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。
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.
curl -fsSL https://raw.githubusercontent.com/tinyhumansai/openhuman/main/scripts/install.sh | bashREADME 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。
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
- 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
- v0.64.14OpenHuman v0.64.142026-10-08 · 2026-10-08 22:05 UTC
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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.
- v0.64.10OpenHuman v0.64.102026-09-30 · 2026-09-30 21:18 UTC
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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-coreand into the appropriatevendor/tiny*modules—keeping core focused on product policy, adapters, and contracts. This includes splitting RPC/server responsibilities, moving channels/sk - v0.64.7OpenHuman v0.64.72026-09-29 · 2026-09-29 01:33 UTC
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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
- v0.64.4OpenHuman v0.64.42026-09-26 · 2026-09-26 00:57 UTC
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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_quitIPC 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
用户点评
登录用户的真实使用记录,可按版本和硬件筛选还没有人点评。第一个说出真实体验的人最有价值。
[!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 task | OpenHuman (vs. median) | Median of the other six | Best of the other six |
|---|---|---|---|
| Wall time (p50) | 19.8 s (2.3x faster) | 46.5 s | 28.4 s (OpenCode) |
| Tokens | 167k (2.6x fewer) | 435k | 370k (Codex) |
| Cost | $0.0077 (-36%) | $0.012 | $0.0077 (OpenCode, a tie) |
| Peak memory | 68 MB (7.7x less) | 523 MB | 123 MB (Codex) |
| CPU time | 1.3 s (8x less) | 10.4 s | 2.9 s (DeepSeek Harness) |
| System prompt | 4.6k tokens (-40%) | 7.7k | 6.2k (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.
| Capability | What OpenHuman ships |
|---|---|
| Configurable memory | Built-in memory over your files, repos, feeds and apps, recalled before every turn with citations, on the memory engine you choose |
| Voice agents | A live voice agent you can interrupt mid-sentence, plus dictation and spoken replies |
| Computer and browser control | Drives a real Chrome browser and your desktop apps, asking before anything it cannot undo |
| Search | Search engines and search agents: Exa and Gemini included, Brave, Tavily, Parallel and more with your own key, grounded answers with citations and Deep Research |
| Tools | Native tools: shell and coder, scraper, documents, image and video generation, cron |
| MCP and skills | MCP servers and skill bundles |
| OAuth integrations | 119 apps through Composio, with triggers that start the agent when something happens |
| Models | Local models (Ollama, LM Studio, MLX) and BYOK for 26 providers, with automatic model routing |
| Channels | 14 messaging channels as agent front ends: Telegram, Discord, iMessage, email and more |
| Workflows | Durable workflow graphs: cron, event or manual triggers, approval steps, resume after a pause |
| Safety | Approval gate, sandboxed execution (OS jail or Docker), Privacy Mode for local-only runs, secrets in the OS keyring |
| Usage tracking | Per-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 Code | Codex | OpenClaw | Hermes Agent | OpenHuman | |
|---|---|---|---|---|---|
| Median task time | 37.8 s | 36.1 s | 62 s | 58.5 s | 🚀 19.8 s |
| Avg tokens per task | 428k | 370k | 442k | 482k | 🚀 167k |
| Peak memory per task | 231 MB | 123 MB | 1.57 GB | 816 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.
- Install Git, Node.js 24+, pnpm 10.10.0, Rust 1.99.1 (with
rustfmtandclippy), CMake, Ninja, ripgrep, and your platform's desktop build prerequisites. - Fork and clone the repo. Run
git submodule update --init --recursive, thenpnpm install. - Run
pnpm devfor UI work orpnpm dev:appfor the desktop app. Before you open a PR, runpnpm typecheck,pnpm format:checkandcargo 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.