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terminal assistant that understands your codebase, edits files, runs commands, and automates development workflows.","maintainers":[{"name":"fengshuangshuang","email":"1306503317@qq.com"},{"name":"maowenhao","email":"winter9445@163.com"},{"name":"yuejunqi","email":"1072971875@qq.com"},{"name":"ljx0304","email":"768167276@qq.com"},{"name":"lemon_zoey","email":"lemon_zoey@163.com"}],"readme":"# Kode - 终端 AI 助手\n\n[![npm version](https://badge.fury.io/js/@shareai-lab%2Fkode.svg)](https://www.npmjs.com/package/@shareai-lab/kode)\n[![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)\n\n[English](README.md) | [贡献指南](CONTRIBUTING.md) | [文档](docs/)\n\n## 🎉 重磅消息：我们已切换至 Apache 2.0 开源协议！\n\n**开发者社区的福音来了！** 为了推动 AI 智能体技术的民主化进程，构建充满活力的创新生态，我们激动地宣布：Kode 已正式从 AGPLv3 协议升级为 **Apache 2.0 开源协议**。\n\n### 这对您意味着什么：\n- ✅ **完全自由**：在任何项目中使用 Kode - 无论是个人项目、商业产品还是企业方案\n- ✅ **无障碍创新**：构建专有解决方案，无需开源您的代码\n- ✅ **极简要求**：仅需保留版权声明和许可信息\n- ✅ **共创未来**：与全球开发者一起，加速世界向 AI 驱动生产的转型\n\n让我们携手共建未来！🚀\n\n## 📢 更新日志\n\n**2025-08-29**：我们添加了 Windows 电脑的运行支持！所有的 Windows 用户现在可以使用你电脑上的 Git Bash、Unix 子系统或 WSL（Windows Subsystem for Linux）来运行 Kode。\n\nKode 是一个强大的 AI 助手，运行在你的终端中。它能理解你的代码库、编辑文件、运行命令，并为你处理整个开发工作流。\n\n> **⚠️ 安全提示**：Kode 默认以 YOLO 模式运行（等同于 Claude 的 `--dangerously-skip-permissions` 标志），跳过所有权限检查以获得最大生产力。YOLO 模式仅建议在安全可信的环境中处理非重要项目时使用。如果您正在处理重要文件或使用能力存疑的模型，我们强烈建议使用 `kode --safe` 启用权限检查和手动审批所有操作。\n> \n> **📊 模型性能建议**：为获得最佳体验，建议使用专为自主任务完成设计的新一代强大模型。避免使用 GPT-4o、Gemini 2.5 Pro 等较老的问答型模型，它们主要针对回答问题进行优化，而非持续的独立任务执行。请选择专门训练用于智能体工作流和扩展推理能力的模型。\n\n## 功能特性\n\n- 🤖 **AI 驱动的助手** - 使用先进的 AI 模型理解并响应你的请求\n- 🔄 **多模型协同** - 灵活切换和组合使用多个 AI 模型，发挥各自优势\n- 📝 **代码编辑** - 直接编辑文件，提供智能建议和改进\n- 🔍 **代码库理解** - 分析项目结构和代码关系\n- 🚀 **命令执行** - 实时运行 shell 命令并查看结果\n- 🛠️ **工作流自动化** - 用简单的提示处理复杂的开发任务\n- 🎨 **交互式界面** - 美观的终端界面，支持语法高亮\n- 🔌 **工具系统** - 可扩展的架构，为不同任务提供专门的工具\n- 💾 **上下文管理** - 智能的上下文处理，保持对话连续性\n\n## 安装\n\n```bash\nnpm install -g @shareai-lab/kode\n```\n\n安装后，你可以使用以下任一命令：\n- `kode` - 主命令\n- `kwa` - Kode With Agent（备选）\n- `kd` - 超短别名\n\n## 使用方法\n\n### 交互模式\n启动交互式会话：\n```bash\nkode\n# 或\nkwa\n# 或\nkd\n```\n\n### 非交互模式\n获取快速响应：\n```bash\nkode -p \"解释这个函数\" main.js\n# 或\nkwa -p \"解释这个函数\" main.js\n```\n\n### Docker 使用说明\n \n```bash\n# 克隆仓库\ngit clone https://github.com/shareAI-lab/Kode.git\ncd Kode\n\n# 本地构建镜像\ndocker build --no-cache -t kode .\n\n# 在你的项目目录中运行\ncd your-project\ndocker run -it --rm \\\n  -v $(pwd):/workspace \\\n  -v ~/.kode:/root/.kode \\\n  -v ~/.kode.json:/root/.kode.json \\\n  -w /workspace \\\n  kode\n```\n\n#### Docker 配置详情\n\n该 Docker 配置包含以下内容：\n\n* **卷挂载（Volume Mounts）**：\n\n  * `$(pwd):/workspace` - 挂载当前项目目录\n  * `~/.kode:/root/.kode` - 在运行间保留 kode 配置目录\n  * `~/.kode.json:/root/.kode.json` - 在运行间保留 kode 全局配置文件\n\n* **工作目录**：容器内工作目录设置为 `/workspace`\n\n* **交互模式**：使用 `-it` 标志以交互式终端方式运行\n\n* **清理**：使用 `--rm` 在退出后自动删除容器\n\n**注意**：\nKode 同时使用 `~/.kode` 目录（存放额外数据，如内存文件）和 `~/.kode.json` 文件（全局配置）。\n\n第一次运行 Docker 命令时会构建镜像，之后的运行会使用缓存镜像以加快启动速度。\n\n你可以通过引导流程（onboarding）来设置模型，或使用 `/model` 命令。\n如果在列表中没有你想要的模型，可以在 `/config` 中手动设置。\n只要你有一个 OpenAI 风格的 API 端点，就可以正常使用。\n\n\n### 常用命令\n\n- `/help` - 显示可用命令\n- `/model` - 更改 AI 模型设置\n- `/config` - 打开配置面板\n- `/cost` - 显示 token 使用量和成本\n- `/clear` - 清除对话历史\n- `/init` - 初始化项目上下文\n\n## 多模型智能协同\n\n与 CC 仅支持单一模型不同，Kode 实现了**真正的多模型协同工作**，让你能够充分发挥不同 AI 模型的独特优势。\n\n### 🏗️ 核心技术架构\n\n#### 1. **ModelManager 多模型管理器**\n我们设计了统一的 `ModelManager` 系统，支持：\n- **模型配置文件（Model Profiles）**：每个模型都有独立的配置文件，包含 API 端点、认证信息、上下文窗口大小、成本等参数\n- **模型指针（Model Pointers）**：用户可以在 `/model` 命令中配置不同用途的默认模型：\n  - `main`：主 Agent 的默认模型\n  - `task`：SubAgent 的默认模型\n  - `reasoning`：预留给未来 ThinkTool 使用\n  - `quick`：用于简单 NLP 任务（如安全性识别、生成标题描述等）的快速模型\n- **动态模型切换**：支持运行时切换模型，无需重启会话，保持上下文连续性\n\n#### 2. **TaskTool 智能任务分发工具**\n专门设计的 `TaskTool`（Architect 工具）实现了：\n- **Subagent 机制**：可以启动多个子代理并行处理任务\n- **模型参数传递**：用户可以在请求中指定 SubAgent 使用的模型\n- **默认模型配置**：SubAgent 默认使用 `task` 指针配置的模型\n\n#### 3. **AskExpertModel 专家咨询工具**\n我们专门设计了 `AskExpertModel` 工具：\n- **专家模型调用**：允许在对话中临时调用特定的专家模型解决疑难问题\n- **模型隔离执行**：专家模型的响应独立处理，不影响主对话流程\n- **知识整合**：将专家模型的见解整合到当前任务中\n\n#### 🎯 灵活的模型切换\n- **Tab 键快速切换**：在输入框按 Tab 键即可快速切换当前对话使用的模型\n- **`/model` 命令**：使用 `/model` 命令配置和管理多个模型配置文件，设置不同用途的默认模型\n- **用户控制**：用户可以随时指定使用特定的模型进行任务处理\n\n#### 🔄 智能的工作分配策略\n\n**架构设计阶段**\n- 使用 **o3 模型** 或 **GPT-5 模型** 探讨系统架构，制定犀利明确的技术方案\n- 这些模型在抽象思维和系统设计方面表现卓越\n\n**方案细化阶段**\n- 使用 **gemini 模型** 深入探讨生产环境的设计细节\n- 利用其在实际工程实践中的深厚积累和平衡的推理能力\n\n**代码实现阶段**\n- 使用 **Qwen Coder 模型**、**Kimi k2 模型** 、**GLM-4.5 模型** 或 **Claude Sonnet 4 模型** 进行具体的代码编写\n- 这些模型在代码生成、文件编辑和工程实现方面性能强劲\n- 支持通过 subagent 并行处理多个编码任务\n\n**疑难问题解决**\n- 遇到复杂问题时，可单独咨询 **o3 模型**、**Claude Opus 4.1 模型** 或 **Grok 4 模型** 等专家模型\n- 获得深度的技术见解和创新的解决方案\n\n#### 💡 实际应用场景\n\n```bash\n# 示例 1：架构设计\n\"用 o3 模型帮我设计一个高并发的消息队列系统架构\"\n\n# 示例 2：多模型协作\n\"先用 GPT-5 模型分析这个性能问题的根本原因，然后用 Claude Sonnet 4 模型编写优化代码\"\n\n# 示例 3：并行任务处理\n\"用 Qwen Coder 模型作为 subagent 同时重构这三个模块\"\n\n# 示例 4：专家咨询\n\"这个内存泄漏问题很棘手，单独问问 Claude Opus 4.1 模型有什么解决方案\"\n\n# 示例 5：代码审查\n\"让 Kimi k2 模型审查这个 PR 的代码质量\"\n\n# 示例 6：复杂推理\n\"用 Grok 4 模型帮我推导这个算法的时间复杂度\"\n\n# 示例 7：方案设计\n\"让 GLM-4.5 模型设计微服务拆分方案\"\n```\n\n### 🛠️ 关键实现机制\n\n#### **配置系统（Configuration System）**\n```typescript\n// 支持多模型配置的示例\n{\n  \"modelProfiles\": {\n    \"o3\": { \"provider\": \"openai\", \"model\": \"o3\", \"apiKey\": \"...\" },\n    \"claude4\": { \"provider\": \"anthropic\", \"model\": \"claude-sonnet-4\", \"apiKey\": \"...\" },\n    \"qwen\": { \"provider\": \"alibaba\", \"model\": \"qwen-coder\", \"apiKey\": \"...\" }\n  },\n  \"modelPointers\": {\n    \"main\": \"claude4\",      // 主对话模型\n    \"task\": \"qwen\",         // 任务执行模型\n    \"reasoning\": \"o3\",      // 推理模型\n    \"quick\": \"glm-4.5\"      // 快速响应模型\n  }\n}\n```\n\n#### **成本追踪系统（Cost Tracking）**\n- **使用统计**：`/cost` 命令查看各模型的 token 使用量和花费\n- **多模型成本对比**：实时追踪不同模型的使用成本\n- **历史记录**：保存每个会话的成本数据\n\n#### **上下文管理器（Context Manager）**\n- **上下文继承**：切换模型时保持对话连续性\n- **上下文窗口适配**：根据不同模型的上下文窗口大小自动调整\n- **会话状态保持**：确保多模型协作时的信息一致性\n\n### 🚀 多模型协同的优势\n\n1. **效率最大化**：每个任务都由最适合的模型处理\n2. **成本优化**：简单任务用轻量模型，复杂任务用强大模型\n3. **并行处理**：多个模型可以同时处理不同的子任务\n4. **灵活切换**：根据任务需求随时切换模型，无需重启会话\n5. **取长补短**：结合不同模型的优势，获得最佳的整体效果\n\n### 📊 与官方实现的对比\n\n| 特性 | Kode | 官方 CC |\n|------|------|---------|\n| 支持模型数量 | 无限制，可配置任意模型 | 仅支持单一 Claude 模型 |\n| 模型切换 | ✅ Tab 键快速切换 | ❌ 需要重启会话 |\n| 并行处理 | ✅ 多个 SubAgent 并行工作 | ❌ 单线程处理 |\n| 成本追踪 | ✅ 多模型成本分别统计 | ❌ 单一模型成本 |\n| 任务模型配置 | ✅ 不同用途配置不同默认模型 | ❌ 所有任务用同一模型 |\n| 专家咨询 | ✅ AskExpertModel 工具 | ❌ 不支持 |\n\n这种多模型协同能力让 Kode 成为真正的 **AI 开发工作台**，而不仅仅是一个单一的 AI 助手。\n\n## 开发\n\nKode 使用现代化工具构建，开发需要 [Bun](https://bun.sh)。\n\n### 安装 Bun\n\n```bash\n# macOS/Linux\ncurl -fsSL https://bun.sh/install | bash\n\n# Windows\npowershell -c \"irm bun.sh/install.ps1 | iex\"\n```\n\n### 设置开发环境\n\n```bash\n# 克隆仓库\ngit clone https://github.com/shareAI-lab/kode.git\ncd kode\n\n# 安装依赖\nbun install\n\n# 在开发模式下运行\nbun run dev\n```\n\n### 构建\n\n```bash\nbun run build\n```\n\n### 测试\n\n```bash\n# 运行测试\nbun test\n\n# 测试 CLI\n./cli.js --help\n```\n\n## 贡献\n\n我们欢迎贡献！请查看我们的[贡献指南](CONTRIBUTING.md)了解详情。\n\n## 许可证\n\nApache 2.0 许可证 - 详见 [LICENSE](LICENSE)。\n\n## 支持\n\n- 📚 [文档](docs/)\n- 🐛 [报告问题](https://github.com/shareAI-lab/kode/issues)\n- 💬 [讨论](https://github.com/shareAI-lab/kode/discussions)\n","readmeFilename":"README.zh-CN.md"}