KLA vs LangSmith
LangSmith is excellent for tracing, evals, and annotation workflows. KLA is built for regulated workflows: decision-time policy gates, approval queues, and auditor-ready evidence exports.
Tracing is necessary. Regulated audits usually ask for decision governance + proof: enforceable policy gates and approvals, packaged as a verifiable evidence bundle (not just raw logs).
For ML platform, compliance, risk, and product teams shipping agentic workflows into regulated environments.
Última actualización: 17 dic 2025 · Versión v1.0 · No es asesoramiento legal.
Para quién es esta página
Un enfoque desde la perspectiva del comprador (sin críticas).
For ML platform, compliance, risk, and product teams shipping agentic workflows into regulated environments.
¿Para qué sirve realmente LangSmith?
Basado en su trabajo principal (y donde se superpone).
LangSmith is built for observing and improving LLM/agent runs: tracing, evaluation tooling, and human annotation workflows, especially when you build on LangChain/LangGraph.
Superposición
- Both help teams understand what happened in a run (inputs, outputs, metadata) and debug failures.
- Both can support sampling and evaluation loops, with different end goals (iteration vs audit deliverables).
- Both can export run data; the difference is whether it’s raw logs/traces or a deliverable-shaped evidence bundle.
En qué es excelente LangSmith
Reconozca qué hace bien la herramienta y luego sepárelo de los resultados de la auditoría.
- Developer-first tracing and debugging for agentic apps.
- Evaluation workflows, including online evaluators with filters and sampling rates.
- Annotation queues for structured human feedback on runs.
- Bulk export of trace data for pipelines and retention workflows.
- Strong fit if you are already deep in LangChain/LangGraph.
Donde los equipos regulados todavía necesitan una capa separada
- Decision-time approval gates for business actions (block until approved), with captured reviewer context as a workflow decision record.
- A clear separation between "human annotation" (after-the-fact review) and "human approval" (enforceable gate) for high-risk actions.
- Deliverable-shaped evidence exports mapped to Annex IV (oversight records, monitoring outcomes, manifest + checksums), not just raw traces.
- Proof layer for long retention: append-only, hash-chained integrity with verification mechanics auditors can validate.
Listo para usar versus construirlo usted mismo
Una división justa entre lo que se envía como flujo de trabajo principal y lo que se ensambla en todos los sistemas.
Fuera de la caja
- Run tracing and debugging for LLM/agent workflows.
- Evaluation tooling (including online evaluators and configurable sampling).
- Human annotation queues for labeling and review.
- Bulk data export of run/trace data.
- Team access controls (plan-dependent).
Posible, pero lo construye usted
- An enforceable approval gate that blocks high-risk actions in production until a reviewer approves (with escalation and overrides).
- Workflow decision records (who approved/overrode what, what they saw, and why) tied to the business action, not only to the run.
- A mapped evidence pack export (Annex IV sections to evidence), with a manifest + checksums suitable for third-party verification.
- Retention, redaction, and integrity posture (e.g., 7+ years, WORM storage, verification drills).
Ejemplo concreto de flujo de trabajo regulado
Un escenario que muestra dónde encaja cada capa.
KYC/AML adverse media escalation
An agent screens a customer, retrieves adverse media, and proposes an escalation/SAR recommendation. The high-risk action (escalation or filing) must be blocked until a designated reviewer approves.
Donde ayuda LangSmith
- Debug which sources were used and why the model made a recommendation.
- Run evals to reduce false positives/false negatives and improve reviewer consistency.
- Export traces for downstream analytics and retention systems.
Donde ayuda KLA
- Enforce a checkpoint that blocks escalation until the right role approves (with escalation rules).
- Capture approval/override decisions as first-class workflow records with context and rationale.
- Export a verifiable evidence bundle mapped to Annex IV and oversight requirements.
Decisión rápida
Cuándo elegir cada uno (y cuándo comprar ambos).
Elija LangSmith cuando
- You primarily need dev tracing/evals and are not being audited on workflow decisions.
- You want a tight loop inside the LangChain ecosystem.
- Your “buyer” is an engineering team optimizing prompts and reliability.
Elija KLA cuando
- Your buyer must produce auditor-ready artifacts (Annex IV, oversight records, monitoring plans).
- You need approvals/overrides to be first-class workflow controls, not notes in a trace.
- You need one-click evidence exports with integrity verification mechanics.
Cuando no comprar KLA
- You only need observability and experimentation tooling for non-regulated apps.
- You already have a workflow engine + ticketing + retention/signing and you’re comfortable assembling evidence bundles yourself.
Si compra ambos
- Use LangSmith for dev iteration and evaluation loops.
- Use KLA to enforce runtime governance (checkpoints + queues) and export evidence packs for audits.
Lo que KLA no hace
- KLA is not a replacement for developer-first tracing/eval tooling used to iterate on prompts.
- KLA is not a prompt playground or prompt-versioning system.
- KLA is not a request gateway/proxy for model calls.
Lazo de control de KLA (Gobernar / Medir / Probar)
Qué significa "evidencia de grado de auditoría" en las primitivas del producto.
Gobernar
- Puntos de control de políticas como código que bloquean o requieren revisión para acciones de alto riesgo.
- Colas de aprobación, escalamiento y anulaciones según roles capturados como registros de decisiones.
Medida
- Revisiones de muestreo por niveles de riesgo (línea de base + explosión durante incidentes o después de cambios).
- Seguimiento de cuasi-incidentes (pasos bloqueados/casi bloqueados) como señal de control medible.
Probar
- registro de auditoría a prueba de manipulaciones, solo para anexar, con marca de tiempo externa y verificación de integridad.
- Evidence Room exporta paquetes (manifiesto + sumas de verificación) para que los auditores puedan verificar de forma independiente.
Nota: algunos controles (SSO, revisión flujos de trabajo, ventanas de retención) dependen del plan. Ver /pricing.
Lista de verificación de RFP (descargable)
Un artefacto para adquisiciones que puede compartir y reenviar.
# Lista de verificación de RFP: KLA vs LangSmith Utilice esto para evaluar si las herramientas de "observabilidad/puerta de enlace/gobernanza" realmente cubren los resultados de auditoría para el agente regulado flujos de trabajo. ## Imprescindible (entregables de auditoría) - Mapeo de exportación estilo Annex IV (campos de documentación técnica -> evidencia) - Registros de supervisión humana (colas de aprobación, escalamiento, anulaciones) - Plan de seguimiento post-comercialización + política de muestreo por niveles de riesgo - Historia de auditoría a prueba de manipulaciones (verificaciones de integridad + retención prolongada) ## Pregúntale a LangSmith (y a su equipo) - Can you enforce decision-time controls (block/review/allow) for high-risk actions in production? - How do you distinguish “human annotation” from “human approval” for business actions? - Can you export a self-contained evidence bundle (manifest + checksums), not just raw logs/traces? - What is the retention posture (e.g., 7+ years) and how can an auditor verify integrity independently? - How do you prove that an approve/stop gate was enforced in production (not just annotated after the fact)?
Fuentes
Referencias públicas utilizadas para mantener esta página precisa e imparcial.
Nota: las capacidades del producto cambian. Si detecta algo desactualizado, infórmelo a través de /contact.