A basic problem facing climate scientists is not the absence of software to analyze data, but rather a shortage of interrelated diagnostic tools that are consistent, flexible, portable, adaptable, efficient, and easy to use. The Climate Data Analysis Tool (CDAT) was originally designed to work with data from the AMIP and CMIP projects that required an open-software paradigm. Consistency is achieved through the open system software framework furnished by the adoption of Python as the underlying framework for CDAT. The power of the Python language guarantees flexibility with modules added to allow access to a variety of data formats with a variety of interfaces. Because of the open system philosophy, the framework will run on any platform. All of the supporting modules are under the open source development guidelines. As modules are added, the system can be adapted to different data structures, diagnostic philosophies, and graphics methods. The Python language is easy to use and debug with a similar syntax to other interpretative languages using array objects. In essence, arrays can be treated as scalars making complex loops unnecessary. The development of the framework for combining and interrelating tasks is the primary goal of this effort.