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.
Decision gist · record as of 2026-08-14
Yes, if you need distributed tracing for GenAI applications and want a lightweight alternative to the full MLflow package. The low install friction, active maintenance, Apache 2.0 license, and zero known vulnerabilities make it safe to deploy. Install only if you have access to a remote MLflow backend (Databricks, SageMaker, Nebius, or self-hosted); local-only tracing is not supported. Do not co-install with the full MLflow package.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later.
- Requires a remote MLflow server backend (Databricks, SageMaker, Nebius, or self-hosted) to log traces; local-only use is not supported.
- Active maintenance with recent release (11 days old).
License · maintenance · safety
permissive license (permissive) — Apache 2.0 permissive license allows commercial and derivative use with minimal restrictions. Attribution and license text preservation required in distributions.
last release 2026-08-03 (11 days) · last repo commit 2026-08-14 · 27,503 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 23,168,516 downloads/mo, #954 on PyPI
Alternatives
Verify before relying
pip install mlflow-tracing
import mlflow
mlflow.set_tracking_uri("databricks")
mlflow.set_experiment("/Path/To/Experiment")
# Enable auto-tracing for supported AI libraries
mlflow.openai.autolog()- Which AI libraries support automatic tracing and whether additional setup is required beyond autolog().
- Performance overhead of tracing instrumentation in high-throughput production scenarios.
- Compatibility guarantees with specific versions of databricks-sdk and opentelemetry packages.
What it is and what it does
MLflow Tracing is a lightweight Python SDK designed to instrument GenAI applications with distributed tracing for observability. It captures execution flows, spans, and metadata from AI library calls and sends them to a remote MLflow backend for centralized monitoring and analysis. The package is intentionally minimal—it includes only the dependencies needed for tracing (cachetools, databricks-sdk, opentelemetry-api, opentelemetry-proto, opentelemetry-sdk, packaging, protobuf, pydantic) and omits MLflow's other features like the tracking server UI, model registry, and evaluation tools.
It supports both automatic tracing via decorators and manual instrumentation through APIs like @trace, mlflow.set_trace_tag, and mlflow.search_traces. The package is designed for production environments where smaller deployment footprint, simpler dependency management, and reduced security surface are priorities. Traces are logged to a remote backend (Databricks offers a free managed option), not stored locally.
Use it for
- Monitor AI library calls and agent execution flows in production without deploying the full MLflow package.
- Trace multi-step AI workflows to identify latency bottlenecks and failure points across supported libraries.
- Centralize observability for serverless or containerized GenAI applications with minimal dependency overhead.
- Instrument custom AI code with manual tracing decorators to log business logic alongside library calls.
- Search and analyze historical traces to debug agent behavior or validate output quality in production.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you need distributed tracing for GenAI applications and want a lightweight alternative to the full MLflow package.
The low install friction, active maintenance, Apache 2.0 license, and zero known vulnerabilities make it safe to deploy. Install only if you have access to a remote MLflow backend (Databricks, SageMaker, Nebius, or self-hosted); local-only tracing is not supported. Do not co-install with the full MLflow package.
Install
mlflow-tracing on PyPI
Before you install
Active maintenance with recent release (11 days old). Low install friction: pure Python wheel with 8 runtime dependencies. No known vulnerabilities. Requires Python 3.10+. Documentation explicitly warns against co-installing with the full MLflow package due to version mismatch risk.
Requires Python 3.10 or later. Requires a remote MLflow server backend (Databricks, SageMaker, Nebius, or self-hosted) to log traces; local-only use is not supported.
License in practice
Apache 2.0 permissive license allows commercial and derivative use with minimal restrictions. Attribution and license text preservation required in distributions.
Quickstart
pip install mlflow-tracing
import mlflow
mlflow.set_tracking_uri("databricks")
mlflow.set_experiment("/Path/To/Experiment")
# Enable auto-tracing for supported AI libraries
mlflow.openai.autolog()Verify before relying
- Which AI libraries support automatic tracing and whether additional setup is required beyond autolog().
- Performance overhead of tracing instrumentation in high-throughput production scenarios.
- Compatibility guarantees with specific versions of databricks-sdk and opentelemetry packages.
Package facts
| License | permissive license permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 8 packagescachetoolsdatabricks-sdkopentelemetry-apiopentelemetry-protoopentelemetry-sdkpackagingprotobufpydantic |
| Maintenance | Actively maintained 11 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 23,168,516 / month, #954 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/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 |
Evidence: mlflow_tracing-3.15.1-py3-none-any.whl
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See also mlflow · weave · mlflow-skinny · opik · opentelemetry-instrumentation-openai-agents · openinference-instrumentation-openai-agents · opentelemetry-instrumentation-openai · literalai · braintrust · opentelemetry-instrumentation-vertexai