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
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
Core Features
Performs semantic-level parsing of unstructured conversations and documents to extract:
- Core declarative information: Strips redundant modifiers, preserving subject-action-object logic
- Structured triples: Automatically extracts entity relationships (e.g.,
MemoryBear → core function → knowledge extraction) as atomic units for graph storage
- Temporal anchoring: Automatically extracts and tags timestamps, enabling time-based knowledge tracing
- Intelligent summarization: Customizable length (50–500 words) and focus; generates concise summaries of 10-page documents in under 3 seconds
Graph Storage (Neo4j)
Graph-first architecture integrated with Neo4j, overcoming the weak relational modeling of traditional databases:
- Supports millions of entities and tens of millions of relational edges
- Covers 12 core relationship types: hierarchical, causal, temporal, logical, and more
- Extracted triples sync directly to Neo4j, automatically building the initial knowledge graph
- Interactive graph visualization with "machine-generated + human-optimized" collaborative management
Hybrid Search
Keyword retrieval + semantic vector retrieval dual-engine fusion:
- Keyword search powered by Elasticsearch for millisecond-level exact matching of structured information
- Semantic vector search via BERT embeddings, recognizing synonyms, near-synonyms, and implicit intent
- Semantic retrieval expands the candidate space; keyword retrieval then performs precise filtering
- Retrieval accuracy reaches 92%, improving 35% over single-mode retrieval
Memory Forgetting Engine
Inspired by the brain's synaptic pruning mechanism, using a dual-dimension model of memory strength and time decay:
- Each knowledge item is assigned an initial memory strength, updated dynamically by usage frequency and association activity
- When strength falls below threshold, knowledge enters a dormancy → decay → clearance three-stage lifecycle
- Redundant knowledge maintained below 8%, reducing waste by over 60% compared to systems without forgetting
Self-Reflection Engine
Scheduled daily reflection process, mimicking human review and retrospection:
- Consistency checks: Detects logical conflicts across related knowledge, flags suspicious records for human review
- Value assessment: Evaluates invocation frequency and association contribution; reinforces high-value knowledge, accelerates decay of low-value knowledge
- Association optimization: Adjusts relationship weights based on recent usage, strengthening high-frequency association paths
FastAPI Service Layer
Unified service architecture exposing two API surfaces:
| API Type |
Path Prefix |
Auth |
Purpose |
| Management API |
/api |
JWT |
System config, permissions, log queries |
| Service API |
/v1 |
API Key |
Knowledge extraction, graph ops, search, forgetting control |
- Average response latency below 50ms, single instance sustaining 1000 QPS
- Auto-generated Swagger documentation
- Docker-ready, compatible with enterprise microservice ecosystems (CRM, OA, R&D management)
Architecture
Celery Multi-Queue Async Architecture (7 worker types in docker-compose):
| Queue |
Worker Type |
Concurrency |
Purpose |
memory_tasks |
threads |
100 |
Memory read/write (asyncio-friendly) |
memory_fast_tasks |
threads |
32 |
High-frequency fast writes (dedicated queue to avoid mutual blocking) |
document_tasks |
prefork |
4 |
Document parsing (CPU-bound) |
periodic_tasks |
prefork |
2 |
Scheduled tasks, reflection engine, various scanners |
workflow_trigger_tasks |
prefork |
2 |
Workflow scheduled triggers |
subscription_state_tasks |
prefork |
4 |
Subscription state transitions (Enterprise) |
subscription_email_tasks |
prefork |
4 |
Subscription expiry reminder emails (Enterprise) |
Additional task queues such as graphrag_tasks, reflection_tasks, and memory_heavy_tasks (GraphRAG construction, second-layer reflection/deduplication, forgetting and insight generation, and other heavy-compute tasks) work together with the standalone task scheduler celery_task_scheduler and Celery Beat.
Benchmarks
Evaluation metrics include F1 score (F1), BLEU-1 (B1), and LLM-as-a-Judge score (J) — higher values indicate better performance.
MemoryBear consistently outperforms competing systems including Mem0, Zep, and LangMem across all four task categories:
Vector version (non-graph): Achieves substantially improved retrieval efficiency while maintaining high accuracy. Overall accuracy surpasses the best existing full-text retrieval methods (72.90 ± 0.19%), while maintaining low latency at both p50 and p95 for Search Latency and Total Latency.
Graph version: Integrating the knowledge graph architecture pushes overall accuracy to a new benchmark (75.00 ± 0.20%), delivering performance metrics that significantly surpass all other methods.
Quick Start
Docker Compose (Recommended)
Prerequisites: Docker Desktop installed.
# 1. Clone the repository
git clone https://github.com/SuanmoSuanyangTechnology/MemoryBear.git
cd MemoryBear/api
# 2. Start base services (PostgreSQL / Neo4j / Redis / Elasticsearch)
# Pull and start these images via Docker Desktop first (see Installation section 3.2)
# 3. Configure environment variables
cp env.example .env
# Edit .env with your database connections and LLM API keys
# 4. Initialize the database
pip install uv && uv sync
alembic upgrade head
# 5. Start API + Celery Workers + Beat scheduler
docker-compose up -d
# 6. Initialize the system and get the admin account
curl -X POST http://127.0.0.1:8002/api/setup
Note: docker-compose.yml includes the API service, Celery Workers, the standalone task scheduler (celery_task_scheduler), and the sandbox service. Base services (PostgreSQL, Neo4j, Redis, Elasticsearch) must be started separately.
Port info: Docker Compose defaults to port 8002; manual startup defaults to port 8000. The installation guide below uses manual startup (8000) as the example.
After startup:
Default admin credentials:
Manual Start
Quick commands below — see Installation for detailed steps.
# Backend
cd api
pip install uv && uv sync
alembic upgrade head
uv run -m app.main
# Frontend (new terminal)
cd web
npm install && npm run dev
Installation
1. Environment Requirements
| Component |
Version |
Purpose |
| Python |
3.12 (3.13+ not supported) |
Backend runtime |
| Node.js |
20.19+ or 22.12+ |
Frontend runtime |
| PostgreSQL |
13+ |
Primary database |
| Neo4j |
4.4+ |
Knowledge graph storage |
| Redis |
6.0+ |
Cache and message queue |
| Elasticsearch |
8.x |
Hybrid search engine |
2. Get the Project
git clone https://github.com/SuanmoSuanyangTechnology/MemoryBear.git
3. Backend API Service
3.1 Install Python Dependencies
# Install uv package manager
pip install uv
# Switch to the API directory
cd api
# Install dependencies
uv sync
# Activate virtual environment
# Windows (PowerShell, inside /api)
.venv\Scripts\Activate.ps1
# Windows (cmd, inside /api)
.venv\Scripts\activate.bat
# macOS / Linux
source .venv/bin/activate
3.2 Install Base Services (Docker Images)
Download Docker Desktop and pull the required images.
PostgreSQL — search → select → pull
Neo4j — pull the same way. When creating the container, map two required ports and set an initial password:
7474: Neo4j Browser7687: Bolt protocol
Important: You must also add the environment variable NEO4J_PLUGINS=["apoc"] to install the APOC plugin (a hard dependency of the backend graph engine; not included in the official image by default). If configuring this via the Docker Desktop GUI is inconvenient, create the container from the command line instead:
docker run -d --name memorybear-neo4j \
-p 7474:7474 -p 7687:7687 \
-e NEO4J_AUTH=neo4j/<initial password, at least 8 chars> \
-e NEO4J_PLUGINS='["apoc"]' \
neo4j:5
Redis — same steps as above.
Elasticsearch
Pull the Elasticsearch 8.x image and create a container, mapping ports 9200 (HTTP API) and 9300 (cluster communication). For initial setup, disable security to simplify configuration:
docker run -d --name elasticsearch \
-p 9200:9200 -p 9300:9300 \
-e "discovery.type=single-node" \
-e "xpack.security.enabled=false" \
elasticsearch:8.15.0
cp env.example .env
Fill in the core configuration in .env:
# Neo4j Graph Database
NEO4J_URI=bolt://localhost:7687
NEO4J_USERNAME=neo4j
NEO4J_PASSWORD=your-password
# PostgreSQL Database
DB_HOST=127.0.0.1
DB_PORT=5432
DB_USER=postgres
DB_PASSWORD=your-password
DB_NAME=redbear-mem
# Set to true on first startup to auto-migrate the database
DB_AUTO_UPGRADE=true
# Redis
REDIS_HOST=127.0.0.1
REDIS_PORT=6379
REDIS_DB=1
REDIS_PASSWORD=
# Celery
REDIS_DB_CELERY_BROKER=1
REDIS_DB_CELERY_BACKEND=2
# Elasticsearch
ELASTICSEARCH_HOST=127.0.0.1
ELASTICSEARCH_PORT=9200
# JWT Secret Key (generate with: openssl rand -hex 32)
SECRET_KEY=your-secret-key-here
3.4 Initialize the PostgreSQL Database
The database connection is read automatically from the DB_* environment variables in .env (migrations/env.py builds the connection string dynamically) — no need to modify alembic.ini.
Apply all migrations to create the full schema:
alembic upgrade head
Tip: When DB_AUTO_UPGRADE=true is set in .env, the API service automatically runs alembic upgrade head on startup.
Migration scripts are located in api/migrations/versions/ — make sure that directory contains migration files before running the command.
3.5 Start the API Service
uv run -m app.main
Access API documentation at http://localhost:8000/docs
3.6 Start Celery Workers (Optional, for async tasks)
# Memory worker (thread pool, asyncio-friendly, high concurrency)
celery -A app.celery_worker.celery_app worker -E --loglevel=info --pool=threads --concurrency=100 --queues=memory_tasks -n memory_worker@%h
# Fast-write worker (thread pool, dedicated queue to avoid blocking normal writes)
celery -A app.celery_worker.celery_app worker -E --loglevel=info --pool=threads --concurrency=32 --queues=memory_fast_tasks -n memory_fast_worker@%h
# Document parsing worker (prefork, CPU-bound)
celery -A app.celery_worker.celery_app worker -E --loglevel=info --pool=prefork --concurrency=4 --queues=document_tasks --max-tasks-per-child=100 -n document_worker@%h
# Periodic tasks worker (reflection engine, various scanners)
celery -A app.celery_worker.celery_app worker -E --loglevel=info --pool=prefork --concurrency=2 --queues=periodic_tasks --max-tasks-per-child=50 -n periodic_worker@%h
# Workflow trigger worker
celery -A app.celery_worker.celery_app worker -E --loglevel=info --pool=prefork --concurrency=2 --queues=workflow_trigger_tasks --max-tasks-per-child=50 -n workflow_trigger_worker@%h
# Beat scheduler
celery -A app.celery_worker.celery_app beat --loglevel=info
# Task scheduler service (enqueue rate limiting and admission control)
python -m app.celery_task_scheduler
Tip: The above are the core workers. For a full deployment (including GraphRAG, subscription/notification, and other Enterprise-edition queue workers), docker-compose up -d is recommended.
4. Frontend Web Application
4.1 Install Dependencies
cd web
npm install
Note: In a corporate intranet, run npm run install:private to install the private component @redbear/memory-brick (use npm run install:private:dev in development). On the public internet, the script auto-detects the environment and skips silently, without affecting startup.
4.2 Update API Proxy Configuration
Edit web/vite.config.ts and point the /api proxy target to the backend API service (the frontend dev server runs on port 5175 by default):
proxy: {
'/api': {
target: 'http://127.0.0.1:8000', // Backend API address — change to your actual deployment address
changeOrigin: true,
},
}
4.3 Start the Frontend Service
npm run dev
5. Initialize the System
# Initialize the database and obtain the super admin account
curl -X POST http://127.0.0.1:8000/api/setup
Super admin credentials:
6. Full Startup Checklist
Step 1 Clone the repository
Step 2 Start base services (PostgreSQL / Neo4j / Redis / Elasticsearch)
Step 3 Configure .env environment variables
Step 4 Run alembic upgrade head to initialize the database
Step 5 uv run -m app.main to start the backend API
Step 6 npm run dev to start the frontend
Step 7 curl -X POST http://127.0.0.1:8000/api/setup to initialize the system
Step 8 Log in to the frontend with the admin account
Tech Stack
| Layer | Technol
MemoryBear is an AI memory system that gives AI agents human-like memory: instead of just storing knowledge statically, it perceives, extracts, associates and forgets information over time.