Trace QA
Analyze agent execution traces to answer questions about what happened, why it failed, how efficient it was, or any other aspect of the run.
Workflow
Always start with overview to understand the trace before diving into details.
- Get the overview first
python scripts/fetch_trace.py <trace_id> overview
This returns metadata (status, duration, tokens, model) and summaries (request, answer preview, tool usage counts). Use this to orient yourself before going deeper.
- Explore steps or LLM calls as needed
Depending on the user's question, drill into the relevant data:
User wants to know... Command
What tools were called and in what order steps [start] [count]
Full input/output of a specific tool call step <N>
How many LLM calls and their token costs llm-calls [start] [count]
What messages were sent to Claude in a specific turn llm-call <N>
Just the final result answer
- Handle long content with segmented reads
When content is large, the script automatically segments output to ~4000 characters. If you see a [CONTINUED: ...] message at the end of output, call the command shown in that message to read the next segment. Repeat until all content is read.
Example sequence:
python scripts/fetch_trace.py <id> step 5
Output ends with: [CONTINUED: use 'step 5 --offset 4000' for next segment]
python scripts/fetch_trace.py <id> step 5 --offset 4000
Output ends with: [CONTINUED: use 'step 5 --offset 8000' for next segment]
python scripts/fetch_trace.py <id> step 5 --offset 8000
Full content now read
Command Reference
Mode Syntax Description
overview
fetch_trace.py <id> overview
Metadata + summary stats
steps
fetch_trace.py <id> steps [start] [count]
Paginated step list (default: 30/page)
step
fetch_trace.py <id> step <N> [--offset <chars>]
Single step full content
llm-calls
fetch_trace.py <id> llm-calls [start] [count]
Paginated LLM call list
llm-call
fetch_trace.py <id> llm-call <N> [--offset <chars>]
Single LLM call full content
answer
fetch_trace.py <id> answer
Final answer only
Common Analysis Patterns
Failure diagnosis: overview → find error → steps list → examine failing step detail
Token efficiency: overview (total tokens) → llm-calls list (per-call breakdown) → identify expensive calls
Behavior understanding: overview → steps list → step details for key tool calls
Tool usage audit: overview (tool summary) → steps list filtered by tool name
Environment
Set API_BASE_URL to override the default API endpoint (http://127.0.0.1:62610 ).