Memobase: User long-term memory system is open source! The secret weapon that allows AI to truly "remember" each user

Introduction:

Are you still worried about AI not being able to remember user information? Memobase is here! This revolutionary long-term memory system can create a unique memory profile for each user, and maintain the consistency of the profile even if the AI ​​agent is replaced. Whether it is a virtual companion, an educational tool, or a personalized assistant, it can achieve an intelligent experience of "the more you use it, the better it understands you." This article will reveal its core technology and come with a 3-minute integration guide!

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1. Core Innovation

• •  Long-term memory of user portraits :
• • Independently store 900+ rounds of conversation history for each user (accuracy increased by 42% compared to mem0)
• • Support cross-AI agent information synchronization
• •  Time perception system :

  # Automatically mark information timeliness
  {
    "preference" : {
      "coffee_type" : { "value" :  "Latte" ,  "expires_at" :  "2025-12-31" }
    }
  }

• •  Controllable memory granularity :
• • Basic information (name/occupation)
• • Dynamic preferences (recent purchases)
• • Hidden features (dialogue style analysis)

2. Performance advantages

How Memobase works?

3. 5-minute rapid integration

1. 1.  Install SDK :

   pip install memobase

2. 2.  Initialize the user :

   from  memobase  import  MemoBaseClient
   mb = MemoBaseClient(project_url= "http://your-domain.com" , api_key= "your-key" )
   user = mb.add_user({ "initial_data" :  "example" })

3. 3.  Conversation memory storage :

   user.insert(ChatBlob(messages=[
       { "role" :  "user" ,  "content" :  "I am sensitive to caffeine" },
       { "role" :  "assistant" ,  "content" :  "Your caffeine taboo has been recorded" }
   ])

4. 4.  Intelligent recall memory :

   # Generate prompt words with memory context
   prompt =  f"""
   <memory>
   {user.context(max_token_size= 500 )}
   </memory>
   The latest question from the user: {new_question}
   """

4. Commercial scenario implementation

• •  E-commerce recommendation :
• • Remember users’ browsing preferences within 3 months
• • Improved cross-platform recommendation consistency by 35%
• •  Online Education :
• • Record students’ incorrect knowledge points
• • Generate personalized learning paths
• •  Healthcare :
• • Long-term tracking of patient symptom changes
• • Warning of abnormal health indicators

5. Advanced Configuration Tips

• •  Dynamic Memory Weights :

  # config/memory_weight.yaml
  basic_info: 
    weight: 1.0 # Fixed high weight   
  preferences:
    decay_rate: 0.7 # decays by 30% every 30 days   

• •  Sensitive information filtering :

  mb = MemoBaseClient(
      privacy_filter=[ "credit card number" ,  "ID number" ]   # Automatic desensitization
  )

Developer Benefit Package

?  Limited time free resources :

• • [Pre-built e-commerce user portrait template: https://github.com/memobase/retail-profile-template
• • [Educational Memory Analysis Toolkit: https://github.com/memobase/edu-memory-kit
• • Quote:

@misc{memobase,
  title={Memobase: User-Centric Long-Term Memory for AI Applications},
  author={Memobase Team},
  year={2025}
}

Summarize:

Memobase redefines the way AI interacts with users. Its innovative time perception + controllable memory architecture makes personalized services truly "long-term effective". Now integrate Memobase and let your AI say goodbye to "goldfish memory"!