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LangSmith

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Debug, monitor, and optimize your LLM applications and AI agents with comprehensive observability.

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Tracked since2026
0 reviews tracked·4 press mentions

The Bottom Line

Entry price

Free plan available, paid tiers above

Biggest pro

Provides deep visibility into non-deterministic LLM behavior

Biggest con

Specific pricing details require visiting a separate page

TL;DR - LangSmith

  • Debug LLM applications with detailed agent tracing.
  • Monitor key business metrics with live dashboards and alerts.
  • Automatically discover usage patterns and systemic issues.
Pricing: Free plan available
Best for: Growing teams

What is LangSmith?

Editorial review
LangSmith is an observability platform designed to help teams build and ship reliable AI agents and LLM applications. It provides robust tracing capabilities to quickly debug non-deterministic LLM app behavior, allowing developers to see step-by-step what their agent is doing and fix issues to improve latency and response quality. The platform also offers live dashboards for monitoring business-critical metrics such as costs, latency, and response quality, with alerting features to notify users of issues and enable drilling down to root causes. Furthermore, LangSmith automatically discovers usage patterns and issues by clustering similar conversations, helping users understand what their users want and identify systemic problems. It integrates seamlessly with various frameworks, including LangChain and LangGraph, and supports OpenTelemetry for unified observability.

Available on: Web

Pros & Cons

Pros

  • Provides deep visibility into non-deterministic LLM behavior
  • Helps improve application performance and response quality
  • Offers comprehensive monitoring and alerting capabilities
  • Identifies systemic issues and user needs automatically
  • Flexible integration with various frameworks and OTel

Cons

  • Specific pricing details require visiting a separate page
  • Self-hosting is only available on the enterprise plan
  • Requires some setup (e.g., environment variables) to get started

Preview

Key Features

Agent tracing for step-by-step debugging of LLM applicationsLive dashboards for monitoring costs, latency, and response qualityAlerts for critical business metricsAutomatic discovery and clustering of similar conversations (Insights Agent)Support for any framework, including LangChain and LangGraphOpenTelemetry (OTel) support for unified observabilitySelf-hosting option for enterprise plansNo added latency to applications

Pricing

Freemium

LangSmith offers a generous free tier with optional paid upgrades for advanced features.

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Reviews

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LangSmith FAQ

How does LangSmith help in debugging LLM applications?

LangSmith provides robust tracing capabilities that allow developers to debug the non-deterministic behavior of LLM applications. It enables a step-by-step view of an agent's actions, which helps in identifying and resolving issues to improve latency and response quality.

Which teams would benefit most from using LangSmith?

Teams that are building and deploying AI agents and LLM applications will find LangSmith most beneficial. It is designed for developers and operations teams who need to ensure the reliability, performance, and quality of their AI-powered products.

How is LangSmith priced?

LangSmith offers a free tier for users to get started. For more extensive usage and additional features, paid plans are available.

Can LangSmith integrate with existing observability tools?

Yes, LangSmith supports OpenTelemetry, allowing for unified observability across different systems. It also integrates seamlessly with frameworks like LangChain and LangGraph.

What kind of limitations should users be aware of with LangSmith?

One limitation is that self-hosting is exclusively available with the enterprise plan. Additionally, getting started with LangSmith requires some initial setup, such as configuring environment variables.

How does LangSmith compare to Langfuse for monitoring AI applications?

LangSmith offers comprehensive observability with robust tracing and live dashboards for debugging and monitoring LLM applications. It focuses on identifying systemic issues and user needs by clustering similar conversations, helping to understand usage patterns.

How does LangSmith assist in optimizing LLM application performance?

LangSmith helps optimize performance by providing live dashboards to monitor business-critical metrics like costs, latency, and response quality. It also includes alerting features that notify users of issues and facilitate drilling down to root causes for improvement.

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