The outcome

Maintain useful production observability without creating an uncontrolled secondary store of prompts, documents, or credentials.

LangSmith is a LLM application platform for tracing, observability, offline and online evaluation, prompt versioning, feedback, and agent deployment. Tracing agent and LLM runs, investigating failures, comparing prompts and models on datasets, collecting feedback, monitoring production quality, and promoting tested versions. This guide narrows that broad capability into one repeatable outcome, with checkpoints that keep the source material and your judgment in the loop.

Before you begin

Set the boundary before the tool starts.

Choose one real task, identify who will use the result, and decide what evidence or test will make the result acceptable. Gather only the source material needed for that task. If the work contains confidential, personal, regulated, or client-owned information, confirm that the platform and account are approved before sharing it.

Troiana principle

AI should make the work easier to inspect. If the workflow removes the source, the owner, or the review step, redesign the workflow.

Step by step

A workflow you can repeat.

  1. 01

    Map every field that tracing can capture, classify sensitivity, choose cloud or self-hosted boundaries, and define sampling, retention, deletion, access, incident, and legal policies.

  2. 02

    Use separate scoped keys and projects per environment, redact before export, disable tracing conditionally for sensitive tenants, and attach only non-secret version and routing metadata.

  3. 03

    Sample normal traffic and fully capture approved error classes, validate parent-child spans and tool outcomes, and alert on safety, schema, latency, cost, and failure signals.

  4. 04

    Restrict workspace and dataset access, review online evaluator filters and spend, test trace and dataset deletion, and verify that tracing can be disabled during an incident.

  5. 05

    Audit access, sampling, retention, redaction, evaluator versions, and unresolved traces regularly, exporting only necessary evidence and documenting every policy change.

Working standard

What good use looks like.

  • Redact before traces leave the app.
  • Separate environments and tenants.
  • Test deletion and tracing shutdown.

Tracing may capture prompts, responses, tool arguments, retrieved documents, personal data, secrets, or proprietary content. Retention differs by tier and datasets can outlive source traces. LLM-as-judge scores are model opinions and require calibration against human decisions. Use least-privilege keys and workspaces, conditional tracing, redaction, explicit retention, deletion tests, evaluator versioning, spend limits, and human review before treating a dashboard score as a release decision.

Official references

Check the current product documentation.

Features, plan limits, availability, and data controls change. These official pages are the starting points used for this collection.