How do you trace and observe tool call latency in production using LangSmith?

Q: How do you trace and observe tool call latency in production using LangSmith?

LangSmith provides automatic end-to-end tracing when `LANGSMITH_TRACING_V2=true` is enabled. Each tool call appears as a nested run within the chain, showing its start time, end time, latency, inputs, outputs, and any errors [citation:4]. For production scale, use the `auto_batch_tracing=True` client setting to enable background batching, reducing performance impact. For high-volume applications, set a sampling rate (e.g., `LANGSMITH_TRACING_SAMPLING_RATE=0.1`) to trace only 10% of requests, maintaining observability while controlling costs [citation:9]. ```python import os from langsmith import Client # Enable tracing os.environ["LANGSMITH_TRACING_V2"] = "true" os.environ["LANGSMITH_API_KEY"] = "lsv2_pt_..." os.environ["LANGSMITH_PROJECT"] = "my-agent" # Enable background batching for production client = Client(auto_batch_tracing=True) # Optional: Sample traces to control volume os.environ["LANGSMITH_TRACING_SAMPLING_RATE"] = "0.1" # 10% of requests # Tag runs for filtering and comparison from langchain_core.runnables import RunnableConfig config = RunnableConfig(tags=["version-2.0", "user-cohort-a"]) result = agent.invoke({"messages": [...]}, config) ``` **Latency**: Each tool call shows duration in the trace timeline **Tool order**: Clear visualization of tool execution sequence **Error tracking**: Failed tool calls show error messages and stack traces **Cost tracking**: For supported providers, token usage and cost appear per tool call **State changes**: Memory reads/writes between tool calls are visible

Difficulty: 8/10

latency tracing, LangSmith integration, monitoring

Trace tool call latency in production by enabling LangSmith tracing, which automatically captures run hierarchies with timing information, and use sampling or tagging to manage data volume.

LangSmith provides automatic end-to-end tracing when LANGSMITH_TRACING_V2=true is enabled. Each tool call appears as a nested run within the chain, showing its start time, end time, latency, inputs, outputs, and any errors [citation:4]. For production scale, use the auto_batch_tracing=True client setting to enable background batching, reducing performance impact. For high-volume applications, set a sampling rate (e.g., LANGSMITH_TRACING_SAMPLING_RATE=0.1) to trace only 10% of requests, maintaining observability while controlling costs [citation:9].

LangSmith Tracing Configuration for Tool Latency

How do you trace and observe tool call latency in production using LangSmith?

Difficulty: 8/10

latency tracing, LangSmith integration, monitoring

Trace tool call latency in production by enabling LangSmith tracing, which automatically captures run hierarchies with timing information, and use sampling or tagging to manage data volume.

LangSmith Tracing Configuration for Tool Latency

How do you trace and observe tool call latency in production using LangSmith?

Trace tool call latency in production by enabling LangSmith tracing, which automatically captures run hierarchies with timing information, and use sampling or tagging to manage data volume.

How do you trace and observe tool call latency in production using LangSmith?

Trace tool call latency in production by enabling LangSmith tracing, which automatically captures run hierarchies with timing information, and use sampling or tagging to manage data volume.

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