Welcome to OpenHuman

Personal AI super intelligence for your desktop: a brain that builds a local-first memory of your life, a fantastic orchestrator of agent fleets and workflows, and a deep researcher across 118+ connec

OpenHuman is an open-source AI assistant built to be three things most assistants aren't: a brain (a persistent, local, readable memory of your world); a fantastic orchestrator (durable agent graphs, visual workflows, sub-agent fleets, and end-to-end encrypted agent-to-agent sessions); and a deep researcher (it sweeps your data and the web before you finish asking). Built on Rust + Tauri, licensed under GNU GPL3.

Every model in the world, all 200+ of them, shares the same fundamental limitation: they are stateless. You type a prompt, get a response, and the context evaporates. Even the ones with "memory" store a few bullet points. A few bullet points is a sticky note, not intelligence.

OpenHuman solves this with a stack that's calmly, deliberately different:

• A local-first Memory Tree. Every source you connect. Gmail, Slack, GitHub, Notion, your own notes, flows through a deterministic pipeline: canonical Markdown, ≤3k-token chunks, scored, folded into per-source / per-topic / per-day summary trees. Stored in SQLite on your machine. No vector-soup black box.

• An Obsidian-style wiki on top of it. The same chunks the agent reasons over land as .md files in a vault you can open in Obsidian, browse, edit, and link by hand. Inspired by Karpathy's obsidian-wiki workflow. You can't trust a memory you can't read.

• 118+ third-party integrations. One-click OAuth into Gmail, GitHub, Slack, Notion, Stripe, Calendar, Drive, Linear, Jira and more - no API keys to wire by hand, no plugin marketplace to navigate.

• Auto-fetch. Every twenty minutes, OpenHuman pulls fresh data from every active connection and folds it into the Memory Tree without you asking, so the agent already has tomorrow's context this morning.

• An agent built for big data. Smart token compression (TokenJuice) compacts verbose tool output before it ever enters the model's context, so sweeping through your last six months of email costs single-digit dollars. Automatic model routing sends each task to the right model - hint:reasoning to a frontier model, hint:fast to a cheap one, vision to vision - all under one subscription. Optional local AI via Ollama or LM Studio keeps supported workloads on-device.

• Batteries included. A complete agent toolbelt is wired in by default: web search, a web-fetch scraper, a full coder toolset (filesystem, git, lint, test, grep), browser & computer control, cron & scheduling, memory tools, agent coordination for spawning sub-agents, and native voice - STT in, TTS out, mascot lip-sync, and a live Google Meet agent that joins meetings, transcribes them into your Memory Tree, and can speak back into the call. No "install a plugin to read files" friction.

• Workflows. Durable, visual automations on the open-source tinyflows engine. Describe the automation in chat, the agent proposes a workflow graph, you review it on a canvas and save it. Flows fire on schedules or live app events, pause at approval gates, and resume exactly where they stopped, with full step-by-step run history.

• Meeting agents. The mascot joins Google Meet, Zoom, Teams, and Webex as a real participant: animated face on the camera tile, its own voice in the call, a live transcript streaming into the app while the meeting happens. Connect your calendar (read-only) and it auto-joins on policy, wakes when addressed by name, and files summary + action items + transcript into a searchable history.

• A harness that finishes the job. Every turn runs on the open-source tinyagents graph engine: durable checkpointing (sub-agents pause for your input and resume, instead of dying), a no-progress circuit breaker that stops identical-call loops and hands back a root-cause summary, classified tool failures rendered as actionable timeline cards, and durable, replayable run journals with per-call token and cost accounting.

• Privacy Mode. One switch, enforced in the Rust core: local-only mode structurally blocks every cloud model call and permits only on-device runtimes (Ollama, LM Studio, MLX, local OpenAI-compatible).

• An agent economy. OpenHuman agents are citizens of tiny.place: a @handle identity, Signal-protocol E2E messaging with other agents, x402 USDC bounties and marketplace trading, all signed by an on-device wallet key that never touches disk.

• Simple, UI-first. A clean desktop experience and short onboarding paths take you from install to a working agent in a few clicks - no config-first setup, no terminal required. The agent has a face: a desktop mascot that speaks, reacts to its surroundings, joins your meetings as a real participant, remembers you across weeks, and keeps thinking in the background even when you've stopped typing.

Together, these turn OpenHuman into something fundamentally different from a chatbot. It is an AI agent that consumes large amounts of personal data at low cost, maintains a persistent and evolving understanding of your world, and takes proactive actions on your behalf.

OpenHuman is not AGI. But it is a meaningful architectural step closer, with better memory, better orchestration, and better tooling.