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AI execution system — controllable, observable, interruptible","maintainers":[{"name":"knox_huang","email":"knox.h@qq.com"}],"readme":"# TaskFlow\n\nTask-driven AI execution system — **controllable, observable, interruptible**.\n\n## 安装\n\n```bash\n# 全局安装 — 命令名为 knox\nnpm install -g @aiknox/taskflow\n\n# 或从源码\nnpm install && npm run build\n```\n\n## 快速开始\n\n```bash\nknox setup          # 配置 API key 和模型\nknox agents         # 查看可用 Agent\nknox run \"Read package.json\"   # 执行任务\n```\n\n---\n\nTaskFlow 是一个由 LLM 驱动的编码代理系统。你给它一个自然语言任务（如\"重构 auth 模块\"），它自主通过工具调用（读文件、写文件、搜索、执行命令）完成，整个过程实时可见。\n\n```\nknox run \"Add a CLI flag --verbose to the logger\"\n```\n\n```\nAgent      default\nModel      claude-sonnet-4-20250514\nPricing    $3/1M in  $15/1M out\nTask       \"Add a CLI flag --verbose to the logger\"\n──────────────────────────────────────────────────\n[0.0s] Agent \"default\" started\n[0.3s]   think Let me first find the logger implementation...\n[0.5s]   search  Logger\n[0.6s]     ok    213ms\n[0.8s]   read    src/logger.ts\n[0.8s]     ok    5ms\n[1.2s]   write   src/logger.ts\n[1.3s]     ok    18ms\n\n✓ Done · 2 turn(s) · in 452 out 128 · $0.003276 (Anthropic) · 1.3s\n```\n\n---\n\n## Quick Start\n\n### 1. 安装\n\n```bash\ngit clone <repo>\ncd TaskFlow\nnpm install\nnpm run build\n```\n\n### 2. 配置\n\n```bash\n# 交互式向导 — 选择 provider、输入 API key、挑选模型\nnode packages/cli/dist/cli.js setup\n```\n\n或手动创建 `.env`：\n\n```env\nAI_PROVIDER=openai\nAI_BASE_URL=https://api.example.com\nAI_API_KEY=sk-xxx\nAI_MODEL=deepseek-v4-flash\n```\n\n### 3. 运行\n\n```bash\n# 查看帮助\nnode packages/cli/dist/cli.js --help\n\n# 执行任务\nnode packages/cli/dist/cli.js run \"Read package.json and summarize its dependencies\"\n\n# 查看历史\nnode packages/cli/dist/cli.js log\n```\n\n### 4. 快捷方式\n\n```bash\nnpm run cli -- run \"your task\"\nnpm run cli -- setup\n```\n\n---\n\n## CLI 命令参考\n\n所有命令通过 `knox` 调用（`npm install -g @aiknox/taskflow` 后可用）。\n\n| 命令 | 用途 |\n|------|------|\n| `knox run <task>` | 执行任务 — Agent 模式，实时流式输出 |\n| `knox` | 交互式会话 — 连续接受任务，保留上下文 |\n| `knox log [taskId]` | 列出历史任务 / 查看事件时间线 |\n| `knox agents` | 列出所有 Agent 配置 |\n| `knox models` | 列出 API 端点可用的模型（标记当前默认模型） |\n| `knox update` | 从 npm 更新 Knox 到最新版本 |\n\n### 选项\n\n| 选项 | 说明 |\n|------|------|\n| `--workspace, -w <path>` | 工作目录（默认: cwd） |\n| `--model, -m <name>` | 覆盖默认模型（默认: .env 中的 AI_MODEL） |\n| `--agent, -a <name>` | Agent 配置名（默认: default） |\n| `--quiet, -q` | 最小输出模式 |\n\n---\n\n## Agent 配置\n\nTaskFlow 使用 YAML 配置文件定义 Agent 行为。默认配置在 `.taskflow/agents/default.yaml`：\n\n```yaml\nname: default\ndescription: General-purpose coding agent\nsystemPromptFile: default.system.md\ntools:\n  - read\n  - write\n  - bash\n  - search\nmaxTurns: 50\nmaxTokens: 16384\ntimeout: 600000      # ms\n```\n\n可以创建多个配置（如 `coder.yaml`、`reviewer.yaml`），通过 `--agent` 切换：\n\n```bash\nknox run --agent coder \"Build the API endpoint\"\nknox agents   # 列出所有可用配置\n```\n\n---\n\n## 工具\n\n| 工具 | 说明 |\n|------|------|\n| `read <path>` | 读取文件或列出目录 |\n| `write <path> <content>` | 写入文件（自动创建中间目录） |\n| `bash <command>` | 在工作目录执行 shell 命令 |\n| `search <pattern> [path]` | grep 搜索文件内容 |\n\n---\n\n## 架构\n\n```\npackages/\n├── schema/      — 核心类型 + 定价数据\n├── scheduler/   — LLM 适配器 (Anthropic/OpenAI/流式) + ToolRunner + 存储\n├── agent/       — Agent 类 + 工具系统 + 配置加载 ← 执行核心\n└── cli/         — CLI 界面\n```\n\n**执行流程**:\n```\n用户输入 → CLI → Agent.run(task)\n  → LLM 流式循环:\n    → 实时 emit thinking/tool_call/tool_result\n  → AgentResult{output, tokenUsage, turns}\n```\n\n---\n\n## 开发\n\n```bash\n# 构建全部\nnpm run build\n\n# 测试全部\nnpm test\n\n# 单包测试\nnpm -w @aiknox/schema run test\nnpm -w @aiknox/scheduler run test\nnpm -w @aiknox/agent run test\nnpm -w @aiknox/cli run test\n\n# 类型检查\nnpm run typecheck\n```\n\n### 发布与更新\n\n维护者一条命令发版、用户 `knox update` 升级，详见 **[RELEASE.md](./RELEASE.md)**。\n\n```bash\nnpm run release    # clean → build → test → 升版本 → 发布 npm\nknox update        # 用户更新\n```\n\n### 添加新模型定价\n\n编辑 `packages/schema/src/pricing.json`，按格式添加条目即可。\n\n---\n\n## License\n\nMIT","readmeFilename":"README.md"}