> ## 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.

# Spans

> AgentOps uses spans to track different types of operations in your agent workflows.

## 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.

## Span Types

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

### Session Spans

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

```python theme={null}
from agentops.sdk.decorators import session

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

### Agent Spans

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

```python theme={null}
from agentops.sdk.decorators import agent

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

### Operation/Task Spans

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

```python theme={null}
from agentops.sdk.decorators import operation

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

### Workflow Spans

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

```python theme={null}
from agentops.sdk.decorators import workflow

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

### LLM Spans

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

## Span Attributes

All spans share a set of common attributes:

| Attribute   | Description                                        |
| ----------- | -------------------------------------------------- |
| span\_id    | A unique identifier for the span                   |
| trace\_id   | The ID of the trace this span belongs to           |
| parent\_id  | The ID of the parent span                          |
| name        | The name of the span                               |
| kind        | The kind of span (session, agent, operation, etc.) |
| start\_time | When the span started                              |
| end\_time   | When the span ended                                |
| attributes  | Custom attributes for the span                     |

Additionally, spans automatically capture:

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

## 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.

## 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.

```python theme={null}
from agentops.sdk.decorators import operation

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

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