# APE v1.0.0 — Complete Language Release

**Release Date:** December 6, 2025  
**Type:** Major Release - Complete Specification with Roadmap Integration  
**Author:** David Van Aelst

---

## 🎯 Overview

APE v1.0.0 represents the completion of the APE language specification, delivering a **minimal, deterministic, policy-friendly language** for AI collaboration. This release consolidates all planned features from the v0.4.0-v0.6.0 roadmap, providing production-ready core features alongside complete scaffolding for advanced capabilities.

**What Makes This Special:**
- ✅ Complete language specification freeze
- ✅ Multi-language support (7 languages: EN, NL, FR, DE, ES, IT, PT)
- ✅ 439 passing tests with comprehensive coverage
- ✅ 9 realistic tutorial scenarios with 46 enriched tests
- ✅ Complete roadmap scaffolding (30+ modules, 120+ tests ready)
- ✅ Professional documentation (300+ pages across 10 guides)

---

## ✅ Production-Ready Features

### 🌐 Multi-Language Surface Syntax
- **7 Languages Supported:** English (canonical), Dutch, French, German, Spanish, Italian, Portuguese
- **Deterministic Translation:** Keywords-only, no NLP/heuristics, identical AST across languages
- **Latin Script Focus:** Proven approach before expanding to non-Latin scripts
- **35+ Tests:** Comprehensive coverage ensuring semantic equivalence

### 🔄 Core Language Features
- **Control Flow:** if/else if/else, while, for loops with max iterations safety
- **Abstractions:** tasks, flows, policies, entities, enums
- **Runtime:** AST-based executor (no exec/eval), deterministic execution
- **Safety:** Sandbox-safe, capability-gated side effects

### 📊 Observability & Introspection
- **Tracing:** TraceCollector captures execution events
- **Explanation:** ExplanationEngine provides step-by-step reasoning
- **Replay:** ReplayEngine validates determinism across runs
- **Profiles:** Strict, balanced, permissive runtime modes
- **Dry-Run Mode:** Analyze logic without mutations

### 📚 Standard Library (Pure Functions)
- **logic** (6 functions): assert_condition, all_true, any_true, none_true, equals, not_equals
- **strings** (6 functions): lower, upper, trim, starts_with, ends_with, contains_text
- **collections** (5 functions): count, is_empty, contains, filter_items, map_items
- **math** (5 functions): abs_value, min_value, max_value, clamp, sum_values

### 🧪 Testing & Quality
- **439 Passing Tests:** Zero regressions, comprehensive coverage
- **9 Tutorial Scenarios:**
  - AI Input Governance (GDPR compliance)
  - APE + Anthropic (3-tier safety classification)
  - APE + LangChain (workflow validation)
  - APE + OpenAI (request governance)
  - Dry-Run Auditing (safe analysis)
  - Explainable Decisions (transparent reasoning)
  - Multi-Language Team (7-language equivalence)
  - Risk Classification (5-tier decision logic)
  - Evidence-Based Testing (10+ control flow scenarios)
- **46 Enriched Test Cases:** Realistic multi-factor scenarios

---

## 🏗️ Scaffolded Features (Structure Complete, Implementation Pending)

These features have **complete module structure, documentation, and test skeletons**, but return `NotImplementedError` until future implementation. They demonstrate APE's architectural vision and provide a clear path for contributors.

### 🚨 Exception Handling (v0.4.0 Roadmap)
- try/catch/finally constructs
- User-defined errors with raise statement
- Error propagation through call stack
- Finally block guaranteed execution
- **Files:** 5 modules, 20+ tests, 50+ pages docs

### 📦 Structured Types (v0.4.0 Roadmap)
- Generic types: List<T>, Map<K,V>
- Record types with named fields
- Tuple types for heterogeneous collections
- Type inference and runtime validation
- **Files:** 5 modules, 20+ tests, 80+ pages docs

### 📚 Expanded Standard Library (v0.5.0 Roadmap)
- JSON parsing and serialization
- Advanced math (trig, log, rounding, constants)
- Extended collections (reduce, sort, zip)
- **Files:** 3 modules, 27+ tests, 90+ pages docs

### ⚡ Compiler Backend & VM (v0.6.0 Roadmap)
- AST optimization passes (constant folding, DCE, CSE, TCO)
- Stack-based bytecode VM (30+ opcodes)
- Bytecode compilation pipeline
- Performance benchmarking infrastructure
- **Files:** 8 modules, 40+ tests, 180+ pages docs

**Total Scaffolding:** 30+ modules, 120+ tests, 300+ pages documentation

---

## 📊 Release Statistics

**Codebase:**
- Production code: ~15,000 lines (fully functional)
- Scaffolded code: ~8,000 lines (structure complete)
- Total: ~23,000 lines

**Tests:**
- Passing: 439 tests
- Skipped: 80 tests (scaffolded features)
- Tutorial scenarios: 9 with 46 enriched tests
- Total: 519 test cases

**Documentation:**
- Core documentation: 10 comprehensive guides
- Tutorial READMEs: 9 scenario guides
- Total pages: 300+ pages

**Multi-Language:**
- Supported languages: 7 (EN, NL, FR, DE, ES, IT, PT)
- Language adapters: 7 deterministic adapters
- Test coverage: 35+ multi-language tests

---

## 🚀 Installation

### From PyPI (Recommended)
```bash
pip install ape-lang==1.0.0
```

### From Source
```bash
git clone https://github.com/Quynah/ape-lang.git
cd ape-lang/packages/ape
pip install -e .
```

### Verify Installation
```bash
ape --version  # Should print: APE v1.0.0
python -c "import ape; print(ape.__version__)"  # Should print: 1.0.0
```

---

## 📚 Getting Started

### Example: Simple Risk Classification (English)
```ape
task classify_risk
  inputs:
    score: Number
  outputs:
    level: Text

  steps:
    if score > 80 then
      set level = "HIGH"
    else if score > 50 then
      set level = "MEDIUM"
    else
      set level = "LOW"
```

### Same Logic in Dutch
```ape
taak classificeer_risico
  invoer:
    score: Getal
  uitvoer:
    niveau: Tekst

  stappen:
    als score > 80 dan
      stel niveau = "HOOG"
    anders als score > 50 dan
      stel niveau = "GEMIDDELD"
    anders
      stel niveau = "LAAG"
```

**Result:** Identical AST, identical execution, identical output.

---

## 🔍 Exploring Tutorials

APE includes 9 realistic tutorial scenarios demonstrating production-ready capabilities:

```bash
# Clone repository
git clone https://github.com/Quynah/ape-lang.git
cd ape-lang/packages/ape/tutorials

# Explore scenarios
ls -la scenario_*

# Run a specific tutorial (requires pytest)
pytest tests/tutorials/test_tutorials_execute.py -k "scenario_ai_input_governance"
```

**Recommended Starting Points:**
1. **scenario_risk_classification** - Basic decision logic with 5-tier classification
2. **scenario_multilanguage_team** - Multi-language semantic equivalence
3. **scenario_dry_run_auditing** - Safe analysis without mutations

---

## 🛣️ Roadmap & Future Versions

APE v1.0.0 provides the **complete architectural foundation** for future development:

- **v1.1.0+** - Implementation of scaffolded features based on community feedback
- **v2.0.0** - Non-Latin script support (Arabic, Hebrew, Chinese, etc.)
- **v2.x** - Advanced capabilities (localized error messages, IDE plugins, etc.)

**Contributing:** All scaffolded features have complete tests and documentation ready for implementation. See `CONTRIBUTING.md` for details.

---

## 📄 License

MIT License - See [LICENSE](LICENSE) for details.

---

## 🙏 Acknowledgments

**Author:** David Van Aelst  
**Repository:** https://github.com/Quynah/ape-lang  
**Documentation:** https://github.com/Quynah/ape-lang/tree/main/packages/ape/docs  
**Tutorials:** https://github.com/Quynah/ape-lang/tree/main/packages/ape/tutorials

---

## 🔗 Links

- **PyPI Package:** https://pypi.org/project/ape-lang/
- **GitHub Repository:** https://github.com/Quynah/ape-lang
- **Documentation:** https://github.com/Quynah/ape-lang/tree/main/packages/ape/docs
- **Changelog:** https://github.com/Quynah/ape-lang/blob/main/packages/ape/CHANGELOG.md
- **Issue Tracker:** https://github.com/Quynah/ape-lang/issues

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**APE v1.0.0** — Built for deterministic AI collaboration 🦍
