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Observability helps you inspect model calls and follow the execution of AI applications across prompts, agents, tools, and workflows. PromptLayer organizes observability data into two artifacts: A trace can contain multiple model calls and therefore link to multiple request logs. Supported direct GenAI spans create request logs, while spans managed by PromptLayer.run() can link to the run’s existing request log.

Where to Go Next

Request Logs

Review individual model calls, errors, inputs, outputs, usage, and cost.

Traces

Inspect the parent-child execution path across model calls, tools, agents, and application code.

Search

Find request logs or traces with content queries, structured filters, and date ranges.

Analytics

Aggregate the current request-log or trace query into charts.

Configure Observability

Once you know which artifact you need, follow the setup path for the code that produces it:

Configure Request Logging

Choose how requests are captured, then add IDs, tags, metadata, scores, and prompt associations.

Configure Tracing

Choose PromptLayer SDK spans, provider auto-instrumentation, a telemetry integration, or OpenTelemetry.
For indexing details, see the Search Data Model.