SuanmoSuanyangTechnology / MemoryBear

# MemoryBear — Empowering AI with Human-Like Memory
**Next-Generation AI Memory Management System · Perceive · Extract · Associate · Forget**
中文 | English
Quick Start · Installation · Core Features · Architecture · Benchmarks · Papers
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## Overview
MemoryBear is a next-generation AI memory system developed by RedBear AI. Its core breakthrough lies in moving beyond the limitations of traditional "static knowledge storage". Inspired by the cognitive mechanisms of biological brains, MemoryBear builds an intelligent knowledge-processing framework that spans the full lifecycle of **perception → extraction → association → forgetting**.
Unlike traditional memory tools that treat knowledge as static data to be retrieved, MemoryBear emulates the hippocampus's memory encoding, the neocortex's knowledge consolidation, and synaptic pruning-based forgetting — enabling knowledge to dynamically evolve with life-like properties. This shifts the relationship between AI and users from **passive lookup** to **proactive cognitive assistance**.
## Papers
| Paper | Description |
|-------|-------------|
| 📄 Memory Bear AI: A Breakthrough from Memory to Cognition | MemoryBear core technical report |
| 📄 Memory Bear AI Memory Science Engine for Multimodal Affective Intelligence | Technical report on multimodal affective intelligence memory engine |
| 📄 A-MBER: Affective Memory Benchmark for Emotion Recognition | Affective memory benchmark dataset |
## Why MemoryBear
### Knowledge Forgetting in Single Models
- **Context window limits**: Mainstream LLMs have 8k–32k token windows. In long conversations, early messages are pushed out, causing responses to lose historical context
- **Static knowledge gap**: Training data is a static snapshot — it cannot absorb personalized information (preferences, history) from live interactions
- **Recency bias**: Transformer self-attention weakens on long-range dependencies, overweighting recent input and ignoring earlier critical information
### Memory Gaps in Multi-Agent Collaboration
- **Data silos**: Different agents (consulting, after-sales, recommendation) maintain isolated memories, forcing users to repeat information
- **Inconsistent dialogue state**: When switching agents, user intent and history labels are not fully passed along, causing service discontinuities
- **Decision conflicts**: Agents with partial memory can produce contradictory responses (e.g., recommending products a user is allergic to)
### Semantic Ambiguity in Reasoning
- Domain jargon, colloquial expressions, and context-dependent references are not accurately encoded, leading to semantic drift in memory interpretation
- Cross-language memory associations fail in multilingual or dialect-rich scenarios
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## Core Features
### Memory Extraction Engine
Performs **semantic-level parsing** of unstructured conversations and documents to extract:
- **Core declarative information**: Strips redundant modifiers, preserving subject-action-object logic
- **Structured tripl