LangSmith
Debug, test, and monitor LLM applications and agents in production with LangChain's observability platform, billed by trace volume and seats.
The verdict on LangSmith: Teams building production LLM apps on LangChain who need full trace visibility into every call LangSmith's deepest integration is still with LangChain and LangGraph: native tracing and the evaluation dataset workflow there are among the most mature in the category. Pricing: Free (5K traces) / $39/seat/mo Plus / Enterprise custom.
Best For
Teams building production LLM apps on LangChain who need full trace visibility into every call
Standout Feature
Native LangChain integration gives trace depth that framework-agnostic tools simply can't match
TL;DR
The obvious choice if you're already on LangChain, less useful if your stack isn't built on it.
Alternatives
Fit Score: use-case reference
How this is scoredLangSmith scored 1 to 5 on five dimensions for the named use cases below. Part of our catalog-wide use-case reference, not a product-quality review rating.
Production LangChain call tracing
4.2/5- Capability
- 5/5
- Reliability
- 4/5
- Value
- 3/5
- Ease
- 4/5
- Compatibility
- 5/5
LangSmith exposes LLM calls in a LangChain application and adds evaluation datasets and regression tests for investigating changes. Native integration reduces instrumentation work; other supported frameworks generally need more wrapping. The 5,000-trace free allowance is small for production, so assess $39/seat/month Plus against the expected workload rather than assuming the free tier will cover a live application.
Editorial judgment grounded in this tool's own published pricing, features, and our existing pros and cons, not a certification or a measured benchmark. Product quality is assessed separately under our review methodology.
Overview
Not yet reviewedProduct quality is assessed from original evidence. How reviews work.
LangSmith is the observability and evaluation platform built by the LangChain team for teams building production LLM applications, it logs every LLM call with full trace context, lets you define evaluation datasets and run automated quality checks, and provides a debugging interface that shows exactly where a chain went wrong. Where Langfuse is framework-agnostic, LangSmith has the tightest integration with LangChain and LangGraph, making it the default choice for teams already in the LangChain ecosystem. The dataset and evaluation workflow is particularly strong for teams that run regular regression testing on their chains.
The source of the legacy catalog rating and review count has not been substantiated. Neither is presented here as a verified review rating or included in review structured data.
Workflow Fit Scores are separate, use-case-specific editorial judgments, not user-review averages. Read the Fit Score methodology.
- • Native LangChain integration provides trace depth that third-party tools can't match
- • Evaluation dataset workflow is among the most mature in the LLM observability category
- • Playground lets you test chains interactively before deploying changes
- • Native LangChain/LangGraph integration is the deepest and most turnkey; other frameworks (OpenAI, Anthropic, CrewAI, AutoGen, and more) are supported but generally need wrapping via the Functional API rather than native tracing
- • Free tier trace limits hit quickly in production
Key Features
- Full LLM call tracing
- LangChain native integration
- Evaluation datasets
- Automated regression testing
- Prompt playground
- Team collaboration
Trust & Data
Verified 2026-09
- Training on your data
- Not publishedLangChain's Data Processing Addendum restricts it to processing customer personal data only for the business purposes specified in the agreement, but neither the privacy policy nor the DPA contains an explicit statement about whether LLM trace and run data, the core data LangSmith handles, is used for AI model training. The LangSmith-specific policy page at smith.langchain.com/privacy rendered as a JavaScript-only shell with no readable content, so this entry relies on LangChain's general privacy policy and DPA rather than a LangSmith-specific document.
- Compliance
- Not published
- Retention
- Per the DPA, customer personal data is retained for the term of the agreement plus a post-termination period until deletion, with ordinary-course archival backups retained as required by law.
- Export
- Not published
Sources: langchain.com/privacy policy, langchain.com/DPA, langchain.com/terms of service
As published by the vendor. Verify independently before purchase decisions.
Our Take
But LangSmith's own docs describe it as a framework-agnostic platform, and LangSmith Deployment explicitly supports OpenAI, Anthropic, CrewAI, Vercel AI SDK, Pydantic AI, Google ADK, Claude Agent SDK, Strands, and AutoGen, so ruling it out entirely for non-LangChain stacks is no longer accurate, evaluate the actual instrumentation and integration depth for your framework rather than assuming a blanket exclusion. The free tier hits its 5,000-trace limit quickly in production, so active applications almost always end up on a paid seat.
Workflows Using This Tool
Step-by-step playbooks whose entries name LangSmith. The step shown is where the workflow uses it.
- AdvancedSetup, then recurring: 1 day setup, then automated. Planning estimate from the workflow entry, not measured time saved.
Build a Multi-Agent Research Pipeline with CrewAI
Step 5 of 5: Monitor with LangSmith
- AdvancedOne-time setup: 2–3 days setup. Planning estimate from the workflow entry, not measured time saved.
Build a Production LLM Evaluation and Monitoring Pipeline
Step 1 of 5: Build Your Golden Dataset
- AdvancedEstimate: 1–2 weeks. Planning estimate from the workflow entry, not measured time saved.
Build an Enterprise MLOps Pipeline
Step 3 of 5: Add LLM Observability
- IntermediateOne-time setup: 1 hour, one-time setup. Planning estimate from the workflow entry, not measured time saved.
Build a Multi-Model Routing Strategy to Cut AI Costs Like an Enterprise
Step 3 of 4: Benchmark Quality vs. Cost
6 workflows in the catalog name LangSmith; the first 4 in catalog order are shown, with step-level mentions first.