azure-ai-evaluation
Microsoft Azure Evaluation Library for Python
Decision gist · record as of 2026-08-14
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.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- 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).
- Low install friction with a pure-Python wheel.
License · maintenance · safety
MIT License (permissive) — MIT License permits unrestricted use, modification, and distribution with minimal restrictions, making it suitable for both open-source and commercial projects.
last release 2026-07-29 (16 days) · last repo commit 2026-08-14 · 5,588 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 341,597 downloads/mo, #7,401 on PyPI
Alternatives
Verify before relying
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."
)- 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
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 on this page. Verify before relying on it.
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
azure-ai-evaluation on PyPI
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.
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).
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."
)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 the current Python release >=3.9 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 12 packagespyjwtazure-identityazure-corenltkazure-storage-blobhttpxpandasopenairuamel.yamlmsrestJinja2aiohttp |
| Maintenance | Actively maintained 16 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 341,597 / month, #7,401 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableLicense :: OSI Approved :: MIT LicenseOperating System :: OS IndependentProgramming Language :: PythonProgramming Language :: Python :: 3Programming Language :: Python :: 3 :: OnlyProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.9 |
Evidence: azure_ai_evaluation-1.18.3-py3-none-any.whl
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