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通用问题解决应用。项目已经从单文件 demo 重构为可维护的 Python 包，支持 CLI、PySide6 桌面 GUI、JSON 配置、运行输出归档、事件记录和基础测试。\n\n默认工作流包含 4 个角色：\n\n- `Problem Analyst`：拆解问题背景、目标、约束、关键矛盾和成功标准\n- `Solution Strategist`：基于分析设计可执行方案\n- `Critical Reviewer`：评审方案并生成完整正式报告\n- `Executive Summarizer`：生成 500 字以内的精简报告\n\n## 目录结构\n\n```text\nsrc/\n  crewai_multiagent_demo/\n    cli.py\n    core/\n      crew_builder.py\n      events.py\n      outputs.py\n      runner.py\n      workflow.py\n    config/\n      loader.py\n      schema.py\n      validation.py\n    domain/\n      agents.py\n      tasks.py\n      run_result.py\n    llm/\n      model_registry.py\n      provider.py\n    gui/\n      pages.py\n      services.py\n      state.py\n      styles.py\n      widgets.py\n    utils/\n      environment.py\n      paths.py\n  main.py\n  gui_app.py\nconfig/\n  agents.json\n  tasks.json\ntests/\n```\n\n`src/main.py` 和 `src/gui_app.py` 是入口；核心业务逻辑在 `crewai_multiagent_demo` 包内。架构分层、扩展点和验证命令见 `docs/ARCHITECTURE.md`。\n\n## 本地环境\n\n为了不污染全局环境，建议把虚拟环境、pip 缓存、CrewAI 缓存和输出都放在项目目录内。\n\n```powershell\npython -m venv .venv\n.\\.venv\\Scripts\\python.exe -m pip install --upgrade pip\n$env:PIP_CACHE_DIR=\"$PWD\\.cache\\pip\"\n.\\.venv\\Scripts\\python.exe -m pip install -r requirements.txt\n```\n\n复制环境变量模板：\n\n```powershell\nCopy-Item .env.template .env\n```\n\n编辑 `.env`：\n\n```text\nDEEPSEEK_API_KEY=sk-...\nDEEPSEEK_BASE_URL=https://api.deepseek.com/v1\nMODEL_VARIANT=flash\nCREWAI_STORAGE_DIR=.cache/crewai\nCREWAI_DISABLE_TELEMETRY=true\nCREWAI_TRACING_ENABLED=false\nCREWAI_TESTING=true\nOTEL_SDK_DISABLED=true\n```\n\n不要提交 `.env`、`.venv`、`.cache` 或 `outputs`。\n\n## CLI\n\n校验配置，不调用模型：\n\n```powershell\n.\\.venv\\Scripts\\python.exe .\\src\\main.py validate\n```\n\n列出模型档位：\n\n```powershell\n.\\.venv\\Scripts\\python.exe .\\src\\main.py list-models\n```\n\n列出当前启用的 agents/tasks：\n\n```powershell\n.\\.venv\\Scripts\\python.exe .\\src\\main.py list-config\n```\n\n运行默认主题：\n\n```powershell\n.\\.venv\\Scripts\\python.exe .\\src\\main.py run\n```\n\n运行自定义主题和模型档位：\n\n```powershell\n.\\.venv\\Scripts\\python.exe .\\src\\main.py run --model flash \"如何降低一个小团队的软件交付延期风险？\"\n.\\.venv\\Scripts\\python.exe .\\src\\main.py run --model pro \"如何设计一个企业内部 AI Agent 平台？\"\n```\n\n可选参数：\n\n```powershell\n.\\.venv\\Scripts\\python.exe .\\src\\main.py run --config-dir .\\config --output-dir .\\outputs --model flash \"你的主题\"\n```\n\n旧用法仍兼容：\n\n```powershell\n.\\.venv\\Scripts\\python.exe .\\src\\main.py --model flash \"你的主题\"\n```\n\n也可以使用 PowerShell 脚本：\n\n```powershell\n.\\run.ps1 -Model flash \"你的主题\"\n```\n\n## 桌面 GUI\n\n桌面 GUI 的用户入口只保留 Windows 可执行文件。首次使用或代码更新后，先在项目目录内构建：\n\n```powershell\n.\\build-gui.ps1\n```\n\n构建完成后启动：\n\n```powershell\n.\\MultiagentStudio.exe\n```\n\n桌面 GUI 支持运行工作流、查看事件和输出、编辑 `agents.json` / `tasks.json`、校验配置、查看历史输出。GUI 层只负责交互展示，实际运行调用 `core.runner.run_workflow`。\n\n`build-gui.ps1` 使用项目内 `.venv` 和 `.cache` 完成打包，避免污染全局 Python 环境。构建脚本会把 `MultiagentStudio.exe` 和运行依赖目录 `_internal/` 放到项目根目录；用户只需要启动 `MultiagentStudio.exe`。\n\n## 配置 Agent 和 Task\n\n默认配置文件就是示例配置：\n\n- `config/agents.json`\n- `config/tasks.json`\n\nAgent 字段：\n\n```json\n{\n  \"id\": \"problem_analyst\",\n  \"role\": \"Problem Analyst\",\n  \"goal\": \"把模糊问题拆成清晰结构。\",\n  \"backstory\": \"角色背景。\",\n  \"enabled\": true\n}\n```\n\nTask 字段：\n\n```json\n{\n  \"id\": \"analysis\",\n  \"name\": \"问题分析\",\n  \"description\": \"围绕主题《{topic}》做分析。\",\n  \"expected_output\": \"结构化中文问题分析。\",\n  \"agent_id\": \"problem_analyst\",\n  \"context_task_ids\": [],\n  \"enabled\": true\n}\n```\n\n添加新 task 时：\n\n1. 在 `agents.json` 中确认存在可用的 `agent_id`。\n2. 在 `tasks.json` 中新增 task。\n3. 用 `context_task_ids` 声明依赖的上游 task。\n4. 运行 `validate` 检查配置。\n\n配置校验会检查重复 id、空字段、不存在或未启用的 agent、缺失 task、禁用 task 被依赖、循环依赖等问题。\n\n## 输出文件\n\n每次运行都会在 `outputs/` 下创建时间戳目录，例如：\n\n```text\noutputs/20260515_142030/\n```\n\n包含：\n\n- `full_report.md`\n- `summary_report.md`\n- `run_metadata.md`\n- `events.json`\n- `tasks/` 下每个 task 的单独输出\n\n`events.json` 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