We've just released Pyrefly v1.3, which adds flexible tools for configuring, suppressing, and baselining errors. Also included: a composable tensor shape DSL that expands experimental tensor shape support for JAX and NumPy, improved DataFrame schema checking for Polars, and a battery of new type checking diagnostics.
Pyrefly is one of the major new Python type-checkers taking the world by storm. It's often touted for its speed, usability, and typing-spec-conformance. But it also offers an extra nifty little feature you may never have heard of: type-coverage.
Let's learn about what it is, why you might want to care about it, and what it can do for you.
Companion post to my PyCon 2026 Typing Summit talk, "From Soundness to Blame: AI-Assisted Formalization of a Tiny Python." This is the first in a series.
Built-in support for attrs is available in Pyrefly as of version 1.2.0-dev.1, and ships in the upcoming 1.2.0 stable release. You get accurate constructor signatures and better type safety for your attrs classes, with no plugins or configuration to set up.
Pyrefly v1.1 is now available 🎉 This is our first minor release since reaching stable v1.0 in May, and it brings improvements across the board: better performance, smarter type checking, and new IDE refactoring tools.
Why aren't tensor shapes part of the type system?
When you write a PyTorch model, the hardest part of composing tensor ops is keeping track of their shapes. The standard practice today is to write shapes down as comments as you go. This talk introduces an experimental Pyrefly feature that brings tensor shapes into Python's type system, so those shape annotations can become inferred type hints instead of comments.
The talk was originally presented at the PyCon US 2026 Typing Summit. The slides and edited transcript are provided below for your convenience.
Want to give it a spin? This feature is available in Pyrefly today, and we'd love to hear your feedback and suggestions.
Does adding type checking to an agentic workflow really help agents?
We ran an experiment recently to determine whether there are improvements in the success rate for completing different kinds of tasks. In theory, having a type checker present should help the agent catch type errors earlier, validate fixes incrementally as it works, and reduce the need for slow, test-driven iterative feedback loops.
This talk was originally presented at the PyCon US 2026 Typing Conference. The slides and edited transcript are provided below for your convenience.
Mypy, Pyrefly, Pyright, ty, Zuban, and possibly more that will come in the future... how are library maintainers expected to cope?
TL;DR: Prioritise running as many type-checkers as possible on your test suite. Run at least one on your source code.
Dify is a large open-source platform for building LLM applications. Its backend is a Python Flask application with workflows, RAG pipelines, model providers, agents, Celery tasks, database migrations, and a large test suite. That makes it a useful case study for Pyrefly adoption: a real codebase where static analysis needs to fit into existing CI without blocking daily work.
The goal was not to make every Pyrefly diagnostic fail CI on day one. That would have been noisy and counterproductive. The better approach was to split the rollout into two CI surfaces: a blocking check for the files we were ready to enforce, and full-project reporting that stayed non-blocking and showed up in PR comments.