matrice-common
Common server utilities for Matrice.ai services
Decision gist · record as of 2026-08-14
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.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.8 or later; API access credentials (access_key and secret_key) must be provided.
- Low install friction with no runtime dependencies.
- Active maintenance status and recent release history (latest 2026-07-29) suggest ongoing support.
License · maintenance · safety
MIT (permissive) — 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.
last release 2026-07-29 (16 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 2,133,057 downloads/mo, #3,266 on PyPI
Alternatives
Verify before relying
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")- 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.
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 on it.
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
Before you install
Low install friction with no runtime dependencies. Active maintenance status and recent release history (latest 2026-07-29) suggest ongoing support.
Requires Python 3.8 or later; API access credentials (access_key and secret_key) must be provided.
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")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
| License | MIT permissive |
| Python support | Supports the current Python release >=3.8 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | None |
| Maintenance | Actively maintained 16 days since the last release |
| First released | |
| Downloads | 2,133,057 / month, #3,266 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 4 - BetaIntended Audience :: DevelopersOperating System :: MacOSOperating System :: Microsoft :: WindowsOperating System :: OS IndependentOperating System :: POSIX :: LinuxProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.8Programming Language :: Python :: 3.9Topic :: Software Development :: Libraries :: Python ModulesTyping :: Typed |
Evidence: matrice_common-1.0.0-py3-none-any.whl
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