OpenHuman: The Local-First AI Agent That Never Forgets Your Context
Tired of explaining your work to AI agents over and over? OpenHuman runs on your machine, remembers everything about your projects and tools, and gives you full access to its memory files. Finally - an AI assistant that works the way you do.
The Memory Problem in AI Agents
Every business owner using AI tools faces the same frustration: you explain your project, your tools, your workflow to the AI... and then you close the window. Next session, you're back to square one, re-explaining everything as if you'd never met. This context amnesia plagues nearly all AI agents today.
OpenHuman was built specifically to solve this problem. Developed by Tiny Humans and launched in beta in May , it's a local-first AI agent that actually remembers your work context between sessions. Unlike other agents that treat memory as an afterthought, OpenHuman is built around its memory system from the ground up.
The key difference: OpenHuman maintains a persistent, editable memory of your work that stays on your machine. You can read exactly what it knows about you in plain text files - a level of transparency no other personal AI offers.
How OpenHuman Works Differently
OpenHuman takes a fundamentally different approach from most AI agents. While tools like Hermes focus on improving at tasks through skill files, OpenHuman focuses on knowing you - your projects, your tools, and how you work.
Written in Rust using the Tauri framework (not Electron), OpenHuman runs efficiently on your local machine without hogging memory. It connects to over 100 services including Gmail, Slack, Notion, and GitHub through one-click sign-ins, then quietly builds a memory tree in the background.
The AutoFetch feature pulls fresh data from connected accounts every 20 minutes, so your agent is always up to date. By the time you sit down in the morning, it already knows about overnight emails, Slack messages, and code commits.
The Three-Layer Memory Tree System
OpenHuman's memory system is its standout feature. Every connected source goes through the same pipeline: cleaned into markdown, chunked into 3,000-token segments, scored, and organized into three types of summary trees:
1. Source Trees
One per connected source (like a Gmail label or Slack channel) that organizes information by origin.
2. Topic Trees
Built around specific entities - people, projects, repositories. The more something appears ("hotness"), the more detailed its tree becomes.
3. Global Tree
A daily digest summarizing everything across all sources, giving you high-level context at a glance.
In practice: When you ask a question, OpenHuman can search a specific source, drill into a topic, or pull from the global summary depending on what you need. All while keeping 70-80% of your data local and private.
Local-First Design and Security
OpenHuman stores all its memory in a folder on your machine (by default in your home directory). There's a small database file for chunks, and a vault of markdown files that open directly in Obsidian. This "memory you can read" philosophy is core to its design.
The team emphasizes you can't trust a memory you can't read. If OpenHuman gets something wrong about you or your work, you can open the file and edit it directly - no black box.
For security-conscious users, OpenHuman can run entirely locally using Ollama with no data ever leaving your machine. Even its default setup ships with a small local model for background processing.
Practical Uses for Business Owners
Once connected to your tools, OpenHuman can handle a wide range of business tasks:
- Morning digest: Summarize who's waiting on you and what they need from overnight emails and Slack
- Project tracking: Cross-reference GitHub commits with Jira/Linear tickets to show what shipped and what's stuck
- Document search: Answer questions about your Notion or Google Drive content without manual hunting
- Meeting prep: Pull everything it knows about attendees and topics before calendar events
- Payment monitoring: Flag unusual Stripe transactions or subscription changes
Because it has browser control and web search built in, you can send it to research topics, write up findings, and drop the results where needed.
How OpenHuman Reduces AI Costs
OpenHuman includes a "token juice" compression layer that squeezes down bulky tool output before it reaches AI models. This is how sweeping through 6 months of email might cost single-digit dollars instead of $20-30 with an uncompressed query.
Independent tests show about 70% cost reduction compared to the company's claimed 80%, but either way represents significant savings for heavy users. OpenHuman also intelligently routes tasks - heavy reasoning goes to frontier models, simple lookups to cheaper options.
Key insight: The memory tree means OpenHuman often has answers without querying external models at all, saving both time and money on common questions about your work.
Getting Started with OpenHuman
Installation is simple - grab the package from tinyhumans.ai for Mac, Windows, or Linux. The team recommends avoiding the terminal install command for production machines since it pipes code directly from the internet.
On first run, you'll choose between local or cloud operation (local is recommended), connect accounts, and set up your model preferences. The interface includes:
- A chat interface where all conversations feed the memory
- Connection management for your linked services
- Intelligence tab showing memory tree stats and visualization
- Direct access to your memory vault in Obsidian
The team suggests starting with a spare machine and 1-2 accounts to evaluate before connecting everything. This lets you verify the memory quality and feel comfortable with the data handling.
Watch the Full Tutorial
See OpenHuman in action with a complete walkthrough of setup and key features (starting at 2:15 in the video). The demo shows how it builds memory trees and gives you direct access to what it knows.
Key Takeaways
OpenHuman represents a significant step forward in personal AI by treating memory as the product rather than a feature. Its local-first design, transparent memory files, and efficient context management solve real pain points for business owners juggling multiple tools.
In summary: OpenHuman remembers what other AI agents forget, keeps your data private, and shows you exactly what it knows. For founders, developers, and anyone living across multiple apps, it's worth evaluating as your AI memory layer.
Frequently Asked Questions
Common questions about OpenHuman
OpenHuman is built around a local-first memory system that actually remembers your context between sessions. Unlike other agents that forget everything when you close the window, OpenHuman maintains a persistent memory tree of your work, projects, and tools.
It also gives you full access to its memory files stored on your machine - you can read and edit exactly what it knows about you. This level of transparency and control is unique in the personal AI space.
- Persistent memory that survives between sessions
- Local-first design keeps your data private
- Full access to memory files stored on your machine
OpenHuman uses a three-layer memory tree system to organize information about your work. All connected data goes through a pipeline that cleans it into markdown, chunks it into segments, and scores it for importance.
Source trees organize information by connected accounts (like one per Gmail label). Topic trees build knowledge around specific entities like people or projects. A global tree creates daily summaries across all sources.
- Source trees: One per connected account or channel
- Topic trees: Built around people, projects, repositories
- Global tree: Daily summaries across all sources
OpenHuman is designed as a local-first application. Your raw data stays on your machine unless you specifically include it in a prompt. The app is open source (GPL-3 licensed) so you can inspect exactly how it handles your information.
For maximum security, you can run OpenHuman with local AI models through Ollama. This ensures nothing ever leaves your computer, not even processed data. The app ships with a small local model for background processing by default.
- Open source code you can audit
- Option to run entirely locally with Ollama
- Raw data stays on your machine by default
OpenHuman supports over 100 services including Gmail, GitHub, Slack, Notion, Stripe, Google Calendar, and more. It offers one-click sign-ins without requiring manual API key setup - just authenticate like you would with any web app.
The AutoFetch feature automatically pulls new data from connected accounts every 20 minutes. This means your agent stays up to date with emails, messages, and commits without you having to manually sync.
- 100+ supported services with one-click auth
- AutoFetch updates data every 20 minutes
- No manual API key setup required
OpenHuman includes a compression layer called token juice that reduces bulky tool output before it reaches AI models. This can dramatically cut the token count (and therefore cost) of queries involving large documents or email threads.
Independent tests show it can cut costs by 70-80% compared to uncompressed queries. It also intelligently routes tasks - heavy reasoning goes to frontier models while simple lookups use cheaper options.
- Token juice compression reduces query sizes
- Intelligent model routing for cost efficiency
- Memory tree often provides answers without external queries
Yes, this is a key differentiator. All of OpenHuman's memory is stored in a vault of markdown files on your machine that you can open and read directly in Obsidian or any text editor. The files are organized by source and topic just like the memory tree.
If the agent gets something wrong about you or your work, you can edit the files directly. This "memory you can read" approach is core to OpenHuman's philosophy - you shouldn't have to trust what an AI claims to know about you.
- All memory stored as editable markdown files
- Files organized by source and topic
- Direct editing capability if information is wrong
Hermes is a self-improving agent that gets better at tasks over time by writing skill files. OpenHuman focuses on knowing you rather than improving at tasks - your projects, your tools, and how you work.
They serve complementary purposes. Hermes serves the work (getting better at completing tasks) while OpenHuman serves you (remembering your context). Many users run both side by side for comprehensive AI assistance.
- Hermes improves at tasks through skill files
- OpenHuman focuses on knowing you and your work
- Can be used together for complete coverage
GrowwStacks helps businesses implement AI automation solutions tailored to their specific needs. Whether you need custom AI agent setups like OpenHuman, workflow automation, or integration with your existing tools, our team can design and deploy solutions that fit your operations.
We offer free 30-minute consultations to discuss your AI automation goals and recommend the best approach for your business. Our experts can help you evaluate tools like OpenHuman, set them up securely, and integrate them with your other systems.
- Custom AI agent implementation
- Workflow automation design
- Free consultation to discuss your needs
Ready for an AI Assistant That Actually Remembers Your Work?
Stop wasting time re-explaining your context to forgetful AI tools. Let GrowwStacks help you implement OpenHuman or other AI solutions tailored to your business needs.