## Summary - 添加第二批核心功能模块的中文翻译(8 个文件):隐私与安全、第三方集成、吉祥物、模型路由、编码器、语音、定时任务、系统与工具 - 修复批次 A 遗留的 12 处未本地化内部链接(因第二批新增目标 `.zh-CN` 文件,之前保留的英文链接现在可指向中文版) - 修复第二批翻译中的 12 处质量问题:错别字、过直译、中英混杂、指向不存在的 `.zh-CN` 链接 - 修复隐私与安全文档中指向 `local-ai.zh-CN.md` 和 `triggers.zh-CN.md` 等尚未翻译文件的错误链接 - 统一 mascot、integrations 等跨模块链接指向,确保中文读者在 zh-CN 文档间流转 - 所有修改仅涉及 `.md` 文档,无代码变更 ## Problem - OpenHuman 中文用户阅读英文文档存在语言障碍 - 第一批汉化(overview + lightweight features)完成后,核心功能模块(integrations、model-routing、native-tools 等)仍无中文版 - 批次 A 的部分链接因目标文件当时未翻译而保留英文版,随着第二批新增 zh-CN 文件,这些链接已过时 ## Solution - 基于英文原文逐文件翻译,遵循术语统一表(vault→存储库、Agent→智能体、LLM/Token 保留英文等) - 翻译完成后运行审计脚本扫描,修复所有未本地化链接、MD040 代码块标识、术语一致性问题 - 对于目标 `.zh-CN.md` 不存在的链接(如 triggers、subconscious、local-ai、agent-coordination),保持指向英文原文,在 Related 中标记后续批次覆盖计划 ## Submission Checklist - [x] I have read the Codex PR Checklist - [x] I have confirmed Type Check passes (`pnpm typecheck`) (N/A: Markdown docs only) - [x] I have confirmed the app builds locally (`pnpm build`) (N/A: Markdown docs only) - [x] I have added tests for this change (N/A: i18n docs do not affect testable logic) - [x] I have updated documentation (N/A: this PR is documentation-only) - [x] I have confirmed no feature flags are required (N/A: no code changes) - [x] I have confirmed Prettier passes (`pnpm format:check`) (N/A: Markdown docs only) ## Impact - Runtime/platform impact: None - Performance/security/migration/compatibility: None ## Related - Follow-up PR(s)/TODOs: - Batch C: subconscious.zh-CN.md, triggers.zh-CN.md, local-ai.zh-CN.md, agent-coordination.zh-CN.md - Batch C: memory-tools.zh-CN.md, meeting-agents.zh-CN.md, developing/cef.zh-CN.md --- ## AI Authored PR Metadata ### Linear Issue - Key: N/A - URL: N/A ### Commit & Branch - Branch: `docs/i18n-batch-b-core-features` - Commit SHA: see PR commits ### Validation Run - [x] `pnpm --filter openhuman-app format:check` — N/A: no code changed - [x] `pnpm typecheck` — N/A: no code changed - [x] Focused tests: N/A - [x] Rust fmt/check: N/A - [x] Tauri fmt/check: N/A ### Validation Blocked - N/A ### Behavior Changes - Intended behavior change: None - User-visible effect: Chinese users can now read core feature docs in zh-CN ### Parity Contract - Legacy behavior preserved: N/A - Guard/fallback/dispatch parity checks: N/A ### Duplicate / Superseded PR Handling - N/A <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit * **Localization** * Updated Simplified Chinese UI strings for vault operations and MCP server/settings. * **Documentation** * Added extensive Chinese documentation covering integrations, mascot/meeting agents, model routing, native tools (voice, web search/scraper, coder, cron, system/tools), memory tree, obsidian wiki, token compression, platform, privacy/security, and subconscious/agent coordination. * **Chores** * Updated ignore rules to exclude AI assistant progress tracking. * Added documentation maintenance and validation scripts. <!-- review_stack_entry_start --> [](https://app.coderabbit.ai/change-stack/tinyhumansai/openhuman/pull/2450?utm_source=github_walkthrough&utm_medium=github&utm_campaign=change_stack) <!-- review_stack_entry_end --> <!-- end of auto-generated comment: release notes by coderabbit.ai --> Co-authored-by: agent:skill-master <skill-master@openclaw> Co-authored-by: Steven Enamakel <enamakel@tinyhumans.ai>
OpenHuman
OpenHuman is your Personal AI super intelligence. Private, Simple and extremely powerful.
Discord • Reddit • X/Twitter • Docs • Follow @senamakel (Creator)
🇺🇸 English | 🇨🇳 简体中文 | 🇯🇵 日本語 | 🇰🇷 한국어 | 🇩🇪 Deutsch
Early Beta: Under active development. Expect rough edges.
To install or get started, either download from the website over at tinyhumans.ai/openhuman or run
# Download DMG, EXEs over at https://tinyhumans.ai/openhuman or run in from your terminal
# For macOS or Linux x64
curl -fsSL https://raw.githubusercontent.com/tinyhumansai/openhuman/main/scripts/install.sh | bash
# For Windows
irm https://raw.githubusercontent.com/tinyhumansai/openhuman/main/scripts/install.ps1 | iex
What is OpenHuman?
OpenHuman is an open-source agentic assistant designed to integrate with you in your daily life. Each bullet links to the deeper writeup in the docs.
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Simple, UI-first & Human A clean desktop experience and short onboarding paths take you from install to a working agent in a few clicks — no config-first setup, no terminal required. The agent has a face: a desktop mascot that speaks, reacts to its surroundings, joins your Google Meets as a real participant, remembers you across weeks, and keeps thinking in the background even when you've stopped typing.
-
118+ third-party integrations with auto-fetch: plug into Gmail, Notion, GitHub, Slack, Stripe, Calendar, Drive, Linear, Jira and the rest of your stack with one-click OAuth. Every connection is exposed to the agent as a typed tool, and every twenty minutes the core walks each active connection and pulls fresh data into the memory tree. No prompts, no polling loops you have to write, so the agent already has tomorrow's context this morning.
Managed integrations are backend-proxied through OpenHuman's Composio connector layer. If you want to run Composio directly instead of using the managed backend path, configure direct mode with your own Composio API key; real-time trigger webhooks then need to be hosted and wired by you.
-
Memory Tree + Obsidian Wiki: a local-first knowledge base built from your data and your activity. Everything you connect is canonicalized into ≤3k-token Markdown chunks, scored, and folded into hierarchical summary trees stored in SQLite on your machine. The same chunks land as
.mdfiles in an Obsidian-compatible vault you can open, browse and edit, inspired by Karpathy's obsidian-wiki workflow. -
Batteries included: web search, a web-fetch scraper, a full coder toolset (filesystem, git, lint, test, grep), and native voice (STT in, ElevenLabs TTS out, mascot lip-sync, live Google Meet agent) are wired in by default. Model routing sends each task to the right LLM (reasoning, fast, or vision) under one subscription. No "install a plugin to read files" friction. Optional local AI via Ollama for on-device workloads.
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Smart token compression (TokenJuice): every tool call, scrape result, email body, and search payload is run through a token compression layer before it touches any LLM Model. HTML is converted to Markdown, long URLs are shortened, and verbose tool output is deduped and summarized via a configurable rule overlay etc... CJK, emoji, and other multi-byte text are preserved grapheme-by-grapheme — never stripped. You get the same information but at a fraction of the tokens. Reducing cost & latency by up to 80%.
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Messaging channels and privacy & security: inbound/outbound across the channels you already use, with workflow data that stays on device, encrypted locally, treated as yours.
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:
- Install Git, Node.js 24+, pnpm 10.10.0, Rust 1.93.0 (
rustfmt+clippy), CMake, Ninja, ripgrep, and the platform desktop build prerequisites. - Fork and clone the repo, then run
git submodule update --init --recursivebeforepnpm installso the vendored Tauri/CEF sources are present. - Use
pnpm devfor web-only UI work,pnpm --filter openhuman-app dev:appfor the desktop shell, and focused checks such aspnpm typecheck,pnpm format:check, andcargo check -p openhuman --libbefore opening a PR.
Deeper docs: Architecture · Getting Set Up · Cloud Deploy.
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.
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 | 🚀 118+ via OAuth |
| Auto-fetch | 🚫 None | 🚫 None | 🚫 None | ✅ 20-min sync into memory |
| 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 + voice |
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