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myICOR vs TinyHumans

Which one should you try?

myICOR

Freemium

Best for Users seeking a local, vendor-independent PKM system

Visit myICOR See full review

TinyHumans

Freemium

Best for Developers and builders wanting local-first AI agent infrastructure

Visit TinyHumans See full review

Pricing, capabilities and platforms side by side. Prices are as of the "Pricing checked" date in the table.

myICOR

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.

TinyHumans

TinyHumans

OpenHuman runs as a desktop app, keeping memory and agent execution on your machine rather than a vendor's cloud — which means your work context, preferences, and knowledge base don't get packaged and sent upstream. NeoCortex handles the memory layer as an API, targeting teams who want deterministic recall baked into production applications. The agent layer is genuinely agentic: the vendor page describes joining meetings, executing code, controlling browsers, and running scheduled tasks autonomously. Where this architecture shows its limits is the managed backend services — even OpenHuman requires account sign-in and model routing that connect to TinyHumans-operated infrastructure, so 'local-first' is partial, not absolute. Teams needing fully air-gapped deployments will hit that wall.

AttributemyICORTinyHumans
PricingFreemiumFreemium
Pricing checkedNot checked yetNot checked yet
Free trialNot statedNot stated
Open sourceNoNo
Has APINoYes
Self-hosted optionYesYes
PlatformsLocal disk (any OS with markdown support)macOS, Windows, Linux
Released—2025
Pros
  • 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.
  • Persistent memory across sessions, so agents accumulate work context over weeks instead of resetting to zero on every launch — which eliminates the re-briefing overhead that makes most AI assistants impractical for ongoing projects.
  • Local-first storage via OpenHuman, so your knowledge base and preferences stay on-device rather than being indexed by a cloud vendor — which matters for users handling sensitive research or proprietary workflows.
  • NeoCortex API exposes the memory layer to production applications, so teams can build context-aware agents without rolling their own vector store and retrieval logic from scratch.
  • Autonomous agent execution — browser control, code execution, meeting participation, scheduled tasks — so multi-step workflows run without requiring manual handoffs at each step.
  • Self-hosted option exists, so teams with infrastructure preferences are not locked into a single deployment model.
Cons
  • 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.
  • OpenHuman's 'local-first' claim is partial: account sign-in and model routing connect to TinyHumans-managed backend services, meaning data does leave the device at the infrastructure layer. Teams under formal compliance requirements — HIPAA, SOC 2, air-gap mandates — hit this wall immediately and will route to a fully self-hostable alternative like a locally-deployed open-source agent stack.
  • The scraped page content provides minimal technical depth on rate limits, latency guarantees, or retrieval precision for NeoCortex — which means teams evaluating it for high-stakes production use have precious little to benchmark against before committing engineering time to integration.
  • With no named alternatives in the market data and a thin public footprint (community links but sparse documentation signals), teams that need proven enterprise support SLAs or a large peer community for troubleshooting will find the risk profile harder to justify against established memory infrastructure providers.

Made up your mind?

myICOR

Freemium

Best for Users seeking a local, vendor-independent PKM system

Visit myICOR See full review

TinyHumans

Freemium

Best for Developers and builders wanting local-first AI agent infrastructure

Visit TinyHumans See full review

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