LangSmith docs

LangSmith provides tools for developing, debugging, and deploying LLM applications. It helps you trace requests, evaluate outputs, test prompts, and manage deployments in one place. LangSmith is framework agnostic, so you can use it with or without LangChain’s open-source libraries langchain and langgraph. Prototype locally, then move to production with integrated monitoring and evaluation to build more reliable AI systems.

Get started

1. Create an account

   Sign up at smith.langchain.com (no credit card required). You can log in with Google, GitHub, or email.

2. Create an API key

   Go to your Settings page → API Keys → Create API Key. Copy the key and save it securely.

Once your account and API key are ready, choose a quickstart to begin building with LangSmith:

Observability

Gain visibility into every step your application takes to debug faster and improve reliability.

Start tracing

Evaluation

Measure and track quality over time to ensure your AI applications are consistent and trustworthy.

Evaluate your app

Deployment

Deploy your agents as LangGraph Servers, ready to scale in production.

Deploy your agents

Prompt Testing

Iterate on prompts with built-in versioning and collaboration to ship improvements faster.

Test your prompts

Studio

Use a visual interface to design, test, and refine applications end-to-end.

Develop with Studio

Hosting

Host LangSmith in the cloud, in your environment, or hybrid to match your infrastructure and compliance needs.

Choose your hosting mode

Workflow

LangSmith combines observability, evaluation, deployment, and hosting in one integrated workflow—from local development to production.

---

Edit the source of this page on GitHub