OpenHuman

What is OpenHuman?

OpenHuman is a personal AI system designed to help you work with your information through a connected set of providers. The product is positioned as private and “extremely powerful,” with a focus on keeping user context and learning tied to your own workflow.

Its core purpose is to remember large amounts of user-provided information (up to 1 billion tokens) and begin assisting once you connect your world—so you don’t have to wait for the system to learn your context.

Key Features

• Single subscription for 30+ providers: One subscription covers the providers needed to build an AI assistant, reducing the need to juggle multiple subscriptions.
• Large memory capacity (up to 1B tokens): OpenHuman can store and use up to 1 billion tokens of memory, allowing the assistant to reference broad personal context.
• Fast start after connecting your world: Once connected, OpenHuman can begin helping immediately rather than requiring days to learn your context.
• Personalized learning from your content: The product learns from information such as your screen, text, and emails, while keeping this information private.
• Local AI model for low-level tasks: A local LLM handles tasks like summarizing and tooling, with the goal of keeping privacy “off the cloud.”
• Simple or advanced setup for tools: You can connect tools like Gmail and Notion in a few clicks, or configure credentials manually for more control.

How to Use OpenHuman

1. Get started by accessing the OpenHuman website and following the setup flow.
2. Connect your tools (“world”)—the site specifically mentions Gmail and Notion—using either the quick connection option or manual credentials for advanced control.
3. Start using the assistant: after connecting, OpenHuman can begin helping immediately using its memory and personalized learning.
4. Provide information and context (including text, emails, and screen content as applicable) so OpenHuman can learn and reference it in future tasks.

Use Cases

• Summarizing email threads and taking follow-ups: Connect Gmail and use OpenHuman to work with email content for summarization and next-step assistance while relying on local handling for low-level tasks.
• Organizing work across notes: Connect Notion to help your assistant interpret and work with your notes and ongoing projects, using its memory capacity to retain context.
• Personal knowledge assistant: Provide background about yourself and your preferences; OpenHuman’s stated “incredible memory” lets it remember up to 1 billion tokens so it can use that information later.
• Screen-based understanding for active tasks: When relevant, use OpenHuman’s ability to learn from information on your screen to support workflows that depend on what you’re currently viewing.
• Building an AI assistant with multiple providers: Use the included set of 30+ providers under one subscription to assemble capabilities without managing many separate subscriptions.

FAQ

• Does OpenHuman use cloud services for all processing? The page states that a local LLM handles low-level tasks like summarizing and tooling to keep privacy off the cloud, but it does not describe every aspect of processing.

• How much information can OpenHuman remember? The site states it can remember up to 1 billion tokens of memory.

• How quickly can I start after setup? After you connect your world, OpenHuman can start helping immediately rather than waiting for days to learn your context.

• What kinds of data can it learn from? The page lists information from your screen, text, and emails, and says this is kept privately.

• Can I connect tools in different ways? Yes. The page describes both simple connections (example: Gmail/Notion in a few clicks) and manual credential configuration for maximum control.

Alternatives

• Local-first personal AI assistants: Alternatives in this category focus on running models locally and integrating with your personal data sources. They differ by which parts are local vs cloud and how tool integrations are managed.
• AI productivity assistants with email/notes integrations: Tools that connect directly to systems like email and notes can help with summaries and organization, typically contrasting with OpenHuman’s emphasis on very large memory and local handling of low-level tasks.
• Custom RAG/workflow setups: If you need a fully configurable system, you can build a retrieval-augmented generation pipeline and connect providers yourself. This may require more engineering compared with OpenHuman’s single-subscription provider approach.
• General-purpose chatbots with user context tools: General AI chat tools can help with Q&A and summarization, but may differ in how persistent memory is handled and whether they support the same provider-based integrations for building an assistant.