⚖️Side-by-Side Comparison

OpenHistory vs Hemory: Which is Better in 2026?

We've compared 2 tools across pricing, features, ratings, and reviews to help you make an informed decision. No paid placements — just facts.

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Feature
OpenHistory logo
OpenHistory

Productivity & Automation

Hemory logo
Hemory

Productivity & Automation

RatingNo reviews yetNo reviews yet
PricingFree

as of Sep 30, 2026

  • Free Trial:$0
  • Starter:$10/mo/mo
  • Pro:$20/mo/mo
  • Max:$50/mo/mo

as of Sep 26, 2026

Pricing ModelFreeFreemium
Free Plan
Free Trial
API Available
Verified
Founded——
HQ——
Team Size1-10—
Platforms——
Top Integrations——
Key Features
  • Permission-scoped activity capture
  • Hourly and daily summaries
  • Local AI agent access
  • No sensitive capture by design
  • No telemetry
  • Always-on voice memory
  • MCP connection to coding agents
  • 100-language transcription + speaker recognition
  • Type-aware summaries & minutes
  • Built-in Q&A agent
Description

OpenHistory is an open-source macOS app that builds a private, searchable timeline of a user's work by observing only the foreground activity they explicitly permit — window names, document context, browser domains, clicks, and visible interface text via macOS Accessibility APIs. It generates hourly and daily summaries and gives approved local AI agents a controlled way to answer questions about what the user did, using Apple's local Foundation Model by default or, optionally, OpenAI, Anthropic, or Kimi. By design, it does not capture screenshots, audio, camera or microphone input, passwords, private browser windows, or low-level keystrokes, and sends no analytics or telemetry of its own. All activity, timelines, summaries, settings, and agent connections are stored in a permission-restricted directory on the user's own Mac. It requires macOS 14 or later and is available on GitHub and via a direct download from its own site.

Hemory is a personal-memory app that listens passively via a phone or Apple Watch you already own and turns your day into a searchable, private memory -- automatically split into moments with speaker labels, not a meeting-transcription tool. It connects over MCP to AI agents like Claude, Codex, or Cursor, so those agents can pull real context from conversations you actually had instead of starting from a blank slate. It is aimed at people who want their AI assistants to have continuity with real-world conversations rather than only chat history.