OpenHuman

The age of super intelligence is here. OpenHuman is your Personal AI super intelligence. Private, Simple and extremely powerful.

Discord • Reddit • X/Twitter • Docs

"The Tet. What a brilliant machine" — Morgan Freeman as he reminisces about alien superintelligence in the movie Oblivion

Early Beta — Under active development. Expect rough edges.

To install or get started, either download from the website over at tinyhumans.ai/openhuman or run

# For MacOS/Linux
curl -fsSL https://raw.githubusercontent.com/tinyhumansai/openhuman/main/scripts/install.sh | bash

# For Windows
irm https://raw.githubusercontent.com/tinyhumansai/openhuman/main/scripts/install.ps1 | iex

What is OpenHuman?

OpenHuman is an open-source agentic assistant that is designed to integrate with you in your daily life. Here's what makes OpenHuman special:

• One subscription, many providers — One assistant wired to skills and backend models so you are not juggling a separate subscription stack for every integration surface.

• Incredible memory — Rust-side memory (store / recall / namespaces) plus optional TinyHumans Neocortex-backed context when configured, so the agent can retain and retrieve more than a single chat window. Channels and ongoing conversations feed the same loop so day-to-day context does not reset every session.

• Screen intelligence — Regular screen capture (on a cadence or when triggered) feeds an on-device pipeline that understands what is on screen, distills it into memory (facts, UI state, workflows), and can propose actions the agent executes for you. OS permissions and capture APIs vary by platform; the goal is your machine first, not shipping raw frames to the cloud by default.

• Voice & meetings — A Local-model speech stack (listen / TTS) let the assistant talk back and capture or work with meeting audio with a privacy-first default when you route inference locally. Transcripts and summaries land in the same memory + agent loop so OpenHuman can follow up: tasks, drafts, calendar nudges, or skill-backed workflows—without treating a meeting as a one-off chat.

• Memory-aware autocomplete — Keyboard autocomplete is built for right-context suggestions: it consults memory namespaces and recent context so completions stay aligned with you, your workspace, and prior sessions—not a blank model every keystroke.

• Runs a local AI model — The Rust core exposes local AI paths (and the desktop bundle can ship local/bundled runners where applicable) for the workloads above—vision snippets, speech helpers, summarization, tooling—so sensitive steps can stay off the cloud when you choose.

• Simple or advanced — Skill setup wizards and defaults for common tools, with room to go deeper via settings, credentials, and core RPC when you need control and privacy.

Architecture: docs/ARCHITECTURE.md. Contributor orientation: CONTRIBUTING.md.

Under the hood (Architecture)

OpenHuman is a desktop monorepo: Rust owns business logic and execution; the UI owns interaction, layout, and OS integration.

Rust (openhuman / openhuman_core). The repo root src/ crate is the brain: JSON-RPC over HTTP (core_server), domain modules (auth, config, memory, skills, channels, screen intelligence, local AI, cron, …), and a QuickJS runtime for sandboxed JavaScript skills. The openhuman binary is built and staged next to the Tauri app so the desktop shell can spawn it as a sidecar. Heavy work—SQLite, sockets, crypto, skill lifecycle—runs there under Tokio, not in the WebView.

UI (app/). Vite + React (TypeScript) implements screens, onboarding, settings, and realtime UX. Redux Toolkit holds client state; Socket.io and the MCP-style client stack stay in sync with the core’s realtime surface. Tauri v2 (app/src-tauri/) is a thin Rust host: windowing, filesystem hooks where needed, and core_rpc_relay—forwarding JSON-RPC from the WebView to the openhuman process so the UI never re-implements domain rules.

Controllers and the RPC surface. Features are exposed as registered controllers: each domain declares schemas (namespace, function name, parameter shapes) and a handler. At runtime, calls are validated, dispatched by method name (e.g. openhuman.auth_get_state, openhuman.local_ai_agent_chat), and return structured outcomes. CLI and HTTP share the same controller catalog, so automation, tests, and the app all hit one contract.

What ties it together: one registry of controllers, one sidecar process for execution, Tauri IPC for shell-only capabilities, and HTTP JSON-RPC for everything else—plus skills and dual-socket behavior documented in the architecture guide.

Read more: docs/ARCHITECTURE.md · Frontend tree: docs/src/README.md · Tauri commands: docs/src-tauri/README.md

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