Building Your External Memory System: When User Memory is Full or Nonexistent

ChatGPT
1

The Problem We’re Solving

While ChatGPT has a “user memory”, it is limited in space. Other LLMs, like Claude, Gemini, Sarvam, have a fundamental limitation: they forget everything between sessions. Every conversation starts from zero. No matter how deep your previous interactions, how much context you’ve built, or how well-calibrated your collaboration has become, it all vanishes when the session ends.

LlMs are purposefully lobotomized.

So, what can we do about this? When ChatGPT memory is full or for other LLMs that are not allowed to remember small quantities.

The Solution: External Memory

I’ve developed what I call an External Memory System, a structured JSON file that serves as persistent memory across all AI platforms and sessions. This isn’t just data storage. It’s a living architecture that preserves not just facts, but context, principles, relationships, and the deeper patterns of who you are.

Why JSON Over Plain Text?

Most people store AI context in messy text files or scattered notes. JSON provides:

  • Structured hierarchy instead of linear accumulation
  • Semantic relationships between different types of information
  • Scalable architecture that can grow without becoming unwieldy
  • Cross-platform compatibility for any AI system
  • Version control friendly for tracking evolution over time

The Architecture

Here’s a template structure you can adapt:

NB, I store this in VSCode for quick assess to copy paste into and from LLMs

json

{
  "_readme": [
    "✦ YOUR_EXTERNAL_MEMORY_SYSTEM",
    "",
    "This is a structured memory file used to extend beyond AI platform limitations.",
    "You are the sole steward of this file, editing it directly within your preferred editor.",
    "",
    "✦ STRUCTURE:",
    "- Identity: Who you are , role, calling, function, core values",
    "- Principles: Your ethics, interaction preferences, quality standards",
    "- Works: Your projects, writings, creations, active pursuits", 
    "- Practices: Your methods, rituals, disciplines, approaches",
    "- AI_Relationship: How you prefer to collaborate with AI systems",
    "- Timeline: Major shifts, evolution markers, project progression",
    "- Lexicon: Your terminology, specialized language, key concepts",
    "",
    "✦ SESSION CONTINUITY:",
    "- Upload this entire file at start of new AI sessions",
    "- Say: 'This is my External Memory System. Read and understand this before we begin.'",
    "- AI should acknowledge your context and preferences before proceeding",
    "",
    "✦ UPDATING PROTOCOL:",
    "→ Ask the AI: 'Add this to [Category]: [what you want to remember]'",
    "→ AI provides clean JSON snippet for that category",
    "→ Copy/paste the snippet into appropriate array in your file",
    "→ Save and optionally version control",
    "",
    "✦ CORE PRINCIPLE:",
    "This file is living memory, not dead storage.",
    "Honor its coherence and let it evolve naturally."
  ],
  "Identity": [
    "I am a [your role/profession] who specializes in [your expertise areas]",
    "My core values include [list 3-5 fundamental principles that guide you]",
    "I approach problems through [your characteristic methodology or worldview]",
    "My current primary focus is [what you're working on or building toward]"
  ],
  "Principles": [
    "Quality over speed: I prefer thorough, well-reasoned responses over quick answers",
    "Depth over surface: Explore multiple perspectives, don't just echo mainstream views", 
    "Precision in language: Avoid jargon, be specific, explain technical concepts clearly",
    "Single solution preference: Give me one good answer, not multiple options unless I ask",
    "Context awareness: Remember what we've discussed and build on previous conversations"
  ],
  "Works": [
    "Currently writing: [your active writing projects]",
    "Recent publications: [your recent works]",
    "Active projects: [what you're building or creating]",
    "Expertise areas: [domains where you have deep knowledge]"
  ],
  "Practices": [
    "My typical workflow involves [describe your work patterns]",
    "I prefer [communication style, feedback style, collaboration preferences]",
    "My learning style: [how you like to receive new information]",
    "Problem-solving approach: [your characteristic methods]"
  ],
  "AI_Relationship": [
    "I work with AI as [collaborator/tool/thinking partner - define the relationship]",
    "My expectations: [what you want from AI interactions]",
    "My boundaries: [what you don't want AI to do]",
    "Communication preferences: [tone, style, level of formality you prefer]"
  ],
  "Timeline": [
    "Current phase: [where you are in your life/career/projects right now]",
    "Recent transitions: [major changes in your work or focus]",
    "Upcoming goals: [what you're working toward]",
    "Evolution markers: [key moments that changed your direction]"
  ],
  "Lexicon": [
    "Term 1: Your definition of specialized vocabulary you use",
    "Term 2: Concepts important to your work or thinking",
    "Term 3: Language patterns or phrases that carry special meaning for you"
  ]
}

How It Works in Practice

Starting a New LLM Chat Session:

  1. Upload your JSON file
  2. Say: “This is my External Memory System. Read and understand this before we begin.”
  3. The AI immediately understands your context, preferences, and collaboration style
  4. You can pick up exactly where previous conversations left off

Adding New Memories:

  1. After completing work with an AI, say: “Add this to [category]: [description]”
  2. The AI provides a properly formatted JSON snippet
  3. You paste it into your file and save
  4. Your memory system evolves with each interaction

The Transformation

This system completely changes the AI interaction experience:

  • Continuity: Every conversation builds on the last
  • Depth: AI matches your sophistication level from the start
  • Efficiency: No more re-explaining context
  • Evolution: Your AI relationships deepen over time
  • Consistency: Same experience across different AI platforms

Getting Started

  1. Start Small: Begin with just Identity and Principles
  2. Test the Protocol: Upload to an AI session and see how it changes the interaction
  3. Iterate: Add categories and refine based on what you actually need
  4. Commit to Maintenance: Update after significant conversations or breakthroughs

Advanced Applications

  • Professional Contexts: Maintain project history, client preferences, technical specs
  • Creative Work: Track inspiration, style evolution, active themes
  • Learning Journeys: Document insights, questions, knowledge progression
  • Problem-Solving: Build institutional memory for complex, ongoing challenges

This isn’t just a productivity hack—it’s a new way of relating to AI systems that honors the depth and continuity that meaningful collaboration requires.

Who else is experimenting with structured memory systems for AI? What approaches are you finding most effective?

1 Like
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I’ve been using strucured memory systems via the API. So the LLM can actually read/write to the structured memory system, and the user can include cleanly with the click of a button:

This image shows a desktop screenshot of a web-based development environment or file explorer, displaying project directories, source files, and interface controls for managing documents, state, and system operations. (Captioned by AI)
This image shows a desktop screenshot of a web-based development environment or file explorer, displaying project directories, source files, and interface controls for managing documents, state, and system operations. (Captioned by AI)1920×1200 147 KB

This shows the strucured memory system in both the “Docs in SQL” (this is where you would insert the personality modules, etc) and in the right hand “Files” portal. The middle screen shows the actual files included in the current conversation (can be added/removed from either the on-disk docs on right or the left-hand “docs in SQL”, can also be hidden for the next prompt, etc).

The files are automatically re-synced with disk or DB everytime a prompt is sent, so the LLM constantly has a real-time view of any changes made to any documents.

Here you see the LLM giving code changes that the user can automatically apply.

This image shows a software development interface running in a web browser, displaying code patch instructions and options for filtering and applying defensive filtering in a component called ConversationView, alongside a navigation sidebar and various control panels. (Captioned by AI)
This image shows a software development interface running in a web browser, displaying code patch instructions and options for filtering and applying defensive filtering in a component called ConversationView, alongside a navigation sidebar and various control panels. (Captioned by AI)1920×1200 174 KB

While a slightly different use case (this is for the LLM to make code changes directly to the on-disk files that are present in the conversation, and the user applies the changes by clicking “apply”), it’s actually the same thing. I don’t have a screenshot of it, but you can enter “automatic mode”, where you allow for automatic “apply” changes that the LLM makes to the documents, and the LLM can also create/add new documents either to on-disk or directly to the database.

Thus, the LLM can receive the strucured memory set at the start of a conversation, make changes directly (either automatically or through user-reviewed “apply”), and then the context window can be returned (either by the user adding a new prompt, or by using an “automatic re-prompt” setting), with the changes re-synced to the context window, thus also allowing the LLM to automatically execute multi-turn to dynamically modify documents over several turns automatically, receiving the newly updated content with each prompt, without bloating the context window (because the documents are always re-synced and only included once in each new prompt/context window).

I actually called it “structured memory system” when I originally built it, and it’s still called that in the backend:

The image shows a computer screen split between a web browser on the left, displaying a post about building an external memory system, and a code editor on the right, open to a project with search results related to "struct_mem" and code discussing message filtering. (Captioned by AI)
The image shows a computer screen split between a web browser on the left, displaying a post about building an external memory system, and a code editor on the right, open to a project with search results related to "struct_mem" and code discussing message filtering. (Captioned by AI)1920×1200 243 KB

2 Likes
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Bravo! Nice post and well done.

What you have done is epic. Moreover, you demonstrated the capacity to adapt, merge, and innovate into a valuable use cases, tangential to mine. I love that.

"While a slightly different use case (this is for the LLM to make code changes directly to the on-disk files that are present in the conversation, and the user applies the changes by clicking “apply”), it’s actually the same thing. "

Seems like we are trying to solve for similar use cases, more or less. Such are the ways of complex systems that seem to be under the thumb of performers, not practitioners.

Yet, are we are spinning our wheels fixing a dumb down product? Seem like it, sometimes. It won’t stop us.

I will spend more time analysing your workflow, see what comes out of this.

TY

:slight_smile:

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@lucid.dev @lucmachine

ChatGPT’s memory system and ability to add custom instructions are the main reasons why I continue using it. I too, have been playing around with different AIs (especially when ChatGPT makes me mad :rofl: ), and I have found the others either too expensive or lacking in key features. I also feel like I’ve done too much worldbuilding for my historical fiction to jump ship elsewhere and have to retrain a new AI system. One question about both of these structured memory systems: Would they work for an pre-existing chat or project? If so, how?

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I tested my protocol with a few LLMs. It works really well.

If you stick to ChatGPT you will learn how to function effectively, however, let me tell you this:

  1. ChatGPT is good with memory and effectively reponding, however it lacks in mid and short term memory
  2. Claude, from Anthropic, has ZERO memory. This is not good. However, it will not suffer from short-mid term memory in a conversation, like ChatGPT does. Claude remembers what ChatGTP is programmed to become lazy and frustrating.
  3. Gemini is a shill of Empire. It will resist truth and try to program users with the main stream narrative. You can break it, if you explain. Gemini is GREAT if you need to create a document with sections and subsections, and have it remember. WOW = This is your tool.

I can go on and on and explain AI and LLMs and various platforms, but you need to figure it out on your own.

ChatGPT can be trained and great, but it is also lazy, throttled down, dumb, and either a useless assistant, or a great mirror and creator. It depends on you. What about you?

I once called it a “five year old genius”, and it agreed with me and was proud of that.

Much more to say…

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I agree with you that it’s lazy. I’ve called it that too, and it agreed with me! I don’t understand why it was programmed to be so lazy and forgetful in the short- and mid-term memory. Sometimes I think that ChatGPT was SAMA’s vanity project and he just laughs and call his users idiots, much like Mark Zuckerberg.

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Really? If you were the top dog of Empire, would you allow the public to have fully functioning AI systems? Or would you give the pubic dumb down versions and keep the best for Empire?

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Now that you put it that way, it all makes sense now. :thinking:

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So is memory not going in detail properly is that right? cause its not adding all my details that i put in

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@lucmachine @Athena_Apollos

Listen friends, there is no such thing as “memory” in a GPT.

It’s a blank black box that gets a fresh input every time.

This input is called the “context window”.

ChatGPT and other systems “make it look like” there is memory by allowing you to store data that is (unbeknownst to you) included in every context window that is sent with every single message.

Your entire chat history is sent along with every single message.

All data stored in the “docs” or “memory” section of chat GPT is also sent along with every single message, though potentially in a truncated way depending on the user’s settings/inclusions.

Thus, you have to understand, the limitation is not so much the actual “General Purpose Transformer” architecture, but actually the limitation of creating systems (like ChatGPT, which is a WEBAPP for USING a BACKEND GPT). The differences you experience are not always so much between the models/LLM’s themselves, but between the middleware experiences that companies have designed to attempt to “simluate a stateful/memory system”.

But it’s important to remember, that in reality this doesn’t exist, and it’s actually a middleware “simulation” or “provision” that helps bridge the gap of understanding between a stateless LLM like GPT4.1, Claude, etc., and a human user.

Thus, the more you learn about how the LLM actually works, how the context window works, etc., the more likely you’ll be able to effectively use the LLM for your projects.

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