LangSmith is a platform designed for developing, debugging, and deploying language model (LM) applications. It provides comprehensive tools for tracing requests, evaluating outputs, testing prompts, and managing deployments in a unified interface. The platform is framework agnostic, meaning you can use it with or without LangChain's open-source libraries.

How does LangSmith provide observability?

LangSmith enables you to gain visibility into every step your application takes to debug faster and improve reliability. It uses traces to log nearly every aspect of LM runs, including metrics such as latency, token count, price of runs, and all types of metadata. The web UI allows you to quickly filter runs based on error percentage, latency, date, or even by text content using natural language.

How does LangSmith help with evaluation?

The platform helps you measure and track quality over time to ensure your AI applications are consistent and trustworthy. LangSmith offers datasets in multiple types, including key-value datasets (for chains and agents with multiple inputs or outputs), and LLM datasets (for string input/output pairs). It provides built-in evaluators for both labeled and unlabeled datasets, allowing you to perform checks such as "Is the output helpful?" or measure the correctness of responses to prompts.

How does LangSmith assist with deployment?

LangSmith allows you to deploy your agents as Agent Servers, ready to scale in production. You can prototype locally and then move to production with integrated monitoring and evaluation to build more reliable AI systems.

What frameworks and libraries does LangSmith work with?

LangSmith works with any framework. If you're already using LangChain or LangGraph, you can simply set one environment variable to get started with tracing your AI application.

Does LangSmith have regional options?

Yes, LangSmith has EU and US regions, with the same legal terms and pricing. EU data can be hosted regardless of location, and the EU region is available on all plans. There may be a small delay between launches to each region depending on the feature, but besides that, they are functionally equivalent—all features supported in the US are supported in the EU and vice versa.

How does LangSmith support multiturn conversations?

LangSmith is designed to trace complex, stateful interactions for applications like chatbots and virtual assistants. By viewing a conversation as a single, continuous trace with multiple turns, developers can understand how context is being passed between requests. This is essential for debugging issues where a chatbot forgets previous parts of the conversation and for improving its ability to engage in coherent, long-running dialogues.

How can I use user feedback to improve my application?

LangSmith's ability to ingest user feedback (such as ratings or flags for bad responses) and link it directly to the corresponding trace creates a powerful quality assurance loop. When a user flags a bad response, the development team can immediately pull up the exact trace that generated it, see the exact inputs, intermediate steps, and model output, and rapidly diagnose the problem to deploy a fix.

What special support does LangSmith provide for Retrieval-Augmented Generation (RAG)?

LangSmith makes it easy to log retrieval steps in RAG applications. The resulting trace shows the initial prompt, the query to the vector database, the retrieved documents, and the final LM call with the combined context, all nested logically.

When does LangSmith pricing go into effect?

If you've been using LangSmith already, your usage became billable starting in July 2024.

LangSmith

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LangSmith helps teams build reliable AI applications. It lets you trace your AI's steps to debug issues fast, monitor important metrics like cost and response quality, and discover common user patterns. This means you can quickly fix problems, understand what users want, and make your AI trustworthy.

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Overview Features Use Cases FAQ Integrations Pricing

About LangSmith

Who It's For

LangSmith helps teams building AI agents and applications. It's for developers who want to ensure their AI tools are reliable and work correctly. If you create chatbots or smart assistants, this platform gives you clear insights into their performance.

What You Get

You get tools to see every step your AI takes, making debugging much easier. It also lets you monitor important things like cost, speed, and the quality of responses using live dashboards. This helps you quickly spot problems and understand how users interact.

How It Works

LangSmith records detailed traces of your AI's actions, so you can find and fix problems fast. It tracks key business metrics and sends alerts when issues arise. The tool also groups similar user conversations automatically to spot common needs. It works with any AI framework.

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Features & Capabilities

🔍 Core Observability

Agent Tracing

Debugs non-deterministic LLM app behavior by visualizing step-by-step agent execution to quickly find and fix failures.

Live Dashboards & Metrics

Monitors business-critical metrics such as costs, latency, and response quality with customizable dashboards and real-time alerts.

Automated Usage Insights

Discovers usage patterns by clustering similar conversations, helping to understand user needs and identify systemic issues.

⚙️ Flexible Integration & Deployment

Framework Agnostic

Works with any LLM application framework, including LangChain and LangGraph, requiring minimal setup.

OpenTelemetry (OTel) Support

Supports OTel to unify your observability stack across services, regardless of the application's programming language.

Enterprise Self-Hosting

Offers a self-hosting option for enterprise plans, allowing the software to run on your Kubernetes cluster for data residency and control.

🔒 Reliability & Data Governance

Zero Latency Impact

Ensures no additional latency is added to your application through an asynchronous, distributed trace collection process.

Data Ownership & Privacy

Guarantees that user data is never used for training purposes, and customers retain full ownership and rights to their data.

Use Cases

Debugging and Optimizing AI Agent Performance

AI engineers face challenges with non-deterministic LLM behavior, making debugging difficult and impacting agent reliability. LangSmith provides step-by-step tracing to pinpoint issues, allowing developers to rapidly diagnose problems and improve response quality and reduce latency for more reliable AI applications.

B2B SaaSFor: AI Engineers

Monitoring Business-Critical LLM Metrics

Go-to-Market Leaders and engineering managers need to track key business metrics like costs, latency, and output quality of their AI applications. LangSmith offers live dashboards and alerts to monitor these critical metrics, enabling teams to proactively identify and address performance bottlenecks or unexpected cost escalations.

Enterprise SoftwareFor: Engineering Managers

Analyzing User Interactions to Resolve Systemic Issues

Product teams struggle to understand how users interact with their AI agents and identify recurring problems that affect user experience. LangSmith automatically clusters similar conversations and integrates user feedback, allowing teams to discover usage patterns, understand user needs, and quickly address systemic issues across their AI applications.

Product DevelopmentFor: Product Managers

Building and Scaling Reliable Conversational AI and RAG Applications

Developers building complex conversational agents or Retrieval-Augmented Generation (RAG) applications need robust tools to ensure context is maintained and retrieval steps are accurate. LangSmith provides specialized tracing for multiturn conversations and RAG workflows, along with integrated monitoring and evaluation, facilitating the development and scalable deployment of highly reliable AI systems.

AI/ML Product DevelopmentFor: ML Engineers

Frequently asked questions

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