We present MetaMo, a unified formal framework for AGI motivational systems, combining category theory, functional analysis and topology to support open-ended agents that self-modify and evolve their own goals and drives. A sequel paper shows how MetaMo maps onto concrete architectures like OpenPsi and MAGUS. MetaMo centers on a composite appraisal-then-decision operator F that carries both comonad (appraisal) and monad (decision) structure – a pseudo-bimonad – enabling clean “feel vs choose” pipelines. It enforces a contractive update law that dynamically keeps motivational states within a designated safe region, and it assumes a tubular topology that guarantees any achievable target lies on a thick, feasible path of incremental steps. From this foundation we extract five meta-motivational design principles: 1) Modular Appraisal-Decision Interface: separate mood updates from goal selection but allow just enough feedback so swapping their order only causes a tiny change; 2) Reciprocal State Simulation: share precise state-translation maps so agents can step into each other’s motivational frames for seamless hand-off and deep empathy; 3) Parallel Motivational Compositionality: run multiple motivational subsystems (e.g. exploration, ethics, service) in parallel and merge their outputs with small coherence corrections; 4) Homeostatic Drive Stability: apply damping so small disturbances fade and tighten control near boundary conditions; 5) Incremental Objective Embodiment: blend partway toward preferred motivational states each cycle, guaranteeing gradual convergence into a feasible "ideal region" without overshoot or destabilization or loss of coherent self-model. We argue that MetaMo guides the design of AGI systems that are stable yet adaptable, capable of safe, incremental self-improvement, trustworthy collaborators in multi-agent communities, and scalable via parallel sub-agents. We illustrate these concepts via a running example of an online research assistant.