Evaluate agent performance and extraction accuracy in LangGraph pipelines.
npx skills add https://github.com/bmsull560/Fabric_4L --skill evals-bmsull560
Please help me install this Agent Skill. Skill: evals Source: https://github.com/bmsull560/Fabric_4L/tree/main/.windsurf/skills/evals Command: npx skills add https://github.com/bmsull560/Fabric_4L --skill evals-bmsull560
Evaluation frameworks for agent performance, output quality, and extraction accuracy across AI pipelines to ensure reliability and measurable improvements.
Run the evaluation suite on your LLM pipeline to start validating metrics and regression baselines.
High-intent search queries and answers about installing and using this skill.
To evaluate LLM extraction accuracy, you can assess predicted extractions against ground truth using precision, recall, and F1 metrics while accounting for semantic similarity. This ensures your AI pipeline maintains reliable output quality.
Regression testing for LLM pipelines provides repeatable tests that guard against performance regressions. You need it when modifying prompts or models to ensure changes do not degrade output quality or agent performance.
Prompt A/B testing facilitates systematic comparison of prompt variants to drive improvements. You can run the evaluation suite on your LLM pipeline to validate metrics and compare different prompt configurations directly.
Yes, you can apply this evaluation framework to benchmark extraction and workflow tasks in LangGraph-based agent systems. It measures agent trajectory quality, tool usage, and costs across end-to-end agent runs.
Agent performance evaluation enforces metrics collection and baseline tracking with integrations to Prometheus and test harnesses. This allows you to monitor workflow quality and operational costs across end-to-end agent runs.
The best way to benchmark AI agent workflow quality is through agent trajectory evaluation, which measures tool usage and costs across end-to-end runs. This establishes repeatable baselines for reliable performance tracking.
Alex1980AlexQuantify search and RAG output quality using standardized retrieval metrics.
nirholasCollect, store, export, and query system metrics via a TypeScript API.
eco2-teamTrack LLM calls, token usage, and costs in LangChain/LangGraph applications.
Himanshu040604Evaluate LLM applications using automated metrics, human feedback, and benchmarking.
