Can this skill help me find why my agent is slow?

Absolutely. It can analyze execution times for individual steps and tool calls, allowing you to identify bottlenecks and optimize agent latency.

Do I need any external tools to use this skill?

Yes, you must have the 'langsmith-fetch' CLI installed via pip and valid LANGSMITH_API_KEY and LANGSMITH_PROJECT environment variables configured.

How does this skill handle sensitive API keys?

The skill relies on your local environment variables. You should ensure your LangSmith API keys are stored securely in your shell configuration or .env files.

What is the primary purpose of the LangSmith Fetch skill?

The skill allows Claude to retrieve and interpret execution data from LangSmith, helping developers debug AI agents, analyze tool calls, and monitor performance directly from the terminal.

Can I export my debugging sessions for later review?

Yes, the skill includes workflows to export traces and threads into organized local directories for archival or team sharing.

LangSmith Fetch Agent Debugger

Name: LangSmith Fetch Agent Debugger
Author: hameed0342j

byhameed0342j

0•

분석 및 모니터링

Debugs LangChain and LangGraph agents by fetching and analyzing execution traces directly from LangSmith Studio within your terminal.

LangSmith Fetch is a specialized diagnostic skill designed for AI engineers working with LangChain and LangGraph frameworks. It enables Claude to act as a terminal-based observability assistant, retrieving real-time traces to investigate tool failures, analyze memory operations, and monitor agent performance. By providing structured deep-dives into execution flows and automated error pattern detection, it significantly reduces the time spent switching between the code editor and the LangSmith web interface, streamlining the debugging process for complex LLM applications.

주요 기능

01Real-time fetching and analysis of recent agent execution traces

02Automated error pattern detection across multiple session traces

03Performance monitoring including token usage and execution latency

040 GitHub stars

05Structured session exports for team collaboration and documentation

06Deep-dive trace investigation to identify specific tool call failures

사용 사례

01Troubleshooting agent logic when a tool call fails or returns unexpected results

02Auditing long-term memory (LTM) and vector store operations during agent runs

03Optimizing execution costs by identifying high-token usage patterns in traces

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add hameed0342j/svh langsmith-fetch

For use in Claude.ai and ChatGPT