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langsmith-fetching

Fetch LangSmith traces by project, date range, or segment ID.

Updated Feb 24, 2026
Search Tags:#debugging#llm#evaluation#data collection#traces#langsmith#ml analysis
One-click install
npx skills add https://github.com/Stream-claims/stream-engineering --skill langsmith-fetching
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: langsmith-fetching
Source: https://github.com/Stream-claims/stream-engineering/tree/main/skills/langsmith-fetching
Command: npx skills add https://github.com/Stream-claims/stream-engineering --skill langsmith-fetching

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill streamlines the process of retrieving LangSmith traces, enabling efficient ML analysis, debugging, and data collection for AI model development and evaluation.

Core Features & Use Cases

  • Fetch LangSmith Traces: Retrieve traces based on project names, date ranges, or specific segment IDs.
  • Analyze LLM Outputs: Examine LLM extraction outputs, decisions, and performance metrics.
  • Collect Training Data: Gather production LLM call data for model retraining and evaluation.
  • Use Case: When a user asks to fetch all traces from the 'extract' project for the last 7 days to analyze extraction performance, this skill can be activated.

Quick Start

Use the langsmith-fetching skill to fetch traces from the 'extract' project for the last 7 days.

Frequently Asked Questions about langsmith-fetching

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I fetch LangSmith traces for ML analysis and debugging?▼

You can fetch LangSmith traces for ML analysis by retrieving them based on project names, date ranges, or specific segment IDs. This streamlines LLM debugging and evaluation by providing direct access to production call data.

What is the best way to collect production LLM call data for model retraining?▼

Fetching LangSmith traces allows you to gather production LLM call data for model retraining and evaluation. You can retrieve specific extraction outputs and decisions by filtering traces via project names or date ranges.

Does fetching LangSmith traces work with both API and Redshift tables?▼

Yes, fetching LangSmith traces supports retrieval via both API and Redshift tables. You can choose between these options based on your specific data volume and recency requirements to optimize extraction performance.

How do I analyze LLM extraction outputs and decisions from specific projects?▼

You can analyze LLM extraction outputs and decisions by fetching traces from a specific project over a defined date range. This allows you to examine performance metrics and isolate extraction behaviors for debugging.

When should I use the API versus Redshift tables to retrieve LangSmith traces?▼

You should use the API to retrieve LangSmith traces for recent data needs, whereas Redshift tables are better suited for handling large data volumes during extensive ML analysis and historical data collection.

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