ami vs CortexaPro AI

ami and CortexaPro AI are both ai agent apps 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.

ami

Ami builds a context graph in SQLite and markdown on disk — tracking entities, relationships, your past decisions, and your writing style — so the agent gets less hand-holdy the more you use it. It maintains a live to-do list and executes recurring busy work by learning how you handle tasks, not by following a static playbook. The self-hosting story is real: no data leaves your machine, no org-level sharing. Where it breaks is scale and surface area — the repo has 3 commits and 4 stars at time of writing, which means production edge cases are yours to discover and debug, not documented anywhere. Teams that need multi-user workflows, audit logs, or a managed API surface will hit the ceiling fast.

CortexaPro AI

The platform covers two distinct audiences: enterprise teams wiring agents into CRM, ERP, HR, and ITSM pipelines, and individual users who want multi-model chat plus life tools in a single interface. The enterprise side offers an agent builder with custom logic, memory, and decision layers, plus role-based access controls and audit logs — the table stakes for any org that will face a compliance review. The Cortexa Launchpad marketplace lets you hand a screenshot or API spec to a purpose-built agent and get production-ready code or UI back. The credit-metering model means costs are trackable, but teams running high-volume pipelines will hit the ceiling of a credit allocation faster than the pricing page suggests.

Attribute	ami	CortexaPro AI
Pricing	Free	Paid
Price	—	$5/mo - $95/mo
Free trial	No	No
Open source	Yes	No
Has API	No	Yes
Self-hosted option	Yes	No
Platforms	Local machine (Node.js)	Web (SaaS)
Pros	Fully local execution with data stored under ~/.ami/ in SQLite and markdown, so your credentials and task history never leave your machine — which means you can connect personal tokens to internal tools without authorizing a third-party cloud service. Context graph memory that tracks entities, relationships, decisions, and writing style across sessions, so the agent improves its accuracy on your specific tasks over time rather than treating every run as a cold start. MIT license with self-hosted deployment, so you own the full stack and can audit, fork, or extend any part of the system without a vendor relationship gating you. Workflow learning from observation rather than manual configuration, so you avoid the setup tax of explicitly scripting every automation — the agent encodes patterns from how you already work.	Agents connect to existing CRM, ERP, ITSM, and HR systems without replacing them, so teams avoid a migration project just to get automation running. Role-based access controls with audit logs and execution tracking on every request, which means compliance reviews have a paper trail rather than a gap where the AI ran. Purpose-built Launchpad agents with scoped inputs and outputs — Screenshot→Component, API Spec→Backend — so developers get production-ready code without writing prompts from scratch each time. Multi-region deployment across four regions with localized compliance handling, so organizations operating across regulatory jurisdictions do not have to build a separate data-routing layer. Credit metering on all agent runs gives finance and engineering a single number to audit, avoiding the surprise overages that come with per-seat or unlimited-call models.
Cons	The project has 3 commits and 4 stars at the time the source page was scraped, which means production edge cases, integration failures, and memory corruption scenarios have no community documentation and no issue tracker history to search — you are debugging from scratch. No API surface is exposed, so any team or tool that needs to call the agent programmatically or integrate it into a pipeline hits a hard wall immediately; teams with that requirement move to an agent framework that exposes an API endpoint. Memory and state are scoped to a single local user under ~/.ami/, with no mechanism described for shared state or multi-user coordination — teams that need a shared task queue or collaborative agent context have to abandon Ami for a hosted alternative before the second team member needs access.	No self-hosted option exists — the platform is cloud-only. Any org under a hard data-residency or air-gap requirement hits this wall before a single agent is built, and those teams move to a self-hostable alternative like Dify or n8n rather than negotiate a carve-out. Credit-metered billing means high-volume production pipelines — document generation or approval workflows running at enterprise scale — exhaust credit allocations before the billing cycle ends. Teams running batch workloads report needing paid upgrades to maintain throughput, which changes the economics that the free tier implied. The platform bundles enterprise orchestration and consumer life tools under one product surface. Engineering leads evaluating the agent builder for production deployments have no clean way to separate the roadmap priorities of a B2B workflow platform from those of a consumer chat app — product direction risk that a pure-play enterprise tool does not carry.

Bottom line

Ami is free while CortexaPro AI is paid; ami is open source; only CortexaPro AI exposes a public API. Choose based on which difference matters most for your workflow.

Frequently asked questions

What is the difference between ami and CortexaPro AI?

ami is Free and open source, while CortexaPro AI is Paid. Compare pricing, free trial, API, platforms, and pros/cons in the table above on AIDiveForge.

Is ami better than CortexaPro AI?

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.