Sravan Yadala@sravanyadala7

Mnemos is a project that provides a universal AI memory layer designed to make interactions with AI systems more useful and consistent. Current AI tools often forget past conversations or lose context when switching between models, sessions, or platforms. This makes it difficult for users to build on previous work or have a continuous experience. Mnemos solves this problem by creating a memory system that persists and recalls context across conversations, tools, and models. The project was developed during a hackathon with the goal of giving developers and users an easy way to add long-term memory to AI-powered applications. It allows AI models to store important details from past interactions and retrieve them later when relevant. This memory can include user preferences, past queries, project information, or any other contextual data that improves the overall experience. Mnemos is designed with flexibility in mind. It combines a backend service and browser integration so that the memory layer can work across different environments. The backend is built to handle persistence and retrieval of context, while the Chrome extension demonstrates how this memory can integrate directly into user workflows. The system is language-agnostic and can support multiple models, making it useful for developers who want to connect memory across various AI tools. This project shows how adding memory to AI systems can turn them from single-session assistants into long-term collaborators. By persisting context, Mnemos enables smoother conversations, smarter tool use, and a more personal AI experience. The hackathon version is an early prototype, but it demonstrates the core idea of creating a universal memory layer that can scale into a reusable framework for developers and users alike.

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