Proposal: User-Owned AI Memory — A Portable Multimodal Memory Format
The idea
I would like to propose a different way of thinking about AI memory.
Today, we generally separate:
- Context windows
- Conversation history
- Model memory
- Uploaded files
- Vector databases / RAG systems
- Agent memory
I believe these concepts could be unified around one simple principle:
«The user’s data should be the AI’s long-term memory — not the model’s context window.»
Instead of making the model itself responsible for holding a limited amount of memory, the AI could use the user’s local or cloud storage as a virtually unlimited long-term memory.
A new concept: AI Memory Containers
Imagine a portable file format such as:
“project.ai”
This would not simply be a Markdown file.
It would be an AI Memory Container capable of containing:
- Text
- Markdown
- PDFs
- Images
- Audio
- Video
- Documents
- Metadata
- Relationships between information
- Search indexes
- Optional embeddings
- Conversation history
- Structured knowledge
The user would own this file and could move it between devices, applications, and potentially different AI models.
For example:
“Mastering_Project_Management.ai”
could contain an entire project:
Mastering_Project_Management.ai
├── Documents
├── PDFs
├── Notes
├── Images
├── Audio
├── Video
├── Conversations
├── Metadata
├── Knowledge Graph
└── Search / Semantic Index
The important point is that the AI would not load everything into its context window.
Instead:
User Question
↓
AI Memory Retrieval
↓
Relevant Files
↓
Relevant Sections / Chunks
↓
Relevant Context
↓
Model Reasoning
↓
Answer
The model only receives what it actually needs.
Context Window vs. Long-Term Memory
I see the architecture more like this:
┌──────────────────────────────┐
│ AI Model │
│ Reasoning Engine │
└──────────────┬───────────────┘
│
Working Memory
Context Window
│
┌──────────────▼───────────────┐
│ Memory Retrieval Layer │
└──────────────┬───────────────┘
│
Long-Term Memory
│
┌──────────────▼───────────────┐
│ User AI Memory │
│ │
│ Text | PDF | Image | Audio │
│ Video | Metadata | Knowledge│
│ Indexes | Conversations │
└───────────────────────────────┘
│
User Storage
HDD / SSD / Cloud
In other words:
Context = working memory
Storage = long-term memory
Model = reasoning engine
This would allow the model to work with terabytes of information without requiring a terabyte-sized context window.
Why this could matter
Imagine a developer has:
- 10 years of source code
- Documentation
- Git repositories
- PDFs
- Architecture diagrams
- Meeting recordings
- Screenshots
- Emails
- Design documents
- Previous AI conversations
Instead of uploading these repeatedly or maintaining separate memory systems for every AI application, the developer could maintain one:
“MyDevelopmentMemory.ai”
An AI assistant could retrieve the relevant information when needed.
The same memory could potentially be used by:
- ChatGPT
- Codex
- Local LLMs
- Other AI assistants
- IDEs
- Agent frameworks
The AI model becomes replaceable.
The user’s memory remains.
The most important part: Model-independent memory
I think this is the real opportunity.
Today, memory can become tightly coupled to a particular AI product.
A better architecture could be:
┌───────────────┐
│ ChatGPT │
└───────┬───────┘
│
┌───────▼───────┐
│ Memory Layer │
└───────┬───────┘
│
┌───────▼───────┐
│ User's │
│ AI Memory │
│ .ai file │
└───────┬───────┘
│
┌───────────┼───────────┐
▼ ▼ ▼
Codex Local LLM Other AI
The user’s knowledge becomes portable.
The model becomes an interchangeable reasoning engine.
Local-first and privacy
This architecture could also improve privacy.
The memory could remain on the user’s machine.
For example:
Local AI Memory
↓
Local Retrieval
↓
Only relevant information
↓
Cloud Model
A cloud model would not need access to the user’s entire memory.
It would receive only the information required to answer the current request.
This could also work with completely local models.
The technical challenge
I don’t think the “.ai” file should simply store embeddings.
Embeddings are model-dependent and may become obsolete.
A better format would preserve:
1. Original content
2. Structured metadata
3. Relationships
4. Version information
5. Optional indexes
6. Optional embeddings
7. A standard retrieval interface
Indexes and embeddings could be regenerated when necessary.
The format should therefore be content-first and model-independent.
A possible standard
Perhaps this could eventually become an open specification:
AI Memory Format (AIMF)
or something similar.
The specification could define:
- Memory containers
- Multimodal content
- Metadata
- Chunking
- Relationships
- Versioning
- Retrieval
- Permissions
- Encryption
- Optional semantic indexes
- Model-independent embeddings
- Memory synchronization
The file extension is actually the least important part.
The important concept is:
«A portable, user-owned, multimodal memory layer for AI.»
Why I think OpenAI could explore this
AI systems are becoming increasingly agentic.
Agents need persistent memory.
But giving an agent a larger context window is not necessarily the right long-term solution.
A better architecture may be:
«Give the AI access to a very large external memory and let it retrieve what it needs.»
This is similar to how humans work:
We don’t consciously hold every piece of information in working memory.
We retrieve information from long-term memory when needed.
AI could work in a similar way.
Final thought
I don’t see this as simply a new file extension.
I see it as a possible shift from:
Model-centric AI
to:
User-centric AI memory.
The user’s knowledge should survive model changes, application changes, and hardwaProposal: User-Owned AI Memory — A Portable Multimodal Memory Format
The idea
I would like to propose a different way of thinking about AI memory.
Today, we generally separate:
- Context windows
- Conversation history
- Model memory
- Uploaded files
- Vector databases / RAG systems
- Agent memory
I believe these concepts could be unified around one simple principle:
«The user’s data should be the AI’s long-term memory — not the model’s context window.»
Instead of making the model itself responsible for holding a limited amount of memory, the AI could use the user’s local or cloud storage as a virtually unlimited long-term memory.
A new concept: AI Memory Containers
Imagine a portable file format such as:
“project.ai”
This would not simply be a Markdown file.
It would be an AI Memory Container capable of containing:
- Text
- Markdown
- PDFs
- Images
- Audio
- Video
- Documents
- Metadata
- Relationships between information
- Search indexes
- Optional embeddings
- Conversation history
- Structured knowledge
The user would own this file and could move it between devices, applications, and potentially different AI models.
For example:
“Mastering_Project_Management.ai”
could contain an entire project:
Mastering_Project_Management.ai
├── Documents
├── PDFs
├── Notes
├── Images
├── Audio
├── Video
├── Conversations
├── Metadata
├── Knowledge Graph
└── Search / Semantic Index
The important point is that the AI would not load everything into its context window.
Instead:
User Question
↓
AI Memory Retrieval
↓
Relevant Files
↓
Relevant Sections / Chunks
↓
Relevant Context
↓
Model Reasoning
↓
Answer
The model only receives what it actually needs.
Context Window vs. Long-Term Memory
I see the architecture more like this:
┌──────────────────────────────┐
│ AI Model │
│ Reasoning Engine │
└──────────────┬───────────────┘
│
Working Memory
Context Window
│
┌──────────────▼───────────────┐
│ Memory Retrieval Layer │
└──────────────┬───────────────┘
│
Long-Term Memory
│
┌──────────────▼───────────────┐
│ User AI Memory │
│ │
│ Text | PDF | Image | Audio │
│ Video | Metadata | Knowledge│
│ Indexes | Conversations │
└───────────────────────────────┘
│
User Storage
HDD / SSD / Cloud
In other words:
Context = working memory
Storage = long-term memory
Model = reasoning engine
This would allow the model to work with terabytes of information without requiring a terabyte-sized context window.
Why this could matter
Imagine a developer has:
- 10 years of source code
- Documentation
- Git repositories
- PDFs
- Architecture diagrams
- Meeting recordings
- Screenshots
- Emails
- Design documents
- Previous AI conversations
Instead of uploading these repeatedly or maintaining separate memory systems for every AI application, the developer could maintain one:
“MyDevelopmentMemory.ai”
An AI assistant could retrieve the relevant information when needed.
The same memory could potentially be used by:
- ChatGPT
- Codex
- Local LLMs
- Other AI assistants
- IDEs
- Agent frameworks
The AI model becomes replaceable.
The user’s memory remains.
The most important part: Model-independent memory
I think this is the real opportunity.
Today, memory can become tightly coupled to a particular AI product.
A better architecture could be:
┌───────────────┐
│ ChatGPT │
└───────┬───────┘
│
┌───────▼───────┐
│ Memory Layer │
└───────┬───────┘
│
┌───────▼───────┐
│ User's │
│ AI Memory │
│ .ai file │
└───────┬───────┘
│
┌───────────┼───────────┐
▼ ▼ ▼
Codex Local LLM Other AI
The user’s knowledge becomes portable.
The model becomes an interchangeable reasoning engine.
Local-first and privacy
This architecture could also improve privacy.
The memory could remain on the user’s machine.
For example:
Local AI Memory
↓
Local Retrieval
↓
Only relevant information
↓
Cloud Model
A cloud model would not need access to the user’s entire memory.
It would receive only the information required to answer the current request.
This could also work with completely local models.
The technical challenge
I don’t think the “.ai” file should simply store embeddings.
Embeddings are model-dependent and may become obsolete.
A better format would preserve:
1. Original content
2. Structured metadata
3. Relationships
4. Version information
5. Optional indexes
6. Optional embeddings
7. A standard retrieval interface
Indexes and embeddings could be regenerated when necessary.
The format should therefore be content-first and model-independent.
A possible standard
Perhaps this could eventually become an open specification:
AI Memory Format (AIMF)
or something similar.
The specification could define:
- Memory containers
- Multimodal content
- Metadata
- Chunking
- Relationships
- Versioning
- Retrieval
- Permissions
- Encryption
- Optional semantic indexes
- Model-independent embeddings
- Memory synchronization
The file extension is actually the least important part.
The important concept is:
«A portable, user-owned, multimodal memory layer for AI.»
Why I think OpenAI could explore this
AI systems are becoming increasingly agentic.
Agents need persistent memory.
But giving an agent a larger context window is not necessarily the right long-term solution.
A better architecture may be:
«Give the AI access to a very large external memory and let it retrieve what it needs.»
This is similar to how humans work:
We don’t consciously hold every piece of information in working memory.
We retrieve information from long-term memory when needed.
AI could work in a similar way.
Final thought
I don’t see this as simply a new file extension.
I see it as a possible shift from:
Model-centric AI
to:
User-centric AI memory.
The user’s knowledge should survive model changes, application changes, and hardwaProposal: User-Owned AI Memory — A Portable Multimodal Memory Format
The idea
I would like to propose a different way of thinking about AI memory.
Today, we generally separate:
- Context windows
- Conversation history
- Model memory
- Uploaded files
- Vector databases / RAG systems
- Agent memory
I believe these concepts could be unified around one simple principle:
«The user’s data should be the AI’s long-term memory — not the model’s context window.»
Instead of making the model itself responsible for holding a limited amount of memory, the AI could use the user’s local or cloud storage as a virtually unlimited long-term memory.
A new concept: AI Memory Containers
Imagine a portable file format such as:
“project.ai”
This would not simply be a Markdown file.
It would be an AI Memory Container capable of containing:
- Text
- Markdown
- PDFs
- Images
- Audio
- Video
- Documents
- Metadata
- Relationships between information
- Search indexes
- Optional embeddings
- Conversation history
- Structured knowledge
The user would own this file and could move it between devices, applications, and potentially different AI models.
For example:
“Mastering_Project_Management.ai”
could contain an entire project:
Mastering_Project_Management.ai
├── Documents
├── PDFs
├── Notes
├── Images
├── Audio
├── Video
├── Conversations
├── Metadata
├── Knowledge Graph
└── Search / Semantic Index
The important point is that the AI would not load everything into its context window.
Instead:
User Question
↓
AI Memory Retrieval
↓
Relevant Files
↓
Relevant Sections / Chunks
↓
Relevant Context
↓
Model Reasoning
↓
Answer
The model only receives what it actually needs.
Context Window vs. Long-Term Memory
I see the architecture more like this:
┌──────────────────────────────┐
│ AI Model │
│ Reasoning Engine │
└──────────────┬───────────────┘
│
Working Memory
Context Window
│
┌──────────────▼───────────────┐
│ Memory Retrieval Layer │
└──────────────┬───────────────┘
│
Long-Term Memory
│
┌──────────────▼───────────────┐
│ User AI Memory │
│ │
│ Text | PDF | Image | Audio │
│ Video | Metadata | Knowledge│
│ Indexes | Conversations │
└───────────────────────────────┘
│
User Storage
HDD / SSD / Cloud
In other words:
Context = working memory
Storage = long-term memory
Model = reasoning engine
This would allow the model to work with terabytes of information without requiring a terabyte-sized context window.
Why this could matter
Imagine a developer has:
- 10 years of source code
- Documentation
- Git repositories
- PDFs
- Architecture diagrams
- Meeting recordings
- Screenshots
- Emails
- Design documents
- Previous AI conversations
Instead of uploading these repeatedly or maintaining separate memory systems for every AI application, the developer could maintain one:
“MyDevelopmentMemory.ai”
An AI assistant could retrieve the relevant information when needed.
The same memory could potentially be used by:
- ChatGPT
- Codex
- Local LLMs
- Other AI assistants
- IDEs
- Agent frameworks
The AI model becomes replaceable.
The user’s memory remains.
The most important part: Model-independent memory
I think this is the real opportunity.
Today, memory can become tightly coupled to a particular AI product.
A better architecture could be:
┌───────────────┐
│ ChatGPT │
└───────┬───────┘
│
┌───────▼───────┐
│ Memory Layer │
└───────┬───────┘
│
┌───────▼───────┐
│ User's │
│ AI Memory │
│ .ai file │
└───────┬───────┘
│
┌───────────┼───────────┐
▼ ▼ ▼
Codex Local LLM Other AI
The user’s knowledge becomes portable.
The model becomes an interchangeable reasoning engine.
Local-first and privacy
This architecture could also improve privacy.
The memory could remain on the user’s machine.
For example:
Local AI Memory
↓
Local Retrieval
↓
Only relevant information
↓
Cloud Model
A cloud model would not need access to the user’s entire memory.
It would receive only the information required to answer the current request.
This could also work with completely local models.
The technical challenge
I don’t think the “.ai” file should simply store embeddings.
Embeddings are model-dependent and may become obsolete.
A better format would preserve:
1. Original content
2. Structured metadata
3. Relationships
4. Version information
5. Optional indexes
6. Optional embeddings
7. A standard retrieval interface
Indexes and embeddings could be regenerated when necessary.
The format should therefore be content-first and model-independent.
A possible standard
Perhaps this could eventually become an open specification:
AI Memory Format (AIMF)
or something similar.
The specification could define:
- Memory containers
- Multimodal content
- Metadata
- Chunking
- Relationships
- Versioning
- Retrieval
- Permissions
- Encryption
- Optional semantic indexes
- Model-independent embeddings
- Memory synchronization
The file extension is actually the least important part.
The important concept is:
«A portable, user-owned, multimodal memory layer for AI.»
Why I think OpenAI could explore this
AI systems are becoming increasingly agentic.
Agents need persistent memory.
But giving an agent a larger context window is not necessarily the right long-term solution.
A better architecture may be:
«Give the AI access to a very large external memory and let it retrieve what it needs.»
This is similar to how humans work:
We don’t consciously hold every piece of information in working memory.
We retrieve information from long-term memory when needed.
AI could work in a similar way.
Final thought
I don’t see this as simply a new file extension.
I see it as a possible shift from:
Model-centric AI
to:
User-centric AI memory.
The user’s knowledge should survive model changes, application changes, and hardware changes.
The model should be able to change.
The memory should remain.
I’d be very interested to hear how the OpenAI team and other developers would approach this architecture, especially regarding retrieval, multimodal indexing, security, synchronization, and model-independent memory.re changes.
The model should be able to change.
The memory should remain.
I’d be very interested to hear how the OpenAI team and other developers would approach this architecture, especially regarding retrieval, multimodal indexing, security, synchronization, and model-independent memory.re changes.
The model should be able to change.
The memory should remain.
I’d be very interested to hear how the OpenAI team and other developers would approach this architecture, especially regarding retrieval, multimodal indexing, security, synchronization, and model-independent memory.