Hot Memory
Captures recent messages, emotion signals, and topic activity for immediate low-latency recall.
OpenQCore · Memory Systems
Generation I · Adaptive Memory Core
Persistent Memory.
Context That Actually Scales.
Memory Systems is OpenQCore’s production memory architecture: a layered engine that captures signals, ranks importance, retrieves relevance, and assembles context blocks for stable long-term intelligence.
Memory Layers
Hot + Cold + Vector + Behavior
Retrieval Model
Ranked + Relevance-Driven
Resilience
Fallbacks + Safe Defaults
Context Output
Recall + Timeline + Style + Relations
Identity Layer
Memory Systems Visual Identity
Use this slot for your final Memory Systems logo. The section is ready for production branding and responsive rendering.
memory systems
Memory Architecture
Layered persistence for real conversational intelligence.
The engine separates short-term interaction state from long-term insight memory, then fuses retrieval and behavior context into one reliable runtime memory flow.
Cold Memory
Stores long-term insights, milestones, message counters, and persistent memory rank over time.
Vector Retrieval Layer
Indexes meaningful content and retrieves semantically similar memories based on relevance and rank thresholds.
Behavior + Relationship Layer
Builds adaptive response style and relationship context from cumulative signals and memory state.
Context Build Pipeline
From raw input to operational memory context.
Each query runs through extraction, retrieval, selection, compression, and context assembly to generate stable memory-aware execution inputs.
01
Load Hot + Cold Memory
02
Extract Query Signals
03
Retrieve Vector + Milestones
04
Select Top Relevant Memories
05
Optional AI Compression
06
Build Recall + Timeline + Style
07
Return Context Block
Core Runtime Modules
smart_extract + cached fallback
Signal extraction with validated fallback and cached recovery when upstream processing fails.
MemoryRanker + threshold persistence
Dynamic memory scoring controls persistence thresholds and promotes meaningful long-term entries.
retrieve_similar + relevance selector
Hybrid retrieval combines vector similarity and ranked relevance filtering for precision.
TimelineEngine + MemoryRecaller
Timeline snippets and recall blocks provide chronological and factual continuity.
BehaviorEngine + style hinting
Behavior mode selection generates response style hints aligned with user memory profile.
RelationshipEngine context layer
Relationship context modeling captures interaction dynamics and continuity cues.
Optional AI compression
Optional LLM compression distills selected memories into compact high-utility context.
Prompt block assembler
Final prompt block merges recall, timeline, relationship, and style into one execution input.
Deploy memory-aware intelligence at production scale.
Build AI systems with durable context, adaptive behavior, and resilient memory retrieval across every user interaction.