Memory System Deep Dive: Persistent Context Across Chats

Magicdoor's memory system keeps useful context available across chats and across supported models. The goal is simple: stop re-explaining the same preferences, background, and recurring constraints every time you switch models. If you are coming from ChatGPT, use this alongside the memory transfer guide.

What memory is good for

Memory works best for information that stays relevant over time:

• communication preferences
• output format preferences
• ongoing projects
• recurring constraints
• personal or professional background that improves future answers

Examples:

• "Prefer concise answers with clear next steps."
• "Use metric units."
• "I work in B2B SaaS marketing."
• "I am currently planning a Q2 product launch."

How it works in practice

When memory is enabled, saved memory items can be included across chats so supported models start with more context about you.

That means:

• a Claude Sonnet 5 chat can use the same saved preferences as a GPT-5.5 chat
• you do not need separate memory setups for each provider
• switching models does not force you to restate the same background information

Good memory habits

Save stable preferences

Add things that are likely to stay useful for weeks or months, not one-off details from a single conversation.

Good candidates:

• preferred writing tone
• job role
• industry context
• default formatting preferences
• long-running project context

Do not over-store temporary details

Avoid filling memory with short-lived notes like:

• today's to-do list
• one-off brainstorming fragments
• throwaway examples
• outdated project milestones

Those belong in the current chat, not long-term memory.

Suggested structure

The easiest pattern is to keep memory in a few buckets:

Preferences

• tone
• formatting
• units
• level of detail

Background

• role
• industry
• technical level
• recurring responsibilities

Current priorities

• active project
• key constraints
• current goals

Managing memory

You should periodically review your saved memory and remove anything outdated. A smaller, cleaner memory set usually works better than a huge list of mixed-quality facts.

Good maintenance moves:

• update job title or responsibilities when they change
• remove completed projects
• refine vague preferences into clear instructions
• delete memory items that no longer help

Example prompts

Add useful memory

Remember that I prefer concise answers with bullet points and concrete next steps.

Remember that I work on growth and lifecycle marketing for a B2B SaaS company.

Update memory

Update my memory: my current priority is launch planning for Q2, not Q1.

Remove stale memory

Remove the memory about my old role. I changed jobs last month.

Cross-model benefit

The main advantage over single-provider memory is that one saved context layer can support multiple models in the same workspace.

That is especially useful when you:

• research with Perplexity
• write with Claude
• plan with GPT-5.5
• switch back and forth during one project

Privacy and control

The practical control points to understand are:

• memory belongs to your account
• you can review, update, and delete saved memory items
• memory can be turned on or off through account preferences

For the current product-level privacy summary, see the Trust page and Terms of Service & Privacy Policy.

FAQ

How much should I store in memory? Store only the information that regularly improves future answers. More is not always better.

What should go in memory vs chat history? Put stable preferences and ongoing context in memory. Keep temporary details inside the current chat.

Does memory work across models? Yes, across supported chat models. That is one of the main reasons to use it on Magicdoor.

Can I change or delete memory later? Yes. You should treat memory as editable working context, not a permanent archive.