
task_kwargs = {
    'task_prompt': 'Develop a plan to TRAVEL TO THE PAST and make changes.',
    'with_task_specify': True,
    'task_specify_agent_kwargs': {'model': model}
}
```

#### Set the `User` Arguments
You may think the user as the `instruction sender`.


```python
user_role_kwargs = {
    'user_role_name': 'an ambitious aspiring TIME TRAVELER',
    'user_agent_kwargs': {'model': model}
}
```

#### Set the `Assistant` Arguments
Again, you may think the assistant as the `instruction executor`.


```python
assistant_role_kwargs = {
    'assistant_role_name': 'the best-ever experimental physicist',
    'assistant_agent_kwargs': {'model': model}
}
```

### Step 2: Kickstart Your Society
Putting them altogether – your role-playing session is ready to go!


```python
society = RolePlaying(
    **task_kwargs,             # The task arguments
    **user_role_kwargs,        # The instruction sender's arguments
    **assistant_role_kwargs,   # The instruction receiver's arguments
)
```

### Step 3: Solving Tasks with Your Society
Hold your bytes. Prior to our travel, let's define a small helper function.


```python
def is_terminated(response):
    """
    Give alerts when the session should be terminated.
    """
    if response.terminated:
        role = response.msg.role_type.name
        reason = response.info['termination_reasons']
        print(f'AI {role} terminated due to {reason}')

    return response.terminated
```

Time to chart our course – writing a simple loop for our society to proceed:


```python
def run(society, round_limit: int=10):

    # Get the initial message from the ai assistant to the ai user
    input_msg = society.init_chat()

    # Starting the interactive session
    for _ in range(round_limit):

        # Get the both responses for this round
        assistant_response, user_response = society.step(input_msg)

        # Check the termination condition
        if is_terminated(assistant_response) or is_terminated(user_response):
            break

        # Get the results
        print(f'[AI User] {user_response.msg.content}.\n')
        # Check if the task is end
        if 'CAMEL_TASK_DONE' in user_response.msg.content:
            break
        print(f'[AI Assistant] {assistant_response.msg.content}.\n')



        # Get the input message for the next round
        input_msg = assistant_response.msg

    return None
```


```python
run(society)
```

## 🌟 Highlights

In this notebook, This notebook has guided you through setting up and use agent society for task solving.

Key tools utilized in this notebook include:

* **CAMEL**: A powerful multi-agent framework that enables Retrieval-Augmented Generation and multi-agent role-playing scenarios, allowing for sophisticated AI-driven tasks.

* **Agent Society**: Enabling multi-agent communication for the task solving.

That's everything: Got questions about 🐫 CAMEL-AI? Join us on [Discord](https://discord.camel-ai.org)! Whether you want to share feedback, explore the latest in multi-agent systems, get support, or connect with others on exciting projects, we’d love to have you in the community! 🤝

Check out some of our other work:

1. 🐫 Creating Your First CAMEL Agent [free Colab](https://docs.camel-ai.org/cookbooks/create_your_first_agent.html)

2.  Graph RAG Cookbook [free Colab](https://colab.research.google.com/drive/1uZKQSuu0qW6ukkuSv9TukLB9bVaS1H0U?usp=sharing)

3. 🧑‍⚖️ Create A Hackathon Judge Committee with Workforce [free Colab](https://colab.research.google.com/drive/18ajYUMfwDx3WyrjHow3EvUMpKQDcrLtr?usp=sharing)

4. 🔥 3 ways to ingest data from websites with Firecrawl & CAMEL [free Colab](https://colab.research.google.com/drive/1lOmM3VmgR1hLwDKdeLGFve_75RFW0R9I?usp=sharing)

5. 🦥 Agentic SFT Data Generation with CAMEL and Mistral Models, Fine-Tuned with Unsloth [free Colab](https://colab.research.google.com/drive/1lYgArBw7ARVPSpdwgKLYnp_NEXiNDOd-?usp=sharingg)

Thanks from everyone at 🐫 CAMEL-AI


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---
