| Tool | OpenPrefs |
|---|---|
| Pricing | Free to try (check official site for current pricing) |
| Category | AI productivity & memory |
| Monetization difficulty | Medium |
In short5 min read
- OpenPrefs stores preferences as structured values that any connected MCP assistant can look up.
- Every read and change is logged with the AI that made it, plus an undo button.
- Per-app permissions let you decide which assistant sees which preferences.
- It is free to try; check the official site for current pricing and limits.
If you run a side business with more than one AI assistant, you probably repeat yourself a lot. You tell one tool your budget, another your working hours, and a third the formatting rules you always use. OpenPrefs, a new tool shared on r/SideProject, tries to end that loop: you state a preference once, and every connected assistant can look it up instead of asking again.
This review covers what OpenPrefs actually does, where it can save a solo operator real time, and a simple framework for deciding what belongs in a shared AI memory and what should never go there. You will finish with a checklist you can run in one sitting.
In this article
The problem: your preferences are scattered across vendors
When memory exists in an assistant today, it usually lives inside that one product. Switch from Claude to ChatGPT for a task and the context stays behind. For someone juggling client work, content and admin with several tools, that means the same instructions get typed again and again, and small inconsistencies creep in.
The founder says the motivation was control: they wanted more say over what AI agents could access and what those agents had stored in memory. OpenPrefs is the result, described as a preference store you own that any AI can connect to.
How OpenPrefs works
The flow, as the founder describes it, has three parts:
- Connect OpenPrefs to your assistants. The founder names Claude, ChatGPT, Muse, and anything that supports MCP (Model Context Protocol).
- Mention a preference in normal chat, such as “I wear a size medium,” “budget is $100,” or “Don’t ping me before 6am.” The AI saves it to OpenPrefs.
- Reuse it. The next time any connected AI needs that information, it looks it up instead of asking you.
The interesting part is not the sync itself but the design choices around it.
| Feature | What it means | Why a solo operator should care |
|---|---|---|
| Structured preferences | “Shirt size: M” is stored as a value, not a paragraph of chat | Assistants get a clean answer instead of guessing from old conversation |
| Real lists with dynamic columns | Favorite brands or watched movies, with extras like star ratings | You can keep reference lists an AI can actually check |
| Activity log | Every read and change, plus which AI made it | You can audit what each assistant touched |
| Undo button | Reverse a change an AI made | Mistaken saves do not quietly stick |
| Per-app permissions | Choose which assistant can see what | Limit exposure per tool |
On pricing, the founder only says it is free to try. Check the official site for current pricing, limits, and any paid tiers before you build a workflow around it.
Where it could save money-making time
OpenPrefs is not a tool that earns money by itself. Its value for a side hustler is indirect: fewer repeated instructions, fewer inconsistent outputs, and less time correcting assistants. Here are three practical ways to use it, based on the kinds of preferences the founder describes.
1. Keep your operating rules consistent across tools
Most freelancers have rules they repeat constantly: a budget ceiling for tool purchases, preferred formats, hours when they do not want notifications. The founder’s own examples (“budget is $100,” “Don’t ping me before 6am”) are exactly this type. Stored as values, these rules can follow you from a research assistant to a writing assistant without a new prompt each time.
2. Maintain reference lists your assistants can check
Because lists support dynamic columns, you could keep a list of preferred suppliers, tools you already pay for, or brands you like working with, each with a rating. When an assistant recommends something, it can check the list first. That cuts the time spent filtering suggestions you would never use.
3. Offer it as part of an AI setup service
If you already help small businesses set up AI assistants, a shared preference store is a natural add-on to an onboarding package: map the owner’s recurring preferences, connect their tools, and configure per-app permissions. This is an idea for your own service, not something the founder promotes, and you should confirm OpenPrefs’ terms before using it on a client’s behalf.
Should you centralize your AI memory? A decision framework
A single store that every assistant reads is convenient, but it also concentrates information. Before you connect everything, sort what you would store into three buckets.
| Type of information | Good fit for a shared store? | Reasoning |
|---|---|---|
| Low-risk working preferences (formats, hours, budgets for tools) | Yes | High repetition, low harm if exposed |
| Personal lists (brands, sizes, favorite places) | Usually | Useful for shopping and planning tasks; limit which apps can read them |
| Client details, contracts, health or financial account data | No | Keep these out; the convenience is not worth the exposure |
| Passwords, API keys, account numbers | Never | No AI memory is the place for credentials |
A few rules make the middle bucket safer:
- Least privilege by default. Give each assistant access only to the preferences its job needs. Per-app permissions exist for this.
- Audit on a schedule. Read the activity log once a week. If an assistant is reading things you did not expect, tighten its permissions.
- Watch for bad saves. An AI may store a one-off remark as a permanent preference. The undo button and log are your correction tools.
- Plan for change. It is a new side project. Ask the founder or check the documentation about exporting your data before you depend on it.
Limitations to keep in mind
- MCP support is required. Assistants that do not support MCP cannot connect, so check each tool in your stack first.
- Setup happens per assistant. The founder is actively asking whether connecting each assistant is clear or confusing, which suggests the setup is still being refined.
- Early stage. The founder is collecting feedback on missing features, so expect changes. There was no public user feedback yet when it launched.
- Pricing beyond the free trial was not shared. Treat long-term cost as unknown until you check.
None of these are dealbreakers for a test, but they matter if you plan to connect client-facing workflows.
How to do it
Use this checklist to test OpenPrefs in one session without over-sharing.
- [ ] Write down 5-10 preferences you repeat to AI tools. The founder asks users this exact question, and it is the fastest way to see if the tool is worth it for you.
- [ ] Sort them using the decision framework above. Keep only low-risk working preferences for the first test.
- [ ] Sign up on the official OpenPrefs site and check current pricing and limits.
- [ ] Connect one MCP-compatible assistant you use daily. Do not connect everything at once.
- [ ] State three preferences in chat and confirm they appear as structured values.
- [ ] Open the activity log and check which AI made each change.
- [ ] Connect a second assistant and set its permissions so it only sees what it needs.
- [ ] Ask the second assistant a task that requires a stored preference and confirm it looks it up instead of asking you.
- [ ] Undo one save to confirm you can reverse mistakes.
- [ ] Review the log weekly for a month, then decide whether to expand what you store.
If the test saves you real time on repeated instructions, expand slowly. If it does not, you have lost an hour and learned what your assistants actually need from you.
Ready to stop repeating yourself to every AI?
Today, list the preferences you type into AI tools most often and keep only the low-risk ones. Then connect a single assistant to OpenPrefs and test whether a second assistant can look those preferences up on its own.
Source: I built OpenPrefs: tell one AI your preferences and every AI you use can read them
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