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What is LangSmith?

LangSmith is an AI agent and LLM observability platform built by LangChain, the company behind the popular LangChain framework. It solves the problem of understanding and debugging complex AI agent behaviors in production by providing complete visibility into agent execution traces. Core capabilities include tracing, monitoring, and insights. Tracing allows developers to see exactly what their agent is doing step by step, pinpointing issues related to latency, cost, and response quality. Monitoring provides real-time dashboards for tracking performance metrics like token usage, error rates, and cost, with online evaluations and alerts. Insights automatically analyze and cluster traces to detect usage patterns and failure modes. LangSmith is designed for teams building AI agents and LLM applications, from individual developers to large enterprises, and integrates with any LLM framework via SDKs for Python, TypeScript, Go, and Java, as well as OpenTelemetry.

AI Tool Review Summary

Performance Score

4.7/5

Content/Output Quality

High, consistent, and on-brand

Interface

Clean and minimal

AI Technology
LLMNLPAgent Tracing
Purpose of Tool

Observability and debugging for AI agents and LLM applications.

Compatibility

Works with any LLM framework via SDKs (Python, TypeScript, Go, Java) and OpenTelemetry.

Pricing

Freemium with paid tiers

Features

Features with the highest value for users are highlighted here.

Agent tracing with step-by-step visibility

Real-time monitoring dashboards

Online LLM-as-judge and code evaluations

SmithDB purpose-built trace database

Unsupervised topic clustering and error analysis

Multi-framework support (OpenAI, Anthropic, LlamaIndex, etc.)

OpenTelemetry integration

Self-hosted and BYOC deployment options

How It Works

1

Install SDK

Add the LangSmith SDK to your project using pip, npm, or Go modules.

2

Instrument Your Code

Wrap your LLM calls and agent logic with tracing decorators or context managers.

3

View Traces

Open the LangSmith dashboard to see detailed execution traces with latency and cost breakdowns.

4

Monitor and Analyze

Set up dashboards and alerts to track performance, and use Insights to discover patterns automatically.

Who Is It For?

AI Engineers

ML Teams

Product Managers

DevOps Engineers

Startups

Enterprise Teams

RAG Developers

Chatbot Builders

Research Labs

Freelance Developers

Pricing

Free

$0/monthly
  • Up to 1,000 traces per month
  • Basic monitoring
  • Community support
Popular

Pro

$99/monthly
  • Up to 100,000 traces per month
  • Advanced monitoring
  • Email support

Enterprise

Custom/monthly
  • Unlimited traces
  • Self-hosted/BYOC
  • Dedicated support

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Pros & Cons

Pros

  • Provides deep observability into complex agent behaviors with sub-second query performance.
  • Flexible deployment options including self-hosted for data residency compliance.

Cons

  • Pricing can become expensive at high trace volumes for production use.
  • Learning curve for teams new to observability concepts and the LangChain ecosystem.

FAQs

Just Launched

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Rating

5.0

Pros

Cons

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Analytics of AI Agent Observability: Tracing, Testing, and Improving Agents

Website traffic and keyword analysis.

Live dataFeb 2026 – Apr 2026

Monthly visits

3.15M

-6.9% vs prior month

Avg. visit duration

00:05:59

M 4 2026 snapshot

Pages / visit

6.63

M 4 2026 snapshot

Bounce rate

42.16%

Lower is better

All traffic · Worldwide

Weekly estimate · Feb 1, 2026 – Apr 29, 2026

630.9K648.92K666.94K684.95K702.97KFeb 1Feb 15Mar 1Mar 15Mar 29Apr 8Apr 22Apr 29

Peak week: 702.97K (Feb 1, 2026)Low week: 630.9K (Apr 1, 2026)WoW: 0.0%Derived from monthly estimates · SimilarWeb-equivalent

Release History

0 releases published

No releases yet.

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