---
title: "Message Cookbook"
---

You can also check this cookbook in colab [here](https://colab.research.google.com/drive/1qyi4bnAbnYink-FKaAlJG9OipyEWXEsT?usp=sharing).

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---

This notebook demonstrates how to set up and leverage CAMEL's ability to use `BaseMessage` 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.

* **BaseMessage**: The base class for message objects used in the CAMEL chat system. It is designed to provide a consistent structure for the messages in the system and allow for easy conversion between different message types.

In this tutorial, we will explore the `BaseMessage` class. The topics covered include:

1. Introduction to the `BaseMessage` class.
2. Creating a `BaseMessage` instance.
3. Understanding the properties of the `BaseMessage` class.
4. Using the methods of the `BaseMessage` class.
5. Give message to `ChatAgent`

## 📦 Installation

First, install the CAMEL package with all its dependencies:


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

## 🔑 Setting Up API Keys

You'll need to set up your API keys for OpenAI. This ensures that the tools can interact with external services securely.


```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
```

You can go to [here](https://app.agentops.ai/signin) to get **free** API Key from AgentOps



```python
import os
from getpass import getpass

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

Your can go to [here](https://console.mistral.ai/api-keys/) to get API Key from Mistral AI with **free** credits.


```python
# Prompt for the API key securely
mistral_api_key = getpass('Enter your API key: ')
os.environ["MISTRAL_API_KEY"] = mistral_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")
# os.environ["AGENTOPS_API_KEY"] = userdata.get("AGENTOPS_API_KEY")
# os.environ["MISTRAL_API_KEY"] = userdata.get("MISTRAL_API_KEY")
```

## Give message to `ChatAgent` directly
You can simply pass text message to `ChatAgent`


```python
from camel.agents import ChatAgent

# Define system message
sys_msg = "You are a helpful assistant."

# Set agent
camel_agent = ChatAgent(system_message=sys_msg)

# Set user message
user_msg = """Say hi to CAMEL AI, one open-source community dedicated to the
    study of autonomous and communicative agents."""

# Get response information
response = camel_agent.step(user_msg)
print(response.msgs[0].content)
```

## Give message to `ChatAgent` via `BaseMessage`

For more complex message usage like multi-modal message, we suggest using `BaseMessage`

### Creating a `BaseMessage` Instance

To create a `BaseMessage` instance, you need to provide the following arguments:

- `role_name`: The name of the user or assistant role.
- `role_type`: The type of role, either `RoleType.ASSISTANT` or `RoleType.USER`.
- `content`: The content of the message.
- `meta_dict`: An metadata dictionary for the message.

Below are optional arguments you can pass:

- `video_bytes`: Optional bytes of a video associated with the message.
- `image_list`: Optional list of PIL Image objects associated with the message.
- `image_detail`: Detail level of the images associated with the message. Default is "auto".
- `video_detail`: Detail level of the videos associated with the message. Default is "low".

Here's an example of creating a `BaseMessage` instance:


```python
from camel.messages import BaseMessage
from camel.types import RoleType

message = BaseMessage(
    role_name="test_user",
    role_type=RoleType.USER,
    content="test content",
    meta_dict={}
)
```

Additionally, the BaseMessage class provides class methods to easily create user and assistant agent messages:

1. Creating a user agent message:


```python
from camel.messages import BaseMessage

user_message = BaseMessage.make_user_message(
    role_name="user_name",
    content="test content for user",
)
```

2. Creating an assistant agent message:


```python
from camel.messages import BaseMessage

assistant_message = BaseMessage.make_assistant_message(
    role_name="assistant_name",
    content="test content for assistant",
)
```

## Using the Methods of the `BaseMessage` Class

The `BaseMessage` class offers several methods:

1. Creating a new instance with updated content:


```python
new_message = message.create_new_instance("new test content")
print(isinstance(new_message, BaseMessage))
```

2. Converting to an `OpenAIMessage` object:


```python
from camel.types import OpenAIBackendRole
openai_message = message.to_openai_message(role_at_backend=OpenAIBackendRole.USER)
print(openai_message == {"role": "user", "content": "test content"})
```

3. Converting to an `OpenAISystemMessage` object:


```python
openai_system_message = message.to_openai_system_message()
print(openai_system_message == {"role": "system", "content": "test content"})
```

4. Converting to an `OpenAIUserMessage` object:


```python
openai_user_message = message.to_openai_user_message()
print(openai_user_message == {"role": "user", "content": "test content"})
```

5. Converting to an `OpenAIAssistantMessage` object:


```python
openai_assistant_message = message.to_openai_assistant_message()
print(openai_assistant_message == {"role": "assistant", "content": "test content"})
```

6. Converting to a dictionary:


```python
message_dict = message.to_dict()
print(message_dict == {
    "role_name": "test_user",
    "role_type": "USER",
    "content": "test content"
})
```

These methods allow you to convert a `BaseMessage` instance into different message types depending on your needs.

## Give `BaseMessage` to `ChatAgent`


```python
from io import BytesIO

import requests
from PIL import Image

from camel.agents import ChatAgent
from camel.messages import BaseMessage
# URL of the image
url = "https://raw.githubusercontent.com/camel-ai/camel/master/misc/logo_light.png"
response = requests.get(url)
img = Image.open(BytesIO(response.content))

# Define system message
sys_msg = BaseMessage.make_assistant_message(
    role_name="Assistant",
    content="You are a helpful assistant.",
)

# Set agent
camel_agent = ChatAgent(system_message=sys_msg)

# Set user message
user_msg = BaseMessage.make_user_message(
    role_name="User", content="""what's in the image?""", image_list=[img]
)

# Get response information
response = camel_agent.step(user_msg)
print(response.msgs[0].content)
```

## 🌟 Highlights

This notebook has guided you through setting up and converting `BaseMessage` to different types of messages. These components play essential roles in the CAMEL chat system, facilitating the creation, management, and interpretation of messages with clarity.

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.
*   **BaseMessage**: The base class for message objects used in the CAMEL chat system. It is designed to provide a consistent structure for the messages in the system and allow for easy conversion between different message types.




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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⭐ *Star us on [GitHub](https://github.com/camel-ai/camel), join our [Discord](https://discord.camel-ai.org), or follow us on [X](https://x.com/camelaiorg)*

---
