Skip to main content

Pyfun

PyPI VS Code Marketplace License: Apache 2.0

Functional programming for the language classrooms already teach.

Pyfun is an F#-inspired, functional-first language that compiles to readable Python. It brings algebraic data types, exhaustive matching, currying, inferred effects, and units of measure to the Python ecosystem, and its Rust compiler checks every one of them before a single line of Python is emitted.

It exists to make functional programming teachable where students already are. CS courses run on Python; learning FP usually means leaving it for Haskell, OCaml, or F# and adopting a whole new ecosystem students rarely touch again. Pyfun keeps them in Python, with no new runtime and no new package manager, and compiles to Python they can read, so every concept stays visible in the code they already understand.

Try it in your browser — no install: write Pyfun, watch it compile to readable Python live, and hit Run to execute it (the compiler runs as WebAssembly, the Python runs in CPython-via-WASM).

Or install it with Python 3.12+:

pip install pyfun-lang
type Shape = Circle float | Rect float float

# `area` handles Circle but forgets Rect, so Pyfun refuses to compile it:
let area s =
  match s:
    case Circle r: 3.14159 * r * r
$ pyfun check shapes.pyfun
error: non-exhaustive match: `Rect _ _` is not matched
 --> 5:3
  |
5 |   match s:
  |   ^^^^^^^^

Plain Python compiles and runs this, then silently returns None the day a Rect reaches it, and you debug the TypeError an hour downstream. Pyfun's Rust compiler checks types, effects, units, and match exhaustiveness before a single line of Python is emitted, then hands you code you can read, diff, and ship.

And when it compiles, the output is the point. There's no runtime library to ship and nothing to read around — your match becomes Python's match/case, one for one:

let grade score =
  match score:
    case s if s >= 90: "A"
    case s if s >= 80: "B"
    case _: "C"
# exactly what `pyfun compile` emits — no wrappers, no runtime:
def grade(score):
    match score:
        case s if s >= 90:
            return "A"
        case s if s >= 80:
            return "B"
        case _:
            return "C"

A Pyfun List is a Python list, a record is a plain class, and extern calls a real library directly (fuller example below).


Made for the classroom

Teaching FP normally forces a detour: a new language, a new toolchain, and a new ecosystem the students abandon the moment the course ends. Pyfun removes the detour.

  • They already have the runtime. Pyfun compiles to plain Python, so anything a student writes runs on the interpreter already installed on every lab machine. No VM, no new package manager.
  • The concepts stay visible. pyfun compile shows the Python your functional code becomes, so a student watches an ADT turn into a class, a match into match/case, and currying into a closure. They learn the idea and how it maps to the imperative code they know.
  • Good habits are enforced, not suggested. The compiler refuses to skip a case, ignore a None, or mutate what should stay immutable, so students learn to handle every path because the tool insists.
  • A small, learnable core. Pyfun is deliberately compact, so the language stays out of the way of the ideas you are teaching.

Pyfun is a real, general-purpose language, not a toy. But teaching is why it exists.


Why Pyfun over plain Python?

Python is the best ecosystem in the world, and even with mypy/pyright bolted on, large Python programs still fail in ways a compiler could have caught. Pyfun keeps the ecosystem and makes the checks mandatory.

Plain Python Pyfun
Type errors mypy/pyright are optional and unsound; they warn, they don't gate found at compile time; no Python is emitted until they pass
None handling AttributeError: 'NoneType'… Option a with exhaustive match; the compiler makes you handle None
Missing case silently falls through, returns None exhaustiveness error with a concrete missing-case witness
Mutation everything is mutable, everywhere immutable by default; let mut + <- is opt-in and tracked
Side effects invisible inferred and tracked; let pure is a compile-checked promise
Units / dimensions a comment and a prayer 100<m> / 10<s> : float<m/s>, checked and then erased
Runtime CPython CPython: Pyfun is Python once compiled

Why not just mypy/pyright? They're a gradual, optional overlay: unsound by design, never required, and one # type: ignore from silence. They report; they don't gate. Pyfun makes the same class of check mandatory: it blocks compilation, infers the signatures pyright often needs spelled out, and there is no untyped Pyfun to fall back to. And you keep the entire Python ecosystem while you do it.


Type-checked Python interop

extern imports any Python callable or value at a Pyfun type. The dotted target is imported for you; the boundary is effectful by default (a Python call can do anything), and pure opts out where you know better. Once imported, the function is a first-class curried Pyfun value: type-checked, effect-tracked, and partially applicable.

extern pure mean:  List float -> float = statistics.mean
extern pure stdev: List float -> float = statistics.stdev

type Summary = { n: int, mean: float, stdev: float }

let summarize xs =
  Summary { n = List.len xs, mean = mean xs, stdev = stdev xs }

let report xs =
  let s = summarize xs
  f"n={s.n} mean={s.mean} sd={s.stdev}"

print (report [1.0, 2.0, 3.0, 4.0])

pyfun compile turns that into Python you'd be happy to have written by hand:

from dataclasses import dataclass
import statistics

@dataclass(frozen=True)
class Summary:
    n: int
    mean: float
    stdev: float

def summarize(xs):
    return Summary(len(xs), statistics.mean(xs), statistics.stdev(xs))

def report(xs):
    s = summarize(xs)
    return f"n={s.n} mean={s.mean} sd={s.stdev}"

print(report([1.0, 2.0, 3.0, 4.0]))
$ pyfun run stats.pyfun
n=4 mean=2.5 sd=1.2909944487358056

Notice what the compiler does:

  • No wrapper layer. statistics.mean(xs) is called directly. List is a Python list, and a record or ADT variant is a frozen @dataclass — so frozen=True even enforces in the Python the immutability Pyfun promises. There is no runtime, no VM, no marshalling.
  • Effects tracked across the boundary. A bare extern is io at full application, so it can't be called from a let pure. Mark it pure (like statistics.mean) and it composes into pure code. You can even annotate other effect labels: extern fetch: string ->{async} string = httpx.get.
  • Exceptions become values. try (parseInt s) : Result int Exception catches whatever the Python side raises and hands you a Result to match on. The imperative FFI edge becomes the FP error type, with errorKind and errorMessage fields.
extern parseInt: string -> int = int          # Python's built-in int()

let safe s = Result.withDefault 0 (try (parseInt s))
print (safe "42")     # 42
print (safe "oops")   # 0   (the ValueError was caught into an Error)

A whistle-stop tour

Everything below type-checks, compiles, and runs today. See examples/hello.pyfun for the exhaustive version.

Algebraic data types, records, and exhaustive matching. None cannot bite you:

type Shape = Circle float | Rect float float

let area s =
  match s:
    case Circle r: 3.14159 * r * r
    case Rect w h: w * h
# forget a case and the compiler reports the missing witness, e.g. `Rect _ _ is not matched`

Decode untrusted JSON into typed data, totally. json.loads hands back an untyped dict that explodes three layers downstream. The built-in Elm-style Decode module turns JSON into your own records — a missing field or wrong type is a value you handle, never an AttributeError an hour later:

type User = { name: string, age: int }

let user =
  Decode.map2 (fun name age -> User { name = name, age = age })
    (Decode.field "name" Decode.string)
    (Decode.field "age" Decode.int)

# Decode.decodeString user : string -> Result User Exception
#   good input   -> Ok (a typed User)
#   missing/bad  -> Error (a value describing exactly what was wrong)

The examples/interop/ cookbook calls json, sqlite3, pathlib, and urllib this way — typed and effect-tracked at the boundary.

Pipelines, currying, composition. F#'s |>, <|, >>, <<, and operator sections (+):

let describe =
  List.filter (fun x -> x > 0)
  >> List.map ((*) 2)
  >> List.fold (+) 0

let total = [1, -2, 3] |> describe    # (1 + 3) * 2 = 8

Inferred effects. Purity is a checked promise, never boilerplate:

let pure add a b = a + b        # OK: no effects
# let pure shout n = print n    # compile error: `print` performs `io`

Units of measure. Dimensional analysis at compile time, erased at runtime:

measure m
measure s
measure kg
measure N = kg m / s^2          # derived aliases expand to base units

let speed = 100<m> / 10<s>      # float<m/s>
let force = 10<N>
# let bad = 100<m> + 10<s>      # compile error: m vs s
let side = sqrt 16.0<m^2>       # float<m>, unit-aware roots

Computation expressions (F#'s showcase feature): result, seq, async, plus your own:

let checked ok v =
  result {                      # railway-oriented; short-circuits on Error
    let! x = if ok then Ok v else Error "bad"
    return x + 1
  }

Rich literals and strings. F-strings, raw strings, triple-quotes, scientific notation, digit separators, hex/octal/binary:

let planck = 6.626e-34
let million = 1_000_000
let mask = 0xFF
let who = "Ada"
let line = f"{who} scored {million} ({String.upper who})"
let path = r"C:\Users\pyfun"    # raw string, backslashes literal

And a standard library that reads like F#'s: module-qualified List / Set / Map / Option / Result / Seq / String (List.map, Map.tryFind, Result.bind, lazy Seq.take, String.split), tuples, active patterns, typed holes for type-driven development, and multi-file projects with import.


How Pyfun compares

A few projects bring functional or statically-typed code to Python. Here is the field, and the bet Pyfun makes within it:

  • Fable compiles real F# to Python, the most capable option by far, because it is F#, with the whole language and a mature ecosystem. The trade-offs: it needs the .NET toolchain, and its output depends on a runtime library (fable_library).
  • Erg is a statically-typed, Python-compatible language with a rich type system and marker-based effect control. It is the closest to Pyfun in ambition, though "rusty"/OO rather than ML-family, with explicit effect annotations.
  • Coconut is a functional superset of Python; static typing is an optional MyPy add-on, so nothing is enforced.
  • Dynamic dialects (Hy, Mochi, Dogelang) are dynamically-typed FP/Lisp languages that run on Python; they share the last column, since they trade static guarantees for Python's dynamism.

Legend: ✅ yes · ⚠️ partial · ➖ different approach · ❌ no

Pyfun Fable Erg Coconut Dynamic dialects
FP-first language (not a Python superset) ⚠️
ML / F#-family syntax
Mandatory static typing
Type inference
Zero annotations required ⚠️ ⚠️
ADTs + enforced exhaustiveness ⚠️ ⚠️
Inferred effects (never annotated)
Units of measure
Computation expressions
Nested record-update ({ p with a.b = v })
Typed holes (type-driven dev)
Chained comparisons (a < b < c) ⚠️
Compiler-as-gatekeeper
Self-contained output (no runtime library)
No .NET / host-runtime toolchain
Python-library interop
Maturity / production use ❌ pre-1.0 ⚠️ Py beta ⚠️ ⚠️
Language surface (built-in constructs) ⚠️ small core ✅ full F# ⚠️ ✅ Python superset
Community, docs, support ❌ solo ⚠️ ⚠️

Pyfun's strengths are the bold rows: self-contained, runtime-free Python output (a List is a list, a record is a plain class), a single dependency-free compiler with no .NET, inferred effects, and a language designed for Python interop first. On several rows it reaches past F# itself, borrowing inferred effects from Koka, typed holes from Haskell and Idris, and Python-style chained comparisons. Every tool here reaches the full Python ecosystem (the interop row), so Pyfun's small core costs nothing in libraries; it just buys simplicity.

Reach for Fable when you want all of F# and are happy to bring the .NET toolchain and a runtime library along. Reach for Pyfun when you want the emitted Python to be a first-class, readable artifact you own outright, or when you are teaching functional programming to people who live in Python.


Getting started

Pyfun runs on the Python you already have. With Python 3.12+ and pip, install the compiler:

pip install pyfun-lang

That puts the pyfun command on your PATH, with no Rust toolchain required. (The PyPI package is pyfun-lang; the command it installs is pyfun.)

Write your first program. Save this as hello.pyfun:

type Shape = Circle float | Rect float float

let area s =
  match s:
    case Circle r: 3.14159 * r * r
    case Rect w h: w * h

print (area (Circle 2.0))

Then run it, type-check it, or see the Python it becomes:

pyfun run     hello.pyfun            # 12.56636
pyfun check   hello.pyfun            # type-check, rustc-style diagnostics
pyfun compile hello.pyfun            # emit readable Python to stdout
pyfun repl                           # interactive REPL

Multi-file projects just work: import Geometry pulls in a sibling geometry.pyfun, and any command drives the whole graph. Clone the repo for a runnable tour in examples/, including a multi-module project (pyfun run examples/modules/main.pyfun).

Building from source (or hacking on the compiler) needs Rust, which auto-selects the pinned 1.96 toolchain:

cargo install --git https://github.com/simontreanor/Pyfun pyfun
# or, from a clone:  cargo install --path .

Editor support

Pyfun ships a dependency-free language server (pyfun lsp) and a VS Code extension. Over resilient analysis that survives a half-typed file, you get:

  • Diagnostics as you type
  • Hover showing the inferred type and effect of any expression, binding, or parameter
  • Go-to-definition and find-references, across files
  • Rename, project-wide, for values, constructors, and types
  • Completion, document symbols, and workspace symbols

Install Pyfun from the VS Code Marketplace (or search "Pyfun" in the Extensions panel); once pyfun is on your PATH (from pip install pyfun-lang), it launches pyfun lsp automatically. Building the extension from source is covered in editors/vscode/DEVELOPMENT.md.


How it works

Pyfun is a dependency-free Rust crate that runs a classic pipeline, and the compiler is the gatekeeper: nothing is emitted until every check passes.

.pyfun ──► lexer ──► parser ──► Hindley–Milner type inference ──► Python-AST IR ──► readable .py
              │         │        (+ effects, units, exhaustiveness)      │
          offside    recursive                                     lowered, not
           rule       descent                                    string-spliced
  • Type inference is full HM with let-generalization: you never annotate a value. The only types you write are in type/extern declarations, and every signature is inferred. It also does unit-of-measure inference (abelian-group unification), effect-row inference, and Maranget-style exhaustiveness with concrete witnesses.
  • Lowering targets a Python-AST IR and emits real, formatted Python: curried functions collapse to n-ary defs and direct calls (closures only for genuine partial application), CEs desugar to their natural Python (async/await, generators, railway Result), and units erase.
  • No CPython fork. Pyfun is a front end for the Python ecosystem, not a competing runtime.

The full language design and rationale live in DESIGN.md.


Status

MVP showcase complete and runnable: ADTs, records, tuples, computation expressions (including user-defined builders), units of measure, mutability, inferred multi-label effects, general Python FFI via extern, a module-qualified standard library, string interpolation, active patterns, typed holes, file-based modules, and a full LSP. See ROADMAP.md for what's next.

This is a solo, actively-developed project: the MVP is feature-complete and runnable, but it's pre-1.0. Expect sharp edges; the language surface is stabilizing but not frozen.


License

Pyfun is free and open source under the Apache License 2.0: use, modify, and redistribute it, including commercially. The accompanying NOTICE names Simon Treanor as the original author; keep it with any redistribution or derivative work.

Copyright © 2026 Simon Treanor.

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

pyfun_lang-0.0.6.tar.gz (458.5 kB view details)

Uploaded Source

Built Distributions

If you're not sure about the file name format, learn more about wheel file names.

pyfun_lang-0.0.6-py3-none-win_amd64.whl (1.0 MB view details)

Uploaded Python 3Windows x86-64

pyfun_lang-0.0.6-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.1 MB view details)

Uploaded Python 3manylinux: glibc 2.17+ x86-64

pyfun_lang-0.0.6-py3-none-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (1.1 MB view details)

Uploaded Python 3manylinux: glibc 2.17+ ARM64

pyfun_lang-0.0.6-py3-none-macosx_11_0_arm64.whl (1.0 MB view details)

Uploaded Python 3macOS 11.0+ ARM64

pyfun_lang-0.0.6-py3-none-macosx_10_12_x86_64.whl (1.1 MB view details)

Uploaded Python 3macOS 10.12+ x86-64

File details

Details for the file pyfun_lang-0.0.6.tar.gz.

File metadata

  • Download URL: pyfun_lang-0.0.6.tar.gz
  • Upload date:
  • Size: 458.5 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.12

File hashes

Hashes for pyfun_lang-0.0.6.tar.gz
Algorithm Hash digest
SHA256 e00858311c32a4f40fa200f384b057974fd230be1c48a50391d493b6b342e484
MD5 81b66d73afbea13bf16555dbedba1dc5
BLAKE2b-256 7215d15d39c4d7e1fa8b6281033aa0951571a2a5dc18048b96322dcccc052557

See more details on using hashes here.

Provenance

The following attestation bundles were made for pyfun_lang-0.0.6.tar.gz:

Publisher: wheels.yml on simontreanor/Pyfun

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file pyfun_lang-0.0.6-py3-none-win_amd64.whl.

File metadata

  • Download URL: pyfun_lang-0.0.6-py3-none-win_amd64.whl
  • Upload date:
  • Size: 1.0 MB
  • Tags: Python 3, Windows x86-64
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.12

File hashes

Hashes for pyfun_lang-0.0.6-py3-none-win_amd64.whl
Algorithm Hash digest
SHA256 406323f2474b4e62aed5f7a26dbdbd6cd5719b2f866632a1dbc80c0da0e37536
MD5 0d4a75579b683fcc63af7476c951dd55
BLAKE2b-256 bee2be589bb771f40c0102f57226a4d72382d934934b4e6475795c7349434964

See more details on using hashes here.

Provenance

The following attestation bundles were made for pyfun_lang-0.0.6-py3-none-win_amd64.whl:

Publisher: wheels.yml on simontreanor/Pyfun

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file pyfun_lang-0.0.6-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pyfun_lang-0.0.6-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 c00810e368a6f125e92fd0fef3a6ce6e51436ed8b4729a0893078661b51cc0b2
MD5 57a416630b2cdd8204c79a13a3f18803
BLAKE2b-256 431c95ebc37d57c47aea4b5c01d96391ac0a74d6640e94b5c43ed81dfd73d503

See more details on using hashes here.

Provenance

The following attestation bundles were made for pyfun_lang-0.0.6-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl:

Publisher: wheels.yml on simontreanor/Pyfun

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file pyfun_lang-0.0.6-py3-none-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for pyfun_lang-0.0.6-py3-none-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 eb3a2e27241b07b5ba2933f54ea6742290d1b53d4c411dce8beeae4f8632fc5d
MD5 52224e19edde8bd888587e857409823c
BLAKE2b-256 2a8161c301c76331da8ed2b7cef893359c1a28684dfd63ebcf2b0a3c9d9846be

See more details on using hashes here.

Provenance

The following attestation bundles were made for pyfun_lang-0.0.6-py3-none-manylinux_2_17_aarch64.manylinux2014_aarch64.whl:

Publisher: wheels.yml on simontreanor/Pyfun

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file pyfun_lang-0.0.6-py3-none-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pyfun_lang-0.0.6-py3-none-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 72c2dae48108732edd558f1d3492193e06ad92916f65410104a2dc1babd39c66
MD5 3a11810e90b4b57dd5f9c7599567a697
BLAKE2b-256 71020b8ae0e996989fcc4fa492c69ac071ac065b85f4000f0031e59e293d2737

See more details on using hashes here.

Provenance

The following attestation bundles were made for pyfun_lang-0.0.6-py3-none-macosx_11_0_arm64.whl:

Publisher: wheels.yml on simontreanor/Pyfun

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file pyfun_lang-0.0.6-py3-none-macosx_10_12_x86_64.whl.

File metadata

File hashes

Hashes for pyfun_lang-0.0.6-py3-none-macosx_10_12_x86_64.whl
Algorithm Hash digest
SHA256 806516ec6d8cf15f2fa10129ae17004748188a14cb4ef1be7f7089edd1c349eb
MD5 64f47b40b0a3650a8ad0d8fcada657ff
BLAKE2b-256 d6b6a6cb4c250ee80c6e9879425f07fb6ac2f02fb3964471a04aeb845cfe0a18

See more details on using hashes here.

Provenance

The following attestation bundles were made for pyfun_lang-0.0.6-py3-none-macosx_10_12_x86_64.whl:

Publisher: wheels.yml on simontreanor/Pyfun

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page