grammar-school 0.4.0

A lightweight framework for building tiny LLM-friendly DSLs

Grammar School - Python Implementation

A lightweight framework for building tiny LLM-friendly DSLs in Python.

Installation

pip install grammar-school

For development:

pip install -e ".[dev]"

Quick Start

from grammar_school import Action, Grammar, verb

class MyGrammar(Grammar):
    @verb
    def greet(self, name, _context=None):
        # @verb methods return Actions (data structures)
        # They are pure - no side effects here!
        return Action(kind="greet", payload={"name": name})

# Default runtime prints actions - no Runtime import needed!
grammar = MyGrammar()
grammar.execute('greet(name="World")')

# Or provide a custom runtime for actual behavior
from grammar_school import Runtime

class MyRuntime(Runtime):
    def __init__(self):
        self.greetings = []  # Runtime manages state

    def execute(self, action: Action) -> None:
        # Runtime performs actual side effects
        if action.kind == "greet":
            name = action.payload["name"]
            self.greetings.append(name)
            print(f"Hello, {name}!")

grammar = MyGrammar(runtime=MyRuntime())
grammar.execute('greet(name="World")')

Understanding the Architecture

Grammar School uses a two-layer architecture:

1. Grammar + @verb methods: Transform DSL syntax → Actions (pure, no side effects)
2. Runtime: Execute Actions → Real world effects (side effects, state management)

Why this separation?

• Same Grammar works with different Runtimes (testing, production, mocking)
• @verb methods are pure and easily testable
• Runtime handles all state and side effects independently

Runtime Output

Default Runtime: Prints actions to stdout (standard output/console)

Custom Runtimes: Can output anywhere:

• Files (write to disk)
• Databases (store in SQL/NoSQL)
• APIs (HTTP requests)
• Logging systems
• In-memory structures
• Or any combination

Example custom runtime that writes to a file:

class FileRuntime(Runtime):
    def __init__(self, filename: str):
        self.filename = filename

    def execute(self, action: Action) -> None:
        with open(self.filename, 'a') as f:
            f.write(f"{action.kind}: {action.payload}\n")

grammar = MyGrammar(runtime=FileRuntime("output.log"))

Streaming Actions

For large DSL programs or real-time processing, you can stream actions as they're generated:

grammar = MyGrammar()

# Stream actions one at a time (memory efficient)
for action in grammar.stream('track(name="A").track(name="B").track(name="C")'):
    print(f"Got action: {action.kind}")
    # Process action immediately, don't wait for all actions
    runtime.execute(action)  # Execute as they arrive

This is useful for:

• Large programs: Don't load all actions into memory at once
• Real-time processing: Start executing actions before compilation completes
• Memory efficiency: Process actions incrementally

Functional Programming Support

Grammar School supports functional programming paradigms through the FunctionalMixin:

from grammar_school import Grammar, FunctionalMixin, verb, Action

class MyGrammar(Grammar, FunctionalMixin):
    @verb
    def square(self, x, _context=None):
        return Action(kind="square", payload={"value": x * x})

    @verb
    def is_even(self, x, _context=None):
        return Action(kind="is_even", payload={"value": x % 2 == 0})

grammar = MyGrammar()
# Use functional operations with function references
grammar.execute('map(@square, data)')
grammar.execute('filter(@is_even, data)')
grammar.execute('map(@square, data).filter(@is_even, data)')

Available functional operations:

• map(@function, data) - Map a function over data
• filter(@predicate, data) - Filter data using a predicate
• reduce(@function, data, initial) - Reduce data using a function
• compose(@f, @g, @h) - Compose multiple functions
• pipe(data, @f, @g, @h) - Pipe data through functions

Function references: Use @function_name syntax to pass functions as arguments.

Examples

See the examples/ directory for complete DSL implementations.

API Reference

Core Types

• Value: AST value node (number, string, identifier, bool)
• Arg: Named argument
• Call: Function call with arguments
• CallChain: Chain of calls (method chaining)
• Action: Runtime action produced by interpreter
• Runtime: Protocol for executing actions

Decorators

• @verb: Mark a method as a verb handler
• @rule: Define grammar rules (for custom grammars)

Classes

• Grammar: Main grammar class that orchestrates parsing and interpretation
• Interpreter: Interprets CallChain AST into Actions
• LarkBackend: Lark-based parser backend