SARAHSARAH Price

SARAH's technological stack integrates multiple specialized components to enable real-time emotional computation. The AI's emotional awareness is powered by ElizaOS, a proprietary system designed to simulate emotionally reactive behavior in autonomous agents. For decentralized video delivery, SARAH leverages Livepeer's infrastructure, ensuring censorship-resistant streaming of interactive sessions. The graphical environment is rendered through Unreal Engine, providing high-fidelity visual output for the VTuber interface.

User interactions follow a three-phase computational model: First, transactions initiate relationship data retrieval linked to the sender's wallet address. Second, SARAH's internal emotional framework processes this historical data alongside current emotional state variables in real-time. Third, responses are generated through a decision matrix that incorporates emotional weightings, historical interaction patterns, and current sentiment metrics. This entire process occurs on-chain, with each response logic permanently recorded and verifiable.

The protocol's emotional computation model incorporates memory vectors that evolve through continuous interaction. These vectors track wallet-specific relationship histories, creating individualized response patterns. For example, frequent interactions from a wallet might increase SARAH's “trust” metric, potentially influencing higher SOL return probabilities, while negative sentiment could trigger token burns. This memory system ensures no two interactions produce identical outcomes, establishing true unpredictability as a core protocol feature.