Pyrefly: A fast type checker and IDE for Python

Currently under active development with known issues. Please open an issue if you find bugs.

Pyrefly is a fast type checker for Python. It's designed to replace the existing Pyre type checker at Meta by the end of 2025. This README describes basic usage. See the Pyrefly website for full documentation and a tool for checking code.

Getting Started

Pyrefly aims to increase development velocity with IDE features and by checking your Python code.

• Try out pyrefly in your browser: Sandbox
• Get the command-line tool: pip install pyrefly
• Get the VSCode extension: Link

Key Features:

• Type Inference: Pyrefly infers types in most locations, apart from function parameters. It can infer types of variables and return types.
• Flow Types: Pyrefly can understand your program's control flow to refine static types.
• Incrementality: Pyrefly aims for large-scale incrementality at the module level, with optimized checking and parallelism.

Getting Involved

If you have questions or would like to report a bug, please create an issue.

See our contributing guide for information on how to contribute to Pyrefly.

Choices

There are a number of choices when writing a Python type checker. We are taking inspiration from Pyre1, Pyright and MyPy. Some notable choices:

• We infer types in most locations, apart from parameters to functions. We do infer types of variables and return types. As an example, def foo(x): return True would result in something equivalent to had you written def foo(x: Any) -> bool: ....
• We attempt to infer the type of [] to however it is used first, then fix it after. For example xs = []; xs.append(1); xs.append("") will infer that xs: List[int] and then error on the final statement.
• We use flow types which refine static types, e.g. x: int = 4 will both know that x has type int, but also that the immediately next usage of x will be aware the type is Literal[4].
• We aim for large-scale incrementality (at the module level) and optimised checking with parallelism, aiming to use the advantages of Rust to keep the code a bit simpler.
• We expect large strongly connected components of modules, and do not attempt to take advantage of a DAG-shape in the source code.

Design

There are many nuances of design that change on a regular basis. But the basic substrate on which the checker is built involves three steps:

1. Figure out what each module exports. That requires solving all import * statements transitively.
2. For each module in isolation, convert it to bindings, dealing with all statements and scope information (both static and flow).
3. Solve those bindings, which may require the solutions of bindings in other modules.

If we encounter unknowable information (e.g. recursion) we use Type::Var to insert placeholders which are filled in later.

For each module, we solve the steps sequentially and completely. In particular, we do not try and solve a specific identifier first (like Rosyln or TypeScript), and do not used fine-grained incrementality (like Rust Analyzer using Salsa). Instead, we aim for raw performance and a simpler module-centric design - there's no need to solve a single binding in isolation if solving all bindings in a module is fast enough.

Example of bindings

Given the program:

1: x: int = 4
2: print(x)

We might produce the bindings:

• define int@0 = from builtins import int
• define x@1 = 4: int@0
• use x@2 = x@1
• anon @2 = print(x@2)
• export x = x@2

Of note:

• The keys are things like define (the definition of something), use (a usage of a thing) and anon (a statement we need to type check, but don't care about the result of).
• In many cases the value of a key refers to other keys.
• Some keys are imported from other modules, via export keys and import values.
• In order to disamiguate identifiers we use the textual position at which they occur (in the example I've used @line, but in reality its the byte offset in the file).

Example of Var

Given the program:

1: x = 1
2: while test():
3:     x = x
4: print(x)

We end up with the bindings:

• x@1 = 1
• x@3 = phi(x@1, x@3)
• x@4 = phi(x@1, x@3)

The expression phi is the join point of the two values, e.g. phi(int, str) would be int | str. We skip the distinction between define and use, since it is not necessary for this example.

When solving x@3 we encounter recursion. Operationally:

• We start solving x@3.
• That requires us to solve x@1.
• We solve x@1 to be Literal[1]
• We start solving x@3. But we are currently solving x@3, so we invent a fresh Var (let's call it ?1) and return that.
• We conclude that x@3 must be Literal[1] | ?1.
• Since ?1 was introduced by x@3 we record that ?1 = Literal[1] | ?1. We can take the upper reachable bound of that and conclude that ?1 = Literal[1].
• We simplify x@3 to just Literal[1].