---
id: strands-agents-evals
version: "1.1.1"
license: Apache-2.0
license_treatment: permissive
maintenance: active
---

# strands-agents-evals — Evaluation framework for Strands

License: permissive · Maintenance: active · Downloads: 155.1K/mo

## What it is and what it does

Strands Evals SDK is a Python framework for systematically evaluating AI agents and language model applications. It provides multiple evaluation modes—output validation with custom rubrics, trajectory analysis of tool usage, trace-based assessment via OpenTelemetry, and automated test generation—allowing developers to measure agent correctness, safety, and behavior across complex interactions. The framework includes built-in LLM-as-a-judge evaluators, multimodal evaluation support, dynamic conversation simulators, failure detection with root-cause analysis, and chaos testing via fault injection. Experiments can be serialized to JSON, versioned, and executed via Python API or CLI.

The package depends on boto3, the OpenTelemetry stack (opentelemetry-api, opentelemetry-sdk, opentelemetry-instrumentation-threading), pydantic, rich, tenacity, typing-extensions, and two internal Strands packages (strands-agents and strands-agents-tools). It targets Python 3.10+ and is actively maintained, with a permissive Apache-2.0 license suitable for both commercial and open-source use.

Use it for:

- Validate LLM output quality and factual accuracy using custom rubrics and LLM judges.
- Analyze agent tool usage sequences and verify correct action ordering in multi-step tasks.
- Detect and diagnose failures in agent sessions with automated root-cause analysis.
- Generate comprehensive test suites from high-level tool or task descriptions.
- Simulate multi-turn conversations with realistic user behavior to stress-test agent resilience.
- Perform adversarial safety testing with built-in attack strategies.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

Strands Evals SDK provides a comprehensive evaluation framework for testing and measuring AI agents and LLM applications, supporting output validation, trajectory analysis, trace-based evaluation, and automated experiment generation.

Yes, if you are actively developing or evaluating AI agents and LLM applications. The framework is actively maintained, has low install friction, carries no security vulnerabilities, and offers a broad evaluation toolkit covering output, trajectory, trace, and adversarial testing. Install with caution if strands-agents and strands-agents-tools are not yet available in your environment.

## Install

pip install strands-agents-evals
uv add strands-agents-evals
poetry add strands-agents-evals

## Installing strands-agents-evals

Before you install: Low install friction with a pure Python wheel distribution. Active maintenance with a release 2 days old. Depends on well-established libraries (boto3, pydantic, rich, opentelemetry stack, tenacity) and two internal Strands packages.

License in practice: Licensed under Apache-2.0 (permissive), allowing free use, modification, and distribution with minimal restrictions—suitable for commercial and open-source projects.

Quickstart:

pip install strands-agents-evals

from strands_agents_evals import Case, Experiment
from strands_agents_evals.evaluators import OutputEvaluator

test_cases = [Case(name="test-1", input="What is 2+2?", expected_output="4")]
evaluators = [OutputEvaluator(rubric="Score 1.0 if correct.")]
experiment = Experiment(cases=test_cases, evaluators=evaluators)
report = experiment.run_evaluations(lambda case: "4")

Requires Python 3.10 or later; depends on strands-agents and strands-agents-tools packages being available.

Verify before relying:

- Whether strands-agents and strands-agents-tools are publicly available or proprietary internal packages.
- Specific LLM models supported by OutputEvaluator and other judge-based evaluators.
- Performance characteristics and scalability limits for large experiment suites.
- Whether the CLI subcommands are fully documented and stable.

## Package facts

- License: Apache-2.0 (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: active
- Downloads: 155.1K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

LLM evaluation framework, AI agent testing, LLM-as-a-judge evaluation, trajectory analysis for agents, experiment generation for AI, agent behavior assessment, OpenTelemetry trace evaluation, llm-evaluation, agent-testing, ai-quality-assurance

[View on SkillFed](https://skillfed.io/packages/strands-agents-evals) · [View on PyPI](https://pypi.org/project/strands-agents-evals/)