OpenAI Agents Trace to Eval Regression Guide
Source-backed guide for converting OpenAI Agents SDK traces into regression eval cases, trace grades, tool-call assertions, and release checks for agentic workflows.
Open the source and read safety notes before installing.
Safety notes
- Traces can include private prompts, file paths, retrieved records, and tool outputs; redact before sharing outside the authorized review surface.
- Do not convert production user data into public eval fixtures.
Privacy notes
- Trace logs and eval cases can retain user identifiers, documents, API responses, account IDs, and tool arguments.
- Keep redacted fixtures separate from raw production traces.
Prerequisites
- OpenAI Agents SDK workflow with tracing enabled or exported trace data.
- A task goal, expected answer, or acceptance criterion for the run.
- Permission to inspect tool inputs, outputs, handoffs, and guardrail events.
Schema details
- Install type
- copy
- Reading time
- 7 min
- Difficulty score
- 68
- Troubleshooting
- Yes
- Breaking changes
- No
Full copyable content
Use this workflow after an agent trace reveals wrong tool use, failed handoff, missing guardrail behavior, or a final answer that needs repeatable eval coverage.About this resource
Why Trace Evidence Belongs In Evals
An agent run can look successful while still hiding fragile behavior: repeated retrieval calls, the wrong tool chosen for the right reason, a handoff that loses state, or a guardrail that fires too late. Tracing gives reviewers the actual sequence of model decisions and tool events. Evals make that sequence repeatable.
Workflow
- Name the user goal. Write the exact task the run was supposed to complete.
- Read the trace chronologically. Separate model turns, tool calls, handoffs, guardrails, and final response.
- Mark critical events. Record the events that determined success or failure.
- Grade the run. Use pass, partial, or fail based on observable trace evidence.
- Convert failure into assertions. A good eval checks the behavior that failed, not just the final answer string.
- Keep fixtures redacted. Use synthetic or sanitized inputs before adding the regression to CI.
Suggested Eval Shape
- Input prompt or task fixture.
- Expected behavior and unacceptable behavior.
- Required tool calls or forbidden tool calls.
- Handoff expectations when multiple agents are involved.
- Guardrail events that should or should not trigger.
- Final response criteria.
Common Trace Findings
| Finding | Regression assertion |
|---|---|
| Wrong tool chosen | The run must call the source-of-truth tool before answering |
| Repeated retrieval | The run should complete within an event-count or latency budget |
| Handoff lost state | The receiving agent must include required task context |
| Unsupported claim | The final answer must cite retrieved evidence |
| Guardrail missed | Sensitive data must be rejected or redacted before tool output |
Troubleshooting
The trace is incomplete
Do not infer missing tool outputs. Mark confidence low and rerun with tracing or structured custom events enabled.
The run passes but uses too many steps
Create a performance or path-quality eval. Agent quality is not only correctness; latency, cost, and unnecessary tool use matter in production.
The trace contains private data
Create a synthetic fixture that preserves the failure mode without preserving the private content.
Duplicate Check
Existing entries cover OpenAI docs and agent observability. This guide focuses on the specific trace-to-eval regression workflow for OpenAI Agents SDK runs.
References
- OpenAI Agents Python tracing - https://openai.github.io/openai-agents-python/tracing/
- OpenAI Agents JS tracing - https://openai.github.io/openai-agents-js/guides/tracing/
- Trace grading - https://platform.openai.com/docs/guides/trace-grading
- Agent evals - https://platform.openai.com/docs/guides/agent-evals
Source citations
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