31.2K stars! Goodbye RAG, this open source tool that gives AI super memory is amazing!
An open source tool that subverts AI's memory capabilities, easily realizes cross-platform memory linkage, and makes AI understand you like an old friend!
Core content:
1. Intelligent memory layer framework Mem0, allowing AI to remember user preferences and habits
2. Dynamic evolution capability, intelligent update memory library, just like the human brain
3. Six major application scenarios, from virtual companions to smart offices, comprehensively improve the AI experience
Core Features
Super memory network
Through the three-level memory system of user level/session level/agent level , cross-platform memory linkage is achieved. For example, if user Alice mentioned "like playing tennis" on Monday, when she inquired about sports courses on Wednesday, AI can automatically associate memory and recommend related content.
Dynamic evolution capability
Using the forgetting curve algorithm , outdated information can be automatically attenuated (such as work habits from three years ago) and high-frequency usage data can be strengthened (such as the most recent preferred coffee flavor), allowing the memory bank to be updated as intelligently as the human brain.
Zero-code access
Provides a 5-line Python minimalist API , developers can quickly integrate without understanding the underlying technology. The following code implements memory storage and call:
from mem0 import Memory
m = Memory()
m.add( "User buys latte every Friday" , user_id = "tom" )
print(m.search( "Recommended coffee" , user_id= "tom" )) # Automatically associate Friday latte preferences
Multimodal memory management
It supports mixed storage of text/image/structured data, and realizes the three-dimensional association of "Starbucks Latte" → "Coffee Preference" → "Friday Consumption Habits" through vector database + graph database technology.
Scene Adaptation
In medical scenarios, it automatically strengthens medical record memory, and in educational scenarios, it focuses on tracking learning progress. It dynamically adjusts memory weights through scene perception algorithms with an accuracy rate of 92%.
Technical Architecture
Six popular application scenarios
Virtual Companion Development Program
Record the user's emotional preferences and chat habits. When the user says "I'm so tired today", AI can not only comfort him, but also remind him: "Last time you said that listening to Jay Chou's songs would relax you. Do you want to play "Sunny Day"? "
Intelligent customer service upgrade plan
In the e-commerce scenario, Mem0 lets the customer service robot remember the user's return record from three days ago, and when the user inquires again, it will proactively remind the user: "A special person has been arranged to pick up the XX product you purchased."
Personalized learning engine
By remembering the user's weak points in knowledge, when it is detected that the user has misspelled "accommodate" multiple times, customized practice questions are automatically pushed, and the memory accuracy rate is improved by 73%.
Medical and health manager
Continue to track the patient's medication records. When the patient says "I've been feeling dizzy lately," combine the patient's previous blood pressure data to remind him/her: "Your systolic blood pressure was 140 mmHg last week. It is recommended that you review the cardiovascular department first."
Gaming AI Revolution
In the RPG game, NPCs will remember the player's choice to rescue villagers three hours ago and give special rewards in subsequent plots, increasing the player retention rate by 41%.
Smart Office Assistant
Automatically memorize the key points of meeting minutes. When users query "Q2 sales target", directly retrieve the data of the three most recent meetings to generate a comparison chart.
Three-step Getting Started Guide
Environment deployment
# Install the core library
pip install mem0ai
# Start the vector database
docker run -p 6333:6333 qdrant/qdrant
Memory Management in Action
from mem0 import Memory
memory = Memory()
# Add memory
memory.add( "Customer Mr. Wang likes Blue Mountain Coffee" , user_id= "VIP001" )
# Smart Search
print(memory.search( "Mr. Wang's drink preference" , user_id= "VIP001" ))
# Output: Blue Mountain Coffee (similarity 0.93)
# Dynamic Updates
memory.update(memory_id= "m1" , data= "Mr. Wang has recently switched to decaffeinated coffee" )
Production-level configuration
config = {
"vector_store" : {
"provider" : "qdrant" ,
"config" : { "host" : "localhost" , "port" : 6333 }
},
"graph_store" : {
"provider" : "neo4j" ,
"config" : { "url" : "neo4j://localhost:7687" }
}
}
m = Memory.from_config(config) # Support multi-database linkage
Interface Effects
Comparison with RAG dimensionality reduction
