LangSmith: Tracing, Evaluation, and Monitoring for LLM Apps

Building an LLM application is one thing. Knowing whether it's actually working — and catching it when it breaks — is another. LangSmith is the observability and evaluation platform built specifically for LLM applications.

What Is LangSmith?

LangSmith is a product by LangChain Inc. that provides:

It integrates natively with LangChain but also works with any LLM framework via the SDK.

Getting Started

With these environment variables set, all LangChain operations are automatically traced. No code changes needed.

Reading a Trace

Each trace in LangSmith shows:

This is invaluable for debugging why an agent took the wrong action or why a RAG retriever returned irrelevant chunks.

Creating Evaluation Datasets

LangSmith lets you build datasets of input/output pairs for systematic testing:

Running Evaluations

Evaluators can be:

Prompt Versioning

LangSmith's Prompt Hub lets you manage prompts as versioned artefacts:

This decouples prompt iteration from code deployment — business users can refine prompts without touching the codebase.

Production Monitoring

In production, LangSmith gives you dashboards for:

You can add user feedback directly from your application:

LangSmith vs Open Alternatives

LangSmith is hosted, easy to set up, and deeply integrated with LangChain. If you want self-hosted and open-source, Langfuse is the closest alternative with overlapping features.

For teams already on LangChain, LangSmith is the natural choice. For polyglot teams using multiple frameworks, Langfuse's framework-agnostic approach may be preferable.

The Real Value

The value of LangSmith isn't in the dashboards — it's in catching regressions before your users do. Teams that invest in evaluation infrastructure ship better AI faster, because they can iterate confidently rather than hoping the prompts still work.

Book a call if you want help setting up LLM observability for your production AI system.