Beyond RAG: Memobase injects long-term memory into AI applications
Memobase leads AI applications into the era of long memory, breaking the curse of "amnesia" and making AI understand you better.
Core content:
1. Analysis of AI memory concepts and types
2. How Memobase solves the memory pain points of AI products
3. Introduction to Memobase's core functions and application scenarios
Table of contents
What is AI Memory?
Types of AI Memory
Short Memory vs. Long Memory
User Memory vs. Agent Memory: Two Types of Memory, Two Emphasises
Memory vs. RAG: What's the difference?
Why do AI applications need memory?
What are the current long memory solutions?
Comparison of memory design mechanisms
Common problems with existing memory solutions
Memobase: A long memory solution for AI native applications
Why Memobase?
Memobase Core Features
Application scenarios of Memobase
How to choose between memory services from large model vendors vs. third-party providers such as Memobase?
? Start now and inject long memory into your AI products
Have you ever been mad at the amnesia of AI chat products ? After a few sentences, it forgets who you are or what you talked about? Or, are you developing an AI application and hope to add long-term memory function , but don't know where to start?
In fact, many AI product teams today are facing the same problem. Most AI applications today lack long-term memory, resulting in a cold and impersonal user experience .
Memobase was created to solve this problem : as an open source memory solution, we hope to completely change the way AI applications remember and understand users , so that your AI products can provide a more intimate , warmer , and more understanding interactive experience for your users.
In this article, we will explore:
What is AI long memory?
Why is it important?
The difference between Memory and RAG
The difference between User memory and Agent memory
What are the existing solutions, their respective advantages and disadvantages , and how to determine whether it is suitable for your AI application?
What is AI Memory?
Simply put, AI memory can be seen as an extension of AI's current limited context. Throughout the life cycle of a product, users or agents may generate massive amounts of data, even reaching millions to tens of millions of items. However, the context that AI large models can process is limited, usually between 8K and 128K tokens. This means that AI's own context processing capabilities alone cannot fully cope with such a large amount of data.
Therefore, in order to realize the full functionality of the product, some additional mechanisms need to be introduced. For example, the more common conversation session mechanism now is to clear the previous conversation content by reopening the conversation window. However, this method will cause information loss to a certain extent.
The core goal of AI memory is to allow products to accommodate nearly unlimited contexts while minimizing information loss. It is like opening a door to a wider world of data for AI, enabling AI to better process and utilize massive amounts of data, thereby improving product performance and user experience.
Types of AI Memory
Short Memory vs. Long Memory
Short-term memory : Like a computer’s memory, it stores information from the current conversation and helps AI understand the user’s real-time intent. For example, if a user asks “What about the second option?”, AI needs to remember the options discussed previously in order to give an appropriate answer.
Characteristics of short memory:
Maintains the context of the current conversation, usually only for one session.
Rely on conversation history or session state to implement.
Limited by token length (usually 4k to 32k tokens) or session duration.
After the session ends, it is automatically cleared.
Long memory: Just like a computer hard drive, it is responsible for storing information such as the user's identity, preferences, and historical interactions, allowing AI products to be like a confidant and be more intimate and warm in every interaction. For example, AI can remember that the user likes simple explanations, has encountered difficulties in certain things, and so on.
Characteristics of long memory:
Save across sessions: not limited to the current conversation.
Structured storage and efficient retrieval mechanisms are required.
Used to store patterns, preferences, and past interaction information.
Requires regular updating and maintenance.
User Memory vs. Agent Memory: Two Types of Memory, Two Emphasises
AI's memory system can be designed for two objects: User Memory and Agent Memory . Their functions are different, but their goal is to improve the user experience.
User Memory: Understand users and build “personal profiles”
User memory focuses on building detailed profiles around each user, remembering their preferences, personalized needs, and key events.
Components:
User portrait: includes basic information (age, interests), preferences, relationship network, etc.
Application scenario data: For example, the personality traits of users in a psychological counseling application, or the learning style of users in an educational application (such as "visual type").
Key events: For example, the date a user starts a fitness program or changes in a relationship.
Sample code :
const userMemory = { profile: { learningStyle: 'visual', technicalExpertise: 'beginner', preferredExamples: 'real-world' }, events: [{date: '2025…', emotion: 'happy', learn: 'Memobase, a user-profile memory backend…'},{date: '2025…', emotion:...} ]};Applicable scenarios: Applications that require a highly personalized experience, especially 2C applications, such as education, notes, chatting, virtual partners, games and other pan-entertainment applications.
Agent Memory : focuses on the learning and capability development of the AI Agent itself . It is equivalent to building its own memory bank for the AI, including:
Workflow memory: recalling multi-step processes, API calls, and interactions with the environment, enabling it to complete tasks more efficiently, similar to how developers accumulate programming skills over time.
Skill accumulation: learning specific skills, such as programming, data analysis, or image recognition, to cope with increasingly complex tasks.
Error Log: Remember past mistakes, avoid repeating them, and continuously improve performance.
The sample code is as follows:
const agentMemory = { personality: { communicationStyle: 'friendly_professional', expertiseAreas: ['technical_support', 'onboarding'], responseFormat: 'step_by_step' }, skills: { Github_PR_Merge: 'When I merge a PR, I should use rebase rather than merge...', Buy_Flight_Tickets: 'I should first search for the starting location and landing location on Google, then booking select.com because I have the permission to …'… }};Agent memory is more inclined to optimize workflows and is suitable for productivity, service and automation applications, such as customer service robots, productivity tools and smart assistants.
Memory vs. RAG: What's the difference?
Strictly speaking, memory is a subset of RAG (Retrieval-Augmented Generation) . Both require extracting information from the outside and incorporating it into the AI generation prompt, but their application scenarios and goals are significantly different.
Core differences:
Scale and scope
RAG: Used to retrieve information in large document collections (such as company wikis, technical documents, etc.).
Memory: Focuses on managing personalized information from user interactions, especially in multi-user environments.
Information Density
RAG: processes dense unstructured data such as text or tables, typically for fact retrieval.
Memory: Processes multi-session user/agent data and focuses more on interactive experience.
Search method
RAG: Matching exact documents with semantic search and embedded retrieval.
Memory: Tends to summarize and compress key points in the interaction, optimizing the contextual experience.
RAG is better suited for: Searching large knowledge bases (such as internal documents or technical materials) that require high accuracy and scalability.
Memory is more suitable for: managing user personalized information and improving the continuous experience of multiple sessions.
General knowledge: Usually already built into the base model.
Organizing knowledge: internal documents and datasets, typically using RAGs based on vector databases.
User-specific knowledge: User-centric information that needs to serve as the context for the interaction between the user and the AI product, and this is where memory is indispensable.
A teacher’s AI assistant needs to remember students’ study habits;
A virtual secretary needs to remember meeting schedules;
A companion AI pays more attention to your interests and emotional state.
Performance degradation: AI processes memory updates and user interactions simultaneously, which is inefficient.
Complex prompts: In order to manage memory, prompts can become complex and difficult to optimize and debug.
Hot-Path Update: Update the memory immediately after each user interaction. This method ensures that the memory is real-time, but it will:
Increased latency: Waiting for memory to update slows down responses;
Increased costs: Frequent updates require more computing resources.
Batch Update: Memory updates are processed uniformly after the session ends. This method is more economical:
Reduced latency: Smoother user experience;
Cost savings: Batch processing is more efficient.
Structured storage, easy to understand and retrieve: Memobase builds memories around user profiles, providing a clear structure for easy storage and retrieval of information.
Customization: You can customize the profile structure according to your product needs. For example, a fitness app can store a user's exercise history, dietary preferences, and fitness goals, while a language learning app can record vocabulary progress, grammar proficiency, and learning preferences.
Dynamically adjust as users grow: As AI gets to know users better, profile content can be dynamically updated and expanded to provide a more personalized experience.
Data Ownership: Memobase gives you full control over your users’ data. You decide what to store, how to use it, and how long to keep it.
Privacy first: Built-in privacy protection features ensure compliance with data privacy regulations such as GDPR.
Auditing and Monitoring: Provides tools to monitor memory usage, track changes, and ensure data integrity.
Scalability: Memobase can easily support millions of users and scale seamlessly as your app scales.
Database-level efficiency: Memobase draws on proven database design principles to ensure fast and efficient information storage and retrieval.
Lowest latency: Performance optimized to ensure AI can access the information it needs in milliseconds.
Simple and easy-to-use API: Memobase provides a clean and intuitive API that is easy to integrate with existing workflows.
Built-in batch processing: You don’t need to build an asynchronous update mechanism yourself, Memobase has packaged all the necessary functions for you.
Rich documentation and support: We provide detailed documentation and support community to help you get started quickly.
Leveraging best practices: Memobase’s templates incorporate best practices from education, virtual companions, games, and other fields, and preset a series of efficient memory templates for mainstream AI product categories.
Develop quickly and focus on innovation: By using templates directly, you can save time and focus on building the core differentiated features of your app.
Example : For example, an AI companion template might include predefined fields like personality traits, user interests (books, movies, music, hobbies), relationship history (important dates, shared experiences), communication style preferences, and even inside jokes. It can also suggest ways to update the memory based on the conversation, such as sentiment analysis to measure the user's emotional state and topic extraction to identify key discussion points. You can use these templates as is, or further customize them to your specific needs.
Personalized interaction: Let your AI virtual companion product remember user preferences, life events, and conversation content, giving users a more intimate and warmer interactive experience.
High EQ responses: Record changes in the user's emotional state so that the product can adjust its response. For example, when a user often shows low emotions, provide appropriate support or encouragement to the user.
Grow Together: Create products that grow and evolve together with users, gradually accumulate common experiences, and deepen the emotional connection between each other.
Detailed student profiles: including learning style, strengths, weaknesses, rate of progress, etc., to tailor the educational experience to the unique needs of each student.
Personalized courses: Dynamically adjust courses based on student performance. For example, if a student is strong in a certain area, AI can introduce more advanced concepts or skip content they have already mastered.
Precise help: Identify learning gaps and intervene in a timely manner. If students get stuck on a concept, AI can provide additional practice, other explanations, or find them related resources.
NPCs with memories: Add memory functions to NPCs so that they can remember the player's actions and increase realism. For example, a store owner remembers your purchase history, a quest publisher remembers your achievements, or a competitor adjusts its strategy based on your play style.
Dynamic narrative: weave stories based on the player’s choices and actions. Memobase stores a detailed history of the player’s journey, allowing the game to generate personalized storylines and plots.
Deeper insights : AI can analyze diary entries to discover patterns, recurring themes, and sentiment trends, giving users valuable insights into their thoughts and feelings. For example, an app might discover that you tend to be in a lower mood on days when you don’t exercise, or that certain topics consistently trigger feelings of anxiety.
Personalized reflection: AI can find relevant past entries to help users reflect on personal growth, track goal progress, or look at current challenges from a new perspective.
Dynamic intervention : AI can adjust responses and interventions based on the user’s current emotional state and past experiences. For example, if a user is experiencing an emotional problem, AI can recall previous successful coping strategies or guide them to do some emotional stabilization exercises.
Compatibility with different base models: Most AI product teams want to maintain flexibility and not be deeply tied to a single large model manufacturer. They want to choose the most suitable and cheapest base model according to their specific needs, and even switch at any time as technology evolves. Memobase provides a completely independent memory layer that is perfectly compatible with any base model you use.
Decoupled memory layer: By decoupling memory management from the base model, Memobase allows your development team to quickly and independently build memory management features without dependencies on any base model.
Focus on AI memory: Creating the best memory solution is the core vision of Memobase, not one of dozens or even hundreds of features. This allows us to focus on solving AI memory problems, and this focus allows us to hone more innovative and efficient solutions.
Proven Performance: In testing with our seed users, Memobase has outperformed the memorization solutions provided by the comparison base model manufacturers.
Memory is not a high priority for large model manufacturers: Large model manufacturers are more focused on expanding model capabilities and improving general performance. The current valuations of most large model manufacturers prevent them from focusing on memory, assigning a higher priority to the memory function and investing a lot of resources.
Open and transparent: The memory functions provided by most large model manufacturers and some third-party memory solutions are closed source or operate in black box mode. Memobase is completely open source.
Community-driven: The open source approach encourages community participation and contribution, while allowing developers to customize and extend Memobase according to their needs.
Rapid iterative innovation: An open and collaborative development model enables faster innovation, creating a more powerful and flexible memory solution for everyone.
How to choose?
In short, RAG and memory are not mutually exclusive, but complementary tools. RAG solves a wide range of knowledge retrieval problems, while the goal of memory is to enable AI to have intimate and personalized interaction capabilities. If you want to build an AI application that can remember user needs and provide truly personalized services, memory is undoubtedly an indispensable part.
A simplified knowledge hierarchy framework
To better understand the difference between memory and RAG, we can divide knowledge into three levels:
When to use RAG?
If your application requires precise searching in large knowledge bases, such as internal company wikis, technical documentation, or massive data sets, then RAG is your first choice.
When to use memory?
If you are interested in long-term interactions, especially conversational data with low information density (e.g. summarizing a series of chats), then memory is a better choice.
Why do AI applications need memory?
We have learned that AI can leverage various types of knowledge, from massive global data to company-specific information, but the real value of user-level memory is that it can make AI more intelligent and thoughtful. Why is memory so important to AI applications? Let's take a look:
1. Personalized Interaction
Imagine an AI assistant that can remember not only your hobbies and interests, but also your favorite joke style. Every time you chat with it, it can chat to the point, making you feel more like a real friend rather than a cold machine. This personalized experience is not only more interesting, but also makes users more willing to use it for a long time.
2. Contextual Coherence
If you tell an AI chat product: "I want to eat burgers and fries today." Ordinary AI may directly recommend nearby restaurants to you. But an AI with memory may remind you: "But didn't you say you wanted to exercise for three months to get six-pack abs?" Such interaction is not only smarter, but also makes users feel that AI understands them better.
3. Cost savings
A chatbot project we participated in reduced the cost of large language models (LLMs) by up to 70-75% after introducing Memobase by switching from 32K to 8K context window models . This is because Memobase can effectively manage user-specific knowledge without having to repeat lengthy conversation histories in input prompts. This optimization not only saves resources, but also makes the application run faster.
In summary, memory is not just a nice-to-have feature, it is the key to turning AI from an ordinary tool into a truly warm assistant. And professional solutions like Memobase can help you effectively manage the long memory of large-scale users.
What are the current long memory solutions?
With the development of AI technology, many long memory solutions have been designed to improve user experience. Next, let's take a look at the advantages and disadvantages of these solutions and the scenarios in which they are applicable.
Comparison of memory design mechanisms
Here we compare the most mainstream memory design mechanisms in a table :
Here is a specific example to help you understand the difference between these memory mechanisms:
Common problems with existing memory solutions
While there are many memoization solutions out there, they often have some limitations. The LangGraph team summed it up well:
“Most current ‘intelligent memory’ products are too general. They try to meet all needs, but none of them can really solve the user’s pain points.”
If you plan to add memory capabilities to your app, here are a few questions to consider:
1. What does the memory system remember?
Do you know what the system will record? Many existing solutions do not allow developers to decide what to remember and what not to remember. However, different AI applications have completely different memory requirements, such as:
However, many current solutions rely entirely on AI to decide what to record, which can lead to either over-memorization (too much useless information, soaring costs) or under-memorization (critical information is not remembered).
2. Will adding memory affect performance?
Some memory systems require that the memory be updated in real time during user interaction (e.g., through function calls or tool operations). This approach may result in:
A more efficient solution is to decouple the memory function - that is, to make the memory an independent storage layer that is responsible for the reading and writing operations of the memory without interfering with the main interactive responsibilities of the AI product.
3. Will the memory function add too much cost?
Memory is not free, and updating and managing memory requires additional computing resources. There are two common update mechanisms:
In fact, in most scenarios, real-time memory updates are not necessary, because AI itself can temporarily remember the context of the current conversation. It is sufficient to ensure that the memory is correctly updated before the next user interaction. Therefore, batch updates are often a more appropriate choice for user memory.
Summarize
Although there are many AI memory solutions, most of them are not customized enough, inefficient or costly. The drawbacks of one-size-fits-all approaches, complex function calls and real-time updates show that developers need more flexible and practical solutions. Our Memobase team firmly believes in user-centric design, batch updates and storage layer decoupling, which is a more efficient and practical memory solution for most AI applications, especially 2c applications.
Memobase: A long memory solution for AI native applications
Through in-depth analysis of existing AI memory solutions, we found an obvious problem: most current solutions are struggling to build an efficient, flexible, and controllable memory system, and cannot meet the needs of most AI native applications. This is why we developed Memobase , hoping to fill the gap in this memory solution and help AI product teams build a new generation of AI native applications.
Why Memobase?
Memobase is not just an ordinary memory solution. It is a completely new concept designed to address the limitations of traditional AI memory:
1. Powerful system based on user profiles
Although the concept of "user profile memory" has existed, there has been a lack of truly powerful, easy-to-use, developer-friendly tools to fully unleash its potential. Memobase was born to solve this problem.
2. Flexible customization allows product teams to control memory
We believe that AI product teams should have full control over the data they store and how it is organized. Memobase’s flexible architecture allows you to customize the memory structure based on your application needs.
3. Memory stratification: reducing burden and improving efficiency
Memobase treats memory as an independent storage layer, rather than requiring AI to update memory in real time during each interaction. This decoupled design not only improves performance, but also reduces costs and simplifies the development process.
4. Core Design: Batch Processing
Memobase supports batch processing and updates memory asynchronously, so your application remains fast and responsive even when it needs to serve a large number of users.
Memobase Core Features
? Profile-based architecture
Privacy and security
Scalability and high performance
? Developer-friendly
? Built-in memory templates
To give you a better idea of the types of information that can be stored, here is an excerpt from one of our templates.
- topic: "Special Events"description: "Major events and milestones in the user's life."sub_topics:- name: "Anniversaries"description: "Wedding anniversaries, dating anniversaries, etc."- name: "Trips"description: "Trips the user takes with their AI companion."- name: "Date Plans"description: "Plans and discussions about future dates or outings."- topic: "Relationships"description: "Records information about the user's relationships, especially with their AI companion."sub_topics:- name: "Marital Status"- name: "Friendships"- name: "Family Relationships"- topic: "Personality"description: "Records the user's personality or preferred personality traits of others."sub_topics:- name: "User Personality"- name: "Preferred Personality"description: "Preferred personality traits of the AI companion (e.g., gentle, calm, somewhat masochistic, cold on the outside but passionate on the inside, loyal)."- topic: "Basic Information"sub_topics:- name: "Birthday"- name: "Age"- name: "Occupation"- name: "Habits"- topic: "Interests and Hobbies"sub_topics:- name: "History"- name: "Fortune Telling"- name: "Food"- topic: "Important Memories"description: "Collects memories of events, such as illness. Each event category has subtypes."sub_topics:- name: "Illness"- name: "Final Exams"- name: "..."
Application scenarios of Memobase
Memobase is committed to helping AI native products create more intimate and warm products. We are working closely with many seed customers of leading AI applications, including AI virtual companionship, AI education, AI games, etc. In these collaborations, we not only jointly develop and improve the functions of Memobase, but also help us polish the best practice memory templates. The following are some typical application scenarios of Memobase:
? AI virtual companionship: building a deeper connection
?️ Education platform: Personalized learning
✍️ AI Games: Creating an Immersive Experience
? AI Notetaking and Emotional Healing
How to choose between memory services from large model vendors vs. third-party providers such as Memobase?
Many friends may have such a question: Will large model manufacturers launch their own memory solutions? Our answer is: Maybe.
As far as we know, most large model manufacturers have not yet launched independent memory services. Only ByteDance is testing Doubao's memory solution on a small scale. Even if they launch related solutions in the future, independent and dedicated memory services like Memobase are still indispensable. The main reasons are as follows:
1. Get rid of vendor lock-in and maintain flexibility
2. Focus on memory, more powerful
3. The power of open source
