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langsmith

Skill445 starsChanged 10 days agoMITView on GitHub

Trace, evaluate, and deploy AI agents and LLM applications with LangSmith. Use when adding observability, running evaluations, engineering prompts, or deploying agents to production.

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  1. Distinctive:

    Use LangSmith when you need to:

    line 17

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

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SKILL.md

LangSmith

LangSmith is a framework-agnostic platform for building, debugging, and deploying AI agents and LLM applications. Trace requests, evaluate outputs, test prompts, and manage deployments all in one place at smith.langchain.com.

When to use

Use LangSmith when you need to:

  • Trace and debug LLM calls, agent steps, retrieval, and tool use
  • Evaluate LLM outputs with automated or human-in-the-loop scoring
  • Engineer prompts with a visual playground and version control
  • Deploy agents to production with the LangGraph-based agent server
  • Monitor production systems with dashboards, alerts, and cost tracking

When NOT to use

  • To build agent logic or LLM pipelines, use LangChain, LangGraph, or Deep Agents instead
  • LangSmith is the platform layer that complements these frameworks

Quick setup

Set two environment variables to enable tracing from any supported framework:

export LANGSMITH_TRACING=true
export LANGSMITH_API_KEY="your-api-key"  # from smith.langchain.com/settings

Install the SDK

# Python
pip install langsmith

# JavaScript/TypeScript
npm install langsmith

Verify tracing

from langsmith import traceable

@traceable
def my_function(query: str) -> str:
    # Your LLM logic here—all calls inside are traced automatically
    return "result"

Core capabilities

CapabilityDescription
ObservabilityTrace every step of your LLM app with automatic or manual instrumentation
EvaluationRun evaluations with code, LLM-as-judge, or composite evaluators
Prompt engineeringCreate, version, and test prompts in a visual playground
Agent deploymentDeploy LangGraph agents with streaming, human-in-the-loop, and durable execution
MonitoringDashboards, alerts, and cost tracking for production workloads

Key documentation

API reference

For SDK class and method details, use the LangChain API Reference site:

  • Browse: https://reference.langchain.com/python/langsmith
  • MCP server: https://reference.langchain.com/mcp

Related skills

  • langchain—Build agents with prebuilt architecture and model integrations
  • langgraph—Orchestrate stateful, durable agent workflows
  • deep-agents—Batteries-included agent harness with planning and subagents

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