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The trajectory is what your app did during a trial: its LLM calls and tool calls. Laminar shows it under the trial’s trace, next to the verifier’s score, so a failing agent evaluation case takes one click to explain. The trace is only as rich as what the app reports: without a trajectory you see the reward but not the calls behind it. How you capture it depends on whether the app is instrumented with Laminar.

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Instrumented apps

If the app is instrumented with Laminar (it calls Laminar.initialize()), there’s nothing to convert. During a run, the plugin sets LMNR_TRAJECTORY_PATH=/logs/agent/trajectory.json in the agent env. With it set, the SDK writes the app’s spans to that file instead of sending them to Laminar, and the plugin attaches them to the trial’s trace: every LLM call with tokens and cost, and tool calls as TOOL spans. The app needs no Laminar API key and no network access to Laminar. This works with the Python SDK (lmnr 0.8.4 and later); TypeScript SDK support is in progress. Make sure the app process actually receives LMNR_TRAJECTORY_PATH, especially if the start script uses env -i. If the app isn’t instrumented yet, adding Laminar is a small production change that also gives you production traces. See Tracing.

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Apps without instrumentation

If it isn’t instrumented, reconstruct the trajectory from the app’s own history. Most products already store the conversation for their UI, for audit, or as agent memory: message tables, run and step tables, LangGraph checkpointers, JSONL transcripts. After the driver finishes, including on failed and timeout since partial trajectories are the most useful, run an exporter that reads this case’s history and writes /logs/agent/trajectory.json in Harbor’s ATIF format:
  • Each message is a step with a sequential step_id (1, 2, 3…), a source (system, user, or agent), a message, and an ISO 8601 timestamp.
  • model_name, tool_calls, and metrics are only allowed on agent steps. Tool results go in that step’s observation.results, paired by source_call_id.
  • History seeded from case.json gets is_copied_context: true, so it’s shown but not mistaken for the agent’s work.
  • Leave metrics unset rather than estimating: stored history usually lacks token counts, costs, and the exact prompt of each call. Say where the trajectory came from in notes.
Don’t write a reconstructed trajectory for an instrumented app, or the agent’s steps show up twice.

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Next steps

Write the agent and verifier

Drive the entry point, wait for a terminal state, and grade the result.

Read a failing trial

Open a low-scoring datapoint and read the app’s run as a transcript.

Track failures with Signals

Detect a failure in production traces, then turn the flagged traces into cases.

Troubleshooting

Fixes for stale images, missing env vars, and empty traces.