LangSmith Observability ↗
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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.
The following sections help you set up and use tracing, monitoring, and observability features:
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.
Access and manage traces via UI or API with filtering, exporting, sharing, and comparison tools.
Create dashboards and set alerts to track performance and get notified when issues arise.
Use rules, webhooks, and online evaluations to streamline observability workflows.
Gather and manage annotations on outputs using queues and inline annotation.
Follow a step-by-step tutorial to trace a Retrieval-Augmented Generation application from start to finish.
For terminology definitions and core concepts, refer to Observability concepts.
Use Polly, LangSmith’s AI assistant, to analyze traces and get AI-powered insights into your application’s performance.
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.
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