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Overview

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

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What are Traces?

Traces in CrewAI Enterprise 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
Traces Overview

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Accessing Traces

1

Navigate to the Traces Tab

Once in your CrewAI Enterprise dashboard, click on the Traces to view all execution records.
2

Select an Execution

You’ll see a list of all crew executions, sorted by date. Click on any execution to view its detailed trace.

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Understanding the Trace Interface

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

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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
Execution Summary

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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
Task List

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3. Final Output

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

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4. Execution Timeline

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

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5. Detailed Task View

When you click on a specific task in the timeline or task list, you’ll see:
Detailed Task View
  • 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

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Using Traces for Debugging

Traces are invaluable for troubleshooting issues with your crews:
1

Identify Failure Points

When a crew execution doesn’t produce the expected results, examine the trace to find where things went wrong. Look for:
  • Failed tasks
  • Unexpected agent decisions
  • Tool usage errors
  • Misinterpreted instructions
Failure Points
2

Optimize Performance

Use execution metrics to identify performance bottlenecks:
  • Tasks that took longer than expected
  • Excessive token usage
  • Redundant tool operations
  • Unnecessary API calls
3

Improve Cost Efficiency

Analyze token usage and cost estimates to optimize your crew’s efficiency:
  • Consider using smaller models for simpler tasks
  • Refine prompts to be more concise
  • Cache frequently accessed information
  • Structure tasks to minimize redundant operations

Need Help?

Contact our support team for assistance with trace analysis or any other CrewAI Enterprise features.