Pebbling AI is a dedicated memory infrastructure designed to enhance AI agent capabilities. By offering vector storage integrations, retrieval-augmented generation support, and customizable memory pruning, it ensures efficient long-term context handling. Developers can define memory schemas, build knowledge graphs, and set retention policies to optimize token usage and relevance. With analytics dashboards, teams monitor memory performance and user engagement. The platform supports multi-agent coordination, allowing separate agents to share and access common knowledge. Whether building conversational bots, virtual assistants, or automated workflows, Pebbling AI streamlines memory management to deliver personalized, context-rich experiences.

MInD is a Python-based memory framework designed to augment LLM-driven AI agents with robust memory capabilities. It enables agents to capture user inputs and system events as episodic logs, condense those logs into semantic summaries, and retrieve contextually relevant memories on demand. With configurable retention policies, similarity search, and automated summarization, MInD maintains a persistent knowledge base that agents consult during inference. This ensures they recall prior interactions accurately, adapt responses based on history, and deliver personalized, coherent dialogues across multiple sessions.