If you use ChatGPT or Claude heavily, you already know the frustration. Every new conversation starts blank. You re-explain your role, your project, and your preferences, over and over, session after session. Several tools now tackle this directly, but they take very different approaches.
Why native memory is not enough
Both ChatGPT and Claude ship with built-in memory. ChatGPT saves facts you tell it explicitly or that it picks up from conversation. Claude offers memory capabilities that depend on the product and plan being used. These are useful starting points.
But native memory mainly covers what you have said inside those chat windows. It does not automatically know about the email thread from last Tuesday, the meeting notes in your workspace, the project brief in a local folder, or the transcript from your last client call. If your real context lives across tools, native memory captures only part of it.
That is the gap third-party memory tools are trying to fill.
The main contenders
The options below range from developer infrastructure to product-specific assistants and no-code memory layers. The important difference is not simply how much each tool can store. It is who can use it, where the context can travel, and whether separate areas of work can stay scoped.
Mem0
Mem0 is primarily developer infrastructure: an API that lets engineering teams embed persistent memory into AI applications they are building. Its graph-based memory model and semantic retrieval make it a strong technical option for product teams.
For individual knowledge workers, it is a more difficult fit. The 2025 source article listed graph memory on a Pro tier at $249 per month. There was no no-code interface for a non-developer to connect Gmail or Notion and immediately query that context inside ChatGPT. A solo consultant or writer would need to build the integration or use a plan designed for engineering organizations.
Best for: developer teams building memory into AI products, rather than individuals looking for a self-serve tool.
Supermemory
Supermemory offers both a developer API and a consumer-facing app. It supports semantic search across saved content and provides a direct interface for working with that memory.
The limitation identified in the 2025 source article was scoping. It did not offer named, role-based memory spaces such as a Work brain and a separate Client brain that could be invoked independently. For someone managing multiple clients or roles, one combined pool can become noisy. Public consumer pricing was also not clearly listed in the source review.
Best for: developers and technically comfortable users who want one unified memory store.
Notion AI
Notion AI is useful inside Notion. It can reference pages, databases, and notes while a person is working within that product, which is valuable for teams that already keep most of their context there.
The tradeoff is portability. Context retrieved inside Notion does not automatically become the same persistent memory inside a separate Claude or ChatGPT conversation. People who need their project material in another AI client may still need a connector or a manual transfer.
Best for: teams whose work and AI workflow remain primarily inside Notion.
Native ChatGPT and Claude memory
Native memory is usually the first option people try, and both platforms have continued to expand their memory features. It is convenient because it is already part of the AI interface.
Its natural boundary is the platform itself. Email, meetings, files, and notes from other tools remain outside that memory unless they are connected or brought into the conversation. Native memory handles preferences and conversational continuity well, but it is not automatically a cross-tool work archive.
Best for: people who primarily work inside one AI product and do not need the same external context across clients.
hippocampOS
hippocampOS extends the memory available to an AI by connecting to sources where work context already lives, including supported email, notes, meeting records, and files. It organizes that material into a persistent memory graph rather than replacing a model's native memory.
Named brains keep roles and projects separate. A person can create a Work brain, a Personal brain, or a brain for a client, then invoke the relevant scope from Claude, ChatGPT, or another supported MCP client. The goal is to retrieve the relevant slice without exposing every saved context to every conversation.
The source article lists the Spark plan at $5 per month when billed annually, with a 7-day refund guarantee. Google user data is not used to train generalized AI models.
Best for: knowledge workers who use more than one AI client and want external work context to remain portable and scoped.
Comparison table
Swipe horizontally to see every column.
| Tool | Best for | Claude and ChatGPT | Pricing | Key limitation |
|---|---|---|---|---|
| Mem0 | Developer teams | Through an API integration | $249/mo for graph memory in the 2025 source review | No no-code self-serve flow for individuals in the source review |
| Supermemory | Developers and technical users | Partial | Not publicly listed in the 2025 source review | No role-based brain scoping in the source review |
| Notion AI | Teams inside Notion | Not as one shared memory across both clients | Bundled with eligible Notion plans | Context remains centered on the Notion workspace |
| Native ChatGPT or Claude memory | Single-platform users | Each platform keeps its own memory | Included with eligible plans | Primarily covers context available to that AI product |
| hippocampOS | Solo knowledge workers | Yes, through supported connections | $5/mo for Spark when billed annually | A newer product with an expanding integration set |
The right memory tool depends on where your context lives and whether you need it to move between AI clients.
How to choose
If you are a developer building memory into a product, API infrastructure such as Mem0 is worth evaluating. If your workflow remains inside Notion, Notion AI may already cover the context you need. Native memory remains the lowest-friction choice when one AI platform contains most of your work.
If your context is spread across email, meeting notes, and project files, the deciding question is portability. Look for a tool that can bring relevant source context into the AI clients you use without collapsing every role or project into one memory pool.
That is the specific problem hippocampOS is designed to address. It leaves source material in intentional brain scopes and makes selected context available through supported AI connections.
Frequently asked questions
Can you give ChatGPT and Claude persistent memory without coding?
Yes. A no-code memory product can connect supported sources and expose relevant context through supported ChatGPT, Claude, or MCP connections without requiring the user to build an API integration.
What is the difference between native memory and a third-party memory tool?
Native memory is controlled by the AI product and centers on context available inside that product. A third-party memory layer can connect external work sources and make selected context available across more than one AI client.
Does hippocampOS work with both Claude and ChatGPT?
hippocampOS provides supported connection paths for Claude, ChatGPT, Claude Code, and other compatible MCP clients.
Is Mem0 suitable for individual users?
Mem0 is primarily developer infrastructure. An individual can use it by building or adopting an integration, but a person seeking a no-code workflow should compare the current setup requirements and pricing first.
What do named brains mean in hippocampOS?
A brain is a scoped memory space for a role, project, or area of life. Selecting one limits retrieval to that context instead of automatically searching everything a person has saved.
How much does hippocampOS cost?
The source article describes Spark at $5 per month when billed annually, with a 7-day refund guarantee. Check the current Plans page before purchasing.
Does hippocampOS use my data to train AI models?
Google user data is not used to train generalized AI models.