Official validators for the JSON Agents specification.
Location: python/
Language: Python 3.8+
Status: ✅ Production Ready (v1.0.0)
Comprehensive validator for JSON Agents manifests with:
- ✅ JSON Schema validation (JSON Schema 2020-12)
- ✅ URI validation (
ajson://scheme per RFC 3986) - ✅ Policy expression validation (Appendix B grammar)
- ✅ Rich CLI with colored output
- ✅ Python API for programmatic use
Quick Start:
cd python/
pip3 install -r requirements.txt
python3 -m jsonagents.cli validate ../../examples/*.jsonDocumentation:
- README.md - Main documentation
- INSTALL.md - Installation guide
- PURPOSE.md - What validators are for
- TEST-RESULTS.md - Test coverage (47/47 passing)
- Node.js and Deno support
- Integration with VS Code extension
- npm package
- High-performance validation
- WASM bindings for browser use
- Command-line tool
- Native performance
- Minimal dependencies
- Container-friendly
| Feature | Python | JS/TS | Rust | Go |
|---|---|---|---|---|
| JSON Schema Validation | ✅ | 🔜 | 🔜 | 🔜 |
| URI Validation | ✅ | 🔜 | 🔜 | 🔜 |
| Policy Expression Parser | ✅ | 🔜 | 🔜 | 🔜 |
| CLI Tool | ✅ | 🔜 | 🔜 | 🔜 |
| Library API | ✅ | 🔜 | 🔜 | 🔜 |
| Test Coverage | 100% | - | - | - |
We welcome validators in other languages! See CONTRIBUTING.md for guidelines.
- ✅ JSON Schema Validation - Validate against official schema
- ✅ URI Validation - Implement RFC 3986 compliant
ajson://parser - ✅ Policy Validation - Parse and validate Appendix B expressions
- ✅ Test Coverage - Minimum 80% code coverage
- ✅ Integration Tests - Pass all examples from
../examples/ - ✅ Documentation - README, installation guide, API docs
- ✅ License - Apache 2.0
Best For: Python developers, data science, ML workflows
Pros: Mature, well-tested, comprehensive CLI
Cons: Slower than compiled languages
Best For: Web developers, Node.js apps, browser validation
Pros: Universal (runs anywhere), npm ecosystem
Cons: Less strict typing than Rust/Go
Best For: High-performance apps, WASM, system tools
Pros: Fastest, memory-safe, WASM support
Cons: Steeper learning curve
Best For: Cloud services, microservices, CLI tools
Pros: Fast, simple, great for containers
Cons: Less rich ecosystem than JS/Python
Building interoperable AI agents, one validator at a time 🚀