abstract: In a collaborative environment where multiple robots and human beings are expected to collaborate to perform a task, it becomes essential for a robot to be aware of multiple agents working in its work environment. A robot must also learn to adapt to different agents in the workspace and conduct its interaction based on the presence of these agents. A theoretical framework was introduced which performs interaction learning from demonstrations in a two-agent work environment, and it is called Interaction Primitives. This document is an in-depth description of the new state of the art Python Framework for Interaction Primitives between two agents in a single as well as multiple task work environment and extension of the original framework in a work environment with multiple agents doing a single task. The original theory of Interaction Primitives has been extended to create a framework which will capture correlation between more than two agents while performing a single task. The new state of the art Python framework is an intuitive, generic, easy to install and easy to use python library which can be applied to use the Interaction Primitives framework in a work environment. This library was tested in simulated environments and controlled laboratory environment. The results and benchmarks of this library are available in the related sections of this document. / Dissertation/Thesis / Masters Thesis Computer Science 2017