LangSmith is LangChain's observability tool — it's excellent if your stack is built on LangChain. Peekr instruments any framework— OpenAI, Anthropic, Gemini, Bedrock, LangChain, CrewAI — with built-in claim-level hallucination scoring and 17 in-process regulatory compliance packs. Here's the honest head-to-head, including where LangSmith is the better choice.
No credit card · MIT license · 17 compliance packs on Pro
The architecture
LangSmith is LangChain's product. Peekr is framework-agnostic. The overlap is tracing and hallucination evaluation; the difference is compliance, framework coupling, and the tracing model.
LangSmith — LangChain's platform
Peekr — in-process, framework-agnostic
Side by side
Green where a capability is present, ✗ where it isn't offered, and — where it's partial. We've kept LangSmith's real strengths in the table.
| Capability | LangSmith | Peekr |
|---|---|---|
| Framework requirement | LangChain preferred (any via SDK) | Any framework, no dependency |
| Tracing model | LangChain callbacks / SDK decorators | Class-level patch, any LLM SDK |
| Claim-level hallucination scoring | —custom evaluator setup required | built-in, every sentence scored |
| In-process regulatory compliance | evaluation infra only, no built-in packs | 17 packs (HIPAA, FDCPA, FINRA…) on Pro |
| Human annotation queues | Yes | not a prompt-testing / labeling platform |
| Evaluation dataset management | Yes | Not offered |
| MIT / self-host in your VPC | —Enterprise self-host only | MIT, self-host any plan |
| Data stays in your process | —traces exported to LangSmith cloud | compliance packs enforced in-process |
| Zero-config OpenAI / Anthropic tracing | —via SDK wrappers | class-level patch, no wrappers |
| Entry paid price | $39/seat/month (Plus) | $29/month (Starter) |
LangSmith pricing: see langchain.com/langsmith for current plans. Peekr pricing: see /login. Neither tool is "insecure" — these are product trade-offs, not faults.
The compliance gap
If your team is building AI in healthcare, fintech, or legal, you need more than an evaluation harness — you need rules enforced on every LLM call, with a tamper-evident audit log. LangSmith can run custom evaluators; Peekr ships 17 ready-to-enable regulatory packs.
17 in-process compliance packs
Enforced in-process on every call · violations blocked + audit-logged · packs updated server-side without SDK redeploy
Pricing
Both have a free tier. The key difference: Peekr's 17 compliance packs ship on the $99/mo Pro plan; LangSmith's compliance and self-host features are Enterprise-only.
LangSmith
Limited daily traces · limited evaluation runs
Unlimited traces · full evaluations · annotation queues
Self-hosted · SSO · advanced compliance
Peekr
10k spans/mo · claim-level hallucination scoring
500k spans
5M spans · all 17 compliance packs
Custom volume · self-host in your own VPC
Where LangSmith wins
Peekr is not a LangChain-native evaluation platform, and we won't pretend otherwise. If any of these is your core need, LangSmith is the more natural choice.
You're deep on LangChain or LangGraph
LangSmith is built by the same team as LangChain. If your agent framework is LangGraph, the native callback integration gives you zero-config tracing of every chain, agent step, and tool call — no extra instrumentation code.
You want annotation queues and human review
LangSmith's annotation queues let your team label LLM outputs, build golden datasets, and run regression evaluations across prompt versions. If this labeling loop is a core part of your workflow, LangSmith has the richer dataset and human-review tooling.
You want LangChain-native evaluation pipelines
LangSmith's evaluator ecosystem is built around LangChain abstractions — off-the-shelf correctness, hallucination, and criteria evaluators that wire directly into LangChain's testing harness. Peekr has built-in claim-level scoring but is not a general-purpose evaluation pipeline builder.
Many teams run both: LangSmith for LangChain-native eval and annotation, Peekr for framework-agnostic tracing and in-process regulatory compliance on production calls.
A note on framework lock-in
Switching frameworks is easier when your observability layer doesn't depend on one.
LangSmith is an excellent product for teams fully committed to the LangChain ecosystem. If you're evaluating whether to stay on LangChain, or if you already run a mix of direct OpenAI calls, Anthropic SDK, and some LangChain chains, a framework-agnostic tracing layer means your observability survives the migration. Peekr patches at the SDK client level, not the framework level — so switching frameworks doesn't break your traces.
FAQ
For framework-agnostic tracing, claim-level hallucination detection, and in-process regulatory compliance, yes. Peekr auto-instruments OpenAI, Anthropic, Gemini, Bedrock, LangChain, CrewAI, and LlamaIndex at the class level — no decorators, no framework dependency. If your stack is fully on LangChain/LangGraph and you primarily want native callback tracing and LangChain-native evaluations, LangSmith is the more natural fit.
No. Peekr instruments any LLM SDK — OpenAI, Anthropic, Gemini, Bedrock, and more — by patching the client at the class level. You do not need LangChain, LangGraph, or any particular agent framework. If you do use LangChain, Peekr traces those calls too.
Peekr calls peekr.instrument() once, before any other imports. It patches the client class so every OpenAI(), AsyncOpenAI(), or anthropic.Anthropic() instance your code creates is automatically traced — including instances inside third-party libraries you didn't write. No decorators or wrappers required. LangSmith's tracing for non-LangChain code works similarly via its own SDK, but its evaluation and annotation tooling is most complete for LangChain-native workflows.
LangSmith provides evaluation infrastructure — you can build custom evaluators to check output against rules. It does not ship pre-built regulatory compliance packs as enforced guardrails. Peekr ships 17 regulatory packs (HIPAA, FDCPA, FINRA, GDPR, EU AI Act, and more) that are enforced in-process on every LLM call: violations are blocked and added to a tamper-evident audit log. This happens inside your own process — customer data does not leave your stack.
Yes. The Peekr SDK is MIT-licensed and runs entirely inside your own process, so you can deploy it in your own VPC with no data leaving your stack. LangSmith has a self-hosted Enterprise offering as well, though its compliance certifications and private deployment sit behind Enterprise pricing.
Auto-instruments OpenAI, Anthropic, Gemini, Bedrock, LangChain, and more. Built-in claim-level hallucination scoring. 17 in-process compliance packs on Pro. MIT-licensed. Free up to 10k spans/month.