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Comprehensive guide for integrating LangSmith to trace, evaluate, and monitor LLM applications and AI agents in production.
This skill provides a detailed implementation guide for using LangSmith to achieve deep observability into LLM applications. It covers the full lifecycle of AI development, from initial debugging and tracing to systematic evaluation and production monitoring.
Activate this skill when you need to diagnose why an agent is failing, compare different prompt versions against a gold dataset, monitor token usage/costs in real-time, or build a regression testing pipeline for AI features.
@traceable decorator, creating datasets, and running systematic evaluations.Compatible with any Python-based agent environment (Claude Code, Cursor, custom LangChain/LlamaIndex agents) that has access to a Python runtime.
This skill has not been reviewed by our automated audit pipeline yet.
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