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.
Dernière mise à jour: 17 déc. 2025 · Version v1.0 · Pas d'avis juridique.
À qui s'adresse cette page
Un cadrage côté acheteur (pas un dunk).
For ML platform, compliance, risk, and product teams shipping agentic workflows into regulated environments.
À quoi sert réellement LangSmith
Fondé dans leur travail principal (et où il se chevauche).
LangSmith is built for observing and improving LLM/agent runs: tracing, evaluation tooling, and human annotation workflows, especially when you build on LangChain/LangGraph.
Chevauchement
- 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.
Les points forts de LangSmith
Reconnaître ce que l'outil fait bien, puis le séparer des produits livrables de la vérification.
- 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.
Lorsque les équipes réglementées ont encore besoin d'une couche séparée
- 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.
Out-of-the-box vs build-it- yourself
Un juste partage entre ce qui expédie comme le workflow primaire et ce que vous assemblez à travers les systèmes.
Clé en main
- 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).
Possible, mais vous le construisez
- 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).
Exemple concret de workflow réglementé
Un scénario qui montre où chaque couche correspond.
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.
Où LangSmith aide
- 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.
Où KLA aide
- 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.
Décision rapide
Quand choisir (et quand acheter les deux).
Choisissez LangSmith lorsque
- 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.
Choisissez KLA lorsque
- 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.
Quand ne pas acheter 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 vous achetez les deux
- Use LangSmith for dev iteration and evaluation loops.
- Use KLA to enforce runtime governance (checkpoints + queues) and export evidence packs for audits.
Ce que KLA ne fait pas
- 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.
La boucle de commande de KLA (Gouvern / Mesure / Prouve)
Qu'est-ce que « preuve de qualité d'audit » signifie dans les produits primitifs.
Gouverner
- Les points de contrôle qui bloquent ou exigent un examen des mesures à haut risque.
- Files d'attente d'approbation contextuelles par rôle
Mesure
- Examens d'échantillonnage selon le degré de risque (base + éclatement pendant les incidents ou après les changements).
- Suivi des quasi-incidents (étapes bloquées / presque bloquées) comme signal de contrôle mesurable.
Prouvez
- Piste d'audit infalsifiable, en append-only, avec horodatage externe et vérification de l'intégrité.
- Les paquets d'exportation Evidence Room (manifest + checksums) permettent aux vérificateurs de vérifier indépendamment.
Remarque : certains contrôles (SSO, examen workflows, fenêtres de rétention) dépendent du plan. Voir / prix.
Liste de contrôle de la DP (téléchargeable)
Un artefact d'achat partageable (contenu de référence).
# Liste de contrôle de la DP : KLA vs LangSmith Utilisez ceci pour évaluer si l'outillage « observabilité / passerelle / gouvernance » couvre réellement les produits livrables de la vérification pour l'agent réglementé workflows. ## Doit avoir (produits livrables de la vérification) - Cartographie des exportations de type Annex IV (champs de documentation technique -> preuves) - Dossiers de surveillance humaine (attentes d'approbation, escalade, interventions) - Plan de surveillance après la mise en marché + politique d'échantillonnage en fonction du risque - Histoire de vérification évidente (vérifications d'intégrité + rétention longue) Demandez LangSmith (et votre équipe) - 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)?
Sources & références
Références publiques utilisées pour garder cette page exacte et équitable.
Remarque : les capacités du produit changent. Si vous remarquez quelque chose de désuet, veuillez le signaler via /contact.