The limitations of direct planning capabilities from Large Language Models (LLMs) have drawn interest in integrating neuro-symbolic approaches within the Automated Planning (AP) and Natural Language Processing (NLP) communities. With the proliferation of related techniques to convert NL to PDDL, we are seeing an ever-increasing set of related methods. To bring them together under a single computational umbrella, we created a unified framework that encompasses the vast majority of existing methods: L2P, 1 an open-source Python library developed to assist users in creating their own frameworks. We hope to see the L2P framework adopted by the community as a repository of existing advancements in LLM model acquisition and relevant papers, ensuring that users have access to the most current research and tools under a common framework for fair comparison.