LangSmith is LangChain's platform for agent observability, evaluation, deployment, monitoring, and debugging.

Is LangSmith only for LangChain apps?

No. LangSmith is framework-agnostic and can trace and evaluate applications built with LangChain, LangGraph, or other agent stacks.

How is LangSmith priced?

LangSmith has Developer at $0/seat/month, Plus at $39/seat/month, and Enterprise custom, with additional usage-based LCU and LSU charges.

What is LangSmith best for?

It is best for teams that need traces, evaluations, monitoring, deployment, and feedback loops for production AI agents.

Can LangSmith run evaluations?

Yes. LangSmith supports offline evaluations before release and online evaluations against production interactions.

What should teams watch for?

Teams should estimate trace volume, retention, LCU/LSU usage, deployment needs, data policies, and evaluation design before scaling.

LangSmith

Name: LangSmith
Brand: LangSmith

Agent observability, evaluation, deployment, and monitoring platform from LangChain.

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What is LangSmith?

TL;DR

LangSmith is an AI engineering platform for tracing, evaluating, testing, deploying, and monitoring LLM applications and agents, with observability and feedback loops for production quality and reliability.

Tracing and observability for agents and LLM apps
Offline and online evaluations for quality monitoring
Deployment, Fleet, Engine, and platform workflows

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About

LangSmith is LangChain's agent engineering platform for observing, evaluating, testing, deploying, and improving LLM applications and AI agents. It helps teams understand what agents actually did by capturing traces, monitoring quality, running evaluations, and turning production behavior into actionable debugging data. The product is framework-agnostic and can be used with LangChain, LangGraph, or other agent stacks. Its core value is visibility: teams can inspect runs, compare experiments, monitor errors and latency, track costs, and build evaluation loops before and after shipping changes. LangSmith is especially useful for production AI teams because traces are often the only reliable record of what an agent saw, decided, and executed. The platform combines observability, evaluation, deployment, Fleet, Engine, and enterprise deployment options for teams that need reliability beyond prototype notebooks. For FutureStack, LangSmith belongs in Developer Tools because serious AI products need more than model access. It is best for engineering and AI teams that want to debug agents, run evals, monitor quality, and deploy workflows with measurable feedback loops.

Use Cases

Agent Observability

Trace multi-step agent runs, tool calls, prompts, and outputs to understand production behavior.

AI Evaluations

Run offline and online evaluations to measure quality and catch regressions as systems change.

Prompt Experiments

Compare prompts, models, and configurations against datasets to identify better-performing approaches.

Production Monitoring

Monitor real user interactions, errors, costs, and quality signals after AI features ship.

Key Features

Tracing and observability for agents and LLM apps
Offline and online evaluations for quality monitoring
Deployment, Fleet, Engine, and platform workflows
Framework-agnostic support across agent stacks

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