mlflow-tracing

MLflow Tracing SDK is an open-source, lightweight Python package that only includes the minimum set of dependencies and functionality to instrument your code/models/agents with MLflow Tracing.

mlflow-tracing Permissive license Copyright 2018 Databricks, Inc. All rights reserved. Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1.… (full text in the JSON record) Active ★ 27,502 v3.15.1 released 2026-08-03

Install

mlflow-tracing on PyPI

pip

pip install mlflow-tracing

uv

uv add mlflow-tracing

poetry

poetry add mlflow-tracing

Package facts

License	Copyright 2018 Databricks, Inc. All rights reserved. Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1.… (full text in the JSON record) (permissive)
Python support	supports the current Python release (>=3.10)
Install friction	low — pure-Python wheel
Runtime dependencies	8 — cachetools, databricks-sdk, opentelemetry-api, opentelemetry-proto, opentelemetry-sdk, packaging, protobuf, pydantic
Maintenance	actively maintained — 10 days since the last release
Last repo commit	2026-08-13
First released	2025-04-30
Popularity	one of the top 1,000 most-downloaded packages on PyPI (30-day window, as of 2026-08-13)
Known vulnerabilities	none known (OSV.dev, checked 2026-08-13)

Evidence: mlflow_tracing-3.15.1-py3-none-any.whl

Keywords: mlflow, ai, databricks

Development Status :: 5 - Production/StableIntended Audience :: DevelopersIntended Audience :: End Users/DesktopIntended Audience :: Information TechnologyIntended Audience :: Science/ResearchLicense :: OSI Approved :: Apache Software LicenseOperating System :: OS IndependentProgramming Language :: Python :: 3.10Topic :: Scientific/Engineering :: Artificial IntelligenceTopic :: Software Development :: Libraries :: Python Modules

About mlflow-tracing

from the package's own PyPI description — quoted content, verbatim

MLflow Tracing: An Open-Source SDK for Observability and Monitoring GenAI Applications🔍

Latest Docs (image) Apache 2 License (image) Slack (image) Twitter (image)

MLflow Tracing (mlflow-tracing) is an open-source, lightweight Python package that only includes the minimum set of dependencies and functionality to instrument your code/models/agents with MLflow Tracing Feature. It is designed to be a perfect fit for production environments where you want:

⚡️ Faster Deployment: The package size and dependencies are significantly smaller than the full MLflow package, allowing...

Read as markdown · JSON record · Source repository · Docs

AI interpretation — verify before relying

AI-generated interpretation of the package facts above; every digit, version, license, or vulnerability id it cites is grounded in the facts already shown on this page

MLflow Tracing is a lightweight Python SDK for instrumenting AI applications with observability and tracing, supporting automatic tracing for popular AI libraries with minimal dependencies.

Low install friction with a pure-Python wheel and eight runtime dependencies. Actively maintained with a release 10 days old from a repository with strong upstream support.

Licensed under Apache 2.0, a permissive license allowing commercial use, modification, and distribution with minimal restrictions—suitable for both proprietary and open-source projects.

Usage

pip install mlflow-tracing

import mlflow

mlflow.set_tracking_uri("databricks")
mlflow.set_experiment("/Path/To/Experiment")

Requires Python >=3.10 and a configured MLflow tracking backend (Databricks, SageMaker, self-hosted, or Nebius).

Verdict: A production-ready, actively maintained tracing SDK designed for GenAI observability with minimal overhead. Apache 2.0 licensing, low dependency footprint, and zero known vulnerabilities make it suitable for deployment in containerized and serverless environments. Requires an external MLflow backend to function.

Needs verification

Whether the eight runtime dependencies (cachetools, databricks-sdk, opentelemetry-api, opentelemetry-proto, opentelemetry-sdk, packaging, protobuf, pydantic) introduce any transitive security concerns not captured in the OSV query.
Performance overhead of automatic tracing for high-throughput AI applications compared to manual instrumentation.
Compatibility guarantees when used alongside the full MLflow package despite the documentation warning against co-installation.

ai application tracinggenai observability monitoringopentelemetry instrumentationllm application logginglightweight tracing sdkmlflow trace integrationproduction ai monitoring