celiums-memory

Search 500,000+ expert knowledge modules by natural language query. Returns ranked results with titles, descriptions, and categories. Use when the user needs technical guidance, best practices, or domain expertise. Behavior: performs hybrid search (full-text + semantic) across the knowledge base, ranks by relevance, returns top N matches. Example queries: "kubernetes horizontal pod autoscaler", "react hooks best practices", "HIPAA compliance checklist".

Load the full content of a knowledge module by its exact name/slug. Returns the complete module text (typically 2,000-20,000 words) with code examples, best practices, and references. Use after forage to read a specific module in full. Behavior: looks up the module by slug, returns full markdown content. If not found, suggests using forage to search. Example: absorb("react-mastery") returns the complete React mastery guide.

Get personalized module recommendations based on a goal or task description. Uses keyword matching and category ranking (no AI inference). Faster than forage for broad exploration. Use when the user describes what they want to achieve and needs guidance on which modules to study. Behavior: analyzes the goal text, matches against module metadata, returns ranked suggestions grouped by relevance. Example: sense("I want to deploy a microservices app on Kubernetes with monitoring").

Browse the entire Celiums knowledge network organized by category. Returns all categories with module counts, top modules per category, and total statistics. Use to explore what knowledge is available, discover categories, or get an overview of the knowledge base. Behavior: queries the module index, groups by category, returns a structured map with counts. No parameters needed — returns the full network overview.

Store information in persistent memory that survives across all sessions and machines. Memories are automatically classified by type (semantic, procedural, episodic) and importance. Use to save facts, preferences, decisions, context, or any information that should be recalled later. Behavior: stores the content with emotional analysis (PAD model), assigns importance score, updates circadian interaction tracking. Scoped to current project by default — use projectId="global" for cross-project memories like user preferences or business decisions.

Search persistent memory using semantic + emotional relevance ranking. Returns memories sorted by relevance, recency, and emotional resonance. Searches current project + global memories by default. Use to retrieve previously stored facts, decisions, preferences, or context. Behavior: performs hybrid retrieval (vector similarity + full-text + emotional resonance), applies spaced activation recall (SAR) filtering, returns ranked results with content, type, importance, and relevance score.