القائمة
Trace, evaluate, and deploy AI agents and LLM applications with LangSmith. Use when adding observability, running evaluations, engineering prompts, or deploying agents to production.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
Use this skill when migrating inline code samples from LangChain docs (MDX files) into external, testable code files that are extracted by this repo’s snippet scripts and used as Mintlify snippets. Applies when extracting code blocks from documentation, creating runnable code samples, using snippet delineators, or wiring snippet output into MDX includes.
Build batteries-included agents with planning, context management, subagent delegation, and sandboxed execution. Use for complex, multi-step tasks that need built-in capabilities.
Build agents with a prebuilt architecture and integrations for any model or tool. Use when creating tool-calling agents, switching model providers, or adding structured output.
Build stateful, durable agent workflows with LangGraph. Use when you need custom graph-based control flow, human-in-the-loop, persistence, or multi-agent orchestration.
Common pandas and matplotlib patterns for data analysis and visualization
Use when the user wants the current date and time written to a file via the bundled script inside the sandbox.
| name | langsmith |
| description | Trace, evaluate, and deploy AI agents and LLM applications with LangSmith. Use when adding observability, running evaluations, engineering prompts, or deploying agents to production. |
| license | MIT |
| compatibility | Framework-agnostic. Works with LangChain, LangGraph, Deep Agents, OpenAI Agents SDK, CrewAI, Pydantic AI, and more. |
| metadata | {"author":"langchain-ai","version":"1.0"} |
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.
Use LangSmith when you need to:
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
# Python
pip install langsmith
# JavaScript/TypeScript
npm install langsmith
from langsmith import traceable
@traceable
def my_function(query: str) -> str:
# Your LLM logic here—all calls inside are traced automatically
return "result"
| Capability | Description |
|---|---|
| Observability | Trace every step of your LLM app with automatic or manual instrumentation |
| Evaluation | Run evaluations with code, LLM-as-judge, or composite evaluators |
| Prompt engineering | Create, version, and test prompts in a visual playground |
| Agent deployment | Deploy LangGraph agents with streaming, human-in-the-loop, and durable execution |
| Monitoring | Dashboards, alerts, and cost tracking for production workloads |
For SDK class and method details, use the LangChain API Reference site:
https://reference.langchain.com/python/langsmithhttps://reference.langchain.com/mcp