Mnexium

Mnexium is a memory infrastructure layer for AI applications that gives agents persistent, structured memory through a simple API. It centralizes long-term memory, conversation history, user profiles, and agent state so that models from OpenAI, Anthropic, and Google can maintain continuity across sessions and channels. The primary purpose of Mnexium is to make it easy for developers to build context-aware, personalized AI systems without managing custom databases or complex retrieval logic themselves.

Mnexium provides APIs for storing and retrieving user-specific data, including past interactions, preferences, and behavioral patterns, enabling agents to adapt responses based on historical context. It supports long-term memory that can be queried and updated over time, allowing agents to “remember” prior conversations and decisions beyond a single session. The platform handles conversation threading, state management, and identity resolution, making it straightforward to associate multiple interactions and channels with the same user. Mnexium is designed to integrate cleanly with existing LLM workflows, supporting major model providers and enabling developers to plug memory into their existing prompt and tool pipelines.