2026 OpenHuman Install Guide: Native Packages, Memory Tree & Ollama From Zero

Maintained by Tiny Humans AI, OpenHuman positions itself as Your Personal AI super intelligence: a cross-platform desktop app built on Rust + Tauri v2 that pulls OAuth data from Gmail, Slack, GitHub, Notion, and 118+ services, compresses tokens, and writes them into a local SQLite-backed Memory Tree so the model can recover cross-week context within minutes (see the official docs).

Before you install, avoid three misconceptions that waste hours every week in community threads:

• Not a “digital human face-swap” stack: you do not download multi-gigabyte .pth weights into checkpoints/ or depend on NVIDIA CUDA for video inference. GUI installers are typically hundreds of megabytes, varying by release.
• Not a pure web chat tab: the core value is background sync + memory-tree retrieval. Closing the window does not erase memory, but first run requires OAuth and index build time.
• Do not trust unverified pipe scripts: the project explicitly warns that curl … | bash cannot verify script byte integrity. Production should prefer Homebrew / signed apt / signed MSI (see install.md).

OpenHuman is not trying to replace every tool in your stack. It aggregates personal context—mail threads, repo activity, calendar fragments—and surfaces them inside a single assistant surface. That is a different problem from spinning up a headless bot on a VPS or rendering synthetic video avatars. Knowing which problem you are solving prevents buying the wrong hardware or following the wrong README fork.

Division of labor with OpenClaw: OpenClaw excels at Telegram/WhatsApp-style messaging-channel bots; OpenHuman excels at desktop super-assistant + Memory Tree + meetings/voice. Both can attach to Ollama, but install paths and always-on patterns differ.

Early Beta means APIs and connector lists shift between releases. Treat “118+ integrations” as a live setting-page count, not a frozen contract. Stars on GitHub crossed roughly 6k in May 2026 with multiple Trending appearances—momentum is real, but you should still read the changelog before pinning a fleet image.

Pick your install channel before you pick your model. A signed package manager path gives you hash or GPG verification; a curl pipe gives you speed and audit risk. The tables below separate host sizing from distribution integrity so you can decide in two minutes instead of reinstalling twice.

Building from source requires Node.js 24+, pnpm, Rust 1.93+, and more (see the repo README). Typical users do not need the source chain. International SaaS dominates the 118+ connector list; region-specific tools such as Feishu or DingTalk may be absent during Beta—verify the live connector list in Settings before you promise stakeholders a integration.

Network topology matters as much as CPU. Memory Tree sync pulls OAuth-backed payloads on a schedule; captive portals, aggressive corporate proxies, and laptop sleep all look like “the assistant forgot me” when the real failure is connectivity. If you plan offline-first inference with Ollama, budget RAM for both the index worker and the model weights simultaneously—Activity Monitor is your honest friend on 16 GB machines.

The fastest path to a passing smoke test is: signed package, version check, wizard, one OAuth source, wait for index, ask a question that requires synced context. Below uses macOS Homebrew; Linux apt and Windows MSI commands appear at the end with the same logic.

brew tap tinyhumansai/openhuman
brew install openhuman
openhuman --version

# Linux Debian/Ubuntu (signed repo, excerpt)
# curl -fsSL .../KEY.gpg | sudo gpg --dearmor -o /etc/apt/keyrings/openhuman.gpg
# sudo apt-get update && sudo apt-get install -y openhuman

1. Choose a channel: production should prefer Homebrew / apt / MSI; do not run curl | bash on production hosts without auditing the script.
2. Install: on macOS, brew install openhuman exposes the openhuman CLI; you can also launch the GUI from Applications or Spotlight.
3. Verify version: compare against GitHub Releases for the current Beta (e.g. v0.54.x). If stale, run brew upgrade openhuman or your system package manager equivalent.
4. Complete first-run wizard: sign in, set timezone and privacy options. Data defaults to local SQLite under your user profile (exact path shown in Settings).
5. Connect one or two OAuth sources: start with Gmail or GitHub for smoke testing; watch Memory Tree for “syncing / indexed” status.
6. Smoke conversation: ask the desktop client to “summarize my GitHub issues from the last week” or “draft a reply from my inbox context” and confirm it cites synced material—not generic hallucination.

Windows users download the .msi from the latest release and install with the standard wizard. Arch users can follow the AUR recipe openhuman-bin in the repository. The npm global package npm install -g openhuman downloads platform binaries on first install and verifies SHA-256; runtime does not depend on Node (per official install.md).

First sync can take longer than the marketing “~20 minute cycle” if you connect many write-capable sources at once. For acceptance testing, read-only connectors first, one domain at a time. Document which account you authorized—OAuth token drift is the silent killer of “it worked yesterday” reports.

After smoke test passes, snapshot your version string and connector list in an internal runbook. Beta upgrades can rename settings keys; a one-line openhuman --version in your ticket template saves debug time when comparing community reports to your fleet.

Memory Tree uses a source tree → topic tree → global tree hierarchy: the bottom layer archives raw snippets per connector (Gmail, Slack, etc.), the middle clusters by topic, and the top layer serves retrieval during chat. Official materials cite roughly a 20-minute incremental sync cadence in current Beta settings, with TokenJuice-style compression to reduce token footprint.

1. Authorize a minimal set: connect one or two read-only sources first; expand after indexing completes to avoid first-run full pulls saturating disk and bandwidth.
2. Watch index status: Settings shows last sync time per connector; long “syncing” states often mean proxy issues or expired OAuth.
3. Install Ollama: on the host, install Ollama and pull a model, e.g. ollama pull qwen2.5:7b (adjust model name to your environment).
4. Point OpenHuman at the local endpoint: in Settings choose Local / Ollama or LM Studio; Base URL is usually http://127.0.0.1:11434/v1 (OpenAI-compatible path per UI labels).
5. Low-VRAM strategy: on 16 GB unified-memory Macs, prefer 7B–8B quantized models; 13B requires monitoring combined Ollama + OpenHuman usage in Activity Monitor.
6. Obsidian sync (optional): if you enable bidirectional notes sync, keep the vault on local SSD—network drives often break file watchers.

ollama serve
ollama pull qwen2.5:7b
# OpenHuman Settings → Local LLM → http://127.0.0.1:11434

Fully offline operation is achievable: skip cloud LLM APIs, run Ollama plus local Memory only—chat and indexing stay on-machine. That pattern fits legal, R&D, or air-gapped document assistant pilots. M-series Macs without discrete GPUs still run 7B-class models; throughput follows unified memory bandwidth more than core count.

LM Studio is a valid alternative when your team already standardized on its UI for model management. The integration principle is identical: OpenAI-compatible local base URL, model name matching what the server exposes, and one process owning the weights. Running two local servers on the same port is a common self-inflicted outage—pick one stack per host unless you deliberately map ports.

Memory quality beats model size for many daily tasks. A well-indexed Gmail + GitHub slice with Qwen2.5 7B often outperforms a 13B model with empty Memory Tree, because the assistant finally has something true to cite. Invest sync patience before you invest GPU dollars.

• License: main repository is GPL-3.0; commercial derivatives must respect copyleft—legal review before embedding in closed products (contrast MIT-licensed OpenClaw).
• Integration scale: official claim of 118+ OAuth service connectors (confirm live list in Settings).
• Storage: Memory data lives in local SQLite, not cloud by default; export or back up the user data directory before uninstall.
• Release cadence: Early Beta; May 2026 community observation put GitHub stars around 6k+ with repeated Trending—always check release changelog before fleet rollout.

Updates: package manager upgrade (brew upgrade / apt upgrade) or MSI reinstall overlay. Before major Beta jumps, export settings screenshots and back up the SQLite directory. Uninstall: remove the app via the OS, then manually delete residual data directories and Ollama model cache if no longer needed.

Enterprise MDM environments should whitelist the OAuth redirect URIs OpenHuman registers—not just the installer binary. Silent installs without a logged-in user session still fail at connector authorization; plan a one-time interactive VNC or in-person wizard for service accounts that need Memory Tree.

When local model responses truncate or loop, check context window settings against Memory retrieval size. Aggressive top-k retrieval plus a small context window produces the illusion of “the model is dumb” when the pipeline is simply over-stuffing prompts. Tune retrieval before swapping models.

Laptops and oversubscribed VPS hosts share three real weaknesses for Memory Tree workloads: ① lid close or sleep stops automatic sync—Monday morning the assistant still feels amnesic; ② pure Linux without a desktop makes OAuth and Tauri GUI first setup painful; ③ noisy neighbor disk IO makes Ollama and indexing contend, producing “installed fine, runs poorly.”

Home broadband adds a fourth weakness: asymmetric upload and CGNAT break stable background sync as surely as sleep does. A machine in a datacenter with predictable routing keeps incremental Memory updates flowing while you travel with only a phone.

If you need OpenHuman with 24/7 incremental memory plus local Qwen2.5, the steadier pattern is a JEXCLOUD bare-metal Mac Mini M4: Homebrew install and Ollama config from this guide, VNC once for OAuth, then SSH for model and log maintenance—no hypervisor overselling, data on rented disk, SQLite and Ollama directories packable before lease end. See pricing for SKUs and regions, order to provision, and help center for remote desktop workflows.

Pilot checklist before you commit a team: pass the six steps here on one host, measure peak RAM with ollama ps during a realistic question, document OAuth owners, then scale—not the reverse. OpenHuman rewards patience on first sync more than heroics on day-one model size.