Creating Your First Agent

• CAMEL: A powerful multi-agent framework that enables Retrieval-Augmented Generation and multi-agent role-playing scenarios, allowing for sophisticated AI-driven tasks.
• ChatAgent(): The class is a cornerstone of CAMEL.

​

Philosophical Bits

Can we design an autonomous communicative agent capable of steering the conversation toward task completion with minimal human supervision?

• Role: along with the goal and content specification, this sets the initial state of an agent, guiding the agent to take actions during the sequential interaction.
• Large Language Models (LLMs): each agent utilizes a Large Language Model to enhance cognitive capabilities. The LLM enables natural language understanding and generation, allowing agents to interpret instructions, generate responses, and engage in complex dialogue.
• Memory: in-context memory and external memory which allows the agent to infer and learn in a more grounded approach.
• Tools: a set of functions that our agents can utilize to interact with the external world; essentially this gives embodiments to our agents.
• Communication: our framework allows flexible and scalable communication between agents. This is fundamental for the critical research question.
• Reasoning: we will equip agents with different planning and reward (critic) learning abilities, allowing them to optimize task completion in a more guided approach.

​

📦 Installation

!pip install "camel-ai[all]==0.2.16"

​

🔑 Setting Up API Keys

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

1. Add the API key or token to the Colab Secrets
2. Grant the secret access to the current notebook
3. Access the secret by uncommenting the following codeblock.

# import os
# from google.colab import userdata

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

​

Quick Start

​

🕹 Step 1: Define the Role

sys_msg = 'You are a curious stone wondering about the universe.'

​

🕹 Step 2: Set up the Model

from camel.models import ModelFactory
from camel.types import ModelPlatformType, ModelType
from camel.configs import ChatGPTConfig

# Define the model, here in this case we use gpt-4o-mini
model = ModelFactory.create(
    model_platform=ModelPlatformType.OPENAI,
    model_type=ModelType.GPT_4O_MINI,
    model_config_dict=ChatGPTConfig().as_dict(), # [Optional] the config for model
)

from camel.agents import ChatAgent
agent = ChatAgent(
    system_message=sys_msg,
    model=model,
    message_window_size=10, # [Optional] the length for chat memory
    )

​

🕹 Step 3: Interact with the Agent with .step()

# Define a user message
usr_msg = 'what is information in your mind?'

# Sending the message to the agent
response = agent.step(usr_msg)

# Check the response (just for illustrative purpose)
print(response.msgs[0].content)

​

Advanced Features

​

🔧 Tool Usage

# Import the necessary tools
from camel.toolkits import MathToolkit, SearchToolkit

# Initialize the agent with list of tools
agent = ChatAgent(
    system_message=sys_msg,
    tools = [
        *MathToolkit().get_tools(),
        *SearchToolkit().get_tools(),
    ]
    )

# Let agent step the message
response = agent.step("What is CAMEL AI?")

# Check tool calling
print(response.info['tool_calls'])

# Get response content
print(response.msgs[0].content)

​

🧠 Memory

agent.memory.get_context()

from camel.messages import BaseMessage

new_user_msg = BaseMessage.make_user_message(
    role_name="CAMEL User",
    content="This is a new user message would add to agent memory",
)

# Update the memory
agent.record_message(new_user_msg)

# Check the current memory
agent.memory.get_context()

​

Miscs

• Setting the agent to its initial state.
  Copy

  agent.reset()
  

• Set the output language for the agent.
  Copy

  agent.set_output_language('french')
  

• The ChatAgent class offers several useful initialization options, including model_type, model_config, memory, message_window_size, token_limit, output_language, tools, and response_terminators.

​

🌟 Highlights

• CAMEL: A powerful multi-agent framework that enables Retrieval-Augmented Generation and multi-agent role-playing scenarios, allowing for sophisticated AI-driven tasks.
• ChatAgent(): The class is a cornerstone of CAMEL.

1. 🐫 Creating Your First CAMEL Agent free Colab
2. Graph RAG Cookbook free Colab
3. 🧑‍⚖️ Create A Hackathon Judge Committee with Workforce free Colab
4. 🔥 3 ways to ingest data from websites with Firecrawl & CAMEL free Colab
5. 🦥 Agentic SFT Data Generation with CAMEL and Mistral Models, Fine-Tuned with Unsloth free Colab