Magic Lab Studio

Observer is an open source AI evaluation and observability framework built by Magic Lab Studio to make AI systems measurable, reproducible, and accountable. As AI systems become increasingly autonomous, developers face a critical challenge: how can they objectively evaluate model behavior, compare deployment strategies, and identify reliability issues before production? For the Meta Global AI Developer Hackathon, we are extending Observer to evaluate Meta's AI models across two inference environments: API hosted inference and local execution using open weights. By running comparable evaluation workloads across both environments, Observer enables developers to investigate differences in response quality, latency, consistency, and operational reliability. Our approach focuses on four capabilities: 1. Cross Environment Evaluation: Compare Meta model execution through an API and local inference under controlled testing conditions. 2. Behavioral Observability: Capture execution traces, outputs, performance metrics, and failure evidence. 3. Reproducible Benchmarking: Apply consistent evaluation criteria to assess model behavior across deployment environments. 4. Regression Detection: Identify changes in performance or behavior that could compromise system reliability. Unlike traditional benchmarks that focus primarily on model scores, Observer emphasizes practical engineering evidence: how AI systems behave under realistic workloads and deployment constraints. Our objective is to demonstrate that AI reliability can be systematically measured, independently of where inference happens. Developed by Magic Lab Studio, Observer represents our vision for an AI engineering ecosystem where model evaluation, observability, and reproducibility are fundamental components of the development lifecycle. Don't just deploy AI. Prove it works.