  The COVID-19 pandemic has intensified the urgency for effective and
accessible mental health interventions in people's daily lives. Mobile Health
(mHealth) solutions, such as AI Chatbots and Mindfulness Apps, have gained
traction as they expand beyond traditional clinical settings to support daily
life. However, the effectiveness of current mHealth solutions is impeded by the
lack of context-awareness, personalization, and modularity to foster their
reusability. This paper introduces CAREForMe, a contextual multi-armed bandit
(CMAB) recommendation framework for mental health. Designed with
context-awareness, personalization, and modularity at its core, CAREForMe
harnesses mobile sensing and integrates online learning algorithms with user
clustering capability to deliver timely, personalized recommendations. With its
modular design, CAREForMe serves as both a customizable recommendation
framework to guide future research, and a collaborative platform to facilitate
interdisciplinary contributions in mHealth research. We showcase CAREForMe's
versatility through its implementation across various platforms (e.g., Discord,
Telegram) and its customization to diverse recommendation features.
