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Recent research highlights the lack of reliability of large language models (LLMs) in tasks requiring complex reasoning. While they can produce impressively fluent text in response to prompts, they can fail on basic reasoning skills, such as recognizing that left is the opposite of right. They struggle even more with grounding such concepts in real-world contexts involving perception and action. Addressing real-world problems, however, typically requires models composed of multiple interdependent learners, with strong capabilities for composition and reasoning. In this talk, I will discuss the reasoning challenges of LLMs and discuss how symbolic representations can enhance neural models by enabling Spatial and Compositional Reasoning over complex linguistic structures, grounding language in visual perception, integrating multiple modalities, and dealing with uncertainty. I will overview recent research in Neurosymbolic (NeSy) modeling and emphasize the need for community-driven libraries to advance this direction. As part of this effort, I will introduce the DomiKnowS framework developed by my team, which combines symbolic and sub-symbolic representations to tackle complex, AI-complete problems, integrating symbolic and logical knowledge seamlessly into deep models and LLMs through a range of underlying algorithms.