ape-lang 0.1.0

Ape — The Because-I-Said-So Language for deterministic AI programming

Ape - AI Programming Language

The very first AIP (AI Programming Language) 🚀

Ape is an AI-native programming language with a strict core and Controlled Deviation System.

Quickstart

Installation

git clone https://github.com/yourusername/ape.git
cd ape
pip install -r requirements.txt  # if exists, or install pytest

CLI Usage

# Parse Ape source to AST
python -m ape parse examples/calculator_basic.ape

# Build IR (Intermediate Representation)
python -m ape ir examples/calculator_basic.ape

# Validate semantics and strictness
python -m ape validate examples/calculator_basic.ape

# Build Python code
python -m ape build examples/calculator_basic.ape --target=python

Your First Ape Program

Create hello.ape:

entity Greeting:
    message: String

task say_hello:
    inputs:
        - name: String
    outputs:
        - greeting: Greeting
    
    constraints:
        - deterministic
    
    steps:
        - create greeting with message
        - return greeting

Then compile it:

python -m ape validate hello.ape
python -m ape build hello.ape --target=python

Core Philosophy

"What is allowed, is fully allowed.
What is forbidden, is strictly forbidden.
What is not declared, does not exist."

What is Implemented

✅ 1. Parser & Tokenizer

• Tokenizer with indentation-aware lexical analysis
• AST nodes for all Ape constructs
• Recursive descent parser for Ape grammar v0.3
• IR Builder transforms AST to Intermediate Representation

Tests: 11 passing

✅ 2. Semantic Validator

• Symbol table for tracking all declarations
• Type checking (entities, enums, builtin types)
• Duplicate definition detection
• Unknown type detection
• Contract validation
• Policy validation
• Deviation validation (RFC-0001)

✅ 3. Strictness Engine

• Ambiguity detection (maybe, possibly, ?, etc.)
• Undeclared behavior detection
• Implicit choice detection (or, choose, etc.)
• Non-determinism detection (random, etc.)
• Deviation bounds validation
• Policy conflict detection

Tests: 19 passing

✅ 4. Python Code Generator

• Entity → Python @dataclass with type hints
• Enum → Python constants class
• Task → Python function with documentation
• Flow → Orchestration function + metadata
• Policy → Python dict structures
• Syntactically valid Python output

Tests: 12 passing

✅ 5. Runtime Support

• RunContext for flow orchestration
• Placeholder for logging, determinism, etc.

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Project Structure

Ape/
├── src/
│   ├── apeparser/          # Parser & Tokenizer
│   │   ├── tokenizer.py
│   │   ├── parser.py
│   │   ├── ast_nodes.py
│   │   └── ir_builder.py
│   ├── apecompiler/        # Compiler & Validation
│   │   ├── ir_nodes.py
│   │   ├── errors.py
│   │   ├── semantic_validator.py
│   │   └── strictness_engine.py
│   ├── apecodegen/         # Code Generation
│   │   └── python_codegen.py
│   └── aperuntime/         # Runtime Support
│       └── core.py
├── tests/                  # Test Suite (49 tests)
│   ├── parser/
│   ├── compiler/semantic/
│   ├── codegen/python/
│   └── examples/           # Example program tests
├── examples/               # Example Ape programs
│   ├── calculator_basic.ape
│   └── README.md
├── generated/              # Generated Python code
├── demo_pipeline.py        # Complete pipeline demo
└── example_generate.py     # Code generation example

Ape Syntax Voorbeeld

entity User:
    id: Integer
    username: String
    email: String

enum UserRole:
    - admin
    - user
    - guest

task CreateUser:
    inputs:
        username: String
        email: String
        role: UserRole
    outputs:
        user: User
    steps:
        - validate username is not empty
        - validate email format
        - create User instance
        - assign role to user
        - return user
    constraints:
        - username must be unique

flow UserRegistrationFlow:
    steps:
        - receive registration request
        - call CreateUser task
        - send welcome email
        - return success

policy SecurityPolicy:
    rules:
        - all passwords must be hashed
        - user data must be encrypted

Usage

Complete Pipeline

from apeparser import parse_ape_source, IRBuilder
from apecompiler.semantic_validator import SemanticValidator
from apecompiler.strictness_engine import StrictnessEngine
from apecodegen.python_codegen import PythonCodeGenerator
from apecompiler.ir_nodes import ProjectNode

# 1. Parse Ape source
ast = parse_ape_source(source, "example.ape")

# 2. Build IR
builder = IRBuilder()
ir_module = builder.build_module(ast, "example.ape")
project = ProjectNode(name="MyProject", modules=[ir_module])

# 3. Validate
validator = SemanticValidator()
errors = validator.validate_project(project)

# 4. Check strictness
engine = StrictnessEngine()
warnings = engine.enforce(project)

# 5. Generate Python
codegen = PythonCodeGenerator(project)
files = codegen.generate()

# 6. Save
for file in files:
    with open(file.path, 'w') as f:
        f.write(file.content)

Running Demos

# Complete pipeline demo
python demo_pipeline.py

# Generate code
python example_generate.py

# Run tests
python -m pytest tests/ -v

# Test calculator example
python -m pytest tests/examples/test_calculator_basic.py -v

Examples

Calculator Examples

1. Calculator Basic (calculator_basic.ape)

A fully deterministic calculator example that demonstrates:

• Strict type checking without ambiguity
• Deterministic constraints
• No controlled deviation
• Complete pipeline from Ape → Python

2. Calculator Smart (calculator_smart.ape)

Demonstrates the Controlled Deviation System (CDS):

• Deterministic for calculations
• Creative freedom for human summary
• Explicit bounds define what can vary
• Rationale explains why deviation is needed

See examples/calculator_basic.ape, examples/calculator_smart.ape and examples/README.md for details.

Test Results

✅ Parser tests:           11 passed
✅ Semantic tests:         19 passed
✅ Codegen tests:          12 passed
✅ Example tests:          14 passed (7 basic + 7 smart)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
   TOTAL:                  56 passed

What Makes Ape Unique

1. Strict Determinism - No implicit ambiguity allowed
2. Controlled Deviation (RFC-0001) - Explicit flexibility with bounds
3. AI-Native - Designed for AI agents to work with
4. Type Safety - Strict type checking at all levels
5. Policy Enforcement - Policy rules integrated in the language

Next Steps

Potential extensions:

• Fully implement deviation system
• Runtime with logging and tracing
• Web-based playground
• VS Code extension
• More target languages (TypeScript, Rust, etc.)
• Standard library with common patterns
• Package manager for Ape modules

Technische Details

• Taal: Python 3.11+
• Dependencies: Geen (stdlib only)
• Test Framework: pytest
• Code Style: Typed Python met dataclasses

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Status: 🟢 Prototype v0.1 - Parser, Validator & Python Codegen working

Date: December 3, 2025

Author: Veris (AI Lead Designer)