openhuman

tinyhumansaiGPL-3.0活跃维护

用 Rust 写的开源 Agent 编排框架,主打极低资源占用,能在 10 美元的 VPS 上同时跑 500 多个 Agent,适合本地优先、注重隐私的个人 AI 助手场景。

The fastest, cheapest, most efficient open-source agent harness. Run more than 500 agents on a $10 VPS.

v0.64.15发布于 2026-10-09 05:08 UTC42,620star +42 / 7 天4,214fork同步于 2026-11-10 11:19 UTC

怎么上手

由 README 提炼,执行前请确认
curl -fsSL https://raw.githubusercontent.com/tinyhumansai/openhuman/main/scripts/install.sh | bash
硬件要求按 README 说明

README 未说明显存要求;强调单进程编译 Rust 极轻量,峰值内存 68 MB、CPU 时间 1.3 s,可在 $10 VPS 上跑 500+ agent。

下载:源码 zip·GitHub Releases(安装包)本站不镜像安装包

项目说明

由 README 自动翻译提炼

开源 agent harness,用 Rust 单进程实现,让一台便宜 VPS 也能跑数百个 agent

用 Rust 写的开源 Agent 编排框架,主打极低资源占用,能在 10 美元的 VPS 上同时跑 500 多个 Agent,适合本地优先、注重隐私的个人 AI 助手场景。

核心能力

  • 用 RLM 递归语言模型压缩工具返回结果,超大结果给句柄供检索
  • Jev 小模型从 1215 个工具中检索,命中率 86.8%
  • 所有功能挂载到统一 Rust 总线,按需加载、故障隔离
  • 内置记忆系统,每轮按 token 预算筛选并附引用
  • agent 可直接操控真实浏览器与桌面应用,支付前自动停止

容易踩的坑

项目处于 early beta,README 明示会有粗糙边缘;安装脚本会校验 checksum,可加 --dry-run 预览;Windows 需改用 PowerShell 的 install.ps1。

内容由模型根据 README 生成,可能不完整或不准确,以官方文档为准。看原文 README

最近发版

5 个版本v0.64.15
  1. v0.64.15OpenHuman v0.64.15最新正式版2026-10-09 · 2026-10-09 05:08 UTC

    The Installed-Build Reliability Upgrade

    This release focuses on keeping installed apps rock-solid—especially when loading key modules like search and memory. Onward and upward! 🎉

    Highlights

    Modules & installed builds 🧩

    A targeted hotfix improves module admission in installed builds so critical modules load correctly again—unblocking both search (tinysearch) and memory (tinymemory) where they could previously fail due to module directory permission checks.

    (#7155) — Thank you @senamakel!

    Contributor Credits

    Thanks to @senamakel for the fix and the fast turnaround!

    Full Compare

    https://github.com/tinyhumansai/openhuman/compare/v0.64.14...v0.64.15

  2. v0.64.14OpenHuman v0.64.142026-10-08 · 2026-10-08 22:05 UTC
    查看更新说明

    The Files, Memory & Voice Power-Up

    This release is a big step forward in day-to-day usability: clearer files and sessions, a major memory evolution, and real-time voice agents—plus a ton of reliability and performance work across the stack. 🎉

    Highlights

    Files & project workspace clarity 🗂️

    Agent outputs now land in a visible Files folder, you can choose where that folder lives, and the app can surface those generated files directly in-place—making it much easier to find and use what your agent created. Large tool outputs are also handled more safely to keep projects tidy. (#6820, #6825, #6833, #6868, #6947) — Thank you @M3gA-Mind and @senamakel!

    Memory v2, imports, and safer organization 🧠

    Memory gets a major upgrade: engine-selectable memory v2, better lifecycle handling around each turn, and stronger portability when switching engines. The Memory UI and provider cards were modernized (including CortexDB), credits/free-period behavior is more consistent (with top-up prompts), and recall becomes time-zone aware.

    Imports and migrations are significantly hardened: clearer “building vs empty” states, smarter batching/retries, better failure visibility with retry controls, more reliable resuming, and improved document ingestion (PDF/Office). Organization also improves with connector-first filing, better GitHub scoping, optional attribution to senders, and multiple erasure/deletion correctness and privacy/security fixes. (#6841, #6843, #6855, #6869, #6873, #6874, #6875, #6892, #6901, #6924, #6949, #6993, #7011, #7012, #7013, #7014, #7034, #7029, #7035, #7036, #7038, #7041, #7043, #7063, #7085, #7089, #7090, #7091, #7094, #7096, #7100, #7102, #7103, #7104, #7105, #7106, [#7107](https://github.

  3. v0.64.10OpenHuman v0.64.102026-09-30 · 2026-09-30 21:18 UTC
    查看更新说明

    The Speed, Structure & Reliability Upgrade

    This release tightens the core experience from end to end—faster first turns, clearer chat feedback, sturdier integrations, and a big internal modularity push that sets us up for even quicker iteration. 🎉

    Highlights

    Performance & responsiveness ⚡

    The agent loop is noticeably snappier: first-turn prompts are dramatically smaller, prompt caching stays warm (improving time-to-first-token), and the managed inference path avoids unnecessary per-call client setup. Tool-output compaction is also enabled by default again—with new “REPL-style” tools over stored results to keep large tool outputs from stalling turns.

    (#6787, #6788, #6823) — Thank you @senamakel!

    Chat UI & approvals polish 💬

    Chat got a round of UX improvements: long source lists now collapse cleanly (with better favicons), web-search rendering no longer leaves awkward gaps, and the silence watchdog now warns instead of killing a running turn—staying in sync with the core’s actual turn state. Approvals are also smoother: answered approvals won’t “ghost” into the background deck, and refusals now carry a clear deny reason that the model can actually read.

    (#6802, #6827, #6800, #6810) — Thank you @senamakel and @M3gA-Mind!

    Memory & knowledge engines 🧠

    Memory is now explicitly selectable: you can choose exactly one active engine, including Local (TinyCortex) or CortexDB via TinyHumans, plus other supported providers. Switching engines can also copy existing memories across—making experimentation and migration much less painful.

    (#6777) — Thank you @senamakel!

    MCP & Composio integrations 🔌

    MCP tooling becomes much more ergonomic and robust: server tools are exposed as stable, individually-addressable tools from a persistent cache, and string-encoded JSON arguments are normalized so calls don’t fail unexpectedly. Refusals from tool listing now tell the model what it can do (and avoid pointing at RPC-only methods). On the Composio side, embedders can give each agent its own credentials, and backend-routed Composio calls now flow through the tinyconnectors module instead of an in-process client.

    (#6767, #6769, #6818, #6689, #6796) — Thank you @senamakel, @M3gA-Mind, and @oxoxDev!

    Security, safety & policy hardening 🛡️

    Security protections are sharper and less noisy: the prompt-injection guard is less likely to false-positive on recalled transcripts, and browser IP blocking now uses the full non-global range definitions (with regression tests). Shell execution reliability also improves in scheduled jobs on Windows, and shell scanning logic is consolidated upstream for consistency.

    (#6776, #6709, #6764, #6790) — Thank you @senamakel, @Avi-Bendetsky, and @sanil-23!

    Architecture & vendor modularity push 🧱

    A major theme of this series is extracting host-independent logic out of openhuman-core and into the appropriate vendor/tiny* modules—keeping core focused on product policy, adapters, and contracts. This includes splitting RPC/server responsibilities, moving channels/sk

  4. v0.64.7OpenHuman v0.64.72026-09-29 · 2026-09-29 01:33 UTC
    查看更新说明

    The Intelligence Upgrade 🎉

    v0.64.4 to v0.64.7 brings 12 PRs across 12 commits, with upgrades across memory, voice, agents, reliability, and developer foundations. Thank you to everyone who contributed to this release. ✨

    Highlights 🚀

    ✅ Tasks, chat & agent workflows

    Task, chat, and agent workflows become easier to steer and inspect, with better task boards, persistent run state, safer delegation, and clearer model/thread controls. (#6672, #6683, #6702, #6760) — Thank you @M3gA-Mind and @senamakel!

    🧩 Integrations, UI & developer foundations

    Integrations, UI polish, and developer foundations also move forward, including clearer connected-account surfaces, meeting-agent wiring, presentation/artifact improvements, and cleaner docs/code organization. (#6668, #6678, #6693, #6699, #6700, #6701, #6704, #6759) — Thank you @senamakel, @M3gA-Mind, and @siddarthkay!

    Contributor Credits 🙌

    • M3gA-Mind (#6672, #6693) — thank you!
    • senamakel (#6668, #6678, #6683, #6700, #6701, #6702, #6704, #6759, #6760) — thank you!
    • siddarthkay (#6699) — thank you!

    Full Compare 🧭

    https://github.com/tinyhumansai/openhuman/compare/v0.64.4...v0.64.7

  5. v0.64.4OpenHuman v0.64.42026-09-26 · 2026-09-26 00:57 UTC
    查看更新说明

    The Better Windows Desktop Upgrade

    This release is all about making the Windows desktop experience more reliable, more polished, and smoother to operate—especially when you’re offline. 🎉

    Highlights

    Offline-ready Windows installers 📦

    Windows desktop installers now bundle all registry-pinned native modules (and their extracted DLLs) directly into the installer, so key features don’t depend on a first-run download. Module resolution now prefers the read-only installer bundle before user cache or network, and the Windows checks/smoke steps were updated to verify offline module loading end-to-end. (#6687) — Thank you @senamakel!

    Sharper Windows window controls & cleaner shutdown 🪟

    Windows gets a more compact first-launch window and improved custom window controls (minimize, maximize/restore, close), with the drag strip staying available during boot and error screens. Shutdown behavior is also fixed so closing properly exits both the desktop host and embedded core (including support for the app_quit IPC command), plus the dev launcher was simplified with earlier architecture mismatch detection. (#6692) — Thank you @stevent95-star!

    Contributor Credits

    Thanks to everyone who contributed to this release: @senamakel, @stevent95-star.

    Full Compare

    https://github.com/tinyhumansai/openhuman/compare/v0.64.3...v0.64.4

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    README 原文

    未翻译

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

    Early beta. OpenHuman is under active development, so expect rough edges.


    Install

    The easiest way is to download the desktop app from tinyhumans.ai/openhuman or the latest release. There is a .dmg for macOS, an .msi or .exe for Windows, and a .deb or .AppImage for Linux.

    Prefer the terminal? The install script picks the right package for your system, checks its checksum and installs it:

    # macOS and Linux
    curl -fsSL https://raw.githubusercontent.com/tinyhumansai/openhuman/main/scripts/install.sh | bash
    
    # Windows (PowerShell)
    irm https://raw.githubusercontent.com/tinyhumansai/openhuman/main/scripts/install.ps1 | iex
    

    To preview what the macOS and Linux script would do, end the command with bash -s -- --dry-run. Other options and troubleshooting are in INSTALL.md.


    Why OpenHuman?

    Most agent harnesses run one heavy process per agent and resend a big prompt on every call. OpenHuman does the same work with far less. It is the only feature-rich open-source harness built for large fleets of agents: 500 of them fit on a $10 server.


    Major innovations

    Most agent harnesses are a simple loop: send everything to the model, wait, repeat. That works for one agent, but it gets slow and expensive quickly, and it falls apart when you run hundreds.

    OpenHuman rethinks the parts that cost the most: how much text the AI has to read, how it finds the right tool, how features load, and how much machine the whole thing needs. The six ideas below are where the speed and savings above come from. Each card links to the docs if you want the details.


    Benchmarks

    We ran seven harnesses on the same SWE-bench tasks, with the same model, API key and container, and metered every call. The setup and results are public in openhuman-benchmarks, so anyone can rerun them. The latest run is swe-x86-1, with ten tasks.

    OpenHuman finished its tasks in less than half the median time, using 2.6x fewer tokens and an eighth of the memory and CPU. It made 114 model calls where the others made 134 to 212, and its whole run cost $0.05 where the others cost $0.08 to $0.35.

    It wins by doing less work per step. The core is compiled Rust in a single process, not Node or Python, so it uses little memory and almost no CPU between model calls. It sends the smallest prompt of the seven, 4.6k tokens with its 20 tools included, so each call is cheaper and comes back faster (1.65 s median, also the fastest). It also needs fewer calls to reach an answer, which is where most of the savings come from.

    Per solved taskOpenHuman (vs. median)Median of the other sixBest of the other six
    Wall time (p50)19.8 s (2.3x faster)46.5 s28.4 s (OpenCode)
    Tokens167k (2.6x fewer)435k370k (Codex)
    Cost$0.0077 (-36%)$0.012$0.0077 (OpenCode, a tie)
    Peak memory68 MB (7.7x less)523 MB123 MB (Codex)
    CPU time1.3 s (8x less)10.4 s2.9 s (DeepSeek Harness)
    System prompt4.6k tokens (-40%)7.7k6.2k (DeepSeek Harness)

    For users

    If you use Claude Code, Codex, OpenClaw or Hermes, you already know the ideas: an agent loop with tools, MCP, skills, BYOK models and memory. OpenHuman has all of them, in a desktop app, a terminal app or a headless server.

    CapabilityWhat OpenHuman ships
    Configurable memoryBuilt-in memory over your files, repos, feeds and apps, recalled before every turn with citations, on the memory engine you choose
    Voice agentsA live voice agent you can interrupt mid-sentence, plus dictation and spoken replies
    Computer and browser controlDrives a real Chrome browser and your desktop apps, asking before anything it cannot undo
    SearchSearch engines and search agents: Exa and Gemini included, Brave, Tavily, Parallel and more with your own key, grounded answers with citations and Deep Research
    ToolsNative tools: shell and coder, scraper, documents, image and video generation, cron
    MCP and skillsMCP servers and skill bundles
    OAuth integrations119 apps through Composio, with triggers that start the agent when something happens
    ModelsLocal models (Ollama, LM Studio, MLX) and BYOK for 26 providers, with automatic model routing
    Channels14 messaging channels as agent front ends: Telegram, Discord, iMessage, email and more
    WorkflowsDurable workflow graphs: cron, event or manual triggers, approval steps, resume after a pause
    SafetyApproval gate, sandboxed execution (OS jail or Docker), Privacy Mode for local-only runs, secrets in the OS keyring
    Usage trackingPer-call cost and token usage, plus replayable run journals

    Start with Getting started or the guides.


    For developers

    OpenHuman is a library-first harness. Add it to your Rust project and call an agent like any other function, with no sidecar or daemon to run. One runtime can hold hundreds of agents, each with its own model, tools, memory and sandbox. 500 of them fit on a $10 VPS, so a product with one agent per customer can start without a cluster.

    use openhuman_embed::{Access, Harness, Provider, Workspace};
    
    let agent = Harness::builder()
        .provider(Provider::openai_compatible("https://api.openai.com/v1", "sk-...").model("gpt-5"))
        .workspace(Workspace::Ephemeral)
        .access(Access::readonly())
        .build()
        .await?;
    
    let reply = agent.run("Summarize what you can see in this directory.").await?;
    println!("{}", reply.reply);
    

    Next: the Rust quickstart, the embedding guide and the developer docs.


    How it compares?

    Here is OpenHuman next to the harnesses most people already use. The top four rows come from our public benchmark: ten SWE-bench coding tasks, with the same model and container for every harness. Each number is per solved task. The rest compare features.

    In short, the others are great for one person working with one agent. OpenHuman does that too, and it is also the open-source option you can embed as a library and scale to a fleet. Products change quickly, so check each project before you decide.

    Claude CodeCodexOpenClawHermes AgentOpenHuman
    Median task time37.8 s36.1 s62 s58.5 s🚀 19.8 s
    Avg tokens per task428k370k442k482k🚀 167k
    Peak memory per task231 MB123 MB1.57 GB816 MB🚀 68 MB
    Avg cost per task$0.04$0.02$0.01$0.0082🚀 $0.0077
    Open source🚫 Proprietary✅ Apache-2.0✅ MIT✅ MIT✅ GPL-3.0
    Agent fleets⚠️ Process per agent⚠️ Process per agent⚠️ Process per agent⚠️ Process per agent🚀 500 agents on a $10 VPS
    Embeddable library⚠️ SDK over a CLI⚠️ SDK over a CLI🚫 None⚠️ Python package🚀 Typed Rust API
    Memory⚠️ Memory files⚠️ Memory files⚠️ Plugin-reliant✅ Self-learning🚀 Recalled every turn, with citations
    Integrations✅ MCP✅ MCP⚠️ BYO⚠️ BYO🚀 119 OAuth apps, MCP, skills
    Messaging channels🚫 None🚫 None✅ Many✅ Several✅ 14, including email
    Browser and desktop⚠️ Browser via MCP⚠️ Browser via MCP✅ Browser✅ Browser✅ Browser and desktop apps
    Workflows🚫 None🚫 None⚠️ Scripts⚠️ Scripts🚀 Visual, agent-drafted
    Model choice⚠️ Anthropic models⚠️ OpenAI-first✅ Any✅ Any✅ Any, with built-in routing

    Contributing

    Read CONTRIBUTING.md, or let an AI coding agent guide you with this prompt.

    1. Install Git, Node.js 24+, pnpm 10.10.0, Rust 1.99.1 (with rustfmt and clippy), CMake, Ninja, ripgrep, and your platform's desktop build prerequisites.
    2. Fork and clone the repo. Run git submodule update --init --recursive, then pnpm install.
    3. Run pnpm dev for UI work or pnpm dev:app for the desktop app. Before you open a PR, run pnpm typecheck, pnpm format:check and cargo check --manifest-path Cargo.toml.

    More in Getting set up, AGENTS.md and the crates overview. Many parts of OpenHuman live in their own repos under vendor/, and they welcome contributions too.

    Contributors get free merch and special access on Discord.

    Star history

    Contributors