LangSmith Observability
LangSmith Observability provides full visibility into your LLM application: from individual traces to production-wide performance metrics.LangSmith works with many frameworks and providers. Browse available integrations to connect your stack including OpenAI, Anthropic, CrewAI, Vercel AI SDK, Pydantic AI, and more.
Get started
Create an account
Sign up at smith.langchain.com (no credit card required).
You can log in with Google, GitHub, or email.
Create an API key
Go to your Settings page → API Keys → Create API Key.
Copy the key and save it securely.
Set up tracing
Add tracing to your app in minutes with environment variables, framework integrations, or the SDK.
Trace a RAG application
Follow a step-by-step tutorial to instrument a retrieval-augmented generation app from start to finish.
Investigate and monitor
View traces
Filter, export, share, and compare traces via the UI or API.
Monitor performance
Build dashboards and set alerts to track quality and catch issues early.
Configure automations
Automate workflows with rules, webhooks, and online evaluations.
Collect feedback
Annotate outputs and gather user feedback using queues or inline annotation.
Find and fix failures with Engine
Automatically detect recurring issues in your traces, diagnose their root cause, and resolve them with LangSmith Engine.
To set up a LangSmith instance, visit the Platform setup section to choose between cloud, hybrid, or self-hosted. All options include observability, evaluation, prompt engineering, and deployment.
Connect these docs to Claude, VSCode, and more via MCP for real-time answers.