Mnexium is a memory infrastructure layer for AI applications. Instead of building custom pipelines to handle conversation history, user profiles, and session context, developers connect once via a single API and get unified memory that works across OpenAI, Anthropic, Gemini, and multi-agent workflows.
The API handles storage, retrieval, and context injection automatically. Relevant memory is surfaced per user or session without managing vector databases, embedding jobs, or sync logic. It supports persistent memory (facts that survive sessions), chat history, structured user records, and live context for ongoing interactions.
Practical use cases include chatbots that remember past conversations, agents that carry state across workflow steps, and personalization layers that adapt responses based on a user's history — regardless of which model or runtime handles the request. Built for teams that want memory to be a solved problem, not a project.