Inspects and manages LangSmith traces, runs, and datasets directly within Claude's terminal environment.
This skill provides a high-performance, context-efficient interface for LangSmith, allowing Claude to act as a powerful AI observability assistant. It enables developers to debug complex AI chains, analyze token costs, and manage evaluation datasets without the overhead of heavy MCP servers. By prioritizing a cache-first workflow and structured JSON outputs, it allows Claude to rapidly search through thousands of runs, filter by metadata, and provide deep insights into application performance and cost efficiency.
Características Principales
01Context-efficient trace and run inspection with mandatory JSON parsing
02Comprehensive dataset and prompt management for evaluation workflows
038 GitHub stars
04Offline-first workflow using local run caching for zero-latency analysis
05Advanced token cost analysis with breakdowns by model and provider
06Powerful regex search and FQL filtering across inputs and outputs
Casos de Uso
01Monitoring and optimizing production costs across multiple LLM projects
02Creating and managing gold-standard evaluation datasets from successful traces
03Debugging failed LLM runs by inspecting intermediate outputs and error logs
