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.
Last updated: Dec 17, 2025 · Version v1.0 · Not legal advice.
Who this page is for
A buyer-side framing (not a dunk).
For ML platform, compliance, risk, and product teams shipping agentic workflows into regulated environments.
What LangSmith is actually for
Grounded in their primary job (and where it overlaps).
LangSmith is built for observing and improving LLM/agent runs: tracing, evaluation tooling, and human annotation workflows — especially when you build on LangChain/LangGraph.
Overlap
- 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.
What LangSmith is excellent at
Recognize what the tool does well, then separate it from audit deliverables.
- 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.
Where regulated teams still need a separate layer
- 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
A fair split between what ships as the primary workflow and what you assemble across systems.
Out of the box
- 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, but you build it
- 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 → evidence), with a manifest + checksums suitable for third-party verification.
- Retention, redaction, and integrity posture (e.g., 7+ years, WORM storage, verification drills).
Concrete regulated workflow example
One scenario that shows where each layer fits.
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.
Where LangSmith helps
- 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.
Where KLA helps
- 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.
Quick decision
When to choose each (and when to buy both).
Choose LangSmith when
- 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.
Choose KLA when
- 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.
When not to buy 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.
If you buy both
- Use LangSmith for dev iteration and evaluation loops.
- Use KLA to enforce runtime governance (checkpoints + queues) and export evidence packs for audits.
What KLA does not do
- 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.
KLA’s control loop (Govern / Measure / Prove)
What “audit-grade evidence” means in product primitives.
Govern
- Policy-as-code checkpoints that block or require review for high-risk actions.
- Role-aware approval queues, escalation, and overrides captured as decision records.
Measure
- Risk-tiered sampling reviews (baseline + burst during incidents or after changes).
- Near-miss tracking (blocked / nearly blocked steps) as a measurable control signal.
Prove
- Tamper-proof, append-only audit trail with external timestamping and integrity verification.
- Evidence Room export bundles (manifest + checksums) so auditors can verify independently.
Note: some controls (SSO, review workflows, retention windows) are plan-dependent — see /pricing.
RFP checklist (downloadable)
A shareable procurement artifact (backlink magnet).
# RFP checklist: KLA vs LangSmith Use this to evaluate whether “observability / gateway / governance” tooling actually covers audit deliverables for regulated agent workflows. ## Must-have (audit deliverables) - Annex IV-style export mapping (technical documentation fields → evidence) - Human oversight records (approval queues, escalation, overrides) - Post-market monitoring plan + risk-tiered sampling policy - Tamper-evident audit story (integrity checks + long retention) ## Ask LangSmith (and your team) - 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
Public references used to keep this page accurate and fair.
Note: product capabilities change. If you spot something outdated, please report it via /contact.