CAMEL-AI is the first large language model (LLM) multi-agent framework and an open-source community dedicated to finding the scaling law of agents. Checkout their comprehensive documentation here.

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Adding AgentOps to CAMEL agents

1

Install the AgentOps SDK

2

Install CAMEL-AI with all dependencies

3

Add AgentOps code to your code

Make sure to call agentops.init before calling any openai, cohere, crew, etc models.

Set your API key as an .env variable for easy access.

Read more about environment variables in Advanced Configuration

4

Run your agent

Execute your program and visit app.agentops.ai/drilldown to observe your CAMEL Agent! 🕵️

After your run, AgentOps prints a clickable url to console linking directly to your session in the Dashboard

Clickable link to session

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Full Examples

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Single Agent Example with Tools

Here’s a simple example of tracking a single CAMEL agent with tools using AgentOps:

import agentops
import os
from camel.agents import ChatAgent
from camel.messages import BaseMessage
from camel.models import ModelFactory
from camel.types import ModelPlatformType, ModelType

# Initialize AgentOps
agentops.init(os.getenv("AGENTOPS_API_KEY"))

# Import toolkits after AgentOps init for tracking
from camel.toolkits import SearchToolkit

# Set up the agent with search tools
sys_msg = BaseMessage.make_assistant_message(
    role_name='Tools calling operator',
    content='You are a helpful assistant'
)

# Configure tools and model
tools = [*SearchToolkit().get_tools()]
model = ModelFactory.create(
    model_platform=ModelPlatformType.OPENAI,
    model_type=ModelType.GPT_4O_MINI,
)

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

# Run the agent
user_msg = 'What is CAMEL-AI.org?'
response = camel_agent.step(user_msg)
print(response)

# End the session
agentops.end_session("Success")

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Multi-Agent Example

Check out the example notebook here to see how to track multi-agent setups.

AutogenCohere