Clarabel is an interior-point solver for conic optimization problems with quadratic objectives. It is based on a homogeneous embedding method originally developed for monotone complementarity problems and adapted for interior-point methods. The solver supports a variety of symmetric and non-symmetric cones, including semidefinite cones, and uses chordal decomposition for efficient solving. CLARABEL is implemented as an open-source solver for the Python CVXPY optimization suite and outperforms other solvers in speed and robustness across a wide range of benchmark problems, particularly for quadratic objectives. The solver uses a primal-dual interior-point method with a homogeneous self-dual embedding approach, and supports both symmetric and nonsymmetric cones. It includes efficient linear solvers and handles large-scale problems with sparse structures. Numerical experiments show that CLARABEL is faster and more robust than competing solvers, especially for quadratic problems. The solver is implemented in both Rust and Julia, with support for various floating-point data types and different linear solvers.Clarabel is an interior-point solver for conic optimization problems with quadratic objectives. It is based on a homogeneous embedding method originally developed for monotone complementarity problems and adapted for interior-point methods. The solver supports a variety of symmetric and non-symmetric cones, including semidefinite cones, and uses chordal decomposition for efficient solving. CLARABEL is implemented as an open-source solver for the Python CVXPY optimization suite and outperforms other solvers in speed and robustness across a wide range of benchmark problems, particularly for quadratic objectives. The solver uses a primal-dual interior-point method with a homogeneous self-dual embedding approach, and supports both symmetric and nonsymmetric cones. It includes efficient linear solvers and handles large-scale problems with sparse structures. Numerical experiments show that CLARABEL is faster and more robust than competing solvers, especially for quadratic problems. The solver is implemented in both Rust and Julia, with support for various floating-point data types and different linear solvers.