How-to guides

Step-by-step guides that cover key tasks and operations in LangSmith.

Tracing

Get started with LangSmith's tracing features to start adding observability to your LLM applications.

• Annotate code for tracing
  • Use @traceable/traceable
  • Wrap the OpenAI API client
  • Use the RunTree API
  • Use the trace context manager (Python only)
• Toggle tracing on and off
• Log traces to specific project
  • Set the destination project statically
  • Set the destination project dynamically
• Set a sampling rate for traces
• Add metadata and tags to traces
• Implement distributed tracing
  • Distributed tracing in Python
  • Distributed tracing in TypeScript
• Access the current span within a traced function
• Log multimodal traces
• Log retriever traces
• Log custom LLM traces
  • Chat-style models
  • Specify model name
  • Stream outputs
  • Manually provide token counts
  • Instruct-style models
• Prevent logging of sensitive data in traces
  • Rule-based masking of inputs and outputs
• Export traces
  • Use filter arguments
  • Use filter query language
• Share or unshare a trace publicly
• Compare traces
• Trace generator functions
• Trace with LangChain
  • Installation
  • Quick start
  • Trace selectively
  • Log to specific project
  • Add metadata and tags to traces
  • Customize run name
  • Access run (span) ID for LangChain invocations
  • Ensure all traces are submitted before exiting
  • Trace without setting environment variables
  • Distributed tracing with LangChain (Python)
  • Interoperability between LangChain (Python) and LangSmith SDK
  • Interoperability between LangChain.JS and LangSmith SDK
• Trace with LangGraph
  • Interoperability between LangChain and LangGraph
  • Interoperability between @traceable/traceable and LangGraph
• Trace with Instructor (Python only)
• Trace with the Vercel AI SDK (JS only)
• Trace without setting environment variables
• Trace using the LangSmith REST API
• Calculate token-based costs for traces
• Bulk Exporting Traces

Monitoring and automations

Leverage LangSmith's powerful monitoring and automations features to make sense of your production data.

• Filter traces in the application
  • Create a filter
  • Filter for intermediate runs (spans)
  • Advanced: filter for intermediate runs (spans) on properties of the root
  • Advanced: filter for runs (spans) whose child runs have some attribute
  • Filter based on inputs and outputs
  • Filter based on input / output key-value pairs
  • Copy the filter
  • Manually specify a raw query in LangSmith query language
  • Use an AI Query to auto-generate a query
• Use monitoring charts
  • Change the time period
  • Slice data by metadata or tag
  • Drill down into specific subsets
• Create dashboards
  • Creating a new dashboard
  • Adding charts to your dashboard
  • Filtering traces in your chart
  • Comparing data within a chart
  • Chart display options
  • Saving and managing charts
  • View a chart in full screen
  • User journeys
• Set up automation rules
  • Create a rule
  • View logs for your automations
• Set up online evaluations
  • Configure online evaluations
  • Set API keys
• Set up webhook notifications for rules
  • Webhook payload
  • Example with Modal
• Set up threads
  • Group traces into threads
  • View threads