---
id: azure-ai-evaluation
version: "1.18.3"
license: MIT License
license_treatment: permissive
maintenance: active
---

# azure-ai-evaluation — Microsoft Azure Evaluation Library for Python

License: permissive · Maintenance: active · Downloads: 341.6K/mo

## What it is and what it does

Azure AI Evaluation is a Python SDK for measuring the quality and safety of generative AI application outputs. It provides a collection of built-in evaluators—including NLP-based metrics like BLEU and ROUGE, AI-assisted quality assessors like Groundedness and Relevance, and safety evaluators for violence, sexual content, and self-harm—alongside an API for running multiple evaluators together on datasets or live applications. You define column mappings to route your data to the right evaluator inputs, and the SDK returns scores and insights that help you understand your model's capabilities and limitations.

The package is designed for teams building on Azure who need systematic, repeatable evaluation of generative AI systems. It integrates with Azure AI Foundry for result tracking and supports both code-based and prompt-based custom evaluators, so you can extend it beyond the built-in metrics. Dependencies include Azure identity and storage libraries, OpenAI for some AI-assisted evaluators, and common data tools like pandas and httpx.

Use it for:

- Score model responses on quality dimensions (relevance, coherence, fluency) before deploying to production
- Run safety evaluations (violence, hate, sexual content) on generated text to catch harmful outputs
- Compare multiple model variants using consistent metrics across a fixed test dataset
- Build custom evaluators for domain-specific quality criteria (e.g., medical accuracy, legal compliance)
- Track evaluation results over time in Azure AI Foundry to monitor model drift or improvement

## Worth the install?

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

Evaluates generative AI application outputs using built-in and custom evaluators that measure quality, safety, and performance through mathematical metrics, NLP scores, and AI-assisted assessment.

Yes, if you are building generative AI applications on Azure and need systematic evaluation. The SDK is actively maintained, has no known vulnerabilities, and provides both out-of-the-box evaluators and extensibility for custom metrics. Requires Python 3.9+, Azure credentials for AI-assisted evaluators, and familiarity with the Azure ecosystem; not a lightweight choice for simple scoring tasks outside Azure.

## Install

pip install azure-ai-evaluation
uv add azure-ai-evaluation
poetry add azure-ai-evaluation

## Installing azure-ai-evaluation

Before you install: Low install friction with a pure-Python wheel. Active maintenance with a recent release (16 days old) and substantial repository engagement (5588 stars). Twelve runtime dependencies including Azure services, OpenAI, and common data libraries introduce moderate complexity but are standard for Azure SDK integrations.

License in practice: MIT License permits unrestricted use, modification, and distribution with minimal restrictions, making it suitable for both open-source and commercial projects.

Quickstart:

pip install azure-ai-evaluation

from azure.ai.evaluation import BleuScoreEvaluator

evaluator = BleuScoreEvaluator()
result = evaluator(
    response="Tokyo is the capital of Japan.",
    ground_truth="The capital of Japan is Tokyo."
)

Python 3.9 or later required. AI-assisted evaluators require Azure AI Foundry Project or Azure OpenAI credentials (subscription_id, resource_group_name, project_name, or endpoint/api_key/deployment).

Verify before relying:

- Performance characteristics when evaluating large datasets or running many evaluators in parallel
- Specific Azure service quotas or rate limits that may affect evaluation throughput
- Whether custom evaluators can be serialized and reused across sessions

## Package facts

- License: MIT License (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: active
- Downloads: 341.6K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

evaluate generative ai outputs, ai model quality metrics, llm evaluation framework, generative ai assessment, ai safety evaluation, prompt response scoring, ai application benchmarking, generative-ai, evaluation-framework, azure-sdk

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