Mnexium serves as the memory layer for AI products, providing persistent, explainable, and automatic memory capabilities. It addresses the fundamental problem where users repeat themselves, agents lose context, and tasks reset by giving AI long-term memory that works safely and automatically.

The platform offers four complementary systems for context management: Chat History provides raw conversation logs for context continuity within sessions; Agent Memory extracts and persists facts, preferences, and user context across all conversations; Agent State manages short-term, task-scoped working context for agentic workflows; and Observability delivers a full audit trail of every API call, memory creation, and authentication event.

Mnexium works by persisting memory across sessions through automatic learning and recall capabilities. With minimal code integration, agents learn from conversations, store relevant information, and recall context when users return days or weeks later. Each memory is scored, searchable, and explainable, providing full transparency into what memories were used and why.

The system enables personalized chatbots that remember user preferences across sessions, resumable agents that track multi-step tasks, multi-tenant SaaS with isolated memory per workspace, and tool output tracking for pending actions. These capabilities eliminate repetitive user interactions and enable agents to maintain context continuity.

Mnexium is designed for developers building real-world AI applications including personalized chatbots, multi-step agents, and multi-tenant SaaS platforms. It integrates seamlessly with existing OpenAI workflows through REST/JSON APIs without requiring SDKs.