Hi, Pyrefly maintainer here. Today we are pleased to share that Pyrefly, a fast type checker and language server for Python, has reached stable v1.0 status, meaning we are confident that Pyrefly is ready for production use.

Pyrefly was first released as an alpha in mid-2025 and followed up with a beta in November of that year. Since then, we have shipped over 60 minor releases: fixing hundreds of bugs, adding the features you’ve been asking for, and improving performance to be one of the fastest tools out there.

This would not have been possible without our amazing open-source community. To everyone who filed GitHub issues, submitted pull requests, gave us feedback at conferences, or joined us on Discord: thank you. Your contributions shaped this release, we’re grateful for every one of them, and we hope you continue being a part of the journey for future releases too.

We've published a blog post explaining what v1.0 means exactly, and what's next for Pyrefly.

Below is a summary of the changes to Pyrefly since the Beta release. The full release notes for v1.0 can be read on our Github.

Pyrefly v1.0 Release Notes

Performance Improvements

We've continued to push Pyrefly's performance since the speed improvements we shared in February. Since beta:

• 2–125x faster updated diagnostics after saving a file (no, that’s not a typo!). Thanks to fine-grained dependency tracking and streaming diagnostics, updates now consistently arrive in milliseconds
• 20–36% faster full type checking on large projects like PyTorch and Pandas
• 2–3x faster initial indexing when Pyrefly first scans your project
• 40–60% less memory usage during both indexing and incremental type checking

(Tested on an M4 Macbook Pro using open-source benchmarks from type_coverage_py and ty_benchmark.)

Compare the performance of Pyrefly and other Python type checkers on our regularly updated benchmarking suite, which runs against 53 popular Python packages.

Configuration Presets

A new preset configuration option provides named bundles of error severities and behavior settings.

See the configuration docs for details.

Onboarding Experience

We’ve made improvements to the out-of-the-box experience for projects without a pyrefly.toml.

• Automatic config synthesis — if you have a mypy or pyright config, Pyrefly automatically migrates your settings and synthesizes an appropriate in-memory Pyrefly config. (This is the same migration that pyrefly init would commit to disk.)
• Basic preset for unconfigured projects — projects with no type checker config get the lightweight “basic” preset, which surfaces only high-confidence errors.
• VS Code status bar — the status bar shows the active preset — e.g. Pyrefly (Basic) or Pyrefly (Legacy) — so you always know which mode is active.
• Type error display settings — new VS Code settings let you control which preset applies to unconfigured files and suppress all diagnostics workspace-wide.

Type Checker Improvements

We've been hard at work making the type checker robust and feature-complete, with a focus on driving down false positives and improving type quality in real-world code bases. Here are some highlights:

• Across the board we've eliminated many sources of false positives in enums, dataclasses, ParamSpec, descriptors, and more.
• Support has been added for more type narrowing patterns, including preserving narrows in nested scopes and recognizing container membership checks.
• Overload resolution was substantially reworked to handle more real-world patterns.
• Pyrefly’s conformance to the Python typing specification has improved from 70% at beta to over 90% today.
• We've added experimental support for tracking tensor dimensions through PyTorch models — see "What's Next" below.

LSP & IDE Improvements

• We've added new refactoring capabilities like Safe Delete (with reference checking) and bulk source.fixAll.
• Navigation is more precise, and hover cards surface richer information for imports, tuples, and NamedTuples.
• Workspace mode is more stable, with multiple crash fixes and improved diagnostic publishing.

Framework & Notebook Support

• Django — Pyrefly has improved support for model relationships, fields, and views, and understands factory_boy factories.
• Pydantic — Pyrefly models Pydantic's runtime behavior more faithfully, with support for lax mode and range constraint validation, and handles more of the Pydantic ecosystem: RootModel, pydantic-settings, and pydantic.dataclasses.
• Pytest integration — We've added Code Lens run buttons for test functions, as well as code actions to annotate fixture return types and parameters.
• Jupyter notebooks — .ipynb IDE support has reached full parity with .py files, with rename, find references, code actions, and document symbols all supported.

Complementary Tooling

Pyrefly ships with tools to aid with adopting type checking in an existing codebase. Two new tools since beta:

• pyrefly coverage report outputs a JSON report with annotation completeness and type completeness metrics per function, class, and module, so you can track coverage over time.
• Baseline files let you snapshot current errors into a JSON file so only new errors are reported, as an alternative to inline suppression comments.

Updated Version Policy

Going forward, we’ll switch from a weekly to monthly cadence for minor (1.x.0) releases, with patch releases in between as-needed for critical fixes. We’ll continue providing release notes for minor versions, so you can see what’s new in each release.

What's Next

• Tensor shape checking — Experimental support for tracking tensor dimensions through PyTorch models and catching shape mismatches statically. Learn more.
• Pyrefly + AI agents — Pyrefly's speed makes it a natural verification step in agentic workflows. See our guide on adding Pyrefly to your agentic loop.
• Continued improvements — We'll keep expanding library support, reducing false positives, and iterating on your feedback. Let us know what you need on Github or Discord.