The Context-First AGI Cognitive Context Engine (CCE) Prototype provides a robust toolkit for developers to implement context-aware AI agents. It leverages vector embeddings to store historical user interactions, enabling efficient retrieval of relevant context snippets. The engine automatically summarizes lengthy conversations to fit within LLM token limits, ensuring continuity and coherence in multi-turn dialogues. Developers can configure context prioritization strategies, manage memory lifecycles, and integrate custom retrieval pipelines. CCE supports modular plugin architectures for embedding providers and storage backends, offering flexibility for scaling across projects. With built-in APIs for storing, querying, and summarizing context, CCE streamlines the creation of personalized conversational applications, virtual assistants, and cognitive agents that require long-term memory retention.

IMMA (Interactive Multi-Modal Memory Agent) is a modular framework designed to enhance conversational AI with persistent memory. It encodes text, image, and other data from past interactions into an efficient memory store, performs semantic retrieval to provide relevant context during new dialogues, and applies summarization and filtering techniques to maintain coherence. IMMA’s APIs enable developers to define custom memory insertion and retrieval policies, integrate multi-modal embeddings, and fine-tune the agent for domain-specific tasks. By managing long-term user context, IMMA supports use cases that require continuity, personalization, and multi-turn reasoning over extended sessions.