Memmy vs sizeless: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Memmy and sizeless — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
Memmy
Memmy
A local-first AI memory layer that lets the desktop app, CLI, and external AI tools share the same long-term context about you.
Key features
- 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.
Best for
- 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.
sizeless
sizeless
Turns a smartphone video of an open trench into a centimetre-accurate 3D point cloud, CAD as-built plan and GIS-ready digital twin of buried utilities.
Key features
- Smartphone capture: Field crews record an open trench with a standard iPhone Pro — no specialist scanning hardware and no separate surveying appointment
- Centimetre-accurate point clouds: Reconstruction algorithms developed at ETH Zurich build a high-resolution 3D point cloud of the excavation from the video alone
- Standards-compliant CAD output: Generates as-built plans in DWG and DXF, with couplings and pipe runs identified and measurements simplified
- 3D digital twin and GIS export: Produces a model of the pipe route including building entries that drops into existing GIS systems
- Works without GPS: Captures basement sections and building entry points where GNSS-based surveying fails
- Immediate backfilling: Because capture takes minutes, trenches close right after filming instead of waiting on a survey crew
- Documentation in about 72 hours: Complete records arrive weeks earlier than conventional surveying, enabling prompt connection billing
- Third-party utility capture: Records crossing utilities and as-laid geometry as unbroken 3D evidence, replacing hand sketches
Best for
- A utility network operator documenting residential service connections without booking a surveyor for every site
- A contractor closing a trench the same day instead of leaving it open pending a survey appointment
- Capturing a building entry point in a basement where GPS-based surveying cannot get a fix
- A district heating project producing as-built DWG plans for regulatory sign-off
- Spotting a laying error in the 3D point cloud before backfilling, while the fix is still cheap
- Feeding as-built pipe geometry into a GIS system for long-term network maintenance planning