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When you click on a trace, you see the full details of that LLM interaction—every step, every token, every cost, and every retrieved document.

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Overview Section

At the top of the trace details page, you’ll see summary information:
Complete summary details of what happened during an interaction.
  • Input - The complete prompt or user question sent to the LLM
  • Output - The full model response
  • Model - Which LLM was used (e.g., gpt-4o-mini)
  • Status - Success ✓ or Error ⚠
  • Timestamp - Exact time this trace occurred
  • Duration - Total execution time
  • Cost - Total cost in USD
  • Tokens - Input tokens + output tokens

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Execution Timeline

For agents or chains with multiple steps, the timeline shows the complete execution flow:
Detailed steps of what your AI system did.
Each node in the timeline represents:
  • LLM calls - When the model was invoked
  • Tool executions - When tools/functions were called
  • Retrieval operations - When documents were fetched
  • Agent steps - Decision points in the agent workflow
Click on any node to see its details:
  • Input to that step
  • Output from that step
  • Duration and cost for that step
  • Tokens used (if LLM call)

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Node Types

Different node types appear with distinct icons:
Node TypeIconDescription
LLM🤖Call to a language model
Tool🔧Function or tool execution
Retriever📄Document retrieval from vector DB
Step📍Generic step in workflow

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Token and Cost Breakdown

See exactly where tokens and costs came from:

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Token Usage

  • Input Tokens - Tokens in the prompt
  • Output Tokens - Tokens in the response
  • Total Tokens - Sum of input + output
For multi-step traces:
  • Per-step token counts
  • Running total as execution progresses
  • Final total at the bottom

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Cost Calculation

Costs are calculated based on:
  • Model pricing (per 1M tokens)
  • Input token count × input price
  • Output token count × output price
Example for gpt-4o-mini: 
Input:  500 tokens × $0.150/1M = $0.000075
Output: 200 tokens × $0.600/1M = $0.000120
Total:                          $0.000195
Multi-step traces show:
  • Cost per step
  • Cumulative cost
  • Total cost for entire trace

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Retrieved Documents

If the trace used RAG (retrieval-augmented generation), you’ll see which documents were retrieved:
Organizational data that gets retrieved by your AI system.
For each retrieved document:
  • Document content - The text that was retrieved
  • Source - Where this document came from
  • Relevance score - How relevant the retriever deemed it (if available)
  • Metadata - Additional fields like timestamps, tags
Click View Full Document to see:
  • Complete document text
  • Full metadata
  • Usage history (how often this doc is retrieved)
Learn more →

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Error Details

If a trace failed, the error section shows:
  • Error Type - Exception class (e.g., RateLimitError, Timeout)
  • Error Message - What went wrong
  • Node Where Error Occurred - Which step failed
  • Stack Trace - Full error traceback (if available)
This helps you:
  • Understand why the trace failed
  • Identify which component broke
  • Fix the root cause

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Common Errors

Error TypeMeaningFix
RateLimitErrorToo many requests to LLM providerImplement rate limiting or backoff
AuthenticationErrorInvalid API keyCheck your LLM provider API key
TimeoutRequest took too longIncrease timeout or optimize prompt
InvalidRequestMalformed request to LLMValidate input format

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Feedback and Comments

At the bottom of the trace, see user feedback:

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User Feedback

If feedback tracking is enabled:
  • 👍 Thumbs Up - Positive feedback count
  • 👎 Thumbs Down - Negative feedback count
Click to see which users gave feedback and when.

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Comments

Team members can leave comments on traces:
  • Click Add Comment
  • Type your note or observation
  • Comments are visible to all team members with access
Use comments for:
  • Noting issues to investigate
  • Documenting why something happened
  • Collaborating on debugging
  • Marking traces for review
Learn more →

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Sharing a Trace

To share a specific trace with team members:
  1. Click Copy Link button
  2. Share the URL
  3. Anyone with project access can view
The URL looks like: 
http://platform.arcbeam.ai/projects/{project-id}/traces/{trace-id}

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Comparing Traces

To compare multiple traces side-by-side:
  1. Open first trace
  2. Click Compare button
  3. Select second trace
  4. View differences in inputs, outputs, costs, and execution flow
Useful for:
  • A/B testing different prompts
  • Comparing model performance
  • Understanding why some queries cost more
  • Debugging inconsistent behavior

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Viewing Source Data

If you’ve connected data sources, click on a retrieved document to see:
  • Full document text - Complete content
  • Source attribution - Original file/URL
  • Metadata - All fields from your vector database
  • Usage stats - How often this document is retrieved
  • Related traces - Other traces that used this document
This connects the dots between what the LLM said and where the information came from.

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What to Look For

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When Debugging Errors

  1. Check the error message - What exactly failed?
  2. Find the failing node - Which step broke?
  3. Review the input to that node - Was the input malformed?
  4. Check recent code changes - Did you change something related?

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When Optimizing Costs

  1. Look at token breakdown - Which steps used most tokens?
  2. Review input length - Can you shorten the prompt?
  3. Check retrieved documents - Are you retrieving too many?
  4. Compare with cheaper models - Could you use gpt-4o-mini instead of gpt-4o?

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When Investigating User Reports

  1. Read input and output - Does the response make sense?
  2. Check retrieved documents - Did it get the right information?
  3. Look for errors - Did anything fail silently?
  4. Review timeline - Was there unusual latency?

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Best Practices

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Add Comments for Context

When you find something interesting:
  • Leave a comment explaining what you found
  • Tag team members who should know
  • Document the resolution if you fix it

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Compare Similar Traces

If you see inconsistent behavior:
  • Find a good trace and a bad trace
  • Compare them side-by-side
  • Identify what’s different

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Check Retrieved Documents

For RAG traces:
  • Always verify the right documents were retrieved
  • If output is wrong, often the retrieved docs are wrong
  • Update your knowledge base if needed

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Next Steps

Retrieved Documents

Understand document retrieval in detail

Collections

Organize traces for review sessions

Error Monitoring

Track and fix errors systematically

Cost Tracking

Optimize costs based on trace data