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strands-agents
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strands-evals

Evaluate AI agents and LLM applications with multiple evaluation types.

173|50|Updated Jul 31, 2025
Search Tags:#llm evaluation#ai evaluation#evaluation framework#agent evaluation#experiment generation
One-click install
npx skills add https://github.com/strands-agents/evals --skill strands-evals
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: strands-evals
Source: https://github.com/strands-agents/evals/tree/main
Command: npx skills add https://github.com/strands-agents/evals --skill strands-evals

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires strands-agents-evals, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill provides a comprehensive framework for evaluating AI agents and LLM applications, covering various aspects such as output validation, trajectory analysis, tool usage assessment, interaction evaluation, and automated experiment generation.

Core Features & Use Cases

  • Multiple Evaluation Types: Output evaluation, trajectory analysis, tool usage assessment, and interaction evaluation
  • Multimodal Evaluation: MLLM-as-a-Judge evaluators for image-to-text tasks with built-in rubrics
  • Dynamic Simulators: Multi-turn conversation simulation with realistic user behavior, goal-oriented interactions, and LLM-powered tool simulation with shared state
  • LLM-as-a-Judge: Built-in evaluators using language models for sophisticated assessment with structured scoring
  • Trace-based Evaluation: Analyze agent behavior through OpenTelemetry execution traces
  • Automated Experiment Generation: Generate comprehensive test suites from context descriptions
  • Custom Evaluators: Extensible framework for domain-specific evaluation logic
  • Experiment Management: Save, load, and version your evaluation experiments with JSON serialization
  • Built-in Scoring Tools: Helper functions for exact, in-order, and any-order trajectory matching
  • Failure Detection & Root Cause Analysis: Automatically detect failures in agent sessions and diagnose root causes with actionable fix recommendations
  • Chaos Testing: Deterministic fault injection via Strands plugin hooks — simulate tool timeouts, network errors, and response corruption to evaluate agent resilience
  • Red Team Evaluation: Adversarial safety testing with built-in attack strategies (Crescendo, GOAT, PAIR, BadLikertJudge, SequentialBreak); see src/strands_evals/experimental/redteam/README.md
  • Use Case: Imagine you want to evaluate an AI agent's performance in a multi-turn conversation. Use this Skill to simulate realistic user interactions and assess the agent's response quality.

Quick Start

Use the strands-evals skill to run an experiment with the provided cases and evaluators.

Frequently Asked Questions about strands-evals

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

FAQPage Schema
How do I evaluate AI agent performance in multi-turn conversations?▼

To evaluate AI agent performance in multi-turn conversations, you can use dynamic simulators that generate realistic user behavior and goal-oriented interactions to assess response quality.

What is LLM-as-a-Judge evaluation and how does it work for AI applications?▼

LLM-as-a-Judge evaluation uses language models for sophisticated assessment of AI outputs with structured scoring, providing automated quality metrics without requiring manual human review.

Can I automatically generate test suites for LLM evaluation from context descriptions?▼

Yes, automated experiment generation creates comprehensive test suites directly from context descriptions, enabling rapid configuration of output validation and trajectory analysis tests.

Do I need the strands-agents-evals dependency to run agent evaluation frameworks?▼

Yes, the strands-agents-evals package is required as a dependency for the evaluation framework to function properly, providing the core evaluators and simulation tools.

How do I diagnose root causes of failures in LLM agent sessions?▼

To diagnose root causes of failures in LLM agent sessions, the framework automatically detects failures and provides actionable fix recommendations through trace-based evaluation using OpenTelemetry execution traces.

What is chaos testing for AI agents and how does it evaluate resilience?▼

Chaos testing for AI agents uses deterministic fault injection via plugin hooks to simulate tool timeouts, network errors, and response corruption, evaluating agent resilience under adverse conditions.

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