Memex vs Meta Muse: A Local-First Approach to Personal AI

A trip to arrange, and a trip to remember

Muse is designed to carry out tasks and continue working after its app closes. That suits a request such as researching options for an upcoming trip.

Now consider what remains afterward: photos, a restaurant you liked, a conversation you want to remember, and a note about what you would do differently next time. In Memex, those fragments become records that agents can organize into cards and knowledge files. You can return to the original material when an AI summary misses the point.

These are different jobs. Decide whether your immediate need is completing an errand or keeping a useful account of your life before comparing feature lists.

How Meta designed Muse · How agentic journaling works

Local records and cloud model calls are separate choices

Memex stores its journal workspace in local files and SQLite. Its agent workflow runs in the app. That gives you a local primary copy; it does not mean every AI operation happens on the phone.

With a directly configured cloud provider, relevant prompts and attachments go to that provider. The optional Memex AI service proxies model requests. An Ollama connection uses the machine hosting that endpoint, which may be another computer on your network. Sync, transcription, and external tools can introduce other data paths.

To try your own provider, open the avatar menu, choose Model Configuration, select the provider, and enter the required API key and base URL. Save a short note and inspect the result. Choose storage, inference, and connected services separately; local storage does not make a cloud model call stay on the phone.

What local-first storage means · Memex model configuration

Muse has safeguards worth comparing fairly

Meta describes a separate Sentinel controlling Muse's external actions, with credentials isolated from the main agent. Those protections address risks in delegated work.

Meta also states that Muse conversations are excluded from its ad systems and offers a training opt-out. This is a different privacy architecture from a local primary journal, rather than evidence that one product has no privacy controls.

For your own decision, separate three questions: where is the archive, who processes the context, and which actions can the agent take? Memex's open code helps you inspect its implementation, but configuring cloud services still requires reviewing those services' terms.

Meta's Muse security architecture · Meta's Muse launch and privacy commitments · Privacy questions for an AI journal

What you can change in Memex

Memex lets you configure a provider and model for individual agents. You can adjust system prompts, add Skills, and scope a custom agent's working directory. Its GPL-3.0 source also lets you build and modify the app under the license.

For a travel-review experiment, give a custom agent this instruction: 'Use the trip records in this workspace. List places I visited, keep my comments separate from your suggestions, and link each conclusion to its source record.' Run it on a small set of notes and inspect the output. You are configuring and evaluating a workflow, rather than enabling a ready-made scheduled review.

Muse also supports custom connectors and skills. Memex's appeal here is the combination of a modifiable app, device-local journal workflows, and provider choice. Setup and maintenance are part of that freedom.

Memex custom agent configuration · Using local models with a journal

What you trade for device-local execution

A phone app must work within its operating system's background execution, battery, and network limits. Do not treat Memex as a cloud computer that stays available around the clock. An external model endpoint must also remain reachable when a task needs it.

Keep a backup of your local archive, and budget for model usage and any hardware or hosting you choose. Start with one small record, check its generated card, and test a provider switch before building a larger workflow. Your archive should remain useful when you change the model behind it.