Inkfold vs myICOR

Inkfold and myICOR are both productivity 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.

Inkfold

Inkfold sits between you and your AI subscriptions, indexing conversation history across ChatGPT, Claude, Gemini, Grok, and others, then injecting the relevant context when you open a new chat on any vendor. The core workflow is capture-remember-inject: import existing chats, let the tool extract entities and decisions, then route new queries through whichever model fits the task with your memory already attached. It works well for solo power users who juggle multiple subscriptions and lose context at every vendor boundary. The ceiling appears fast for teams: shared memory and the admin console are paid-only features, and the product is still in private alpha — so the reliability guarantees you need before connecting production workflows do not yet exist in public form.

myICOR

The system is a local markdown folder pre-loaded with a six-person AI team: a routing orchestrator (Larry), a research specialist (Pax), a capture agent (Penn), and others — each with a named contract and a session journal so the next model picks up where the last one left off. You bring your own LLM; the folder supplies the memory. Research produces structured notes in place, drafts inherit your established voice, and weekly review prompts surface stale items automatically. The ceiling appears when you need real-time data, API integrations, or collaborative editing — none of that is in the folder. Teams that need those reach for purpose-built tools alongside this one.

Attribute	Inkfold	myICOR
Pricing	Paid	Paid
Price	$7/month	—
Free trial	No	No
Open source	No	No
Has API	No	No
Self-hosted option	No	Yes
Platforms	Web app, CLI, mobile app	Local disk (any OS with markdown support)
Pros	Cross-vendor memory injection, so you stop re-establishing project context every time you switch from Claude to GPT or Gemini — the AI you open next already knows the decisions the last one helped you make. Bring-your-own-key support for every major vendor, so your queries route through your own API credentials and you control cost exposure instead of burning through a fixed monthly message cap. Three retention modes — indexed, E2E-encrypted private, and incognito — so you can keep sensitive conversations opaque to the platform without giving up the memory layer entirely. Shared team memory with pooled message credits, so context one teammate builds in Claude is available when the next person opens a GPT session without anyone manually syncing notes. Local model support via Ollama, so teams that run self-hosted LLMs for compliance or cost reasons can still participate in the shared memory layer alongside cloud vendors.	LLM-agnostic folder architecture, so switching from Claude to Gemini mid-project is a matter of opening the same folder in a different app — no re-pasting context, no lost session history. Persistent agent journals mean each specialist picks up from the last session, so you stop spending the first ten minutes of every AI conversation re-explaining who you are and what you're working on. Plain markdown on your local disk means zero migration risk — if the vendor disappears tomorrow, every note, contract, and workflow you built is still readable by any text editor or LLM. Larry's routing layer matches requests to the right specialist automatically, so you don't have to remember which prompt style triggers good research versus good drafting — the team handles the handoff. Open-source scaffold under CC BY-NC-SA 4.0, so you can inspect, fork, and extend the agent contracts without waiting on a vendor roadmap or paying for access to the base system.
Cons	The product is in private alpha, meaning you request access rather than sign up — teams that need a stable, documented API or guaranteed uptime before connecting real workflows have no current path to that on the public tier. No self-hosted deployment option exists for the memory layer itself, so organizations in regulated environments that cannot route conversation metadata through a third-party cloud must either wait for a custom enterprise contract or build their own context store — at which point Inkfold is not in the picture. Shared memory, the admin console, and audit logs are all paid-only features, so a team evaluating the free tier to validate fit will not encounter the core multi-user capability until after they have committed to payment. There is no documented API surface, which means you cannot programmatically read from or write to Inkfold memory inside your own applications or CI pipelines — teams that want memory as a service they call from code will move to a vector-store-backed solution like a self-managed RAG pipeline instead.	The folder has no mechanism for live data: API calls, web scraping, calendar reads, and CRM syncs are all outside its scope. Teams that need agents to pull live information must wire up a separate integration layer and maintain it alongside the folder — which is a second system to debug. There is no multi-user collaboration model. Two people cannot edit the same folder simultaneously with conflict resolution. Teams of more than one person sharing a PKM workspace hit this wall immediately and typically move the shared layer to a tool with real-time sync — Notion, Obsidian Sync, or a shared Git repo — while keeping individual folders local. No hosted inference or built-in LLM access means every new user must already have API credentials or a local model running before the team scaffold does anything. For non-technical users who came for the AI workflows, the setup friction before first use is real and the docs leave meaningful configuration detail to the user to figure out. The agent team is fixed at the scaffold level — expanding it requires running Nolan's eight-step hiring procedure, which is a prompt-driven workflow inside the folder. Teams used to GUI-based agent builders who want to add a specialist in two clicks will find the process slower and more text-heavy than competing tools that offer visual agent creation.

Bottom line

Inkfold and myICOR are closely matched on pricing model, openness, and API availability — pick by feature set and platform support in the table above.

Frequently asked questions

What is the difference between Inkfold and myICOR?

Inkfold is Paid, while myICOR is Paid. Compare pricing, free trial, API, platforms, and pros/cons in the table above on AIDiveForge.

Is Inkfold better than myICOR?

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.