Monolithos vs MiroFish

A side-by-side comparison built from both listings. Blank cells mean the company has not given that detail; we never guess.

Monolithos compared with MiroFish
Detail	Monolithos	MiroFish
What it does	Personal Agent OS built from memory you own	A simple and universal swarm intelligence engine
Stage	Not given	Not given
Founded	Not given	Not given
Based in	Not given	Not given
Pricing model	Freemium	Freemium
Pricing	Not given	Starter: $7.50/month (monthly) or $9/month (annual); Pro: $15.83/month (monthly) or $19/month (annual); Premium: $40.83/month (monthly) or $49/month (annual). Annual plans save 17%.
Key features	User-owned memory in Markdown and YAML Eight modules: Memory, Flow, Black Hole, Minutes, Codex, Prism, Audio, Facet Multi-step AI tasks with human approval checkpoints Meeting transcription sealed into memory Markdown-to-presentation generation with Prism Audio generation from notes Visual asset library with provenance tracking Private domain boundaries to exclude content from cloud AI Project mode to bind files and conversations into sealed context Compatible with existing Obsidian vaults without migration	Upload PDF, Markdown, and text documents to seed simulations Convert source material into ontology and graph structure Run parallel simulations with up to 1 million agents per run Generate prediction reports with key risks and confidence signals Deep interaction through ReportAgent or direct character interviews Dual-surface simulation across Twitter and Reddit style channels Follow-up questions in the same workspace for continued analysis Build executable scenarios for public opinion, market, policy, and narrative use cases
What makes it different	Memory and data remain on your device and under your ownership, never used for model training Files stay readable as plain Markdown and YAML with any text editor; no vendor lock-in possible Works with existing Obsidian vaults natively without import or conversion Private-domain content stays out of cloud AI context automatically AI recall includes sources so you can see what answers are based on	Inspectable AI forecasts treated as exploratory hypotheses before operational decisions Review simulations before upload with the Forecast Explorer gallery Graph-native simulation that exposes actor incentives, narrative spread, and reaction chains Templates and guides for recurring scenario types like crises and product launches Keeps judgment with humans on scenario boundaries and high-stakes operating calls
Community votes	0	0
DR (Domain Rating by Ahrefs)	3 no change since last check	56 no change since last check
Trust Flow (Majestic)	9	5
Citation Flow (Majestic)	21	30
Referring domains (Majestic)	22	160

4 details filled in for both companies.

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