Memmy vs Visiby: Features, Pricing & Which Is Better (2026)

A side-by-side comparison of Memmy and Visiby — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.

Memmy

Freemium

A local-first AI memory layer that lets the desktop app, CLI, and external AI tools share the same long-term context about you.

Self-Evolving Long-Term Memory: Automatically ingests your local AI collaboration history and structures scattered conversations and decisions into evolving memory.
Cross-Tool Memory Relay: Switch between Cursor, Claude, and other agents without re-explaining project context — Memmy carries decisions across tools.
Local-First Architecture: Memory stays on your machine, so preferences and history are not tied to a single vendor's cloud.
Desktop App and CLI Access: Read and write the same memory from the desktop app or the command line, so scripts and agents share one source of truth.
External Agent Integration: Expose the same memory to other AI agents so every tool has consistent knowledge of your preferences and decisions.
Preference and Decision Capture: Records rules like preferred frameworks and stylistic choices so you never need to repeat them in each new session.
Cross-Platform Downloads: Native builds for Mac ARM64 and Windows x64.
Free Trial Tokens: New users get a large token allotment to try Memmy against their real workflow before paying.

Switching Between Cursor and Claude Code: Carry project decisions, style, and open questions across coding assistants without re-briefing.
Personal Assistant Continuity: Keep a consistent long-term memory of your goals and preferences across chat, coding, and writing agents.
Team Onboarding via Shared Memory: Bootstrap a new agent on the same structured memory another agent has been building up.
Local-Only Knowledge Work: Store preferences and history on-device for privacy-sensitive workflows.
CLI-Driven Automation: Use the CLI to seed or query memory from scripts that run alongside interactive AI sessions.
Long Projects: Maintain evolving technical decisions across weeks of work with multiple AI tools without drift.

FNA Technology

Paid

AI visibility platform that tracks how ChatGPT, Perplexity, Claude, Gemini and AI Overviews cite your brand, and ships fixes.

AI Citation Tracking: Continuously samples roughly 50,000 prompts per week across ChatGPT, Claude, Perplexity, Gemini and Google AI Overviews to record where and how a brand is cited.
Per-Engine Visibility Scoring: Reports a composite AI Visibility score plus share of voice and prompts won or lost, broken out engine by engine so declines can be traced to a specific model.
Prompts & Citations Explorer: Lets teams open any tracked prompt and read the actual model answer to see which competitor was named and why.
Brand Entity Analysis: Maps the adjectives each engine associates with your brand versus competitors and suggests reframing plays to change that portrait.
Competitor Intelligence: Tracks rival citation share on comparison and 'alternatives to' prompts, highlighting categories where a competitor dominates.
Prioritized Action Plan: Converts findings into P0/P1 recommendations such as schema additions or comparison pages, each with a time estimate and projected score gain.
Site Audit for AI Parseability: Audits pages for missing entity definitions, structured Q&A data and other signals that prevent models from citing the site correctly.
White-Label Reporting and API: Higher tiers add white-label client reports, SSO/SAML and API access for agencies managing multiple brands.

AI Search Monitoring: Marketing teams track whether ChatGPT and Perplexity recommend their product or a competitor on high-intent category prompts.
Competitive Benchmarking: Brands quantify how much citation share a named rival is capturing on 'alternatives to' and 'best of' queries.
Content Prioritization: Content teams decide which pages to write or refresh based on which prompts are currently missed rather than on keyword volume alone.
Technical AEO Audits: SEO specialists find pages lacking FAQ schema or entity markers that keep answer engines from parsing them.
Agency Client Reporting: Agencies run pooled prompt tracking across multiple client workspaces and deliver white-label AI visibility reports.
Executive Reporting: Operators present a weekly digest showing search clicks alongside AI citation share to explain traffic shifts leadership sees.