This paper describes the development of a Framework for benchmarking and comparing signal-extraction and noise-interference-removal methods that are applicable to interferometric Gravitational Wave detector systems. The primary use is towards comparing signal and noise extraction techniques at LISA frequencies from multiple (possibly confused) ,gravitational wave sources. The Framework includes extensive hybrid learning/classification algorithms, as well as post-processing regularization methods, and is based on a unique plug-and-play (component) architecture. Published methods for signal extraction and interference removal at LISA Frequencies are being encoded, as well as multiple source noise models, so that the stiffness of GW Sensitivity Space can be explored under each combination of methods. Furthermore, synthetic datasets and source models can be created and imported into the Framework, and specific degraded numerical experiments can be run to test the flexibility of the analysis methods. The Framework also supports use of full current LISA Testbeds, Synthetic data systems, and Simulators already in existence through plug-ins and wrappers, thus preserving those legacy codes and systems in tact. Because of the component-based architecture, all selected procedures can be registered or de-registered at run-time, and are completely reusable, reconfigurable, and modular
