https://github.com/omnixs/orion
C++ DMN Business Rules Engine - Fast decision table evaluation with FEEL
https://github.com/omnixs/orion
ai-generated-code business-rules cpp cpp-library cpp23 decision-tables dmn feel-expressions rules-engine
Last synced: about 2 months ago
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C++ DMN Business Rules Engine - Fast decision table evaluation with FEEL
- Host: GitHub
- URL: https://github.com/omnixs/orion
- Owner: omnixs
- License: apache-2.0
- Created: 2025-11-21T20:11:18.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2026-01-13T16:37:46.000Z (2 months ago)
- Last Synced: 2026-01-13T19:55:28.307Z (2 months ago)
- Topics: ai-generated-code, business-rules, cpp, cpp-library, cpp23, decision-tables, dmn, feel-expressions, rules-engine
- Language: C++
- Size: 33.3 MB
- Stars: 2
- Watchers: 0
- Forks: 0
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
- Security: SECURITY.md
- Notice: NOTICE
Awesome Lists containing this project
README
# ORION — Optimized Rule Integration & Operations Native
[](CONTRIBUTING.md)
[](https://github.com/omnixs/orion/releases/latest)
[](LICENSE)
[](https://github.com/omnixs/orion/actions/workflows/ci-full.yml)
[](https://en.cppreference.com/w/cpp/23)
[](https://www.omg.org/spec/DMN/)
[](dat/tck-baselines/2.1.1/)
A native C++ implementation of the DMN™ (Decision Model and Notation) specification,
focusing on **decision tables** with an emphasis on performance, correctness, and simple integration.
> **🤖 AI-Generated Project:** All source code is generated by AI agents via task prompts. See [AI-Only Development Workflow](#ai-only-development-workflow) below.
- **Language:** C++23
- **License:** Apache-2.0 (see `LICENSE`)
- **Status:** Early stage (APIs may evolve)
- **DMN Compliance:** Level 1 and Level 2 compatible
- **Goals:** fast rule evaluation, clean table model, clear extensibility points
## Why Orion?
- **Native C++23** - No JVM dependency, designed for high-performance backend integration
- **DMN Standards Focus** - Implements DMN 1.5 Level 2 with 100% TCK compliance (126/126 tests)
- **Lightweight & Embeddable** - Static library suitable for embedding in larger systems
- **Production-Quality Testing** - 3,814 automated tests (279 unit + 3,535 TCK)
- **AI-Only Codebase** - Demonstrates rigorous AI-generated code with human oversight
## Download
Download the latest release from [GitHub Releases](https://github.com/omnixs/orion/releases/latest):
- **Linux**: `orion-X.Y.Z-linux-x64.tar.gz`
- **Windows**: `orion-X.Y.Z-windows-x64.zip`
Each release includes:
- Static libraries (`liborion_lib.a` / `orion_lib.lib`)
- Headers (`include/orion/`)
- CMake configuration files
- Command-line tools (`orion_app`, `orion_tck_runner`)
- TCK test baseline for the version
- License files
**Verify downloads** with the corresponding `.sha256` checksum files.
## Features
- Decision Table evaluation compatible with DMN L1/L2 (hit policies, FEEL expressions)
- **Decision Requirements Graph (DRG)** evaluation with topological sorting and dependency resolution
- Business Knowledge Models (BKM) support
- Portable C++ API under `orion::api` namespace
- FEEL (Friendly Enough Expression Language) expression evaluator
- Pluggable logger interface (optional spdlog integration)
- Deterministic tests and benchmarks
- DMN TCK (Technology Compatibility Kit) test runner
## Current Limitations
**DMN Feature Coverage:**
- ✅ Level 2: 100% (125/125 tests) - Decision tables with UNIQUE, FIRST, COLLECT hit policies
- ⏳ Level 3: 11.2% (375/3,351 tests) - Partial FEEL expression support
- ❌ Boxed expressions, decision services, and advanced DMN features not yet implemented
**FEEL Language Support:**
- ✅ Basic arithmetic, comparisons, boolean logic, string operations
- ✅ Built-in functions: abs(), substring(), string length, matches()
- ⏳ Limited date/time, list, and context operations
- ❌ Advanced functions, quantified expressions (some, every)
**API Stability:**
- Version 2.1.1 released: FEEL function evaluation improvements
- Breaking change from v1.x: evaluate() now uses nlohmann::json directly
- Thread-safety: Single-threaded usage model (share-nothing across threads)
- See [CHANGELOG.md](CHANGELOG.md) for version history
- See [CHANGELOG.md](CHANGELOG.md) for version history
## Quick Start
### Prerequisites
- C++23 compatible compiler (GCC 11+, Clang 15+, MSVC 2022+)
- CMake 3.26 or later
- vcpkg package manager
**Dependencies** (automatically installed by vcpkg):
- nlohmann-json - JSON library
- PCRE2 - Perl-compatible regular expressions (for FEEL `matches()` function)
- CTRE - Compile-time regular expressions (internal implementation)
- spdlog - Logging framework
- RapidXML - XML parsing
- Boost.Test - Unit testing (dev only)
- Google Benchmark - Performance testing (dev only)
### Setup vcpkg
```bash
git clone https://github.com/Microsoft/vcpkg.git ~/vcpkg
cd ~/vcpkg
./bootstrap-vcpkg.sh # or bootstrap-vcpkg.bat on Windows
```
### Build Orion
```bash
git clone https://github.com/omnixs/orion.git orion
cd orion
# Initialize DMN TCK test submodule (required for running tests)
git submodule update --init --recursive
# Configure with vcpkg
cmake -S . -B build \
-DCMAKE_BUILD_TYPE=Release \
-DCMAKE_TOOLCHAIN_FILE=~/vcpkg/scripts/buildsystems/vcpkg.cmake \
-DVCPKG_TARGET_TRIPLET=x64-linux
# Build
cmake --build build -j
# Run tests (fast selective mode - runs passing TCK tests only)
./build/tst_orion --log_level=test_suite
# Or run comprehensive mode (all TCK tests - slower)
export ORION_TCK_RUN_ALL=1
./build/tst_orion --log_level=test_suite
```
**Note**: The DMN TCK (Test Compatibility Kit) is included as a git submodule in `dat/dmn-tck/`.
If you skip the submodule initialization, the build will succeed but some tests will fail due to missing test data files.
**Performance Tip**: Unit tests run in selective mode by default (0.9s vs 25s), executing only passing Level 3 TCK tests.
Level 2 tests always run in full (DMN compliance requirement). Set `ORION_TCK_RUN_ALL=1` for comprehensive validation.
### Minimal Usage Example
```cpp
#include
#include
#include
int main() {
// Create the business rules engine
orion::api::BusinessRulesEngine engine;
// Load DMN model from XML string or file
std::string dmn_xml = "...";
auto result = engine.load_dmn_model(dmn_xml);
if (!result) {
std::cerr << "Error: " << result.error() << std::endl;
return 1;
}
// Create input context as JSON
nlohmann::json input = {
{"age", 25},
{"category", "premium"}
};
// Evaluate decision (native JSON API - zero-copy)
nlohmann::json result = engine.evaluate(input);
std::cout << "Result: " << result.dump() << std::endl;
return 0;
}
```
See `docs/examples/consumer-project/` for a complete working example with build configuration.
## Examples & Documentation
- **[Architecture Overview](docs/architecture.md)** - Components, data flow, repository structure
- **[Testing Guide](docs/testing.md)** - Build instructions, running unit tests and TCK
- **[AI Development Story](docs/ai-dev-story.md)** - How this project was built using AI-only workflow
- **[Consumer Project Example](docs/examples/consumer-project/)** - Integrate Orion into your CMake project
- **[DMN Examples](dat/tst/)** - Sample DMN files for testing
## Getting Help
**Questions & Support:**
- 🐛 **Bug Reports**: [Open an issue](https://github.com/omnixs/orion/issues/new) with reproduction steps
- 💡 **Feature Requests**: [Open an issue](https://github.com/omnixs/orion/issues/new) describing use case
- 📖 **Documentation**: See [docs/](docs/) for architecture, testing, and AI workflow guides
- 💬 **Usage Questions**: [Open an issue](https://github.com/omnixs/orion/issues) with "question" label
**Before Opening an Issue:**
1. Check existing issues for duplicates
2. Review [docs/testing.md](docs/testing.md) for test execution help
3. Include DMN file, input JSON, and error messages for bug reports
## Contributing
We welcome issues and PRs! Please read:
- `CONTRIBUTING.md` (AI-only workflow, DCO, coding guidelines)
- `CODE_OF_CONDUCT.md` (Contributor Covenant)
**Note:** This project follows a strict **AI-Only Policy**. All source code changes must be generated by AI agents via task prompts.
### AI-Only Development Workflow
This project uses a **two-phase task-based workflow**:
**Phase 1: Create Task** (Planning)
1. Create an issue describing the work
2. Tell the AI: "Create a task file for [goal] using [template]"
3. AI creates task file in `.github/tasks/` (may ask clarifying questions if needed)
4. Review and refine the task file (directly or using AI)
**Phase 2: Execute Task** (Implementation)
1. Tell the AI: "Execute"
2. AI creates feature branch, implements changes, runs tests, fills retrospective
3. Review the PR and merge
**Key files:**
- `.github/copilot-instructions.md` - Complete AI agent instructions
- `.github/prompts/*.md` - Task templates for different work types
- `.github/tasks/*.md` - Individual task files (one per issue)
See `CONTRIBUTING.md` for complete details.
## Security
See `SECURITY.md` for reporting vulnerabilities.
## License
Copyright (c) 2025 ORION contributors.
Licensed under the Apache License, Version 2.0. See `LICENSE` for details.
## Trademarks
DMN™ is a trademark of the Object Management Group (OMG). Any other names are the
property of their respective owners and used for identification only.