LangSmith logo

LangSmith

Freemium

Hosted platform from LangChain for tracing, evaluating, and monitoring LLM applications.

Visit website

Pricing

Freemium

Type

Automation

Languages

Python, JavaScript, TypeScript

// VERDICT

Reach for LangSmith when you want hosted tracing plus evaluation for LLM apps - debugging runs and gating on eval scores. Skip it when you want open-source self-hosting (Langfuse/Phoenix) or code-only evals without a platform.

Best for

LangChain's platform for tracing, evaluating and monitoring LLM apps - capture every run, build eval datasets, score outputs and debug chains/agents, whether or not you use LangChain.

Avoid when

You want a fully open-source self-hosted tool, or a lightweight code-only eval without a platform.

CI/CD fit

SDK tracing · eval datasets · CI eval runs

Languages

Python · JavaScript · TypeScript

Team fit

LLM app teams · LangChain users (and non-users) · Dev/QA debugging + evaluating LLMs

Setup

Easy

Maintenance

Low

Learning

Intermediate

Licence

Freemium

// BEST FOR

  • Tracing every LLM/chain/agent run for debugging
  • Building eval datasets from traced runs
  • Scoring outputs (code or LLM-as-judge) against datasets
  • Monitoring LLM apps in production
  • Works with or without LangChain
  • Catching regressions when prompts/models change

// AVOID WHEN

  • You require fully open-source self-hosting
  • A lightweight code-only eval is enough
  • You can't send traces to a hosted service
  • You're not building LLM apps
  • Only prompt comparison is needed (PromptFoo)
  • Turnkey on-prem is mandatory

// QUICK START

pip install langsmith   # or npm i langsmith
# set LANGCHAIN_TRACING_V2 + API key -> runs are traced;
# define datasets + evaluators, run evals in CI

// ALTERNATIVES TO CONSIDER

ToolChoose it when
LangfuseYou want open-source, self-hostable tracing + eval.
Arize PhoenixYou want open-source observability with OpenTelemetry.
BraintrustYou want a managed eval-first platform with datasets.

// FEATURES

  • Distributed tracing for chains, agents, and tool calls
  • Datasets and evaluation runs with custom evaluators
  • Prompt playground with versioning and side-by-side compare
  • Production monitoring with feedback capture
  • Annotation queues for human review

// PROS

  • Best-in-class tracing UX for LangChain and LangGraph apps
  • Works with non-LangChain code via the SDK
  • Generous free tier for individual developers
  • Tight loop between debugging traces and turning failures into evals

// CONS

  • Closed-source SaaS — self-hosting limited to enterprise tier
  • Pricing scales with trace volume and can surprise teams
  • Tightest experience reserved for the LangChain ecosystem

// EXAMPLE QA WORKFLOW

  1. Enable LangSmith tracing via SDK/env

  2. Capture runs from your LLM app

  3. Build eval datasets from traced runs

  4. Define evaluators and score outputs

  5. Gate CI on eval scores/regressions

  6. Monitor production and feed back new cases

// RELATED QA.CODES RESOURCES

Cheat sheets