langfuse

Debug LLM agent failures by querying trace and observation data to inspect errors, warnings, and detailed logs. Investigate specific agent runs to identify the root cause of performance issues and unexpected behaviors within your application.

Analyze LLM API costs and latency by summarizing resource usage metrics from observation data. Break down token counts and execution times across different traces to optimize overall performance and manage operational expenses effectively.

Yes, you need an API key to query LLM traces and datasets. Accessing detailed prompt histories, observation scores, and agent run metrics requires authenticating REST API requests with valid credentials to retrieve the observability data securely.

Yes, you can audit prompt histories and dataset versions for consistency. Query the REST API to review different prompt iterations and dataset states, ensuring that changes to your LLM workflows maintain expected behavior over time.

LLM observability queries may return limited trace data due to restricted API key permissions or filtering parameters. Ensure your credentials have sufficient access rights and adjust your REST API request parameters to broaden the scope of retrieved logs.