arize
A helper library to interact with Arize AI APIs
Decision gist · record as of 2026-08-14
Yes, if you are building or operating LLM applications on the Arize platform. The package is actively maintained, has no security vulnerabilities, uses permissive licensing, and integrates cleanly with OpenTelemetry. Install friction is low. However, it is tightly coupled to Arize's hosted service—you will need API credentials and an active account to use it meaningfully. Not suitable if you need a standalone, self-hosted observability solution.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later; API credentials (api_key and space_key) needed to connect to Arize services.
- Low install friction with a pure Python wheel and 17 well-established runtime dependencies.
- The package is actively maintained with a release 4 days old and supports modern Python versions (3.10–3.14).
License · maintenance · safety
Apache-2.0 (permissive) — Licensed under Apache-2.0 (permissive), allowing free use, modification, and distribution in both open-source and commercial projects with minimal restrictions.
last release 2026-08-10 (4 days) · last repo commit 2026-08-10 · 57 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 951,220 downloads/mo, #4,656 on PyPI
Alternatives
Verify before relying
pip install arize
from arize.client import Client
client = Client(api_key='your-api-key', space_key='your-space-key')
# Log spans, datasets, experiments, or evaluations via client methods- Whether the 17 runtime dependencies (numpy, pandas, pyarrow, protobuf, opentelemetry-*, pydantic, etc.) are all required for basic usage or if some are optional.
- Performance characteristics when logging large volumes of spans or ML data.
- Whether the package supports offline mode or requires live connectivity to Arize services.
What it is and what it does
Arize is the official Python SDK for the Arize AI engineering platform, a service that helps teams develop, evaluate, and observe LLM applications and AI agents in production. The package provides a client interface to log traces (using OpenTelemetry instrumentation), stream ML inference data, manage versioned datasets, run experiments, create and manage prompts and evaluators, and monitor application performance. It integrates with OpenTelemetry for distributed tracing and supports both real-time streaming and batch operations.
The library is built on top of 17 runtime dependencies including numpy, pandas, pyarrow, pydantic, and the OpenTelemetry SDK and exporters, making it a comprehensive observability client. It's actively maintained (last release 4 days old), supports Python 3.10 through 3.14, and carries no known security vulnerabilities. The package is in Alpha status and positioned for teams building production LLM systems who need centralized logging, evaluation, and monitoring.
Use it for
- Instrument LLM applications with OpenTelemetry to capture traces and spans for debugging and performance analysis.
- Log classification, object detection, and other ML model predictions with associated metadata for monitoring.
- Create and manage versioned datasets of examples for experimentation, evaluation, and fine-tuning workflows.
- Run experiments to evaluate changes to prompts, LLMs, and retrieval strategies against benchmark datasets.
- Manage prompts, evaluators, and tasks as versioned artifacts with labels and lifecycle tracking.
- Monitor and observe AI application behavior in production using centralized tracing and evaluation dashboards.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are building or operating LLM applications on the Arize platform.
The package is actively maintained, has no security vulnerabilities, uses permissive licensing, and integrates cleanly with OpenTelemetry. Install friction is low. However, it is tightly coupled to Arize's hosted service—you will need API credentials and an active account to use it meaningfully. Not suitable if you need a standalone, self-hosted observability solution.
Install
arize on PyPI
Before you install
Low install friction with a pure Python wheel and 17 well-established runtime dependencies. The package is actively maintained with a release 4 days old and supports modern Python versions (3.10–3.14). No known vulnerabilities.
Requires Python 3.10 or later; API credentials (api_key and space_key) needed to connect to Arize services.
License in practice
Licensed under Apache-2.0 (permissive), allowing free use, modification, and distribution in both open-source and commercial projects with minimal restrictions.
Quickstart
pip install arize
from arize.client import Client
client = Client(api_key='your-api-key', space_key='your-space-key')
# Log spans, datasets, experiments, or evaluations via client methodsVerify before relying
- Whether the 17 runtime dependencies (numpy, pandas, pyarrow, protobuf, opentelemetry-*, pydantic, etc.) are all required for basic usage or if some are optional.
- Performance characteristics when logging large volumes of spans or ML data.
- Whether the package supports offline mode or requires live connectivity to Arize services.
Package facts
| License | Apache-2.0 permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 17 packagesnumpyopeninference-semantic-conventionsopentelemetry-exporter-otlp-proto-commonopentelemetry-exporter-otlp-proto-grpcopentelemetry-sdkopentelemetry-semantic-conventionspandasprotobufpyarrowpydanticpython-dateutilrequests-futuresrequeststqdmtyping-extensionsurllib3wrapt |
| Maintenance | Actively maintained 4 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 951,220 / month, #4,656 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 3 - AlphaIntended Audience :: DevelopersProgramming Language :: PythonProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Topic :: Scientific/Engineering :: Artificial IntelligenceTopic :: Software Development :: Libraries :: Python ModulesTopic :: System :: LoggingTopic :: System :: Monitoring |
Evidence: arize-8.46.0-py3-none-any.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “AI evaluation and experimentation”
- arizeArize is a Python client library for interacting with the Arize AI…
- arize-phoenixPhoenix is an open-source AI observability platform that traces LLM…
- openfeature-provider-flagsmithConnects the OpenFeature SDK to Flagsmith for server-side feature…
Give your agent the search over MCP, or paste the wish link into any chat.
More Python Modules packages
Converts domain names between Unicode and ASCII-compatible encoding (Punycode) according to IDNA 2008 and Unicode Technical Standard 46, with security validation and broader script coverage than the standard library.
Install it if you work with internationalized domain names, need to validate domains, or use HTTP clients that depend on it transitively.
Setuptools is a Python build backend and package management tool that handles building, distributing, and installing Python packages, including support for C/C++ extension modules.
PyYAML parses and emits YAML 1.1 data format, enabling serialization and deserialization of configuration files and Python objects to and from human-readable YAML text.
Pydantic validates Python data structures against type hints, coercing and checking input at runtime to ensure it matches a declared schema.
Provides reusable metadata objects for use with PEP-593 `typing.Annotated` to express common constraints like bounds, collection sizes, and predicates on types.
Install it if you use or build libraries that need to express type constraints in a standardized, inspectable way—or if you want to annotate your own types with…
Provides runtime tools to inspect and introspect Python type annotations, enabling programmatic examination of type hints at execution time.
See also arize-otel · openinference-instrumentation-haystack · openinference-instrumentation-litellm · arize-phoenix-client · arize-phoenix · openinference-instrumentation-anthropic · openinference-instrumentation-pydantic-ai · anima-python · arize-phoenix-evals · langfuse