A reproducible evaluation framework for AI systems, retrieval systems, agents, and model experiments.
Agent workflows require standardized, reproducible testing to prevent behavioral regressions and verify model accuracy.
- Provide structured model evaluation templates\n- Track dataset test metrics over iteration runs\n- Implement pipeline assertion checks\n
- nongoal\n- nongoal\n
A Python framework using pytest structures and JSON schemas to define, evaluate, and track run benchmarks.
Clone the repository and see language-specific tool setups.
Refer to examples directory for minimal usage.
See CONTRIBUTING.md for details.
Run language-specific testing framework commands.
- Establish baseline interfaces
- Add unit test coverage
- Integrate telemetry tracking
See CONTRIBUTING.md.
Apache-2.0 License.