Can it help me find specific tool call errors?

Yes, the skill can analyze JSON trace data to identify which tool failed, what the error message was, and what the agent was trying to accomplish at that moment.

Do I need a LangSmith account to use this?

Yes, you must have a LangSmith API key and an active project set up in LangSmith Studio for the skill to fetch execution data.

Does this skill support LangGraph workflows?

Absolutely. It is specifically designed to handle the complex execution flows and trace structures of both standard LangChain agents and LangGraph state machines.

How do I install the required components?

You need to install the langsmith-fetch CLI via pip and set your LANGSMITH_API_KEY and LANGSMITH_PROJECT environment variables.

What is the main benefit of the LangSmith Fetcher skill?

It allows you to debug LangChain agents directly within your Claude Code terminal by pulling real-time execution data and traces from LangSmith without switching to a browser.

LangSmith Trace Fetcher

Name: LangSmith Trace Fetcher
Author: QuyDatSadBoy

byQuyDatSadBoy

Analytics & Monitoring

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

About

This skill empowers developers to troubleshoot autonomous agents built with LangChain or LangGraph by retrieving real-time execution data. It automates the process of identifying failed tool calls, analyzing memory operations, and pinpointing performance bottlenecks without leaving the terminal. By integrating with the langsmith-fetch CLI, it provides structured insights into trace logs, token usage, and error patterns, making it an essential tool for refining agentic workflows and improving LLM observability.

Key Features

Automated error pattern recognition and failure frequency reporting
0 GitHub stars
Detailed metrics for execution latency and token consumption analysis
Deep-dive analysis of specific trace IDs for root cause detection
Fetch and filter recent traces with customizable timeframes and limits
Local export of debug sessions for team collaboration and documentation

Use Cases

Monitoring agent performance and cost by analyzing token usage patterns
Troubleshooting failed tool calls or logic loops in complex LangGraph agents
Investigating memory storage and retrieval issues in long-term memory systems

About

Key Features

Automated error pattern recognition and failure frequency reporting
0 GitHub stars
Detailed metrics for execution latency and token consumption analysis
Deep-dive analysis of specific trace IDs for root cause detection
Fetch and filter recent traces with customizable timeframes and limits
Local export of debug sessions for team collaboration and documentation

Use Cases

Monitoring agent performance and cost by analyzing token usage patterns
Troubleshooting failed tool calls or logic loops in complex LangGraph agents
Investigating memory storage and retrieval issues in long-term memory systems