openhuman README

The project's README from github.com/tinyhumansai/openhuman. As one markdown file: README.md.

OpenHuman

The Tet

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

🕸️ The orchestrator

🔬 The deep researcher & doer

🧍 Human, private, yours

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 context-building diagram

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:

OpenHuman orchestration diagram

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.

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.

OpenHuman workflow canvas

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 1.93.0 (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 Tauri/CEF sources are present.
  3. Use pnpm dev for web-only UI work, pnpm --filter openhuman-app dev:app for the desktop shell, and focused checks such as pnpm typecheck, pnpm format:check, and cargo check -p openhuman --lib before opening a PR.

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

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OpenHuman contributors

openhuman (tinyhumansai/openhuman)

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42.1K ·GitHub
Openhuman
What is this?OpenHuman is a local-first personal AI system that combines a persistent memory of your data, an orchestrator that runs agent fleets on durable graphs, and a deep researcher that sweeps your files and the web for you. It is written in Rust and ships as a desktop app.
Is this just a chatbot, or something bigger?It is more than a chat window: it is an agent harness built from three pieces - a persistent local memory, an orchestrator that runs fleets of agents on durable checkpointed graphs, and a researcher that proactively pulls your data and the web.
Do I need my own API keys or can I use a local model?No key is required out of the box: managed Exa web search is bundled with the subscription, but you can bring your own Exa key, point any workload at your own provider key, or run a fully local Ollama model instead.
Is this ready for daily use?Not fully - the project labels itself Early Beta and under active development, so rough edges are expected.
Which platforms does it run on?It installs as a native desktop app with packages for Homebrew (macOS), Debian/Ubuntu .deb and AUR (Linux), plus graphical installers for other platforms via the website or GitHub Releases.
What happens to my data?Data is stored on-device encrypted with OS-keyring secrets, approval gates, and opt-in sandboxing, and a single Privacy Mode switch keeps every inference call on your machine, enforced inside the Rust core.
What is TokenJuice?TokenJuice compresses tool output before it hits the model, cutting the same information down by up to 80% fewer tokens, which is what keeps OpenHuman's large memory affordable to run.
How does OpenHuman compare to OpenClaw or Hermes Agent?The README's comparison table shows OpenHuman offering a Memory Tree plus Obsidian vault, built-in agent graph orchestration with E2E-encrypted agent-to-agent messaging, visual durable workflows, and one subscription covering search, media and model routing - versus plugin-reliant memory and single-loop orchestration in OpenClaw and Hermes Agent.
What is the Memory Tree and Obsidian wiki?It is how OpenHuman stores context: your data gets compressed into scored Markdown trees kept in SQLite on your machine and mirrored as an editable Obsidian vault, instead of an opaque vector database.
What messaging channels can the agent reach me on?17 channels are supported, including Telegram, Discord, Slack, WhatsApp, Signal, and iMessage, plus native email over IMAP IDLE and SMTP.
What are similar projects?hyperfide, EnvoyMesh, atlas, obsidian-mind, Orbital, ruflo, chubbyskills, obsidian-second-brain
Which topics is it in?second-brain, personal-ai, obsidian, whisper, ai-assistant, tauri, harness, agent-memory, oauth, workflow-automation, agent-orchestration, knowledge-base
Creator
tinyhumansai
Size
332.5 MB
Stars
42.1K
Updated
CommitOct 18Releasev0.64.15Oct 09
Age
7mo
Commits · 90 Days