Table of Contents

01 · What is OpenHuman and What Can It Do?

OpenHuman is a local-first, open-source AI agent platform developed by TinyHumans, available on macOS, Windows, and Linux desktops. Unlike a typical chatbot, OpenHuman builds a persistent Memory Tree—a locally stored SQLite database that ingests your emails, calendars, Slack threads, and code repositories into structured knowledge. Every conversation can then draw on that context to give precise, personalized responses.

In 2026, OpenHuman added a Meeting Agent, multimodal context awareness, and Obsidian vault integration. For developers, researchers, and content creators, it acts less like a tool and more like a digital counterpart that continuously learns your work patterns. The critical advantage: all core data stays on your machine, never touching a remote server unless you explicitly configure a hosted model endpoint.

02 · Three Common Installation Problems

Before walking through the install steps, let's diagnose the three failure modes that catch most first-time users:

Python version conflicts. macOS ships with Python 2.7 or 3.8 in system paths. Running pip install openhuman without a virtual environment targets the wrong interpreter, cascading into dozens of broken dependencies. OpenHuman requires Python 3.10 or higher.
Model weight download failures. HuggingFace can be slow or blocked in certain regions; files are multi-gigabyte with no built-in resume. Baidu Pan throttles free accounts; ModelScope requires account verification. Each channel has its own friction, and most guides don't explain the trade-offs.
GPU backend mismatch. Windows users frequently install the wrong PyTorch build for their CUDA version. macOS Intel has no CUDA at all. Apple Silicon users need MPS (Metal Performance Shaders) rather than CUDA—yet most online tutorials only cover the CUDA path, leaving M-chip owners confused.

This guide resolves all three. Each platform gets its own verified command sequence.

03 · Hardware Requirements and Platform Matrix

Three data points worth anchoring before you proceed: the official minimum RAM is 4 GB, but running a local AI model alongside OpenHuman requires 16 GB or more; Memory Tree initial sync takes roughly 20 minutes; Apple Silicon MPS inference runs approximately 5–8× faster than pure CPU mode.

Item	Minimum	Recommended	Notes
OS	macOS 12 / Win 10 / Ubuntu 20.04	macOS 14+ / Win 11 / Ubuntu 22.04	Apple Silicon: macOS 13+
RAM	4 GB	16 GB+	Large mailbox + local model needs 16 GB+
GPU	None (CPU mode)	NVIDIA RTX / Apple M-series	Inference speed gap: ~5–10×
Storage	10 GB	50 GB+ SSD	Memory Tree grows with data volume
macOS install	Homebrew brew install openhuman		Handles signing automatically
Linux install	apt signed repo or AUR		Ubuntu 20.04 LTS+
Windows install	MSI signed installer		Pair with CUDA 12.x for GPU use

04 · Environment Preparation

Python Version Management

OpenHuman requires Python 3.10 or higher. Use pyenv or conda to isolate versions and avoid touching the system Python.

                        # Verify current Python

python3 --version   # Must be 3.10+

# macOS: install Python 3.11 via pyenv

brew install pyenv

pyenv install 3.11.9 && pyenv global 3.11.9

# Create a dedicated virtual environment

python3 -m venv openhuman_env

source openhuman_env/bin/activate   # Linux/macOS

openhuman_env\Scripts\activate       # Windows

GPU Backend Notes

If you're on Apple Silicon (M1–M4), OpenHuman automatically activates Metal Performance Shaders. No CUDA installation needed. For NVIDIA GPU users on Windows or Linux, install CUDA Toolkit 12.x and the matching PyTorch build from pytorch.org before proceeding.

05 · Five-Step Installation Walkthrough

Step 1 — macOS: Homebrew (recommended)

                        # Add the official tap and install

brew tap humansai/openhuman

brew install openhuman

# Verify

openhuman --version

Homebrew verifies the bottle hash and handles all dependencies automatically. Once installed, open the app from your Applications folder or launch it from the terminal.

Step 2 — Linux: Signed apt Repository

                        sudo apt-get install -y gnupg2 curl ca-certificates

curl -fsSL https://tinyhumansai.github.io/openhuman/apt/KEY.gpg \

  | sudo gpg --dearmor -o /etc/apt/keyrings/openhuman.gpg

echo "deb [signed-by=/etc/apt/keyrings/openhuman.gpg arch=amd64] \

  https://tinyhumansai.github.io/openhuman/apt stable main" \

  | sudo tee /etc/apt/sources.list.d/openhuman.list

sudo apt-get update && sudo apt-get install -y openhuman

Arch Linux: yay -S openhuman-bin

Step 3 — Windows: Signed MSI

Download the signed MSI from tinyhumans.ai/openhuman and run the installer. For GPU acceleration, confirm CUDA 12.x is installed first. The installer adds OpenHuman to the Start Menu and sets up the background sync service automatically.

Step 4 — First Login and Permissions

On first launch, sign in with GitHub or Google. macOS will prompt for Accessibility and Input Monitoring permissions (used for voice shortcuts). Grant them via System Settings, or skip them and enable later under Settings → Automation & Channels.

For a fully self-hosted setup with your own API key, switch to Custom / Local mode in Settings and edit the config file:

                        # macOS config path:

# ~/Library/Application Support/openhuman/config.toml

[model]

provider = "custom"

base_url = "https://api.deepseek.com"   # any OpenAI-compatible endpoint

api_key  = "<YOUR_API_KEY>"

[memory]

backend  = "local"   # default local SQLite

Step 5 — Connect Data Sources and Wait for Memory Tree Init

Open the Integrations panel, connect your three most important data sources via OAuth (Gmail, GitHub, Slack, etc.), then let the app run in the background. The first full sync takes approximately 20 minutes. After that, incremental syncs complete in seconds. Do not close the app during initial ingestion.

06 · First-Run Validation and Error Lookup

Send a simple test prompt after initial setup to confirm the assistant is responding. Then verify your Memory Tree has populated under the Knowledge panel before adding more integrations.

Error	Cause	Fix
ModuleNotFoundError: openhuman	pip installed to wrong Python env	Activate virtual env, reinstall
MPS not available	PyTorch version lacks MPS support	Install PyTorch ≥ 2.0 for macOS arm64
Memory DB locked	Multiple instances contesting SQLite	Kill all openhuman processes, restart
Integration OAuth failed	System clock out of sync	Sync system time, retry OAuth
Ingest timeout	Too many sources on first run	Connect one source at a time, batch adds

07 · Advanced: Local AI and Web UI Setup

Connecting Ollama for Fully Offline Operation

To run OpenHuman entirely offline, point its model backend to a local Ollama instance. Edit config.toml:

                        [model]

provider = "custom"

base_url = "http://localhost:11434"

model    = "deepseek-r1:14b"   # any model already pulled in Ollama

On Apple Silicon M4, a 14B quantized model in Ollama delivers around 18–25 tokens/second—sufficient for conversational workloads. For heavier inference (70B+), consider a Mac Mini M4 with 64 GB+ unified memory or a Mac Studio.

Memory Database Paths

macOS: ~/Library/Application Support/openhuman/memory.db
Linux: ~/.local/share/openhuman/memory.db
Windows: %APPDATA%\openhuman\memory.db

You can link the Memory Tree directory to an Obsidian vault, letting OpenHuman read and write your personal knowledge base notes directly. This is one of the most popular integrations among researchers and writers.

For a guide on running OpenHuman alongside OpenClaw on a single rented Mac, see: 2026 OpenClaw & OpenHuman on a Rented Mac Mini M4.

08 · Why Renting a Mac Beats Building Your Own Setup

Self-hosting OpenHuman on your own hardware is perfectly viable—but it comes with friction worth naming honestly before you commit:

Upfront hardware cost. A Mac Mini M4 with 16 GB starts at around $600; a Mac Studio with 512 GB unified memory exceeds $3,000. Most users want to try OpenHuman for a few days before that commitment makes sense.
Resource contention on your main machine. Running Ollama and OpenHuman's background sync on your daily driver frequently causes browser, Xcode, or design app slowdowns. A dedicated machine avoids the conflict entirely.
Complex path and dependency management on Windows/Linux. CUDA version matrices, WSL2 compatibility quirks, and path separator differences routinely consume hours on a first install. Clean macOS environments sidestep most of these edge cases.
Non-upgradeable unified memory. Apple Silicon solders RAM to the SoC. If your workload grows beyond your current tier, you replace the entire machine rather than adding a DIMM.

Renting a Mac Mini M4 or Mac Studio by the day gives you a clean macOS environment with MPS acceleration ready to go. You validate the full OpenHuman workflow at low cost—no dependency tangle, no impact on your primary machine. When you're confident the setup fits your workflow, then decide whether a long-term rental or outright purchase makes financial sense.

09 · FAQ

Can I run OpenHuman without a GPU?

Yes. CPU mode works fine for users relying on cloud API endpoints (OpenAI, DeepSeek). The Memory Tree indexing pipeline is not GPU-dependent. Inference via a local model will be noticeably slower in CPU mode—plan for 2–4× longer response times compared to MPS on an M-series chip.

Does OpenHuman support Apple Silicon natively?

Yes. The Homebrew bottle is compiled as a native arm64 binary. MPS activation is automatic on macOS 13+ with Apple Silicon. Performance is significantly better than Rosetta 2 emulation.

How do I update to a new version?

macOS: brew upgrade openhuman. Linux: sudo apt-get install --only-upgrade openhuman. Windows: download the new MSI and install over the existing version. Config files and the Memory Tree database are preserved across updates.

Is OpenHuman free for commercial use?

The core codebase is MIT-licensed and free for personal use. Commercial deployments should review the latest terms at tinyhumans.ai, particularly for hosted API features which carry separate licensing terms.

Also relevant: 2026 Hermes Agent Installation Guide—another local-first AI agent that pairs well with OpenHuman in a Mac-based workflow.