What is AI Memory?

You've built months of context in ChatGPT. Your projects, your preferences, how you like responses formatted. Then you try Claude for a different task and realize: you're starting from scratch.

This is the reality of AI memory today. Not that it doesn't exist - ChatGPT has memory, Claude has Projects, Gemini remembers things. The problem is that each AI keeps its memory locked inside its own walls. Your context doesn't travel with you.

AI memory is the technology that stores your context across AI interactions. But the question isn't whether AI can remember you anymore. It's whether that memory works everywhere you need it.

This guide explains what AI memory is, how different types work, and why cross-platform memory is becoming essential for anyone who uses multiple AI tools.

The real problem with AI memory today

Here's what most people get wrong about AI memory: they think the problem is that AI forgets you. That was true in 2023. It's not the main issue anymore.

ChatGPT launched memory features in 2024. Claude introduced Projects for persistent context. Google's Gemini remembers your preferences. The major AI providers have solved basic memory.

The real problems are different now.

Platform lock-in

Every AI provider wants you to build context with them specifically. The more context you build in ChatGPT, the harder it becomes to try Claude. The more you invest in Claude Projects, the more you lose by switching to GPT-4.

This is vendor lock-in, AI edition. Your memory becomes the chain that keeps you on one platform.

No visibility or control

What has ChatGPT actually remembered about you? Can you see the full list? Edit specific memories? Export them if you want to leave?

Native AI memory is largely a black box. You know something is being remembered, but you have limited visibility and control over what's stored.

Context fragmentation

Most AI power users don't use just one tool. They might use Claude for writing, GPT-4 for coding, Gemini for research, and Llama for privacy-sensitive tasks.

Each of these tools maintains its own isolated memory. The context you build in one doesn't transfer to another. You end up with fragmented pieces of yourself scattered across multiple platforms.

Starting over constantly

Want to try the new model everyone's talking about? Get ready to re-explain your role, your projects, your preferences. Every time you explore a new AI tool, you start from zero.

This friction actively discourages experimentation. You stick with what knows you, even if something better exists.

How AI memory actually works

Understanding AI memory requires separating the different technical approaches.

Native AI memory

When ChatGPT or Claude "remembers" something, they're storing information in their own systems and injecting it into future conversations. Here's the basic flow:

1. You share information during a conversation
2. The AI identifies memorable context (automatically or when you ask it to remember)
3. That context gets stored on the provider's servers
4. In future conversations, relevant memories are retrieved and added to the prompt

This approach works well for single-platform use. The limitation is that the memory only exists within that provider's ecosystem.

Context windows and retrieval

Modern AI has limited context windows - the amount of information it can consider at once. Memory systems work around this by:

• Storing your full context in a database
• Using semantic search to find relevant pieces
• Injecting only the relevant context into each conversation

This is why AI doesn't dump everything it knows about you into every response. It retrieves what seems relevant to your current question.

Cross-platform memory layers

A newer approach adds a memory layer that sits between you and all your AI providers. Instead of each AI maintaining its own memory:

1. You interact with any AI through the memory layer
2. The layer captures and stores context from all interactions
3. When you use any AI, relevant context is provided from your unified memory
4. Your context follows you across ChatGPT, Claude, Gemini, and others

This solves the portability problem. Tools like Onoma provide this kind of cross-platform memory, working with 14 models from 7 different providers.

Types of AI memory solutions

Different approaches serve different needs. Here's how they compare.

Native provider memory

Examples: ChatGPT Memory, Claude Projects, Gemini

How it works: Memory stored and managed by the AI provider

Advantages:

• Already integrated - no additional tools
• Free with your existing subscription
• Simple to enable and use

Limitations:

• Platform lock-in: only works with that one provider
• Limited visibility into what's stored
• No portability - can't take your memory elsewhere
• Basic organization options

Native memory is a starting point, not a complete solution.

Cross-platform memory layers

Examples: Onoma, Mem0, custom RAG implementations

How it works: A layer between you and all AI providers that maintains unified memory

Advantages:

• Same memory across all AI providers
• Full visibility and control over stored context
• Portability - your memory is yours
• Advanced organization with features like automatic Spaces
• Features like adaptive routing to pick the best model

Considerations:

• Additional tool to set up
• May have subscription costs
• Requires routing through the memory layer

For users of multiple AI tools, this approach eliminates the biggest friction points.

Local-first memory

Examples: AnythingLLM, custom vector database setups

How it works: Memory stored entirely on your own hardware

Advantages:

• Maximum control - nothing leaves your device
• Works with local models
• No dependency on external services

Limitations:

• Requires technical setup
• May need powerful hardware
• Limited to desktop use
• Maintenance burden

Best for developers with specific requirements and the time to maintain infrastructure.

Developer memory APIs

Examples: Zep, LangMem, MemGPT

How it works: APIs and libraries for building memory into AI applications

Advantages:

• Maximum customization
• Integration with existing systems
• Full control over implementation

Limitations:

• Requires development work
• Ongoing maintenance
• Not a consumer-ready solution

These are tools for building memory into products, not for end-user consumption.

Why cross-platform memory matters now

The AI landscape is fragmenting in a useful way. Different models excel at different things.

Model specialization

GPT-4 remains strong for general tasks. Claude excels at writing and nuanced analysis. Gemini integrates with Google's ecosystem. Llama offers open-source flexibility. Mistral provides fast, efficient responses.

Using just one AI means missing capabilities that others do better.

Rapid model evolution

New models launch constantly. Sticking with one provider means missing innovations from others. But the cost of switching - losing your built-up context - creates artificial friction.

With cross-platform memory, trying a new model doesn't mean starting over.

The multi-model workflow

Power users are increasingly building workflows that use different models for different tasks:

• Claude for drafting content
• GPT-4 for coding and technical work
• Gemini for research and web-connected tasks
• Llama for privacy-sensitive local processing

This only works smoothly if context transfers between them.

Cost optimization

Different models have different pricing. Sometimes you want GPT-4's power; sometimes a faster, cheaper model works fine. Cross-platform memory lets you choose the right model for each task without losing context.

What to look for in AI memory solutions

If you're evaluating memory solutions, here are the factors that matter.

Provider coverage

How many AI models does it support? Onoma supports 14 models from 7 providers - OpenAI, Anthropic, Google, xAI, Groq, Mistral, and more. Fewer providers means less flexibility.

Visibility and control

Can you see exactly what's stored? Edit individual memories? Delete things you don't want kept? Export everything? The more control, the better.

Organization

Does context get organized automatically, or do you manage folders manually? Automatic organization (like Spaces that separate work from personal) reduces overhead.

Model selection

Some solutions let you choose which model handles each request. Others go further with adaptive routing that automatically picks the best model for your question. Side-by-side comparison lets you see how different models approach the same question.

Privacy options

For sensitive work, look for options like local PII processing that strips personal identifiers before reaching AI providers. EU data residency matters for European users.

Setup complexity

How long does it take to start using it? Minutes vs hours matters for adoption.

Setting up cross-platform AI memory

Here's how to get started with different approaches.

Using native memory (quickest start)

ChatGPT Memory:

1. Open Settings
2. Go to Personalization
3. Enable Memory
4. Optionally manage specific memories

Claude Projects:

1. Create a new Project
2. Add context in the project knowledge section
3. Use conversations within that project

This gets you basic memory within each platform.

Using a cross-platform memory layer

For memory that works across all providers:

1. Sign up for a tool like Onoma
2. Connect your AI providers
3. Start using AI through the memory layer
4. Context captures automatically and travels with you

Setup typically takes a few minutes.

Building custom memory

For developers wanting full control:

1. Choose a vector database (Pinecone, Weaviate, Chroma)
2. Implement context capture from AI interactions
3. Build retrieval logic for relevant context
4. Create interfaces for memory management

Expect significant development and ongoing maintenance.

Common questions about AI memory

Does AI memory slow down conversations?

No noticeable impact. Context retrieval happens in milliseconds. Most users find conversations feel faster because they skip the context-setting phase.

How much can AI memory store?

Varies by solution. Native memory has provider-set limits. Cross-platform solutions typically offer generous or unlimited storage. The practical limit is usually what's useful to retrieve.

What if wrong or outdated context gets retrieved?

Good memory systems let you review and edit stored context. You can correct inaccuracies, remove outdated information, or adjust what gets retrieved.

Can I use AI memory with local models?

Yes. Cross-platform memory layers work with local models like Llama the same way they work with cloud providers. Your context follows you regardless of where the AI runs.

What happens to my memory if I stop using a service?

With native memory, your data typically stays with the provider. With third-party memory layers, look for export capabilities. You should own your data.

Is my memory data private?

Depends on the solution. Native AI memory lives on provider servers. Some cross-platform solutions offer privacy features like local PII processing and EU data residency. Evaluate based on your needs.

The future of AI memory

AI memory is evolving rapidly.

Smarter retrieval

Better understanding of what context matters for each conversation. Less irrelevant information, more relevant context when you need it.

Team memory

Shared context for teams - everyone's AI understanding project background, team preferences, and shared knowledge without manual synchronization.

Deeper tool integration

Memory that works across email, documents, calendars, and other productivity tools. Not just AI conversations, but your full work context.

Agent memory

As AI agents take on more autonomous tasks, they need persistent memory to maintain context across operations. Research is advancing in how agents should structure and use memory.

Key takeaways

AI memory exists. The question is whether it works across your full AI workflow.

• Native AI memory works - ChatGPT, Claude, Gemini all remember you now
• The problem is lock-in - each provider's memory is trapped in their platform
• Cross-platform memory solves this - one memory layer across all your AI tools
• Visibility and control matter - see what's stored, edit it, export it
• Model flexibility increases - try new AI without losing context
• Setup is straightforward - connect your providers and start using AI normally

For users of multiple AI tools, cross-platform memory isn't optional - it's essential for a smooth workflow.

Ready to try AI memory that works everywhere? Start with Onoma free - 14 models, 7 providers, one memory that follows you.