MIT 许可、本地优先的跨平台内容发现 Agent；通过伴生 DSH 客户端插件（dsh-openbiliclaw）以第四栏面板 + 22 个 Agent Bridge 工具的方式接入 DeepSeek Harness。
DSH 适配
兼容
作者声明
安全审计
未审计
最后核验
2026-08-21
许可证
MIT
01它能帮你完成什么？
Use OpenBiliClaw's cross-platform personalized content discovery directly inside DeepSeek Harness
The OpenBiliClaw panel (recommendations / content library / chat / profile / settings) embedded as DSH's 4th column, plus 22 Agent Bridge tools that let DSH agents read recommendations, answer interest probes and close the learning loop
DeepSeek Harness users who want a personalized, local-first content feed alongside their agent work
Discover content across Bilibili, Xiaohongshu, Douyin, YouTube, X, Zhihu, Reddit, Linux.do, Bangumi, V2EX, Weibo and the open web from a single local agent
Cross-platform recommendations with friend-style explanations, unified into one candidate pool with diversity quotas and dedup
Users whose interests are fragmented across platforms and who want one recommendation agent instead of each platform's own feed
Keep the recommendation profile and all data fully local while tuning it via chat
A five-layer soul profile (MBTI, cognitive style, deep needs) and recommendation history stored only in local SQLite, updated by Socratic chat and feedback
Privacy-conscious users who refuse cloud recommendation profiles and want to own their data
02如何接入 DeepSeek Harness？
前置条件
- DeepSeek Harness installed (the DSH client plugin runs inside it)
- OpenBiliClaw backend deployed locally — desktop installers for macOS / Windows from Latest Release, or Python 3.11+ for script / manual deployment
- Your own LLM API key (LLM calls use your key by default); optional local Ollama + bge-m3 embedding
- A Chrome-compatible browser (Chrome / Edge / Brave / Arc / Vivaldi / Opera) or Safari (macOS) for the browser extension that connects platform login sessions
安装步骤
- 01
Deploy the OpenBiliClaw backend: download the desktop installer (macOS .dmg / Windows .exe) from the Latest Release page, or paste the one-line prompt from the README to an AI coding assistant for a customizable deployment
- 02
Install the DSH client plugin from the companion repository github.com/whiteguo233/dsh-openbiliclaw to bring OpenBiliClaw into DeepSeek Harness
- 03
In the browser with the extension installed, log in to Bilibili (default source) or opt in to Xiaohongshu / Douyin / YouTube / X / Zhihu / Reddit / Linux.do / V2EX / Weibo to initialize your profile
- 04
The DSH panel then works through the local backend API, with the 4th column and 22 Agent Bridge tools available in DSH
验证接入成功
作者未说明
03DSH 适配与能力边界
Installed inside DeepSeek Harness via the companion DSH client plugin (dsh-openbiliclaw), which embeds the OpenBiliClaw panel as DSH's 4th column and registers 22 Agent Bridge tools
Cross-platform content discovery
Your logged-in platform sessions (via the browser extension) and opt-in source selection→Candidates from Bilibili / Xiaohongshu / Douyin / YouTube / X / Zhihu / Reddit / Linux.do / Bangumi / V2EX / Weibo / open web unified into one evaluated pool
Reads content from external platform sites using your existing browser login sessionsSends relevant content to the LLM / embedding providers you configureFive-layer soul profile with interest probes
Your cross-platform behavior, feedback and chat→A deep profile (MBTI, cognitive style, deep needs) plus proactive interest / avoidance probes that only become filters after confirmation
Writes profile and memory data to local SQLiteAgent Bridge for agent hosts (incl. DSH)
An agent host such as DeepSeek Harness / OpenClaw / Claude Code calling the versioned Agent Bridge→Proactive recommendations, interest probes, multi-turn durable chat, profile reads, on-demand multi-source recommendations and idempotent feedback write-back
Local-first storage & BYO LLM
Your own LLM API key (or experimental Codex CLI OAuth reuse); optional local Ollama + bge-m3 embedding→All behavior, recommendation, chat and profile data kept in local SQLite; no OpenBiliClaw-operated cloud account
Slim installers auto-download the bge-m3 vector model (~1.1GB) on first launchRequests are sent to the LLM / embedding endpoints you configure
04适合谁？何时不该用？
适合
- DeepSeek Harness users who want a personalized, local-first content feed alongside their agent work
- Users whose interests are fragmented across platforms and who want one recommendation agent instead of each platform's own feed
- Privacy-conscious users who refuse cloud recommendation profiles and want to own their data
不适合
- The DSH integration is not built into this repository: it ships as a separate client plugin in github.com/whiteguo233/dsh-openbiliclaw, and the OpenBiliClaw local backend must be running for the DSH panel to work.
- Desktop installers are experimental pre-releases: ad-hoc signed and not notarized, so macOS may block first launch and Windows SmartScreen may warn; the project itself notes this channel suits quick trials, not development.
05兼容性、维护与安全提示
- The DSH integration is not built into this repository: it ships as a separate client plugin in github.com/whiteguo233/dsh-openbiliclaw, and the OpenBiliClaw local backend must be running for the DSH panel to work.
- Desktop installers are experimental pre-releases: ad-hoc signed and not notarized, so macOS may block first launch and Windows SmartScreen may warn; the project itself notes this channel suits quick trials, not development.
- The system relies on your own LLM key by default (built-in Gemini / DeepSeek / OpenAI / Claude / OpenRouter / Ollama or any OpenAI-compatible service), so model quota and cost are on you; background LLM budget caps and embedding circuit breaking exist precisely because unattended runs can burn quota.
MIT · actively maintained (latest release openbiliclaw-v0.3.208, 2026-08-18)
06常见问题
OpenBiliClaw 如何接入 DeepSeek Harness？
通过独立的 DSH 客户端插件（github.com/whiteguo233/dsh-openbiliclaw）实现：它把 OpenBiliClaw 装进 DeepSeek Harness，界面常驻第四栏（推荐 / 内容库 / 对话 / 画像 / 设置），并注册 22 个 Agent Bridge 工具，DSH 里的 Agent 可以读推荐、答探测、把反馈写回学习闭环。
是原生集成还是 MCP？
属于 DSH 客户端插件，而非内置原生运行时：DSH 面板只调用本地 OpenBiliClaw 后端 API，平台登录会话与 Cookie 同步仍由浏览器插件承担。
接入需要什么前置条件？
需要已安装 DeepSeek Harness、本地部署好的 OpenBiliClaw 后端（macOS / Windows 桌面安装包，或 Python 3.11+ 脚本 / 手动部署）、你自己的 LLM API Key，以及装有 OpenBiliClaw 浏览器插件的 Chrome 系浏览器（或 macOS Safari）用于连接内容平台。
我的数据会流向哪里？
默认数据流是：浏览器插件 → 你配置的本地 OpenBiliClaw 后端 → 本机 SQLite，不会发送到开发者运营的服务器。如果你配置了云端 LLM / embedding 服务，相关内容会按你的配置发送给对应服务商。
哪些平台需要登录？
B 站、小红书、抖音、YouTube、X、知乎、Reddit 通过插件复用你浏览器里的现有登录会话；Linux.do、Bangumi、V2EX 与微博可匿名做公开发现，个人信号初始化则需要已登录会话或公开用户名。
07相关的 DSH 工作流
open-design
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DeepSeek Harness 的开源 AI 设计插件，本地优先桌面应用，让编码智能体变身设计引擎，生成原型、落地页、幻灯片、图片与视频，支持多种格式导出。
picgo
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PicGo：面向高效创作者的图片上传神器，支持 Obsidian、Typora、VS Code 等编辑器，以及 S3、GitHub、Cloudflare R2、Imgur、阿里云 OSS 等 60+ 图床，粘贴即上传。
dsh-desktop
作者 anywhere-labs
为 DeepSeek Harness (DSH) 插件生态打造的现代化桌面端解决方案。万物皆「插件」，桌面本身也是「插件」。
deepseek-harness-desktop
作者 dsh-tauri-desk
DeepSeek Harness Tauri 桌面版 | Only 5mb installer, zero environment setup, preset plugins, Windows / macOS / Linux.
08数据与来源
新增 **DSH 客户端插件** —— 把 OpenBiliClaw 装进 [DeepSeek Harness](https://github.com/deepseek-ai/DeepSeek-Harness)：DSH 界面常驻第四栏（推荐…
以及把同一套面板搬进 DSH Web 界面的 [DSH 客户端插件](https://github.com/whiteguo233/dsh-openbiliclaw)（第四栏 + 22 个 Agent Bridge 工具）。桌面端、移动端…
页面基于项目公开文档、仓库元数据和 DSH Plugins 的结构化解析生成；最后核验于 2026-08-21。发现错误？提交更正。
最佳 DeepSeek Harness 插件
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