Traces

Overview

Traces provide comprehensive visibility into your crew executions, helping you monitor performance, debug issues, and optimize your AI agent workflows.

What are Traces?

Traces in CrewAI AMP are detailed execution records that capture every aspect of your crew's operation, from initial inputs to final outputs. They record:

• Agent thoughts and reasoning
• Task execution details
• Tool usage and outputs
• Token consumption metrics
• Execution times
• Cost estimates

Accessing Traces

Understanding the Trace Interface

The trace interface is divided into several sections, each providing different insights into your crew's execution:

1. Execution Summary

The top section displays high-level metrics about the execution:

• Total Tokens: Number of tokens consumed across all tasks
• Prompt Tokens: Tokens used in prompts to the LLM
• Completion Tokens: Tokens generated in LLM responses
• Requests: Number of API calls made
• Execution Time: Total duration of the crew run
• Estimated Cost: Approximate cost based on token usage

2. Tasks & Agents

This section shows all tasks and agents that were part of the crew execution:

• Task name and agent assignment
• Agents and LLMs used for each task
• Status (completed/failed)
• Individual execution time of the task

3. Final Output

Displays the final result produced by the crew after all tasks are completed.

4. Execution Timeline

A visual representation of when each task started and ended, helping you identify bottlenecks or parallel execution patterns.

5. Detailed Task View

When you click on a specific task in the timeline or task list, you'll see:

• Task Key: Unique identifier for the task
• Task ID: Technical identifier in the system
• Status: Current state (completed/running/failed)
• Agent: Which agent performed the task
• LLM: Language model used for this task
• Start/End Time: When the task began and completed
• Execution Time: Duration of this specific task
• Task Description: What the agent was instructed to do
• Expected Output: What output format was requested
• Input: Any input provided to this task from previous tasks
• Output: The actual result produced by the agent

Using Traces for Debugging

Traces are invaluable for troubleshooting issues with your crews:

- Failed tasks
- Unexpected agent decisions
- Tool usage errors
- Misinterpreted instructions

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  ![Failure Points](/images/enterprise/failure.png)
</Frame>

- Tasks that took longer than expected
- Excessive token usage
- Redundant tool operations
- Unnecessary API calls

- Consider using smaller models for simpler tasks
- Refine prompts to be more concise
- Cache frequently accessed information
- Structure tasks to minimize redundant operations

Performance and batching

CrewAI batches trace uploads to reduce overhead on high-volume runs:

• A TraceBatchManager buffers events and sends them in batches via the Plus API client
• Reduces network chatter and improves reliability on flaky connections
• Automatically enabled in the default trace listener; no configuration needed

This yields more stable tracing under load while preserving detailed task/agent telemetry.