This paper introduces the Smac Planner, an open-source search-based planning framework with multiple algorithm implementations including 2D-A*, Hybrid-A*, and State Lattice planners. The work addresses the lack of performant and available feasible planners for mobile and surface robotics research. The Smac Planner is designed to be a templated, C++-based framework that enables the creation of search-based planning algorithms with minimal code. It provides three planners: 2D-A*, Hybrid-A*, and State Lattice, each tailored for different robotic applications. The framework is integrated into ROS 2's Nav2, which powers thousands of robots in research and industry. The Smac Planner introduces Cost-Aware planners, which are specifically designed for mobile roboticists. These planners incorporate cost maps to account for environmental constraints, enabling kinematically feasible paths. The Cost-Aware Obstacle Heuristic is a key component that guides the search away from obstacles while respecting grid map constraints. This heuristic is admissible and allows for efficient path planning by avoiding dead-ends and unnecessary turns. The Smac Planner also includes traversal penalty functions that influence the search behavior of the planners. These penalties help in creating smooth and reliable paths by penalizing non-straight motion and unnecessary turning. The framework also adjusts motion primitives based on the grid resolution, ensuring that the planners can operate efficiently in various environments. The paper presents experiments comparing the performance of the Smac Planners with other planning frameworks such as NavFn and SBPL. The results show that the Smac Planners, particularly the Cost-Aware Hybrid-A* and State Lattice, outperform these frameworks in terms of speed and path quality. The Smac Planners are able to generate shorter and more feasible paths, especially in complex environments. The Smac Planner is also tested in real-world scenarios, such as a large warehouse, where it demonstrates effective path planning in confined spaces. The framework's integration with ROS 2's Nav2 enables it to be used in a wide range of robotic applications, including car-like, legged, and large non-circular robots. Overall, the Smac Planner provides a robust and efficient solution for kinematically feasible path planning in mobile and surface robotics, filling a critical gap in the field. The open-source nature of the framework allows for easy integration and customization, making it a valuable tool for researchers and developers in the robotics community.This paper introduces the Smac Planner, an open-source search-based planning framework with multiple algorithm implementations including 2D-A*, Hybrid-A*, and State Lattice planners. The work addresses the lack of performant and available feasible planners for mobile and surface robotics research. The Smac Planner is designed to be a templated, C++-based framework that enables the creation of search-based planning algorithms with minimal code. It provides three planners: 2D-A*, Hybrid-A*, and State Lattice, each tailored for different robotic applications. The framework is integrated into ROS 2's Nav2, which powers thousands of robots in research and industry. The Smac Planner introduces Cost-Aware planners, which are specifically designed for mobile roboticists. These planners incorporate cost maps to account for environmental constraints, enabling kinematically feasible paths. The Cost-Aware Obstacle Heuristic is a key component that guides the search away from obstacles while respecting grid map constraints. This heuristic is admissible and allows for efficient path planning by avoiding dead-ends and unnecessary turns. The Smac Planner also includes traversal penalty functions that influence the search behavior of the planners. These penalties help in creating smooth and reliable paths by penalizing non-straight motion and unnecessary turning. The framework also adjusts motion primitives based on the grid resolution, ensuring that the planners can operate efficiently in various environments. The paper presents experiments comparing the performance of the Smac Planners with other planning frameworks such as NavFn and SBPL. The results show that the Smac Planners, particularly the Cost-Aware Hybrid-A* and State Lattice, outperform these frameworks in terms of speed and path quality. The Smac Planners are able to generate shorter and more feasible paths, especially in complex environments. The Smac Planner is also tested in real-world scenarios, such as a large warehouse, where it demonstrates effective path planning in confined spaces. The framework's integration with ROS 2's Nav2 enables it to be used in a wide range of robotic applications, including car-like, legged, and large non-circular robots. Overall, the Smac Planner provides a robust and efficient solution for kinematically feasible path planning in mobile and surface robotics, filling a critical gap in the field. The open-source nature of the framework allows for easy integration and customization, making it a valuable tool for researchers and developers in the robotics community.