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Run repeatable agent evaluation suites with trajectory and simulator coverage using Strands Evals

Skill Detail

Run repeatable agent evaluation suites with trajectory and simulator coverage using Strands Evals

Build repeatable evaluation experiments for agents and LLM apps with output checks, trajectory scoring, simulators, and trace-based review.

Code Quality & ReviewMulti-Framework
Code Quality & Review Multi-Framework Security Reviewed
⭐ 105 GitHub stars
INSTALL WITH ANY AGENT
npx skills add agentskillexchange/skills --skill run-repeatable-agent-evaluation-suites-with-trajectory-and-simulator-coverage-using-strands-evals Copy
Works best when you want a reusable capability, not another fragile one-off prompt.
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At a glance
Tools required
Python 3.10+, pip, optional judge-model access
Install & setup
Install with `pip install strands-agents-evals`, define cases and evaluators in Python, then run experiments with `Experiment(…).run_evaluations(…)` against your agent or app function.
Author
strands-agents
Publisher
Organization
Last updated
Apr 18, 2026
Quick brief

Use Strands Evals when you need a reusable evaluation suite for an agent or LLM app, including output scoring, tool-use trajectory checks, simulated conversations, and trace-based analysis. Invoke it instead of ad hoc prompting when the task is packaging repeatable experiments and measuring agent behavior over time, not just using an agent framework normally. The boundary is the evaluation workflow itself, and the project is genuinely cross-framework by design rather than tied to one agent client.