The paper introduces CLARABEL, an open-source interior-point solver for convex optimization problems with conic constraints and quadratic objectives. The method is based on a homogeneous embedding technique originally developed for monotone complementarity problems and adapted to handle conic constraints. CLARABEL supports a variety of symmetric and non-symmetric cones, including semidefinite cones, and employs chordal decomposition methods for semidefinite cones. The solver is implemented in both Rust and Julia, with interfaces for Python, C, C++, and R. Numerical experiments show that CLARABEL outperforms several state-of-the-art solvers, including commercial and open-source solvers, in terms of speed and robustness, particularly for problems with quadratic objectives. The solver is currently integrated into the Python CVXPY optimization suite.The paper introduces CLARABEL, an open-source interior-point solver for convex optimization problems with conic constraints and quadratic objectives. The method is based on a homogeneous embedding technique originally developed for monotone complementarity problems and adapted to handle conic constraints. CLARABEL supports a variety of symmetric and non-symmetric cones, including semidefinite cones, and employs chordal decomposition methods for semidefinite cones. The solver is implemented in both Rust and Julia, with interfaces for Python, C, C++, and R. Numerical experiments show that CLARABEL outperforms several state-of-the-art solvers, including commercial and open-source solvers, in terms of speed and robustness, particularly for problems with quadratic objectives. The solver is currently integrated into the Python CVXPY optimization suite.