Tracking Agents - AgentOps

All operations are automatically associated with the agent that originated them. Agents are given a name which is what you will see in the dashboard.

This operation is labeled with the name of the Agent that originated it

The example below creates an agent class with a custom name:

from agentops.sdk.decorators import agent

@agent(name='ResearchAgent')
class MyAgent:
    def __init__(self):
        # Agent initialization
        pass
        
    # Agent methods

If you don’t specify a name, the agent will use the class name by default:

@agent
class ResearchAgent:
    # This agent will have the name "ResearchAgent"
    pass

Nesting Operations Under Agents

Operations performed by an agent should be decorated with the @operation decorator to ensure they’re properly nested under the agent:

from agentops.sdk.decorators import agent, operation

@agent
class ResearchAgent:
    @operation
    def search_web(self, query):
        # Search implementation
        return results
        
    @operation
    def analyze_data(self, data):
        # Analysis implementation
        return analysis

Session Context

Agents should be created within a session context to ensure proper tracing:

from agentops.sdk.decorators import session, agent, operation

@agent
class ResearchAgent:
    @operation
    def perform_research(self, topic):
        # Research implementation
        return results

@session
def research_workflow(topic):
    agent = ResearchAgent()
    return agent.perform_research(topic)
    
# Run the session
result = research_workflow("quantum computing")

Tracking LLM Calls Recording Operations