LLM observability and evaluation platform by LangChain for tracing, debugging, and monitoring production AI agents and RAG pipelines.

Overview

LangSmith is LangChain’s commercial platform for tracing, evaluating, and monitoring LLM applications. If you are building with LangChain, LangGraph, or almost any LLM framework, LangSmith provides the debugging, testing, and production-monitoring infrastructure that turns a fragile prototype into a service you can operate. Every execution of your app — an agent run, an evaluator run, or a playground session — becomes a trace containing each LLM call, tool use, and retrieval step, with inputs, outputs, latency, token usage, and errors all visible.

Beyond tracing, LangSmith adds a prompt playground for side-by-side iteration, datasets and evaluators for offline and online testing, annotation queues for human review, and online evaluations that run on live traffic. In our evaluation its core strength is closing the loop between building and operating: you can capture real production traces, turn them into an evaluation dataset, and block a prompt regression before it ships. The 2026 pricing also bundles LangGraph Cloud deployment, Fleet agents, and an Engine for autonomous failure diagnosis, so the platform is expanding from observability into a fuller agent-operations stack.

Key Features

  • End-to-end tracing — every LLM call, tool, and retrieval step in a run is inspectable with latency, tokens, and errors.
  • Prompt playground — test and compare prompt variants side by side without rerunning your app.
  • Datasets and evaluators — build regression-proofing suites from production traces and run them offline or online.
  • Annotation queues — structured human review of model outputs inside the same UI.
  • LangGraph deployment — one-click deploy and horizontally scale long-running agents with auth and cron scheduling.
  • Engine and Fleet — autonomous trace clustering that diagnoses failures and suggests prompt or code fixes.

Pricing

PlanPriceFor
Developer$0 /seat1 seat, 5,000 base traces/mo, 14-day retention, community support
Plus$39/seat/moUnlimited seats, 10k traces + $2.50/1k overage, 400-day retention, email support
EnterpriseCustomSSO/RBAC, self-host or hybrid, SLA, custom retention and volume

Comparison

vs. LangChain: LangChain the framework is open-source and free; LangSmith is the paid platform that makes LangChain apps observable and testable. You can use one without the other, but together they cover build and operate.

vs. Dify: Dify is a full visual agent-building platform with its own built-in observability, whereas LangSmith assumes you build elsewhere and focuses purely on tracing and evaluation. Pick Dify to build and ship, LangSmith to deeply monitor a code-first stack.

vs. n8n: n8n is a general workflow automator you can wire to LLMs, with basic execution logs; LangSmith is purpose-built LLM observability with evaluations and datasets. Choose n8n for broad automation, LangSmith when trace-level debugging of AI calls is the priority.

Compare alternatives

Side-by-side with the 3 closest alternatives.

ToolCategoryPricingVisit
LangSmith (this) code, agentsFrom $0/mo Site ↗
LangChaincode, agentsFrom $0/mo Site ↗
Difyagents, productivityFrom $0/mo Site ↗
n8nagents, productivity, codeFrom $0/mo Site ↗
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Editor’s Review
4.4/5
Pros
  • +Native tracing for LangChain, LangGraph, and most LLM frameworks out of the box
  • +Strong evaluation and dataset tooling for regression-proofing prompts
  • +Free Developer tier with 5,000 traces/month for prototyping
Cons
  • Per-seat pricing plus trace overages get expensive at production scale
  • Best value is inside the LangChain ecosystem; weaker as a neutral tool
  • Retention tiers (14 vs 400 days) materially change the bill

LangSmith is the observability layer most teams reach for once an LLM app leaves the notebook. Its tracing makes a multi-step agent legible — every LLM call, tool use, and retrieval step is inspectable — and the evaluation datasets turn prompt changes from guesswork into measured diffs. The catch is the billing model: seats plus per-trace overages mean a chatty production agent can surprise you, and the deepest features assume you are already in the LangChain world. As a neutral, framework-agnostic observer it is good but not uniquely so.

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