Key points at a glance
- Both make the AI remember you across conversations, but they differ greatly in granularity and controllability.
- Enclave's memory is structured, manageable item by item, and shared across multiple experts.
- Enclave is self-hostable and your memory lives only on your machine; ChatGPT's memory is stored in the platform's cloud.
Side-by-side comparison
| Dimension | Enclave | ChatGPT memory |
|---|---|---|
| Who it serves | Multiple experts share one memory | A single assistant |
| Memory form | Structured, recalled by relevance | Mostly summary-based |
| Control granularity | View / edit / delete item by item | View / delete some entries |
| Proactive use | Experts proactively follow up based on memory | Largely passive responses |
| Data location | Lives only on your machine when self-hosted | Stored in the platform's cloud |
| Open source | MIT open source | Closed source |
How to choose
If you're already on ChatGPT and just need it to remember a few preferences across chats, its built-in memory is enough; if you want a memory that's structured, controllable item by item, shared across multiple experts, and able to live only on your own machine, Enclave goes a step further. You can also use both.
FAQ
Does Enclave's AI really have long-term memory?
Yes. Enclave uses a structured long-term memory system rather than a simple context window, so experts can still cite specific things you said months ago — not a vague "I think you mentioned that." Every memory is yours to view, edit, or delete at any time.Is my conversation data safe? Will it be used for training?
In self-hosted mode, your conversations live only on your own drive — they never leave your machine and are never used by any third party for training. The model layer can connect to cloud APIs like OpenAI and Anthropic, or be swapped for local Ollama / vLLM to run fully offline; you decide which kind of conversation goes to which model.Which large language models does Enclave support?
Enclave's model layer is fully swappable: OpenAI, Anthropic, Google, DeepSeek, and local Ollama / vLLM can all be configured. Different characters can even use different models, freely allocated by scenario and cost.
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