matrice-common

What it is and what it does

matrice_common is a utility library for Matrice.ai backend services, bundling authentication, API communication, and streaming abstractions into a single package. It provides token-based authentication with automatic refresh, an RPC client supporting both synchronous and asynchronous HTTP methods, and a unified streaming interface for Kafka and Redis. The library also includes error logging with deduplication, caching decorators, and session management for project lifecycle operations.

The package is designed as a shared foundation for Matrice.ai microservices, eliminating the need to reimplement common patterns across multiple services. It has no external runtime dependencies, making installation straightforward. Type hints are included throughout, and the library supports Python 3.8 through 3.12 across Linux, macOS, and Windows.

Use it for:

• Build Matrice.ai backend services that need standardized authentication, RPC communication, and error tracking without reimplementing these patterns.
• Implement streaming data pipelines using a single API that abstracts over both Kafka and Redis backends.
• Create async-first applications that make concurrent API requests to Matrice.ai endpoints with automatic token refresh.
• Set up centralized error logging and deduplication across multiple services to reduce noise in monitoring systems.
• Manage project lifecycle operations (creation, configuration) within Matrice.ai classification workflows.

Worth the install?

AI-flagged interpretation of the facts on this page — verify before relying

matrice_common provides authentication, RPC client, streaming, and utility components for Matrice.ai services, including token management, API communication, Kafka/Redis streaming, and error logging.

Yes. The package has low install friction, no external dependencies, active maintenance, and a permissive MIT license. It is positioned as a shared utility for Matrice.ai services and carries no known vulnerabilities. Install if you are building services within the Matrice.ai ecosystem; otherwise, it is not applicable.

Install

matrice-common on PyPI

pip install matrice-common

uv add matrice-common

poetry add matrice-common

Installing matrice-common

Before you install

Low install friction with no runtime dependencies. Active maintenance status and recent release history (latest 2026-07-29) suggest ongoing support.

License in practice

MIT license permits commercial and private use with minimal restrictions; you may use, modify, and distribute this package freely provided you include the license notice.

Quickstart

pip install matrice-common

from matrice_common.rpc import RPC
from matrice_common.session import create_session

rpc = RPC(access_key="key", secret_key="secret")
response = rpc.get("/v1/endpoint")

session = create_session(access_key="key", secret_key="secret")
project = session.create_classification_project(name="My Project")

Requires Python 3.8 or later; API access credentials (access_key and secret_key) must be provided.

Verify before relying

• Whether Sentry integration for error logging is optional or required at runtime.
• Specific performance characteristics of the streaming abstraction layer compared to direct Kafka/Redis clients.
• Whether the package is production-ready despite Beta development status classification.

Package facts

Evidence: matrice_common-1.0.0-py3-none-any.whl

Keywords: matrice, common, utilities, mypyc, compiled

Tags

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