Long-horizon embodied tasks require policies that execute many dependent actions before task success can be observed. Representing policies as executable control pro- grams (code-as-policy) enables their decision logic to be inspected and revised after rollout evaluation. Revised programs can then be executed and compared by rollout performance, framing policy improvement as execution-guided program search. Evo- lutionary methods driven by large language models (LLMs) provide a natural mecha- nism for this search by generating variants and selecting high-performing candidates. However, existing approaches primarily select among independently generated vari- ants and lack a sequential local improvement phase. We introduce MEMENTO, a memory-guided single-elite memetic framework for code-as-policy evolution. ME- MENTO first evolves a rollout evaluator that maps policy rollouts to scalar fitness and structured feedback metrics. Fitness selects accepted candidates and the next elite, while feedback metrics condition policy proposals generated by memory-guided hill-climbing, macro-mutation, and crossover. We evaluate MEMENTO on two long- horizon embodied domains: Robosuite Franka Tower-of-Hanoi manipulation and AI2- THOR household interaction. MEMENTO outperforms Eureka and REvolve, adapted as code-as-policy evolutionary baselines, in task success and generalization to held- out Robosuite object configurations and unseen AI2-THOR scenes. Ablations show that zero-shot generation and unevolved evaluators fail to solve either domain, and that removing policy-search branches reduces performance. Finally, we deploy the best-evolved Robosuite policy on a physical Franka robot, demonstrating the feasibil- ity of sim-to-real transfer of the evolved code-as-policy. Code, prompts, and videos are available at: https://github.com/sygkounas/MEMENTO.

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