Add DeepSeek Harness intro article: plugin-based agent framework (zh+en)

src/content/guides-en/deepseek-harness-intro.md

@@ -0,0 +1,153 @@
+---
+title: "Getting Started with DeepSeek Harness: The Open-Source Agent Framework Where Everything Is a Plugin"
+description: "DeepSeek Harness (dsh) is DeepSeek AI's official open-source agent harness — 64k+ stars, MIT licensed, built on an 'everything is a plugin' architecture. This guide explains what it is, how its plugin-based design differs from traditional frameworks like LangChain and CrewAI, how to get started, and what you can build with it during developer preview."
+date: "2026-08-14"
+article_type: explainer
+tags: [deepseek, harness, agent-framework, dsh, plugin, cordis]
+pillar: workflow
+content_status: keep
+locale_strategy: mirrored
+draft: false
+---
+
+## Bottom Line
+
+DeepSeek Harness (the CLI command is `dsh`) is not another AI coding tool. It's DeepSeek AI's official open-source **agent harness** — a framework for building, orchestrating, and running AI agents.
+
+Unlike Claude Code or Codex, which are ready-to-use coding agents, DeepSeek Harness is more like an **agent factory**: it gives you a plugin-based architecture foundation, and you assemble your own agents by plugging in models, tools, and workflows as plugins.
+
+> ⚠️ **Important**: DeepSeek Harness is currently in **developer preview** and iterating rapidly. Compatibility-breaking changes are expected. Facts in this guide are current as of 2026-08-14; always refer to the official docs.
+
+---
+
+## 1. What Is DeepSeek Harness?
+
+[DeepSeek Harness](https://github.com/deepseek-ai/deepseek-harness) (`dsh`) is an open-source agent framework developed by DeepSeek AI, MIT licensed, written in TypeScript, with 64k+ GitHub stars.
+
+Its core design philosophy is one sentence: **Everything is a Plugin.**
+
+This isn't just a slogan — it's an architectural constraint driven by the underlying engine, [Cordis](https://github.com/cordiverse/cordis). Cordis's design is described in the paper "A Programming Paradigm for Spatiotemporal Composability" — in short, it enables software components to compose freely in both time and space dimensions.
+
+In DeepSeek Harness:
+
+- **Models are plugins**: You can plug in DeepSeek's own models, OpenAI, Anthropic, or any other provider
+- **Tools are plugins**: File I/O, shell execution, network requests, MCP servers — all plugins
+- **Workflows are plugins**: Agent lifecycle, task orchestration, context management — also plugins
+
+This means you don't "write chains in LangChain, configure agents in AutoGen" — in DeepSeek Harness, everything is the same abstraction: a plugin.
+
+---
+
+## 2. Core Architecture: Everything Is a Plugin
+
+### How It Differs from Traditional Agent Frameworks
+
+| Dimension | Traditional (LangChain / AutoGen) | DeepSeek Harness |
+|---|---|---|
+| **Architecture model** | Pipeline-based: chain → agent → tool, layered abstractions | Plugin-based: everything is a plugin, unified abstraction |
+| **Composition** | Predefined chains (SequentialChain, RouterChain) | Spatiotemporal composition: plugins compose freely in time and space |
+| **Extensibility** | Extend by inheriting or implementing specific interfaces | Register a plugin — no framework code changes needed |
+| **Underlying engine** | Custom or LangChain Expression Language | Cordis (independent open-source project, formal paper) |
+| **Coding agent capability** | Depends on function calling | Native agent harness, built-in tool execution pipeline |
+
+**Key difference**: Traditional frameworks give you "a set of blocks and a manual." DeepSeek Harness gives you "LEGO bricks where the plugins are the bricks themselves — you decide how to assemble them."
+
+### The Cordis Engine
+
+Cordis is the runtime core of DeepSeek Harness. Its "spatiotemporal composability" solves a real problem: agent behavior is not linear — it might be interrupted while executing task A, handle event B, then return to A. Traditional frameworks handle this with callbacks or state machines; Cordis supports it natively through plugin composition.
+
+You don't need to understand Cordis's paper to use DeepSeek Harness. But knowing the engine is there helps you understand why it "feels" different from LangChain.
+
+---
+
+## 3. Relationship with Claude Code / Codex and Similar Tools
+
+This isn't a "DeepSeek Harness vs. Claude Code" choice — they operate at different levels.
+
+| Dimension | Claude Code / Codex | DeepSeek Harness |
+|---|---|---|
+| **Positioning** | Coding Agent (helps you write code) | Agent Harness (helps you build agents) |
+| **Usage** | Ready to use, CLI or IDE plugin | Framework; requires assembling plugins and configuration |
+| **Model binding** | Bound to Claude / GPT models | Model-agnostic; plug in any provider |
+| **Use case** | Individual developer daily coding | Building custom agent workflows, agent products |
+| **Maturity** | Production-ready | Developer preview |
+
+**You can use both**: Build a code review agent in DeepSeek Harness with DeepSeek models, while continuing to use Claude Code for daily coding. They don't conflict.
+
+---
+
+## 4. Getting Started
+
+### 5-Minute Quick Start
+
+```bash
+# Prerequisite: Node.js installed
+npx @deepseek-ai/dsh web
+```
+
+The Web UI starts at `http://127.0.0.1:3080` by default. Open your browser and you're in.
+
+### Running from Source
+
+```bash
+git clone https://github.com/deepseek-ai/deepseek-harness.git
+cd deepseek-harness
+pnpm install
+pnpm run build
+pnpm dsh web
+```
+
+### Configuring Providers
+
+In the Web UI, configure your model provider (DeepSeek API, OpenAI API, etc.), then create agents and start working. See the official [Web UI guide](https://github.com/deepseek-ai/deepseek-harness/tree/master/docs/user/guide) for details.
+
+### Python SDK
+
+In addition to the Web UI and CLI, DeepSeek Harness offers a Python SDK for integration into Python projects.
+
+---
+
+## 5. Plugin Ecosystem and Community
+
+### Plugins
+
+DeepSeek Harness plugins are discoverable through the GitHub [`dsh-plugin` topic](https://github.com/topics/dsh-plugin). You can:
+
+- Browse existing community plugins
+- Tag your own plugin with `dsh-plugin` to make it discoverable
+- Follow the official [plugin development guide](https://github.com/deepseek-ai/deepseek-harness/tree/master/docs) to write your own
+
+### Community
+
+- **Discord**: [DeepSeek Harness Discord](https://discord.gg/Ycq5dCaS4)
+- **China**: WeCom group + official WeChat account (see the official README)
+- **GitHub Discussions**: For feedback and bug reports
+
+---
+
+## 6. Who It's For / Not For
+
+**Good fit**:
+
+- Developers wanting to build custom agent workflows (not just "let AI write code," but "let AI follow your process")
+- Engineers interested in plugin-based architectures and agent framework design
+- Teams already using DeepSeek models who want to build agent products on top
+- Early adopters willing to accept developer preview instability for early access
+
+**Not a fit**:
+
+- Anyone just looking for a ready-to-use AI coding tool — use Claude Code, Codex, or Cursor instead
+- Production environments with zero tolerance for breaking changes — wait for stable release
+- Anyone who doesn't want to learn a new framework — DeepSeek Harness has a steeper learning curve than coding agents
+
+---
+
+## Summary
+
+DeepSeek Harness's value isn't "yet another agent tool." It's **a different way to build agents**.
+
+When you're no longer satisfied with "let AI write this piece of code," and instead want "let AI follow my designed process, use my specified tools, and work continuously within my defined boundaries" — that's when you need an agent harness. DeepSeek Harness's plugin-based architecture is one of the most radical answers to this need in the open-source world today.
+
+It's still in developer preview, but the direction is clear: **not giving you an agent, but giving you a factory to build agents.**
+
+> Further reading: Our [Harness category overview](/en/guides/meta-harness-2026) compares Omnigent, Conductor, Zed ACP, and other harness products.

src/content/guides/deepseek-harness-intro.md

@@ -0,0 +1,153 @@
+---
+title: "DeepSeek Harness 入门：一切皆插件的开源 Agent 框架"
+description: "DeepSeek Harness（dsh）是 DeepSeek AI 官方的开源 agent harness，64k+ stars，MIT 许可，架构核心是「一切皆插件」。本文讲清它是什么、插件化架构与 LangChain/CrewAI 等传统框架的区别、怎么上手、以及 developer preview 阶段你能用它做什么。"
+date: "2026-08-14"
+article_type: explainer
+tags: [deepseek, harness, agent-framework, dsh, plugin, cordis]
+pillar: workflow
+content_status: keep
+locale_strategy: mirrored
+draft: false
+---
+
+## 先说结论
+
+DeepSeek Harness（CLI 命令 `dsh`）不是又一个 AI 编程工具。它是 DeepSeek AI 官方开源的 **agent harness**——一个让你构建、编排和运行 AI Agent 的框架。
+
+和 Claude Code、Codex 这类"拿来就用的 coding agent"不同，DeepSeek Harness 更像一个**Agent 工厂**：它提供插件化的架构底座，你把模型、工具、工作流作为插件接入，组装出你自己的 Agent。
+
+> ⚠️ **重要提醒**：DeepSeek Harness 目前处于 **developer preview** 阶段，迭代速度极快，会有破坏性变更。本文事实截至 2026-08-14，请以官方文档为准。
+
+---
+
+## 一、DeepSeek Harness 是什么
+
+[DeepSeek Harness](https://github.com/deepseek-ai/deepseek-harness)（`dsh`）是 DeepSeek AI 开发的开源 agent 框架，MIT 许可，TypeScript 编写，GitHub 64k+ stars。
+
+它的核心设计理念就一句话：**Everything is a Plugin（一切皆插件）**。
+
+这个理念不是口号，而是由底层引擎 [Cordis](https://github.com/cordiverse/cordis) 驱动的架构约束。Cordis 的设计思想来自论文《A Programming Paradigm for Spatiotemporal Composability》（时空可组合性编程范式）——简单说，就是让软件组件在时间和空间两个维度上都能自由组合。
+
+在 DeepSeek Harness 里：
+
+- **模型是插件**：你可以接 DeepSeek 自己的模型，也可以接 OpenAI、Anthropic 或其他 provider
+- **工具是插件**：文件读写、Shell 执行、网络请求、MCP server——都是插件
+- **工作流是插件**：agent 的生命周期、任务编排、上下文管理——也是插件
+
+这意味着你不需要"在 LangChain 里写 chain、在 AutoGen 里配 agent"——在 DeepSeek Harness 里，一切都是同一种抽象：插件。
+
+---
+
+## 二、核心架构：一切皆插件
+
+### 与传统 agent 框架的区别
+
+| 维度 | 传统框架（LangChain/AutoGen） | DeepSeek Harness |
+|---|---|---|
+| **架构模型** | 管线式：chain → agent → tool 逐层抽象 | 插件式：一切是插件，统一抽象 |
+| **组合方式** | 预定义链（SequentialChain、RouterChain） | 时空可组合：插件在时间和空间维度自由组合 |
+| **扩展性** | 继承或实现特定接口 | 注册插件即可，无需改框架代码 |
+| **底层引擎** | 自研或基于 LangChain 表达式语言 | Cordis（独立开源项目，有正式论文） |
+| **编码 agent 能力** | 依赖工具调用（function calling） | 原生支持 agent harness，工具执行管线内置 |
+
+**关键区别**：传统框架是"给你一套积木，按说明书搭"；DeepSeek Harness 是"给你一套乐高，插件就是积木本身，怎么搭你自己定"。
+
+### Cordis 引擎
+
+Cordis 是 DeepSeek Harness 的运行时核心。它的"时空可组合性"解决了一个实际问题：agent 的行为不是线性的——它可能在执行任务 A 的过程中被中断、去处理事件 B、然后回来继续 A。传统框架用回调或状态机处理这种场景，Cordis 用插件组合天然支持。
+
+对使用者来说，你不需要理解 Cordis 的论文才能用 DeepSeek Harness。但知道底层有这个引擎，能帮你理解为什么它和 LangChain 的"感觉"不一样。
+
+---
+
+## 三、和 Claude Code / Codex 等工具的关系
+
+这不是一个"选 DeepSeek Harness 还是选 Claude Code"的问题——它们是不同层级的东西。
+
+| 维度 | Claude Code / Codex | DeepSeek Harness |
+|---|---|---|
+| **定位** | Coding Agent（帮你写代码） | Agent Harness（帮你构建 Agent） |
+| **使用方式** | 开箱即用，CLI 或 IDE 插件 | 框架，需要组装插件和配置 |
+| **模型绑定** | 绑定 Claude / GPT 模型 | 模型无关，插件化接入任意 provider |
+| **适用场景** | 个人开发者日常编码 | 构建自定义 Agent 工作流、Agent 产品 |
+| **成熟度** | 生产可用 | Developer preview |
+
+**你可以两者都用**：在 DeepSeek Harness 里接 DeepSeek 模型构建一个代码审查 Agent，同时日常编码继续用 Claude Code。它们不冲突。
+
+---
+
+## 四、快速上手
+
+### 5 分钟跑起来
+
+```bash
+# 前置条件：Node.js 已安装
+npx @deepseek-ai/dsh web
+```
+
+Web UI 默认在 `http://127.0.0.1:3080` 启动。打开浏览器就能看到界面。
+
+### 从源码跑
+
+```bash
+git clone https://github.com/deepseek-ai/deepseek-harness.git
+cd deepseek-harness
+pnpm install
+pnpm run build
+pnpm dsh web
+```
+
+### 配置 Provider
+
+在 Web UI 中配置你的模型 provider（DeepSeek API、OpenAI API 等），然后就可以创建 Agent 并开始使用了。详细配置见官方 [Web UI 指南](https://github.com/deepseek-ai/deepseek-harness/tree/master/docs/user/guide)。
+
+### Python SDK
+
+除了 Web UI 和 CLI，DeepSeek Harness 还提供 Python SDK，方便在 Python 项目中集成。
+
+---
+
+## 五、插件生态与社区
+
+### 插件
+
+DeepSeek Harness 的插件通过 GitHub 的 [`dsh-plugin` topic](https://github.com/topics/dsh-plugin) 来发现。你可以：
+
+- 浏览社区已有的插件
+- 把自己的插件打上 `dsh-plugin` topic 让它被搜索到
+- 参考官方文档 [插件开发指南](https://github.com/deepseek-ai/deepseek-harness/tree/master/docs) 写自己的插件
+
+### 社区
+
+- **Discord**：[DeepSeek Harness Discord](https://discord.gg/Ycq5dCaS4)
+- **国内**：企微群 + 公众号（见官方 README）
+- **GitHub Discussions**：反馈和 bug 报告
+
+---
+
+## 六、适合谁 / 不适合谁
+
+**适合**：
+
+- 想构建自定义 Agent 工作流的开发者（不只是"让 AI 写代码"，而是"让 AI 按你的流程干活"）
+- 对插件化架构感兴趣、想深入理解 Agent 框架设计的工程师
+- 已经在用 DeepSeek 模型、想基于它构建 Agent 产品的团队
+- 愿意接受 developer preview 的不稳定性来换取早期体验
+
+**不适合**：
+
+- 只想找一个开箱即用的 AI 编程工具——直接用 Claude Code、Codex 或 Cursor
+- 对破坏性变更零容忍的生产环境——等 stable release
+- 不想学新框架、只想用现成方案——DeepSeek Harness 的学习曲线比直接用 coding agent 高
+
+---
+
+## 总结
+
+DeepSeek Harness 的价值不在"又一个 agent 工具"，而在"**一种不同的 agent 构建方式**"。
+
+当你不再满足于"让 AI 帮我写这段代码"，而是想"让 AI 按我设计的流程、用我指定的工具、在我定义的边界内持续工作"——这时候你就需要一个 agent harness。DeepSeek Harness 的插件化架构，是目前开源世界里对这个需求最激进的回答之一。
+
+它还在 developer preview，但方向已经很清楚：**不是给你一个 agent，而是给你一个造 agent 的工厂。**
+
+> 延伸阅读：站内 [Harness 品类综述](/zh/guides/meta-harness-2026) 对比了 Omnigent、Conductor、Zed ACP 等同类 harness 产品，可配合阅读。