CogniMemo vs sizeless: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of CogniMemo and sizeless — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
CogniMemo
CogniMemo
Persistent, context-aware memory layer that connects data and apps to AI for smarter, human-like reasoning.
Key features
- Persistent Memory Storage: Stores structured and unstructured information across sessions so agents and applications retain long-term context and user history.
- Context-Aware Retrieval: Returns relevant memories based on contextual signals to inform model outputs and reduce irrelevant or repetitive responses.
- Connectors & Integrations: Ingests data from external sources (apps, docs, databases, APIs) via connectors or ingestion pipelines to centralize knowledge for recall.
- APIs and SDKs: Provides programmatic interfaces for reading, writing, querying, and managing memories so developers can integrate persistence into agents and apps quickly.
- Linking & Metadata Management: Organizes memories with links, tags, and metadata to enable semantic search and relationship-aware recall across related items.
- Privacy & Access Controls: Configurable retention, sharing, and access settings to help teams control where memories are stored and who can access sensitive context.
- Web application for saving and organizing information (login at app.cognimemo.com)
- Agent memory primitives (repository tagline: "Memory for AI Agents in 6 lines of code")
- Repository contains Docker Compose configuration for containerized deployment
- Python project structure (pyproject.toml, poetry.lock) indicating a Python backend
- Database migration tooling present (alembic.ini) suggesting persistent storage with migrations
- Environment configuration (.env.template) and entrypoint scripts for deployment
- Support for notebooks, tools, and logs in the repo structure (indicative of developer tooling / examples)
Best for
- Personal Assistant Memory: Enable virtual assistants to remember preferences, past interactions, and contextual details to provide personalized follow-ups and recommendations.
- Agent-Oriented Workflows: Give multi-step AI agents persistent state so they can recall prior tasks, variables, and outcomes across sessions for continuous task completion.
- Customer Support: Maintain long-term customer histories and conversation threads so support agents and bots can resolve issues faster without repeated questions.
- Knowledge Management: Centralize company documents, notes, and insights into a searchable memory to surface relevant information during research and decision-making.
- Developer Prototyping: Rapidly add persistent memory to prototypes and applications via SDKs and APIs to test memory-driven features and agent behaviors.
- Research & R&D Context: Preserve experimental context, configurations, and observations so models and teams can reason over historical research data and iterations.
- Personal or team knowledge base and note-taking with AI-assisted organization
- Providing persistent memory storage for AI agents (agent state, conversation memory, embeddings store)
- Self-hosted deployment for privacy-sensitive memory storage using Docker and Python
- Developer integration and experimentation with agent memory primitives and tooling
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