> ## Documentation Index
> Fetch the complete documentation index at: https://docs.agentops.ai/llms.txt
> Use this file to discover all available pages before exploring further.
# Camel AI
> Track and analyze CAMEL agents with AgentOps
[CAMEL-AI](https://www.camel-ai.org/) 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](https://docs.camel-ai.org/).
## Adding AgentOps to CAMEL agents
```bash pip theme={null}
pip install agentops
```
```bash poetry theme={null}
poetry add agentops
```
```bash pip theme={null}
pip install "camel-ai[all]==0.2.11"
```
```bash poetry theme={null}
poetry add "camel-ai[all]==0.2.11"
```
Make sure to call `agentops.init` before calling any `openai`, `cohere`, `crew`, etc models.
```python python theme={null}
import agentops
agentops.init()
# your code here
agentops.end_session("Success") # Success|Fail|Indeterminate
```
Set your API key as an `.env` variable for easy access.
```python .env theme={null}
AGENTOPS_API_KEY=
```
Read more about environment variables in [Advanced Configuration](/v1/usage/advanced-configuration)
Execute your program and visit [app.agentops.ai/drilldown](https://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
## Full Examples
### Single Agent Example with Tools
Here's a simple example of tracking a single CAMEL agent with tools using AgentOps:
```python theme={null}
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")
```
### Multi-Agent Example
Check out the example notebook [here](https://github.com/AgentOps-AI/agentops/tree/main/examples/camel_examples/camelai-multi-agent-example.ipynb) to see how to track multi-agent setups.