What problem does it solve? Debugging LangChain and LangGraph agents requires digging through LangSmith Studio manually to find traces, errors, and tool call details. This Skill brings trace fetching and analysis directly into your terminal workflow so you can diagnose agent failures, memory issues, and performance bottlenecks without leaving your development environment. ## Core Features & Use Cases - Recent Trace Debugging: Fetch traces from the last N minutes and get a structured report of errors, tool calls, execution times, and token usage. - Deep Trace Analysis: Investigate a specific trace ID to see the full execution flow, identify the exact failure point, and receive root cause analysis with suggested fixes. - Error Detection & Session Export: Scan recent traces for error patterns across agents and tools, or export full debug sessions with traces and threads for sharing and archiving. - Use Case: Your agent suddenly stops responding. Run a quick trace fetch for the last 5 minutes, discover a Neo4j connection timeout in the search_nodes tool, and get a recommendation to add retry logic. ## Quick Start Ask the AI to debug your agent by fetching the last 5 minutes of LangSmith traces and summarizing any errors, tool calls, and token usage.