Python powers today's AI revolution, from machine learning frameworks to agentic workflows and data science pipelines. But for years, Python's packaging ecosystem has lagged behind developer expectations: slow installs, painful dependency resolution, and tooling fragmentation.
This is where uv comes in. And now, paired with Snyk, teams can ensure speed doesn't come at the cost of security.
Why uv is winning over Python developers.
Built by Astral, uv is a modern, high-performance Python package manager and resolver, designed to be a drop-in replacement for teams using pip, pip-tools, poetry, and other Python packaging tools.
Since its launch 2 years ago, uv has seen explosive adoption:
- 80K stars on GitHub
- Serving 500 million requests per day
- Becoming the tool of choice for popular AI native projects like FastMCP, Pydantic, BentoML, Instructor, Outlines, and Antropic's Python SDK
At Snyk, we quickly adopted uv internally–both for application development and for features like agent-scan in Evo.
Recognizing the need for supply chain security
When teams evaluate a new tool, two questions always come up:
- Is it secure?
- Will it integrate with our existing toolchain?
Shortly after uv's release, developers in the Python community started asking whether uv could support exporting dependencies in standard SBOM formats. Without that, integrating uv projects into security and compliance pipelines would create friction.
We saw the same demand from Snyk customers eager to adopt uv but needing a seamless way to maintain supply chain visibility.
At the same time, we feel it's important that we not only support but actively contribute to open standards and the ecosystems that are important to developers.
So, we partnered directly with the uv maintainers to solve it. Together, we contributed support for native CycloneDX export, making it easier for adopters to integrate with downstream tools and for tool providers to build on top of uv in a scalable way.
Using uv and Snyk together
With CycloneDX support now available in uv, securing a project is straightforward.
Step 1: Export a CycloneDX SBOM from uv
Generate a CycloneDX SBOM in JSON format that includes their project's dependencies:
uv export --format cyclonedxStep 2: Test the SBOM with Snyk
Using Snyk, this SBOM can then be tested for vulnerabilities and license compliance issues. Developers get clear visibility into both security and license risks directly from their uv-managed dependencies:
snyk sbom test ——file=sbom.jsonSecuring uv projects at inception
SBOM export was just the beginning. While scanning exported artifacts works well, we wanted to make the experience even more seamless for developers using uv. So we built native uv support directly into:
- The Snyk CLI
- IDE integrations
- Agentic workflows
Native support for uv is currently available to Enterprise customers as part of a private preview to gather feedback ahead of an Early Access launch planned for all customers and free users in April 2026.
Coming soon:
Our goal is simple: If you're building with uv, security should feel built in — not bolted on. As uv is quickly becoming the modern standard for Python package management, Snyk is committed to ensuring that there is never a trade-off between speed/performance and security.
By combining uv's high-performance dependency resolution with Snyk's industry-leading AI security platform, teams can confidently build, install, and secure their AI-native applications from inception.
Get started today
With uv and Snyk together, you don't have to choose between speed and security. Reach out to your Snyk account representative to learn more about uv support. To learn more about how Snyk supports Python developers, check out our User Docs.
And if you're building AI-native applications in Python, now is the time to rethink your supply chain security strategy. Learn more in our AI Security Crisis in Python report to discover the real risks impacting Python's AI ecosystem and what engineering teams can do to stay ahead.