reference.md

multilingualprogramming Reference

This document is the detailed reference for the project.

Overview

multilingualprogramming is a Python framework for multilingual programming. It supports writing source code with keywords, numerals, and literals from multiple human languages while mapping everything to a shared semantic model.

Python compatibility baseline:

• compatibility_matrix.md
• compatibility_roadmap.md

Version and release status:

• Package version: multilingualprogramming/version.py
• Release notes: CHANGELOG.md

Supported Languages

• English
• French
• Spanish
• German
• Italian
• Portuguese
• Polish
• Dutch
• Swedish
• Danish
• Finnish
• Hindi
• Arabic
• Bengali
• Tamil
• Chinese (Simplified)
• Japanese

Core Components

Numeral System

• MPNumeral
• UnicodeNumeral
• RomanNumeral
• ComplexNumeral
• FractionNumeral
• NumeralConverter

Key capabilities:

• arithmetic across numeral scripts
• conversion across scripts
• Unicode fraction handling
• scientific notation helpers

Keyword and Concept Model

• KeywordRegistry
• KeywordValidator

Key capabilities:

• concept -> keyword lookup (COND_IF -> if, si, etc.)
• keyword -> concept reverse lookup
• supported-language discovery
• ambiguity/completeness checks

Date and Time

• MPDate
• MPTime
• MPDatetime

Key capabilities:

• multilingual parsing and formatting
• script-aware numeric rendering

Frontend (Lexing, Parsing, Semantic Analysis)

• Lexer
• Parser
• AST node model in multilingualprogramming/parser/ast_nodes.py
• core.semantic_analyzer.SemanticAnalyzer
• ASTPrinter

Key capabilities:

• Unicode-aware tokenization
• AST generation from multilingual source
• scope and symbol checks
• multilingual semantic error messages

Runtime and Execution

• PythonCodeGenerator
• WATCodeGenerator
• RuntimeBuiltins
• ProgramExecutor
• REPL

Key capabilities:

• transpile multilingual semantic IR to Python source
• execute full pipeline: source -> tokens -> optional normalization -> AST -> semantic IR -> checks -> Python/WAT -> runtime
• inject multilingual runtime builtins
• interactive REPL with language switching and Python, WAT, and Rust/Wasmtime preview modes

AI Runtime

• AIRuntime — singleton registry that dispatches AI calls to the active provider
• AIProvider — abstract base class for LLM backends
• AnthropicProvider — concrete provider backed by the Anthropic Messages API
• OpenAIProvider — concrete provider backed by the OpenAI Python SDK
• OllamaProvider — concrete provider backed by a local Ollama instance

Key capabilities:

• prompt(model, template) — single-turn text completion
• think(model, template) — extended chain-of-thought reasoning (returns Reasoning)
• generate(model, template, target_type) — structured / JSON-mode generation
• stream(model, template) — token-by-token streaming (returns Iterator[StreamChunk])
• embed(model, text) — text embedding (returns EmbeddingVector)
• extract / classify / plan / transcribe / retrieve — specialised AI operations
• provider registration: AIRuntime.register(AnthropicProvider())

Model reference literals in source (@claude-sonnet, @claude-haiku, …) resolve to full model IDs via AnthropicProvider._MODEL_ALIASES.

Concrete provider SDKs are optional dependencies. Install multilingualprogramming[ai] to get OpenAI, Anthropic, and Ollama support.

Reactive / UI Runtime

• ReactiveEngine — engine managing observable Signal objects
• Signal — a value cell that notifies subscribers on change
• CanvasNode — a named UI canvas region
• stream_to_view(signal, target) — bind a signal stream to a view target

Key capabilities:

• observe name = value / on name.change: reactive declarations
• canvas name: / render target = value canvas and rendering
• view target = signal binding

Structured Concurrency Runtime

• Channel — typed async FIFO channel backed by asyncio.Queue

Key capabilities:

• channel<T>() creates a Channel (unbounded or with capacity)
• await ch.send(value) / await ch.receive() — async message passing
• async for item in ch: — iteration until channel is closed
• par [ expr1, expr2, … ] — parallel fan-out; lowers to asyncio.gather()
• spawn expr — background task; lowers to asyncio.create_task()

Observability Runtime

• ml_trace(value, label) — record a TraceEvent and return value unchanged
• ml_cost(value) — return (value, CostInfo) with token and latency data
• ml_explain(value) — return (value, explanation_text) from the model

Key capabilities:

• transparent: original result always flows through unchanged
• TraceEvent / CostInfo data classes for structured inspection
• global trace log via get_trace_log() / clear_trace_log()

Placement Runtime

• @local / @edge / @cloud — deployment target annotations

Key capabilities:

• decorators attach __ml_placement__ to any function or agent
• Python backend executes locally; a distributed backend routes on the hint
• get_placement(fn) — inspect the placement of any callable

Agent Memory and Coordination Runtime

• MemoryStore / ml_memory(name, scope) — named key-value stores
• Swarm — pool of named sub-agents with fan-out and delegation
• ml_delegate(swarm_or_agent, …) — async message to an agent
• swarm_decorator — @swarm(agents=[…]) decorator factory

Key capabilities:

• memory scopes: "session" (in-process), "persistent" (JSON file), "shared" (swarm-wide)
• Swarm.broadcast(message) — fan-out to all sub-agents concurrently
• delegate(agent, message) in source lowers to await ml_delegate(…)

Language Features

AI-native constructs

Effects must be declared on the enclosing function or agent with uses ai:

fn summarise(text: str) -> str uses ai:
    return prompt @claude-sonnet: "Summarise: " + text

fn reasoning_demo() uses ai:
    let r = think @claude-sonnet:
        What are the implications of AI-native programming?
    print(r.conclusion)

fn typed_output() uses ai:
    let result: SentimentLabel = generate @claude-sonnet: "Classify: great product"

Available AI keywords (all 17 languages supported — see keywords.json):

Concept	English	French	Japanese
prompt	prompt	requête / requete	プロンプト
think	think	réfléchir	考える
generate	generate	générer	生成する
stream	stream	diffuser	ストリーム
embed	embed	incorporer	埋め込む
extract	extract	extraire	抽出する
classify	classify	classifier	分類する
plan	plan	planifier	計画する
transcribe	transcribe	transcrire	書き起こす
retrieve	retrieve	récupérer	取得する

Agent and tool declarations:

@tool(description="Search the web")
fn web_search(query: str) -> str uses net:
    pass

@agent(model=@claude-sonnet)
fn researcher(question: str) -> str uses ai, net:
    return prompt @claude-sonnet: question

Structured concurrency

# Parallel fan-out — all branches run concurrently, results returned as tuple
let results = parallel [
    prompt @claude-sonnet: "Answer A",
    prompt @claude-sonnet: "Answer B"
]

# Background task — returns immediately with a future
let task = spawn long_running_operation()

# Typed channel — async FIFO between tasks
let ch = channel()
spawn producer(ch)
let item = ch.receive()

All concurrency keywords are multilingual:

Concept	English	French	Japanese
parallel	par / parallel	parallèle	並列
spawn	spawn / launch	lancer	起動
channel	channel	canal	チャネル
send	send	envoyer	送る
receive	receive	recevoir	受信

Observability

fn monitored() uses ai:
    # trace — log timing; value passes through unchanged
    let result = trace(prompt @claude-sonnet: "Hello", "my-label")

    # cost — returns (value, CostInfo) with token counts
    let answer, info = cost(prompt @claude-sonnet: "What is AI?")
    print(info)   # CostInfo(model='claude-sonnet-4-6', tokens=42, latency=1200ms)

    # explain — returns (value, explanation_text)
    let value, why = explain(answer)

Distributed placement

@local
fn preprocess(data: str) -> str:
    pass          # hint: run on local machine

@edge
fn classify_fast(img: str) -> str uses ai:
    pass          # hint: run at the network edge

@cloud
@agent(model=@claude-sonnet)
fn heavy_reasoning(prompt: str) -> str uses ai:
    pass          # hint: run in the cloud

Placement keywords are multilingual (local/lokal/स्थानीय/本地/ローカル …).

Agent memory and coordination

fn with_memory() uses ai:
    # Named session store (dict-like)
    let facts = memory("facts")
    facts["answer"] = "Paris"

    # Persistent across runs
    let cache = memory("cache", scope="persistent")

@swarm(agents=[researcher, writer, reviewer])
fn team_coordinator(task: str) -> str uses ai:
    # Fan-out to two sub-agents simultaneously
    let draft, review = parallel [
        delegate(writer, task),
        delegate(reviewer, task)
    ]
    return prompt @claude-sonnet: "Merge: " + draft + "\n" + review

Memory scopes: "session" (default, in-process), "persistent" (JSON-backed file), "shared" (swarm-wide in-process).

General language features

The implementation includes support for:

• booleans and None, including identity checks (is, is not)
• control flow (if/else, for, while)
• async constructs (async def, await, async for, async with)
• functions and classes
• imports (import, from ... import ..., aliases with as)
• assertions
• exception handling (try, except, else, finally)
• chained assignment
• slices (a[1:3], a[::-1])
• comprehensions (list, dict, generator), including nested for clauses
• default parameters, *args, **kwargs
• tuple unpacking
• decorators
• f-strings
• triple-quoted strings

Example (nested comprehension):

let rows = [[1, 2], [3, 4]]
let flat = [x for row in rows for x in row]
print(flat)  # [1, 2, 3, 4]

Example (async for / async with):

import asyncio

class AsyncCtx:
    async def __aenter__(self):
        return 1
    async def __aexit__(self, exc_type, exc, tb):
        return False

async def main(xs):
    let total = 0
    async with AsyncCtx() as base:
        async for i in xs:
            total = total + i + base
    return total

print(asyncio.run(main([1, 2, 3])))

API Entry Points

Most commonly used imports:

from multilingualprogramming import (
    MPNumeral,
    KeywordRegistry,
    MPDate,
    Lexer,
    Parser,
    ASTPrinter,
    PythonCodeGenerator,
    ProgramExecutor,
    REPL,
)

from multilingualprogramming.core.semantic_analyzer import SemanticAnalyzer

CLI and REPL

Run interactive mode:

python -m multilingualprogramming repl
python -m multilingualprogramming repl --lang fr
python -m multilingualprogramming repl --show-python
python -m multilingualprogramming repl --show-wat
python -m multilingualprogramming repl --show-rust

REPL commands:

• :help
• :language <code>
• :python — toggle generated Python display
• :wat — toggle generated WAT (WebAssembly Text) display
• :rust — toggle generated Rust/Wasmtime bridge code display
• :reset
• :kw [XX]
• :ops [XX]
• :q

REPL Language Smoke Tests

Use these two snippets to quickly validate each language in REPL.

Snippet A (variables + print):

<LET> x = 2
<LET> y = 3
<PRINT>(x + y)

Snippet B (for loop):

<LET> total = 0
<FOR> i <IN> <RANGE_ALIAS>(4):
    total = total + i
<PRINT>(total)

Built-in aliases are also available for selected universal functions. Both the universal name and localized alias work. Example (French):

afficher(intervalle(4))
afficher(longueur([10, 20, 30]))
afficher(somme([1, 2, 3]))

Language-specific forms:

English (en)

let x = 2
let y = 3
print(x + y)

let total = 0
for i in range(4):
    total = total + i
print(total)

French (fr)

soit x = 2
soit y = 3
afficher(x + y)

soit somme = 0
pour i dans intervalle(4):
    somme = somme + i
afficher(somme)

Spanish (es)

sea x = 2
sea y = 3
imprimir(x + y)

sea suma = 0
para i en rango(4):
    suma = suma + i
imprimir(suma)

German (de)

sei x = 2
sei y = 3
ausgeben(x + y)

sei summe = 0
für i in bereich(4):
    summe = summe + i
ausgeben(summe)

Italian (it)

sia x = 2
sia y = 3
stampa(x + y)

sia totale = 0
per i in intervallo(4):
    totale = totale + i
stampa(totale)

Portuguese (pt)

seja x = 2
seja y = 3
imprimir(x + y)

seja soma = 0
para i em intervalo(4):
    soma = soma + i
imprimir(soma)

Hindi (hi)

मान x = 2
मान y = 3
छापो(x + y)

मान योग = 0
के_लिए i में परास(4):
    योग = योग + i
छापो(योग)

Arabic (ar)

ليكن x = 2
ليكن y = 3
اطبع(x + y)

ليكن المجموع = 0
لكل i في مدى(4):
    المجموع = المجموع + i
اطبع(المجموع)

Bengali (bn)

ধরি x = 2
ধরি y = 3
ছাপাও(x + y)

ধরি মোট = 0
জন্য i মধ্যে পরিসর(4):
    মোট = মোট + i
ছাপাও(মোট)

Tamil (ta)

இருக்கட்டும் x = 2
இருக்கட்டும் y = 3
அச்சிடு(x + y)

இருக்கட்டும் மொத்தம் = 0
ஒவ்வொரு i இல் வரம்பு(4):
    மொத்தம் = மொத்தம் + i
அச்சிடு(மொத்தம்)

Chinese (zh)

令 x = 2
令 y = 3
打印(x + y)

令 总计 = 0
对于 i 里 范围(4):
    总计 = 总计 + i
打印(总计)

Japanese (ja)

変数 x = 2
変数 y = 3
表示(x + y)

変数 合計 = 0
毎 i 中 範囲(4):
    合計 = 合計 + i
表示(合計)

Examples

Runnable examples are documented in:

• examples/README.md

Preferred starter examples:

• examples/hello_en.multi
• examples/hello_fr.multi
• examples/cross_import_main_en.multi

Complete feature coverage examples:

• examples/complete_features_*.multi (one file per supported language; .ml also supported)

Run:

python -m multilingualprogramming run examples/complete_features_en.multi --lang en
python -m multilingualprogramming run examples/complete_features_fr.multi --lang fr
python -m multilingualprogramming run examples/complete_features_es.multi --lang es

Run all examples from repository root:

python -m examples.arithmetic
python -m examples.numeral_extended
python -m examples.keywords
python -m examples.datetime_example
python -m examples.lexer_example
python -m examples.parser_example
python -m examples.ast_example
python -m examples.multilingual_parser_example
python -m examples.codegen_example
python -m examples.multilingual_codegen_example
python -m examples.semantic_example
python -m examples.executor_example

Development

python -m pytest -q
python -m pylint $(git ls-files '*.py')

Related Docs

• Project quick start: README.md
• Design overview: design.md
• Core formal specification: core_spec.md
• Frontend translation contracts: frontend_contracts.md
• Related work and differentiation: related_work.md
• Controlled language scope: cnl_scope.md
• Evaluation plan: evaluation_plan.md
• Word order and syntax naturalness: word_order_and_naturalness.md
• Standard library localization strategy: stdlib_localization.md
• Translation governance: translation_guidelines.md
• Development and debugging guide: development.md
• Python compatibility matrix: compatibility_matrix.md
• Python 3.12 compatibility roadmap: compatibility_roadmap.md
• Usage snippets: USAGE.md
• Examples guide: examples/README.md
• French programming guide: fr/programmation.md
• Language onboarding: language_onboarding.md

License

• Code: GPLv3+
• Documentation/content: CC BY-SA 4.0