Mnexium serves as the memory layer for AI products, providing persistent, explainable, and automatic memory capabilities. It addresses the problem of users repeating themselves, agents losing context, and tasks resetting 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; Agent Memory extracts and persists facts, preferences, and context about users across all conversations; Agent State manages short-term, task-scoped working context for agentic workflows; and Observability provides a full audit trail of every API call, memory creation, and auth event.

Mnexium works by persisting memory across sessions through automatic learning and recall. With just a few lines of code, agents learn from conversations, store what matters, and recall relevant context when users return days or weeks later. Every memory is scored, searchable, and explainable, with full observability 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 emails, tickets, or payments.

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