Mother Brain vs Reyn

Mother Brain and Reyn are both personal assistants tracked by AIDiveForge. Below is a side-by-side comparison of pricing, capabilities, platforms, and ownership — sourced from each tool's live website and verified before publishing.

Mother Brain

Mother Brain provides persistent AI memory with document and codebase indexing, vectorization, and real-time team collaboration on a self-hosted infrastructure. The vendor states the tool is designed for developers who need an AI assistant that retains context across sessions rather than starting cold each time. The self-hosted model means your code and documents never leave your own infrastructure, which matters for teams with compliance requirements. Scraped page content is sparse, so specific throughput limits, supported LLM backends, and integration depth cannot be confirmed from sourced material. Teams evaluating this should request direct documentation on vector store configuration and team permission models before committing.

Reyn

Reyn passively records screen activity and surfaces it through a Q&A interface — ask what you worked on yesterday, and it pulls an answer from your actual session history, not from a search index you remembered to populate. A morning email digest recaps open items and recent completions, so you're not reconstructing your week at standup. The workflow capture feature watches you complete a process once, then documents the steps — which is useful for handing off SOPs without writing them from scratch. The hard ceiling appears the moment you need this on Windows or in a team context: Reyn is Mac-only and the data model is per-device, not shared. Teams that need collaborative activity logging or cross-platform coverage will find no path forward here.

Attribute	Mother Brain	Reyn
Pricing	Paid	Paid
Price	$25/year	$20/mo
Free trial	No	No
Open source	No	No
Has API	No	No
Self-hosted option	Yes	No
Platforms	macOS, Windows, Linux	Mac
Pros	Persistent memory across sessions, so developers stop re-explaining codebase structure to an AI that forgot everything since the last conversation. Self-hosted deployment, which means proprietary source code and documents never leave your own infrastructure — removing a category of compliance risk that cloud-only tools cannot eliminate. Real-time team collaboration on a shared AI context, so multiple developers work from the same indexed knowledge base rather than maintaining separate, diverging chat histories. Document and codebase vectorization built in, so teams avoid the engineering overhead of standing up their own retrieval pipeline just to give an AI assistant persistent context.	On-device screen journaling means your screen contents — client work, code, internal docs — never transit a vendor's cloud, so you get AI-powered recall without the data exposure that cloud activity trackers carry. Live screen context at query time, not just historical indexing, so answers about 'what am I looking at right now' are grounded in the actual present state of your desktop rather than a stale snapshot. Workflow capture documents a process from a single live run-through, so you can hand off SOPs to a teammate without separately writing documentation after the fact. Morning email digest surfaces open items and recent completions automatically, so you're not reconstructing your week from memory at the start of each day. Multi-provider AI support, so you're not locked to one model vendor — if API costs or model quality shift, you switch providers without changing how your screen data is stored.
Cons	The sourced vendor page provides no technical documentation on supported LLM backends, indexing capacity ceilings, or API surface — teams that require this detail before procurement cannot complete due diligence from public materials alone, and evaluation stalls. No free tier exists, so teams cannot run a trial against a real codebase before committing; teams that need hands-on validation before budget sign-off will move to a competitor that offers an evaluation path. Real-time collaboration feature set and permission model are undocumented in sourced material — teams managing access controls across multiple contributors cannot confirm whether the tool meets their requirements without direct vendor engagement, which adds procurement lead time.	Reyn is Mac-only with no Windows support or self-hosted option described anywhere on the vendor page — a user who splits their work across platforms loses the journal entirely for their non-Mac sessions, and there is no documented path to extend coverage. The data model is per-device and there is no API or shared workspace feature, which means workflow documentation captured by Reyn cannot be accessed by a teammate directly from the tool — teams expecting a shared activity log or collaborative SOP repository will need a separate system, at which point Reyn becomes a personal note-taking layer rather than a team workflow tool. AI inference routes through external model providers, so the local-first claim applies only to raw screen data — queries still require an outbound call to whichever AI provider you configure, which means teams operating in fully air-gapped environments cannot use Reyn as described.

Bottom line

Mother Brain and Reyn are closely matched on pricing model, openness, and API availability — pick by feature set and platform support in the table above.

Frequently asked questions

What is the difference between Mother Brain and Reyn?

Mother Brain is Paid, while Reyn is Paid. Compare pricing, free trial, API, platforms, and pros/cons in the table above on AIDiveForge.

Is Mother Brain better than Reyn?

It depends on your workflow. Use the side-by-side attributes (pricing, open source, API, self-hosted, platforms) to decide. AIDiveForge does not rank a universal winner — we publish verified facts so you can choose.