CAMEL - Build Multi-Agent AI Systems

You can also check this cookbook in colab here ⭐ Star us on Github, join our Discord or follow our X Goal: Track and analysis the running of CAMEL Single Agent and Multiple Agents including LLMs and Tools usage

📦 Installation

First, install the CAMEL and AgentOps package with all its dependencies:

%pip install camel-ai[all]==0.2.16
%pip install agentops==0.3.10

🔑 Setting Up API Keys

import agentops
import os
from getpass import getpass

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

You can go to here to get free API Key from AgentOps

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

# 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")

🤖 Run CAMEL Single Agent with Tool by using AgentOps to track the whole process!

Import required modules from CAMEL.

from camel.agents import ChatAgent
from camel.configs import ChatGPTConfig
from camel.messages import BaseMessage
from camel.models import ModelFactory
from camel.types import ModelPlatformType, ModelType

Initialize AgentOps, you need to import toolkits after init of agentops so that the tool usage would be tracked.

AGENTOPS_API_KEY = os.getenv("AGENTOPS_API_KEY")
agentops.init(AGENTOPS_API_KEY, default_tags=["CAMEL X AgentOps Single Agent with Tool Example"])

from camel.toolkits import SearchToolkit

Set one Agent with Search Tools

# Define system message
sys_msg = BaseMessage.make_assistant_message(
    role_name='Tools calling opertor', content='You are a helpful assistant.'
)

# Set model config
tools = [*SearchToolkit().get_tools()]


model = ModelFactory.create(
    model_platform=ModelPlatformType.OPENAI,
    model_type=ModelType.GPT_4O_MINI,
)

# Set agent
camel_agent = ChatAgent(
    system_message=sys_msg,
    model=model,
    tools=tools,
)

Set user query and run the agent

# Define a user message
usr_msg = 'What is CAMEL-AI.org?'

# Get response information
response = camel_agent.step(usr_msg)
print(response)


agentops.end_session("Success")

🎉 Go to the AgentOps link shown above, you will be able to see the detailed record for this running like below.

🤖🤖 Run CAMEL Multi-agent with Tool by using AgentOps to track the whole process!

Import required modules

from typing import List

from colorama import Fore

from camel.agents.chat_agent import FunctionCallingRecord
from camel.configs import ChatGPTConfig
from camel.models import ModelFactory
from camel.societies import RolePlaying
from camel.types import ModelPlatformType, ModelType
from camel.utils import print_text_animated

import agentops

Initialize AgentOps, you need to import toolkits after init of agentops so that the tool usage would be tracked.

agentops.start_session(tags=["CAMEL X AgentOps Multi-agent with Tools."])

from camel.toolkits import (
    SearchToolkit,
    MathToolkit,
)

Set your task prompt

task_prompt = (
    "Assume now is 2024 in the Gregorian calendar, "
    "estimate the current age of University of Oxford "
    "and then add 10 more years to this age, "
    "and get the current weather of the city where "
    "the University is located."
)

Set tools for the assistant agent, we wish the agent would be able to do mathmatic calculation and search information from websites

tools = [
    *MathToolkit().get_tools(),
    *SearchToolkit().get_tools(),
]

Set up Role Playing session

role_play_session = RolePlaying(
    assistant_role_name="Searcher",
    user_role_name="Professor",
    assistant_agent_kwargs=dict(
        model=ModelFactory.create(
            model_platform=ModelPlatformType.OPENAI,
            model_type=ModelType.GPT_4O_MINI,
        ),
        tools=tools,
    ),
    user_agent_kwargs=dict(
        model=ModelFactory.create(
            model_platform=ModelPlatformType.OPENAI,
            model_type=ModelType.GPT_4O_MINI,
        ),
    ),
    task_prompt=task_prompt,
    with_task_specify=False,
)

Print the system message and task prompt

print(
    Fore.GREEN
    + f"AI Assistant sys message:\n{role_play_session.assistant_sys_msg}\n"
)
print(Fore.BLUE + f"AI User sys message:\n{role_play_session.user_sys_msg}\n")

print(Fore.YELLOW + f"Original task prompt:\n{task_prompt}\n")
print(
    Fore.CYAN
    + "Specified task prompt:"
    + f"\n{role_play_session.specified_task_prompt}\n"
)
print(Fore.RED + f"Final task prompt:\n{role_play_session.task_prompt}\n")

Set terminate rule and print the chat message

n = 0
input_msg = role_play_session.init_chat()
while n < 50:
    n += 1
    assistant_response, user_response = role_play_session.step(input_msg)

    if assistant_response.terminated:
        print(
            Fore.GREEN
            + (
                "AI Assistant terminated. Reason: "
                f"{assistant_response.info['termination_reasons']}."
            )
        )
        break
    if user_response.terminated:
        print(
            Fore.GREEN
            + (
                "AI User terminated. "
                f"Reason: {user_response.info['termination_reasons']}."
            )
        )
        break

    # Print output from the user
    print_text_animated(
        Fore.BLUE + f"AI User:\n\n{user_response.msg.content}\n"
    )

    # Print output from the assistant, including any function
    # execution information
    print_text_animated(Fore.GREEN + "AI Assistant:")
    tool_calls: List[FunctionCallingRecord] = assistant_response.info[
        'tool_calls'
    ]
    for func_record in tool_calls:
        print_text_animated(f"{func_record}")
    print_text_animated(f"{assistant_response.msg.content}\n")

    if "CAMEL_TASK_DONE" in user_response.msg.content:
        break

    input_msg = assistant_response.msg

End the AgentOps session

agentops.end_session("Success")

🎉 Go to the AgentOps link shown above, you will be able to see the detailed record for the multi-agent running like below.

​📦 Installation

​🔑 Setting Up API Keys

​🤖 Run CAMEL Single Agent with Tool by using AgentOps to track the whole process!

​🎉 Go to the AgentOps link shown above, you will be able to see the detailed record for this running like below.

​🤖🤖 Run CAMEL Multi-agent with Tool by using AgentOps to track the whole process!

​🎉 Go to the AgentOps link shown above, you will be able to see the detailed record for the multi-agent running like below.