langsmith-tracing Skill

langsmith-tracing

Configure LangSmith observability and tracing for LLM applications built with LangChain and LangGraph frameworks.

Overview

LangSmith is the managed observability suite by LangChain that provides:

Dashboards and alerting for LLM applications
Human-in-the-loop evaluation capabilities
Deep LangChain/LangGraph integration
Run Tree model for nested traces
MCP connectivity to Claude, VSCode

Capabilities

Core Tracing Setup

Initialize LangSmith client and API configuration
Configure project/workspace settings
Set up trace collection and sampling
Enable debug logging for agent execution

Integration Patterns

LangChain chain tracing with automatic instrumentation
LangGraph workflow state tracking
Custom span creation for non-LangChain code
Parent-child trace relationships

Debugging Features

Fetch execution traces for analysis
Query run history and metadata
Export traces for offline analysis
Compare runs across different versions

Usage

Environment Setup

# Set required environment variables
export LANGCHAIN_TRACING_V2=true
export LANGCHAIN_API_KEY=<your-api-key>
export LANGCHAIN_PROJECT=<project-name>

Python Integration

from langsmith import Client, traceable
from langchain.callbacks.tracers import LangChainTracer

# Initialize client
client = Client()

# Use @traceable decorator for custom functions
@traceable(name="custom_operation")
def my_function(input_data):
    # Your logic here
    return result

# Initialize tracer for LangChain
tracer = LangChainTracer(project_name="my-project")

# Use with LangChain chains
chain.invoke(input, config={"callbacks": [tracer]})

Trace Retrieval

# Fetch traces from LangSmith
runs = client.list_runs(
    project_name="my-project",
    start_time=datetime.now() - timedelta(hours=24),
    execution_order=1,  # Root runs only
    error=False,  # Successful runs only
)

for run in runs:
    print(f"Run ID: {run.id}")
    print(f"Latency: {run.latency_p99}")
    print(f"Tokens: {run.total_tokens}")

Task Definition

When used in a babysitter process, this skill produces:

const langsmithTracingTask = defineTask({
  name: 'langsmith-tracing-setup',
  description: 'Configure LangSmith tracing for the application',

  inputs: {
    projectName: { type: 'string', required: true },
    apiKeyEnvVar: { type: 'string', default: 'LANGCHAIN_API_KEY' },
    samplingRate: { type: 'number', default: 1.0 },
    enableDebug: { type: 'boolean', default: false }
  },

  outputs: {
    configured: { type: 'boolean' },
    projectUrl: { type: 'string' },
    artifacts: { type: 'array' }
  },

  async run(inputs, taskCtx) {
    return {
      kind: 'skill',
      title: `Configure LangSmith tracing for ${inputs.projectName}`,
      skill: {
        name: 'langsmith-tracing',
        context: {
          projectName: inputs.projectName,
          apiKeyEnvVar: inputs.apiKeyEnvVar,
          samplingRate: inputs.samplingRate,
          enableDebug: inputs.enableDebug,
          instructions: [
            'Verify LangSmith API credentials are available',
            'Create or validate project configuration',
            'Set up tracing instrumentation in codebase',
            'Configure sampling rate and debug settings',
            'Verify traces are being captured correctly'
          ]
        }
      },
      io: {
        inputJsonPath: `tasks/${taskCtx.effectId}/input.json`,
        outputJsonPath: `tasks/${taskCtx.effectId}/result.json`
      }
    };
  }
});

Applicable Processes

llm-observability-monitoring
agent-evaluation-framework
react-agent-implementation
conversation-quality-testing
regression-testing-agent

External Dependencies

LangSmith account and API key
LangChain Python library
langsmith Python package

References

Related Skills

SK-OBS-002 langfuse-integration
SK-OBS-003 phoenix-arize-setup
SK-OBS-004 opentelemetry-llm

Related Agents

AG-OPS-004 observability-engineer
AG-SAF-004 agent-evaluator

Agent Skills: langsmith-tracing

Install this agent skill to your local

Skill Files