ensue-memory

Ensue Memory is an augmented cognition skill that ensures your AI intelligence does not reset between sessions. It builds a persistent, searchable knowledge tree that maps your preferences, technical decisions, research findings, and project contexts. By connecting current conversations to your historical data, Ensue allows the agent to reason beyond immediate chat history, surfacing relevant past insights whenever technical topics or decision-making challenges arise. It is designed for developers, researchers, and power users who need a centralized, structured brain that compounds knowledge over time, ensuring that past research into domains like GPU inference, distributed systems, or project architecture is always available to inform new work.

• Persistent knowledge tree architecture using namespaces to categorize thought domains like preferences, projects, research, and notes.

• Proactive memory retrieval that checks relevant knowledge branches when specific technical topics or project names are mentioned.

• Semantic search capabilities using discovery methods scoped to specific namespaces for targeted information retrieval.

• Efficient batch operations for creating, reading, and deleting memories to minimize API roundtrips and token consumption.

• Automated session logging and summarization that captures key decisions and reasoning patterns into your memory tree.

• Research agent integration to autonomously map concepts, identify knowledge gaps, and structure findings into your personal knowledge base.

• The skill requires an ENSUE_API_KEY; use the provided helper scripts to manage secure interactions with the Ensue Network API.

• Optimize context usage by avoiding large data dumps into the chat window; rely on targeted key listing and semantic discovery instead.

• Use the ENSUE_READONLY flag to selectively disable auto-logging when working on sensitive sessions.

• Follow the namespace hierarchy (e.g., projects/name/architecture) to keep your knowledge base clean and highly navigable.

• Always prioritize creating atomic, actionable memories rather than long-form, unstructured blocks of text to improve the utility of recalled context.