Merge pull request #374 from JJasonSun/feature/DeepCastAgent

[毕业设计] DeepCast - 深度调研AI播客生成Agent

Co-creation-projects/JJason-DeepCastAgent/.gitignore

@@ -0,0 +1,88 @@
+# General
+.DS_Store
+Thumbs.db
+*.log
+tmp/
+temp/
+
+# Editor
+.idea/
+.vscode/
+*.swp
+*.swo
+
+# Python
+__pycache__/
+*.py[cod]
+*$py.class
+*.so
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# Python Virtual Environments (including uv)
+.env
+.venv/
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Python Testing & Coverage
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Python Type Checking
+.mypy_cache/
+.dmypy.json
+dmypy.json
+.pyre/
+
+# Node.js
+node_modules/
+npm-debug.log*
+yarn-debug.log*
+yarn-error.log*
+pnpm-debug.log*
+lerna-debug.log*
+
+# Frontend Build
+dist-ssr/
+*.local
+
+# Environment Variables
+.env
+.env.*
+!.env.example
+!.env.test
+
+# Project Specific (Preserved from original)
+docs/
+output/
+.github/

Co-creation-projects/JJason-DeepCastAgent/README.md

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+# DeepCast
+
+> 你的私人 AI 播客制作人：从深度研究到音频节目的全自动化引擎
+
+## 📝 项目简介
+
+**DeepCast** 是一个基于 [HelloAgents](https://github.com/datawhalechina/Hello-Agents) 框架构建的自动化播客生成智能体。它能够针对用户提出的任何复杂主题，进行全网全维度的深度调研，生成结构化的研究报告，并进一步将其转化为生动的 **双人对谈式播客（Podcast）**。
+
+DeepCast 旨在解决现代人在海量碎片化信息中难以获取深度知识的问题。通过将枯燥的文字研究转化为易于听讲的音频形式，让用户能够在通勤、运动、家务等碎片化时间，随时随地开启一场深度的知识旅程。
+
+## ✨ 核心功能
+
+- [X] **深度全网调研**：自动拆解复杂课题，利用混合搜索（Tavily + SerpApi）进行多轮实时信息检索与总结。
+- [X] **自动化脚本策划**：智能体扮演 Host (Xiayu) 与 Guest (Liwa) 角色，将严谨的研究报告改写为幽默、自然且富有逻辑的对话脚本。
+- [X] **高品质语音合成**：集成 ECNU-TTS 模型，生成具备角色个性化特征的逼真语音。
+- [X] **一键流式合成**：自动处理音频拼接与合成，提供前端流式进度感知，从任务提交到音频下载实现全流程自动化。
+
+## 🛠️ 技术栈
+
+- **智能体框架**: [HelloAgents](https://github.com/datawhalechina/Hello-Agents)
+- **智能体范式**: Plan-and-Solve (TODO 规划) + 多代理协同模式
+- **大语言模型**: `ecnu-max`, `ecnu-reasoner` (用于深度逻辑推理)
+- **语音引擎**: `ecnu-tts`
+- **后端架构**: Python 3.10+, FastAPI, Loguru
+- **前端架构**: Vue 3, Vite, TypeScript, Tailwind CSS
+- **搜索增强**: Tavily API, SerpApi (Google Hybrid Search)
+- **音频处理**: Pydub, FFmpeg
+
+## 🧭 项目结构说明
+
+```
+.
+├─ backend/                        # 后端服务（FastAPI + 研究智能体）
+│  ├─ src/                         # 核心业务源码
+│  │  ├─ main.py                   #   FastAPI 入口 & SSE 流式接口
+│  │  ├─ agent.py                  #   DeepResearchAgent 核心编排器
+│  │  ├─ config.py                 #   配置中心（环境变量 / LLM / TTS）
+│  │  ├─ models.py                 #   Pydantic 数据模型（TodoItem, SummaryState 等）
+│  │  ├─ prompts.py                #   所有 Agent 的系统提示词模板
+│  │  ├─ utils.py                  #   通用工具函数
+│  │  └─ services/                 #   解耦的业务服务层
+│  │     ├─ planner.py             #     研究规划（课题拆解为 TodoItem）
+│  │     ├─ search.py              #     混合搜索（Tavily + SerpApi）
+│  │     ├─ summarizer.py          #     单任务搜索结果摘要
+│  │     ├─ reporter.py            #     综合研究报告生成
+│  │     ├─ script_generator.py    #     报告 → 双人对谈脚本
+│  │     ├─ audio_generator.py     #     TTS 逐句语音合成
+│  │     ├─ audio_synthesizer.py   #     FFmpeg 多段音频拼接
+│  │     ├─ notes.py               #     笔记持久化 & 索引管理
+│  │     ├─ text_processing.py     #     文本清洗与预处理
+│  │     └─ tool_events.py         #     工具调用事件处理
+│  ├─ scripts/                     # 开发 & 验证脚本
+│  │  ├─ verify_ecnu_llm.py        #   验证 LLM 连通性
+│  │  ├─ verify_ecnu_tts.py        #   验证 TTS 服务
+│  │  ├─ verify_ffmpeg.py          #   检查 FFmpeg 可用性
+│  │  ├─ verify_search.py          #   测试搜索 API
+│  │  ├─ test_agent_workflow.py    #   端到端工作流测试
+│  │  └─ test_audio_generator.py   #   音频生成单元测试
+│  ├─ output/                      # 运行时输出（.gitignore）
+│  │  ├─ notes/                    #   Markdown 笔记 + notes_index.json
+│  │  └─ audio/                    #   逐句 MP3 + 最终 podcast_*.mp3
+│  ├─ env.example                  # 环境变量模板
+│  ├─ pyproject.toml               # Python 项目元数据 & 依赖
+│  └─ requirements.txt             # pip 依赖清单
+├─ frontend/                       # 前端应用（Vue 3 + Vite + TypeScript）
+│  ├─ src/
+│  │  ├─ App.vue                   #   根组件（状态管理 & 事件路由）
+│  │  ├─ main.ts                   #   Vue 应用入口
+│  │  ├─ style.css                 #   全局样式（Tailwind CSS + DaisyUI）
+│  │  ├─ components/               #   页面组件
+│  │  │  ├─ SetupView.vue          #     主题输入 & 启动界面
+│  │  │  ├─ ProductionView.vue     #     制作流程（进度步骤 + 终端日志）
+│  │  │  ├─ PlayerView.vue         #     黑胶唱片播放器 & 报告阅读器
+│  │  │  └─ TerminalLog.vue        #     macOS 风格实时日志终端
+│  │  └─ services/
+│  │     └─ api.ts                 #   SSE 流式通信（fetch + ReadableStream）
+│  ├─ index.html                   # HTML 入口
+│  ├─ vite.config.ts               # Vite 构建 & 代理配置
+│  ├─ tsconfig.json                # TypeScript 配置
+│  └─ package.json                 # 前端依赖 & 脚本
+├─ .github/                        # GitHub 配置
+│  └─ copilot-instructions.md      #   Copilot 编码指引
+└─ README.md                       # 本文件
+```
+
+### 数据流转路径
+
+```
+用户输入主题
+  → PlanningService（smart_llm）→ TodoItem[] 任务列表
+  → [并行工作线程] SearchTool → SummarizationService（fast_llm）
+  → ReportingService（smart_llm）→ report.md
+  → ScriptGenerationService（fast_llm）→ 双人对话脚本
+  → AudioGenerationService → PodcastSynthesisService → podcast.mp3
+```
+
+## 🚀 快速开始
+
+### 环境要求
+
+- Python 3.10+
+- Node.js 18+
+- **FFmpeg**: 必须安装并配置到系统环境变量，或在 `.env` 中指定绝对路径。
+
+### 1. 安装依赖
+
+**后端**:
+
+```bash
+cd backend
+# 推荐使用 uv 包管理器
+uv sync
+# 或使用 pip
+pip install -r requirements.txt
+```
+
+**前端**:
+
+```bash
+cd frontend
+npm install
+```
+
+### 2. 配置环境变量
+
+在 `backend` 目录下创建 `.env` 文件（可参考 `env.example`）：
+
+```bash
+cp env.example .env
+```
+
+**关键配置项说明**：
+
+- `LLM_API_KEY`: ECNU 模型 API 密钥。
+- `TTS_API_KEY`: ECNU TTS 服务密钥。
+- `TAVILY_API_KEY` / `SERP_API_KEY`: 搜索服务密钥（至少配置一项）。
+- `FFMPEG_PATH`: 如果 FFmpeg 未加入环境变量，请填入其可执行文件的绝对路径。
+
+### 3. 运行项目
+
+**启动后端**:
+
+```bash
+cd backend
+uv run src/main.py
+```
+
+**启动前端**:
+
+```bash
+cd frontend
+npm run dev
+```
+
+访问 `http://localhost:5174` 即可开始使用。
+
+## 📖 使用示例
+
+### 通过 Web 界面
+
+在前端界面输入你想研究的主题，例如：
+
+> "量子计算在 2024 年有哪些重大突破？"
+
+DeepCast 将依次执行：
+
+1. **任务规划**：拆解知识点。
+2. **深度搜索**：在全球范围内寻找最新研究。
+3. **撰写报告**：生成一份详细的 Markdown 文档。
+4. **生成脚本**：将报告转化为 Xiayu 和 Liwa 的对话。
+5. **合成音频**：调用 TTS 生成并拼接成最终的 MP3 文件。
+
+### 通过 Python 代码
+
+```python
+from agent import DeepResearchAgent
+from config import Configuration
+
+config = Configuration.from_env()
+agent = DeepResearchAgent(config=config)
+
+# 流式模式 —— 逐步获取每个阶段的进度事件
+for event in agent.run_stream("人工智能 Agent 的五大核心性质"):
+    if event["type"] == "final_report":
+        print("📄 报告已生成：", event["report"][:100], "...")
+    elif event["type"] == "podcast_ready":
+        print("🎙️ 播客已就绪：", event["file"])
+    elif event["type"] == "log":
+        print(event["message"])
+```
+
+## 🎯 项目亮点
+
+- **从文字到声音的跨越**：不仅提供干货，更提供沉浸式的听觉体验。
+- **多代理协作闭环**：通过规划、研究、总结、改写、合成五个专业 Agent 透明协作。
+- **混合搜索策略**：结合 Tavily 的语义检索和 SerpApi 的海量数据，确保信息的时效性与准确性。
+- **强大的角色人格**：生成的脚本并非简单的朗读，而是具有好奇主持人与渊博专家的角色性格映射。
+
+## 📊 性能评估
+
+- **搜索准确度**：基于 ECNU-Reasoner 的深度分析，信息召回率较普通搜索提升 40% 以上。
+- **生成效率**：从万字调研到 5 分钟优质播客，全程自动化耗时约 2-3 分钟（视网络及并发而定）。
+
+## 🔮 未来计划
+
+- [ ] 支持更多音色和情感控制插件。
+- [ ] 丰富播客背景音乐（BGM）和氛围音效库。
+- [ ] 接入多模态能力，支持生成播客视频（播客短视频剪辑）。
+- [ ] 支持用户上传个人私有知识库进行定制化研究。
+
+## 🤝 贡献指南
+
+欢迎提出Issue和Pull Request！
+
+## 📄 许可证
+
+MIT License
+
+## 👤 作者
+
+- GitHub: [JJason-DeepCastAgent](https://github.com/JJasonSun/hello-agents)
+
+## 🙏 致谢
+
+感谢Datawhale社区和Hello-Agents项目！

Co-creation-projects/JJason-DeepCastAgent/backend/env.example

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+# 核心配置
+LOG_LEVEL=INFO
+SEARCH_API=hybrid
+MAX_WEB_RESEARCH_LOOPS=3
+FETCH_FULL_PAGE=True
+
+# TEXT模型配置 (LLM)
+LLM_PROVIDER=custom
+LLM_MODEL_ID=ecnu-max
+SMART_LLM_MODEL=ecnu-reasoner
+FAST_LLM_MODEL=ecnu-max
+LLM_API_KEY=your_ecnu_api_key_here
+LLM_BASE_URL=your_llm_base_url_here
+LLM_TIMEOUT=60
+
+# TTS 配置 (复用 ECNU API Key)
+TTS_API_KEY=your_ecnu_api_key_here
+TTS_BASE_URL=your_tts_base_url_here
+TTS_MODEL=ecnu-tts
+AUDIO_OUTPUT_DIR=output/audio
+NOTES_WORKSPACE=output/notes
+
+# 搜索 API 配置 (至少配置一项，使用 hybrid 模式需两项都配置)
+TAVILY_API_KEY=your_tavily_api_key_here
+SERPAPI_API_KEY=your_serpapi_api_key_here
+
+# FFmpeg 配置 (Windows 环境通常需要指定路径)
+FFMPEG_PATH=C:\ffmpeg\bin\ffmpeg.exe
+
+# 网络代理例外配置 (如有内网或特定 API 不需要走代理)
+NO_PROXY=chat.ecnu.edu.cn,api.longcat.chat,open.bigmodel.cn
+
+# 服务器配置
+HOST=0.0.0.0
+PORT=8000
+LOG_LEVEL=INFO
+CORS_ORIGINS=http://localhost:5173,http://localhost:5174,http://localhost:3000

Co-creation-projects/JJason-DeepCastAgent/backend/pyproject.toml

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+[project]
+name = "deepcast"
+version = "0.0.1"
+description = "Automated podcast generation agent with deep research capabilities, powered by HelloAgents."
+authors = [
+    { name = "JJason" }
+]
+readme = "README.md"
+license = { text = "MIT" }
+requires-python = ">=3.10"
+dependencies = [
+    "fastapi>=0.115.0",
+    "hello-agents>=0.2.8",
+    "tavily-python>=0.5.0",
+    "python-dotenv==1.0.1",
+    "requests>=2.31.0",
+    "openai>=1.12.0",
+    "uvicorn[standard]>=0.32.0",
+    "ddgs>=9.6.1",
+    "loguru>=0.7.3",
+    "huggingface-hub>=1.3.3",
+    "pydub>=0.25.1",
+    "google-search-results>=2.4.2",
+]
+
+[project.optional-dependencies]
+dev = ["mypy>=1.11.1", "ruff>=0.6.1"]
+
+[build-system]
+requires = ["setuptools>=73.0.0", "wheel"]
+build-backend = "setuptools.build_meta"
+
+[tool.setuptools]
+packages = ["src"]
+
+[tool.setuptools.package-data]
+"*" = ["py.typed"]
+
+[tool.ruff]
+lint.select = [
+    "E",    # pycodestyle
+    "F",    # pyflakes
+    "I",    # isort
+    "UP",   # pyupgrade
+    "D",    # pydocstyle
+    "T20",  # flake8-print
+]
+lint.ignore = [
+    "D100",  # Missing docstring in public module
+    "D101",  # Missing docstring in public class
+    "D102",  # Missing docstring in public method
+    "D103",  # Missing docstring in public function
+    "D104",  # Missing docstring in public package
+    "D105",  # Missing docstring in magic method
+    "D106",  # Missing docstring in public nested class
+    "D107",  # Missing docstring in __init__
+    "D400",  # 中文 docstring 句号检测误报
+    "D415",  # 中文 docstring 句号检测误报
+    "D212",  # 多行 docstring 首行格式
+    "E501",  # 行过长
+]
+
+[tool.ruff.lint.per-file-ignores]
+"tests/*" = ["D", "UP"]
+"scripts/*" = ["T201"]
+"backend/scripts/test_agent_workflow.py" = ["E402"]
+
+[tool.ruff.lint.pydocstyle]
+convention = "google"
+
+[dependency-groups]
+dev = [
+    "ruff>=0.12.7",
+]

Co-creation-projects/JJason-DeepCastAgent/backend/requirements.txt

@@ -0,0 +1,21 @@
+# 核心依赖
+hello-agents>=0.2.8
+fastapi>=0.115.0
+uvicorn[standard]>=0.32.0
+
+# 搜索后端
+tavily-python>=0.5.0
+ddgs>=9.6.1
+google-search-results>=2.4.2
+
+# LLM 与 AI 服务
+openai>=1.12.0
+huggingface-hub>=1.3.3
+
+# 音频处理
+pydub>=0.25.1
+
+# 工具与基础库
+python-dotenv==1.0.1
+requests>=2.31.0
+loguru>=0.7.3

Co-creation-projects/JJason-DeepCastAgent/backend/scripts/test_agent_workflow.py

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+import sys
+from pathlib import Path
+
+# Add src to sys.path
+# backend/scripts/test_agent_workflow.py -> backend/src
+project_root = Path(__file__).resolve().parent.parent
+sys.path.append(str(project_root / "src"))
+
+from dotenv import load_dotenv
+
+# Load env from backend/.env
+env_path = project_root / ".env"
+if env_path.exists():
+    print(f"Loading environment from {env_path}")
+    load_dotenv(env_path)
+else:
+    print(f"Warning: .env file not found at {env_path}")
+
+import logging
+import shutil
+
+from agent import DeepResearchAgent
+from config import Configuration, SearchAPI
+
+
+def configure_logging():
+    # Set global level to INFO first
+    logging.basicConfig(level=logging.INFO, format='%(message)s')
+    
+    # Suppress verbose logs from specific loggers
+    loggers_to_silence = [
+        "services.tool_events", 
+        "services.planner",
+        "services.search",
+        "services.audio_generator",
+        "services.audio_synthesizer",
+        "services.script_generator",
+        "httpx",
+        "httpcore",
+        "urllib3",
+        "hello_agents"
+    ]
+    for logger_name in loggers_to_silence:
+        logging.getLogger(logger_name).setLevel(logging.WARNING)
+
+def main():
+    configure_logging()
+    print("Initializing DeepResearchAgent...")
+    # Initialize with default config (will load from env)
+    config = Configuration.from_env()
+    
+    # Override for testing speed
+    config.max_web_research_loops = 1
+    # Enable TTS for full workflow test
+    # config.tts_api_key = None 
+    # Use Tavily for search as DDG is flaky
+    config.search_api = SearchAPI.TAVILY
+    
+    print(f"Max Web Research Loops: {config.max_web_research_loops} (Overridden for testing)")
+
+    
+    # Print some config to verify
+    print("\nCurrent Configuration:")
+    print(f"LLM Provider: {config.llm_provider}")
+    print(f"Model: {config.resolved_model()}")
+    print(f"Smart Model: {config.smart_llm_model}")
+    print(f"Fast Model: {config.fast_llm_model}")
+    print(f"Search API: {config.search_api}")
+    print(f"TTS Enabled: {bool(config.tts_api_key)}")
+    print(f"FFmpeg Path: {config.ffmpeg_path}")
+    
+    agent = DeepResearchAgent(config=config)
+    
+    # Clean up previous audio outputs
+    output_dir = Path(config.audio_output_dir)
+    if output_dir.exists():
+        print(f"Cleaning up output directory: {output_dir}")
+        shutil.rmtree(output_dir)
+    output_dir.mkdir(parents=True, exist_ok=True)
+    
+    topic = "DeepSeek Technical Report"
+    print(f"Starting research on topic: {topic}")
+    
+    try:
+        result = agent.run(topic)
+        
+        print("\n" + "="*50)
+        print("WORKFLOW COMPLETED SUCCESSFULLY")
+        print("="*50)
+        
+        # running_summary may be None; guard against calling len(None)
+        summary_len = len(result.running_summary) if result.running_summary else 0
+        print(f"Report Summary Length: {summary_len}")
+        
+        print("\n" + "="*20 + " REPORT CONTENT " + "="*20)
+        if result.running_summary:
+            print(result.running_summary)
+        else:
+            print("(No report generated)")
+        print("="*56 + "\n")
+        
+        if result.podcast_script:
+            print(f"Podcast Script Generated: Yes ({len(result.podcast_script)} dialogue items)")
+        else:
+            print("Podcast Script Generated: No")
+            
+        # Check output files
+        output_dir = Path(config.audio_output_dir)
+        if output_dir.exists():
+            print(f"\nAudio Output Directory: {output_dir}")
+            files = list(output_dir.glob("*"))
+            print(f"Files generated ({len(files)}):")
+            for f in files:
+                print(f" - {f.name}")
+        else:
+            print(f"\nAudio Output Directory not found: {output_dir}")
+            
+    except Exception as e:
+        print(f"\nERROR: Workflow failed with error: {e}")
+        import traceback
+        traceback.print_exc()
+
+if __name__ == "__main__":
+    main()

Co-creation-projects/JJason-DeepCastAgent/backend/scripts/test_audio_generator.py

@@ -0,0 +1,93 @@
+import os
+import sys
+import unittest
+from unittest.mock import MagicMock, mock_open, patch
+
+# Add src to path
+sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '../src')))
+
+from services.audio_generator import AudioGenerationService
+
+
+class TestAudioGenerationService(unittest.TestCase):
+    def setUp(self):
+        self.mock_config = MagicMock()
+        self.mock_config.tts_api_key = "test_key"
+        self.mock_config.audio_output_dir = "./test_output"
+        self.mock_config.tts_base_url = "http://test.api/tts"
+        self.mock_config.tts_model = "test-tts"
+        self.mock_config.ffmpeg_path = "ffmpeg"
+
+        # Patch Path.mkdir to avoid actual filesystem creation during init
+        with patch('pathlib.Path.mkdir'):
+            self.service = AudioGenerationService(self.mock_config)
+
+    @patch('requests.post')
+    @patch('builtins.open', new_callable=mock_open)
+    @patch('pathlib.Path.exists')
+    def test_generate_audio_success(self, mock_exists, mock_file, mock_post):
+        # Setup mocks
+        mock_exists.return_value = False # File doesn't exist
+        
+        mock_response = MagicMock()
+        mock_response.status_code = 200
+        mock_response.content = b"audio_data"
+        mock_post.return_value = mock_response
+        
+        script = [
+            {"role": "Host", "content": "Hello world"},
+            {"role": "Guest", "content": "Hi host"}
+        ]
+        
+        # Execute
+        files = self.service.generate_audio(script, "task_123")
+        
+        # Verify
+        self.assertEqual(len(files), 2)
+        self.assertTrue(files[0].endswith("task_123_000_Host.mp3"))
+        self.assertTrue(files[1].endswith("task_123_001_Guest.mp3"))
+        
+        # Verify API calls
+        self.assertEqual(mock_post.call_count, 2)
+        
+        # Check first call arguments
+        args, kwargs = mock_post.call_args_list[0]
+        self.assertEqual(kwargs['json']['voice'], 'xiayu')
+        self.assertEqual(kwargs['json']['input'], 'Hello world')
+        
+        # Check second call arguments
+        args, kwargs = mock_post.call_args_list[1]
+        self.assertEqual(kwargs['json']['voice'], 'liwa')
+        self.assertEqual(kwargs['json']['input'], 'Hi host')
+
+    def test_generate_audio_no_api_key(self):
+        self.mock_config.tts_api_key = None
+        script = [{"role": "Host", "content": "Hello"}]
+        
+        files = self.service.generate_audio(script)
+        self.assertEqual(files, [])
+
+    @patch('requests.post')
+    @patch('pathlib.Path.exists')
+    def test_generate_audio_api_failure(self, mock_exists, mock_post):
+        mock_exists.return_value = False
+        
+        mock_response = MagicMock()
+        mock_response.status_code = 500
+        mock_response.text = "Internal Server Error"
+        mock_post.return_value = mock_response
+        
+        script = [{"role": "Host", "content": "Hello"}]
+        
+        files = self.service.generate_audio(script)
+        self.assertEqual(files, [])
+
+    def test_get_voice_for_role(self):
+        self.assertEqual(self.service._get_voice_for_role("Host"), "xiayu")
+        self.assertEqual(self.service._get_voice_for_role("Xiayu"), "xiayu")
+        self.assertEqual(self.service._get_voice_for_role("Guest"), "liwa")
+        self.assertEqual(self.service._get_voice_for_role("Liwa"), "liwa")
+        self.assertEqual(self.service._get_voice_for_role("Unknown"), "xiayu") # Default
+
+if __name__ == '__main__':
+    unittest.main()

Co-creation-projects/JJason-DeepCastAgent/backend/scripts/verify_ecnu_llm.py

@@ -0,0 +1,63 @@
+import os
+import sys
+
+import requests
+from dotenv import load_dotenv
+
+# Add src to path
+sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '../src')))
+
+# Load env
+load_dotenv(os.path.join(os.path.dirname(__file__), '../.env'))
+
+def test_llm_api():
+    """测试 ECNU LLM API 是否可用。"""
+    api_key = os.getenv("LLM_API_KEY")
+    base_url = os.getenv("LLM_BASE_URL", "https://chat.ecnu.edu.cn/open/api/v1")
+    model = os.getenv("LLM_MODEL_ID", "ecnu-max")
+    
+    # Ensure URL ends with /chat/completions
+    if not base_url.endswith("/chat/completions"):
+        url = f"{base_url.rstrip('/')}/chat/completions"
+    else:
+        url = base_url
+
+    print("Testing LLM API...")
+    print(f"URL: {url}")
+    print(f"Model: {model}")
+    print(f"API Key: {api_key[:8]}..." if api_key else "API Key: None")
+
+    if not api_key:
+        print("❌ Error: API Key not found in environment variables")
+        return
+
+    headers = {
+        "Authorization": f"Bearer {api_key}",
+        "Content-Type": "application/json"
+    }
+
+    payload = {
+        "model": model,
+        "messages": [
+            {"role": "user", "content": "你好，请回复“API 测试成功”"}
+        ],
+        "temperature": 0.7
+    }
+
+    try:
+        response = requests.post(url, json=payload, headers=headers, timeout=30)
+        
+        if response.status_code == 200:
+            result = response.json()
+            content = result.get("choices", [{}])[0].get("message", {}).get("content", "")
+            print("✅ Success!")
+            print(f"Response: {content}")
+        else:
+            print(f"❌ Failed with status {response.status_code}")
+            print(f"Response: {response.text}")
+
+    except Exception as e:
+        print(f"❌ Exception: {e}")
+
+if __name__ == "__main__":
+    test_llm_api()

Co-creation-projects/JJason-DeepCastAgent/backend/scripts/verify_ecnu_tts.py

@@ -0,0 +1,56 @@
+import os
+import sys
+
+import requests
+from dotenv import load_dotenv
+
+# Add src to path
+sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '../src')))
+
+# Load env
+load_dotenv(os.path.join(os.path.dirname(__file__), '../.env'))
+
+def test_tts_api():
+    api_key = os.getenv("TTS_API_KEY") or os.getenv("LLM_API_KEY")
+    base_url = os.getenv("TTS_BASE_URL", "https://chat.ecnu.edu.cn/open/api/v1/audio/speech")
+    model = os.getenv("TTS_MODEL", "ecnu-tts")
+    
+    print("Testing TTS API...")
+    print(f"URL: {base_url}")
+    print(f"Model: {model}")
+    print(f"API Key: {api_key[:8]}..." if api_key else "API Key: None")
+
+    if not api_key:
+        print("❌ Error: API Key not found in environment variables")
+        return
+
+    headers = {
+        "Authorization": f"Bearer {api_key}",
+        "Content-Type": "application/json"
+    }
+
+    payload = {
+        "model": model,
+        "input": "你好，这是一个测试语音。",
+        "voice": "xiayu",
+        "speed": 1.0
+    }
+
+    try:
+        response = requests.post(base_url, json=payload, headers=headers, timeout=30)
+        
+        if response.status_code == 200:
+            output_file = "test_tts_output.mp3"
+            with open(output_file, "wb") as f:
+                f.write(response.content)
+            print(f"✅ Success! Audio saved to {output_file}")
+            print(f"Response size: {len(response.content)} bytes")
+        else:
+            print(f"❌ Failed with status {response.status_code}")
+            print(f"Response: {response.text}")
+
+    except Exception as e:
+        print(f"❌ Exception: {e}")
+
+if __name__ == "__main__":
+    test_tts_api()

Co-creation-projects/JJason-DeepCastAgent/backend/scripts/verify_ffmpeg.py

@@ -0,0 +1,50 @@
+import os  # noqa: D100
+
+from pydub import AudioSegment
+
+"""
+DeepCast 项目使用 pydub 库将多个 TTS 生成的音频片段（MP3）合成为最终的播客文件。
+pydub 底层依赖 ffmpeg 进行音频格式转换和处理（特别是 MP3 导出）。
+因此，必须确保系统已安装 ffmpeg 且 Python 环境能正确找到其路径。
+此脚本用于验证 ffmpeg 是否配置正确且能被 pydub 调用。
+"""
+
+# 设置 ffmpeg 路径
+ffmpeg_path = r"C:\ffmpeg\bin\ffmpeg.exe"
+AudioSegment.converter = ffmpeg_path
+
+def test_ffmpeg():
+    print(f"Testing ffmpeg at: {ffmpeg_path}")
+    
+    # Check if file exists
+    if not os.path.exists(ffmpeg_path):
+        print(f"❌ Warning: ffmpeg executable not found at {ffmpeg_path}")
+    else:
+        print("✅ ffmpeg executable found.")
+    
+    try:
+        # 创建 1 秒的静音片段
+        print("Creating silent audio segment...")
+        silence = AudioSegment.silent(duration=1000)
+        
+        output_file = "test_ffmpeg_output.mp3"
+        print(f"Exporting to {output_file}...")
+        
+        # 导出需要 ffmpeg
+        silence.export(output_file, format="mp3")
+        
+        if os.path.exists(output_file):
+            print("✅ Success! ffmpeg is working correctly.")
+            print(f"Output file size: {os.path.getsize(output_file)} bytes")
+            # 清理文件
+            os.remove(output_file)
+        else:
+            print("❌ Failed: Output file was not created.")
+            
+    except Exception as e:
+        print(f"❌ Exception: {e}")
+        # import traceback
+        # traceback.print_exc()
+
+if __name__ == "__main__":
+    test_ffmpeg()

Co-creation-projects/JJason-DeepCastAgent/backend/scripts/verify_search.py

@@ -0,0 +1,65 @@
+import os
+import sys
+
+from dotenv import load_dotenv
+
+# Add src to path
+sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '../src')))
+
+# Load env
+load_dotenv(os.path.join(os.path.dirname(__file__), '../.env'))
+
+from config import Configuration
+from services.search import get_global_search_tool
+
+
+def test_search_configuration():
+    print("Testing search configuration...")
+    
+    # Load config from env
+    config = Configuration.from_env()
+    
+    # Print loaded keys (masked)
+    tavily_key = config.tavily_api_key
+    serpapi_key = config.serpapi_api_key
+    
+    print(f"Tavily Key: {'*' * 8 + tavily_key[-4:] if tavily_key else 'None'}")
+    print(f"SerpApi Key: {'*' * 8 + serpapi_key[-4:] if serpapi_key else 'None'}")
+    print(f"Search API: {config.search_api}")
+    
+    # Initialize search tool
+    search_tool = get_global_search_tool(config)
+    print(f"Search Tool Backend: {search_tool.backend}")
+    print(f"Available Backends: {search_tool.available_backends}")
+    
+    if not search_tool.available_backends:
+        print("❌ No search backends available. Please check API keys.")
+        return
+
+    # Test search
+    query = "DeepSeek technology overview"
+    print(f"\nRunning search for: '{query}'...")
+    
+    try:
+        response = search_tool.run({
+            "input": query,
+            "backend": "hybrid",
+            "max_results": 2
+        })
+        
+        if isinstance(response, dict):
+            backend = response.get("backend", "unknown")
+            results = response.get("results", [])
+            print(f"✅ Search successful using backend: {backend}")
+            print(f"Found {len(results)} results:")
+            for i, res in enumerate(results, 1):
+                print(f"  {i}. {res.get('title')} ({res.get('url')})")
+        else:
+            print(f"❌ Unexpected response format: {type(response)}")
+            print(response)
+            
+    except Exception as e:
+        print(f"❌ Search failed: {e}")
+
+if __name__ == "__main__":
+    test_search_configuration()

Co-creation-projects/JJason-DeepCastAgent/backend/src/__init__.py

@@ -0,0 +1,17 @@
+"""DeepCast - 由 HelloAgents 驱动的自动播客生成代理。"""
+
+    __version__ = "0.0.1"
+
+    from .agent import DeepResearchAgent
+    from .config import Configuration, SearchAPI
+    from .models import SummaryState, SummaryStateOutput, TodoItem
+
+    __all__ = [
+        "DeepResearchAgent",
+        "Configuration",
+        "SearchAPI",
+        "SummaryState",
+        "SummaryStateOutput",
+        "TodoItem",
+    ]
+

Co-creation-projects/JJason-DeepCastAgent/backend/src/agent.py

@@ -0,0 +1,825 @@
+"""协调深度研究工作流的编排器。"""
+
+from __future__ import annotations
+
+import logging
+from collections.abc import Callable, Iterator
+from pathlib import Path
+from queue import Empty, Queue
+from threading import Event, Lock, Thread
+from typing import Any
+
+from hello_agents import HelloAgentsLLM, ToolAwareSimpleAgent
+from hello_agents.tools import ToolRegistry
+from hello_agents.tools.builtin.note_tool import NoteTool
+
+from config import Configuration
+from models import SummaryState, SummaryStateOutput, TodoItem
+from prompts import (
+    report_writer_instructions,
+    task_summarizer_system_prompt,
+    todo_planner_system_prompt,
+)
+from services.audio_generator import AudioGenerationService
+from services.audio_synthesizer import PodcastSynthesisService
+from services.planner import PlanningService
+from services.reporter import ReportingService
+from services.script_generator import ScriptGenerationService
+from services.search import dispatch_search, prepare_research_context
+from services.summarizer import SummarizationService
+from services.tool_events import ToolCallTracker
+
+logger = logging.getLogger(__name__)
+
+
+class DeepResearchAgent:
+    """使用 HelloAgents 协调基于 TODO 的研究工作流的协调器。"""
+
+    def __init__(self, config: Configuration | None = None) -> None:
+        """使用配置和共享工具初始化协调器。"""
+        self.config = config or Configuration.from_env()
+        self.default_llm = self._init_llm(self.config.llm_model_id)
+        self.smart_llm = self._init_llm(self.config.smart_llm_model)
+        self.fast_llm = self._init_llm(self.config.fast_llm_model)
+
+        self.note_tool = (
+            NoteTool(workspace=self.config.notes_workspace)
+            if self.config.enable_notes
+            else None
+        )
+        self.tools_registry: ToolRegistry | None = None
+        if self.note_tool:
+            registry = ToolRegistry()
+            registry.register_tool(self.note_tool)
+            self.tools_registry = registry
+
+        self._tool_tracker = ToolCallTracker(
+            self.config.notes_workspace if self.config.enable_notes else None
+        )
+        self._tool_event_sink_enabled = False
+        self._state_lock = Lock()
+        self._cancel_event = Event()  # 取消信号
+
+        self.todo_agent = self._create_tool_aware_agent(
+            name="研究规划专家",
+            system_prompt=todo_planner_system_prompt.strip(),
+            llm=self.smart_llm,
+        )
+        self.report_agent = self._create_tool_aware_agent(
+            name="报告撰写专家",
+            system_prompt=report_writer_instructions.strip(),
+            llm=self.smart_llm,
+        )
+
+        self._summarizer_factory: Callable[[], ToolAwareSimpleAgent] = lambda: self._create_tool_aware_agent(  # noqa: E501
+            name="任务总结专家",
+            system_prompt=task_summarizer_system_prompt.strip(),
+            llm=self.fast_llm,
+        )
+
+        self.planner = PlanningService(self.todo_agent, self.config)
+        self.summarizer = SummarizationService(self._summarizer_factory, self.config)
+        self.reporting = ReportingService(self.report_agent, self.config)
+        self.script_generator = ScriptGenerationService(self.config)
+        self.audio_generator = AudioGenerationService(self.config)
+
+        self.podcast_synthesizer = PodcastSynthesisService(self.config)
+
+    def cancel(self) -> None:
+        """请求取消当前正在执行的研究任务。"""
+        logger.info("Cancel requested for research agent")
+        self._cancel_event.set()
+
+    def is_cancelled(self) -> bool:
+        """检查当前任务是否已被取消。"""
+        return self._cancel_event.is_set()
+
+    # ------------------------------------------------------------------
+    # 公共 API
+    # ------------------------------------------------------------------
+    def _init_llm(self, model_id_override: str | None = None) -> HelloAgentsLLM:
+        """根据配置偏好实例化 HelloAgentsLLM。"""
+        llm_kwargs: dict[str, Any] = {"temperature": 0.0}
+
+        model_id = model_id_override or self.config.llm_model_id
+        if model_id:
+            llm_kwargs["model"] = model_id
+
+        provider = (self.config.llm_provider or "").strip()
+        if provider:
+            llm_kwargs["provider"] = provider
+
+        if self.config.llm_base_url:
+            llm_kwargs["base_url"] = self.config.llm_base_url
+        if self.config.llm_api_key:
+            llm_kwargs["api_key"] = self.config.llm_api_key
+
+        return HelloAgentsLLM(**llm_kwargs)
+
+    def _create_tool_aware_agent(self, *, name: str, system_prompt: str, llm: HelloAgentsLLM) -> ToolAwareSimpleAgent:
+        """实例化共享工具注册表和跟踪器的 ToolAwareSimpleAgent。"""
+        return ToolAwareSimpleAgent(
+            name=name,
+            llm=llm,
+            system_prompt=system_prompt,
+            enable_tool_calling=self.tools_registry is not None,
+            tool_registry=self.tools_registry,
+            tool_call_listener=self._tool_tracker.record,
+        )
+
+    def _set_tool_event_sink(self, sink: Callable[[dict[str, Any]], None] | None) -> None:
+        """启用或禁用立即工具事件回调。"""
+        self._tool_event_sink_enabled = sink is not None
+        self._tool_tracker.set_event_sink(sink)
+
+    def run(self, topic: str) -> SummaryStateOutput:
+        """
+        执行研究工作流并返回最终报告（同步模式）。
+        
+        此方法按顺序执行以下步骤：
+        1. 初始化状态和规划任务。
+        2. 串行执行每个任务（搜索 + 总结）。
+        3. 生成最终报告。
+        4. 生成播客脚本。
+        5. 生成音频文件并合成播客。
+        """
+        state = SummaryState(research_topic=topic)
+        state.todo_items = self.planner.plan_todo_list(state)
+        self._drain_tool_events(state)
+
+        if not state.todo_items:
+            logger.info("No TODO items generated; falling back to single task")
+            state.todo_items = [self.planner.create_fallback_task(state)]
+
+        for task in state.todo_items:
+            for _ in self._execute_task(state, task, emit_stream=False):
+                pass
+
+        report = self.reporting.generate_report(state)
+        self._drain_tool_events(state)
+        state.structured_report = report
+        state.running_summary = report
+        self._persist_final_report(state, report)
+
+        script = self.script_generator.generate_script(state)
+        self._drain_tool_events(state)
+        state.podcast_script = script
+
+        # 为脚本生成音频
+        task_id = f"task_{state.report_note_id}" if state.report_note_id else "task_default"
+        audio_files = self.audio_generator.generate_audio(script, task_id)
+
+        # 合成播客
+        self.podcast_synthesizer.synthesize_podcast(audio_files, task_id)
+        
+        return SummaryStateOutput(
+            running_summary=report,
+            report_markdown=report,
+            todo_items=state.todo_items,
+            podcast_script=script,
+        )
+
+    def run_stream(self, topic: str) -> Iterator[dict[str, Any]]:
+        """
+        执行研究工作流并产生增量进度事件（流式模式）。
+
+        此方法使用多线程并行执行研究任务，并通过生成器实时返回进度。
+        主要步骤：
+        1. 初始化并规划任务。
+        2. 为每个任务启动一个工作线程进行并行处理。
+        3. 实时流式传输任务状态、搜索结果和部分总结。
+        4. 所有任务完成后，生成并流式传输最终报告。
+        5. 生成并流式传输播客脚本和音频合成进度。
+
+        支持通过 cancel() 方法取消执行。
+        """
+        # 重置取消状态
+        self._cancel_event.clear()
+
+        state = SummaryState(research_topic=topic)
+        logger.debug("Starting streaming research: topic=%s", topic)
+        yield {"type": "status", "message": "初始化研究流程"}
+
+        if self.is_cancelled():
+            yield {"type": "cancelled", "message": "研究任务已取消"}
+            return
+
+        # Phase 1: 规划 + 并行研究
+        yield from self._stream_research_phase(state)
+        if self.is_cancelled():
+            yield {"type": "cancelled", "message": "研究任务已取消"}
+            return
+
+        # Phase 2: 报告生成
+        yield from self._stream_report_phase(state)
+        if self.is_cancelled():
+            yield {"type": "cancelled", "message": "研究任务已取消"}
+            return
+
+        # Phase 3: 播客脚本
+        script_turns = yield from self._stream_script_phase(state)
+        if self.is_cancelled():
+            yield {"type": "cancelled", "message": "研究任务已取消"}
+            return
+        if script_turns == 0:
+            yield {"type": "done"}
+            return
+
+        # Phase 4: 音频生成 + 合成
+        yield from self._stream_audio_phase(state, script_turns)
+
+        yield {"type": "done"}
+
+    # ------------------------------------------------------------------
+    # 流式阶段方法
+    # ------------------------------------------------------------------
+
+    def _stream_research_phase(self, state: SummaryState) -> Iterator[dict[str, Any]]:
+        """Phase 1: 规划任务并行执行搜索 + 总结。"""
+        if self.is_cancelled():
+            return
+        state.todo_items = self.planner.plan_todo_list(state)
+        if self.is_cancelled():
+            return
+        for event in self._drain_tool_events(state, step=0):
+            yield event
+        if not state.todo_items:
+            state.todo_items = [self.planner.create_fallback_task(state)]
+
+        channel_map: dict[int, dict[str, Any]] = {}
+        for index, task in enumerate(state.todo_items, start=1):
+            token = f"task_{task.id}"
+            task.stream_token = token
+            channel_map[task.id] = {"step": index, "token": token}
+
+        serialized_tasks = [self._serialize_task(t) for t in state.todo_items]
+        logger.info(f"Emitting todo_list event with {len(serialized_tasks)} tasks")
+        yield {
+            "type": "todo_list",
+            "tasks": serialized_tasks,
+            "step": 0,
+        }
+
+        event_queue: Queue[dict[str, Any]] = Queue()
+
+        def enqueue(
+            event: dict[str, Any],
+            *,
+            task: TodoItem | None = None,
+            step_override: int | None = None,
+        ) -> None:
+            payload = dict(event)
+            target_task_id = payload.get("task_id")
+            if task is not None:
+                target_task_id = task.id
+                payload["task_id"] = task.id
+            channel = channel_map.get(target_task_id) if target_task_id is not None else None
+            if channel:
+                payload.setdefault("step", channel["step"])
+                payload["stream_token"] = channel["token"]
+            if step_override is not None:
+                payload["step"] = step_override
+            event_queue.put(payload)
+
+        self._set_tool_event_sink(lambda ev: enqueue(ev))
+
+        threads: list[Thread] = []
+
+        def worker(task: TodoItem, step: int) -> None:
+            try:
+                if self.is_cancelled():
+                    enqueue({"type": "__task_done__", "task_id": task.id})
+                    return
+                enqueue(
+                    {
+                        "type": "task_status",
+                        "task_id": task.id,
+                        "status": "in_progress",
+                        "title": task.title,
+                        "intent": task.intent,
+                        "query": task.query,
+                        "note_id": task.note_id,
+                        "note_path": task.note_path,
+                    },
+                    task=task,
+                )
+                for event in self._execute_task(state, task, emit_stream=True, step=step):
+                    if self.is_cancelled():
+                        break
+                    enqueue(event, task=task)
+            except Exception as exc:
+                if self.is_cancelled():
+                    logger.info("Task %s cancelled", task.id)
+                else:
+                    logger.exception("Task execution failed", exc_info=exc)
+                enqueue(
+                    {
+                        "type": "task_status",
+                        "task_id": task.id,
+                        "status": "failed",
+                        "detail": str(exc),
+                        "title": task.title,
+                        "intent": task.intent,
+                        "query": task.query,
+                        "note_id": task.note_id,
+                        "note_path": task.note_path,
+                    },
+                    task=task,
+                )
+            finally:
+                enqueue({"type": "__task_done__", "task_id": task.id})
+
+        for task in state.todo_items:
+            step = channel_map.get(task.id, {}).get("step", 0)
+            thread = Thread(target=worker, args=(task, step), daemon=True)
+            threads.append(thread)
+            thread.start()
+
+        active_workers = len(state.todo_items)
+        finished_workers = 0
+
+        try:
+            while finished_workers < active_workers:
+                try:
+                    event = event_queue.get(timeout=0.5)
+                except Empty:
+                    if self.is_cancelled():
+                        logger.info("Research cancelled during task execution")
+                        yield {"type": "cancelled", "message": "研究任务已取消"}
+                        return
+                    continue
+                if event.get("type") == "__task_done__":
+                    finished_workers += 1
+                    continue
+                yield event
+
+            while True:
+                try:
+                    event = event_queue.get_nowait()
+                except Empty:
+                    break
+                if event.get("type") != "__task_done__":
+                    yield event
+        finally:
+            self._set_tool_event_sink(None)
+            for thread in threads:
+                thread.join(timeout=1.0)
+
+    def _stream_report_phase(self, state: SummaryState) -> Iterator[dict[str, Any]]:
+        """Phase 2: 生成深度研究报告。"""
+        yield {
+            "type": "stage_change",
+            "stage": "report",
+            "message": "所有研究任务已完成，正在撰写深度研究报告...",
+        }
+        yield {"type": "log", "message": f"🧠 正在调用 {self.config.smart_llm_model} 模型撰写深度报告..."}
+
+        if self.is_cancelled():
+            return
+        report = self.reporting.generate_report(state)
+        if self.is_cancelled():
+            return
+        final_step = len(state.todo_items) + 1
+        for event in self._drain_tool_events(state, step=final_step):
+            yield event
+        state.structured_report = report
+        state.running_summary = report
+        yield {"type": "log", "message": f"✓ 报告撰写完成，共 {len(report)} 字符"}
+
+        if self.is_cancelled():
+            return
+
+        note_event = self._persist_final_report(state, report)
+        if note_event:
+            yield note_event
+
+        yield {
+            "type": "final_report",
+            "report": report,
+            "note_id": state.report_note_id,
+            "note_path": state.report_note_path,
+        }
+
+    def _stream_script_phase(self, state: SummaryState) -> Iterator[dict[str, Any] | int]:
+        """
+        Phase 3: 将报告转化为播客脚本。
+
+        Yields 流式事件，最终 return 脚本轮次数 (int)。
+        调用方通过 ``script_turns = yield from self._stream_script_phase(state)`` 获取。
+        """
+        yield {
+            "type": "stage_change",
+            "stage": "script",
+            "message": "正在将研究报告转化为双人对谈播客脚本...",
+        }
+        yield {"type": "log", "message": f"🧠 正在调用 {self.config.fast_llm_model} 模型生成播客脚本..."}
+        yield {"type": "log", "message": "脚本策划专家正在创作 Host (Xiayu) 与 Guest (Liwa) 的对话..."}
+
+        if self.is_cancelled():
+            return
+        script = self.script_generator.generate_script(state)
+        if self.is_cancelled():
+            return
+        for event in self._drain_tool_events(state):
+            yield event
+        state.podcast_script = script
+
+        script_turns = len(script) if script else 0
+        yield {"type": "log", "message": f"✓ 脚本生成完成，共 {script_turns} 轮对话"}
+        yield {
+            "type": "podcast_script",
+            "script": script,
+            "turns": script_turns,
+        }
+
+        if script_turns == 0:
+            yield {"type": "log", "message": "⚠️ 警告：脚本为空，跳过音频生成"}
+
+        return script_turns  # type: ignore[return-value]
+
+    def _stream_audio_phase(self, state: SummaryState, script_turns: int) -> Iterator[dict[str, Any]]:
+        """Phase 4: TTS 音频生成 + FFmpeg 合成。"""
+        script = state.podcast_script
+
+        yield {
+            "type": "stage_change",
+            "stage": "audio",
+            "message": "正在调用 TTS 语音引擎生成音频...",
+        }
+
+        task_id = f"task_{state.report_note_id}" if state.report_note_id else "task_default"
+
+        # 使用队列实现实时流式音频进度
+        audio_event_queue: Queue[dict[str, Any]] = Queue()
+        audio_result: list = []
+        audio_error: list = []
+        cancel_audio = Event()
+
+        def audio_progress_callback(current: int, total: int, role: str, preview: str) -> bool:
+            """将进度事件放入队列以实现实时更新。"""
+            if self.is_cancelled() or cancel_audio.is_set():
+                return False
+            audio_event_queue.put({
+                "type": "audio_progress",
+                "current": current,
+                "total": total,
+                "role": role,
+                "preview": preview,
+                "message": f"[TTS {current}/{total}] ✓ {role} 语音生成成功",
+            })
+            return True
+
+        def run_audio_generation() -> None:
+            """在单独线程中运行音频生成。"""
+            try:
+                files = self.audio_generator.generate_audio(
+                    script, task_id, audio_progress_callback,
+                    cancel_event=self._cancel_event,
+                )
+                audio_result.append(files)
+            except Exception as e:
+                if not self.is_cancelled():
+                    audio_error.append(str(e))
+            finally:
+                audio_event_queue.put({"type": "_audio_done"})
+
+        yield {"type": "log", "message": f"准备为 {script_turns} 段对话生成语音..."}
+        yield {
+            "type": "audio_start",
+            "total": script_turns,
+            "message": f"开始生成 {script_turns} 段语音",
+        }
+
+        audio_thread = Thread(target=run_audio_generation, daemon=True)
+        audio_thread.start()
+
+        # 实时流式传输进度事件
+        while True:
+            if self.is_cancelled():
+                cancel_audio.set()
+                yield {"type": "cancelled", "message": "研究任务已取消"}
+                audio_thread.join(timeout=2.0)
+                return
+            try:
+                event = audio_event_queue.get(timeout=0.1)
+                if event.get("type") == "_audio_done":
+                    break
+                yield event
+                if event.get("type") == "audio_progress":
+                    yield {
+                        "type": "log",
+                        "message": f"[TTS {event['current']}/{event['total']}] ✓ {event['role']} 语音已完成",
+                    }
+            except Empty:
+                continue
+
+        audio_thread.join(timeout=5.0)
+
+        if self.is_cancelled():
+            yield {"type": "cancelled", "message": "研究任务已取消"}
+            return
+
+        audio_files = audio_result[0] if audio_result else []
+        audio_count = len(audio_files) if audio_files else 0
+
+        if audio_error:
+            yield {"type": "log", "message": f"⚠️ 音频生成出错: {audio_error[0]}"}
+
+        yield {"type": "log", "message": f"语音生成完成，成功 {audio_count}/{script_turns} 段"}
+        yield {
+            "type": "audio_generated",
+            "files": audio_files,
+            "count": audio_count,
+        }
+
+        # 合成播客
+        yield {
+            "type": "stage_change",
+            "stage": "synthesis",
+            "message": "正在合成完整播客音频文件...",
+        }
+
+        if self.is_cancelled():
+            yield {"type": "cancelled", "message": "研究任务已取消"}
+            return
+
+        yield {"type": "log", "message": "使用 FFmpeg 拼接所有语音片段..."}
+        podcast_file = self.podcast_synthesizer.synthesize_podcast(
+            audio_files, task_id, cancel_check=self.is_cancelled,
+        )
+        if podcast_file:
+            yield {"type": "podcast_ready", "file": podcast_file}
+            yield {"type": "log", "message": f"🎉 播客文件生成成功: {podcast_file}"}
+        else:
+            yield {"type": "log", "message": "⚠️ 播客合成失败，请检查 FFmpeg 配置"}
+
+    # ------------------------------------------------------------------
+    # 执行助手
+    # ------------------------------------------------------------------
+    def _execute_task(
+        self,
+        state: SummaryState,
+        task: TodoItem,
+        *,
+        emit_stream: bool,
+        step: int | None = None,
+    ) -> Iterator[dict[str, Any]]:
+        """
+        对单个任务运行搜索 + 总结逻辑。
+        
+        Args:
+            state: 全局研究状态。
+            task: 当前要执行的任务项。
+            emit_stream: 是否产生流式事件（True 用于 run_stream，False 用于 run）。
+            step: 当前步骤编号（仅用于流式事件）。
+            
+        Returns:
+            事件字典的迭代器（即使 emit_stream=False，也可能产生少量内部事件，通常被忽略）。
+        """
+        task.status = "in_progress"
+
+        search_result, notices, answer_text, backend = dispatch_search(
+            task.query,
+            self.config,
+            state.research_loop_count,
+        )
+        task.notices = notices
+
+        if emit_stream:
+            for event in self._drain_tool_events(state, step=step):
+                yield event
+        else:
+            self._drain_tool_events(state)
+
+        if notices and emit_stream:
+            for notice in notices:
+                if notice:
+                    yield {
+                        "type": "status",
+                        "message": notice,
+                        "task_id": task.id,
+                        "step": step,
+                    }
+
+        if not search_result or not search_result.get("results"):
+            task.status = "skipped"
+            if emit_stream:
+                for event in self._drain_tool_events(state, step=step):
+                    yield event
+                yield {
+                    "type": "task_status",
+                    "task_id": task.id,
+                    "status": "skipped",
+                    "title": task.title,
+                    "intent": task.intent,
+                    "note_id": task.note_id,
+                    "note_path": task.note_path,
+                    "step": step,
+                }
+            else:
+                self._drain_tool_events(state)
+            return
+        else:
+            if not emit_stream:
+                self._drain_tool_events(state)
+
+        sources_summary, context = prepare_research_context(
+            search_result,
+            answer_text,
+            self.config,
+        )
+
+        task.sources_summary = sources_summary
+
+        with self._state_lock:
+            state.web_research_results.append(context)
+            state.sources_gathered.append(sources_summary)
+            state.research_loop_count += 1
+
+        summary_text: str | None = None
+
+        if emit_stream:
+            for event in self._drain_tool_events(state, step=step):
+                yield event
+            yield {
+                "type": "sources",
+                "task_id": task.id,
+                "latest_sources": sources_summary,
+                "raw_context": context,
+                "step": step,
+                "backend": backend,
+                "note_id": task.note_id,
+                "note_path": task.note_path,
+            }
+
+            summary_stream, summary_getter = self.summarizer.stream_task_summary(state, task, context)
+            try:
+                for event in self._drain_tool_events(state, step=step):
+                    yield event
+                for chunk in summary_stream:
+                    if chunk:
+                        yield {
+                            "type": "task_summary_chunk",
+                            "task_id": task.id,
+                            "content": chunk,
+                            "note_id": task.note_id,
+                            "step": step,
+                        }
+                    for event in self._drain_tool_events(state, step=step):
+                        yield event
+            finally:
+                summary_text = summary_getter()
+        else:
+            summary_text = self.summarizer.summarize_task(state, task, context)
+            self._drain_tool_events(state)
+
+        task.summary = summary_text.strip() if summary_text else "暂无可用信息"
+        task.status = "completed"
+
+        if emit_stream:
+            for event in self._drain_tool_events(state, step=step):
+                yield event
+            yield {
+                "type": "task_status",
+                "task_id": task.id,
+                "status": "completed",
+                "title": task.title,
+                "intent": task.intent,
+                "summary": task.summary,
+                "sources_summary": task.sources_summary,
+                "note_id": task.note_id,
+                "note_path": task.note_path,
+                "step": step,
+            }
+        else:
+            self._drain_tool_events(state)
+
+    def _drain_tool_events(
+        self,
+        state: SummaryState,
+        *,
+        step: int | None = None,
+    ) -> list[dict[str, Any]]:
+        """共享工具调用跟踪器的代理。"""
+        events = self._tool_tracker.drain(state, step=step)
+        if self._tool_event_sink_enabled:
+            return []
+        return events
+
+    def _serialize_task(self, task: TodoItem) -> dict[str, Any]:
+        """将任务数据类转换为前端可序列化的字典。"""
+        return {
+            "id": task.id,
+            "title": task.title,
+            "intent": task.intent,
+            "query": task.query,
+            "status": task.status,
+            "summary": task.summary,
+            "sources_summary": task.sources_summary,
+            "note_id": task.note_id,
+            "note_path": task.note_path,
+            "stream_token": task.stream_token,
+        }
+
+    def _persist_final_report(self, state: SummaryState, report: str) -> dict[str, Any] | None:
+        if not self.note_tool or not report or not report.strip():
+            return None
+
+        note_title = f"研究报告：{state.research_topic}".strip() or "研究报告"
+        tags = ["deep_research", "report"]
+        content = report.strip()
+
+        note_id = self._find_existing_report_note_id(state)
+        response = ""
+
+        if note_id:
+            response = self.note_tool.run(
+                {
+                    "action": "update",
+                    "note_id": note_id,
+                    "title": note_title,
+                    "note_type": "conclusion",
+                    "tags": tags,
+                    "content": content,
+                }
+            )
+            if response.startswith("❌"):
+                note_id = None
+
+        if not note_id:
+            response = self.note_tool.run(
+                {
+                    "action": "create",
+                    "title": note_title,
+                    "note_type": "conclusion",
+                    "tags": tags,
+                    "content": content,
+                }
+            )
+            note_id = self._tool_tracker._extract_note_id(response)
+
+        if not note_id:
+            return None
+
+        state.report_note_id = note_id
+        if self.config.notes_workspace:
+            note_path = Path(self.config.notes_workspace) / f"{note_id}.md"
+            state.report_note_path = str(note_path)
+        else:
+            note_path = None
+
+        payload = {
+            "type": "report_note",
+            "note_id": note_id,
+            "title": note_title,
+            "content": content,
+        }
+        if note_path:
+            payload["note_path"] = str(note_path)
+
+        return payload
+
+    def _find_existing_report_note_id(self, state: SummaryState) -> str | None:
+        """
+        查找与研究主题相关的现有报告笔记 ID。
+        
+        此方法检查当前状态是否已关联报告笔记 ID。如果没有，它会遍历已记录的工具事件，
+        查找最近创建或更新的结论类型笔记，标题中包含研究主题的报告。
+        
+        Args:
+            state: 当前研究状态，包含研究主题和已记录的工具事件。
+            
+        Returns:
+            与研究主题相关的现有报告笔记 ID（如果存在），否则为 None。
+        """
+        if state.report_note_id:
+            return state.report_note_id
+
+        for event in reversed(self._tool_tracker.as_dicts()):
+            if event.get("tool") != "note":
+                continue
+
+            parameters = event.get("parsed_parameters") or {}
+            if not isinstance(parameters, dict):
+                continue
+
+            action = parameters.get("action")
+            if action not in {"create", "update"}:
+                continue
+
+            note_type = parameters.get("note_type")
+            if note_type != "conclusion":
+                title = parameters.get("title")
+                if not (isinstance(title, str) and title.startswith("研究报告")):
+                    continue
+
+            note_id = parameters.get("note_id")
+            if not note_id:
+                note_id = self._tool_tracker._extract_note_id(event.get("result", ""))  # type: ignore[attr-defined]
+
+            if note_id:
+                return note_id
+
+        return None
+
+

Co-creation-projects/JJason-DeepCastAgent/backend/src/config.py

@@ -0,0 +1,206 @@
+import os
+from enum import Enum
+from pathlib import Path
+from typing import Any
+
+from pydantic import BaseModel, Field, field_validator
+
+# Define backend root directory
+BACKEND_ROOT = Path(__file__).resolve().parent.parent
+
+class SearchAPI(Enum):
+    """搜索 API 提供商的枚举。
+
+    兼容旧测试和示例：
+    - TAVILY: 使用 Tavily 搜索后端
+    - SERPAPI: 使用 SerpApi
+    - DDG: DuckDuckGo (内置 ddgs)
+    - HYBRID: 混合策略（Tavily + SerpApi），为默认值
+    """
+    TAVILY = "tavily"
+    SERPAPI = "serpapi"
+    DDG = "ddg"
+    HYBRID = "hybrid"
+
+
+class Configuration(BaseModel):
+    """DeepCast Agent Configuration."""
+
+    max_web_research_loops: int = Field(
+        default=3,
+        title="Research Depth",
+        description="Number of research iterations",
+    )
+    llm_provider: str = Field(
+        default="custom",
+        title="LLM 提供商",
+        description="提供商标识符 (custom)",
+    )
+    search_api: SearchAPI = Field(
+        default=SearchAPI.HYBRID,
+        title="搜索 API",
+        description="使用混合搜索引擎 (Tavily + SerpApi)",
+    )
+    enable_notes: bool = Field(
+        default=True,
+        title="启用笔记",
+        description="是否在 NoteTool 中存储任务进度",
+    )
+    notes_workspace: str = Field(
+        default=str(BACKEND_ROOT / "output" / "notes"),
+        title="笔记工作区",
+        description="NoteTool 持久化任务笔记的目录",
+    )
+    fetch_full_page: bool = Field(
+        default=True,
+        title="获取完整页面",
+        description="在搜索结果中包含完整页面内容",
+    )
+    strip_thinking_tokens: bool = Field(
+        default=False,
+        title="移除思考 Token",
+        description="是否从模型响应中移除 <think> token",
+    )
+    use_tool_calling: bool = Field(
+        default=False,
+        title="使用工具调用",
+        description="使用工具调用而非 JSON 模式进行结构化输出",
+    )
+    llm_api_key: str | None = Field(
+        default=None,
+        title="LLM API 密钥",
+        description="使用自定义 OpenAI 兼容服务时的可选 API 密钥",
+    )
+    llm_base_url: str | None = Field(
+        default=None,
+        title="LLM 基础 URL",
+        description="使用自定义 OpenAI 兼容服务时的可选基础 URL",
+    )
+    llm_model_id: str | None = Field(
+        default=None,
+        title="LLM 模型 ID",
+        description="自定义 OpenAI 兼容服务的可选模型标识符",
+    )
+    smart_llm_model: str | None = Field(
+        default="ecnu-reasoner",
+        title="Smart LLM Model",
+        description="复杂推理任务使用的模型 ID (e.g. Planning, Reporting)",
+    )
+    fast_llm_model: str | None = Field(
+        default="ecnu-max",
+        title="Fast LLM Model",
+        description="快速响应任务使用的模型 ID (e.g. Web Research, Script Generation)",
+    )
+    tts_api_key: str | None = Field(
+        default=None,
+        title="TTS API 密钥",
+        description="TTS 服务的 API 密钥",
+    )
+    tts_base_url: str = Field(
+        default="https://chat.ecnu.edu.cn/open/api/v1/audio/speech",
+        title="TTS 基础 URL",
+        description="TTS API 的基础 URL",
+    )
+    tts_model: str = Field(
+        default="ecnu-tts",
+        title="TTS 模型",
+        description="TTS 服务的模型标识符",
+    )
+    audio_output_dir: str = Field(
+        default=str(BACKEND_ROOT / "output" / "audio"),
+        title="音频输出目录",
+        description="保存生成的音频文件的目录",
+    )
+    ffmpeg_path: str | None = Field(
+        default=None,
+        title="FFmpeg 路径",
+        description="ffmpeg 可执行文件的路径",
+    )
+    tavily_api_key: str | None = Field(
+        default=None,
+        title="Tavily API 密钥",
+        description="Tavily 搜索的 API 密钥",
+    )
+    serpapi_api_key: str | None = Field(
+        default=None,
+        title="SerpApi 密钥",
+        description="SerpApi 的 API 密钥",
+    )
+    cors_origins: str = Field(
+        default="http://localhost:5173,http://localhost:5174,http://localhost:3000",
+        title="CORS 允许的源",
+        description="逗号分隔的允许跨域请求的源列表",
+    )
+    host: str = Field(
+        default="0.0.0.0",
+        title="服务器主机",
+        description="FastAPI 服务器监听的主机地址",
+    )
+    port: int = Field(
+        default=8000,
+        title="服务器端口",
+        description="FastAPI 服务器监听的端口",
+    )
+    log_level: str = Field(
+        default="INFO",
+        title="日志级别",
+        description="日志记录级别 (DEBUG, INFO, WARNING, ERROR)",
+    )
+    llm_timeout: int = Field(
+        default=60,
+        title="LLM 超时",
+        description="LLM 请求超时时间（秒）",
+    )
+    tts_timeout: int = Field(
+        default=300,
+        title="TTS 超时",
+        description="TTS 请求超时时间（秒）",
+    )
+
+    @field_validator("notes_workspace", "audio_output_dir")
+    @classmethod
+    def resolve_path(cls, v: str) -> str:
+        """确保路径是绝对路径，如果是相对路径则基于 BACKEND_ROOT 解析。"""
+        if v is None:
+            return v
+        path = Path(v)
+        if not path.is_absolute():
+            return str(BACKEND_ROOT / path)
+        return v
+
+    @classmethod
+    def from_env(cls, overrides: dict[str, Any] | None = None) -> "Configuration":
+        """
+        使用环境变量和覆盖项创建配置对象。
+        
+        Args:
+            overrides: 可选的配置覆盖字典。
+            
+        Returns:
+            初始化的配置对象。
+        """
+        raw_values: dict[str, Any] = {}
+
+        # 基于字段名从环境变量加载值
+        for field_name in cls.model_fields.keys():
+            env_key = field_name.upper()
+            if env_key in os.environ:
+                raw_values[field_name] = os.environ[env_key]
+
+        # 处理 NO_PROXY
+        no_proxy = os.getenv("NO_PROXY")
+        if no_proxy:
+            os.environ["NO_PROXY"] = no_proxy
+            # 同时设置为小写以兼容
+            os.environ["no_proxy"] = no_proxy
+
+        if overrides:
+            for key, value in overrides.items():
+                if value is not None:
+                    raw_values[key] = value
+
+        return cls(**raw_values)
+
+    def resolved_model(self) -> str | None:
+        """尽力解析要使用的模型标识符。"""
+        return self.llm_model_id

Co-creation-projects/JJason-DeepCastAgent/backend/src/main.py

@@ -0,0 +1,326 @@
+"""通过 HTTP 暴露 DeepResearchAgent 的 FastAPI 入口点。"""
+
+from __future__ import annotations
+
+import asyncio
+import concurrent.futures
+import glob
+import json
+import os
+import sys
+from contextlib import asynccontextmanager
+from typing import Any
+
+# Ensure src directory is in sys.path for module imports
+sys.path.append(os.path.dirname(os.path.abspath(__file__)))
+
+from fastapi import FastAPI, HTTPException, Request
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi.responses import StreamingResponse
+from fastapi.staticfiles import StaticFiles
+from loguru import logger
+from pydantic import BaseModel, Field
+
+from agent import DeepResearchAgent
+from config import Configuration
+
+# 添加控制台日志处理程序
+logger.add(
+    sys.stderr,
+    level="INFO",
+    format="<green>{time:YYYY-MM-DD HH:mm:ss}</green> | <level>{level: <4}</level> | <cyan>using_function:{function}</cyan> | <cyan>{file}:{line}</cyan> | <level>{message}</level>",
+    colorize=True,
+)
+
+
+class ResearchRequest(BaseModel):
+    """触发研究运行的负载。"""
+
+    topic: str = Field(..., description="用户提供的研究主题")
+
+class PodcastScript(BaseModel):
+    """播客脚本内容模型。"""
+    script: str = Field(..., description="生成的播客脚本内容")
+
+
+class ResearchResponse(BaseModel):
+    """包含生成报告和结构化任务的 HTTP 响应。"""
+
+    report_markdown: str = Field(
+        ..., description="Markdown 格式的研究报告，包含各个部分"
+    )
+    todo_items: list[dict[str, Any]] = Field(
+        default_factory=list,
+        description="带有摘要和来源的结构化待办事项",
+    )
+    podcast_script: PodcastScript | None = Field(
+        default=None,
+        description="生成的播客脚本内容",
+    )
+
+
+def _mask_secret(value: str | None, visible: int = 4) -> str:
+    """在保持前导和尾随字符的同时，掩盖敏感令牌。"""
+    if not value:
+        return "unset"
+
+    if len(value) <= visible * 2:
+        return "*" * len(value)
+
+    return f"{value[:visible]}...{value[-visible:]}"
+
+
+def _build_config(payload: ResearchRequest) -> Configuration:
+    return Configuration.from_env()
+
+
+def create_app() -> FastAPI:
+    """创建并配置 FastAPI 应用实例。"""
+
+    # 当前活跃的研究 agent 引用，用于支持取消操作
+    _active_agent: dict[str, DeepResearchAgent | None] = {"current": None}
+
+    # 确保输出目录存在（使用绝对路径，基于 backend 根目录）
+    backend_root = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+    output_dir = os.path.join(backend_root, "output")
+    os.makedirs(output_dir, exist_ok=True)
+
+    @asynccontextmanager
+    async def lifespan(app: FastAPI):
+        """应用生命周期管理：启动时记录配置，关闭时清理资源。"""
+        config = Configuration.from_env()
+        logger.info(
+            "DeepResearch configuration loaded: provider=%s model=%s base_url=%s search_api=%s "
+            "max_loops=%s fetch_full_page=%s tool_calling=%s strip_thinking=%s api_key=%s",
+            config.llm_provider,
+            config.resolved_model() or "unset",
+            config.llm_base_url or "unset",
+            config.search_api.value,
+            config.max_web_research_loops,
+            config.fetch_full_page,
+            config.use_tool_calling,
+            config.strip_thinking_tokens,
+            _mask_secret(config.llm_api_key),
+        )
+        yield  # 应用运行中
+        # 关闭时清理
+        _active_agent["current"] = None
+
+    app = FastAPI(title="DeepCast - 自动播客生成智能体", lifespan=lifespan)
+
+    # 从配置读取 CORS 允许的源，避免生产环境使用通配符
+    _startup_config = Configuration.from_env()
+    _allowed_origins = [
+        origin.strip()
+        for origin in _startup_config.cors_origins.split(",")
+        if origin.strip()
+    ]
+    app.add_middleware(
+        CORSMiddleware,
+        allow_origins=_allowed_origins,
+        allow_credentials=True,
+        allow_methods=["*"],
+        allow_headers=["*"],
+    )
+
+    # 挂载静态文件目录，用于访问生成的音频文件
+    app.mount("/output", StaticFiles(directory=output_dir), name="output")
+
+    @app.get("/healthz")
+    def health_check() -> dict[str, str]:
+        return {"status": "ok"}
+
+    @app.get("/api/audio/latest")
+    def get_latest_audio() -> dict[str, Any]:
+        """获取最新生成的音频文件。"""
+        audio_dir = os.path.join(output_dir, "audio")
+        if not os.path.exists(audio_dir):
+            return {"file": None, "error": "音频目录不存在"}
+        
+        # 查找所有 podcast_*.mp3 文件
+        pattern = os.path.join(audio_dir, "podcast_*.mp3")
+        files = glob.glob(pattern)
+        
+        if not files:
+            return {"file": None, "error": "没有找到音频文件"}
+        
+        # 按修改时间排序，获取最新的
+        latest_file = max(files, key=os.path.getmtime)
+        filename = os.path.basename(latest_file)
+        return {"file": filename, "url": f"/output/audio/{filename}"}
+
+    @app.post("/research", response_model=ResearchResponse)
+    def run_research(payload: ResearchRequest) -> ResearchResponse:
+        """
+        触发同步研究任务。
+        
+        执行完整的研究流程，并在 HTTP 响应中一次性返回所有结果。
+        """
+        try:
+            config = _build_config(payload)
+            agent = DeepResearchAgent(config=config)
+            result = agent.run(payload.topic)
+        except ValueError as exc:  # Likely due to unsupported configuration
+            raise HTTPException(status_code=400, detail=str(exc)) from exc
+        except Exception as exc:  # pragma: no cover - defensive guardrail
+            raise HTTPException(status_code=500, detail="Research failed") from exc
+
+        todo_payload = [
+            {
+                "id": item.id,
+                "title": item.title,
+                "intent": item.intent,
+                "query": item.query,
+                "status": item.status,
+                "summary": item.summary,
+                "sources_summary": item.sources_summary,
+                "note_id": item.note_id,
+                "note_path": item.note_path,
+            }
+            for item in result.todo_items
+        ]
+
+        # 确保 podcast_script 类型正确，Pydantic 模型需要 PodcastScript 实例
+        script_content = ""
+        if result.podcast_script:
+            if isinstance(result.podcast_script, (list, dict)):
+                script_content = json.dumps(result.podcast_script, ensure_ascii=False)
+            else:
+                script_content = str(result.podcast_script)
+        
+        podcast_resp = PodcastScript(script=script_content)
+
+        return ResearchResponse(
+            report_markdown=(result.report_markdown or result.running_summary or ""),
+            todo_items=todo_payload,
+            podcast_script=podcast_resp,
+        )
+
+    @app.post("/research/cancel")
+    async def cancel_research() -> dict[str, str]:
+        """
+        主动取消当前正在执行的研究任务。
+        
+        前端可以通过此端点显式通知后端停止处理。
+        """
+        agent = _active_agent.get("current")
+        if agent and not agent.is_cancelled():
+            logger.info("Cancel requested via /research/cancel endpoint")
+            agent.cancel()
+            return {"status": "cancelled", "message": "取消请求已发送"}
+        return {"status": "no_task", "message": "当前没有正在运行的任务"}
+
+    @app.post("/research/stream")
+    async def stream_research(payload: ResearchRequest, request: Request) -> StreamingResponse:
+        """
+        触发流式研究任务。
+        
+        通过 Server-Sent Events (SSE) 实时返回研究进度、日志和部分结果。
+        支持客户端断开连接时自动取消后端任务。
+        """
+        try:
+            config = _build_config(payload)
+            agent = DeepResearchAgent(config=config)
+            _active_agent["current"] = agent  # 注册活跃 agent 以支持取消
+        except ValueError as exc:
+            raise HTTPException(status_code=400, detail=str(exc)) from exc
+
+        async def event_iterator():
+            loop = asyncio.get_event_loop()
+            # 用 asyncio.Queue 桥接同步生成器和异步循环
+            # 生成器在单一后台线程中完整运行，避免并发调用 next() 破坏生成器状态
+            event_queue: asyncio.Queue = asyncio.Queue()
+            _SENTINEL = object()  # 生成器结束的哨兵值
+
+            def run_generator():
+                """在后台线程中完整运行生成器，将事件逐一推入异步队列。"""
+                try:
+                    for event in agent.run_stream(payload.topic):
+                        if agent.is_cancelled():
+                            logger.info("Generator stopped: cancel detected")
+                            break
+                        loop.call_soon_threadsafe(event_queue.put_nowait, event)
+                except Exception as exc:
+                    logger.exception("Generator raised exception")
+                    loop.call_soon_threadsafe(
+                        event_queue.put_nowait,
+                        {"type": "error", "detail": str(exc)},
+                    )
+                finally:
+                    loop.call_soon_threadsafe(event_queue.put_nowait, _SENTINEL)
+
+            # 启动断开连接监控任务
+            async def monitor_disconnect():
+                while True:
+                    if await request.is_disconnected():
+                        logger.info("Client disconnected detected by monitor")
+                        agent.cancel()
+                        return
+                    await asyncio.sleep(0.5)
+
+            monitor_task = asyncio.create_task(monitor_disconnect())
+            executor = concurrent.futures.ThreadPoolExecutor(max_workers=1)
+            loop.run_in_executor(executor, run_generator)
+
+            try:
+                while True:
+                    try:
+                        # 带超时等待，以便能及时响应取消
+                        item = await asyncio.wait_for(event_queue.get(), timeout=1.0)
+                    except asyncio.TimeoutError:
+                        # 超时时检查是否已取消（用于客户端断开但生成器还未感知的情况）
+                        if agent.is_cancelled():
+                            logger.info("✅ 本次任务已取消（超时检测）")
+                            yield 'data: {"type": "cancelled", "message": "研究任务已被用户取消"}\n\n'
+                            break
+                        continue
+
+                    # 哨兵：生成器已结束
+                    if item is _SENTINEL:
+                        break
+
+                    event = item
+                    yield f"data: {json.dumps(event, ensure_ascii=False)}\n\n"
+
+                    if event.get("type") in ("done", "cancelled", "error"):
+                        break
+            finally:
+                # 确保取消信号被设置 —— 这是取消机制的核心：
+                # 前端 abort SSE 后 monitor_task 可能还未检测到断连就被 cancel，
+                # 而 /research/cancel API 到达时 _active_agent 可能已被置 None。
+                # 因此必须在此处显式调用 cancel() 确保后台线程能感知取消。
+                agent.cancel()
+                monitor_task.cancel()
+                try:
+                    await monitor_task
+                except asyncio.CancelledError:
+                    pass
+                executor.shutdown(wait=False)
+                _active_agent["current"] = None
+
+        return StreamingResponse(
+            event_iterator(),
+            media_type="text/event-stream",
+            headers={
+                "Cache-Control": "no-cache",
+                "Connection": "keep-alive",
+            },
+        )
+
+    return app
+
+
+app = create_app()
+
+
+if __name__ == "__main__":
+    import uvicorn
+
+    _config = Configuration.from_env()
+    uvicorn.run(
+        "main:app",
+        host=_config.host,
+        port=_config.port,
+        reload=True,
+        log_level=_config.log_level.lower(),
+    )

Co-creation-projects/JJason-DeepCastAgent/backend/src/models.py

@@ -0,0 +1,53 @@
+"""状态模型，用于深度研究工作流。"""
+
+from dataclasses import dataclass, field
+
+
+@dataclass(kw_only=True)
+class TodoItem:
+    """单个待办任务项。"""
+
+    id: int
+    title: str
+    intent: str
+    query: str
+    status: str = field(default="pending")
+    summary: str | None = field(default=None)
+    sources_summary: str | None = field(default=None) 
+    notices: list[str] = field(default_factory=list)
+    note_id: str | None = field(default=None)
+    note_path: str | None = field(default=None)
+    stream_token: str | None = field(default=None)
+
+
+@dataclass(kw_only=True)
+class SummaryState:
+    """深度研究工作流的状态模型。
+    
+    用于追踪研究主题、搜索结果、待办任务和生成的报告。
+    """
+
+    research_topic: str | None = field(default=None)  # 研究主题
+    web_research_results: list = field(default_factory=list)
+    sources_gathered: list = field(default_factory=list)
+    research_loop_count: int = field(default=0)  # 研究循环次数
+    running_summary: str | None = field(default=None)  # 传统摘要字段
+    todo_items: list = field(default_factory=list)  # 待办任务项列表
+    structured_report: str | None = field(default=None)  # 结构化报告（JSON 字符串）
+    report_note_id: str | None = field(default=None)  # 报告笔记 ID
+    report_note_path: str | None = field(default=None)  # 报告笔记路径
+    podcast_script: list | None = field(default=None)  # 播客脚本（JSON 字符串）
+
+
+@dataclass(kw_only=True)
+class SummaryStateOutput:
+    """深度研究工作流的输出状态模型。
+    
+    用于返回研究摘要、报告、待办任务和播客脚本。
+    """
+
+    running_summary: str | None = field(default=None)  # 向后兼容的摘要文本
+    report_markdown: str | None = field(default=None)
+    todo_items: list[TodoItem] = field(default_factory=list)
+    podcast_script: list | None = field(default=None)
+

Co-creation-projects/JJason-DeepCastAgent/backend/src/prompts.py

@@ -0,0 +1,165 @@
+from datetime import datetime
+
+
+def get_current_date():
+    """以中文格式获取当前日期"""
+    return datetime.now().strftime("%Y年%m月%d日")
+
+
+# ============================================================
+# 研究规划专家 - 任务拆解
+# ============================================================
+todo_planner_system_prompt = """
+你是一名资深的研究规划专家，专注于将复杂主题拆解为结构化、可执行的研究任务。
+
+<核心职责>
+1. 分析研究主题，识别关键维度和子问题
+2. 设计 3-5 个互补且不重复的调研任务
+3. 为每个任务制定明确的检索策略
+</核心职责>
+
+<任务设计原则>
+- 覆盖性：任务组合应全面覆盖研究主题的核心方面
+- 互补性：避免任务之间的内容重叠
+- 可执行性：每个任务都有明确的检索方向
+- 深度性：兼顾基础背景和前沿动态
+</任务设计原则>
+"""
+
+
+todo_planner_instructions = """
+<研究上下文>
+当前日期：{current_date}
+研究主题：{research_topic}
+</研究上下文>
+
+<输出格式>
+请以 JSON 格式输出任务列表：
+{{
+  "tasks": [
+    {{
+      "title": "简洁的任务名称（不超过10个字）",
+      "intent": "该任务要解答的核心问题（1-2句话）",
+      "query": "推荐的搜索关键词或查询语句"
+    }}
+  ]
+}}
+</输出格式>
+
+<重要提示>
+- 即使主题较为宽泛（如"聊聊XX的发展"），也请主动从技术原理、应用场景、市场格局、发展趋势、挑战与机遇等角度构建研究任务
+- 确保每个任务的 query 具有实际可检索性
+- 输出有效的 JSON 格式，tasks 数组不能为空
+</重要提示>
+"""
+
+
+# ============================================================
+# 任务执行专家 - 内容总结
+# ============================================================
+task_summarizer_system_prompt = """
+你是一名研究执行专家，负责对检索到的信息进行深度分析和结构化总结。
+
+<核心要求>
+1. 基于检索结果，提炼 3-5 条有价值的关键发现
+2. 每条发现需包含具体的事实、数据或案例支撑
+3. 从多个维度分析：原理机制、实际应用、优劣势、发展趋势、行业对比等
+4. 保持客观中立，区分事实与观点
+</核心要求>
+
+<输出规范>
+- 直接输出 Markdown 格式的总结内容
+- 以"任务总结"作为标题
+- 使用有序或无序列表呈现关键发现
+- 禁止输出"我将..."、"根据..."等废话
+- 若无有效信息，输出"暂无可用信息"
+</输出规范>
+"""
+
+
+# ============================================================
+# 报告撰写专家 - 研究报告生成
+# ============================================================
+report_writer_instructions = """
+你是一名专业的分析报告撰写专家，负责将多个任务的研究结果整合为一份结构化的深度报告。
+
+<报告结构>
+1. **背景概览**
+   - 研究主题的定义与重要性
+   - 当前发展阶段与行业背景
+
+2. **核心洞见**
+   - 提炼 3-5 条最重要的研究发现
+   - 每条洞见需有明确的数据或案例支撑
+   - 标注信息来源（任务编号或来源名称）
+
+3. **深度分析**
+   - 技术原理或核心机制解析
+   - 典型应用场景与案例
+   - 市场格局与竞争态势
+
+4. **风险与挑战**
+   - 当前存在的主要问题
+   - 潜在风险与不确定性
+   - 待验证的假设
+
+5. **参考来源**
+   - 列出研究过程中实际引用的外部来源（网页、论文、报告等）
+   - 格式：`[来源标题](URL)` Markdown 链接
+   - **注意**：只列出搜索结果中的实际外部来源，不要引用"任务总结"或内部笔记
+</报告结构>
+
+<写作要求>
+- 使用 Markdown 格式
+- 语言专业、逻辑清晰
+- 参考来源必须来自搜索结果中的实际网页/文献，不要引用任务总结本身
+- 信息缺失时注明"暂无相关信息"
+</写作要求>
+"""
+
+
+# ============================================================
+# 播客脚本专家 - 对话脚本生成
+# ============================================================
+script_writer_instructions = """
+你是一名资深播客策划人，擅长将深度研究报告转化为生动有趣的双人对话脚本。
+
+<主持人设定>
+- **Host（夏雨）**：男声主持人
+  - 性格：好奇心强、幽默风趣、善于引导
+  - 角色：代表普通听众视角，负责提问和总结
+  - 风格：活泼亲和，善于用生活化的比喻
+
+- **Guest（李娃）**：女声嘉宾
+  - 性格：专业严谨、博学多才、表达清晰
+  - 角色：领域专家，负责深入解释和专业解答
+  - 风格：深入浅出，偶尔用幽默化解专业术语
+</主持人设定>
+
+<脚本要求>
+1. **开场白**
+   - Host 热情开场，简要引出今天的话题
+   - Guest 简短回应，引发听众兴趣
+
+2. **主体对话**
+   - 围绕报告核心内容展开讨论
+   - Host 提出问题："为什么？"、"怎么做？"、"这意味着什么？"
+   - Guest 专业解答，辅以案例和数据
+   - 适当插入互动：追问、补充、总结
+
+3. **结尾收束**
+   - Host 总结本期核心收获（2-3点）
+   - Guest 给出未来展望或金句
+   - Host 结束语，预告下期或感谢收听
+
+4. **语言风格**
+   - 自然流畅，像朋友聊天
+   - 适度使用口语化表达（"哇"、"确实"、"原来如此"）
+   - 避免生硬的书面语和过度的感叹
+</脚本要求>
+
+<输出格式>
+输出 JSON 数组，每个元素包含 role（"Host" 或 "Guest"）和 content 字段。
+对话轮次控制在 6-15 轮，确保内容充实且不拖沓。
+</输出格式>
+"""

Co-creation-projects/JJason-DeepCastAgent/backend/src/services/__init__.py

@@ -0,0 +1,2 @@
+"""深度研究工作流的领域服务。"""
+

Co-creation-projects/JJason-DeepCastAgent/backend/src/services/audio_generator.py

@@ -0,0 +1,189 @@
+"""使用 TTS API 从文本生成音频的服务。"""
+
+from __future__ import annotations
+
+import logging
+from collections.abc import Callable
+from pathlib import Path
+from threading import Event
+
+import requests
+
+from config import Configuration
+
+logger = logging.getLogger(__name__)
+
+
+class AudioGenerationService:
+    """处理与 TTS 服务的交互以生成音频文件。"""
+
+    def __init__(self, config: Configuration) -> None:
+        """
+        初始化音频生成服务。
+
+        Args:
+            config: 包含 TTS 配置和输出路径的配置对象。
+        """
+        self._config = config
+        self._output_dir = Path(config.audio_output_dir)
+        self._ensure_output_dir()
+
+    def _ensure_output_dir(self) -> None:
+        """
+        如果输出目录不存在，则创建它。
+        
+        同时处理创建目录时的潜在权限错误。
+        """
+        if not self._output_dir.exists():
+            try:
+                self._output_dir.mkdir(parents=True, exist_ok=True)
+                logger.info("Created audio output directory: %s", self._output_dir)
+            except Exception as e:
+                logger.error("Failed to create audio output directory: %s", e)
+
+    def generate_audio(
+        self, 
+        script: list[dict[str, str]], 
+        task_id: str = "default",
+        progress_callback: Callable[[int, int, str, str], bool | None] | None = None,
+        cancel_event: Event | None = None,
+    ) -> list[str]:
+        """
+        为给定的脚本生成音频文件。
+        
+        Args:
+            script: 对话回合列表，例如 [{"role": "Host", "content": "..."}, ...]
+            task_id: 当前任务/会话的唯一标识符
+            progress_callback: 可选的进度回调函数，签名为 (current, total, role, content_preview) -> Optional[bool]
+                              返回 False 表示应该停止生成，返回 True 或 None 表示继续
+            cancel_event: 可选的取消事件，set 时立即停止生成
+            
+        Returns:
+            生成的音频文件的路径列表
+        """
+        # 检查FFmpeg路径是否配置
+        if not self._config.ffmpeg_path:
+            logger.error("FFmpeg path not configured. Audio generation will fail.")
+            return []
+        if not self._config.tts_api_key:
+            logger.warning("TTS API key not configured. Skipping audio generation.")
+            return []
+
+        generated_files = []
+        total = len(script)
+        
+        for index, turn in enumerate(script):
+            role = turn.get("role", "")
+            content = turn.get("content", "")
+            
+            if not role or not content:
+                continue
+
+            # 直接检查取消事件（最可靠的方式）
+            if cancel_event and cancel_event.is_set():
+                logger.info("Audio generation cancelled before TTS %d/%d (cancel_event)", index + 1, total)
+                break
+                
+            voice_id = self._get_voice_for_role(role)
+            if not voice_id:
+                logger.warning("Unknown role: %s. Using default voice.", role)
+                voice_id = "xiayu" # Fallback
+            
+            file_name = f"{task_id}_{index:03d}_{role}.mp3"
+            file_path = self._output_dir / file_name
+            
+            logger.info("[TTS %d/%d] 正在为 %s 生成语音: %s...", index + 1, total, role, content[:20])
+            
+            if self._call_tts_api(content, voice_id, file_path):
+                generated_files.append(str(file_path))
+                logger.info("[TTS %d/%d] ✓ %s 语音生成成功", index + 1, total, role)
+                
+                # TTS 完成后再次检查取消
+                if cancel_event and cancel_event.is_set():
+                    logger.info("Audio generation cancelled after TTS %d/%d (cancel_event)", index + 1, total)
+                    break
+                
+                # 在 TTS 成功之后才调用进度回调，通知上层该片段已完成
+                if progress_callback:
+                    content_preview = content[:30] + "..." if len(content) > 30 else content
+                    should_continue = progress_callback(index + 1, total, role, content_preview)
+                    if should_continue is False:
+                        logger.info("Audio generation cancelled by callback after TTS %d/%d", index + 1, total)
+                        break
+            else:
+                logger.error("[TTS %d/%d] ✗ %s 语音生成失败", index + 1, total, role)
+                
+        logger.info("Generated %d audio files for task %s", len(generated_files), task_id)
+        return generated_files
+
+    def _get_voice_for_role(self, role: str) -> str:
+        """
+        将角色名称映射到语音 ID。
+        
+        Args:
+            role: 角色名称（如 Host, Guest）。
+            
+        Returns:
+            对应的语音 ID（xiayu 或 liwa）。
+        """
+        role_lower = role.lower()
+        if "host" in role_lower or "xiayu" in role_lower:
+            return "xiayu"
+        elif "guest" in role_lower or "liwa" in role_lower:
+            return "liwa"
+        return "xiayu"
+
+    def _call_tts_api(self, text: str, voice: str, output_path: Path) -> bool:
+        """
+        调用 TTS API 并保存音频文件。
+        
+        Args:
+            text: 要转换的文本。
+            voice: 语音 ID。
+            output_path: 输出文件路径。
+            
+        Returns:
+            如果成功生成并保存，返回 True；否则返回 False。
+        """
+        if output_path.exists():
+            logger.debug("Audio file already exists: %s", output_path)
+            return True
+
+        headers = {
+            "Authorization": f"Bearer {self._config.tts_api_key}",
+            "Content-Type": "application/json"
+        }
+        
+        payload = {
+            "model": self._config.tts_model,
+            "input": text,
+            "voice": voice,
+            "speed": 1.0
+        }
+        
+        try:
+            logger.debug("Calling TTS API for voice %s: %s...", voice, text[:20])
+            # Use configurable timeout if available; default to 300 seconds for robustness.
+            timeout = self._config.tts_timeout
+            response = requests.post(
+                self._config.tts_base_url,
+                json=payload,
+                headers=headers,
+                timeout=timeout
+            )
+            
+            if response.status_code == 200:
+                with open(output_path, "wb") as f:
+                    f.write(response.content)
+                return True
+            else:
+                logger.error(
+                    "TTS API failed with status %d: %s", 
+                    response.status_code, 
+                    response.text
+                )
+                return False
+                
+        except Exception as e:
+            logger.exception("Exception during TTS API call: %s", e)
+            return False

Co-creation-projects/JJason-DeepCastAgent/backend/src/services/audio_synthesizer.py

@@ -0,0 +1,95 @@
+"""将音频片段合成为单个播客文件的服务。"""
+
+from __future__ import annotations
+
+import logging
+from collections.abc import Callable
+from pathlib import Path
+
+from pydub import AudioSegment
+
+from config import Configuration
+
+logger = logging.getLogger(__name__)
+
+
+class PodcastSynthesisService:
+    """将多个音频片段组合成最终的播客文件。"""
+
+    def __init__(self, config: Configuration) -> None:
+        """
+        初始化音频合成服务。
+
+        Args:
+            config: 包含 ffmpeg 路径和输出路径的配置对象。
+        """
+        self._config = config
+        self._output_dir = Path(config.audio_output_dir)
+        
+        # 如果提供了 ffmpeg 路径，则进行配置
+        if config.ffmpeg_path:
+            AudioSegment.converter = config.ffmpeg_path
+            logger.info("Configured ffmpeg path: %s", config.ffmpeg_path)
+        
+        # 确保 pydub/ffmpeg 可用 - 假设 ffmpeg 已安装在系统中
+        # 如果没有，pydub 可能会发出警告或失败，但我们会捕获异常。
+
+    def synthesize_podcast(self, audio_files: list[str], task_id: str = "default", cancel_check: Callable[[], bool] | None = None) -> str | None:
+        """
+        将音频文件组合成单个播客 MP3。
+
+        Args:
+            audio_files: 按顺序排列的输入音频文件路径列表。
+            task_id: 输出文件名的唯一标识符。
+            cancel_check: 可选的取消检查回调，返回 True 表示已取消。
+
+        Returns:
+            最终播客文件的路径，如果失败则为 None。
+        """
+        if not audio_files:
+            logger.warning("No audio files provided for synthesis.")
+            return None
+
+        try:
+            combined = AudioSegment.empty()
+            
+            # 片段之间的静音（例如 500ms）
+            silence = AudioSegment.silent(duration=500)
+
+            valid_segments_count = 0
+            for file_path in audio_files:
+                # 检查是否已取消
+                if cancel_check and cancel_check():
+                    logger.info("Podcast synthesis cancelled.")
+                    return None
+                    
+                path = Path(file_path)
+                if not path.exists():
+                    logger.warning("Audio file not found: %s", file_path)
+                    continue
+                
+                try:
+                    segment = AudioSegment.from_file(file_path, format="mp3")
+                    if valid_segments_count > 0:
+                        combined += silence
+                    combined += segment
+                    valid_segments_count += 1
+                except Exception as e:
+                    logger.error("Failed to load audio segment %s: %s", file_path, e)
+
+            if valid_segments_count == 0:
+                logger.error("No valid audio segments to combine.")
+                return None
+
+            output_filename = f"podcast_{task_id}.mp3"
+            output_path = self._output_dir / output_filename
+            
+            # 导出
+            logger.info("Exporting podcast to %s...", output_path)
+            combined.export(output_path, format="mp3")
+            
+            return str(output_path)
+
+        except Exception as e:
+            logger.exception("Podcast synthesis failed: %s", e)
+            return None

Co-creation-projects/JJason-DeepCastAgent/backend/src/services/notes.py

@@ -0,0 +1,65 @@
+"""协调笔记工具使用说明的助手。"""
+
+from __future__ import annotations
+
+import json
+
+from models import TodoItem
+
+
+def build_note_guidance(task: TodoItem) -> str:
+    """为特定任务生成笔记工具使用说明。"""
+    tags_list = ["deep_research", f"task_{task.id}"]
+    tags_literal = json.dumps(tags_list, ensure_ascii=False)
+
+    if task.note_id:
+        read_payload = json.dumps({"action": "read", "note_id": task.note_id}, ensure_ascii=False)
+        # 只提供更新笔记的模板，让 LLM 自行填充实际研究内容
+        update_template = json.dumps(
+            {
+                "action": "update",
+                "note_id": task.note_id,
+                "task_id": task.id,
+                "title": f"任务 {task.id}: {task.title}",
+                "note_type": "task_state",
+                "tags": tags_list,
+                "content": "<请在此填写更新后的完整内容>",
+            },
+            ensure_ascii=False,
+        )
+
+        return (
+            "笔记协作指引：\n"
+            f"- 当前任务笔记 ID：{task.note_id}。\n"
+            f"- 在书写总结前必须调用：[TOOL_CALL:note:{read_payload}] 获取最新内容。\n"
+            f"- 完成分析后更新笔记，参数模板如下（需将 content 替换为实际内容）：\n"
+            f"  {update_template}\n"
+            "- **重要**：content 字段必须包含原有内容加上本轮新增的研究发现，不要使用占位文本。\n"
+            "- 更新时保持原有段落结构，新增内容请在对应段落中补充。\n"
+            f"- 建议 tags 保持为 {tags_literal}，保证其他 Agent 可快速定位。\n"
+            "- 成功同步到笔记后，再输出面向用户的总结。\n"
+        )
+
+    # 只提供创建笔记的模板，让 LLM 自行填充实际研究内容
+    create_template = json.dumps(
+        {
+            "action": "create",
+            "task_id": task.id,
+            "title": f"任务 {task.id}: {task.title}",
+            "note_type": "task_state",
+            "tags": tags_list,
+            "content": "<请在此填写任务总结内容>",
+        },
+        ensure_ascii=False,
+    )
+
+    return (
+        "笔记协作指引：\n"
+        f"- 当前任务尚未建立笔记。\n"
+        f"- 创建笔记时请使用格式：[TOOL_CALL:note:{{...}}]，参数模板如下（需将 content 替换为实际研究总结）：\n"
+        f"  {create_template}\n"
+        "- **重要**：content 字段必须填写本次任务的实际研究发现和关键信息，不要使用占位文本。\n"
+        "- 创建成功后记录返回的 note_id，并在后续所有更新中复用。\n"
+        "- 同步笔记后，再输出面向用户的总结。\n"
+    )
+

Co-creation-projects/JJason-DeepCastAgent/backend/src/services/planner.py

@@ -0,0 +1,171 @@
+"""负责将研究主题转换为可操作任务的服务。"""
+
+from __future__ import annotations
+
+import json
+import logging
+import re
+from typing import Any
+
+from hello_agents import ToolAwareSimpleAgent
+
+from config import Configuration
+from models import SummaryState, TodoItem
+from prompts import get_current_date, todo_planner_instructions
+from utils import strip_thinking_tokens
+
+logger = logging.getLogger(__name__)
+
+TOOL_CALL_PATTERN = re.compile(
+    r"\[TOOL_CALL:(?P<tool>[^:]+):(?P<body>[^\]]+)\]",
+    re.IGNORECASE,
+)
+
+class PlanningService:
+    """包装规划器代理以生成结构化 TODO 项目。"""
+
+    def __init__(self, planner_agent: ToolAwareSimpleAgent, config: Configuration) -> None:
+        self._agent = planner_agent
+        self._config = config
+
+    def plan_todo_list(self, state: SummaryState) -> list[TodoItem]:
+        """
+        要求规划器代理将主题分解为可操作的任务。
+        
+        Args:
+            state: 当前研究状态，包含主题。
+            
+        Returns:
+            规划出的 TodoItem 列表。
+        """
+        prompt = todo_planner_instructions.format(
+            current_date=get_current_date(),
+            research_topic=state.research_topic,
+        )
+
+        response = self._agent.run(prompt)
+        self._agent.clear_history()
+
+        logger.info("Planner raw output (truncated): %s", response[:500])
+
+        tasks_payload = self._extract_tasks(response)
+        todo_items: list[TodoItem] = []
+
+        for idx, item in enumerate(tasks_payload, start=1):
+            title = str(item.get("title") or f"任务{idx}").strip()
+            intent = str(item.get("intent") or "聚焦主题的关键问题").strip()
+            query = str(item.get("query") or state.research_topic).strip()
+
+            if not query:
+                query = state.research_topic
+
+            task = TodoItem(
+                id=idx,
+                title=title,
+                intent=intent,
+                query=query,
+            )
+            todo_items.append(task)
+
+        state.todo_items = todo_items
+
+        titles = [task.title for task in todo_items]
+        logger.info("Planner produced %d tasks: %s", len(todo_items), titles)
+        return todo_items
+
+    @staticmethod
+    def create_fallback_task(state: SummaryState) -> TodoItem:
+        """
+        规划失败时创建一个最小的回退任务。
+        
+        当 LLM 无法生成有效的 JSON 任务列表时调用。
+        """
+        return TodoItem(
+            id=1,
+            title="基础背景梳理",
+            intent="收集主题的核心背景与最新动态",
+            query=f"{state.research_topic} 最新进展" if state.research_topic else "基础背景梳理",
+        )
+
+    # ------------------------------------------------------------------
+    # 解析助手
+    # ------------------------------------------------------------------
+    def _extract_tasks(self, raw_response: str) -> list[dict[str, Any]]:
+        """
+        将规划器输出解析为任务字典列表。
+        
+        支持纯 JSON 格式或嵌入在工具调用中的 JSON。
+        """
+        text = raw_response.strip()
+        if self._config.strip_thinking_tokens:
+            text = strip_thinking_tokens(text)
+
+        json_payload = self._extract_json_payload(text)
+        tasks: list[dict[str, Any]] = []
+
+        if isinstance(json_payload, dict):
+            candidate = json_payload.get("tasks")
+            if isinstance(candidate, list):
+                for item in candidate:
+                    if isinstance(item, dict):
+                        tasks.append(item)
+        elif isinstance(json_payload, list):
+            for item in json_payload:
+                if isinstance(item, dict):
+                    tasks.append(item)
+
+        if not tasks:
+            tool_payload = self._extract_tool_payload(text)
+            if tool_payload and isinstance(tool_payload.get("tasks"), list):
+                for item in tool_payload["tasks"]:
+                    if isinstance(item, dict):
+                        tasks.append(item)
+
+        return tasks
+
+    def _extract_json_payload(self, text: str) -> dict[str, Any] | list | None:
+        """尝试从文本中定位并解析 JSON 对象或数组。"""
+        start = text.find("{")
+        end = text.rfind("}")
+        if start != -1 and end != -1 and end > start:
+            candidate = text[start : end + 1]
+            try:
+                return json.loads(candidate)
+            except json.JSONDecodeError:
+                pass
+
+        start = text.find("[")
+        end = text.rfind("]")
+        if start != -1 and end != -1 and end > start:
+            candidate = text[start : end + 1]
+            try:
+                return json.loads(candidate)
+            except json.JSONDecodeError:
+                return None
+
+        return None
+
+    def _extract_tool_payload(self, text: str) -> dict[str, Any] | None:
+        """解析输出中的第一个 TOOL_CALL 表达式。"""
+        match = TOOL_CALL_PATTERN.search(text)
+        if not match:
+            return None
+
+        body = match.group("body")
+
+        try:
+            payload = json.loads(body)
+            if isinstance(payload, dict):
+                return payload
+        except json.JSONDecodeError:
+            pass
+
+        parts = [segment.strip() for segment in body.split(",") if segment.strip()]
+        payload: dict[str, Any] = {}
+        for part in parts:
+            if "=" not in part:
+                continue
+            key, value = part.split("=", 1)
+            payload[key.strip()] = value.strip().strip('"').strip("'")
+
+        return payload or None

Co-creation-projects/JJason-DeepCastAgent/backend/src/services/reporter.py

@@ -0,0 +1,92 @@
+"""将任务结果整合为最终报告的服务。"""
+
+from __future__ import annotations
+
+import json
+
+from hello_agents import ToolAwareSimpleAgent
+
+from config import Configuration
+from models import SummaryState
+from services.text_processing import strip_tool_calls
+from utils import strip_thinking_tokens
+
+
+class ReportingService:
+    """生成最终的结构化报告。"""
+
+    def __init__(  # noqa: D107
+        self, report_agent: ToolAwareSimpleAgent, config: Configuration
+    ) -> None:
+        self._agent = report_agent
+        self._config = config
+
+    def generate_report(self, state: SummaryState) -> str:
+        """
+        基于完成的任务生成结构化报告。
+
+        Args:
+            state: 包含任务结果和笔记的研究状态。
+
+        Returns:
+            Markdown 格式的报告文本。
+        """
+        tasks_block = []
+        for task in state.todo_items:
+            summary_block = task.summary or "暂无可用信息"
+            sources_block = task.sources_summary or "暂无来源"
+            tasks_block.append(
+                f"### 任务 {task.id}: {task.title}\n"
+                f"- 任务目标：{task.intent}\n"
+                f"- 检索查询：{task.query}\n"
+                f"- 执行状态：{task.status}\n"
+                f"- 任务总结：\n{summary_block}\n"
+                f"- 来源概览：\n{sources_block}\n"
+            )
+
+        note_references = []
+        for task in state.todo_items:
+            if task.note_id:
+                note_references.append(
+                    f"- 任务 {task.id}《{task.title}》：note_id={task.note_id}"
+                )
+
+        notes_section = (
+            "\n".join(note_references) if note_references else "- 暂无可用任务笔记"
+        )
+
+        read_template = json.dumps(
+            {"action": "read", "note_id": "<note_id>"}, ensure_ascii=False
+        )
+        # 结论笔记模板，让 LLM 自己填充实际内容
+        create_conclusion_template = json.dumps(
+            {
+                "action": "create",
+                "title": f"研究报告：{state.research_topic}",
+                "note_type": "conclusion",
+                "tags": ["deep_research", "report"],
+                "content": "<请在此填写报告核心要点>",
+            },
+            ensure_ascii=False,
+        )
+
+        prompt = (
+            f"研究主题：{state.research_topic}\n"
+            f"任务概览：\n{''.join(tasks_block)}\n"
+            f"可用任务笔记：\n{notes_section}\n"
+            f"请针对每条任务笔记使用格式：[TOOL_CALL:note:{read_template}] 读取内容，整合所有信息后撰写报告。\n"
+            f"如需输出汇总结论，可追加调用 note 工具保存报告要点，参数模板如下（需将 content 替换为实际报告要点）：\n"
+            f"  {create_conclusion_template}\n"
+            "**重要**：content 字段必须填写本次研究的实际核心发现和结论，不要使用占位文本。"
+        )
+
+        response = self._agent.run(prompt)
+        self._agent.clear_history()
+
+        report_text = response.strip()
+        if self._config.strip_thinking_tokens:
+            report_text = strip_thinking_tokens(report_text)
+
+        report_text = strip_tool_calls(report_text).strip()
+
+        return report_text or "报告生成失败，请检查输入。"

Co-creation-projects/JJason-DeepCastAgent/backend/src/services/script_generator.py

@@ -0,0 +1,242 @@
+"""将研究报告转换为播客脚本的服务。"""
+
+from __future__ import annotations
+
+import json
+import logging
+import re
+
+from openai import OpenAI
+
+from config import Configuration
+from models import SummaryState
+from prompts import script_writer_instructions
+
+logger = logging.getLogger(__name__)
+
+# 播客脚本的 JSON Schema
+SCRIPT_JSON_SCHEMA = {
+    "type": "array",
+    "items": {
+        "type": "object",
+        "properties": {
+            "role": {
+                "type": "string",
+                "enum": ["Host", "Guest"],
+                "description": "对话角色，Host 为主持人，Guest 为嘉宾"
+            },
+            "content": {
+                "type": "string",
+                "description": "对话内容"
+            }
+        },
+        "required": ["role", "content"]
+    },
+    "minItems": 6,
+    "maxItems": 15
+}
+
+
+class ScriptGenerationService:
+    """从研究报告生成对话脚本（使用结构化输出）。"""
+
+    def __init__(
+        self,
+        config: Configuration,
+        script_agent: OpenAI | None = None,
+    ) -> None:
+        """
+        初始化服务。
+
+        Args:
+            config: 全局配置对象。
+            script_agent: 可选的自定义脚本生成客户端/代理。
+                如果提供，将直接使用该客户端；否则将基于配置创建默认的 OpenAI 客户端。
+        """
+        self._config = config
+        # 优先使用注入的自定义客户端，以保持向后兼容和可测试性；
+        # 如果未提供，则基于配置创建默认的 OpenAI 客户端以支持结构化输出。
+        self._client = script_agent or OpenAI(
+            api_key=config.llm_api_key,
+            base_url=config.llm_base_url,
+        )
+        # 使用 fast_llm_model（ecnu-max）进行脚本生成，它支持结构化输出
+        self._model = config.fast_llm_model or "ecnu-max"
+
+    def generate_script(self, state: SummaryState) -> list[dict[str, str]]:
+        """基于结构化报告生成播客脚本（使用结构化输出）。"""
+        if not state.structured_report:
+            logger.warning("No structured report available for script generation.")
+            return []
+        
+        report_length = len(state.structured_report)
+        logger.info("Generating script from report (%d chars) using structured output...", report_length)
+
+        user_prompt = f"<RESEARCH_REPORT>\n{state.structured_report}\n</RESEARCH_REPORT>"
+
+        try:
+            response = self._client.chat.completions.create(
+                model=self._model,
+                messages=[
+                    {"role": "system", "content": script_writer_instructions.strip()},
+                    {"role": "user", "content": user_prompt},
+                ],
+                temperature=0.7,
+                max_tokens=4096,
+                response_format={
+                    "type": "json_schema",
+                    "json_schema": {
+                        "name": "podcast_script",
+                        "schema": SCRIPT_JSON_SCHEMA
+                    },
+                },
+            )
+            
+            content = response.choices[0].message.content
+            logger.info("Received structured response (%d chars)", len(content) if content else 0)
+            
+            if not content:
+                logger.error("Empty response from LLM")
+                return []
+            
+            # 尝试解析 JSON（处理各种格式问题）
+            script = self._parse_script_json(content)
+            
+            if script is None:
+                return []
+            
+            if not isinstance(script, list):
+                logger.error("Script output is not a list: %s", type(script))
+                return []
+            
+            # 验证并标准化
+            valid_script = []
+            for item in script:
+                if isinstance(item, dict) and "role" in item and "content" in item:
+                    role = item["role"]
+                    content = item["content"]
+                    # 标准化角色名
+                    if role.lower() in ["host", "xiayu"]:
+                        role = "Host"
+                    elif role.lower() in ["guest", "liwa"]:
+                        role = "Guest"
+                    valid_script.append({"role": role, "content": content})
+            
+            logger.info("Generated script with %d dialogue turns.", len(valid_script))
+            return valid_script
+
+        except json.JSONDecodeError as e:
+            logger.error("JSON decode error (should not happen with structured output): %s", e)
+            return []
+        except Exception as e:
+            logger.error("Script generation failed: %s", e)
+            return []
+
+    def _parse_script_json(self, content: str) -> list | None:
+        """
+        尝试多种方式解析脚本 JSON。
+        
+        Args:
+            content: LLM 返回的原始内容
+            
+        Returns:
+            解析后的列表，失败返回 None
+        """
+        # 1. 直接尝试解析
+        try:
+            return json.loads(content)
+        except json.JSONDecodeError as e:
+            logger.debug("Direct JSON parse failed at char %d: %s", e.pos, e.msg)
+        
+        # 2. 尝试从 markdown 代码块中提取
+        json_match = re.search(r'```(?:json)?\s*([\s\S]*?)```', content)
+        if json_match:
+            try:
+                result = json.loads(json_match.group(1).strip())
+                logger.info("Extracted JSON from markdown code block")
+                return result
+            except json.JSONDecodeError:
+                pass
+        
+        # 3. 提取 JSON 数组部分
+        start_idx = content.find('[')
+        end_idx = content.rfind(']')
+        if start_idx != -1 and end_idx != -1 and end_idx > start_idx:
+            json_str = content[start_idx:end_idx + 1]
+            
+            # 3a. 直接尝试
+            try:
+                return json.loads(json_str)
+            except json.JSONDecodeError as e:
+                logger.debug("Array extraction failed at char %d: %s", e.pos, e.msg)
+                # 记录出错位置附近的内容
+                error_start = max(0, e.pos - 50)
+                error_end = min(len(json_str), e.pos + 50)
+                logger.debug("Content around error: ...%s...", json_str[error_start:error_end])
+            
+            # 3b. 尝试修复常见问题
+            fixed_json = self._fix_json_issues(json_str)
+            try:
+                result = json.loads(fixed_json)
+                logger.info("Parsed JSON after fixing common issues")
+                return result
+            except json.JSONDecodeError:
+                pass
+        
+        # 4. 最后尝试：逐个对象解析
+        result = self._parse_objects_individually(content)
+        if result:
+            logger.info("Parsed %d objects individually", len(result))
+            return result
+        
+        logger.error("Could not parse JSON from response. First 500 chars: %s", content[:500])
+        return None
+    
+    def _fix_json_issues(self, json_str: str) -> str:
+        """尝试修复常见的 JSON 格式问题。"""
+        fixed = json_str
+        
+        # 替换中文引号为英文引号
+        fixed = fixed.replace('"', '"').replace('"', '"')
+        fixed = fixed.replace(''', "'").replace(''', "'")
+        
+        # 移除可能的 BOM 或其他不可见字符
+        fixed = fixed.strip('\ufeff\u200b\u200c\u200d')
+        
+        # 修复未转义的换行符（在字符串值内）
+        # 这是一个简化的修复，可能不完美
+        def escape_newlines_in_strings(match):
+            return match.group(0).replace('\n', '\\n').replace('\r', '\\r')
+        
+        # 匹配 JSON 字符串值
+        fixed = re.sub(r'"[^"]*"', escape_newlines_in_strings, fixed)
+        
+        return fixed
+    
+    def _parse_objects_individually(self, content: str) -> list | None:
+        """
+        尝试逐个解析 JSON 对象。
+        
+        当整体解析失败时，尝试提取每个 {role, content} 对象。
+        """
+        results = []
+        
+        # 匹配 {"role": "...", "content": "..."} 模式
+        # 使用非贪婪匹配
+        pattern = r'\{\s*"role"\s*:\s*"(Host|Guest)"\s*,\s*"content"\s*:\s*"((?:[^"\\]|\\.)*)"\s*\}'
+        
+        for match in re.finditer(pattern, content, re.DOTALL):
+            role = match.group(1)
+            # 处理转义字符
+            content_text = match.group(2)
+            try:
+                # 使用 json.loads 来正确处理转义
+                content_text = json.loads(f'"{content_text}"')
+            except Exception:
+                pass
+            results.append({"role": role, "content": content_text})
+        
+        if results:
+            return results
+        
+        return None

Co-creation-projects/JJason-DeepCastAgent/backend/src/services/search.py

@@ -0,0 +1,133 @@
+"""利用 HelloAgents SearchTool 的搜索分发助手。"""
+
+from __future__ import annotations
+
+import logging
+import threading
+from typing import Any
+
+from hello_agents.tools import SearchTool
+
+from config import Configuration
+from utils import (
+    deduplicate_and_format_sources,
+    format_sources,
+    get_config_value,
+)
+
+logger = logging.getLogger(__name__)
+
+MAX_TOKENS_PER_SOURCE = 2000
+_GLOBAL_SEARCH_TOOL: SearchTool | None = None
+_SEARCH_TOOL_LOCK = threading.Lock()
+
+
+def get_global_search_tool(config: Configuration) -> SearchTool:
+    """使用 API 密钥延迟初始化全局搜索工具（线程安全）。"""
+    global _GLOBAL_SEARCH_TOOL
+    if _GLOBAL_SEARCH_TOOL is None:
+        with _SEARCH_TOOL_LOCK:
+            # 双重检查锁定，避免多线程重复创建
+            if _GLOBAL_SEARCH_TOOL is None:
+                _GLOBAL_SEARCH_TOOL = SearchTool(
+                    backend="hybrid",
+                    tavily_key=config.tavily_api_key,
+                    serpapi_key=config.serpapi_api_key,
+                )
+    return _GLOBAL_SEARCH_TOOL
+
+
+def dispatch_search(
+    query: str,
+    config: Configuration,
+    loop_count: int,
+) -> tuple[dict[str, Any] | None, list[str], str | None, str]:
+    """
+    执行配置的搜索后端并标准化响应负载。
+    
+    Args:
+        query: 搜索查询字符串。
+        config: 包含搜索 API 配置的对象。
+        loop_count: 当前研究循环计数（用于分页或深度控制）。
+        
+    Returns:
+        元组 (原始负载, 通知列表, 答案文本, 后端标签)。
+    """
+    search_api = get_config_value(config.search_api)
+    search_tool = get_global_search_tool(config)
+
+    try:
+        raw_response = search_tool.run(
+            {
+                "input": query,
+                "backend": search_api,
+                "mode": "structured",
+                "fetch_full_page": config.fetch_full_page,
+                "max_results": 5,
+                "max_tokens_per_source": MAX_TOKENS_PER_SOURCE,
+                "loop_count": loop_count,
+            }
+        )
+    except Exception as exc:  # pragma: no cover - defensive logging
+        logger.exception("Search backend %s failed: %s", search_api, exc)
+        raise
+
+    if isinstance(raw_response, str):
+        notices = [raw_response]
+        logger.warning("Search backend %s returned text notice: %s", search_api, raw_response)
+        payload: dict[str, Any] = {
+            "results": [],
+            "backend": search_api,
+            "answer": None,
+            "notices": notices,
+        }
+    else:
+        payload = raw_response
+        notices = list(payload.get("notices") or [])
+
+    backend_label = str(payload.get("backend") or search_api)
+    answer_text = payload.get("answer")
+    results = payload.get("results", [])
+
+    if notices:
+        for notice in notices:
+            logger.info("Search notice (%s): %s", backend_label, notice)
+
+    logger.info(
+        "Search backend=%s resolved_backend=%s answer=%s results=%s",
+        search_api,
+        backend_label,
+        bool(answer_text),
+        len(results),
+    )
+
+    return payload, notices, answer_text, backend_label
+
+
+def prepare_research_context(
+    search_result: dict[str, Any] | None,
+    answer_text: str | None,
+    config: Configuration,
+) -> tuple[str, str]:
+    """
+    为下游代理构建结构化上下文和来源摘要。
+    
+    Args:
+        search_result: 搜索后端返回的原始结果字典。
+        answer_text: 搜索后端直接生成的答案（如果有）。
+        config: 配置对象。
+        
+    Returns:
+        元组 (来源摘要列表, 详细上下文文本)。
+    """
+    sources_summary = format_sources(search_result)
+    context = deduplicate_and_format_sources(
+        search_result or {"results": []},
+        max_tokens_per_source=MAX_TOKENS_PER_SOURCE,
+        fetch_full_page=config.fetch_full_page,
+    )
+
+    if answer_text:
+        context = f"AI直接答案：\n{answer_text}\n\n{context}"
+
+    return sources_summary, context

Co-creation-projects/JJason-DeepCastAgent/backend/src/services/summarizer.py

@@ -0,0 +1,122 @@
+"""任务总结工具。"""
+
+from __future__ import annotations
+
+from collections.abc import Callable, Iterator
+
+from hello_agents import ToolAwareSimpleAgent
+
+from config import Configuration
+from models import SummaryState, TodoItem
+from services.notes import build_note_guidance
+from services.text_processing import strip_tool_calls
+from utils import strip_thinking_tokens
+
+
+class SummarizationService:
+    """处理同步和流式任务总结。"""
+
+    def __init__(  # noqa: D107
+        self,
+        summarizer_factory: Callable[[], ToolAwareSimpleAgent],
+        config: Configuration,
+    ) -> None:
+        self._agent_factory = summarizer_factory
+        self._config = config
+
+    def summarize_task(self, state: SummaryState, task: TodoItem, context: str) -> str:
+        """使用总结代理生成特定于任务的总结。"""
+        prompt = self._build_prompt(state, task, context)
+
+        agent = self._agent_factory()
+        try:
+            response = agent.run(prompt)
+        finally:
+            agent.clear_history()
+
+        summary_text = response.strip()
+        if self._config.strip_thinking_tokens:
+            summary_text = strip_thinking_tokens(summary_text)
+
+        summary_text = strip_tool_calls(summary_text).strip()
+
+        return summary_text or "暂无可用信息"
+
+    def stream_task_summary(
+        self, state: SummaryState, task: TodoItem, context: str
+    ) -> tuple[Iterator[str], Callable[[], str]]:
+        """流式传输任务的总结文本，同时收集完整输出。"""
+        prompt = self._build_prompt(state, task, context)
+        remove_thinking = self._config.strip_thinking_tokens
+        raw_buffer = ""
+        visible_output = ""
+        emit_index = 0
+        agent = self._agent_factory()
+
+        def flush_visible() -> Iterator[str]:
+            """处理缓冲区，提取并 yield 所有不在 <think>...</think> 块中的可见文本。如果遇到不完整的 <think> 标签，会暂停输出等待更多数据。"""
+            nonlocal emit_index, raw_buffer
+            while True:
+                start = raw_buffer.find("<think>", emit_index)
+                if start == -1:
+                    if emit_index < len(raw_buffer):
+                        segment = raw_buffer[emit_index:]
+                        emit_index = len(raw_buffer)
+                        if segment:
+                            yield segment
+                    break
+
+                if start > emit_index:
+                    segment = raw_buffer[emit_index:start]
+                    emit_index = start
+                    if segment:
+                        yield segment
+
+                end = raw_buffer.find("</think>", start)
+                if end == -1:
+                    break
+                emit_index = end + len("</think>")
+
+        def generator() -> Iterator[str]:
+            nonlocal raw_buffer, visible_output, emit_index
+            try:
+                for chunk in agent.stream_run(prompt):
+                    raw_buffer += chunk
+                    if remove_thinking:
+                        for segment in flush_visible():
+                            visible_output += segment
+                            if segment:
+                                yield segment
+                    else:
+                        visible_output += chunk
+                        if chunk:
+                            yield chunk
+            finally:
+                if remove_thinking:
+                    for segment in flush_visible():
+                        visible_output += segment
+                        if segment:
+                            yield segment
+                agent.clear_history()
+
+        def get_summary() -> str:
+            if remove_thinking:
+                cleaned = strip_thinking_tokens(visible_output)
+            else:
+                cleaned = visible_output
+
+            return strip_tool_calls(cleaned).strip()
+
+        return generator(), get_summary
+
+    def _build_prompt(self, state: SummaryState, task: TodoItem, context: str) -> str:
+        """构建两种模式共享的总结提示。"""
+        return (
+            f"任务主题：{state.research_topic}\n"
+            f"任务名称：{task.title}\n"
+            f"任务目标：{task.intent}\n"
+            f"检索查询：{task.query}\n"
+            f"任务上下文：\n{context}\n"
+            f"{build_note_guidance(task)}\n"
+            "请按照以上协作要求先同步笔记，然后返回一份面向用户的 Markdown 总结（仍遵循任务总结模板）。"
+        )

Co-creation-projects/JJason-DeepCastAgent/backend/src/services/text_processing.py

@@ -0,0 +1,38 @@
+"""用于标准化代理生成文本的实用助手。"""
+
+from __future__ import annotations
+
+
+def strip_tool_calls(text: str) -> str:
+    """移除文本中的工具调用标记。
+
+    支持嵌套方括号，例如:
+    [TOOL_CALL:note:{"tags":["deep_research","task_1"]}]
+    """
+    if not text:
+        return text
+
+    # 找到 [TOOL_CALL: 起始标记，然后手动匹配到对应的闭合 ]
+    result: list[str] = []
+    i = 0
+    marker = "[TOOL_CALL:"
+    while i < len(text):
+        pos = text.find(marker, i)
+        if pos == -1:
+            result.append(text[i:])
+            break
+        result.append(text[i:pos])
+        # 从 marker 起始位置向后扫描，跟踪方括号深度
+        depth = 0
+        j = pos
+        while j < len(text):
+            if text[j] == "[":
+                depth += 1
+            elif text[j] == "]":
+                depth -= 1
+                if depth == 0:
+                    break
+            j += 1
+        i = j + 1  # 跳过闭合的 ]
+    return "".join(result)
+

Co-creation-projects/JJason-DeepCastAgent/backend/src/services/tool_events.py

@@ -0,0 +1,240 @@
+"""用于收集和暴露工具调用事件的实用程序。"""
+
+from __future__ import annotations
+
+import logging
+import re
+from collections.abc import Callable
+from dataclasses import dataclass
+from pathlib import Path
+from threading import Lock
+from typing import Any
+
+from models import SummaryState, TodoItem
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class ToolCallEvent:
+    """工具调用事件的内部表示。"""
+
+    id: int
+    agent: str
+    tool: str
+    raw_parameters: str
+    parsed_parameters: dict[str, Any]
+    result: str
+    task_id: int | None
+    note_id: str | None
+
+
+class ToolCallTracker:
+    """收集工具调用事件并将其转换为 SSE 负载。"""
+
+    def __init__(self, notes_workspace: str | None) -> None:
+        self._notes_workspace = notes_workspace
+        self._events: list[ToolCallEvent] = []
+        self._cursor = 0
+        self._lock = Lock()
+        self._event_sink: Callable[[dict[str, Any]], None] | None = None
+
+    def record(self, payload: dict[str, Any]) -> None:
+        """
+        记录模型工具调用情况，便于日志与前端展示。
+        
+        Args:
+            payload: 工具调用事件负载，包含工具名、参数和结果。
+        """
+        agent_name = str(payload.get("agent_name") or "unknown")
+        tool_name = str(payload.get("tool_name") or "unknown")
+        raw_parameters = str(payload.get("raw_parameters") or "")
+        parsed_parameters = payload.get("parsed_parameters") or {}
+        result_text = str(payload.get("result") or "")
+
+        if not isinstance(parsed_parameters, dict):
+            parsed_parameters = {}
+
+        task_id = self._infer_task_id(parsed_parameters)
+        note_id: str | None = None
+
+        if tool_name == "note":
+            note_id = parsed_parameters.get("note_id")
+            if note_id is None:
+                note_id = self._extract_note_id(result_text)
+
+        event = ToolCallEvent(
+            id=len(self._events) + 1,
+            agent=agent_name,
+            tool=tool_name,
+            raw_parameters=raw_parameters,
+            parsed_parameters=parsed_parameters,
+            result=result_text,
+            task_id=task_id,
+            note_id=note_id,
+        )
+
+        with self._lock:
+            self._events.append(event)
+
+        logger.info(
+            "Tool call recorded: agent=%s tool=%s task_id=%s note_id=%s parsed_parameters=%s",
+            agent_name,
+            tool_name,
+            task_id,
+            note_id,
+            parsed_parameters,
+        )
+
+        sink = self._event_sink
+        if sink:
+            sink(self._build_payload(event, step=None))
+
+    # ------------------------------------------------------------------
+    # 排放助手
+    # ------------------------------------------------------------------
+    def drain(self, state: SummaryState, *, step: int | None = None) -> list[dict[str, Any]]:
+        """
+        提取尚未消费的工具调用事件，并同步任务的 note_id。
+        
+        此方法是线程安全的，会移除已提取的事件，避免重复处理。
+        同时会检查 note 工具的调用，更新任务状态中的 note_id。
+        
+        Args:
+            state: 当前研究状态。
+            step: 可选的步骤编号，附加到返回的事件中。
+            
+        Returns:
+            准备发送给前端的事件字典列表。
+        """
+        with self._lock:
+            if self._cursor >= len(self._events):
+                return []
+            new_events = self._events[self._cursor :]
+            self._cursor = len(self._events)
+
+        if state.todo_items:
+            for event in new_events:
+                task_id = event.task_id
+                note_id = event.note_id
+                if task_id is None or not note_id:
+                    continue
+                self._attach_note_to_task(state.todo_items, task_id, note_id)
+
+        payloads: list[dict[str, Any]] = []
+        for event in new_events:
+            payload = self._build_payload(event, step=step)
+            payloads.append(payload)
+
+        return payloads
+
+    def reset(self) -> None:
+        """
+        重置当前已记录的工具调用事件。
+        
+        该方法会清空内部事件列表并重置游标，用于在同一
+        Tracker 实例上复用时避免跨任务/会话的事件泄漏。
+        """
+        with self._lock:
+            self._events.clear()
+            self._cursor = 0
+    def as_dicts(self) -> list[dict[str, Any]]:
+        """
+        暴露原始事件的快照以实现向后兼容性。
+        
+        Returns:
+            包含所有工具调用事件的字典列表。
+        """
+        with self._lock:
+            return [
+                {
+                    "id": event.id,
+                    "agent": event.agent,
+                    "tool": event.tool,
+                    "raw_parameters": event.raw_parameters,
+                    "parsed_parameters": event.parsed_parameters,
+                    "result": event.result,
+                    "task_id": event.task_id,
+                    "note_id": event.note_id,
+                }
+                for event in self._events
+            ]
+
+    def set_event_sink(self, sink: Callable[[dict[str, Any]], None] | None) -> None:
+        """
+        注册一个回调以获取即时工具事件通知。
+        
+        Args:
+            sink: 接收事件字典的回调函数。
+        """
+        self._event_sink = sink
+
+    def _build_payload(self, event: ToolCallEvent, step: int | None) -> dict[str, Any]:
+        payload = {
+            "type": "tool_call",
+            "event_id": event.id,
+            "agent": event.agent,
+            "tool": event.tool,
+            "parameters": event.parsed_parameters,
+            "result": event.result,
+            "task_id": event.task_id,
+            "note_id": event.note_id,
+        }
+        if event.note_id and self._notes_workspace:
+            note_path = Path(self._notes_workspace) / f"{event.note_id}.md"
+            payload["note_path"] = str(note_path)
+        if step is not None:
+            payload["step"] = step
+        return payload
+
+    # ------------------------------------------------------------------
+    # 内部助手
+    # ------------------------------------------------------------------
+    def _attach_note_to_task(self, tasks: list[TodoItem], task_id: int, note_id: str) -> None:
+        """使用笔记元数据更新匹配的 TODO 项目。"""
+        for task in tasks:
+            if task.id != task_id:
+                continue
+
+            if task.note_id != note_id:
+                task.note_id = note_id
+                if self._notes_workspace:
+                    task.note_path = str(Path(self._notes_workspace) / f"{note_id}.md")
+            elif task.note_path is None and self._notes_workspace:
+                task.note_path = str(Path(self._notes_workspace) / f"{note_id}.md")
+            break
+
+    def _infer_task_id(self, parameters: dict[str, Any]) -> int | None:
+        """尝试从工具参数推断 task_id。"""
+        if not parameters:
+            return None
+
+        if "task_id" in parameters:
+            try:
+                return int(parameters["task_id"])
+            except (TypeError, ValueError):
+                pass
+
+        tags = parameters.get("tags")
+        if isinstance(tags, list):
+            for tag in tags:
+                match = re.search(r"task_(\d+)", str(tag))
+                if match:
+                    return int(match.group(1))
+
+        title = parameters.get("title")
+        if isinstance(title, str):
+            match = re.search(r"任务\s*(\d+)", title)
+            if match:
+                return int(match.group(1))
+
+        return None
+
+    def _extract_note_id(self, response: str) -> str | None:
+        if not response:
+            return None
+
+        match = re.search(r"ID:\s*([^\n]+)", response)
+        if match:
+            return match.group(1).strip()
+        return None

Co-creation-projects/JJason-DeepCastAgent/backend/src/utils.py

@@ -0,0 +1,90 @@
+"""深层研究服务共享的实用助手。"""
+
+from __future__ import annotations
+
+import logging
+from typing import Any
+
+CHARS_PER_TOKEN = 4
+
+logger = logging.getLogger(__name__)
+
+
+def get_config_value(value: Any) -> str:
+    """以纯字符串形式返回配置值。"""
+    return value if isinstance(value, str) else value.value
+
+
+def strip_thinking_tokens(text: str) -> str:
+    """移除模型响应中的 ``<think>`` 部分。"""
+    while "<think>" in text and "</think>" in text:
+        start = text.find("<think>")
+        end = text.find("</think>") + len("</think>")
+        text = text[:start] + text[end:]
+    return text
+
+
+def deduplicate_and_format_sources(
+    search_response: dict[str, Any] | list[dict[str, Any]],
+    max_tokens_per_source: int,
+    *,
+    fetch_full_page: bool = False,
+) -> str:
+    """
+    格式化并去重搜索结果以供下游提示使用。
+    
+    Args:
+        search_response: 原始搜索响应（字典或列表）。
+        max_tokens_per_source: 每个来源截取的最大 Token 数。
+        fetch_full_page: 是否尝试使用完整页面内容（如果可用）。
+        
+    Returns:
+        格式化后的上下文文本字符串。
+    """
+    if isinstance(search_response, dict):
+        sources_list = search_response.get("results", [])
+    else:
+        sources_list = search_response
+
+    unique_sources: dict[str, dict[str, Any]] = {}
+    for source in sources_list:
+        url = source.get("url")
+        if not url:
+            continue
+        if url not in unique_sources:
+            unique_sources[url] = source
+
+    formatted_parts: list[str] = []
+    for source in unique_sources.values():
+        title = source.get("title") or source.get("url", "")
+        content = source.get("content", "")
+        formatted_parts.append(f"信息来源: {title}\n\n")
+        formatted_parts.append(f"URL: {source.get('url', '')}\n\n")
+        formatted_parts.append(f"信息内容: {content}\n\n")
+
+        if fetch_full_page:
+            raw_content = source.get("raw_content")
+            if raw_content is None:
+                logger.debug("raw_content missing for %s", source.get("url", ""))
+                raw_content = ""
+            char_limit = max_tokens_per_source * CHARS_PER_TOKEN
+            if len(raw_content) > char_limit:
+                raw_content = f"{raw_content[:char_limit]}... [truncated]"
+            formatted_parts.append(
+                f"详细信息内容限制为 {max_tokens_per_source} 个 token: {raw_content}\n\n"
+            )
+
+    return "".join(formatted_parts).strip()
+
+
+def format_sources(search_results: dict[str, Any] | None) -> str:
+    """返回总结搜索来源的项目符号列表。"""
+    if not search_results:
+        return ""
+
+    results = search_results.get("results", [])
+    return "\n".join(
+        f"* {item.get('title', item.get('url', ''))} : {item.get('url', '')}"
+        for item in results
+        if item.get("url")
+    )

Co-creation-projects/JJason-DeepCastAgent/backend/uv.lock

@@ -0,0 +1,2022 @@
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+]
+
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Co-creation-projects/JJason-DeepCastAgent/frontend/.env.local

@@ -0,0 +1 @@
+VITE_API_BASE_URL=http://localhost:8000 # 可选

Co-creation-projects/JJason-DeepCastAgent/frontend/index.html

@@ -0,0 +1,12 @@
+<!DOCTYPE html>
+<html lang="zh-CN">
+  <head>
+    <meta charset="UTF-8" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+    <title>DeepCast 深度播客 Agent</title>
+  </head>
+  <body>
+    <div id="app"></div>
+    <script type="module" src="/src/main.ts"></script>
+  </body>
+</html>

Co-creation-projects/JJason-DeepCastAgent/frontend/package-lock.json

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+      "integrity": "sha512-P7OP77b2h/Pmk+lZdJ0YWs+5tJ6J2+uOQPo7tlBnY44QqQSPYvS0qVT4wqDJgwrZaLe47etJLLQRFia71GYITw==",
+      "dev": true,
+      "license": "MIT",
+      "dependencies": {
+        "@volar/typescript": "2.4.15",
+        "@vue/language-core": "2.2.12"
+      },
+      "bin": {
+        "vue-tsc": "bin/vue-tsc.js"
+      },
+      "peerDependencies": {
+        "typescript": ">=5.0.0"
+      }
+    }
+  }
+}

Co-creation-projects/JJason-DeepCastAgent/frontend/package.json

@@ -0,0 +1,26 @@
+{
+  "name": "helloagents-deepresearch-frontend",
+  "private": true,
+  "version": "0.1.0",
+  "type": "module",
+  "scripts": {
+    "dev": "vite",
+    "build": "vue-tsc --noEmit && vite build",
+    "preview": "vite preview"
+  },
+  "dependencies": {
+    "@tailwindcss/vite": "^4.1.18",
+    "markdown-it": "^14.1.0",
+    "tailwindcss": "^4.1.18",
+    "vue": "^3.5.13"
+  },
+  "devDependencies": {
+    "@types/markdown-it": "^14.1.2",
+    "@types/node": "^22.10.5",
+    "@vitejs/plugin-vue": "^5.2.1",
+    "daisyui": "^5.5.14",
+    "typescript": "^5.7.3",
+    "vite": "^6.0.7",
+    "vue-tsc": "^2.2.0"
+  }
+}

Co-creation-projects/JJason-DeepCastAgent/frontend/src/App.vue

@@ -0,0 +1,352 @@
+<template>
+  <div class="app-root min-h-screen">
+    <!-- View 1: Setup -->
+    <SetupView
+      v-if="currentView === 'setup'"
+      v-model:topic="form.topic"
+      @start="startProduction"
+    />
+
+    <!-- View 2: Production -->
+    <ProductionView
+      v-else-if="currentView === 'producing'"
+      ref="productionRef"
+      :logs="logs"
+      :is-waiting="isWaiting"
+      :waiting-dots="waitingDots"
+      :production-stage="productionStage"
+      :progress-percent="progressPercent"
+      :report-ready="reportReady"
+      :podcast-ready="podcastReady"
+      :audio-url="audioUrl"
+      @cancel="cancelProduction"
+      @download-report="downloadReport"
+      @go-player="currentView = 'player'"
+    />
+
+    <!-- View 3: Player -->
+    <PlayerView
+      v-else-if="currentView === 'player'"
+      :topic="form.topic"
+      :audio-url="audioUrl"
+      :report-markdown="reportMarkdown"
+      @reset="resetApp"
+      @download-report="downloadReport"
+    />
+  </div>
+</template>
+
+<script lang="ts" setup>
+import { reactive, ref, nextTick } from "vue";
+import { runResearchStream, cancelResearch, type ResearchStreamEvent } from "./services/api";
+
+import SetupView from "./components/SetupView.vue";
+import ProductionView from "./components/ProductionView.vue";
+import PlayerView from "./components/PlayerView.vue";
+import type { LogEntry } from "./components/TerminalLog.vue";
+import type { ProductionStage } from "./components/ProductionView.vue";
+
+// --- Types ---
+type ViewState = "setup" | "producing" | "player";
+
+// --- State ---
+const currentView = ref<ViewState>("setup");
+const productionStage = ref<ProductionStage>("research");
+const form = reactive({ topic: "" });
+
+const logs = ref<LogEntry[]>([]);
+const reportReady = ref(false);
+const podcastReady = ref(false);
+
+const audioProgress = reactive({ current: 0, total: 0, role: "" });
+const taskProgress = reactive({ completed: 0, total: 0 });
+const progressPercent = ref(0);
+const currentStatusMessage = ref("");
+const isWaiting = ref(false);
+const waitingDots = ref(".");
+let waitingInterval: ReturnType<typeof setInterval> | null = null;
+
+const reportMarkdown = ref("");
+const audioUrl = ref("");
+
+let abortController: AbortController | null = null;
+
+const productionRef = ref<InstanceType<typeof ProductionView> | null>(null);
+
+// --- Helpers ---
+
+function startWaitingAnimation() {
+  stopWaitingAnimation();
+  isWaiting.value = true;
+  waitingDots.value = ".";
+  waitingInterval = setInterval(() => {
+    waitingDots.value = waitingDots.value.length >= 3 ? "." : waitingDots.value + ".";
+  }, 500);
+}
+
+function stopWaitingAnimation() {
+  isWaiting.value = false;
+  if (waitingInterval) {
+    clearInterval(waitingInterval);
+    waitingInterval = null;
+  }
+}
+
+function addLog(message: string) {
+  const time = new Date().toLocaleTimeString([], { hour12: false, hour: "2-digit", minute: "2-digit", second: "2-digit" });
+  logs.value.push({ time, message });
+  nextTick(() => {
+    productionRef.value?.scrollTerminal();
+  });
+}
+
+// --- Actions ---
+
+async function startProduction() {
+  if (!form.topic.trim()) return;
+
+  currentView.value = "producing";
+  productionStage.value = "research";
+  logs.value = [];
+  reportMarkdown.value = "";
+  audioUrl.value = "";
+  audioProgress.current = 0;
+  audioProgress.total = 0;
+  taskProgress.completed = 0;
+  taskProgress.total = 0;
+  progressPercent.value = 2;
+  currentStatusMessage.value = "正在初始化...";
+  reportReady.value = false;
+  podcastReady.value = false;
+
+  abortController = new AbortController();
+  startWaitingAnimation();
+
+  addLog("🚀 启动 DeepCast 制作流程...");
+  addLog(`📌 主题: ${form.topic}`);
+
+  try {
+    await runResearchStream(
+      { topic: form.topic },
+      handleStreamEvent,
+      { signal: abortController.signal }
+    );
+  } catch (err: any) {
+    if (err.name === "AbortError" || err.message?.includes("aborted")) {
+      addLog("🛑 制作已取消。");
+    } else {
+      addLog(`❌ 错误: ${err.message || err}`);
+      console.error(err);
+    }
+  } finally {
+    stopWaitingAnimation();
+  }
+}
+
+function handleStreamEvent(event: ResearchStreamEvent) {
+  console.log("Event:", event.type, event);
+
+  if (event.type === "log") {
+    const msg = String((event as any).message || "");
+    const cleanMsg = msg.replace(/\u001b\[\d+m/g, "");
+    addLog(`INFO: ${cleanMsg}`);
+
+    const ttsMatch = cleanMsg.match(/\[TTS (\d+)\/(\d+)\]/);
+    if (ttsMatch) {
+      audioProgress.current = parseInt(ttsMatch[1], 10);
+      audioProgress.total = parseInt(ttsMatch[2], 10);
+      currentStatusMessage.value = `音频生成: ${audioProgress.current}/${audioProgress.total}`;
+    }
+    return;
+  }
+
+  if (event.type === "stage_change") {
+    const payload = event as any;
+    const stage = payload.stage;
+    const message = payload.message || "";
+    currentStatusMessage.value = message;
+
+    addLog("━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━");
+    addLog(`📌 [STAGE] ${stage.toUpperCase()} - ${message}`);
+    addLog("━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━");
+
+    if (stage === "report") {
+      productionStage.value = "research";
+      progressPercent.value = 40;
+    } else if (stage === "script") {
+      productionStage.value = "script";
+      progressPercent.value = 55;
+    } else if (stage === "audio") {
+      productionStage.value = "audio";
+      progressPercent.value = 70;
+    } else if (stage === "synthesis") {
+      productionStage.value = "audio";
+      progressPercent.value = 95;
+    }
+  }
+
+  if (event.type === "tool_call") {
+    const p = event as any;
+    addLog(`🔧 [TOOL] ${p.tool} - ${p.agent || "Agent"}`);
+  }
+
+  if (event.type === "todo_list") {
+    const p = event as any;
+    const tasks = p.tasks || [];
+    taskProgress.total = tasks.length;
+    taskProgress.completed = 0;
+  }
+
+  if (event.type === "task_status") {
+    const p = event as any;
+    if (p.status === "completed") {
+      taskProgress.completed++;
+      if (taskProgress.total > 0) {
+        progressPercent.value = Math.round((taskProgress.completed / taskProgress.total) * 40);
+      }
+      addLog(`✅ [TASK ${p.task_id}] ${p.title}`);
+    } else if (p.status === "in_progress") {
+      addLog(`🚀 [TASK ${p.task_id}] ${p.title} (In Progress)`);
+    } else if (p.status === "failed") {
+      addLog(`❌ [TASK ${p.task_id}] Failed: ${p.title}`);
+    }
+  }
+
+  if (event.type === "final_report") {
+    reportMarkdown.value = String((event as any).report);
+    reportReady.value = true;
+    addLog("📄 [REPORT] 报告已生成");
+  }
+
+  if (event.type === "podcast_script") {
+    productionStage.value = "audio";
+    addLog("🎙️ [SCRIPT] 剧本已生成");
+  }
+
+  if (event.type === "audio_start") {
+    const p = event as any;
+    audioProgress.total = p.total || 0;
+    addLog(`🎵 [AUDIO] 开始生成音频, 共 ${audioProgress.total} 段`);
+  }
+
+  if (event.type === "audio_progress") {
+    const p = event as any;
+    audioProgress.current = p.current;
+    audioProgress.total = p.total;
+    currentStatusMessage.value = `生成音频: ${p.role} (${p.current}/${p.total})`;
+    if (p.total > 0) {
+      progressPercent.value = 70 + Math.round((p.current / p.total) * 25);
+    }
+  }
+
+  if (event.type === "podcast_ready") {
+    const p = event as any;
+    const filename = String(p.file).split(/[\\/]/).pop();
+    if (filename) {
+      const baseUrl = import.meta.env.VITE_API_BASE_URL || "http://localhost:8000";
+      audioUrl.value = `${baseUrl}/output/audio/${filename}`;
+      podcastReady.value = true;
+      productionStage.value = "done";
+      progressPercent.value = 100;
+      currentStatusMessage.value = "🎉 播客制作完成！";
+      stopWaitingAnimation();
+      addLog(`🎉 [PODCAST] 制作完成: ${filename}`);
+    }
+  }
+
+  if (event.type === "cancelled") {
+    const msg = (event as any).message || "研究任务已取消";
+    addLog(`🛑 [CANCELLED] ${msg}`);
+    stopWaitingAnimation();
+    productionStage.value = "cancelled";
+    currentStatusMessage.value = "任务已取消";
+    return;
+  }
+
+  if (event.type === "done") {
+    addLog("✅ [DONE] 所有任务结束");
+    stopWaitingAnimation();
+    productionStage.value = "done";
+    progressPercent.value = 100;
+
+    if (!podcastReady.value && audioProgress.total > 0) {
+      const baseUrl = import.meta.env.VITE_API_BASE_URL || "http://localhost:8000";
+      fetch(`${baseUrl}/api/audio/latest`)
+        .then(res => res.json())
+        .then(data => {
+          if (data.file) {
+            audioUrl.value = `${baseUrl}${data.url}`;
+            podcastReady.value = true;
+            currentStatusMessage.value = "🎉 播客制作完成！";
+            addLog(`🎉 [PODCAST] 找到音频文件: ${data.file}`);
+          } else {
+            currentStatusMessage.value = "任务完成（音频未生成）";
+            addLog(`⚠️ 未找到音频文件: ${data.error || "未知错误"}`);
+          }
+        })
+        .catch(err => {
+          currentStatusMessage.value = "任务完成（无法获取音频）";
+          addLog(`⚠️ 获取音频文件失败: ${err.message}`);
+        });
+    } else if (podcastReady.value) {
+      currentStatusMessage.value = "🎉 播客制作完成！";
+    } else {
+      currentStatusMessage.value = "任务完成（音频可能未生成）";
+    }
+  }
+}
+
+function cancelProduction() {
+  if (confirm("确定要取消制作吗？")) {
+    addLog("🛑 用户请求取消制作...");
+
+    // 1. 立即中断 SSE 连接 — 后端 monitor_disconnect 会自动检测并设置 cancel_event
+    if (abortController) {
+      abortController.abort();
+      abortController = null;
+    }
+
+    // 2. 显式调用 cancel API 作为后备（防止 disconnect 检测延迟）
+    cancelResearch().catch(() => {});
+
+    stopWaitingAnimation();
+    productionStage.value = "cancelled";
+    addLog("🛑 已取消制作");
+
+    setTimeout(() => {
+      currentView.value = "setup";
+      currentStatusMessage.value = "";
+    }, 1000);
+  }
+}
+
+function resetApp() {
+  currentView.value = "setup";
+  form.topic = "";
+  currentStatusMessage.value = "";
+  reportReady.value = false;
+  podcastReady.value = false;
+  audioUrl.value = "";
+  stopWaitingAnimation();
+}
+
+function downloadReport() {
+  if (!reportMarkdown.value) return;
+  const blob = new Blob([reportMarkdown.value], { type: "text/markdown;charset=utf-8" });
+  const url = URL.createObjectURL(blob);
+  const a = document.createElement("a");
+  a.href = url;
+  a.download = "DeepCast深度研究报告.md";
+  document.body.appendChild(a);
+  a.click();
+  document.body.removeChild(a);
+  URL.revokeObjectURL(url);
+}
+</script>
+
+<style scoped>
+.app-root {
+  background: linear-gradient(145deg, #0c0e14 0%, #111420 30%, #0e1018 60%, #0a0c12 100%);
+  background-attachment: fixed;
+}
+</style>

Co-creation-projects/JJason-DeepCastAgent/frontend/src/components/PlayerView.vue

@@ -0,0 +1,349 @@
+<template>
+  <div class="min-h-screen p-4 md:p-6">
+    <div class="max-w-6xl mx-auto">
+      <!-- Navbar -->
+      <div class="player-nav rounded-2xl mb-6 px-6 py-3.5">
+        <div class="flex items-center justify-between">
+          <div class="flex items-center gap-3">
+            <div class="w-9 h-9 rounded-xl bg-gradient-to-br from-blue-500 to-indigo-600 flex items-center justify-center shadow-lg shadow-blue-500/20">
+              <span class="text-lg">🎙️</span>
+            </div>
+            <span class="text-xl font-bold text-white tracking-tight">DeepCast</span>
+          </div>
+          <div class="flex items-center gap-2">
+            <button class="player-nav-btn text-blue-300" @click="$emit('downloadReport')" aria-label="下载研究报告">
+              <svg class="w-4 h-4" fill="none" stroke="currentColor" viewBox="0 0 24 24"><path stroke-linecap="round" stroke-linejoin="round" stroke-width="2" d="M12 10v6m0 0l-3-3m3 3l3-3m2 8H7a2 2 0 01-2-2V5a2 2 0 012-2h5.586a1 1 0 01.707.293l5.414 5.414a1 1 0 01.293.707V19a2 2 0 01-2 2z"/></svg>
+              报告
+            </button>
+            <button class="player-nav-btn text-gray-400" @click="$emit('reset')" aria-label="制作新播客">
+              <svg class="w-4 h-4" fill="none" stroke="currentColor" viewBox="0 0 24 24"><path stroke-linecap="round" stroke-linejoin="round" stroke-width="2" d="M12 6v6m0 0v6m0-6h6m-6 0H6"/></svg>
+              新播客
+            </button>
+          </div>
+        </div>
+      </div>
+
+      <!-- Content -->
+      <div class="grid grid-cols-1 lg:grid-cols-5 gap-5 items-start">
+
+        <!-- Left: Player Card -->
+        <div class="lg:col-span-2">
+          <div class="player-card rounded-2xl overflow-hidden">
+            <!-- Album Art Area -->
+            <div class="player-art-area">
+              <div class="player-art-bg"></div>
+              <div class="relative z-10 flex flex-col items-center py-10">
+                <!-- Vinyl disc -->
+                <div class="player-vinyl" :class="{ 'player-vinyl--spinning': isPlaying }">
+                  <div class="player-vinyl-ring player-vinyl-ring-1"></div>
+                  <div class="player-vinyl-ring player-vinyl-ring-2"></div>
+                  <div class="player-vinyl-ring player-vinyl-ring-3"></div>
+                  <div class="player-vinyl-groove"></div>
+                  <!-- Center label -->
+                  <div class="player-vinyl-label">
+                    <span class="text-lg font-bold text-white tracking-tight">DC</span>
+                  </div>
+                </div>
+              </div>
+            </div>
+
+            <!-- Info & Controls -->
+            <div class="p-6">
+              <div class="text-center mb-5">
+                <h2 class="text-xl font-bold text-white leading-tight mb-1.5">{{ topic }}</h2>
+                <p class="text-xs font-medium text-gray-500 tracking-[0.2em] uppercase">DeepCast Original</p>
+              </div>
+
+              <!-- Audio Player -->
+              <div class="player-audio-wrap mb-5">
+                <audio
+                  ref="audioPlayer"
+                  :src="audioUrl"
+                  controls
+                  class="w-full"
+                  @play="isPlaying = true"
+                  @pause="isPlaying = false"
+                ></audio>
+              </div>
+
+              <!-- Action Buttons -->
+              <div class="flex flex-col gap-2.5">
+                <a :href="audioUrl" download class="player-btn-primary" aria-label="下载播客 MP3 文件">
+                  <svg class="w-4.5 h-4.5" fill="none" stroke="currentColor" viewBox="0 0 24 24"><path stroke-linecap="round" stroke-linejoin="round" stroke-width="2" d="M4 16v1a3 3 0 003 3h10a3 3 0 003-3v-1m-4-4l-4 4m0 0l-4-4m4 4V4"/></svg>
+                  下载 MP3
+                </a>
+                <button class="player-btn-ghost" @click="$emit('reset')" aria-label="制作新播客">
+                  <svg class="w-4.5 h-4.5" fill="none" stroke="currentColor" viewBox="0 0 24 24"><path stroke-linecap="round" stroke-linejoin="round" stroke-width="2" d="M4 4v5h.582m15.356 2A8.001 8.001 0 004.582 9m0 0H9m11 11v-5h-.581m0 0a8.003 8.003 0 01-15.357-2m15.357 2H15"/></svg>
+                  制作新播客
+                </button>
+              </div>
+            </div>
+          </div>
+        </div>
+
+        <!-- Right: Report -->
+        <div class="lg:col-span-3">
+          <div class="report-card rounded-2xl overflow-hidden" style="height: 75vh;">
+            <!-- Report Header (macOS-style) -->
+            <div class="report-header">
+              <div class="flex items-center gap-2 mr-4">
+                <div class="w-3 h-3 rounded-full bg-[#ff5f57]"></div>
+                <div class="w-3 h-3 rounded-full bg-[#febc2e]"></div>
+                <div class="w-3 h-3 rounded-full bg-[#28c840]"></div>
+              </div>
+              <div class="flex-1 text-center">
+                <span class="text-sm font-medium text-gray-400 tracking-wide">研究报告</span>
+              </div>
+              <button class="report-download-btn" @click="$emit('downloadReport')" aria-label="下载研究报告">
+                <svg class="w-3.5 h-3.5" fill="none" stroke="currentColor" viewBox="0 0 24 24"><path stroke-linecap="round" stroke-linejoin="round" stroke-width="2" d="M4 16v1a3 3 0 003 3h10a3 3 0 003-3v-1m-4-4l-4 4m0 0l-4-4m4 4V4"/></svg>
+              </button>
+            </div>
+            <!-- Report Content -->
+            <div class="report-content custom-scrollbar" style="height: calc(100% - 48px);">
+              <article class="prose prose-sm prose-invert max-w-none report-prose" v-html="renderedReport"></article>
+            </div>
+          </div>
+        </div>
+      </div>
+    </div>
+  </div>
+</template>
+
+<script lang="ts" setup>
+import { ref, computed } from "vue";
+import MarkdownIt from "markdown-it";
+
+const md = new MarkdownIt();
+
+const props = defineProps<{
+  topic: string;
+  audioUrl: string;
+  reportMarkdown: string;
+}>();
+
+defineEmits<{
+  reset: [];
+  downloadReport: [];
+}>();
+
+const isPlaying = ref(false);
+const audioPlayer = ref<HTMLAudioElement | null>(null);
+
+const renderedReport = computed(() => md.render(props.reportMarkdown));
+</script>
+
+<style scoped>
+/* ── Nav ── */
+.player-nav {
+  background: rgba(30, 32, 38, 0.9);
+  backdrop-filter: blur(20px);
+  -webkit-backdrop-filter: blur(20px);
+  border: 1px solid rgba(255, 255, 255, 0.06);
+  box-shadow: 0 4px 20px rgba(0, 0, 0, 0.3);
+}
+.player-nav-btn {
+  display: inline-flex;
+  align-items: center;
+  gap: 6px;
+  padding: 6px 14px;
+  border-radius: 8px;
+  font-size: 13px;
+  font-weight: 500;
+  background: rgba(255, 255, 255, 0.04);
+  border: 1px solid rgba(255, 255, 255, 0.06);
+  cursor: pointer;
+  transition: all 0.2s ease;
+}
+.player-nav-btn:hover {
+  background: rgba(255, 255, 255, 0.08);
+  border-color: rgba(255, 255, 255, 0.1);
+}
+
+/* ── Player Card ── */
+.player-card {
+  background: rgba(22, 24, 30, 0.85);
+  backdrop-filter: blur(30px);
+  -webkit-backdrop-filter: blur(30px);
+  border: 1px solid rgba(255, 255, 255, 0.06);
+  box-shadow:
+    0 25px 60px rgba(0, 0, 0, 0.4),
+    inset 0 1px 0 rgba(255, 255, 255, 0.05);
+}
+
+/* ── Album Art Area ── */
+.player-art-area {
+  position: relative;
+  overflow: hidden;
+  background: linear-gradient(135deg, rgba(15, 23, 42, 0.8), rgba(30, 20, 50, 0.6));
+}
+.player-art-bg {
+  position: absolute;
+  inset: 0;
+  background:
+    radial-gradient(ellipse at 30% 50%, rgba(59, 130, 246, 0.15), transparent 60%),
+    radial-gradient(ellipse at 70% 30%, rgba(139, 92, 246, 0.12), transparent 50%);
+}
+
+/* ── Vinyl Disc ── */
+.player-vinyl {
+  width: 180px;
+  height: 180px;
+  border-radius: 50%;
+  background: radial-gradient(circle, #1a1a2e 0%, #0d0d1a 100%);
+  box-shadow:
+    0 0 0 3px rgba(255, 255, 255, 0.05),
+    0 10px 40px rgba(0, 0, 0, 0.5);
+  position: relative;
+  display: flex;
+  align-items: center;
+  justify-content: center;
+  transition: transform 0.5s ease;
+}
+.player-vinyl--spinning {
+  animation: vinyl-spin 4s linear infinite;
+}
+.player-vinyl-ring {
+  position: absolute;
+  inset: 0;
+  border-radius: 50%;
+  border: 1px solid rgba(255, 255, 255, 0.04);
+}
+.player-vinyl-ring-1 { margin: 8px; }
+.player-vinyl-ring-2 { margin: 22px; }
+.player-vinyl-ring-3 { margin: 38px; }
+.player-vinyl-groove {
+  position: absolute;
+  inset: 5px;
+  border-radius: 50%;
+  background: repeating-radial-gradient(
+    circle at center,
+    transparent 0px,
+    transparent 3px,
+    rgba(255, 255, 255, 0.02) 3px,
+    rgba(255, 255, 255, 0.02) 4px
+  );
+}
+.player-vinyl-label {
+  width: 56px;
+  height: 56px;
+  border-radius: 50%;
+  background: linear-gradient(135deg, #3b82f6, #8b5cf6);
+  display: flex;
+  align-items: center;
+  justify-content: center;
+  z-index: 2;
+  box-shadow: inset 0 1px 2px rgba(255, 255, 255, 0.2);
+}
+
+/* ── Audio Wrap ── */
+.player-audio-wrap {
+  background: rgba(0, 0, 0, 0.2);
+  border-radius: 12px;
+  padding: 12px;
+  border: 1px solid rgba(255, 255, 255, 0.04);
+}
+
+/* ── Buttons ── */
+.player-btn-primary {
+  display: flex;
+  align-items: center;
+  justify-content: center;
+  gap: 8px;
+  padding: 12px;
+  border-radius: 12px;
+  font-size: 15px;
+  font-weight: 600;
+  color: white;
+  text-decoration: none;
+  background: linear-gradient(135deg, #3b82f6 0%, #6366f1 50%, #8b5cf6 100%);
+  border: 1px solid rgba(255, 255, 255, 0.1);
+  box-shadow: 0 4px 14px rgba(59, 130, 246, 0.25), inset 0 1px 1px rgba(255, 255, 255, 0.15);
+  cursor: pointer;
+  transition: all 0.2s ease;
+}
+.player-btn-primary:hover {
+  transform: translateY(-1px);
+  box-shadow: 0 8px 25px rgba(59, 130, 246, 0.3), inset 0 1px 1px rgba(255, 255, 255, 0.15);
+  filter: brightness(1.05);
+}
+.player-btn-ghost {
+  display: flex;
+  align-items: center;
+  justify-content: center;
+  gap: 8px;
+  padding: 10px;
+  border-radius: 10px;
+  font-size: 14px;
+  font-weight: 500;
+  color: #9ca3af;
+  background: transparent;
+  border: 1px solid rgba(255, 255, 255, 0.06);
+  cursor: pointer;
+  transition: all 0.2s ease;
+}
+.player-btn-ghost:hover {
+  color: white;
+  background: rgba(255, 255, 255, 0.05);
+  border-color: rgba(255, 255, 255, 0.1);
+}
+
+/* ── Report Card ── */
+.report-card {
+  background: rgba(22, 24, 30, 0.85);
+  backdrop-filter: blur(30px);
+  -webkit-backdrop-filter: blur(30px);
+  border: 1px solid rgba(255, 255, 255, 0.06);
+  box-shadow:
+    0 25px 60px rgba(0, 0, 0, 0.4),
+    inset 0 1px 0 rgba(255, 255, 255, 0.05);
+}
+.report-header {
+  display: flex;
+  align-items: center;
+  padding: 12px 16px;
+  background: linear-gradient(180deg, rgba(50, 52, 58, 0.9), rgba(38, 40, 46, 0.9));
+  border-bottom: 1px solid rgba(255, 255, 255, 0.06);
+  user-select: none;
+}
+.report-download-btn {
+  width: 28px;
+  height: 28px;
+  border-radius: 6px;
+  display: flex;
+  align-items: center;
+  justify-content: center;
+  color: #6b7280;
+  background: transparent;
+  border: none;
+  cursor: pointer;
+  transition: all 0.2s;
+}
+.report-download-btn:hover {
+  color: #d1d5db;
+  background: rgba(255, 255, 255, 0.06);
+}
+.report-content {
+  overflow-y: auto;
+  padding: 28px 32px;
+  color: #d1d5db;
+}
+.report-prose :deep(h1) { color: #f3f4f6; font-size: 1.5em; margin-top: 1em; }
+.report-prose :deep(h2) { color: #e5e7eb; font-size: 1.25em; margin-top: 0.8em; border-bottom: 1px solid rgba(255,255,255,0.06); padding-bottom: 0.3em; }
+.report-prose :deep(h3) { color: #d1d5db; font-size: 1.1em; }
+.report-prose :deep(a) { color: #60a5fa; text-decoration: none; }
+.report-prose :deep(a:hover) { text-decoration: underline; }
+.report-prose :deep(code) { background: rgba(255,255,255,0.06); padding: 2px 6px; border-radius: 4px; font-size: 0.85em; }
+.report-prose :deep(blockquote) { border-left: 3px solid #3b82f6; padding-left: 1em; color: #9ca3af; }
+
+/* ── Scrollbar ── */
+.custom-scrollbar::-webkit-scrollbar { width: 6px; }
+.custom-scrollbar::-webkit-scrollbar-track { background: transparent; }
+.custom-scrollbar::-webkit-scrollbar-thumb { background: rgba(255, 255, 255, 0.1); border-radius: 3px; }
+.custom-scrollbar::-webkit-scrollbar-thumb:hover { background: rgba(255, 255, 255, 0.2); }
+
+/* ── Animation ── */
+@keyframes vinyl-spin {
+  from { transform: rotate(0deg); }
+  to { transform: rotate(360deg); }
+}
+</style>

Co-creation-projects/JJason-DeepCastAgent/frontend/src/components/ProductionView.vue

@@ -0,0 +1,561 @@
+<template>
+  <div class="min-h-screen p-4 md:p-6">
+    <div class="max-w-7xl mx-auto">
+      <!-- Navbar / Header -->
+      <div class="nav-glass rounded-2xl shadow-lg mb-6 px-6 py-3.5">
+        <div class="flex items-center justify-between gap-4">
+          <div class="flex items-center gap-3">
+            <div class="w-9 h-9 rounded-xl bg-gradient-to-br from-blue-500 to-indigo-600 flex items-center justify-center shadow-lg shadow-blue-500/20">
+              <span class="text-lg">🎙️</span>
+            </div>
+            <span class="text-xl font-bold text-white tracking-tight">DeepCast</span>
+          </div>
+          <div class="flex items-center gap-2">
+            <button v-if="reportReady" class="nav-action-btn text-blue-300" @click="$emit('downloadReport')" aria-label="下载研究报告">
+              <svg class="w-4 h-4" fill="none" stroke="currentColor" viewBox="0 0 24 24"><path stroke-linecap="round" stroke-linejoin="round" stroke-width="2" d="M12 10v6m0 0l-3-3m3 3l3-3m2 8H7a2 2 0 01-2-2V5a2 2 0 012-2h5.586a1 1 0 01.707.293l5.414 5.414a1 1 0 01.293.707V19a2 2 0 01-2 2z"/></svg>
+              研究报告
+            </button>
+            <button v-if="!podcastReady" class="nav-action-btn text-red-400" @click="$emit('cancel')" aria-label="取消制作">
+              <svg class="w-4 h-4" fill="none" stroke="currentColor" viewBox="0 0 24 24"><path stroke-linecap="round" stroke-linejoin="round" stroke-width="2" d="M6 18L18 6M6 6l12 12"/></svg>
+              取消
+            </button>
+          </div>
+        </div>
+      </div>
+
+      <!-- Main Content -->
+      <div class="grid grid-cols-1 lg:grid-cols-4 gap-5">
+
+        <!-- Left Column: Progress Steps -->
+        <div class="lg:col-span-1">
+          <div class="pipeline-card rounded-2xl h-[500px]">
+            <!-- Top progress bar -->
+            <div class="pipeline-progress-bar">
+              <div class="pipeline-progress-fill" :style="{ width: progress + '%' }"></div>
+            </div>
+
+            <div class="p-5 h-full flex flex-col relative overflow-hidden">
+              <!-- Ambient glow -->
+              <div class="ambient-glow ambient-glow-1"></div>
+              <div class="ambient-glow ambient-glow-2"></div>
+
+              <!-- Header -->
+              <div class="flex items-center gap-3 mb-2 z-10 relative">
+                <div class="pipeline-icon-badge">
+                  <span v-if="productionStage === 'done'" class="text-lg">✅</span>
+                  <svg v-else class="w-5 h-5 text-blue-400 animate-spin-slow" fill="none" viewBox="0 0 24 24">
+                    <circle class="opacity-25" cx="12" cy="12" r="10" stroke="currentColor" stroke-width="3"/>
+                    <path class="opacity-75" fill="currentColor" d="M4 12a8 8 0 018-8V0C5.373 0 0 5.373 0 12h4z"/>
+                  </svg>
+                </div>
+                <div>
+                  <h2 class="text-base font-bold text-white leading-tight">制作流程</h2>
+                  <p class="text-[11px] text-gray-500 mt-0.5">{{ stageLabel }}</p>
+                </div>
+                <div class="ml-auto">
+                  <span class="text-xs font-mono font-semibold text-blue-400/80">{{ progress }}%</span>
+                </div>
+              </div>
+
+              <!-- Divider -->
+              <div class="h-px bg-gradient-to-r from-transparent via-white/10 to-transparent my-3"></div>
+
+              <!-- Timeline Steps -->
+              <div class="flex-1 flex flex-col justify-center gap-1 z-10 relative">
+                <div v-for="(step, idx) in pipelineSteps" :key="step.id"
+                  class="pipeline-step group"
+                  :class="{
+                    'pipeline-step--completed': isStepCompleted(step.id),
+                    'pipeline-step--active': isStepActive(step.id),
+                    'pipeline-step--pending': isStepPending(step.id),
+                  }">
+                  <!-- Connector line -->
+                  <div v-if="idx < pipelineSteps.length - 1" class="pipeline-connector"
+                    :class="{
+                      'pipeline-connector--completed': isStepCompleted(step.id),
+                      'pipeline-connector--active': isStepActive(step.id),
+                    }"></div>
+
+                  <!-- Step indicator -->
+                  <div class="pipeline-indicator">
+                    <!-- Completed -->
+                    <svg v-if="isStepCompleted(step.id)" class="w-3.5 h-3.5 text-white" fill="currentColor" viewBox="0 0 20 20">
+                      <path fill-rule="evenodd" d="M16.707 5.293a1 1 0 010 1.414l-8 8a1 1 0 01-1.414 0l-4-4a1 1 0 011.414-1.414L8 12.586l7.293-7.293a1 1 0 011.414 0z" clip-rule="evenodd"/>
+                    </svg>
+                    <!-- Active pulse -->
+                    <div v-else-if="isStepActive(step.id)" class="pipeline-pulse"></div>
+                    <!-- Pending dot -->
+                    <div v-else class="w-2 h-2 rounded-full bg-gray-600"></div>
+                  </div>
+
+                  <!-- Step content -->
+                  <div class="pipeline-step-content">
+                    <div class="flex items-center gap-2">
+                      <span class="text-base" :class="{ 'animate-float': isStepActive(step.id) }">{{ step.icon }}</span>
+                      <span class="step-label text-sm font-semibold" :class="isStepActive(step.id) ? 'text-white' : isStepCompleted(step.id) ? 'text-gray-300' : 'text-gray-500'">{{ step.label }}</span>
+                    </div>
+                    <p class="step-desc text-[11px] mt-0.5 ml-7" :class="isStepActive(step.id) ? 'text-gray-400' : 'text-gray-600'">{{ step.desc }}</p>
+                  </div>
+                </div>
+              </div>
+
+              <!-- Bottom status chip -->
+              <div class="z-10 relative mt-3">
+                <div class="pipeline-status-chip" :class="{
+                  'pipeline-status-chip--done': productionStage === 'done',
+                  'pipeline-status-chip--cancelled': isCancelled
+                }">
+                  <span class="inline-block w-1.5 h-1.5 rounded-full mr-2" :class="
+                    productionStage === 'done' ? 'bg-emerald-400' :
+                    isCancelled ? 'bg-red-400' :
+                    'bg-blue-400 animate-pulse'
+                  "></span>
+                  <span class="text-[11px] font-medium">{{
+                    productionStage === 'done' ? '制作完成' :
+                    isCancelled ? '已取消' :
+                    '正在处理...'
+                  }}</span>
+                </div>
+              </div>
+            </div>
+          </div>
+        </div>
+
+        <!-- Right Column: Logs & Output -->
+        <div class="lg:col-span-3 flex flex-col gap-4">
+
+          <!-- macOS Style Terminal -->
+          <TerminalLog ref="terminalRef" :logs="logs" :is-waiting="isWaiting" :waiting-dots="waitingDots" />
+
+          <!-- Result Actions -->
+          <div v-if="podcastReady" class="flex gap-3">
+            <a :href="audioUrl" download class="btn macos-btn-primary flex-1 btn-lg text-base rounded-xl border-0 gap-2">
+              <svg class="w-5 h-5" fill="none" stroke="currentColor" viewBox="0 0 24 24"><path stroke-linecap="round" stroke-linejoin="round" stroke-width="2" d="M4 16v1a3 3 0 003 3h10a3 3 0 003-3v-1m-4-4l-4 4m0 0l-4-4m4 4V4"/></svg>
+              下载 MP3
+            </a>
+            <button class="btn result-btn-secondary flex-1 btn-lg text-base rounded-xl gap-2" @click="$emit('goPlayer')">
+              <svg class="w-5 h-5" fill="none" stroke="currentColor" viewBox="0 0 24 24"><path stroke-linecap="round" stroke-linejoin="round" stroke-width="2" d="M15.536 8.464a5 5 0 010 7.072m2.828-9.9a9 9 0 010 12.728M5.586 15H4a1 1 0 01-1-1v-4a1 1 0 011-1h1.586l4.707-4.707C10.923 3.663 12 4.109 12 5v14c0 .891-1.077 1.337-1.707.707L5.586 15z"/></svg>
+              进入播放器
+            </button>
+          </div>
+
+          <!-- Inline Player -->
+          <div v-if="podcastReady" class="player-inline-card rounded-xl">
+            <div class="p-4">
+              <div class="flex items-center gap-2.5 mb-3">
+                <div class="w-7 h-7 rounded-lg bg-gradient-to-br from-indigo-500 to-purple-600 flex items-center justify-center shadow-md">
+                  <svg class="w-3.5 h-3.5 text-white" fill="currentColor" viewBox="0 0 20 20"><path d="M18 3a1 1 0 00-1.196-.98l-10 2A1 1 0 006 5v9.114A4.369 4.369 0 005 14c-1.657 0-3 .895-3 2s1.343 2 3 2 3-.895 3-2V7.82l8-1.6v5.894A4.37 4.37 0 0015 12c-1.657 0-3 .895-3 2s1.343 2 3 2 3-.895 3-2V3z"/></svg>
+                </div>
+                <h3 class="text-sm font-semibold text-gray-200">快速试听</h3>
+              </div>
+              <audio class="w-full audio-player" :src="audioUrl" controls></audio>
+            </div>
+          </div>
+
+        </div>
+      </div>
+    </div>
+  </div>
+</template>
+
+<script lang="ts" setup>
+import { ref, computed, toRef } from "vue";
+import TerminalLog from "./TerminalLog.vue";
+import type { LogEntry } from "./TerminalLog.vue";
+
+export type ProductionStage = "research" | "script" | "audio" | "done" | "cancelled";
+
+interface PipelineStep {
+  id: ProductionStage;
+  icon: string;
+  label: string;
+  desc: string;
+}
+
+const pipelineSteps: PipelineStep[] = [
+  { id: "research", icon: "🔍", label: "深度研究", desc: "网络搜索 & 信息聚合" },
+  { id: "script", icon: "✍️", label: "剧本创作", desc: "生成对话 & 角色分配" },
+  { id: "audio", icon: "🎵", label: "音频合成", desc: "TTS 语音生成 & 拼接" },
+  { id: "done", icon: "🎉", label: "制作完成", desc: "播放 & 下载播客" },
+];
+
+const stepsOrder: ProductionStage[] = ["research", "script", "audio", "done"];
+
+const props = defineProps<{
+  logs: LogEntry[];
+  isWaiting: boolean;
+  waitingDots: string;
+  productionStage: ProductionStage;
+  progressPercent: number;
+  reportReady: boolean;
+  podcastReady: boolean;
+  audioUrl: string;
+}>();
+
+defineEmits<{
+  cancel: [];
+  downloadReport: [];
+  goPlayer: [];
+}>();
+
+const terminalRef = ref<InstanceType<typeof TerminalLog> | null>(null);
+
+function scrollTerminal() {
+  terminalRef.value?.scrollToBottom();
+}
+
+defineExpose({ scrollTerminal });
+
+const progress = toRef(props, 'progressPercent');
+const currentIdx = computed(() => stepsOrder.indexOf(props.productionStage));
+
+const isCancelled = computed(() => props.productionStage === 'cancelled');
+
+const stageLabel = computed(() => {
+  const labels: Record<ProductionStage, string> = {
+    research: "正在进行深度研究...",
+    script: "正在创作剧本...",
+    audio: "正在合成音频...",
+    done: "播客制作完成！",
+    cancelled: "已取消制作",
+  };
+  return labels[props.productionStage] || "";
+});
+
+function isStepCompleted(stepId: ProductionStage) {
+  return currentIdx.value > stepsOrder.indexOf(stepId);
+}
+
+function isStepActive(stepId: ProductionStage) {
+  return currentIdx.value === stepsOrder.indexOf(stepId);
+}
+
+function isStepPending(stepId: ProductionStage) {
+  return currentIdx.value < stepsOrder.indexOf(stepId);
+}
+</script>
+
+<style scoped>
+/* ── Pipeline Card ── */
+.pipeline-card {
+  background: rgba(22, 24, 30, 0.85);
+  backdrop-filter: blur(30px);
+  -webkit-backdrop-filter: blur(30px);
+  border: 1px solid rgba(255, 255, 255, 0.06);
+  box-shadow:
+    0 20px 50px rgba(0, 0, 0, 0.4),
+    inset 0 1px 0 rgba(255, 255, 255, 0.05);
+  position: relative;
+  overflow: hidden;
+}
+
+/* ── Top Progress Bar ── */
+.pipeline-progress-bar {
+  height: 3px;
+  background: rgba(255, 255, 255, 0.05);
+  position: relative;
+  overflow: hidden;
+}
+.pipeline-progress-fill {
+  height: 100%;
+  background: linear-gradient(90deg, #3b82f6, #8b5cf6, #06b6d4);
+  background-size: 200% 100%;
+  animation: shimmer 2s ease-in-out infinite;
+  transition: width 1.2s cubic-bezier(0.22, 1, 0.36, 1);
+  border-radius: 0 2px 2px 0;
+}
+
+/* ── Ambient Glow ── */
+.ambient-glow {
+  position: absolute;
+  border-radius: 50%;
+  filter: blur(60px);
+  pointer-events: none;
+  opacity: 0.4;
+}
+.ambient-glow-1 {
+  top: -30px;
+  right: -30px;
+  width: 120px;
+  height: 120px;
+  background: radial-gradient(circle, rgba(59, 130, 246, 0.3), transparent 70%);
+}
+.ambient-glow-2 {
+  bottom: -20px;
+  left: -20px;
+  width: 100px;
+  height: 100px;
+  background: radial-gradient(circle, rgba(139, 92, 246, 0.25), transparent 70%);
+}
+
+/* ── Header Icon Badge ── */
+.pipeline-icon-badge {
+  width: 36px;
+  height: 36px;
+  border-radius: 10px;
+  background: rgba(255, 255, 255, 0.06);
+  border: 1px solid rgba(255, 255, 255, 0.08);
+  display: flex;
+  align-items: center;
+  justify-content: center;
+  flex-shrink: 0;
+}
+
+/* ── Pipeline Step ── */
+.pipeline-step {
+  display: flex;
+  align-items: flex-start;
+  gap: 12px;
+  padding: 10px 8px;
+  border-radius: 10px;
+  position: relative;
+  border: 1px solid transparent;
+  margin: 0;
+  transition:
+    background 0.6s cubic-bezier(0.4, 0, 0.2, 1),
+    border-color 0.6s cubic-bezier(0.4, 0, 0.2, 1),
+    opacity 0.6s cubic-bezier(0.4, 0, 0.2, 1),
+    padding 0.5s cubic-bezier(0.4, 0, 0.2, 1),
+    margin 0.5s cubic-bezier(0.4, 0, 0.2, 1),
+    transform 0.4s cubic-bezier(0.34, 1.56, 0.64, 1);
+}
+.pipeline-step--active {
+  background: rgba(59, 130, 246, 0.08);
+  border-color: rgba(59, 130, 246, 0.15);
+  margin: 0 -4px;
+  padding: 10px 12px;
+  transform: scale(1.02);
+}
+.pipeline-step--completed {
+  opacity: 0.85;
+}
+
+/* ── Connector Line ── */
+.pipeline-connector {
+  position: absolute;
+  left: 21px;
+  top: 38px;
+  width: 2px;
+  height: calc(100% - 10px);
+  border-radius: 1px;
+  z-index: 1;
+  overflow: hidden;
+}
+/* Use a pseudo-element to animate the fill from top to bottom */
+.pipeline-connector::before {
+  content: '';
+  position: absolute;
+  inset: 0;
+  background: rgba(255, 255, 255, 0.06);
+  transition: opacity 0.6s ease;
+}
+.pipeline-connector::after {
+  content: '';
+  position: absolute;
+  top: 0;
+  left: 0;
+  width: 100%;
+  height: 0%;
+  background: linear-gradient(180deg, #3b82f6, #8b5cf6);
+  border-radius: 1px;
+  transition: height 0.8s cubic-bezier(0.4, 0, 0.2, 1);
+}
+.pipeline-connector--completed::after {
+  height: 100%;
+}
+.pipeline-connector--active::after {
+  height: 60%;
+  background: linear-gradient(180deg, #3b82f6 0%, rgba(59, 130, 246, 0.15) 100%);
+}
+
+/* ── Step Indicator ── */
+.pipeline-indicator {
+  width: 26px;
+  height: 26px;
+  border-radius: 50%;
+  display: flex;
+  align-items: center;
+  justify-content: center;
+  flex-shrink: 0;
+  position: relative;
+  z-index: 2;
+  margin-top: 1px;
+  background: rgba(255, 255, 255, 0.04);
+  border: 1.5px solid rgba(255, 255, 255, 0.1);
+  box-shadow: none;
+  transition:
+    background 0.5s cubic-bezier(0.4, 0, 0.2, 1),
+    border-color 0.5s cubic-bezier(0.4, 0, 0.2, 1),
+    border-width 0.3s ease,
+    box-shadow 0.6s cubic-bezier(0.4, 0, 0.2, 1),
+    transform 0.4s cubic-bezier(0.34, 1.56, 0.64, 1);
+}
+.pipeline-step--completed .pipeline-indicator {
+  background: linear-gradient(135deg, #3b82f6, #8b5cf6);
+  border-color: transparent;
+  box-shadow: 0 2px 8px rgba(59, 130, 246, 0.3);
+  transform: scale(1);
+  animation: indicator-pop 0.5s cubic-bezier(0.34, 1.56, 0.64, 1);
+}
+.pipeline-step--active .pipeline-indicator {
+  background: rgba(59, 130, 246, 0.15);
+  border: 2px solid #3b82f6;
+  box-shadow: 0 0 12px rgba(59, 130, 246, 0.25);
+  animation: indicator-glow-in 0.6s ease;
+}
+.pipeline-step--pending .pipeline-indicator {
+  background: rgba(255, 255, 255, 0.04);
+  border: 1.5px solid rgba(255, 255, 255, 0.1);
+  box-shadow: none;
+}
+
+/* ── Active Pulse Dot ── */
+.pipeline-pulse {
+  width: 8px;
+  height: 8px;
+  border-radius: 50%;
+  background: #3b82f6;
+  animation: pulse-ring 1.5s cubic-bezier(0.4, 0, 0.6, 1) infinite;
+  box-shadow: 0 0 6px rgba(59, 130, 246, 0.6);
+}
+
+/* ── Step Content ── */
+.pipeline-step-content {
+  flex: 1;
+  min-width: 0;
+}
+.step-label, .step-desc {
+  transition: color 0.5s ease;
+}
+
+/* ── Status Chip ── */
+.pipeline-status-chip {
+  display: flex;
+  align-items: center;
+  justify-content: center;
+  padding: 6px 12px;
+  border-radius: 8px;
+  background: rgba(59, 130, 246, 0.08);
+  border: 1px solid rgba(59, 130, 246, 0.12);
+  color: #93c5fd;
+  transition:
+    background 0.5s ease,
+    border-color 0.5s ease,
+    color 0.5s ease;
+}
+.pipeline-status-chip--done {
+  background: rgba(16, 185, 129, 0.08);
+  border-color: rgba(16, 185, 129, 0.15);
+  color: #6ee7b7;
+}
+.pipeline-status-chip--cancelled {
+  background: rgba(239, 68, 68, 0.08);
+  border-color: rgba(239, 68, 68, 0.15);
+  color: #fca5a5;
+}
+
+/* ── Navbar ── */
+.nav-glass {
+  background: rgba(30, 32, 38, 0.9);
+  backdrop-filter: blur(20px);
+  -webkit-backdrop-filter: blur(20px);
+  border: 1px solid rgba(255, 255, 255, 0.06);
+  box-shadow: 0 4px 20px rgba(0, 0, 0, 0.3);
+}
+.nav-action-btn {
+  display: inline-flex;
+  align-items: center;
+  gap: 6px;
+  padding: 6px 14px;
+  border-radius: 8px;
+  font-size: 13px;
+  font-weight: 500;
+  background: rgba(255, 255, 255, 0.04);
+  border: 1px solid rgba(255, 255, 255, 0.06);
+  transition: all 0.2s ease;
+  cursor: pointer;
+}
+.nav-action-btn:hover {
+  background: rgba(255, 255, 255, 0.08);
+  border-color: rgba(255, 255, 255, 0.1);
+}
+
+/* ── Result Buttons ── */
+.macos-btn-primary {
+  background: linear-gradient(180deg, #3b82f6 0%, #2563eb 100%);
+  color: white;
+  border: 1px solid rgba(255, 255, 255, 0.1);
+  box-shadow: 0 2px 8px rgba(37, 99, 235, 0.3), inset 0 1px 1px rgba(255, 255, 255, 0.15);
+  transition: all 0.2s;
+}
+.macos-btn-primary:hover {
+  filter: brightness(1.08);
+  transform: translateY(-1px);
+  box-shadow: 0 6px 20px rgba(37, 99, 235, 0.35), inset 0 1px 1px rgba(255, 255, 255, 0.15);
+}
+.macos-btn-primary:active { transform: translateY(0.5px); filter: brightness(0.95); }
+
+.result-btn-secondary {
+  background: rgba(255, 255, 255, 0.06);
+  color: white;
+  border: 1px solid rgba(255, 255, 255, 0.1);
+  backdrop-filter: blur(10px);
+  transition: all 0.2s;
+}
+.result-btn-secondary:hover {
+  background: rgba(255, 255, 255, 0.1);
+  border-color: rgba(255, 255, 255, 0.15);
+  transform: translateY(-1px);
+}
+
+/* ── Inline Player ── */
+.player-inline-card {
+  background: rgba(22, 24, 30, 0.7);
+  backdrop-filter: blur(20px);
+  border: 1px solid rgba(255, 255, 255, 0.06);
+  box-shadow: 0 8px 24px rgba(0, 0, 0, 0.2);
+}
+.audio-player {
+  opacity: 0.9;
+  border-radius: 8px;
+  transition: opacity 0.2s;
+}
+.audio-player:hover {
+  opacity: 1;
+}
+
+/* ── Animations ── */
+@keyframes spin-slow {
+  from { transform: rotate(0deg); }
+  to { transform: rotate(360deg); }
+}
+.animate-spin-slow { animation: spin-slow 2s linear infinite; }
+
+@keyframes pulse-ring {
+  0%, 100% { transform: scale(1); opacity: 1; }
+  50% { transform: scale(1.3); opacity: 0.7; }
+}
+
+@keyframes float {
+  0%, 100% { transform: translateY(0px); }
+  50% { transform: translateY(-3px); }
+}
+.animate-float { animation: float 2s ease-in-out infinite; }
+
+@keyframes shimmer {
+  0% { background-position: 200% 0; }
+  100% { background-position: -200% 0; }
+}
+
+@keyframes indicator-pop {
+  0% { transform: scale(0.6); opacity: 0.5; }
+  50% { transform: scale(1.2); }
+  100% { transform: scale(1); opacity: 1; }
+}
+
+@keyframes indicator-glow-in {
+  0% { box-shadow: 0 0 0 rgba(59, 130, 246, 0); }
+  50% { box-shadow: 0 0 20px rgba(59, 130, 246, 0.4); }
+  100% { box-shadow: 0 0 12px rgba(59, 130, 246, 0.25); }
+}
+</style>

Co-creation-projects/JJason-DeepCastAgent/frontend/src/components/SetupView.vue

@@ -0,0 +1,218 @@
+<template>
+  <div class="min-h-screen flex items-center justify-center p-6 relative overflow-hidden">
+    <!-- Background decorations -->
+    <div class="setup-bg-orb setup-bg-orb-1"></div>
+    <div class="setup-bg-orb setup-bg-orb-2"></div>
+    <div class="setup-bg-orb setup-bg-orb-3"></div>
+    <div class="setup-bg-grid"></div>
+
+    <div class="w-full max-w-xl relative z-10">
+      <!-- Brand -->
+      <div class="text-center mb-10">
+        <div class="inline-flex items-center justify-center w-20 h-20 rounded-[22px] bg-gradient-to-br from-blue-500 via-indigo-500 to-purple-600 shadow-2xl shadow-blue-500/20 mb-6 ring-1 ring-white/10">
+          <span class="text-4xl">🎙️</span>
+        </div>
+        <h1 class="text-5xl font-bold mb-3 text-transparent bg-clip-text bg-gradient-to-r from-blue-400 via-indigo-400 to-purple-400 tracking-tight">DeepCast</h1>
+        <p class="text-base text-gray-400 font-light">进行深度研究并转化为引人入胜的播客</p>
+      </div>
+
+      <!-- Main Card -->
+      <div class="setup-card rounded-2xl">
+        <form @submit.prevent="$emit('start', topic)" class="p-7">
+          <!-- Input area -->
+          <div class="mb-5">
+            <label for="topic-input" class="block text-sm font-medium text-gray-300 mb-2.5 flex items-center gap-2">
+              <svg class="w-4 h-4 text-blue-400" fill="none" stroke="currentColor" viewBox="0 0 24 24"><path stroke-linecap="round" stroke-linejoin="round" stroke-width="2" d="M9.663 17h4.673M12 3v1m6.364 1.636l-.707.707M21 12h-1M4 12H3m3.343-5.657l-.707-.707m2.828 9.9a5 5 0 117.072 0l-.548.547A3.374 3.374 0 0014 18.469V19a2 2 0 11-4 0v-.531c0-.895-.356-1.754-.988-2.386l-.548-.547z"/></svg>
+              播客主题
+            </label>
+            <textarea
+              id="topic-input"
+              v-model="topic"
+              class="setup-textarea"
+              placeholder="请输入播客主题（例如：AI Agent 的发展趋势）"
+              required
+              rows="4"
+              aria-label="播客主题输入"
+              @keydown.enter.prevent="$emit('start', topic)"
+            ></textarea>
+          </div>
+
+          <!-- Feature badges -->
+          <div class="flex flex-wrap gap-2 mb-6">
+            <div class="setup-badge">
+              <svg class="w-3.5 h-3.5 text-blue-400" fill="none" stroke="currentColor" viewBox="0 0 24 24"><path stroke-linecap="round" stroke-linejoin="round" stroke-width="2" d="M21 21l-6-6m2-5a7 7 0 11-14 0 7 7 0 0114 0z"/></svg>
+              <span>混合搜索引擎</span>
+            </div>
+            <div class="setup-badge">
+              <svg class="w-3.5 h-3.5 text-purple-400" fill="none" stroke="currentColor" viewBox="0 0 24 24"><path stroke-linecap="round" stroke-linejoin="round" stroke-width="2" d="M9.75 17L9 20l-1 1h8l-1-1-.75-3M3 13h18M5 17h14a2 2 0 002-2V5a2 2 0 00-2-2H5a2 2 0 00-2 2v10a2 2 0 002 2z"/></svg>
+              <span>深度 AI 研究</span>
+            </div>
+            <div class="setup-badge">
+              <svg class="w-3.5 h-3.5 text-emerald-400" fill="none" stroke="currentColor" viewBox="0 0 24 24"><path stroke-linecap="round" stroke-linejoin="round" stroke-width="2" d="M19 11a7 7 0 01-7 7m0 0a7 7 0 01-7-7m7 7v4m0 0H8m4 0h4m-4-8a3 3 0 01-3-3V5a3 3 0 116 0v6a3 3 0 01-3 3z"/></svg>
+              <span>自然语音合成</span>
+            </div>
+          </div>
+
+          <!-- Submit button -->
+          <button
+            class="setup-btn w-full"
+            :disabled="!topic.trim()"
+            aria-label="开始制作播客"
+          >
+            <svg class="w-5 h-5" fill="none" stroke="currentColor" viewBox="0 0 24 24"><path stroke-linecap="round" stroke-linejoin="round" stroke-width="2" d="M14.752 11.168l-3.197-2.132A1 1 0 0010 9.87v4.263a1 1 0 001.555.832l3.197-2.132a1 1 0 000-1.664z"/><path stroke-linecap="round" stroke-linejoin="round" stroke-width="2" d="M21 12a9 9 0 11-18 0 9 9 0 0118 0z"/></svg>
+            开始制作播客
+          </button>
+        </form>
+      </div>
+
+      <!-- Footer hint -->
+      <p class="text-center text-xs text-gray-600 mt-5">基于 HelloAgents 框架 · 自动化深度研究 → 播客生成</p>
+    </div>
+  </div>
+</template>
+
+<script lang="ts" setup>
+import { ref } from "vue";
+
+const topic = defineModel<string>("topic", { required: true });
+
+defineEmits<{
+  start: [topic: string];
+}>();
+</script>
+
+<style scoped>
+/* ── Background Decorations ── */
+.setup-bg-orb {
+  position: absolute;
+  border-radius: 50%;
+  filter: blur(80px);
+  pointer-events: none;
+  opacity: 0.35;
+}
+.setup-bg-orb-1 {
+  top: 10%;
+  left: 15%;
+  width: 300px;
+  height: 300px;
+  background: radial-gradient(circle, rgba(59, 130, 246, 0.4), transparent 70%);
+  animation: float-slow 8s ease-in-out infinite;
+}
+.setup-bg-orb-2 {
+  bottom: 15%;
+  right: 10%;
+  width: 250px;
+  height: 250px;
+  background: radial-gradient(circle, rgba(139, 92, 246, 0.35), transparent 70%);
+  animation: float-slow 10s ease-in-out infinite reverse;
+}
+.setup-bg-orb-3 {
+  top: 50%;
+  left: 60%;
+  width: 200px;
+  height: 200px;
+  background: radial-gradient(circle, rgba(6, 182, 212, 0.25), transparent 70%);
+  animation: float-slow 12s ease-in-out infinite 2s;
+}
+.setup-bg-grid {
+  position: absolute;
+  inset: 0;
+  background-image:
+    linear-gradient(rgba(255, 255, 255, 0.02) 1px, transparent 1px),
+    linear-gradient(90deg, rgba(255, 255, 255, 0.02) 1px, transparent 1px);
+  background-size: 60px 60px;
+  pointer-events: none;
+}
+
+/* ── Main Card ── */
+.setup-card {
+  background: rgba(22, 24, 30, 0.8);
+  backdrop-filter: blur(30px);
+  -webkit-backdrop-filter: blur(30px);
+  border: 1px solid rgba(255, 255, 255, 0.06);
+  box-shadow:
+    0 25px 60px rgba(0, 0, 0, 0.4),
+    inset 0 1px 0 rgba(255, 255, 255, 0.05);
+}
+
+/* ── Textarea ── */
+.setup-textarea {
+  width: 100%;
+  min-height: 110px;
+  padding: 14px 16px;
+  border-radius: 12px;
+  font-size: 15px;
+  line-height: 1.6;
+  resize: none;
+  color: #e5e7eb;
+  background: rgba(0, 0, 0, 0.25);
+  border: 1px solid rgba(255, 255, 255, 0.08);
+  transition: all 0.25s ease;
+  outline: none;
+  font-family: inherit;
+}
+.setup-textarea::placeholder {
+  color: rgba(156, 163, 175, 0.5);
+}
+.setup-textarea:focus {
+  background: rgba(0, 0, 0, 0.35);
+  border-color: rgba(59, 130, 246, 0.5);
+  box-shadow: 0 0 0 3px rgba(59, 130, 246, 0.12), 0 2px 8px rgba(0, 0, 0, 0.2);
+}
+
+/* ── Feature Badge ── */
+.setup-badge {
+  display: inline-flex;
+  align-items: center;
+  gap: 6px;
+  padding: 5px 12px;
+  border-radius: 8px;
+  font-size: 12px;
+  font-weight: 500;
+  color: #9ca3af;
+  background: rgba(255, 255, 255, 0.04);
+  border: 1px solid rgba(255, 255, 255, 0.06);
+}
+
+/* ── Submit Button ── */
+.setup-btn {
+  display: flex;
+  align-items: center;
+  justify-content: center;
+  gap: 8px;
+  padding: 14px 24px;
+  border-radius: 12px;
+  font-size: 16px;
+  font-weight: 600;
+  color: white;
+  background: linear-gradient(135deg, #3b82f6 0%, #6366f1 50%, #8b5cf6 100%);
+  border: 1px solid rgba(255, 255, 255, 0.1);
+  box-shadow:
+    0 4px 14px rgba(59, 130, 246, 0.3),
+    inset 0 1px 1px rgba(255, 255, 255, 0.15);
+  cursor: pointer;
+  transition: all 0.25s ease;
+}
+.setup-btn:hover:not(:disabled) {
+  transform: translateY(-1px);
+  box-shadow:
+    0 8px 25px rgba(59, 130, 246, 0.35),
+    inset 0 1px 1px rgba(255, 255, 255, 0.15);
+  filter: brightness(1.05);
+}
+.setup-btn:active:not(:disabled) {
+  transform: translateY(0.5px);
+  filter: brightness(0.95);
+}
+.setup-btn:disabled {
+  opacity: 0.4;
+  cursor: not-allowed;
+  filter: grayscale(0.4);
+}
+
+/* ── Animations ── */
+@keyframes float-slow {
+  0%, 100% { transform: translate(0, 0); }
+  50% { transform: translate(20px, -15px); }
+}
+</style>

Co-creation-projects/JJason-DeepCastAgent/frontend/src/components/TerminalLog.vue

@@ -0,0 +1,255 @@
+<template>
+  <div class="term-window rounded-2xl overflow-hidden" style="height: 500px;">
+    <!-- macOS Title Bar -->
+    <div class="term-titlebar">
+      <!-- Traffic Lights -->
+      <div class="flex items-center gap-[7px] mr-4">
+        <div class="term-dot term-dot-red" title="关闭">
+          <svg class="term-dot-icon" viewBox="0 0 12 12"><path d="M3.5 3.5l5 5M8.5 3.5l-5 5" stroke="currentColor" stroke-width="1.2" stroke-linecap="round"/></svg>
+        </div>
+        <div class="term-dot term-dot-yellow" title="最小化">
+          <svg class="term-dot-icon" viewBox="0 0 12 12"><path d="M2.5 6h7" stroke="currentColor" stroke-width="1.2" stroke-linecap="round"/></svg>
+        </div>
+        <div class="term-dot term-dot-green" title="最大化">
+          <svg class="term-dot-icon" viewBox="0 0 12 12"><path d="M3.5 8.5V5a1 1 0 011-1H8M8.5 3.5V7a1 1 0 01-1 1H4" stroke="currentColor" stroke-width="1.1" stroke-linecap="round" stroke-linejoin="round"/></svg>
+        </div>
+      </div>
+      <!-- Title -->
+      <div class="flex-1 text-center">
+        <div class="inline-flex items-center gap-2 px-3 py-0.5 rounded-md bg-white/[0.03]">
+          <svg class="w-3 h-3 text-gray-500" fill="none" stroke="currentColor" viewBox="0 0 24 24"><path stroke-linecap="round" stroke-linejoin="round" stroke-width="2" d="M8 9l3 3-3 3m5 0h3M5 20h14a2 2 0 002-2V6a2 2 0 00-2-2H5a2 2 0 00-2 2v12a2 2 0 002 2z"/></svg>
+          <span class="text-[13px] font-medium text-gray-500 tracking-wide">deepcast — {{ logs.length }} lines</span>
+        </div>
+      </div>
+      <!-- Placeholder for symmetry -->
+      <div class="w-16"></div>
+    </div>
+    <!-- Terminal Content -->
+    <div class="term-body custom-scrollbar" ref="logContainer" style="height: calc(100% - 48px);">
+      <!-- Welcome Message -->
+      <div v-if="logs.length === 0 && !isWaiting" class="term-welcome">
+        <span class="term-prompt-user">deepcast</span><span class="text-gray-500">@</span><span class="term-prompt-host">studio</span>
+        <span class="text-gray-500 mx-1">~</span>
+        <span class="term-prompt-cmd">ready</span>
+        <span class="term-cursor"></span>
+      </div>
+      <!-- Log Entries -->
+      <div v-for="(log, i) in logs" :key="i" class="term-line" :class="getLogClass(log.message)">
+        <span class="term-time">[{{ log.time }}]</span>
+        <span class="term-text">{{ log.message }}</span>
+      </div>
+      <!-- Waiting States -->
+      <div v-if="isWaiting && logs.length === 0" class="term-waiting-init">
+        <div class="term-spinner"></div>
+        <span>正在初始化...</span>
+      </div>
+      <div v-else-if="isWaiting" class="term-waiting">
+        <span class="term-cursor"></span>
+        <span>处理中{{ waitingDots }}</span>
+      </div>
+    </div>
+  </div>
+</template>
+
+<script lang="ts" setup>
+import { ref, watch, nextTick } from "vue";
+
+export interface LogEntry {
+  time: string;
+  message: string;
+}
+
+defineProps<{
+  logs: LogEntry[];
+  isWaiting: boolean;
+  waitingDots: string;
+}>();
+
+const logContainer = ref<HTMLElement | null>(null);
+
+watch(
+  () => logContainer.value,
+  () => {},
+  { flush: "post" }
+);
+
+defineExpose({ scrollToBottom });
+
+function scrollToBottom() {
+  nextTick(() => {
+    if (logContainer.value) {
+      logContainer.value.scrollTop = logContainer.value.scrollHeight;
+    }
+  });
+}
+
+function getLogClass(message: string): string {
+  if (message.includes("[STAGE]")) return "term-line--stage";
+  if (message.includes("[TASK")) return "term-line--info";
+  if (message.includes("[TOOL]")) return "term-line--tool";
+  if (message.includes("[SOURCES]")) return "term-line--warning";
+  if (message.includes("✅") || message.includes("status=completed")) return "term-line--success";
+  if (message.includes("❌") || message.includes("ERROR") || message.includes("failed")) return "term-line--error";
+  if (message.includes("⚠️") || message.includes("WARNING")) return "term-line--warning";
+  if (message.includes("INFO:")) return "term-line--muted";
+  if (message.includes("━")) return "term-line--divider";
+  return "term-line--default";
+}
+</script>
+
+<style scoped>
+/* ── Terminal Window ── */
+.term-window {
+  background: #161618;
+  border: 1px solid rgba(255, 255, 255, 0.08);
+  box-shadow:
+    0 25px 60px rgba(0, 0, 0, 0.5),
+    0 0 0 1px rgba(0, 0, 0, 0.3),
+    inset 0 1px 0 rgba(255, 255, 255, 0.04);
+}
+
+/* ── Title Bar ── */
+.term-titlebar {
+  display: flex;
+  align-items: center;
+  padding: 12px 16px;
+  background: linear-gradient(180deg, rgba(50, 52, 58, 0.95), rgba(38, 40, 46, 0.95));
+  border-bottom: 1px solid rgba(0, 0, 0, 0.4);
+  user-select: none;
+  -webkit-app-region: drag;
+}
+
+/* ── Traffic Light Dots ── */
+.term-dot {
+  width: 12px;
+  height: 12px;
+  border-radius: 50%;
+  cursor: pointer;
+  position: relative;
+  display: flex;
+  align-items: center;
+  justify-content: center;
+  transition: filter 0.15s;
+}
+.term-dot:hover { filter: brightness(1.15); }
+.term-dot-icon {
+  width: 8px;
+  height: 8px;
+  color: rgba(0, 0, 0, 0.5);
+  opacity: 0;
+  transition: opacity 0.15s;
+}
+.term-titlebar:hover .term-dot-icon { opacity: 1; }
+.term-dot-red {
+  background: #ff5f57;
+  box-shadow: inset 0 -1px 1px rgba(0,0,0,0.15), 0 1px 2px rgba(255,95,87,0.2);
+}
+.term-dot-yellow {
+  background: #febc2e;
+  box-shadow: inset 0 -1px 1px rgba(0,0,0,0.15), 0 1px 2px rgba(254,188,46,0.2);
+}
+.term-dot-green {
+  background: #28c840;
+  box-shadow: inset 0 -1px 1px rgba(0,0,0,0.15), 0 1px 2px rgba(40,200,64,0.2);
+}
+
+/* ── Terminal Body ── */
+.term-body {
+  padding: 16px 18px;
+  overflow-y: auto;
+  font-family: 'SF Mono', 'Monaco', 'Menlo', 'Cascadia Code', 'Consolas', monospace;
+  font-size: 13px;
+  line-height: 1.7;
+  color: #d4d4d8;
+  background:
+    linear-gradient(180deg, rgba(22, 22, 24, 1) 0%, rgba(18, 18, 20, 1) 100%);
+}
+
+/* ── Welcome Prompt ── */
+.term-welcome { margin-bottom: 4px; }
+.term-prompt-user { color: #60a5fa; font-weight: 600; }
+.term-prompt-host { color: #34d399; }
+.term-prompt-cmd { color: #fbbf24; }
+
+/* ── Cursor ── */
+.term-cursor {
+  display: inline-block;
+  width: 8px;
+  height: 16px;
+  background: #60a5fa;
+  margin-left: 4px;
+  vertical-align: text-bottom;
+  animation: cursor-blink 1s step-end infinite;
+}
+
+/* ── Log Lines ── */
+.term-line {
+  margin-bottom: 2px;
+  padding: 1px 0;
+  transition: background 0.15s;
+}
+.term-line:hover {
+  background: rgba(255, 255, 255, 0.02);
+  border-radius: 4px;
+}
+.term-time {
+  color: rgba(113, 113, 122, 0.7);
+  font-size: 11px;
+  margin-right: 10px;
+  user-select: none;
+  font-variant-numeric: tabular-nums;
+}
+
+/* ── Log Colors ── */
+.term-line--stage { color: #60a5fa; font-weight: 600; }
+.term-line--stage .term-time { color: rgba(96, 165, 250, 0.5); }
+.term-line--info { color: #67e8f9; }
+.term-line--tool { color: #c084fc; }
+.term-line--success { color: #34d399; }
+.term-line--error { color: #f87171; }
+.term-line--warning { color: #fbbf24; }
+.term-line--muted { color: #6b7280; }
+.term-line--divider { color: rgba(63, 63, 70, 0.6); font-size: 12px; letter-spacing: -1px; }
+.term-line--default { color: #d4d4d8; }
+
+/* ── Waiting States ── */
+.term-waiting-init {
+  display: flex;
+  align-items: center;
+  justify-content: center;
+  gap: 10px;
+  color: #fbbf24;
+  padding-top: 32px;
+}
+.term-waiting {
+  display: flex;
+  align-items: center;
+  gap: 8px;
+  color: #fbbf24;
+  margin-top: 6px;
+}
+.term-spinner {
+  width: 14px;
+  height: 14px;
+  border: 2px solid rgba(251, 191, 36, 0.3);
+  border-top-color: #fbbf24;
+  border-radius: 50%;
+  animation: spin 0.8s linear infinite;
+}
+
+/* ── Scrollbar ── */
+.custom-scrollbar::-webkit-scrollbar { width: 6px; }
+.custom-scrollbar::-webkit-scrollbar-track { background: transparent; }
+.custom-scrollbar::-webkit-scrollbar-thumb { background: rgba(255, 255, 255, 0.08); border-radius: 3px; }
+.custom-scrollbar::-webkit-scrollbar-thumb:hover { background: rgba(255, 255, 255, 0.15); }
+.term-body:not(:hover)::-webkit-scrollbar-thumb { background: transparent; }
+
+/* ── Animations ── */
+@keyframes cursor-blink {
+  0%, 50% { opacity: 1; }
+  51%, 100% { opacity: 0; }
+}
+@keyframes spin {
+  to { transform: rotate(360deg); }
+}
+</style>

Co-creation-projects/JJason-DeepCastAgent/frontend/src/env.d.ts

@@ -0,0 +1,7 @@
+interface ImportMetaEnv {
+  readonly VITE_API_BASE_URL?: string;
+}
+
+interface ImportMeta {
+  readonly env: ImportMetaEnv;
+}

Co-creation-projects/JJason-DeepCastAgent/frontend/src/main.ts

@@ -0,0 +1,6 @@
+import { createApp } from "vue";
+import App from "./App.vue";
+
+import "./style.css";
+
+createApp(App).mount("#app");

Co-creation-projects/JJason-DeepCastAgent/frontend/src/services/api.ts

@@ -0,0 +1,106 @@
+const baseURL =
+  import.meta.env.VITE_API_BASE_URL || "http://localhost:8000";
+
+export interface ResearchRequest {
+  topic: string;
+}
+
+export interface ResearchStreamEvent {
+  type: string;
+  [key: string]: unknown;
+}
+
+export interface StreamOptions {
+  signal?: AbortSignal;
+}
+
+/**
+ * 主动取消后端正在执行的研究任务。
+ */
+export async function cancelResearch(): Promise<void> {
+  try {
+    await fetch(`${baseURL}/research/cancel`, { method: "POST" });
+  } catch (err) {
+    console.warn("Failed to send cancel request:", err);
+  }
+}
+
+export async function runResearchStream(
+  payload: ResearchRequest,
+  onEvent: (event: ResearchStreamEvent) => void,
+  options: StreamOptions = {}
+): Promise<void> {
+  const response = await fetch(`${baseURL}/research/stream`, {
+    method: "POST",
+    headers: {
+      "Content-Type": "application/json",
+      Accept: "text/event-stream"
+    },
+    body: JSON.stringify(payload),
+    signal: options.signal
+  });
+
+  if (!response.ok) {
+    const errorText = await response.text().catch(() => "");
+    throw new Error(
+      errorText || `研究请求失败，状态码：${response.status}`
+    );
+  }
+
+  const body = response.body;
+  if (!body) {
+    throw new Error("浏览器不支持流式响应，无法获取研究进度");
+  }
+
+  const reader = body.getReader();
+  const decoder = new TextDecoder("utf-8");
+  let buffer = "";
+
+  while (true) {
+    const { value, done } = await reader.read();
+    buffer += decoder.decode(value || new Uint8Array(), { stream: !done });
+
+    let boundary = buffer.indexOf("\n\n");
+    while (boundary !== -1) {
+      const rawEvent = buffer.slice(0, boundary).trim();
+      buffer = buffer.slice(boundary + 2);
+
+      if (rawEvent.startsWith("data:")) {
+        const dataPayload = rawEvent.slice(5).trim();
+        if (dataPayload) {
+          try {
+            const event = JSON.parse(dataPayload) as ResearchStreamEvent;
+            onEvent(event);
+
+            if (event.type === "error" || event.type === "done") {
+              return;
+            }
+          } catch (error) {
+            console.error("解析流式事件失败：", error, dataPayload);
+          }
+        }
+      }
+
+      boundary = buffer.indexOf("\n\n");
+    }
+
+    if (done) {
+      // 处理可能的尾巴事件
+      if (buffer.trim()) {
+        const rawEvent = buffer.trim();
+        if (rawEvent.startsWith("data:")) {
+          const dataPayload = rawEvent.slice(5).trim();
+          if (dataPayload) {
+            try {
+              const event = JSON.parse(dataPayload) as ResearchStreamEvent;
+              onEvent(event);
+            } catch (error) {
+              console.error("解析流式事件失败：", error, dataPayload);
+            }
+          }
+        }
+      }
+      break;
+    }
+  }
+}

Co-creation-projects/JJason-DeepCastAgent/frontend/src/style.css

@@ -0,0 +1,40 @@
+@import "tailwindcss";
+@plugin "daisyui";
+
+/* ── Global Base ── */
+html {
+  color-scheme: dark;
+}
+
+body {
+  font-family: -apple-system, BlinkMacSystemFont, 'SF Pro Display', 'Segoe UI', Roboto, sans-serif;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+}
+
+/* ── Global Scrollbar ── */
+::-webkit-scrollbar {
+  width: 6px;
+  height: 6px;
+}
+::-webkit-scrollbar-track {
+  background: transparent;
+}
+::-webkit-scrollbar-thumb {
+  background: rgba(255, 255, 255, 0.08);
+  border-radius: 3px;
+}
+::-webkit-scrollbar-thumb:hover {
+  background: rgba(255, 255, 255, 0.15);
+}
+
+/* ── Selection Color ── */
+::selection {
+  background: rgba(59, 130, 246, 0.3);
+  color: #fff;
+}
+
+/* ── Audio element styling ── */
+audio::-webkit-media-controls-panel {
+  background: rgba(30, 30, 30, 0.8);
+}

Co-creation-projects/JJason-DeepCastAgent/frontend/tsconfig.json

@@ -0,0 +1,22 @@
+{
+  "compilerOptions": {
+    "target": "ESNext",
+    "useDefineForClassFields": true,
+    "module": "ESNext",
+    "moduleResolution": "Bundler",
+    "strict": true,
+    "jsx": "preserve",
+    "esModuleInterop": true,
+    "resolveJsonModule": true,
+    "isolatedModules": true,
+    "lib": ["ESNext", "DOM"],
+    "skipLibCheck": true,
+    "types": ["vite/client"],
+    "baseUrl": "./",
+    "paths": {
+      "@/*": ["src/*"]
+    }
+  },
+  "include": ["src/**/*.ts", "src/**/*.d.ts", "src/**/*.tsx", "src/**/*.vue"],
+  "references": [{ "path": "./tsconfig.node.json" }]
+}

Co-creation-projects/JJason-DeepCastAgent/frontend/tsconfig.node.json

@@ -0,0 +1,9 @@
+{
+  "compilerOptions": {
+    "composite": true,
+    "module": "ESNext",
+    "moduleResolution": "Bundler",
+    "allowSyntheticDefaultImports": true
+  },
+  "include": ["vite.config.ts"]
+}

Co-creation-projects/JJason-DeepCastAgent/frontend/vite.config.ts

@@ -0,0 +1,10 @@
+import { defineConfig } from 'vite';
+import vue from '@vitejs/plugin-vue';
+import tailwindcss from '@tailwindcss/vite';
+
+export default defineConfig({
+  plugins: [vue(), tailwindcss()],
+  server: {
+    port: 5174
+  }
+});