OpenHuman 是你的个人 AI 超级智能：一个能记住一切的大脑、出色的编排器，以及深度研究员。本地优先、简单且强大。

早期 Beta：项目仍在积极开发中，可能存在不够完善之处。

OpenHuman 并非 AGI。但凭借更好的记忆、更好的编排能力和更好的工具链，它在架构上迈出了更接近 AGI 的重要一步。

🎉 上线一周内，OpenHuman 连续九天成为 GitHub 排名第一的趋势仓库。

可从 tinyhumans.ai/openhuman 或 GitHub Releases 页面下载安装程序。

如需通过终端安装（Homebrew、Debian/Ubuntu .deb、AUR、安装脚本及各平台说明），请参阅 INSTALL.md。

OpenHuman 具备多数助手所没有的三项能力：大脑，为你的世界建立持久的本地记忆；出色的编排器，可在持久化图结构上运行 Agent 集群；以及深度研究员，在你尚未问完问题前便可检索你的数据和 Web。以下每一项均链接至文档中的详细说明。

• Memory Tree + Obsidian Wiki：将你的数据压缩为存放在本机 SQLite 中、带评分的 Markdown 树，并镜像为可打开和编辑的 Obsidian vault。没有“向量汤”式黑箱。

• 100+ OAuth 集成、5,000+ MCP 服务器、90,000+ Skills：一键接入 Gmail、Notion、GitHub、Slack 以及你技术栈中的其他服务。自动抓取每 20 分钟为大脑补充一次信息，因此它能在今天早晨掌握明天的上下文。

• 目标与待办事项：支持长期目标、按线程持久保存的目标，以及每个对话共享的看板。

• TokenJuice：工具输出在送入模型前会被压缩；信息相同，Token 最多减少 80%。没有它，如此庞大的大脑将难以负担。

• 工作流：Agent 提议自动化方案；你可在画布上审阅并保存。基于开源 tinyflows 的持久化、触发驱动、审批门控执行。

• 能够完成任务的 harness：在开源 tinyagents 上运行带检查点的图结构任务。卡住的 Agent 可被引导，停止的 Agent 会返回根因，每次运行均可回放并展示真实的逐调用成本。

• 始终在线的分脑：快速反射 Agent 对入站请求进行分流，深度推理核心则在“潜意识”的引导下将任务委派给工作 Agent 集群。

• Agent 经济体：通过 tiny.place 上的 @handle、采用 Signal 加密的 Agent 间编排、x402 USDC 悬赏与交易机制协作。密钥绝不落盘。

🔬 深度研究员与执行者

• 开箱即用：由 Exa 驱动的托管式 Web 搜索包含在 OpenHuman 订阅中，无需 API key；也可使用你自己的 Exa key，直接通过自己的 Exa 账户和账单进行搜索。此外还提供爬虫、代码工具集、真实的浏览器，以及进程内 Whisper 支持的原生语音。模型路由可在同一订阅下为不同工作负载选择合适的 LLM。该订阅是默认选项，而不是锁定机制：你可将任意工作负载指向自己的提供商 key 或完全本地的 Ollama 模型，并按需混用三者。

• 图像与视频生成：可在同一订阅下，直接将 Seedream/SeedEdit 图像与 Seedance/Veo 视频生成到你的工作区中。

• 17 个消息渠道：Telegram、Discord、Slack、WhatsApp、Signal、iMessage……并支持原生电子邮件（IMAP IDLE + SMTP）。你的 Agent 可在你已经使用的渠道中联系你。

• 简单、UI 优先且以人为本：无需配置文件或终端，只需几次点击即可从安装完成到可工作的 Agent。它还有一张脸：一个会说话、会反应并记得你的吉祥物。

• 隐私与安全：设备端加密数据、审批门控、OS keyring 密钥管理和可选沙箱。此外还提供 隐私模式：打开一个开关，所有推理均不会离开你的设备，这一机制由 Rust 核心强制执行。

• 主题与 Theme Studio：提供五个主题系列与完整的可视化编辑器，可导出为 JSON。

几分钟建立上下文，而非数周

OpenHuman 是首个能在几分钟内了解你的 Agent harness。其灵感来自 Karpathy 的 LLM Knowledgebase。大多数 Agent 都是从零开始：Hermes 通过观察你的工作来学习；OpenClaw 则等待插件搬运上下文。无论哪种方式，都要花费数天乃至数周，Agent 才能充分了解你的技术栈并真正派上用场。

OpenHuman 会总结并压缩你的所有文档、电子邮件和聊天记录；同时创建记忆图谱，让你的 Agent 记住关于你的一切。

OpenHuman 跳过了等待过程。连接你的账户，让自动抓取以 20 分钟为周期在本地拉取数据，随后由 Memory Trees 将所有内容压缩为 Markdown 文件，并智能存储在 Karpathy 风格的 Obsidian wiki 中。

只需一次同步，Agent 就能获得你收件箱、日历、代码库、文档和消息的完整（压缩后）上下文。无需训练期，也无需“再给它几周时间”。它将成为受你控制的另一个你。

如果你已经在其他编程 Agent 中自行托管 agentmemory，OpenHuman 提供一个可选的 Memory 后端代理至该服务。在 config.toml 中设置 memory.backend = "agentmemory"，同一个持久化存储即可同时为 OpenHuman、Claude Code、Cursor、Codex 和 OpenCode 提供支持。设置方法请参阅 agentmemory backend 页面。

编排器，而不是聊天机器人

大多数 Agent harness 在单个循环中运行一个 Agent。OpenHuman 是一个 编排器：

Agent 间消息通过 Signal 协议端到端加密传输，因此你可以连接任何工具（Claude Code、Codex、OpenClaw、Hermes），并使用 OpenHuman 编排你的全部 Agent 和工具。

• 图，而非循环：对话轮次以基于 tinyagents 的带检查点图结构运行。它们可暂停等待人工操作，在重启后仍能存活，并从运行中断处恢复。
• 子 Agent 集群：专家 Agent 可向下衍生三层；卡住的 Agent 会转化为根因报告。
• 加密的 Agent 间协作：各实例通过具有 x402 支付能力的 Signal 协议 E2E 会话互相编排。服务器绝不会看到明文。

深受 n8n 和 Zapier 启发，工作流将同样可视化、触发驱动的自动化能力带给你的 Agent，不同之处在于，工作流由 Agent 为你构建。提出一个自动化需求，它就会提议一个方案：一个 tinyflows 图；你可在可视化画布上审阅后再保存。

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 : 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.

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.

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.

• 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.

• 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.

• An agent economy : a @handle on tiny.place, Signal-encrypted agent-to-agent orchestration, x402 USDC bounties and trading. Keys never touch disk.

🔬 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.

• 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.

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

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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