Documentation Index
Fetch the complete documentation index at: https://docs.camel-ai.org/llms.txt
Use this file to discover all available pages before exploring further.
You can also check this cookbook in colab here.
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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.
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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
!pip install "camel-ai==0.2.16"
🔑 Setting Up API Keys
You’ll need to set up your API keys for OpenAI.
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
# import os
# from google.colab import userdata
# os.environ["OPENAI_API_KEY"] = userdata.get("OPENAI_API_KEY")
Quick Start
# 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
)
Set the Task Arguments
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.
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.
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!
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.
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
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
🌟 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! 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:
-
🐫 Creating Your First CAMEL Agent free Colab
-
Graph RAG Cookbook free Colab
-
🧑⚖️ Create A Hackathon Judge Committee with Workforce free Colab
-
🔥 3 ways to ingest data from websites with Firecrawl & CAMEL free Colab
-
🦥 Agentic SFT Data Generation with CAMEL and Mistral Models, Fine-Tuned with Unsloth free Colab
Thanks from everyone at 🐫 CAMEL-AI
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