[opensourcedex]

openhuman is a free, open source ai interaction & interfaces project written in Rust and released under GPL-3.0. It has 42,503 GitHub stars, 4,209 forks and 189 open issues, and was last pushed 7 hours ago. On this registry it ranks #14 of 156 tracked projects in AI Interaction & Interfaces, with 5 head-to-head comparisons available. It gained 2,271 stars over the last 30 tracked days.

What is openhuman?

OpenHuman is an open source, GPL-3.0-licensed agent harness written in Rust and shipped as a Tauri desktop application, aimed at individuals who want a personal AI assistant whose memory, orchestration, and research tooling run local-first on their own machine.

What it is

OpenHuman is a desktop agent harness that the README describes as three things at once: a brain that builds a persistent, local memory of your world, an orchestrator that runs fleets of agents on durable graphs, and a deep researcher that sweeps your data and the web before you finish asking. It is written in Rust, licensed GPL-3.0, and packaged as a Tauri desktop application. Its memory layer, the Memory Tree, compresses personal data into scored Markdown trees stored in SQLite on the local machine and mirrors them as an Obsidian vault that can be opened and edited by hand. The project is currently labelled an Early Beta and states plainly that it is under active development and that rough edges should be expected.

The concrete problem it addresses is the opacity and impermanence of assistant memory, together with the difficulty of steering long-running multi-agent work. The specific thing the README positions it against is vector-database memory, which it calls a "vector-soup black box"; OpenHuman instead keeps a human-readable Markdown and SQLite record that the user owns and can edit. On the orchestration side, agent work runs as checkpointed graphs on the project's own open-source tinyagents library, with trigger-driven, approval-gated workflows on tinyflows, so a run that stalls can be steered, a run that halts reports a root cause, and past runs replay with real per-call costs. It lives in the MCP and LLM agent ecosystem, connecting to 5,000+ MCP servers and 100+ OAuth providers rather than operating as a closed tool.

Key capabilities

  • Memory Tree and Obsidian Wiki: personal data compressed into scored Markdown trees in local SQLite, mirrored as an editable Obsidian vault, offered as the alternative to a "vector-soup black box".
  • TokenJuice compression: tool output is compressed before it reaches the model, which the README states delivers the same information with up to 80% fewer tokens.
  • Integrations: 100+ OAuth integrations including Gmail, Notion, GitHub, and Slack, plus 5,000+ MCP servers and 90,000+ Skills, with Auto-fetch feeding the brain every 20 minutes.
  • Workflows: the agent proposes an automation, the user reviews it on a canvas, and saved automations run as durable, trigger-driven, approval-gated runs on tinyflows.
  • Agent harness and split-brain orchestration: checkpointed graph runs on tinyagents where stuck agents are steered and halted ones return a root cause, while a fast reflex agent triages inbound traffic and a deep reasoning core delegates to worker fleets.
  • Goals and Todos: long-term goals, durable per-thread goals, and a shared kanban board per conversation.
  • Batteries included: managed Exa-powered web search with no API key, scraper, coder toolset, browser, native voice with in-process Whisper, and model routing that picks an LLM per workload on one subscription, which can also be pointed at your own provider key or a fully local model.

Who uses it and how

  • Individuals building a private second brain over their own mail, notes, and code, who want the memory written to disk as Markdown rather than held in a hosted vector store.
  • Obsidian users who want agent memory to land in a vault they already open and edit, with Auto-fetch refreshing context every 20 minutes.
  • Privacy-focused desktop users who route every workload to their own provider key or to a fully local model instead of relying on the bundled subscription.
  • Automation builders who need fleets of agents running durable graphs with approval gates, per-call cost accounting, and replayable runs.
  • Developers who want to reach their existing tooling through MCP servers and the Skills catalogue rather than writing bespoke connectors.

Getting started

Installers are downloaded from tinyhumans.ai/openhuman or from the GitHub Releases page. Terminal installs are documented in INSTALL.md and cover Homebrew, a Debian/Ubuntu .deb package, AUR, and install scripts, with platform notes.

How it compares

The facts provided name no comparable tools or paid products that OpenHuman replaces, so as far as this registry entry is concerned it stands alone in its category. Its distinguishing characteristics are therefore internal to the description: a GPL-3.0 licence, a local-first SQLite and Markdown memory store, and a desktop Tauri application rather than a hosted service.

When to use it — and when not to

A self-hoster takes on the local SQLite memory store, the Obsidian vault mirror, MCP and OAuth integration credentials, and either the bundled subscription or separate provider keys. The project is an Early Beta with 189 open issues and an explicit warning about rough edges, so it is not a fit for anyone who needs a stable, production-critical assistant today, and the desktop form factor does not suit headless or server-side deployment. Check the GPL-3.0 terms before building derivative or redistributed work on it.

project readme (upstream, from github) — read inline

OpenHuman

tinyhumansai%2Fopenhuman | Trendshift OpenHuman - An open source AI harness built with the human in mind | Product Hunt OpenHuman - An open source AI harness built with the human in mind | Product Hunt

OpenHuman - An open source AI harness built with the human in mind | Product Hunt OpenHuman - An open source AI harness built with the human in mind | Product Hunt

OpenHuman is your personal AI super intelligence: a brain that remembers everything, a fantastic orchestrator, a deep researcher. Local-first, simple, powerful.

Discussions • Discord • Reddit • X/Twitter • Docs • Follow @senamakel (Creator)

🇺🇸 English | 🇨🇳 简体中文 | 🇯🇵 日本語 | 🇰🇷 한국어 | 🇩🇪 Deutsch | 🇵🇰 اردو

Early Beta Latest Release GitHub Stars License

Early Beta: Under active development. Expect rough edges.

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

🎉 Within one week of launch, OpenHuman became the number one trending repository on GitHub for nine days in a row.

Install

Download installers from tinyhumans.ai/openhuman or from the GitHub Releases page.

For terminal installs (Homebrew, Debian/Ubuntu .deb, AUR, install scripts, and platform notes), see INSTALL.md.

What is OpenHuman?

OpenHuman is three things most assistants aren't: a brain that builds a persistent, local memory of your world; a fantastic orchestrator that runs fleets of agents on durable graphs; and a deep researcher that sweeps your data and the web before you finish asking. Every bullet links to the deeper writeup in the docs.

🧠 The brain

  • Memory Tree + Obsidian Wiki: your data compressed into scored Markdown trees in SQLite on your machine, mirrored as an Obsidian vault you can open and edit. No vector-soup black box.
  • 100+ OAuth integrations, 5,000+ MCP servers, 90,000+ Skills: one click into Gmail, Notion, GitHub, Slack and the rest of your stack. Auto-fetch feeds the brain every 20 minutes, so it has tomorrow's context this morning.
  • Goals & Todos: long-term goals, durable per-thread goals, and a shared kanban board per conversation.
  • TokenJuice: tool output compressed before it hits the model: same information, up to 80% fewer tokens. A brain this big would be unaffordable without it.

🕸️ The orchestrator

  • Workflows: the agent proposes the automation; you review it on a canvas and save. Durable, trigger-driven, approval-gated runs on open-source tinyflows.
  • A harness that finishes the job: checkpointed graph runs on open-source tinyagents. Stuck agents get steered, halted ones return a root cause, and every run replays with real per-call costs.
  • A split brain, always on: a fast reflex agent triages inbound traffic while a deep reasoning core delegates to worker fleets, steered by the subconscious.

🔬 The deep researcher & doer

  • Batteries included: managed web search, powered by Exa, is included with your OpenHuman subscription and needs no API key; bring your own Exa key to search directly on your own Exa account and billing. Plus scraper, coder toolset, a real browser, and native voice with in-process Whisper. Model routing picks the right LLM per workload on one subscription. That subscription is a default, not a lock-in: point any workload at your own provider key or a fully local Ollama model, and mix the three however you like.
  • Image & video generation: Seedream/SeedEdit images and Seedance/Veo video, straight into your workspace on the same subscription.
  • 17 messaging channels: Telegram, Discord, Slack, WhatsApp, Signal, iMessage… plus native email (IMAP IDLE + SMTP). Your agent reaches you where you already are.

🧍 Human, private, yours

  • Simple, UI-first & Human: install to working agent in a few clicks, with no config files and no terminal. And it has a face: a mascot that speaks, reacts, and remembers you.
  • Privacy & security: on-device encrypted data, approval gate, OS-keyring secrets, and opt-in sandboxing. There is also Privacy Mode: flip one switch and no inference leaves your machine, enforced in the Rust core.
  • Themes & Theme Studio: five theme families plus a full visual editor, exportable as JSON.

Context in minutes, not weeks

OpenHuman is the first agent harness that gets to know you in minutes. Inspired by Karpathy's LLM Knowledgebase. Most agents start cold. Hermes learns by watching you work; OpenClaw waits for plugins to ferry context in. Either way, you spend days or weeks before the agent knows enough about your stack to be genuinely useful.

OpenHuman summarizes and compresses all your documents, emails & chats; and creates a memory graph that lets your agent remember everything about you.

OpenHuman skips the wait. Connect your accounts, let auto-fetch pull data locally on a 20-minute loop, and then have Memory Trees compress everything into Markdown files stored intelligently in a Karpathy-style Obsidian wiki.

In just one sync pass, the agent has full (compressed) context of your inbox, your calendar, your repos, your docs, your messages. No training period. No "give it a few weeks.". It becomes you, controlled by you.

Already self-host agentmemory across other coding agents? OpenHuman ships an optional Memory backend that proxies to it. Set memory.backend = "agentmemory" in config.toml and the same durable store powers OpenHuman alongside Claude Code, Cursor, Codex, and OpenCode. See the agentmemory backend page for setup.

An orchestrator, not a chatbot

Most agent harnesses run one agent in one loop. OpenHuman is an orchestrator:

Agent-to-agent messaging runs over Signal-protocol end-to-end encryption, so you can connect anything (Claude Code, Codex, OpenClaw, Hermes) and use OpenHuman to orchestrate all of your agents and tools.

  • Graphs, not loops: turns run as checkpointed graphs on tinyagents. They pause for a human, survive a restart, and resume mid-run.
  • Sub-agent fleets: specialists spawn three levels deep; stuck agents become root-cause reports.
  • Agent-to-agent, encrypted: instances orchestrate each other over Signal-protocol E2E sessions with x402 payments. No server ever sees plaintext.

Workflows you can see

Heavily inspired by n8n and Zapier, workflows bring the same visual, trigger-driven automation to your agent, except the agent builds them for you. Ask for an automation and it proposes one: a tinyflows graph you review on a visual canvas before saving.

The agent proposes the workflow; you review it on a canvas and save it.

Saved workflows are durable and trigger-driven. They fire on schedules, webhooks, or channel events, survive restarts, and gate side effects behind approvals.

OpenHuman vs Other Agent Harnesses

High-level comparison (products evolve, so verify against each vendor). OpenHuman is built to minimize vendor sprawl, keep workflow knowledge on-device, and give the agent a persistent memory of your data, not only chat.

Claude Cowork OpenClaw Hermes Agent OpenHuman
Open-source 🚫 Proprietary ✅ MIT ✅ MIT ✅ GNU
Simple to start ✅ Desktop + CLI ⚠️ Terminal-first ⚠️ Terminal-first ✅ Clean UI, minutes
Cost ⚠️ Sub + add-ons ⚠️ BYO models ⚠️ BYO models ✅ One sub + TokenJuice
Memory ✅ Chat-scoped ⚠️ Plugin-reliant ✅ Self-learning 🚀 Memory Tree + Obsidian vault, optional agentmemory backend
Integrations ⚠️ Few connectors ⚠️ BYO ⚠️ BYO 🚀 100+ OAuth · 5k+ MCP · 90k+ Skills
Auto-fetch 🚫 None 🚫 None 🚫 None ✅ 20-min sync into memory
Orchestration ⚠️ Sub-tasks ⚠️ Single loop ⚠️ Single loop 🚀 Agent graphs + checkpoints + E2E-encrypted A2A
Workflows 🚫 None ⚠️ Scripts ⚠️ Scripts 🚀 Visual, durable, agent-proposed, approval-gated
Meetings 🚫 None 🚫 None 🚫 None 🚀 Joins Meet/Zoom/Teams/Webex, speaks, live transcript
Messaging channels 🚫 None ⚠️ A few ⚠️ A few ✅ 17 incl. native email (IMAP/SMTP)
Local-only mode 🚫 Cloud-only ⚠️ BYO local ⚠️ BYO local ✅ One-switch enforced Privacy Mode
Observability 🚫 Opaque ⚠️ Logs ⚠️ Logs ✅ Replayable run journals + per-call cost accounting
API sprawl 🚫 Extra keys 🚫 BYOK 🚫 Multi-vendor ✅ One account
Model routing 🚫 Single model ⚠️ Manual ⚠️ Manual ✅ Built-in
Native tools ✅ Code-only ✅ Code-only ✅ Code-only ✅ Code + search + scraper + browser + voice + media gen

Contributing from source

New contributor? Start with CONTRIBUTING.md for the fork/PR workflow and local validation commands, or use the copy-paste AI-agent prompt in CONTRIBUTING-BEGINNERS.md. The short path is:

  1. Install Git, Node.js 24+, pnpm 10.10.0, Rust 1.96.1 (rustfmt + clippy), CMake, Ninja, ripgrep, and the platform desktop build prerequisites.
  2. Fork and clone the repo, then run git submodule update --init --recursive before pnpm install so the vendored Rust dependencies under vendor/ (tinyagents, tinyflows, tinychannels, tinymemory, motosan-ai-oauth, ...) resolve.
  3. Use pnpm dev for web-only UI work, pnpm --filter openhuman-app dev:app (macOS) or pnpm dev:app:win (Windows) for the desktop shell, and focused checks such as pnpm typecheck, pnpm format:check, and cargo check -p openhuman --lib before opening a PR.

The Rust workspace under crates/ splits into crates/openhuman-core (package openhuman: the core plus the openhuman-core CLI), crates/openhuman-app (the Tauri desktop shell, built as a separate Cargo world), crates/openhuman-embed (the library facade for embedding the core), crates/openhuman-rpc (shared RPC contracts and client), and crates/openhuman-tui (the terminal client). See Building the Rust core and AGENTS.md for the full layout.

Deeper docs: Architecture · Getting Set Up · Cloud Deploy.

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Frequently asked questions

Is openhuman free to use?

openhuman is open source under the GPL-3.0 licence. There is no licence fee and no seat count — you can self-host it or, where the project offers one, pay a vendor for a managed version instead.

What does openhuman do?

OpenHuman is an open source agent harness with local-first memory, agent orchestration, and workflows

What is openhuman written in?

openhuman is primarily written in Rust. Its source is publicly available at https://github.com/tinyhumansai/openhuman, and it has 42,503 GitHub stars.