LangSmith Observability provides full visibility into your LLM application: from individual traces to production-wide performance metrics.Documentation Index
Fetch the complete documentation index at: https://docs.langchain.com/llms.txt
Use this file to discover all available pages before exploring further.
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
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.
Analyze traces with Polly
LangSmith’s built-in AI assistant analyzes your traces and surfaces insights about performance, errors, and quality—without manual investigation.
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.