Spans

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Spans Overview

In AgentOps v0.4, the concept of “Events” has been replaced with “Spans” for all operation tracking. Spans represent different types of operations and are organized hierarchically.

A span is a unit of work or operation in your agent workflow. Spans are created using decorators and are automatically nested to create a hierarchical trace of your agent’s execution.

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Span Types

AgentOps supports several types of spans, each representing a different kind of operation:

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Session Spans

Session spans serve as the root for all other spans. They represent a complete execution of your agent workflow.

from agentops.sdk.decorators import session

@session
def my_workflow():
    # Your session code here
    return result

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Agent Spans

Agent spans represent operations performed by a specific agent. They are typically children of session spans.

from agentops.sdk.decorators import agent

@agent
class MyAgent:
    def __init__(self, name):
        self.name = name

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Operation/Task Spans

Operation spans represent specific tasks or operations performed by an agent. They are typically children of agent spans.

from agentops.sdk.decorators import operation

@operation
def process_data(data):
    # Process the data
    return result

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Workflow Spans

Workflow spans represent a sequence of operations that form a workflow. They can contain multiple operations.

from agentops.sdk.decorators import workflow

@workflow
def my_workflow(data):
    # Workflow implementation
    return result

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LLM Spans

LLM spans are automatically created when you make calls to supported LLM providers. They represent interactions with language models.

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Span Attributes

All spans share a set of common attributes:

Additionally, spans automatically capture:

• Input parameters to the decorated function
• Return values from the decorated function
• Exceptions that occur during execution

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Span Hierarchy

Spans are organized hierarchically to create a trace of your agent’s execution. The typical hierarchy is:

1. Session (root)
2. Agent
3. Operation/Task
4. Nested Operations

This hierarchy allows you to visualize the flow of your agent’s execution and understand how different operations relate to each other.

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Error Handling

When an exception occurs within a decorated function, it’s automatically recorded in the span. This allows you to easily identify and debug errors in your agent workflow.

from agentops.sdk.decorators import operation

@operation
def risky_operation():
    # This exception will be recorded in the span
    raise ValueError("Something went wrong")