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For the complete documentation index for agents and LLMs, see llms.txt.

Trace Your Pipelines

Traces are detailed records of every step in the search process that hel you understand, analyze, and troubleshoot your AI pipelines. You can trace your pipelines through logs, built-in traces, or one of the integrated services.

Tracing vs Debugging​

Tracing and debugging both help you understand how your pipeline runs, but they serve different purposes.

Tracing captures the full journey of a query — from the user's input through each processing step to the final result. A trace usually includes timestamps, the original query and its transformations, generated embeddings, retrieved documents, scoring and ranking decisions, as well as any errors or latency issues. Tracing shows how data flows through the system and when each step occurs, giving you a clear picture of what happened end to end. Tools like Langfuse or Weave make this information easy to visualize and explore.

Debugging is about getting to the root of problems. It involves analyzing detailed logs to pinpoint errors, misconfigurations, or unexpected behavior. While tracing shows you the "what" and "when," debugging helps you uncover the "why."

AI-powered applications are often complex and opaque, making it hard to understand what’s happening under the hood. When something goes wrong, tracing and debugging let you lift the veil. They’re essential for fixing issues, improving performance, and gaining insight into how your models behave. Whether you're tracking down high latency, investigating odd query results, or exploring search patterns, these tools give you the visibility you need to take action.

Tools for Debugging​

  • Pipeline logs: Capture operational details, such as the component that generated the log, log level, timestamp, and log message. They're available in the Builder on the Analytics tab, but also after running the pipeline in Builder or Playground.
  • Pipeline debugger: Offers live component-level logs available from Builder.
  • Remote debug with a VS Code tunnel.

Tools for Tracing​

You can trace your pipelines through built-in traces or one of the integrated services.

  • Built-in traces: Available on the Analytics tab in the Builder. These traces capture the details of every pipeline run to help you undersand what happened during a query: which components ran, how long they took, and any errors that occurred.
  • Integrated services:
    • Langfuse: Provides deep tracing for application-level observability, including detailed spans, latency tracking, and cross-service dependencies.
    • Weights & Biases Weave: Captures ML-specific telemetry to help you track your pipeline's latency, token-count, and more.

Debug Your Pipelines​

See the following instructions, depending on how you want to debug:

Trace Your Pipelines​

To trace your pipelines, you can use built-in traces or one of the integrated services: