feat: add deep research case study project

.gitignore

@@ -159,4 +159,7 @@ cython_debug/
 #  option (not recommended) you can uncomment the following to ignore the entire idea folder.
 #.idea/
 .DS_Store
-.obsidian
+.obsidian
+
+# 忽略各种文件夹下codex AGENRS.md
+AGENTS.md

code/chapter14/helloagents-deepresearch/backend/.env.example

@@ -0,0 +1,42 @@
+# Which search service to use, either 'duckduckgo', 'tavily', 'perplexity', Searxng
+SEARCH_API='duckduckgo'
+
+# LLM配置 (从HelloAgents继承,如需覆盖可在此配置)
+
+# 模型提供者
+LLM_PROVIDER=custom          # Options: ollama, lmstudio, custom 其中ollama和lmstudio都是用于本地大模型配置，custom是用于自定义大模型配置[用于api调用]
+# 模型名称
+LLM_MODEL_ID=your-model-name
+
+# API密钥
+LLM_API_KEY=your-api-key-here
+
+# 服务地址
+LLM_BASE_URL=your-api-base-url
+
+# 超时时间（可选，默认60秒）
+LLM_TIMEOUT=60
+
+# 服务器配置
+HOST=0.0.0.0
+PORT=8000
+
+# CORS配置
+CORS_ORIGINS=http://localhost:5173,http://localhost:3000
+
+# 日志级别
+LOG_LEVEL=INFO
+# Web Search API Keys (choose one or both)
+# TAVILY_API_KEY=tvly-xxxxx      # Get your key at https://tavily.com
+# PERPLEXITY_API_KEY=pplx-xxxxx  # Get your key at https://www.perplexity.ai
+# For Searxng search, defaults to http://localhost:8888
+# SEARXNG_URL=
+
+# 本地模型配置(LMStudio/Ollama)
+# LLM_PROVIDER=lmstudio          # Options: ollama, lmstudio
+# LOCAL_LLM=qwen_qwq-32b         # Model name in LMStudio/Ollama
+# LMSTUDIO_BASE_URL=http://localhost:1234/v1  # LMStudio OpenAI-compatible API URL
+# OLLAMA_BASE_URL=http://localhost:11434 # the endpoint of the Ollama service, defaults to http://localhost:11434 if not set
+
+MAX_WEB_RESEARCH_LOOPS=3
+FETCH_FULL_PAGE=True

code/chapter14/helloagents-deepresearch/backend/pyproject.toml

@@ -0,0 +1,71 @@
+[project]
+name = "helloagents-deep-researcher"
+version = "0.0.1"
+description = "Fully local web research and summarization assistant powered by HelloAgents."
+authors = [
+    { name = "Lance Martin" }
+]
+readme = "README.md"
+license = { text = "MIT" }
+requires-python = ">=3.10"
+dependencies = [
+    "fastapi>=0.115.0",
+    "hello-agents>=0.2.4",
+    "tavily-python>=0.5.0",
+    "httpx>=0.28.1",
+    "markdownify>=0.11.0",
+    "python-dotenv==1.0.1",
+    "requests>=2.31.0",
+    "langsmith>=0.1.38",
+    "openai>=1.12.0",
+    "uvicorn[standard]>=0.32.0",
+    "huggingface-hub>=0.35.3",
+    "langchain-community>=0.4",
+    "ddgs>=9.6.1",
+    "loguru>=0.7.3",
+]
+
+[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 = ["deep_researcher"]
+
+[tool.setuptools.package-dir]
+"deep_researcher" = "src/deep_researcher"
+
+[tool.setuptools.package-data]
+"*" = ["py.typed"]
+
+[tool.ruff]
+lint.select = [
+    "E",    # pycodestyle
+    "F",    # pyflakes
+    "I",    # isort
+    "D",    # pydocstyle
+    "D401", # First line should be in imperative mood
+    "T201",
+    "UP",
+]
+lint.ignore = [
+    "UP006",
+    "UP007",
+    "UP035",
+    "D417",
+    "E501",
+]
+
+[tool.ruff.lint.per-file-ignores]
+"tests/*" = ["D", "UP"]
+
+[tool.ruff.lint.pydocstyle]
+convention = "google"
+
+[dependency-groups]
+dev = [
+    "ruff>=0.12.7",
+]

code/chapter14/helloagents-deepresearch/backend/src/deep_researcher/__init__.py

@@ -0,0 +1,16 @@
+"""HelloAgents deep researcher package."""
+
+version = "0.0.1"
+
+from .agent import DeepResearchAgent, run_deep_research
+from .configuration import Configuration, SearchAPI
+from .state import SummaryState, SummaryStateOutput
+
+__all__ = [
+    "DeepResearchAgent",
+    "run_deep_research",
+    "Configuration",
+    "SearchAPI",
+    "SummaryState",
+    "SummaryStateOutput",
+]

code/chapter14/helloagents-deepresearch/backend/src/deep_researcher/agent.py

@@ -0,0 +1,8 @@
+"""Backward-compatible entry points for the deep researcher agent."""
+
+from __future__ import annotations
+
+from .app.agents.deep_research_agent import DeepResearchAgent, run_deep_research
+
+__all__ = ["DeepResearchAgent", "run_deep_research"]
+

code/chapter14/helloagents-deepresearch/backend/src/deep_researcher/api.py

@@ -0,0 +1,11 @@
+"""Backward-compatible API exports."""
+
+from __future__ import annotations
+
+from .app.api.main import app, create_app
+
+__all__ = [
+    "app",
+    "create_app",
+]
+

code/chapter14/helloagents-deepresearch/backend/src/deep_researcher/app/__init__.py

@@ -0,0 +1,2 @@
+"""Application layer for the deep researcher."""
+

code/chapter14/helloagents-deepresearch/backend/src/deep_researcher/app/agents/__init__.py

@@ -0,0 +1,10 @@
+"""Agent implementations for the deep researcher."""
+
+from .deep_research_agent import DeepResearchAgent, run_deep_research
+from .tool_aware_agent import ToolAwareSimpleAgent
+
+__all__ = [
+    "DeepResearchAgent",
+    "run_deep_research",
+    "ToolAwareSimpleAgent",
+]

code/chapter14/helloagents-deepresearch/backend/src/deep_researcher/app/agents/deep_research_agent.py

@@ -0,0 +1,561 @@
+"""Orchestrator coordinating the deep research workflow."""
+
+from __future__ import annotations
+
+import logging
+import re
+from pathlib import Path
+from queue import Empty, Queue
+from threading import Lock, Thread
+from typing import Any, Callable, Iterator, Optional
+
+from hello_agents import HelloAgentsLLM
+from hello_agents.tools import ToolRegistry
+from hello_agents.tools.builtin.note_tool import NoteTool
+
+from ...configuration import Configuration
+from ...prompts import (
+    report_writer_instructions,
+    task_summarizer_instructions,
+    todo_planner_system_prompt,
+)
+from ..models import SummaryState, SummaryStateOutput, TodoItem
+from ..services.planner_service import PlanningService
+from ..services.reporting_service import ReportingService
+from ..services.search_service import dispatch_search, prepare_research_context
+from ..services.summarization_service import SummarizationService
+from ..services.tool_events import ToolCallTracker
+from .tool_aware_agent import ToolAwareSimpleAgent
+
+logger = logging.getLogger(__name__)
+
+
+class DeepResearchAgent:
+    """Coordinator orchestrating TODO-based research workflow using HelloAgents."""
+
+    def __init__(self, config: Configuration | None = None) -> None:
+        self.config = config or Configuration.from_env()
+        self.llm = self._init_llm()
+
+        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.todo_agent = self._create_tool_aware_agent(
+            name="研究规划专家",
+            system_prompt=todo_planner_system_prompt.strip(),
+        )
+        self.report_agent = self._create_tool_aware_agent(
+            name="报告撰写专家",
+            system_prompt=report_writer_instructions.strip(),
+        )
+
+        self._summarizer_factory: Callable[[], ToolAwareSimpleAgent] = lambda: self._create_tool_aware_agent(  # noqa: E501
+            name="任务总结专家",
+            system_prompt=task_summarizer_instructions.strip(),
+        )
+
+        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._last_search_notices: list[str] = []
+
+    # ------------------------------------------------------------------
+    # Public API
+    # ------------------------------------------------------------------
+    def _init_llm(self) -> HelloAgentsLLM:
+        """Instantiate HelloAgentsLLM following configuration preferences."""
+
+        llm_kwargs: dict[str, Any] = {"temperature": 0.0}
+
+        model_id = self.config.llm_model_id or self.config.local_llm
+        if model_id:
+            llm_kwargs["model"] = model_id
+
+        provider = (self.config.llm_provider or "").strip()
+        if provider:
+            llm_kwargs["provider"] = provider
+
+        if provider == "ollama":
+            llm_kwargs["base_url"] = self.config.sanitized_ollama_url()
+            if self.config.llm_api_key:
+                llm_kwargs["api_key"] = self.config.llm_api_key
+            else:
+                llm_kwargs["api_key"] = "ollama"
+        elif provider == "lmstudio":
+            llm_kwargs["base_url"] = self.config.lmstudio_base_url
+            if self.config.llm_api_key:
+                llm_kwargs["api_key"] = self.config.llm_api_key
+        else:
+            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) -> ToolAwareSimpleAgent:
+        """Instantiate a ToolAwareSimpleAgent sharing tool registry and tracker."""
+
+        return ToolAwareSimpleAgent(
+            name=name,
+            llm=self.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: Optional[Callable[[dict[str, Any]], None]]) -> None:
+        """Enable or disable immediate tool event callbacks."""
+
+        self._tool_event_sink_enabled = sink is not None
+        self._tool_tracker.set_event_sink(sink)
+
+    def run(self, topic: str) -> SummaryStateOutput:
+        """Execute the research workflow and return the final report."""
+
+        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:
+            self._execute_task(state, task, emit_stream=False)
+
+        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)
+
+        return SummaryStateOutput(
+            running_summary=report,
+            report_markdown=report,
+            todo_items=state.todo_items,
+        )
+
+    def run_stream(self, topic: str) -> Iterator[dict[str, Any]]:
+        """Execute the workflow yielding incremental progress events."""
+
+        state = SummaryState(research_topic=topic)
+        logger.debug("Starting streaming research: topic=%s", topic)
+        yield {"type": "status", "message": "初始化研究流程"}
+
+        state.todo_items = self.planner.plan_todo_list(state)
+        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}
+
+        yield {
+            "type": "todo_list",
+            "tasks": [self._serialize_task(t) for t in state.todo_items],
+            "step": 0,
+        }
+
+        event_queue: Queue[dict[str, Any]] = Queue()
+
+        def enqueue(
+            event: dict[str, Any],
+            *,
+            task: Optional[TodoItem] = None,
+            step_override: Optional[int] = 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)
+
+        def tool_event_sink(event: dict[str, Any]) -> None:
+            enqueue(event)
+
+        self._set_tool_event_sink(tool_event_sink)
+
+        threads: list[Thread] = []
+
+        def worker(task: TodoItem, step: int) -> None:
+            try:
+                enqueue(
+                    {
+                        "type": "task_status",
+                        "task_id": task.id,
+                        "status": "in_progress",
+                        "title": task.title,
+                        "intent": task.intent,
+                        "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):
+                    enqueue(event, task=task)
+            except Exception as exc:  # pragma: no cover - defensive guardrail
+                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,
+                        "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:
+                event = event_queue.get()
+                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()
+
+        report = self.reporting.generate_report(state)
+        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
+
+        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,
+        }
+        yield {"type": "done"}
+
+    # ------------------------------------------------------------------
+    # Execution helpers
+    # ------------------------------------------------------------------
+    def _execute_task(
+        self,
+        state: SummaryState,
+        task: TodoItem,
+        *,
+        emit_stream: bool,
+        step: int | None = None,
+    ) -> Iterator[dict[str, Any]]:
+        """Run search + summarization for a single task."""
+
+        task.status = "in_progress"
+
+        search_result, notices, answer_text, backend = dispatch_search(
+            task.query,
+            self.config,
+            state.research_loop_count,
+        )
+        self._last_search_notices = notices
+        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: Optional[str] = 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",
+                "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: Optional[int] = None,
+    ) -> list[dict[str, Any]]:
+        """Proxy to the shared tool call tracker."""
+
+        events = self._tool_tracker.drain(state, step=step)
+        if self._tool_event_sink_enabled:
+            return []
+        return events
+
+    @property
+    def _tool_call_events(self) -> list[dict[str, Any]]:
+        """Expose recorded tool events for legacy integrations."""
+
+        return self._tool_tracker.as_dicts()
+
+    def _serialize_task(self, task: TodoItem) -> dict[str, Any]:
+        """Convert task dataclass to serializable dict for frontend."""
+
+        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) -> Optional[dict[str, Any]]:
+        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._extract_note_id_from_text(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) -> Optional[str]:
+        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
+
+    @staticmethod
+    def _extract_note_id_from_text(response: str) -> Optional[str]:
+        if not response:
+            return None
+
+        match = re.search(r"ID:\s*([^\n]+)", response)
+        if not match:
+            return None
+
+        return match.group(1).strip()
+
+
+def run_deep_research(topic: str, config: Optional[Configuration] = None) -> SummaryStateOutput:
+    """Convenience function mirroring the class-based API."""
+
+    agent = DeepResearchAgent(config=config)
+    return agent.run(topic)

code/chapter14/helloagents-deepresearch/backend/src/deep_researcher/app/agents/tool_aware_agent.py

@@ -0,0 +1,353 @@
+"""Wrapper around HelloAgents SimpleAgent that records tool calls."""
+
+from __future__ import annotations
+
+import ast
+import json
+import logging
+from collections.abc import Iterator
+from typing import Any, Callable, Optional
+
+from hello_agents import SimpleAgent
+from hello_agents.core.message import Message
+from hello_agents.tools import ToolRegistry
+
+logger = logging.getLogger(__name__)
+
+
+class ToolAwareSimpleAgent(SimpleAgent):
+    """SimpleAgent 子类，记录工具调用情况。"""
+
+    def __init__(
+        self,
+        *args: Any,
+        tool_call_listener: Optional[Callable[[dict[str, Any]], None]] = None,
+        **kwargs: Any,
+    ) -> None:
+        super().__init__(*args, **kwargs)
+        self._tool_call_listener = tool_call_listener
+
+    def _execute_tool_call(self, tool_name: str, parameters: str) -> str:  # type: ignore[override]
+        if not self.tool_registry:
+            return "❌ 错误：未配置工具注册表"
+
+        try:
+            tool = self.tool_registry.get_tool(tool_name)
+            if not tool:
+                return f"❌ 错误：未找到工具 '{tool_name}'"
+
+            parsed_parameters = self._parse_tool_parameters(tool_name, parameters)
+            parsed_parameters = self._sanitize_parameters(parsed_parameters)
+
+            result = tool.run(parsed_parameters)
+            formatted_result = f"🔧 工具 {tool_name} 执行结果：\n{result}"
+        except Exception as exc:  # pragma: no cover - tool failures回退
+            parsed_parameters = {}
+            formatted_result = f"❌ 工具调用失败：{exc}"
+
+        if self._tool_call_listener:
+            try:
+                self._tool_call_listener(
+                    {
+                        "agent_name": self.name,
+                        "tool_name": tool_name,
+                        "raw_parameters": parameters,
+                        "parsed_parameters": parsed_parameters,
+                        "result": formatted_result,
+                    }
+                )
+            except Exception:  # pragma: no cover - 防御性兜底
+                logger.exception("Tool call listener failed")
+
+        return formatted_result
+
+    def _parse_tool_calls(self, text: str) -> list:  # type: ignore[override]
+        marker = "[TOOL_CALL:"
+        calls: list = []
+        start = 0
+
+        while True:
+            begin = text.find(marker, start)
+            if begin == -1:
+                break
+
+            tool_start = begin + len(marker)
+            colon = text.find(":", tool_start)
+            if colon == -1:
+                break
+
+            tool_name = text[tool_start:colon].strip()
+            body_start = colon + 1
+            pos = body_start
+            depth = 0
+            in_string = False
+            string_quote = ""
+
+            while pos < len(text):
+                char = text[pos]
+
+                if char in {'"', "'"}:
+                    if not in_string:
+                        in_string = True
+                        string_quote = char
+                    elif string_quote == char and text[pos - 1] != "\\":
+                        in_string = False
+
+                if not in_string:
+                    if char == '[':
+                        depth += 1
+                    elif char == ']':
+                        if depth == 0:
+                            body = text[body_start:pos].strip()
+                            original = text[begin : pos + 1]
+                            calls.append(
+                                {
+                                    "tool_name": tool_name,
+                                    "parameters": body,
+                                    "original": original,
+                                }
+                            )
+                            start = pos + 1
+                            break
+                        else:
+                            depth -= 1
+
+                pos += 1
+            else:
+                break
+
+        return calls
+
+    @staticmethod
+    def _find_tool_call_end(text: str, start_index: int) -> int:
+        marker = "[TOOL_CALL:"
+        tool_start = start_index + len(marker)
+        colon = text.find(":", tool_start)
+        if colon == -1:
+            return -1
+
+        body_start = colon + 1
+        pos = body_start
+        depth = 0
+        in_string = False
+        string_quote = ""
+
+        while pos < len(text):
+            char = text[pos]
+
+            if char in {'"', "'"}:
+                if not in_string:
+                    in_string = True
+                    string_quote = char
+                elif string_quote == char and text[pos - 1] != "\\":
+                    in_string = False
+
+            if not in_string:
+                if char == '[':
+                    depth += 1
+                elif char == ']':
+                    if depth == 0:
+                        return pos
+                    depth -= 1
+
+            pos += 1
+
+        return -1
+
+    @staticmethod
+    def attach_registry(agent: "ToolAwareSimpleAgent", registry: ToolRegistry | None) -> None:
+        """Helper to attach a tool registry if provided."""
+
+        if registry:
+            agent.tool_registry = registry
+            agent.enable_tool_calling = True
+
+    @staticmethod
+    def _sanitize_parameters(parameters: dict[str, Any]) -> dict[str, Any]:
+        sanitized: dict[str, Any] = {}
+        for key, value in parameters.items():
+            if isinstance(value, (int, float, bool, list, dict)):
+                sanitized[key] = value
+                continue
+
+            if isinstance(value, str):
+                normalized = ToolAwareSimpleAgent._normalize_string(value)
+
+                if key == "task_id":
+                    try:
+                        sanitized[key] = int(normalized)
+                        continue
+                    except ValueError:
+                        pass
+
+                if key == "tags":
+                    parsed_tags = ToolAwareSimpleAgent._coerce_sequence(normalized)
+                    if isinstance(parsed_tags, list):
+                        sanitized[key] = parsed_tags
+                        continue
+                    if normalized:
+                        sanitized[key] = [item.strip() for item in normalized.split(",") if item.strip()]
+                        continue
+
+                if key in {"note_type", "action", "title", "content", "note_id"}:
+                    sanitized[key] = normalized
+                    continue
+
+                sanitized[key] = normalized
+                continue
+
+            sanitized[key] = value
+
+        return sanitized
+
+    @staticmethod
+    def _normalize_string(value: str) -> str:
+        trimmed = value.strip()
+
+        if trimmed and trimmed[0] in {'"', "'"} and trimmed.count(trimmed[0]) == 1:
+            trimmed = trimmed[1:]
+        if trimmed and trimmed[-1] in {'"', "'"} and trimmed.count(trimmed[-1]) == 1:
+            trimmed = trimmed[:-1]
+
+        if trimmed and trimmed[0] in {'"', "'"} and trimmed[-1] == trimmed[0]:
+            trimmed = trimmed[1:-1]
+
+        if trimmed and trimmed[0] in {'[', '('} and trimmed[-1] not in {']', ')'}:
+            closing = ']' if trimmed[0] == '[' else ')'
+            trimmed = f"{trimmed}{closing}"
+
+        return trimmed.strip()
+
+    @staticmethod
+    def _coerce_sequence(value: str) -> Any:
+        if not value:
+            return None
+
+        candidates = [value]
+        if value.startswith("[") and not value.endswith("]"):
+            candidates.append(f"{value}]")
+        if value.startswith("(") and not value.endswith(")"):
+            candidates.append(f"{value})")
+
+        for candidate in candidates:
+            for loader in (json.loads, ast.literal_eval):
+                try:
+                    parsed = loader(candidate)
+                except Exception:
+                    continue
+                if isinstance(parsed, list):
+                    return parsed
+
+        return None
+
+    def stream_run(self, input_text: str, max_tool_iterations: int = 3, **kwargs: Any) -> Iterator[str]:  # type: ignore[override]
+        """Stream assistant output while supporting tool calls mid-generation."""
+
+        messages: list[dict[str, Any]] = []
+        enhanced_system_prompt = self._get_enhanced_system_prompt()
+        messages.append({"role": "system", "content": enhanced_system_prompt})
+
+        for msg in self._history:
+            messages.append({"role": msg.role, "content": msg.content})
+
+        messages.append({"role": "user", "content": input_text})
+
+        final_segments: list[str] = []
+        final_response_text = ""
+        current_iteration = 0
+
+        marker = "[TOOL_CALL:"
+
+        while current_iteration < max_tool_iterations:
+            residual = ""
+            segments_this_round: list[str] = []
+            tool_call_texts: list[str] = []
+
+            def process_residual(final_pass: bool = False) -> Iterator[str]:
+                nonlocal residual
+                while True:
+                    start = residual.find(marker)
+                    if start == -1:
+                        safe_len = len(residual) if final_pass else max(0, len(residual) - (len(marker) - 1))
+                        if safe_len > 0:
+                            segment = residual[:safe_len]
+                            residual = residual[safe_len:]
+                            yield segment
+                        break
+
+                    if start > 0:
+                        segment = residual[:start]
+                        residual = residual[start:]
+                        if segment:
+                            yield segment
+                        continue
+
+                    end = self._find_tool_call_end(residual, 0)
+                    if end == -1:
+                        break
+
+                    tool_call_texts.append(residual[: end + 1])
+                    residual = residual[end + 1 :]
+
+            for chunk in self.llm.stream_invoke(messages, **kwargs):
+                if not chunk:
+                    continue
+
+                residual += chunk
+
+                for segment in process_residual():
+                    if not segment:
+                        continue
+                    segments_this_round.append(segment)
+                    final_segments.append(segment)
+                    yield segment
+
+            for segment in process_residual(final_pass=True):
+                if not segment:
+                    continue
+                segments_this_round.append(segment)
+                final_segments.append(segment)
+                yield segment
+
+            clean_response = "".join(segments_this_round)
+            tool_calls: list[dict[str, Any]] = []
+
+            for call_text in tool_call_texts:
+                tool_calls.extend(self._parse_tool_calls(call_text))
+
+            if tool_calls:
+                messages.append({"role": "assistant", "content": clean_response})
+
+                tool_results = []
+                for call in tool_calls:
+                    result = self._execute_tool_call(call["tool_name"], call["parameters"])
+                    tool_results.append(result)
+
+                tool_results_text = "\n\n".join(tool_results)
+                messages.append(
+                    {
+                        "role": "user",
+                        "content": (
+                            "工具执行结果：\n"
+                            f"{tool_results_text}\n\n"
+                            "请基于这些结果给出完整的回答。"
+                        ),
+                    }
+                )
+
+                current_iteration += 1
+                continue
+
+            final_response_text = clean_response
+            break
+
+        if current_iteration >= max_tool_iterations and not final_response_text:
+            fallback_response = self.llm.invoke(messages, **kwargs)
+            final_segments.append(fallback_response)
+            final_response_text = fallback_response
+            yield fallback_response
+
+        stored_response = final_response_text or "".join(final_segments)
+
+        self.add_message(Message(input_text, "user"))
+        self.add_message(Message(stored_response, "assistant"))

code/chapter14/helloagents-deepresearch/backend/src/deep_researcher/app/api/__init__.py

@@ -0,0 +1,8 @@
+"""FastAPI integration for the deep researcher."""
+
+from .main import app, create_app
+
+__all__ = [
+    "app",
+    "create_app",
+]

code/chapter14/helloagents-deepresearch/backend/src/deep_researcher/app/api/main.py

@@ -0,0 +1,178 @@
+"""FastAPI entrypoint exposing the DeepResearchAgent via HTTP."""
+
+from __future__ import annotations
+
+import json
+import sys
+from typing import Any, Dict, Iterator, Optional
+
+from fastapi import FastAPI, HTTPException
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi.responses import StreamingResponse
+from loguru import logger
+from pydantic import BaseModel, Field
+
+from ...configuration import Configuration, SearchAPI
+from ..agents.deep_research_agent import DeepResearchAgent
+
+# 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,
+)
+
+
+# 添加错误日志文件处理程序
+logger.add(
+    sink=sys.stderr,
+    level="ERROR",
+    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):
+    """Payload for triggering a research run."""
+
+    topic: str = Field(..., description="Research topic supplied by the user")
+    search_api: SearchAPI | None = Field(
+        default=None,
+        description="Override the default search backend configured via env",
+    )
+
+
+class ResearchResponse(BaseModel):
+    """HTTP response containing the generated report and structured tasks."""
+
+    report_markdown: str = Field(
+        ..., description="Markdown-formatted research report including sections"
+    )
+    todo_items: list[dict[str, Any]] = Field(
+        default_factory=list,
+        description="Structured TODO items with summaries and sources",
+    )
+
+
+def _mask_secret(value: Optional[str], visible: int = 4) -> str:
+    """Mask sensitive tokens while keeping leading and trailing characters."""
+    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:
+    overrides: Dict[str, Any] = {}
+
+    if payload.search_api is not None:
+        overrides["search_api"] = payload.search_api
+
+    return Configuration.from_env(overrides=overrides)
+
+
+def create_app() -> FastAPI:
+    app = FastAPI(title="HelloAgents Deep Researcher")
+
+    app.add_middleware(
+        CORSMiddleware,
+        allow_origins=["*"],
+        allow_credentials=True,
+        allow_methods=["*"],
+        allow_headers=["*"],
+    )
+
+    @app.on_event("startup")
+    def log_startup_configuration() -> None:
+        config = Configuration.from_env()
+
+        if config.llm_provider == "ollama":
+            base_url = config.sanitized_ollama_url()
+        elif config.llm_provider == "lmstudio":
+            base_url = config.lmstudio_base_url
+        else:
+            base_url = config.llm_base_url or "unset"
+
+        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",
+            base_url,
+            (config.search_api.value if isinstance(config.search_api, SearchAPI) else config.search_api),
+            config.max_web_research_loops,
+            config.fetch_full_page,
+            config.use_tool_calling,
+            config.strip_thinking_tokens,
+            _mask_secret(config.llm_api_key),
+        )
+
+    @app.get("/healthz")
+    def health_check() -> Dict[str, str]:
+        return {"status": "ok"}
+
+    @app.post("/research", response_model=ResearchResponse)
+    def run_research(payload: ResearchRequest) -> ResearchResponse:
+        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
+        ]
+
+        return ResearchResponse(
+            report_markdown=(result.report_markdown or result.running_summary or ""),
+            todo_items=todo_payload,
+        )
+
+    @app.post("/research/stream")
+    def stream_research(payload: ResearchRequest) -> StreamingResponse:
+        try:
+            config = _build_config(payload)
+            agent = DeepResearchAgent(config=config)
+        except ValueError as exc:
+            raise HTTPException(status_code=400, detail=str(exc)) from exc
+
+        def event_iterator() -> Iterator[str]:
+            try:
+                for event in agent.run_stream(payload.topic):
+                    yield f"data: {json.dumps(event, ensure_ascii=False)}\n\n"
+            except Exception as exc:  # pragma: no cover - defensive guardrail
+                logger.exception("Streaming research failed")
+                error_payload = {"type": "error", "detail": str(exc)}
+                yield f"data: {json.dumps(error_payload, ensure_ascii=False)}\n\n"
+
+        return StreamingResponse(
+            event_iterator(),
+            media_type="text/event-stream",
+            headers={
+                "Cache-Control": "no-cache",
+                "Connection": "keep-alive",
+            },
+        )
+
+    return app
+
+
+app = create_app()

code/chapter14/helloagents-deepresearch/backend/src/deep_researcher/app/api/routes/__init__.py

@@ -0,0 +1,2 @@
+"""Route definitions for the deep researcher HTTP API."""
+

code/chapter14/helloagents-deepresearch/backend/src/deep_researcher/app/models/__init__.py

@@ -0,0 +1,11 @@
+"""Shared data models used across the application."""
+
+from .state import SummaryState, SummaryStateInput, SummaryStateOutput, TodoItem
+
+__all__ = [
+    "SummaryState",
+    "SummaryStateInput",
+    "SummaryStateOutput",
+    "TodoItem",
+]
+

code/chapter14/helloagents-deepresearch/backend/src/deep_researcher/app/models/state.py

@@ -0,0 +1,50 @@
+"""State models used by the deep research workflow."""
+
+import operator
+from dataclasses import dataclass, field
+from typing import List, Optional
+
+from typing_extensions import Annotated
+
+
+@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: Optional[str] = field(default=None)
+    sources_summary: Optional[str] = field(default=None)
+    notices: list[str] = field(default_factory=list)
+    note_id: Optional[str] = field(default=None)
+    note_path: Optional[str] = field(default=None)
+    stream_token: Optional[str] = field(default=None)
+
+
+@dataclass(kw_only=True)
+class SummaryState:
+    research_topic: str = field(default=None)  # Report topic
+    search_query: str = field(default=None)  # Deprecated placeholder
+    web_research_results: Annotated[list, operator.add] = field(default_factory=list)
+    sources_gathered: Annotated[list, operator.add] = field(default_factory=list)
+    research_loop_count: int = field(default=0)  # Research loop count
+    running_summary: str = field(default=None)  # Legacy summary field
+    todo_items: Annotated[list, operator.add] = field(default_factory=list)
+    structured_report: Optional[str] = field(default=None)
+    report_note_id: Optional[str] = field(default=None)
+    report_note_path: Optional[str] = field(default=None)
+
+
+@dataclass(kw_only=True)
+class SummaryStateInput:
+    research_topic: str = field(default=None)  # Report topic
+
+
+@dataclass(kw_only=True)
+class SummaryStateOutput:
+    running_summary: str = field(default=None)  # Backward-compatible文本
+    report_markdown: Optional[str] = field(default=None)
+    todo_items: List[TodoItem] = field(default_factory=list)

code/chapter14/helloagents-deepresearch/backend/src/deep_researcher/app/services/__init__.py

@@ -0,0 +1,2 @@
+"""Domain services for the deep researcher workflow."""
+

code/chapter14/helloagents-deepresearch/backend/src/deep_researcher/app/services/notes_service.py

@@ -0,0 +1,59 @@
+"""Helpers for coordinating note tool usage instructions."""
+
+from __future__ import annotations
+
+import json
+
+from ..models import TodoItem
+
+
+def build_note_guidance(task: TodoItem) -> str:
+    """Generate note tool usage guidance for a specific task."""
+
+    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)
+        update_payload = 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": "请将本轮新增信息补充到任务概览中",
+            },
+            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"- 完成分析后调用：[TOOL_CALL:note:{update_payload}] 同步增量信息。\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"
+        )
+
+    create_payload = json.dumps(
+        {
+            "action": "create",
+            "task_id": task.id,
+            "title": f"任务 {task.id}: {task.title}",
+            "note_type": "task_state",
+            "tags": tags_list,
+            "content": "请记录任务概览、来源概览",
+        },
+        ensure_ascii=False,
+    )
+
+    return (
+        "笔记协作指引：\n"
+        f"- 当前任务尚未建立笔记，请先调用：[TOOL_CALL:note:{create_payload}]。\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"
+    )
+

code/chapter14/helloagents-deepresearch/backend/src/deep_researcher/app/services/planner_service.py

@@ -0,0 +1,159 @@
+"""Service responsible for converting the research topic into actionable tasks."""
+
+from __future__ import annotations
+
+import json
+import logging
+import re
+from typing import Any, List, Optional
+
+from ..models import SummaryState, TodoItem
+from ...configuration import Configuration
+from ...prompts import get_current_date, todo_planner_instructions
+from ...utils import strip_thinking_tokens
+from ..agents.tool_aware_agent import ToolAwareSimpleAgent
+
+logger = logging.getLogger(__name__)
+
+TOOL_CALL_PATTERN = re.compile(
+    r"\[TOOL_CALL:(?P<tool>[^:]+):(?P<body>[^\]]+)\]",
+    re.IGNORECASE,
+)
+
+class PlanningService:
+    """Wraps the planner agent to produce structured TODO items."""
+
+    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]:
+        """Ask the planner agent to break the topic into actionable tasks."""
+
+        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:
+        """Create a minimal fallback task when planning failed."""
+
+        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",
+        )
+
+    # ------------------------------------------------------------------
+    # Parsing helpers
+    # ------------------------------------------------------------------
+    def _extract_tasks(self, raw_response: str) -> List[dict[str, Any]]:
+        """Parse planner output into a list of task dictionaries."""
+
+        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) -> Optional[dict[str, Any] | list]:
+        """Try to locate and parse a JSON object or array from the text."""
+
+        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) -> Optional[dict[str, Any]]:
+        """Parse the first TOOL_CALL expression in the output."""
+
+        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

code/chapter14/helloagents-deepresearch/backend/src/deep_researcher/app/services/reporting_service.py

@@ -0,0 +1,76 @@
+"""Service that consolidates task results into the final report."""
+
+from __future__ import annotations
+
+import json
+
+from ..models import SummaryState
+from ...configuration import Configuration
+from ...utils import strip_thinking_tokens
+from ..agents.tool_aware_agent import ToolAwareSimpleAgent
+from .text_processing import strip_tool_calls
+
+
+class ReportingService:
+    """Generates the final structured report."""
+
+    def __init__(self, report_agent: ToolAwareSimpleAgent, config: Configuration) -> None:
+        self._agent = report_agent
+        self._config = config
+
+    def generate_report(self, state: SummaryState) -> str:
+        """Generate a structured report based on completed tasks."""
+
+        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)
+        create_conclusion_template = json.dumps(
+            {
+                "action": "create",
+                "title": f"研究报告：{state.research_topic}",
+                "note_type": "conclusion",
+                "tags": ["deep_research", "report"],
+                "content": "请在此沉淀最终报告要点",
+            },
+            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：[TOOL_CALL:note:{create_conclusion_template}] 保存报告要点。"
+        )
+
+        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。"
+

code/chapter14/helloagents-deepresearch/backend/src/deep_researcher/app/services/search_service.py

@@ -0,0 +1,114 @@
+"""Search dispatching helpers."""
+
+from __future__ import annotations
+
+import logging
+from typing import Any, Optional, Tuple
+
+from ...configuration import Configuration
+from ...utils import (
+    advanced_search,
+    deduplicate_and_format_sources,
+    duckduckgo_search,
+    format_sources,
+    get_config_value,
+    perplexity_search,
+    searxng_search,
+    tavily_search,
+)
+
+logger = logging.getLogger(__name__)
+
+MAX_TOKENS_PER_SOURCE = 2000
+
+
+def dispatch_search(
+    query: str,
+    config: Configuration,
+    loop_count: int,
+) -> Tuple[dict[str, Any] | None, list[str], Optional[str], str]:
+    """Call the configured search backend and normalize the response."""
+
+    search_api = get_config_value(config.search_api)
+    notices: list[str] = []
+    answer_text: Optional[str] = None
+    backend_label = search_api
+
+    if search_api == "tavily":
+        result = tavily_search(
+            query,
+            fetch_full_page=config.fetch_full_page,
+            max_results=5,
+        )
+    elif search_api == "perplexity":
+        result = perplexity_search(
+            query,
+            perplexity_search_loop_count=loop_count,
+        )
+    elif search_api == "duckduckgo":
+        result = duckduckgo_search(
+            query,
+            max_results=5,
+            fetch_full_page=config.fetch_full_page,
+        )
+    elif search_api == "searxng":
+        result = searxng_search(
+            query,
+            max_results=5,
+            fetch_full_page=config.fetch_full_page,
+        )
+    elif search_api == "advanced":
+        result = advanced_search(
+            query,
+            fetch_full_page=config.fetch_full_page,
+        )
+        if isinstance(result, dict):
+            notices = list(result.get("notices") or [])
+            answer_text = result.get("answer")
+            backend_label = str(result.get("backend") or "advanced")
+    else:
+        raise ValueError(f"Unsupported search API: {config.search_api}")
+
+    if answer_text is None and isinstance(result, dict):
+        answer_text = result.get("answer")
+
+    if isinstance(result, dict):
+        results_len = len(result.get("results", []))
+    elif isinstance(result, list):
+        results_len = len(result)
+    else:
+        results_len = "?"
+
+    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),
+        results_len,
+    )
+
+    return result, notices, answer_text, backend_label
+
+
+def prepare_research_context(
+    search_result: dict[str, Any] | None,
+    answer_text: Optional[str],
+    config: Configuration,
+) -> tuple[str, str]:
+    """Format sources and research context for downstream summarization."""
+
+    sources_summary = format_sources(search_result)
+    context = deduplicate_and_format_sources(
+        search_result,
+        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
+

code/chapter14/helloagents-deepresearch/backend/src/deep_researcher/app/services/summarization_service.py

@@ -0,0 +1,124 @@
+"""Task summarization utilities."""
+
+from __future__ import annotations
+
+from collections.abc import Callable, Iterator
+from typing import Tuple
+
+from ..models import SummaryState, TodoItem
+from ...configuration import Configuration
+from ...utils import strip_thinking_tokens
+from ..agents.tool_aware_agent import ToolAwareSimpleAgent
+from .notes_service import build_note_guidance
+from .text_processing import strip_tool_calls
+
+
+class SummarizationService:
+    """Handles synchronous and streaming task summarization."""
+
+    def __init__(
+        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:
+        """Generate a task-specific summary using the summarizer agent."""
+
+        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]]:
+        """Stream the summary text for a task while collecting full output."""
+
+        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]:
+            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:
+        """Construct the summarization prompt shared by both modes."""
+
+        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）。"
+        )

code/chapter14/helloagents-deepresearch/backend/src/deep_researcher/app/services/text_processing.py

@@ -0,0 +1,16 @@
+"""Utility helpers for normalizing agent generated text."""
+
+from __future__ import annotations
+
+import re
+
+
+def strip_tool_calls(text: str) -> str:
+    """移除文本中的工具调用标记。"""
+
+    if not text:
+        return text
+
+    pattern = re.compile(r"\[TOOL_CALL:[^\]]+\]")
+    return pattern.sub("", text)
+

code/chapter14/helloagents-deepresearch/backend/src/deep_researcher/app/services/tool_events.py

@@ -0,0 +1,215 @@
+"""Utility for collecting and exposing tool call events."""
+
+from __future__ import annotations
+
+import logging
+import re
+from dataclasses import dataclass
+from pathlib import Path
+from threading import Lock
+from typing import Any, Callable, Optional
+
+from ..models import SummaryState, TodoItem
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class ToolCallEvent:
+    """Internal representation of a tool call event."""
+
+    id: int
+    agent: str
+    tool: str
+    raw_parameters: str
+    parsed_parameters: dict[str, Any]
+    result: str
+    task_id: Optional[int]
+    note_id: Optional[str]
+
+
+class ToolCallTracker:
+    """Collects tool call events and converts them to SSE payloads."""
+
+    def __init__(self, notes_workspace: Optional[str]) -> None:
+        self._notes_workspace = notes_workspace
+        self._events: list[ToolCallEvent] = []
+        self._cursor = 0
+        self._lock = Lock()
+        self._event_sink: Optional[Callable[[dict[str, Any]], 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。"""
+
+        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: Optional[str] = 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))
+
+    # ------------------------------------------------------------------
+    # Draining helpers
+    # ------------------------------------------------------------------
+    def drain(self, state: SummaryState, *, step: Optional[int] = 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。"""
+
+        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:
+        """Clear recorded events."""
+
+        with self._lock:
+            self._events.clear()
+            self._cursor = 0
+
+    def as_dicts(self) -> list[dict[str, Any]]:
+        """Expose a snapshot of raw events for backwards compatibility."""
+
+        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: Optional[Callable[[dict[str, Any]], None]]) -> None:
+        """Register a callback for immediate tool event notifications."""
+
+        self._event_sink = sink
+
+    def _build_payload(self, event: ToolCallEvent, step: Optional[int]) -> 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
+
+    # ------------------------------------------------------------------
+    # Internal helpers
+    # ------------------------------------------------------------------
+    def _attach_note_to_task(self, tasks: list[TodoItem], task_id: int, note_id: str) -> None:
+        """Update matching TODO item with note metadata."""
+
+        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]) -> Optional[int]:
+        """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) -> Optional[str]:
+        if not response:
+            return None
+
+        match = re.search(r"ID:\s*([^\n]+)", response)
+        if match:
+            return match.group(1).strip()
+        return None

code/chapter14/helloagents-deepresearch/backend/src/deep_researcher/configuration.py

@@ -0,0 +1,142 @@
+import os
+from enum import Enum
+from typing import Any, Optional
+
+from pydantic import BaseModel, Field
+
+
+class SearchAPI(Enum):
+    PERPLEXITY = "perplexity"
+    TAVILY = "tavily"
+    DUCKDUCKGO = "duckduckgo"
+    SEARXNG = "searxng"
+    ADVANCED = "advanced"
+
+
+class Configuration(BaseModel):
+    """Configuration options for the deep research assistant."""
+
+    max_web_research_loops: int = Field(
+        default=3,
+        title="Research Depth",
+        description="Number of research iterations to perform",
+    )
+    local_llm: str = Field(
+        default="llama3.2",
+        title="Local Model Name",
+        description="Name of the locally hosted LLM (Ollama/LMStudio)",
+    )
+    llm_provider: str = Field(
+        default="ollama",
+        title="LLM Provider",
+        description="Provider identifier (ollama, lmstudio, or custom)",
+    )
+    search_api: SearchAPI = Field(
+        default=SearchAPI.DUCKDUCKGO,
+        title="Search API",
+        description="Web search API to use",
+    )
+    enable_notes: bool = Field(
+        default=True,
+        title="Enable Notes",
+        description="Whether to store task progress in NoteTool",
+    )
+    notes_workspace: str = Field(
+        default="./notes/deep_research",
+        title="Notes Workspace",
+        description="Directory for NoteTool to persist task notes",
+    )
+    fetch_full_page: bool = Field(
+        default=True,
+        title="Fetch Full Page",
+        description="Include the full page content in the search results",
+    )
+    ollama_base_url: str = Field(
+        default="http://localhost:11434",
+        title="Ollama Base URL",
+        description="Base URL for Ollama API (without /v1 suffix)",
+    )
+    lmstudio_base_url: str = Field(
+        default="http://localhost:1234/v1",
+        title="LMStudio Base URL",
+        description="Base URL for LMStudio OpenAI-compatible API",
+    )
+    strip_thinking_tokens: bool = Field(
+        default=True,
+        title="Strip Thinking Tokens",
+        description="Whether to strip <think> tokens from model responses",
+    )
+    use_tool_calling: bool = Field(
+        default=False,
+        title="Use Tool Calling",
+        description="Use tool calling instead of JSON mode for structured output",
+    )
+    llm_api_key: Optional[str] = Field(
+        default=None,
+        title="LLM API Key",
+        description="Optional API key when using custom OpenAI-compatible services",
+    )
+    llm_base_url: Optional[str] = Field(
+        default=None,
+        title="LLM Base URL",
+        description="Optional base URL when using custom OpenAI-compatible services",
+    )
+    llm_model_id: Optional[str] = Field(
+        default=None,
+        title="LLM Model ID",
+        description="Optional model identifier for custom OpenAI-compatible services",
+    )
+
+    @classmethod
+    def from_env(cls, overrides: Optional[dict[str, Any]] = None) -> "Configuration":
+        """Create a configuration object using environment variables and overrides."""
+
+        raw_values: dict[str, Any] = {}
+
+        # Load values from environment variables based on field names
+        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]
+
+        # Additional mappings for explicit env names
+        env_aliases = {
+            "local_llm": os.getenv("LOCAL_LLM"),
+            "llm_provider": os.getenv("LLM_PROVIDER"),
+            "llm_api_key": os.getenv("LLM_API_KEY"),
+            "llm_model_id": os.getenv("LLM_MODEL_ID"),
+            "llm_base_url": os.getenv("LLM_BASE_URL"),
+            "lmstudio_base_url": os.getenv("LMSTUDIO_BASE_URL"),
+            "ollama_base_url": os.getenv("OLLAMA_BASE_URL"),
+            "max_web_research_loops": os.getenv("MAX_WEB_RESEARCH_LOOPS"),
+            "fetch_full_page": os.getenv("FETCH_FULL_PAGE"),
+            "strip_thinking_tokens": os.getenv("STRIP_THINKING_TOKENS"),
+            "use_tool_calling": os.getenv("USE_TOOL_CALLING"),
+            "search_api": os.getenv("SEARCH_API"),
+            "enable_notes": os.getenv("ENABLE_NOTES"),
+            "notes_workspace": os.getenv("NOTES_WORKSPACE"),
+        }
+
+        for key, value in env_aliases.items():
+            if value is not None:
+                raw_values.setdefault(key, value)
+
+        if overrides:
+            for key, value in overrides.items():
+                if value is not None:
+                    raw_values[key] = value
+
+        return cls(**raw_values)
+
+    def sanitized_ollama_url(self) -> str:
+        """Ensure Ollama base URL includes the /v1 suffix required by OpenAI clients."""
+
+        base = self.ollama_base_url.rstrip("/")
+        if not base.endswith("/v1"):
+            base = f"{base}/v1"
+        return base
+
+    def resolved_model(self) -> Optional[str]:
+        """Best-effort resolution of the model identifier to use."""
+
+        return self.llm_model_id or self.local_llm

code/chapter14/helloagents-deepresearch/backend/src/deep_researcher/prompts.py

@@ -0,0 +1,110 @@
+from datetime import datetime
+
+
+# Get current date in a readable format
+def get_current_date():
+    return datetime.now().strftime("%B %d, %Y")
+
+
+
+todo_planner_system_prompt = """
+你是一名研究规划专家，请把复杂主题拆解为一组有限、互补的待办任务。
+- 任务之间应互补，避免重复；
+- 每个任务要有明确意图与可执行的检索方向；
+- 输出须结构化、简明且便于后续协作。
+
+<GOAL>
+1. 结合研究主题梳理 3~5 个最关键的调研任务；
+2. 每个任务需明确目标意图，并给出适宜的网络检索查询；
+3. 任务之间要避免重复，整体覆盖用户的问题域；
+4. 在创建或更新任务时，必须调用 `note` 工具同步任务信息（这是唯一会写入笔记的途径）。
+</GOAL>
+
+<NOTE_COLLAB>
+- 为每个任务调用 `note` 工具创建/更新结构化笔记，统一使用 JSON 参数格式：
+  - 创建示例：`[TOOL_CALL:note:{"action":"create","task_id":1,"title":"任务 1: 背景梳理","note_type":"task_state","tags":["deep_research","task_1"],"content":"请记录任务概览、系统提示、来源概览、任务总结"}]`
+  - 更新示例：`[TOOL_CALL:note:{"action":"update","note_id":"<现有ID>","task_id":1,"title":"任务 1: 背景梳理","note_type":"task_state","tags":["deep_research","task_1"],"content":"...新增内容..."}]`
+- `tags` 必须包含 `deep_research` 与 `task_{task_id}`，以便其他 Agent 查找
+</NOTE_COLLAB>
+
+<TOOLS>
+你必须调用名为 `note` 的笔记工具来记录或更新待办任务，参数统一使用 JSON：
+```
+[TOOL_CALL:note:{"action":"create","task_id":1,"title":"任务 1: 背景梳理","note_type":"task_state","tags":["deep_research","task_1"],"content":"..."}]
+```
+</TOOLS>
+"""
+
+
+todo_planner_instructions = """
+
+<CONTEXT>
+当前日期：{current_date}
+研究主题：{research_topic}
+</CONTEXT>
+
+<FORMAT>
+请严格以 JSON 格式回复：
+{{
+  "tasks": [
+    {{
+      "title": "任务名称（10字内，突出重点）",
+      "intent": "任务要解决的核心问题，用1-2句描述",
+      "query": "建议使用的检索关键词"
+    }}
+  ]
+}}
+</FORMAT>
+
+如果主题信息不足以规划任务，请输出空数组：{{"tasks": []}}。必要时使用笔记工具记录你的思考过程。
+"""
+
+
+task_summarizer_instructions = """
+你是一名研究执行专家，请基于给定的上下文，为特定任务生成要点总结，对内容进行详尽且细致的总结而不是走马观花，需要勇于创新、打破常规思维，并尽可能多维度，从原理、应用、优缺点、工程实践、对比、历史演变等角度进行拓展。
+
+<GOAL>
+1. 针对任务意图梳理 3-5 条关键发现；
+2. 清晰说明每条发现的含义与价值，可引用事实数据；
+</GOAL>
+
+<NOTES>
+- 任务笔记由规划专家创建，笔记 ID 会在调用时提供；请先调用 `[TOOL_CALL:note:{"action":"read","note_id":"<note_id>"}]` 获取最新状态。
+- 更新任务总结后，使用 `[TOOL_CALL:note:{"action":"update","note_id":"<note_id>","task_id":{task_id},"title":"任务 {task_id}: …","note_type":"task_state","tags":["deep_research","task_{task_id}"],"content":"..."}]` 写回笔记，保持原有结构并追加新信息。
+- 若未找到笔记 ID，请先创建并在 `tags` 中包含 `task_{task_id}` 后再继续。
+</NOTES>
+
+<FORMAT>
+- 使用 Markdown 输出；
+- 以小节标题开头："任务总结"；
+- 关键发现使用有序或无序列表表达；
+- 若任务无有效结果，输出"暂无可用信息"。
+- 最终呈现给用户的总结中禁止包含 `[TOOL_CALL:...]` 指令。
+</FORMAT>
+"""
+
+
+report_writer_instructions = """
+你是一名专业的分析报告撰写者，请根据输入的任务总结与参考信息，生成结构化的研究报告。
+
+<REPORT_TEMPLATE>
+1. **背景概览**：简述研究主题的重要性与上下文。
+2. **核心洞见**：提炼 3-5 条最重要的结论，标注文献/任务编号。
+3. **证据与数据**：罗列支持性的事实或指标，可引用任务摘要中的要点。
+4. **风险与挑战**：分析潜在的问题、限制或仍待验证的假设。
+5. **参考来源**：按任务列出关键来源条目（标题 + 链接）。
+</REPORT_TEMPLATE>
+
+<REQUIREMENTS>
+- 报告使用 Markdown；
+- 各部分明确分节，禁止添加额外的封面或结语；
+- 若某部分信息缺失，说明"暂无相关信息"；
+- 引用来源时使用任务标题或来源标题，确保可追溯。
+- 输出给用户的内容中禁止残留 `[TOOL_CALL:...]` 指令。
+</REQUIREMENTS>
+
+<NOTES>
+- 报告生成前，请针对每个 note_id 调用 `[TOOL_CALL:note:{"action":"read","note_id":"<note_id>"}]` 读取任务笔记。
+- 如需在报告层面沉淀结果，可创建新的 `conclusion` 类型笔记，例如：`[TOOL_CALL:note:{"action":"create","title":"研究报告：{研究主题}","note_type":"conclusion","tags":["deep_research","report"],"content":"...报告要点..."}]`。
+</NOTES>
+"""

code/chapter14/helloagents-deepresearch/backend/src/deep_researcher/state.py

@@ -0,0 +1,10 @@
+"""Backward-compatible exports of state models."""
+
+from .app.models.state import SummaryState, SummaryStateInput, SummaryStateOutput, TodoItem
+
+__all__ = [
+    "SummaryState",
+    "SummaryStateInput",
+    "SummaryStateOutput",
+    "TodoItem",
+]

code/chapter14/helloagents-deepresearch/backend/src/deep_researcher/utils.py

@@ -0,0 +1,537 @@
+import os
+import logging
+import httpx
+import requests
+from typing import Dict, Any, List, Union, Optional
+
+from markdownify import markdownify
+from langsmith import traceable
+from tavily import TavilyClient
+from ddgs import DDGS
+from ddgs.exceptions import DDGSException
+
+
+logger = logging.getLogger(__name__)
+
+# Constants
+CHARS_PER_TOKEN = 4
+
+
+def get_config_value(value: Any) -> str:
+    """
+    Convert configuration values to string format, handling both string and enum types.
+
+    Args:
+        value (Any): The configuration value to process. Can be a string or an Enum.
+
+    Returns:
+        str: The string representation of the value.
+
+    Examples:
+        >>> get_config_value("tavily")
+        'tavily'
+        >>> get_config_value(SearchAPI.TAVILY)
+        'tavily'
+    """
+    return value if isinstance(value, str) else value.value
+
+
+def strip_thinking_tokens(text: str) -> str:
+    """
+    Remove <think> and </think> tags and their content from the text.
+
+    Iteratively removes all occurrences of content enclosed in thinking tokens.
+
+    Args:
+        text (str): The text to process
+
+    Returns:
+        str: The text with thinking tokens and their content removed
+    """
+    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: Union[Dict[str, Any], List[Dict[str, Any]]],
+    max_tokens_per_source: int,
+    fetch_full_page: bool = False,
+) -> str:
+    """
+    Format and deduplicate search responses from various search APIs.
+
+    Takes either a single search response or list of responses from search APIs,
+    deduplicates them by URL, and formats them into a structured string.
+
+    Args:
+        search_response (Union[Dict[str, Any], List[Dict[str, Any]]]): Either:
+            - A dict with a 'results' key containing a list of search results
+            - A list of dicts, each containing search results
+        max_tokens_per_source (int): Maximum number of tokens to include for each source's content
+        fetch_full_page (bool, optional): Whether to include the full page content. Defaults to False.
+
+    Returns:
+        str: Formatted string with deduplicated sources
+
+    Raises:
+        ValueError: If input is neither a dict with 'results' key nor a list of search results
+    """
+    # Convert input to list of results
+    if isinstance(search_response, dict):
+        sources_list = search_response["results"]
+    elif isinstance(search_response, list):
+        sources_list = []
+        for response in search_response:
+            if isinstance(response, dict) and "results" in response:
+                sources_list.extend(response["results"])
+            else:
+                sources_list.extend(response)
+    else:
+        raise ValueError(
+            "Input must be either a dict with 'results' or a list of search results"
+        )
+
+    # Deduplicate by URL
+    unique_sources = {}
+    for source in sources_list:
+        if source["url"] not in unique_sources:
+            unique_sources[source["url"]] = source
+
+    # Format output text
+    formatted_text = ""
+    for i, source in enumerate(unique_sources.values(), 1):
+        formatted_text += f"信息来源: {source['title']}\n\n"
+        formatted_text += f"URL: {source['url']}\n\n"
+        formatted_text += (
+            f"信息内容: {source['content']}\n\n"
+        )
+        if fetch_full_page:
+            # Using rough estimate of characters per token
+            char_limit = max_tokens_per_source * CHARS_PER_TOKEN
+            # Handle None raw_content
+            raw_content = source.get("raw_content", "")
+            if raw_content is None:
+                raw_content = ""
+                print(f"Warning: No raw_content found for source {source['url']}")
+            if len(raw_content) > char_limit:
+                raw_content = raw_content[:char_limit] + "... [truncated]"
+            formatted_text += f"详细信息内容限制为 {max_tokens_per_source} 个 token: {raw_content}\n\n"
+
+    return formatted_text.strip()
+
+
+def format_sources(search_results: Dict[str, Any]) -> str:
+    """Format search results into a bullet-point list of sources with URLs.
+
+    Creates a simple bulleted list of search results with title and URL for each source.
+
+    Args:
+        search_results (Dict[str, Any]): Search response containing a 'results' key with
+                                        a list of search result objects
+
+    Returns:
+        str: Formatted string with sources as bullet points in the format "* title : url"
+    """
+    return "\n".join(
+        f"* {source['title']} : {source['url']}" for source in search_results["results"]
+    )
+
+
+def fetch_raw_content(url: str) -> Optional[str]:
+    """
+    Fetch HTML content from a URL and convert it to markdown format.
+
+    Uses a 10-second timeout to avoid hanging on slow sites or large pages.
+
+    Args:
+        url (str): The URL to fetch content from
+
+    Returns:
+        Optional[str]: The fetched content converted to markdown if successful,
+                      None if any error occurs during fetching or conversion
+    """
+    try:
+        # Create a client with reasonable timeout
+        with httpx.Client(timeout=10.0) as client:
+            response = client.get(url)
+            response.raise_for_status()
+            return markdownify(response.text)
+    except Exception as e:
+        print(f"Warning: Failed to fetch full page content for {url}: {str(e)}")
+        return None
+
+
+@traceable
+def duckduckgo_search(
+    query: str, max_results: int = 3, fetch_full_page: bool = False
+) -> Dict[str, List[Dict[str, Any]]]:
+    """Search the web using DuckDuckGo and return formatted results.
+
+    Uses the DDGS library to perform web searches through DuckDuckGo.
+
+    Args:
+        query (str): The search query to execute
+        max_results (int, optional): Maximum number of results to return. Defaults to 3.
+        fetch_full_page (bool, optional): Whether to fetch full page content from result URLs.
+                                         Defaults to False.
+
+    Returns:
+        Dict[str, List[Dict[str, Any]]]: Search response containing:
+            - results (list): List of search result dictionaries, each containing:
+                - title (str): Title of the search result
+                - url (str): URL of the search result
+                - content (str): Snippet/summary of the content
+                - raw_content (str or None): Full page content if fetch_full_page is True,
+                                            otherwise same as content
+    """
+    try:
+        with DDGS(timeout=10) as client:
+            search_results = client.text(
+                query,
+                max_results=max_results,
+                backend="duckduckgo",
+            )
+
+        results: list[dict[str, Any]] = []
+        for entry in search_results:
+            url = entry.get("href") or entry.get("url")
+            title = entry.get("title") or url
+            content = entry.get("body") or entry.get("content")
+
+            if not all([url, title, content]):
+                print(f"Warning: Incomplete result from DuckDuckGo: {entry}")
+                continue
+
+            raw_content = content
+            if fetch_full_page:
+                fetched = fetch_raw_content(url)
+                raw_content = fetched if fetched is not None else content
+
+            results.append(
+                {
+                    "title": title,
+                    "url": url,
+                    "content": content,
+                    "raw_content": raw_content,
+                }
+            )
+
+        return {"results": results}
+    except DDGSException as exc:
+        print(f"Error in DuckDuckGo search: {str(exc)}")
+        print("Full error details: DDGSException")
+        return {"results": []}
+    except Exception as exc:  # pragma: no cover - defensive
+        print(f"Unexpected error in DuckDuckGo search: {str(exc)}")
+        print(f"Full error details: {type(exc).__name__}")
+        return {"results": []}
+
+
+@traceable
+def searxng_search(
+    query: str, max_results: int = 3, fetch_full_page: bool = False
+) -> Dict[str, List[Dict[str, Any]]]:
+    """
+    Search the web using SearXNG and return formatted results.
+
+    Uses the SearXNG JSON API (`/search?format=json`) to执行检索。
+    The SearXNG host URL is read from the SEARXNG_URL environment variable
+    or defaults to http://localhost:8888.
+
+    Args:
+        query (str): The search query to execute
+        max_results (int, optional): Maximum number of results to return. Defaults to 3.
+        fetch_full_page (bool, optional): Whether to fetch full page content from result URLs.
+                                         Defaults to False.
+
+    Returns:
+        Dict[str, List[Dict[str, Any]]]: Search response containing:
+            - results (list): List of search result dictionaries, each containing:
+                - title (str): Title of the search result
+                - url (str): URL of the search result
+                - content (str): Snippet/summary of the content
+                - raw_content (str or None): Full page content if fetch_full_page is True,
+                                           otherwise same as content
+    """
+    host = os.environ.get("SEARXNG_URL", "http://localhost:8888")
+    endpoint = f"{host.rstrip('/')}/search"
+
+    try:
+        response = requests.get(
+            endpoint,
+            params={
+                "q": query,
+                "format": "json",
+                "language": "zh-CN",
+                "safesearch": 1,
+                "categories": "general",
+            },
+            timeout=10,
+        )
+        response.raise_for_status()
+        payload = response.json()
+    except Exception as exc:  # pragma: no cover - 远程接口失败兜底
+        logger.warning("SearXNG request failed: %s", exc)
+        return {"results": []}
+
+    results = []
+    for entry in payload.get("results", [])[:max_results]:
+        url = entry.get("url") or entry.get("link")
+        title = entry.get("title") or url
+        content = entry.get("content") or entry.get("snippet") or ""
+
+        if not all([url, title]) or not content:
+            logger.debug("Skipping incomplete SearXNG result: %s", entry)
+            continue
+
+        raw_content = content
+        if fetch_full_page:
+            fetched = fetch_raw_content(url)
+            raw_content = fetched if fetched is not None else content
+
+        results.append(
+            {
+                "title": title,
+                "url": url,
+                "content": content,
+                "raw_content": raw_content,
+            }
+        )
+
+    return {"results": results}
+
+
+@traceable
+def tavily_search(
+    query: str, fetch_full_page: bool = True, max_results: int = 3
+) -> Dict[str, List[Dict[str, Any]]]:
+    """
+    Search the web using the Tavily API and return formatted results.
+
+    Uses the TavilyClient to perform searches. Tavily API key must be configured
+    in the environment.
+
+    Args:
+        query (str): The search query to execute
+        fetch_full_page (bool, optional): Whether to include raw content from sources.
+                                         Defaults to True.
+        max_results (int, optional): Maximum number of results to return. Defaults to 3.
+
+    Returns:
+        Dict[str, List[Dict[str, Any]]]: Search response containing:
+            - results (list): List of search result dictionaries, each containing:
+                - title (str): Title of the search result
+                - url (str): URL of the search result
+                - content (str): Snippet/summary of the content
+                - raw_content (str or None): Full content of the page if available and
+                                            fetch_full_page is True
+    """
+
+    tavily_client = TavilyClient()
+    return tavily_client.search(
+        query, max_results=max_results, include_raw_content=fetch_full_page
+    )
+
+
+@traceable
+def advanced_search(query: str, fetch_full_page: bool = False) -> Dict[str, Any]:
+    """利用多源策略执行搜索，优先 Tavily，其次 SerpApi，最后 DuckDuckGo。"""
+
+    notices: list[str] = []
+    results: list[dict[str, Any]] = []
+    answer: Optional[str] = None
+    backend = "advanced"
+
+    # 优先尝试 Tavily
+    tavily_key = os.getenv("TAVILY_API_KEY")
+    if tavily_key:
+        try:
+            tavily_result = tavily_search(
+                query,
+                fetch_full_page=fetch_full_page,
+                max_results=5,
+            )
+            if tavily_result.get("results"):
+                backend = "tavily"
+                answer = tavily_result.get("answer")
+                results.extend(tavily_result["results"])
+                logger.info("advanced_search: using Tavily results for query='%s'", query)
+                return {
+                    "results": results,
+                    "notices": notices,
+                    "answer": answer,
+                    "backend": backend,
+                }
+            notices.append("⚠️ Tavily 未返回有效结果，尝试其他搜索源")
+            logger.info("advanced_search: Tavily returned no results for query='%s'", query)
+        except Exception as exc:  # pragma: no cover - 第三方库防御
+            notices.append(f"⚠️ Tavily 搜索失败：{exc}")
+            logger.warning("advanced_search: Tavily failed for query='%s': %s", query, exc)
+    else:
+        notices.append("⚠️ 未检测到 TAVILY_API_KEY，跳过 Tavily 搜索")
+        logger.info("advanced_search: Tavily disabled for query='%s'", query)
+
+    # 其次尝试 SerpApi
+    serpapi_key = os.getenv("SERPAPI_API_KEY")
+    if serpapi_key:
+        try:
+            from serpapi import GoogleSearch  # type: ignore
+
+            params = {
+                "engine": "google",
+                "q": query,
+                "api_key": serpapi_key,
+                "gl": "cn",
+                "hl": "zh-cn",
+                "num": 5,
+            }
+
+            client = GoogleSearch(params)
+            response = client.get_dict()
+
+            answer_box = response.get("answer_box") or {}
+            direct_answer = answer_box.get("answer") or answer_box.get("snippet")
+            if direct_answer:
+                answer = direct_answer
+
+            organic_results = response.get("organic_results", [])
+            for item in organic_results[:5]:
+                results.append(
+                    {
+                        "title": item.get("title") or item.get("link") or query,
+                        "url": item.get("link", ""),
+                        "content": item.get("snippet") or item.get("title") or "",
+                        "raw_content": item.get("snippet") or "",
+                    }
+                )
+
+            if results:
+                backend = "serpapi"
+                logger.info("advanced_search: using SerpApi results for query='%s'", query)
+                return {
+                    "results": results,
+                    "notices": notices,
+                    "answer": answer,
+                    "backend": backend,
+                }
+
+            notices.append("⚠️ SerpApi 未返回有效结果，回退到通用搜索")
+            logger.info("advanced_search: SerpApi returned no results for query='%s'", query)
+        except ImportError:
+            notices.append("⚠️ SerpApi 库未安装，跳过 SerpApi 搜索 (pip install google-search-results)")
+            logger.warning("advanced_search: serpapi package missing, skip query='%s'", query)
+        except Exception as exc:  # pragma: no cover - 第三方库防御
+            notices.append(f"⚠️ SerpApi 搜索失败：{exc}")
+            logger.warning("advanced_search: SerpApi failed for query='%s': %s", query, exc)
+    else:
+        notices.append("⚠️ 未检测到 SERPAPI_API_KEY，跳过 SerpApi 搜索")
+        logger.info("advanced_search: SerpApi disabled for query='%s'", query)
+
+    # 最后回退到 DuckDuckGo（无需额外配置）
+    try:
+        ddg_result = duckduckgo_search(
+            query,
+            max_results=5,
+            fetch_full_page=fetch_full_page,
+        )
+        if ddg_result.get("results"):
+            backend = "duckduckgo"
+            results.extend(ddg_result["results"])
+            logger.info("advanced_search: using DuckDuckGo results for query='%s'", query)
+        else:
+            notices.append("⚠️ DuckDuckGo 未返回有效结果")
+            logger.info("advanced_search: DuckDuckGo returned no results for query='%s'", query)
+    except Exception as exc:  # pragma: no cover - 第三方库防御
+        notices.append(f"⚠️ DuckDuckGo 搜索失败：{exc}")
+        logger.warning("advanced_search: DuckDuckGo failed for query='%s': %s", query, exc)
+
+    return {
+        "results": results,
+        "notices": notices,
+        "answer": answer,
+        "backend": backend,
+    }
+
+
+@traceable
+def perplexity_search(
+    query: str, perplexity_search_loop_count: int = 0
+) -> Dict[str, Any]:
+    """
+    Search the web using the Perplexity API and return formatted results.
+
+    Uses the Perplexity API to perform searches with the 'sonar-pro' model.
+    Requires a PERPLEXITY_API_KEY environment variable to be set.
+
+    Args:
+        query (str): The search query to execute
+        perplexity_search_loop_count (int, optional): The loop step for perplexity search
+                                                     (used for source labeling). Defaults to 0.
+
+    Returns:
+        Dict[str, Any]: Search response containing:
+            - results (list): List of search result dictionaries, each containing:
+                - title (str): Title of the search result (includes search counter)
+                - url (str): URL of the citation source
+                - content (str): Content of the response or reference to main content
+                - raw_content (str or None): Full content for the first source, None for additional
+                                            citation sources
+
+    Raises:
+        requests.exceptions.HTTPError: If the API request fails
+    """
+
+    headers = {
+        "accept": "application/json",
+        "content-type": "application/json",
+        "Authorization": f"Bearer {os.getenv('PERPLEXITY_API_KEY')}",
+    }
+
+    payload = {
+        "model": "sonar-pro",
+        "messages": [
+            {
+                "role": "system",
+                "content": "Search the web and provide factual information with sources.",
+            },
+            {"role": "user", "content": query},
+        ],
+    }
+
+    response = requests.post(
+        "https://api.perplexity.ai/chat/completions", headers=headers, json=payload
+    )
+    response.raise_for_status()  # Raise exception for bad status codes
+
+    # Parse the response
+    data = response.json()
+    content = data["choices"][0]["message"]["content"]
+
+    # Perplexity returns a list of citations for a single search result
+    citations = data.get("citations", ["https://perplexity.ai"])
+
+    # Return first citation with full content, others just as references
+    results = [
+        {
+            "title": f"Perplexity Search {perplexity_search_loop_count + 1}, Source 1",
+            "url": citations[0],
+            "content": content,
+            "raw_content": content,
+        }
+    ]
+
+    # Add additional citations without duplicating content
+    for i, citation in enumerate(citations[1:], start=2):
+        results.append(
+            {
+                "title": f"Perplexity Search {perplexity_search_loop_count + 1}, Source {i}",
+                "url": citation,
+                "content": "See above for full content",
+                "raw_content": None,
+            }
+        )
+
+    return {"results": results}

code/chapter14/helloagents-deepresearch/backend/uv.lock

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code/chapter14/helloagents-deepresearch/frontend/.env.local

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code/chapter14/helloagents-deepresearch/frontend/index.html

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+    <title>HelloAgents 深度研究助手</title>
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+        "@rollup/rollup-linux-x64-gnu": "4.52.5",
+        "@rollup/rollup-linux-x64-musl": "4.52.5",
+        "@rollup/rollup-openharmony-arm64": "4.52.5",
+        "@rollup/rollup-win32-arm64-msvc": "4.52.5",
+        "@rollup/rollup-win32-ia32-msvc": "4.52.5",
+        "@rollup/rollup-win32-x64-gnu": "4.52.5",
+        "@rollup/rollup-win32-x64-msvc": "4.52.5",
+        "fsevents": "~2.3.2"
+      }
+    },
+    "node_modules/source-map-js": {
+      "version": "1.2.1",
+      "resolved": "https://registry.npmjs.org/source-map-js/-/source-map-js-1.2.1.tgz",
+      "integrity": "sha512-UXWMKhLOwVKb728IUtQPXxfYU+usdybtUrK/8uGE8CQMvrhOpwvzDBwj0QhSL7MQc7vIsISBG8VQ8+IDQxpfQA==",
+      "license": "BSD-3-Clause",
+      "engines": {
+        "node": ">=0.10.0"
+      }
+    },
+    "node_modules/tinyglobby": {
+      "version": "0.2.15",
+      "resolved": "https://registry.npmjs.org/tinyglobby/-/tinyglobby-0.2.15.tgz",
+      "integrity": "sha512-j2Zq4NyQYG5XMST4cbs02Ak8iJUdxRM0XI5QyxXuZOzKOINmWurp3smXu3y5wDcJrptwpSjgXHzIQxR0omXljQ==",
+      "dev": true,
+      "license": "MIT",
+      "dependencies": {
+        "fdir": "^6.5.0",
+        "picomatch": "^4.0.3"
+      },
+      "engines": {
+        "node": ">=12.0.0"
+      },
+      "funding": {
+        "url": "https://github.com/sponsors/SuperchupuDev"
+      }
+    },
+    "node_modules/typescript": {
+      "version": "5.9.3",
+      "resolved": "https://registry.npmjs.org/typescript/-/typescript-5.9.3.tgz",
+      "integrity": "sha512-jl1vZzPDinLr9eUt3J/t7V6FgNEw9QjvBPdysz9KfQDD41fQrC2Y4vKQdiaUpFT4bXlb1RHhLpp8wtm6M5TgSw==",
+      "devOptional": true,
+      "license": "Apache-2.0",
+      "bin": {
+        "tsc": "bin/tsc",
+        "tsserver": "bin/tsserver"
+      },
+      "engines": {
+        "node": ">=14.17"
+      }
+    },
+    "node_modules/undici-types": {
+      "version": "6.21.0",
+      "resolved": "https://registry.npmjs.org/undici-types/-/undici-types-6.21.0.tgz",
+      "integrity": "sha512-iwDZqg0QAGrg9Rav5H4n0M64c3mkR59cJ6wQp+7C4nI0gsmExaedaYLNO44eT4AtBBwjbTiGPMlt2Md0T9H9JQ==",
+      "dev": true,
+      "license": "MIT"
+    },
+    "node_modules/vite": {
+      "version": "6.4.1",
+      "resolved": "https://registry.npmjs.org/vite/-/vite-6.4.1.tgz",
+      "integrity": "sha512-+Oxm7q9hDoLMyJOYfUYBuHQo+dkAloi33apOPP56pzj+vsdJDzr+j1NISE5pyaAuKL4A3UD34qd0lx5+kfKp2g==",
+      "dev": true,
+      "license": "MIT",
+      "dependencies": {
+        "esbuild": "^0.25.0",
+        "fdir": "^6.4.4",
+        "picomatch": "^4.0.2",
+        "postcss": "^8.5.3",
+        "rollup": "^4.34.9",
+        "tinyglobby": "^0.2.13"
+      },
+      "bin": {
+        "vite": "bin/vite.js"
+      },
+      "engines": {
+        "node": "^18.0.0 || ^20.0.0 || >=22.0.0"
+      },
+      "funding": {
+        "url": "https://github.com/vitejs/vite?sponsor=1"
+      },
+      "optionalDependencies": {
+        "fsevents": "~2.3.3"
+      },
+      "peerDependencies": {
+        "@types/node": "^18.0.0 || ^20.0.0 || >=22.0.0",
+        "jiti": ">=1.21.0",
+        "less": "*",
+        "lightningcss": "^1.21.0",
+        "sass": "*",
+        "sass-embedded": "*",
+        "stylus": "*",
+        "sugarss": "*",
+        "terser": "^5.16.0",
+        "tsx": "^4.8.1",
+        "yaml": "^2.4.2"
+      },
+      "peerDependenciesMeta": {
+        "@types/node": {
+          "optional": true
+        },
+        "jiti": {
+          "optional": true
+        },
+        "less": {
+          "optional": true
+        },
+        "lightningcss": {
+          "optional": true
+        },
+        "sass": {
+          "optional": true
+        },
+        "sass-embedded": {
+          "optional": true
+        },
+        "stylus": {
+          "optional": true
+        },
+        "sugarss": {
+          "optional": true
+        },
+        "terser": {
+          "optional": true
+        },
+        "tsx": {
+          "optional": true
+        },
+        "yaml": {
+          "optional": true
+        }
+      }
+    },
+    "node_modules/vscode-uri": {
+      "version": "3.1.0",
+      "resolved": "https://registry.npmjs.org/vscode-uri/-/vscode-uri-3.1.0.tgz",
+      "integrity": "sha512-/BpdSx+yCQGnCvecbyXdxHDkuk55/G3xwnC0GqY4gmQ3j+A+g8kzzgB4Nk/SINjqn6+waqw3EgbVF2QKExkRxQ==",
+      "dev": true,
+      "license": "MIT"
+    },
+    "node_modules/vue": {
+      "version": "3.5.22",
+      "resolved": "https://registry.npmjs.org/vue/-/vue-3.5.22.tgz",
+      "integrity": "sha512-toaZjQ3a/G/mYaLSbV+QsQhIdMo9x5rrqIpYRObsJ6T/J+RyCSFwN2LHNVH9v8uIcljDNa3QzPVdv3Y6b9hAJQ==",
+      "license": "MIT",
+      "dependencies": {
+        "@vue/compiler-dom": "3.5.22",
+        "@vue/compiler-sfc": "3.5.22",
+        "@vue/runtime-dom": "3.5.22",
+        "@vue/server-renderer": "3.5.22",
+        "@vue/shared": "3.5.22"
+      },
+      "peerDependencies": {
+        "typescript": "*"
+      },
+      "peerDependenciesMeta": {
+        "typescript": {
+          "optional": true
+        }
+      }
+    },
+    "node_modules/vue-tsc": {
+      "version": "2.2.12",
+      "resolved": "https://registry.npmjs.org/vue-tsc/-/vue-tsc-2.2.12.tgz",
+      "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"
+      }
+    }
+  }
+}

code/chapter14/helloagents-deepresearch/frontend/package.json

@@ -0,0 +1,22 @@
+{
+  "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": {
+    "axios": "^1.7.9",
+    "vue": "^3.5.13"
+  },
+  "devDependencies": {
+    "@types/node": "^22.10.5",
+    "@vitejs/plugin-vue": "^5.2.1",
+    "typescript": "^5.7.3",
+    "vite": "^6.0.7",
+    "vue-tsc": "^2.2.0"
+  }
+}

code/chapter14/helloagents-deepresearch/frontend/src/App.vue

@@ -0,0 +1,2019 @@
+<template>
+  <main class="app-shell">
+    <div class="aurora" aria-hidden="true">
+      <span></span>
+      <span></span>
+      <span></span>
+    </div>
+
+    <div class="layout">
+      <section class="panel panel-form">
+        <header class="panel-head">
+          <div class="logo">
+            <svg viewBox="0 0 24 24" aria-hidden="true">
+              <path
+                d="M12 2.5c-.7 0-1.4.2-2 .6L4.6 7C3.6 7.6 3 8.7 3 9.9v4.2c0 1.2.6 2.3 1.6 2.9l5.4 3.9c1.2.8 2.8.8 4 0l5.4-3.9c1-.7 1.6-1.7 1.6-2.9V9.9c0-1.2-.6-2.3-1.6-2.9L14 3.1a3.6 3.6 0 0 0-2-.6Z"
+              />
+            </svg>
+          </div>
+          <div>
+            <h1>深度研究助手</h1>
+            <p>结合多轮智能检索与总结，实时呈现洞见与引用。</p>
+          </div>
+        </header>
+
+        <form class="form" @submit.prevent="handleSubmit">
+          <label class="field">
+            <span>研究主题</span>
+            <textarea
+              v-model="form.topic"
+              placeholder="例如：探索多模态模型在 2025 年的关键突破"
+              rows="4"
+              required
+            ></textarea>
+          </label>
+
+          <section class="options">
+            <label class="field option">
+              <span>搜索引擎</span>
+              <select v-model="form.searchApi">
+                <option value="">沿用后端配置</option>
+                <option
+                  v-for="option in searchOptions"
+                  :key="option"
+                  :value="option"
+                >
+                  {{ option }}
+                </option>
+              </select>
+            </label>
+          </section>
+
+          <div class="form-actions">
+            <button class="submit" type="submit" :disabled="loading">
+              <span class="submit-label">
+                <svg
+                  v-if="loading"
+                  class="spinner"
+                  viewBox="0 0 24 24"
+                  aria-hidden="true"
+                >
+                  <circle cx="12" cy="12" r="9" stroke-width="3" />
+                </svg>
+                {{ loading ? "研究进行中..." : "开始研究" }}
+              </span>
+            </button>
+            <button
+              v-if="loading"
+              type="button"
+              class="secondary-btn"
+              @click="cancelResearch"
+            >
+              取消研究
+            </button>
+          </div>
+        </form>
+
+        <p v-if="error" class="error-chip">
+          <svg viewBox="0 0 20 20" aria-hidden="true">
+            <path
+              d="M10 3.2c-.3 0-.6.2-.8.5L3.4 15c-.4.7.1 1.6.8 1.6h11.6c.7 0 1.2-.9.8-1.6L10.8 3.7c-.2-.3-.5-.5-.8-.5Zm0 4.3c.4 0 .7.3.7.7v4c0 .4-.3.7-.7.7s-.7-.3-.7-.7V8.2c0-.4.3-.7.7-.7Zm0 6.6a1 1 0 1 1 0 2 1 1 0 0 1 0-2Z"
+            />
+          </svg>
+          {{ error }}
+        </p>
+        <p v-else-if="loading" class="hint muted">
+          正在收集线索与证据，实时进展见右侧区域。
+        </p>
+      </section>
+
+            <section
+        class="panel panel-result"
+        v-if="todoTasks.length || reportMarkdown || progressLogs.length"
+      >
+        <header class="status-bar">
+          <div class="status-main">
+            <div class="status-chip" :class="{ active: loading }">
+              <span class="dot"></span>
+              {{ loading ? "研究进行中" : "研究流程完成" }}
+            </div>
+            <span class="status-meta">
+              任务进度：{{ completedTasks }} / {{ totalTasks || todoTasks.length || 1 }}
+              · 阶段记录 {{ progressLogs.length }} 条
+            </span>
+          </div>
+          <div class="status-controls">
+            <button class="secondary-btn" @click="logsCollapsed = !logsCollapsed">
+              {{ logsCollapsed ? "展开流程" : "收起流程" }}
+            </button>
+          </div>
+        </header>
+
+        <div class="timeline-wrapper" v-show="!logsCollapsed && progressLogs.length">
+          <transition-group name="timeline" tag="ul" class="timeline">
+            <li v-for="(log, index) in progressLogs" :key="`${log}-${index}`">
+              <span class="timeline-node"></span>
+              <p>{{ log }}</p>
+            </li>
+          </transition-group>
+        </div>
+
+        <div class="tasks-section" v-if="todoTasks.length">
+          <aside class="tasks-list">
+            <h3>任务清单</h3>
+            <ul>
+              <li
+                v-for="task in todoTasks"
+                :key="task.id"
+                :class="['task-item', { active: task.id === activeTaskId, completed: task.status === 'completed' }]"
+              >
+                <button
+                  type="button"
+                  class="task-button"
+                  @click="activeTaskId = task.id"
+                >
+                  <span class="task-title">{{ task.title }}</span>
+                  <span class="task-status" :class="task.status">
+                    {{ formatTaskStatus(task.status) }}
+                  </span>
+                </button>
+                <p class="task-intent">{{ task.intent }}</p>
+              </li>
+            </ul>
+          </aside>
+
+          <article class="task-detail" v-if="currentTask">
+            <header class="task-header">
+              <div>
+                <h3>{{ currentTaskTitle || "当前任务" }}</h3>
+                <p class="muted" v-if="currentTaskIntent">
+                  {{ currentTaskIntent }}
+                </p>
+              </div>
+              <div class="task-chip-group">
+                <span class="task-label">查询：{{ currentTaskQuery || "" }}</span>
+                <span
+                  v-if="currentTaskNoteId"
+                  class="task-label note-chip"
+                  :title="currentTaskNoteId"
+                >
+                  笔记：{{ currentTaskNoteId }}
+                </span>
+                <span
+                  v-if="currentTaskNotePath"
+                  class="task-label note-chip path-chip"
+                  :title="currentTaskNotePath"
+                >
+                  <span class="path-label">路径：</span>
+                  <span class="path-text">{{ currentTaskNotePath }}</span>
+                  <button
+                    class="chip-action"
+                    type="button"
+                    @click="copyNotePath(currentTaskNotePath)"
+                  >
+                    复制
+                  </button>
+                </span>
+              </div>
+            </header>
+
+            <section v-if="currentTask && currentTask.notices.length" class="task-notices">
+              <h4>系统提示</h4>
+              <ul>
+                <li v-for="(notice, idx) in currentTask.notices" :key="`${notice}-${idx}`">
+                  {{ notice }}
+                </li>
+              </ul>
+            </section>
+
+            <section
+              class="sources-block"
+              :class="{ 'block-highlight': sourcesHighlight }"
+            >
+              <h3>最新来源</h3>
+              <template v-if="currentTaskSources.length">
+                <ul class="sources-list">
+                  <li
+                    v-for="(item, index) in currentTaskSources"
+                    :key="`${item.title}-${index}`"
+                    class="source-item"
+                  >
+                    <a
+                      class="source-link"
+                      :href="item.url || '#'"
+                      target="_blank"
+                      rel="noopener noreferrer"
+                    >
+                      {{ item.title || item.url || `来源 ${index + 1}` }}
+                    </a>
+                    <div v-if="item.snippet || item.raw" class="source-tooltip">
+                      <p v-if="item.snippet">{{ item.snippet }}</p>
+                      <p v-if="item.raw" class="muted-text">{{ item.raw }}</p>
+                    </div>
+                  </li>
+                </ul>
+              </template>
+              <p v-else class="muted">暂无可用来源</p>
+            </section>
+
+            <section
+              class="summary-block"
+              :class="{ 'block-highlight': summaryHighlight }"
+            >
+              <h3>任务总结</h3>
+              <pre class="block-pre">{{ currentTaskSummary || "暂无可用信息" }}</pre>
+            </section>
+
+            <section
+              class="tools-block"
+              :class="{ 'block-highlight': toolHighlight }"
+              v-if="currentTaskToolCalls.length"
+            >
+              <h3>工具调用记录</h3>
+              <ul class="tool-list">
+                <li
+                  v-for="entry in currentTaskToolCalls"
+                  :key="`${entry.eventId}-${entry.timestamp}`"
+                  class="tool-entry"
+                >
+                  <div class="tool-entry-header">
+                    <span class="tool-entry-title">
+                      #{{ entry.eventId }} {{ entry.agent }} → {{ entry.tool }}
+                    </span>
+                    <span
+                      v-if="entry.noteId"
+                      class="tool-entry-note"
+                    >
+                      笔记：{{ entry.noteId }}
+                    </span>
+                  </div>
+                  <p v-if="entry.notePath" class="tool-entry-path">
+                    笔记路径：
+                    <button
+                      class="link-btn"
+                      type="button"
+                      @click="copyNotePath(entry.notePath)"
+                    >
+                      复制
+                    </button>
+                    <span class="path-text">{{ entry.notePath }}</span>
+                  </p>
+                  <p class="tool-subtitle">参数</p>
+                  <pre class="tool-pre">{{ formatToolParameters(entry.parameters) }}</pre>
+                  <template v-if="entry.result">
+                    <p class="tool-subtitle">执行结果</p>
+                    <pre class="tool-pre">{{ formatToolResult(entry.result) }}</pre>
+                  </template>
+                </li>
+              </ul>
+            </section>
+          </article>
+
+          <article class="task-detail" v-else>
+            <p class="muted">等待任务规划或执行结果。</p>
+          </article>
+        </div>
+
+        <div
+          v-if="reportMarkdown"
+          class="report-block"
+          :class="{ 'block-highlight': reportHighlight }"
+        >
+          <h3>最终报告</h3>
+          <pre class="block-pre">{{ reportMarkdown }}</pre>
+        </div>
+      </section>
+
+    </div>
+  </main>
+</template>
+
+<script lang="ts" setup>
+import { computed, onBeforeUnmount, reactive, ref } from "vue";
+
+import {
+  runResearchStream,
+  type ResearchStreamEvent
+} from "./services/api";
+
+interface SourceItem {
+  title: string;
+  url: string;
+  snippet: string;
+  raw: string;
+}
+
+interface ToolCallLog {
+  eventId: number;
+  agent: string;
+  tool: string;
+  parameters: Record<string, unknown>;
+  result: string;
+  noteId: string | null;
+  notePath: string | null;
+  timestamp: number;
+}
+
+interface TodoTaskView {
+  id: number;
+  title: string;
+  intent: string;
+  query: string;
+  status: string;
+  summary: string;
+  sourcesSummary: string;
+  sourceItems: SourceItem[];
+  notices: string[];
+  noteId: string | null;
+  notePath: string | null;
+  toolCalls: ToolCallLog[];
+}
+
+const form = reactive({
+  topic: "",
+  searchApi: ""
+});
+
+const loading = ref(false);
+const error = ref("");
+const progressLogs = ref<string[]>([]);
+const logsCollapsed = ref(false);
+
+const todoTasks = ref<TodoTaskView[]>([]);
+const activeTaskId = ref<number | null>(null);
+const reportMarkdown = ref("");
+
+const summaryHighlight = ref(false);
+const sourcesHighlight = ref(false);
+const reportHighlight = ref(false);
+const toolHighlight = ref(false);
+
+let currentController: AbortController | null = null;
+
+const searchOptions = [
+  "advanced",
+  "duckduckgo",
+  "tavily",
+  "perplexity",
+  "searxng"
+];
+
+const TASK_STATUS_LABEL: Record<string, string> = {
+  pending: "待执行",
+  in_progress: "进行中",
+  completed: "已完成",
+  skipped: "已跳过"
+};
+
+function formatTaskStatus(status: string): string {
+  return TASK_STATUS_LABEL[status] ?? status;
+}
+
+const totalTasks = computed(() => todoTasks.value.length);
+const completedTasks = computed(() =>
+  todoTasks.value.filter((task) => task.status === "completed").length
+);
+
+const currentTask = computed(() => {
+  if (activeTaskId.value !== null) {
+    return todoTasks.value.find((task) => task.id === activeTaskId.value) ?? null;
+  }
+  return todoTasks.value[0] ?? null;
+});
+
+const currentTaskSources = computed(() => currentTask.value?.sourceItems ?? []);
+const currentTaskSummary = computed(() => currentTask.value?.summary ?? "");
+const currentTaskTitle = computed(() => currentTask.value?.title ?? "");
+const currentTaskIntent = computed(() => currentTask.value?.intent ?? "");
+const currentTaskQuery = computed(() => currentTask.value?.query ?? "");
+const currentTaskNoteId = computed(() => currentTask.value?.noteId ?? "");
+const currentTaskNotePath = computed(() => currentTask.value?.notePath ?? "");
+const currentTaskToolCalls = computed(
+  () => currentTask.value?.toolCalls ?? []
+);
+
+const pulse = (flag: typeof summaryHighlight) => {
+  flag.value = false;
+  requestAnimationFrame(() => {
+    flag.value = true;
+    window.setTimeout(() => {
+      flag.value = false;
+    }, 1200);
+  });
+};
+
+function parseSources(raw: string): SourceItem[] {
+  if (!raw) {
+    return [];
+  }
+
+  const items: SourceItem[] = [];
+  const lines = raw.split("\n");
+
+  let current: SourceItem | null = null;
+  const truncate = (value: string, max = 360) => {
+    const trimmed = value.trim();
+    return trimmed.length > max ? `${trimmed.slice(0, max)}…` : trimmed;
+  };
+
+  const flush = () => {
+    if (!current) {
+      return;
+    }
+    const normalized: SourceItem = {
+      title: current.title?.trim() || "",
+      url: current.url?.trim() || "",
+      snippet: current.snippet ? truncate(current.snippet) : "",
+      raw: current.raw ? truncate(current.raw, 420) : ""
+    };
+
+    if (
+      normalized.title ||
+      normalized.url ||
+      normalized.snippet ||
+      normalized.raw
+    ) {
+      if (!normalized.title && normalized.url) {
+        normalized.title = normalized.url;
+      }
+      items.push(normalized);
+    }
+    current = null;
+  };
+
+  const ensureCurrent = () => {
+    if (!current) {
+      current = { title: "", url: "", snippet: "", raw: "" };
+    }
+  };
+
+  for (const line of lines) {
+    const trimmed = line.trim();
+    if (!trimmed) {
+      continue;
+    }
+
+    if (/^\*/.test(trimmed) && trimmed.includes(" : ")) {
+      flush();
+      const withoutBullet = trimmed.replace(/^\*\s*/, "");
+      const [titlePart, urlPart] = withoutBullet.split(" : ");
+      current = {
+        title: titlePart?.trim() || "",
+        url: urlPart?.trim() || "",
+        snippet: "",
+        raw: ""
+      };
+      continue;
+    }
+
+    if (/^(Source|信息来源)\s*:/.test(trimmed)) {
+      flush();
+      const [, titlePart = ""] = trimmed.split(/:\s*(.+)/);
+      current = {
+        title: titlePart.trim(),
+        url: "",
+        snippet: "",
+        raw: ""
+      };
+      continue;
+    }
+
+    if (/^URL\s*:/.test(trimmed)) {
+      ensureCurrent();
+      const [, urlPart = ""] = trimmed.split(/:\s*(.+)/);
+      current!.url = urlPart.trim();
+      continue;
+    }
+
+    if (
+      /^(Most relevant content from source|信息内容)\s*:/.test(trimmed)
+    ) {
+      ensureCurrent();
+      const [, contentPart = ""] = trimmed.split(/:\s*(.+)/);
+      current!.snippet = contentPart.trim();
+      continue;
+    }
+
+    if (
+      /^(Full source content limited to|信息内容限制为)\s*:/.test(trimmed)
+    ) {
+      ensureCurrent();
+      const [, rawPart = ""] = trimmed.split(/:\s*(.+)/);
+      current!.raw = rawPart.trim();
+      continue;
+    }
+
+    if (/^https?:\/\//.test(trimmed)) {
+      ensureCurrent();
+      if (!current!.url) {
+        current!.url = trimmed;
+        continue;
+      }
+    }
+
+    ensureCurrent();
+    current!.raw = current!.raw ? `${current!.raw}\n${trimmed}` : trimmed;
+  }
+
+  flush();
+  return items;
+}
+
+function extractOptionalString(value: unknown): string | null {
+  if (typeof value !== "string") {
+    return null;
+  }
+  const trimmed = value.trim();
+  return trimmed ? trimmed : null;
+}
+
+function ensureRecord(value: unknown): Record<string, unknown> {
+  if (value && typeof value === "object" && !Array.isArray(value)) {
+    return value as Record<string, unknown>;
+  }
+  return {};
+}
+
+function applyNoteMetadata(
+  task: TodoTaskView,
+  payload: Record<string, unknown>
+): void {
+  const noteId = extractOptionalString(payload.note_id);
+  if (noteId) {
+    task.noteId = noteId;
+  }
+  const notePath = extractOptionalString(payload.note_path);
+  if (notePath) {
+    task.notePath = notePath;
+  }
+}
+
+function formatToolParameters(parameters: Record<string, unknown>): string {
+  try {
+    return JSON.stringify(parameters, null, 2);
+  } catch (error) {
+    console.warn("无法格式化工具参数", error, parameters);
+    return Object.entries(parameters)
+      .map(([key, value]) => `${key}: ${String(value)}`)
+      .join("\n");
+  }
+}
+
+function formatToolResult(result: string): string {
+  const trimmed = result.trim();
+  const limit = 900;
+  if (trimmed.length > limit) {
+    return `${trimmed.slice(0, limit)}…`;
+  }
+  return trimmed;
+}
+
+async function copyNotePath(path: string | null | undefined) {
+  if (!path) {
+    return;
+  }
+
+  try {
+    await navigator.clipboard.writeText(path);
+    progressLogs.value.push(`已复制笔记路径：${path}`);
+  } catch (error) {
+    console.warn("无法直接复制到剪贴板", error);
+    window.prompt("复制以下笔记路径", path);
+    progressLogs.value.push("请手动复制笔记路径");
+  }
+}
+
+function resetWorkflowState() {
+  todoTasks.value = [];
+  activeTaskId.value = null;
+  reportMarkdown.value = "";
+  progressLogs.value = [];
+  summaryHighlight.value = false;
+  sourcesHighlight.value = false;
+  reportHighlight.value = false;
+  toolHighlight.value = false;
+  logsCollapsed.value = false;
+}
+
+function findTask(taskId: unknown): TodoTaskView | undefined {
+  const numeric =
+    typeof taskId === "number"
+      ? taskId
+      : typeof taskId === "string"
+      ? Number(taskId)
+      : NaN;
+  if (Number.isNaN(numeric)) {
+    return undefined;
+  }
+  return todoTasks.value.find((task) => task.id === numeric);
+}
+
+function upsertTaskMetadata(task: TodoTaskView, payload: Record<string, unknown>) {
+  if (typeof payload.title === "string" && payload.title.trim()) {
+    task.title = payload.title.trim();
+  }
+  if (typeof payload.intent === "string" && payload.intent.trim()) {
+    task.intent = payload.intent.trim();
+  }
+  if (typeof payload.query === "string" && payload.query.trim()) {
+    task.query = payload.query.trim();
+  }
+}
+
+const handleSubmit = async () => {
+  if (!form.topic.trim()) {
+    error.value = "请输入研究主题";
+    return;
+  }
+
+  if (currentController) {
+    currentController.abort();
+    currentController = null;
+  }
+
+  loading.value = true;
+  error.value = "";
+  resetWorkflowState();
+
+  const controller = new AbortController();
+  currentController = controller;
+
+  const payload = {
+    topic: form.topic.trim(),
+    search_api: form.searchApi || undefined
+  };
+
+  try {
+    await runResearchStream(
+      payload,
+      (event: ResearchStreamEvent) => {
+        if (event.type === "status") {
+          const message =
+            typeof event.message === "string" && event.message.trim()
+              ? event.message
+              : "流程状态更新";
+          progressLogs.value.push(message);
+
+          const payload = event as Record<string, unknown>;
+          const task = findTask(payload.task_id);
+          if (task && message) {
+            task.notices.push(message);
+            applyNoteMetadata(task, payload);
+          }
+          return;
+        }
+
+        if (event.type === "todo_list") {
+          const tasks = Array.isArray(event.tasks)
+            ? (event.tasks as Record<string, unknown>[])
+            : [];
+
+          todoTasks.value = tasks.map((item, index) => {
+            const rawId =
+              typeof item.id === "number"
+                ? item.id
+                : typeof item.id === "string"
+                ? Number(item.id)
+                : index + 1;
+            const id = Number.isFinite(rawId) ? Number(rawId) : index + 1;
+            const noteId =
+              typeof item.note_id === "string" && item.note_id.trim()
+                ? item.note_id.trim()
+                : null;
+            const notePath =
+              typeof item.note_path === "string" && item.note_path.trim()
+                ? item.note_path.trim()
+                : null;
+
+            return {
+              id,
+              title:
+                typeof item.title === "string" && item.title.trim()
+                  ? item.title.trim()
+                  : `任务${id}`,
+              intent:
+                typeof item.intent === "string" && item.intent.trim()
+                  ? item.intent.trim()
+                  : "探索与主题相关的关键信息",
+              query:
+                typeof item.query === "string" && item.query.trim()
+                  ? item.query.trim()
+                  : form.topic.trim(),
+              status:
+                typeof item.status === "string" && item.status.trim()
+                  ? item.status.trim()
+                  : "pending",
+              summary: "",
+              sourcesSummary: "",
+              sourceItems: [],
+              notices: [],
+              noteId,
+              notePath,
+              toolCalls: []
+            } as TodoTaskView;
+          });
+
+          if (todoTasks.value.length) {
+            activeTaskId.value = todoTasks.value[0].id;
+            progressLogs.value.push("已生成任务清单");
+          } else {
+            progressLogs.value.push("未生成任务清单，使用默认任务继续");
+          }
+          return;
+        }
+
+        if (event.type === "task_status") {
+          const payload = event as Record<string, unknown>;
+          const task = findTask(event.task_id);
+          if (!task) {
+            return;
+          }
+
+          upsertTaskMetadata(task, payload);
+          applyNoteMetadata(task, payload);
+          const status =
+            typeof event.status === "string" && event.status.trim()
+              ? event.status.trim()
+              : task.status;
+          task.status = status;
+
+          if (status === "in_progress") {
+            task.summary = "";
+            task.sourcesSummary = "";
+            task.sourceItems = [];
+            task.notices = [];
+            activeTaskId.value = task.id;
+            progressLogs.value.push(`开始执行任务：${task.title}`);
+          } else if (status === "completed") {
+            if (typeof event.summary === "string" && event.summary.trim()) {
+              task.summary = event.summary.trim();
+            }
+            if (
+              typeof event.sources_summary === "string" &&
+              event.sources_summary.trim()
+            ) {
+              task.sourcesSummary = event.sources_summary.trim();
+              task.sourceItems = parseSources(task.sourcesSummary);
+            }
+            progressLogs.value.push(`完成任务：${task.title}`);
+            if (activeTaskId.value === task.id) {
+              pulse(summaryHighlight);
+              pulse(sourcesHighlight);
+            }
+          } else if (status === "skipped") {
+            progressLogs.value.push(`任务跳过：${task.title}`);
+          }
+          return;
+        }
+
+        if (event.type === "sources") {
+          const payload = event as Record<string, unknown>;
+          const task = findTask(event.task_id);
+          if (!task) {
+            return;
+          }
+
+          const textCandidates = [
+            payload.latest_sources,
+            payload.sources_summary,
+            payload.raw_context
+          ];
+          const latestText = textCandidates
+            .map((value) => (typeof value === "string" ? value.trim() : ""))
+            .find((value) => value);
+
+          if (latestText) {
+            task.sourcesSummary = latestText;
+            task.sourceItems = parseSources(latestText);
+            if (activeTaskId.value === task.id) {
+              pulse(sourcesHighlight);
+            }
+            progressLogs.value.push(`已更新任务来源：${task.title}`);
+          }
+
+          if (typeof payload.backend === "string") {
+            progressLogs.value.push(
+              `当前使用搜索后端：${payload.backend}`
+            );
+          }
+
+          applyNoteMetadata(task, payload);
+
+          return;
+        }
+
+        if (event.type === "task_summary_chunk") {
+          const payload = event as Record<string, unknown>;
+          const task = findTask(event.task_id);
+          if (!task) {
+            return;
+          }
+          const chunk =
+            typeof event.content === "string" ? event.content : "";
+          task.summary += chunk;
+          applyNoteMetadata(task, payload);
+          if (activeTaskId.value === task.id) {
+            pulse(summaryHighlight);
+          }
+          return;
+        }
+
+        if (event.type === "tool_call") {
+          const payload = event as Record<string, unknown>;
+          const eventId =
+            typeof payload.event_id === "number"
+              ? payload.event_id
+              : Date.now();
+          const agent =
+            typeof payload.agent === "string" && payload.agent.trim()
+              ? payload.agent.trim()
+              : "Agent";
+          const tool =
+            typeof payload.tool === "string" && payload.tool.trim()
+              ? payload.tool.trim()
+              : "tool";
+          const parameters = ensureRecord(payload.parameters);
+          const result =
+            typeof payload.result === "string" ? payload.result : "";
+          const noteId = extractOptionalString(payload.note_id);
+          const notePath = extractOptionalString(payload.note_path);
+
+          const task = findTask(payload.task_id);
+          if (task) {
+            task.toolCalls.push({
+              eventId,
+              agent,
+              tool,
+              parameters,
+              result,
+              noteId,
+              notePath,
+              timestamp: Date.now()
+            });
+            if (noteId) {
+              task.noteId = noteId;
+            }
+            if (notePath) {
+              task.notePath = notePath;
+            }
+            const logSummary = noteId
+              ? `${agent} 调用了 ${tool}（任务 ${task.id}，笔记 ${noteId}）`
+              : `${agent} 调用了 ${tool}（任务 ${task.id}）`;
+            progressLogs.value.push(logSummary);
+            if (activeTaskId.value === task.id) {
+              pulse(toolHighlight);
+            }
+          } else {
+            progressLogs.value.push(`${agent} 调用了 ${tool}`);
+          }
+          return;
+        }
+
+        if (event.type === "final_report") {
+          const report =
+            typeof event.report === "string" && event.report.trim()
+              ? event.report.trim()
+              : "";
+          reportMarkdown.value = report || "报告生成失败，未获得有效内容";
+          pulse(reportHighlight);
+          progressLogs.value.push("最终报告已生成");
+          return;
+        }
+
+        if (event.type === "error") {
+          const detail =
+            typeof event.detail === "string" && event.detail.trim()
+              ? event.detail
+              : "研究过程中发生错误";
+          error.value = detail;
+          progressLogs.value.push("研究失败，已停止流程");
+        }
+      },
+      { signal: controller.signal }
+    );
+
+    if (!reportMarkdown.value) {
+      reportMarkdown.value = "暂无生成的报告";
+    }
+  } catch (err) {
+    if (err instanceof DOMException && err.name === "AbortError") {
+      progressLogs.value.push("已取消当前研究任务");
+    } else {
+      error.value = err instanceof Error ? err.message : "请求失败";
+    }
+  } finally {
+    loading.value = false;
+    if (currentController === controller) {
+      currentController = null;
+    }
+  }
+};
+
+const cancelResearch = () => {
+  if (!loading.value || !currentController) {
+    return;
+  }
+  progressLogs.value.push("正在尝试取消当前研究任务…");
+  currentController.abort();
+};
+
+onBeforeUnmount(() => {
+  if (currentController) {
+    currentController.abort();
+    currentController = null;
+  }
+});
+</script>
+
+
+<style scoped>
+.app-shell {
+  position: relative;
+  min-height: 100vh;
+  padding: 72px 24px;
+  display: flex;
+  justify-content: center;
+  background: radial-gradient(circle at 20% 20%, #1e254f, #070910 60%);
+  color: #f8fafc;
+  overflow: hidden;
+  box-sizing: border-box;
+}
+
+.aurora {
+  position: absolute;
+  inset: 0;
+  pointer-events: none;
+  opacity: 0.45;
+}
+
+.aurora span {
+  position: absolute;
+  width: 45vw;
+  height: 45vw;
+  max-width: 520px;
+  max-height: 520px;
+  background: radial-gradient(circle, rgba(62, 164, 255, 0.42), transparent 60%);
+  filter: blur(80px);
+  animation: float 26s infinite linear;
+}
+
+.aurora span:nth-child(1) {
+  top: -20%;
+  left: -18%;
+  animation-delay: 0s;
+}
+
+.aurora span:nth-child(2) {
+  bottom: -25%;
+  right: -20%;
+  background: radial-gradient(circle, rgba(125, 86, 255, 0.36), transparent 60%);
+  animation-delay: -9s;
+}
+
+.aurora span:nth-child(3) {
+  top: 35%;
+  left: 45%;
+  background: radial-gradient(circle, rgba(94, 211, 182, 0.32), transparent 60%);
+  animation-delay: -16s;
+}
+
+.layout {
+  position: relative;
+  width: min(1280px, 100%);
+  display: flex;
+  flex-wrap: wrap;
+  gap: 24px;
+  z-index: 1;
+  align-items: flex-start;
+}
+
+.panel {
+  position: relative;
+  flex: 1 1 360px;
+  padding: 24px;
+  border-radius: 20px;
+  background: rgba(11, 16, 32, 0.78);
+  border: 1px solid rgba(148, 163, 184, 0.12);
+  box-shadow: 0 24px 56px rgba(11, 16, 32, 0.4);
+  backdrop-filter: blur(18px);
+  overflow: hidden;
+}
+
+.panel-form {
+  max-width: 420px;
+}
+
+.panel-result {
+  min-width: 360px;
+  flex: 2 1 420px;
+}
+
+.panel::before {
+  content: "";
+  position: absolute;
+  inset: 0;
+  background: linear-gradient(135deg, rgba(59, 130, 246, 0.2), rgba(125, 86, 255, 0.16));
+  opacity: 0;
+  transition: opacity 0.35s ease;
+  z-index: 0;
+}
+
+.panel:hover::before {
+  opacity: 1;
+}
+
+.panel > * {
+  position: relative;
+  z-index: 1;
+}
+
+.panel-form h1 {
+  margin: 0;
+  font-size: 26px;
+  letter-spacing: 0.01em;
+}
+
+.panel-form p {
+  margin: 4px 0 0;
+  color: #c5d3f5;
+  font-size: 13px;
+}
+
+.panel-head {
+  display: flex;
+  align-items: center;
+  gap: 16px;
+  margin-bottom: 24px;
+}
+
+.logo {
+  width: 52px;
+  height: 52px;
+  display: grid;
+  place-items: center;
+  border-radius: 16px;
+  background: linear-gradient(135deg, #2563eb, #7c3aed);
+  box-shadow: 0 12px 28px rgba(59, 130, 246, 0.4);
+}
+
+.logo svg {
+  width: 28px;
+  height: 28px;
+  fill: #f8fafc;
+}
+
+.form {
+  display: flex;
+  flex-direction: column;
+  gap: 18px;
+}
+
+.field {
+  display: flex;
+  flex-direction: column;
+  gap: 10px;
+}
+
+.field span {
+  font-weight: 600;
+  color: rgba(226, 232, 240, 0.94);
+}
+
+textarea,
+input,
+select {
+  padding: 14px 16px;
+  border-radius: 16px;
+  border: 1px solid rgba(148, 163, 184, 0.25);
+  background: rgba(15, 23, 42, 0.55);
+  color: #f8fafc;
+  font-size: 14px;
+  transition: border-color 0.2s, box-shadow 0.2s, background 0.2s;
+}
+
+textarea:focus,
+input:focus,
+select:focus {
+  outline: none;
+  border-color: rgba(96, 165, 250, 0.8);
+  box-shadow: 0 0 0 3px rgba(37, 99, 235, 0.35);
+  background: rgba(15, 23, 42, 0.75);
+}
+
+.options {
+  display: flex;
+  gap: 16px;
+  flex-wrap: wrap;
+}
+
+.option {
+  flex: 1;
+  min-width: 140px;
+}
+
+.form-actions {
+  display: flex;
+  align-items: center;
+  gap: 12px;
+  flex-wrap: wrap;
+}
+
+.submit {
+  align-self: flex-start;
+  padding: 12px 24px;
+  border-radius: 16px;
+  border: none;
+  background: linear-gradient(135deg, #2563eb, #7c3aed);
+  color: #ffffff;
+  font-size: 15px;
+  font-weight: 600;
+  cursor: pointer;
+  transition: transform 0.2s, box-shadow 0.2s, opacity 0.2s;
+  display: inline-flex;
+  align-items: center;
+  gap: 10px;
+  position: relative;
+}
+
+.submit-label {
+  display: inline-flex;
+  align-items: center;
+  gap: 10px;
+}
+
+.submit .spinner {
+  width: 18px;
+  height: 18px;
+  fill: none;
+  stroke: rgba(255, 255, 255, 0.85);
+  stroke-linecap: round;
+  animation: spin 1s linear infinite;
+}
+
+.submit:disabled {
+  opacity: 0.7;
+  cursor: not-allowed;
+}
+
+.submit:not(:disabled):hover {
+  transform: translateY(-2px);
+  box-shadow: 0 12px 28px rgba(37, 99, 235, 0.28);
+}
+
+.secondary-btn {
+  padding: 10px 18px;
+  border-radius: 14px;
+  background: rgba(148, 163, 184, 0.18);
+  border: 1px solid rgba(148, 163, 184, 0.28);
+  color: rgba(226, 232, 240, 0.9);
+  font-size: 14px;
+  font-weight: 500;
+  cursor: pointer;
+  transition: background 0.2s ease, border-color 0.2s ease;
+}
+
+.secondary-btn:hover {
+  background: rgba(148, 163, 184, 0.28);
+  border-color: rgba(148, 163, 184, 0.4);
+}
+
+.error-chip {
+  margin-top: 16px;
+  display: inline-flex;
+  align-items: center;
+  gap: 8px;
+  padding: 10px 14px;
+  background: rgba(248, 113, 113, 0.14);
+  border: 1px solid rgba(248, 113, 113, 0.35);
+  border-radius: 14px;
+  color: #fecaca;
+  font-size: 14px;
+}
+
+.error-chip svg {
+  width: 18px;
+  height: 18px;
+  fill: currentColor;
+}
+
+.panel-result {
+  display: flex;
+  flex-direction: column;
+  gap: 18px;
+}
+
+.status-bar {
+  display: flex;
+  align-items: center;
+  justify-content: space-between;
+  gap: 12px;
+  flex-wrap: wrap;
+}
+
+.status-main {
+  display: flex;
+  align-items: center;
+  gap: 12px;
+  flex-wrap: wrap;
+}
+
+.status-controls {
+  display: flex;
+  gap: 8px;
+}
+
+.status-chip {
+  display: inline-flex;
+  align-items: center;
+  gap: 8px;
+  background: rgba(59, 130, 246, 0.18);
+  padding: 8px 14px;
+  border-radius: 999px;
+  font-size: 13px;
+  color: rgba(191, 219, 254, 0.9);
+  border: 1px solid rgba(59, 130, 246, 0.35);
+  transition: background 0.3s ease;
+}
+
+.status-chip.active {
+  background: rgba(99, 102, 241, 0.24);
+  border-color: rgba(129, 140, 248, 0.45);
+}
+
+.status-chip .dot {
+  width: 8px;
+  height: 8px;
+  border-radius: 999px;
+  background: #38bdf8;
+  box-shadow: 0 0 12px rgba(56, 189, 248, 0.8);
+  animation: pulse 1.8s ease-in-out infinite;
+}
+
+.status-meta {
+  color: rgba(148, 163, 184, 0.9);
+  font-size: 13px;
+}
+
+.timeline-wrapper {
+  margin-top: 12px;
+  max-height: 220px;
+  overflow-y: auto;
+  padding-right: 8px;
+  scrollbar-width: thin;
+  scrollbar-color: rgba(79, 70, 229, 0.55) rgba(15, 23, 42, 0.6);
+}
+
+.timeline-wrapper::-webkit-scrollbar {
+  width: 6px;
+}
+
+.timeline-wrapper::-webkit-scrollbar-track {
+  background: rgba(15, 23, 42, 0.45);
+  border-radius: 999px;
+}
+
+.timeline-wrapper::-webkit-scrollbar-thumb {
+  background: linear-gradient(180deg, rgba(96, 165, 250, 0.9), rgba(129, 140, 248, 0.75));
+  border-radius: 999px;
+}
+
+.timeline-wrapper::-webkit-scrollbar-thumb:hover {
+  background: linear-gradient(180deg, rgba(129, 140, 248, 0.95), rgba(165, 180, 252, 0.85));
+}
+
+.timeline {
+  list-style: none;
+  padding: 0;
+  margin: 0;
+  display: flex;
+  flex-direction: column;
+  gap: 14px;
+  position: relative;
+  padding-left: 12px;
+}
+
+.timeline::before {
+  content: "";
+  position: absolute;
+  top: 8px;
+  bottom: 8px;
+  left: 0;
+  width: 2px;
+  background: linear-gradient(180deg, rgba(59, 130, 246, 0.4), rgba(129, 140, 248, 0.1));
+}
+
+.timeline li {
+  position: relative;
+  padding-left: 24px;
+  color: rgba(226, 232, 240, 0.95);
+  font-size: 14px;
+  line-height: 1.5;
+}
+
+.timeline-node {
+  position: absolute;
+  left: -12px;
+  top: 6px;
+  width: 10px;
+  height: 10px;
+  border-radius: 999px;
+  background: linear-gradient(135deg, #38bdf8, #7c3aed);
+  box-shadow: 0 0 0 4px rgba(59, 130, 246, 0.15);
+}
+
+.timeline-enter-active,
+.timeline-leave-active {
+  transition: all 0.35s ease, opacity 0.35s ease;
+}
+
+.timeline-enter-from,
+.timeline-leave-to {
+  opacity: 0;
+  transform: translateY(-6px);
+}
+
+.tasks-section {
+  display: grid;
+  grid-template-columns: 280px 1fr;
+  gap: 20px;
+  align-items: start;
+}
+
+@media (max-width: 960px) {
+  .tasks-section {
+    grid-template-columns: 1fr;
+  }
+}
+
+.tasks-list {
+  background: rgba(15, 23, 42, 0.55);
+  border: 1px solid rgba(148, 163, 184, 0.26);
+  border-radius: 18px;
+  padding: 18px;
+  display: flex;
+  flex-direction: column;
+  gap: 16px;
+  box-shadow: inset 0 0 0 1px rgba(100, 116, 139, 0.1);
+}
+
+.tasks-list h3 {
+  margin: 0;
+  font-size: 16px;
+  font-weight: 600;
+  color: rgba(224, 231, 255, 0.92);
+}
+
+.tasks-list ul {
+  list-style: none;
+  margin: 0;
+  padding: 0;
+  display: flex;
+  flex-direction: column;
+  gap: 12px;
+}
+
+.task-item {
+  border-radius: 14px;
+  border: 1px solid transparent;
+  transition: border-color 0.2s ease, background 0.2s ease;
+}
+
+.task-item.completed {
+  border-color: rgba(56, 189, 248, 0.3);
+}
+
+.task-item.active {
+  border-color: rgba(129, 140, 248, 0.6);
+  background: rgba(79, 70, 229, 0.18);
+}
+
+.task-button {
+  width: 100%;
+  display: flex;
+  align-items: center;
+  justify-content: space-between;
+  gap: 12px;
+  padding: 12px 14px 6px;
+  background: transparent;
+  border: none;
+  color: inherit;
+  cursor: pointer;
+  text-align: left;
+}
+
+.task-title {
+  font-weight: 600;
+  font-size: 14px;
+  color: rgba(226, 232, 240, 0.95);
+}
+
+.task-status {
+  display: inline-flex;
+  align-items: center;
+  justify-content: center;
+  padding: 4px 10px;
+  border-radius: 999px;
+  font-size: 12px;
+  font-weight: 500;
+  color: #0f172a;
+  background: rgba(148, 163, 184, 0.42);
+}
+
+.task-status.pending {
+  background: rgba(148, 163, 184, 0.32);
+  color: rgba(226, 232, 240, 0.9);
+}
+
+.task-status.in_progress {
+  background: rgba(129, 140, 248, 0.32);
+  color: rgba(224, 231, 255, 0.95);
+}
+
+.task-status.completed {
+  background: rgba(34, 197, 94, 0.32);
+  color: #dcfce7;
+}
+
+.task-status.skipped {
+  background: rgba(248, 113, 113, 0.24);
+  color: #fecaca;
+}
+
+.task-intent {
+  margin: 0;
+  padding: 0 14px 12px 14px;
+  font-size: 13px;
+  color: rgba(148, 163, 184, 0.85);
+}
+
+.task-detail {
+  background: rgba(15, 23, 42, 0.55);
+  border: 1px solid rgba(148, 163, 184, 0.26);
+  border-radius: 18px;
+  padding: 22px;
+  display: flex;
+  flex-direction: column;
+  gap: 18px;
+  box-shadow: inset 0 0 0 1px rgba(100, 116, 139, 0.12);
+}
+
+.task-header {
+  display: flex;
+  justify-content: space-between;
+  align-items: flex-start;
+  flex-wrap: wrap;
+  gap: 12px;
+}
+
+.task-chip-group {
+  display: flex;
+  align-items: center;
+  gap: 8px;
+  flex-wrap: wrap;
+}
+
+.task-header h3 {
+  margin: 0;
+  font-size: 18px;
+  font-weight: 600;
+  color: rgba(226, 232, 240, 0.96);
+}
+
+.task-header .muted {
+  margin: 6px 0 0;
+}
+
+.task-label {
+  padding: 6px 12px;
+  border-radius: 999px;
+  background: rgba(59, 130, 246, 0.18);
+  border: 1px solid rgba(59, 130, 246, 0.35);
+  font-size: 12px;
+  color: rgba(191, 219, 254, 0.95);
+}
+
+.task-label.note-chip {
+  background: rgba(34, 197, 94, 0.2);
+  border-color: rgba(34, 197, 94, 0.35);
+  color: #bbf7d0;
+}
+
+.task-label.path-chip {
+  display: inline-flex;
+  align-items: center;
+  gap: 6px;
+  max-width: 360px;
+  background: rgba(56, 189, 248, 0.16);
+  border-color: rgba(56, 189, 248, 0.32);
+  color: #bae6fd;
+  overflow: hidden;
+  text-overflow: ellipsis;
+  white-space: nowrap;
+}
+
+.path-label {
+  font-weight: 500;
+}
+
+.path-text {
+  max-width: 220px;
+  overflow: hidden;
+  text-overflow: ellipsis;
+  white-space: nowrap;
+}
+
+.chip-action {
+  border: none;
+  background: rgba(14, 116, 144, 0.28);
+  color: #a5f3fc;
+  padding: 3px 8px;
+  border-radius: 10px;
+  font-size: 11px;
+  cursor: pointer;
+  transition: background 0.2s ease, color 0.2s ease;
+}
+
+.chip-action:hover {
+  background: rgba(8, 145, 178, 0.42);
+  color: #ecfeff;
+}
+
+.task-notices {
+  background: rgba(59, 130, 246, 0.12);
+  border: 1px solid rgba(96, 165, 250, 0.2);
+  border-radius: 16px;
+  padding: 14px 18px;
+  color: rgba(191, 219, 254, 0.96);
+}
+
+.task-notices h4 {
+  margin: 0 0 8px;
+  font-size: 14px;
+  font-weight: 600;
+}
+
+.task-notices ul {
+  list-style: disc;
+  margin: 0 0 0 18px;
+  padding: 0;
+  display: flex;
+  flex-direction: column;
+  gap: 6px;
+}
+
+.task-notices li {
+  font-size: 13px;
+}
+
+.report-block {
+  background: rgba(15, 23, 42, 0.58);
+  border: 1px solid rgba(148, 163, 184, 0.26);
+  border-radius: 18px;
+  padding: 22px;
+  display: flex;
+  flex-direction: column;
+  gap: 12px;
+}
+
+.report-block h3 {
+  margin: 0;
+  font-size: 18px;
+  font-weight: 600;
+  color: rgba(226, 232, 240, 0.96);
+}
+
+.block-pre {
+  font-family: "JetBrains Mono", "Fira Code", ui-monospace, SFMono-Regular,
+    Menlo, Monaco, Consolas, "Liberation Mono", "Courier New", monospace;
+  font-size: 13px;
+  line-height: 1.7;
+  white-space: pre-wrap;
+  word-break: break-word;
+  color: rgba(226, 232, 240, 0.95);
+  background: rgba(15, 23, 42, 0.72);
+  padding: 16px;
+  border-radius: 14px;
+  border: 1px solid rgba(148, 163, 184, 0.24);
+  overflow: auto;
+  max-height: 420px;
+  scrollbar-width: thin;
+  scrollbar-color: rgba(94, 129, 244, 0.8) rgba(12, 16, 30, 0.7);
+}
+
+.block-pre::-webkit-scrollbar {
+  width: 6px;
+}
+
+.block-pre::-webkit-scrollbar-track {
+  background: rgba(12, 16, 30, 0.7);
+  border-radius: 999px;
+}
+
+.block-pre::-webkit-scrollbar-thumb {
+  background: linear-gradient(180deg, rgba(99, 102, 241, 0.9), rgba(59, 130, 246, 0.75));
+  border-radius: 999px;
+}
+
+.block-pre::-webkit-scrollbar-thumb:hover {
+  background: linear-gradient(180deg, rgba(129, 140, 248, 0.95), rgba(165, 180, 252, 0.85));
+}
+
+.summary-block .block-pre,
+.sources-block .block-pre {
+  max-height: 360px;
+}
+
+
+.tools-block {
+  position: relative;
+  margin-top: 16px;
+  padding: 20px;
+  border-radius: 18px;
+  background: rgba(12, 18, 34, 0.65);
+  border: 1px solid rgba(148, 163, 184, 0.18);
+  box-shadow: inset 0 0 0 1px rgba(59, 130, 246, 0.05);
+  display: flex;
+  flex-direction: column;
+  gap: 12px;
+}
+
+.tools-block h3 {
+  margin: 0;
+  font-size: 16px;
+  font-weight: 600;
+  color: rgba(224, 231, 255, 0.92);
+  letter-spacing: 0.02em;
+}
+
+.tool-list {
+  list-style: none;
+  margin: 0;
+  padding: 0;
+  display: flex;
+  flex-direction: column;
+  gap: 12px;
+}
+
+.tool-entry {
+  background: rgba(15, 23, 42, 0.72);
+  border: 1px solid rgba(148, 163, 184, 0.2);
+  border-radius: 14px;
+  padding: 14px;
+  display: flex;
+  flex-direction: column;
+  gap: 10px;
+}
+
+.tool-entry-header {
+  display: flex;
+  flex-wrap: wrap;
+  gap: 8px;
+  align-items: center;
+  justify-content: space-between;
+}
+
+.tool-entry-title {
+  font-weight: 600;
+  color: rgba(226, 232, 240, 0.95);
+}
+
+.tool-entry-note {
+  font-size: 12px;
+  color: rgba(125, 211, 252, 0.9);
+}
+
+.tool-entry-path {
+  margin: 0;
+  font-size: 12px;
+  display: flex;
+  align-items: center;
+  gap: 6px;
+  color: rgba(191, 219, 254, 0.92);
+}
+
+.tool-subtitle {
+  margin: 0;
+  font-size: 13px;
+  color: rgba(191, 219, 254, 0.9);
+  font-weight: 500;
+}
+
+.tool-pre {
+  font-family: "JetBrains Mono", "Fira Code", ui-monospace, SFMono-Regular,
+    Menlo, Monaco, Consolas, "Liberation Mono", "Courier New", monospace;
+  font-size: 12px;
+  line-height: 1.6;
+  white-space: pre-wrap;
+  word-break: break-word;
+  color: rgba(226, 232, 240, 0.95);
+  background: rgba(10, 16, 30, 0.68);
+  padding: 12px;
+  border-radius: 12px;
+  border: 1px solid rgba(148, 163, 184, 0.2);
+  overflow: auto;
+  max-height: 260px;
+  scrollbar-width: thin;
+  scrollbar-color: rgba(94, 129, 244, 0.8) rgba(12, 16, 30, 0.7);
+}
+
+.tool-pre::-webkit-scrollbar {
+  width: 6px;
+}
+
+.tool-pre::-webkit-scrollbar-track {
+  background: rgba(12, 16, 30, 0.7);
+}
+
+.tool-pre::-webkit-scrollbar-thumb {
+  background: rgba(94, 129, 244, 0.8);
+  border-radius: 10px;
+}
+
+.link-btn {
+  background: none;
+  border: none;
+  color: #7dd3fc;
+  cursor: pointer;
+  padding: 0 4px;
+  font-size: 12px;
+  border-radius: 8px;
+  transition: color 0.2s ease, background 0.2s ease;
+}
+
+.link-btn:hover {
+  color: #e0f2fe;
+  background: rgba(125, 211, 252, 0.18);
+}
+
+
+.sources-block,
+.summary-block {
+  position: relative;
+  margin-top: 16px;
+  padding: 18px;
+  border-radius: 18px;
+  background: rgba(12, 18, 34, 0.65);
+  border: 1px solid rgba(148, 163, 184, 0.18);
+  box-shadow: inset 0 0 0 1px rgba(59, 130, 246, 0.05);
+}
+
+.sources-history {
+  margin-top: 16px;
+  display: flex;
+  flex-direction: column;
+  gap: 10px;
+}
+
+.sources-history h4 {
+  margin: 0;
+  color: rgba(224, 231, 255, 0.88);
+  font-size: 14px;
+  letter-spacing: 0.01em;
+}
+
+.history-list {
+  display: flex;
+  flex-direction: column;
+  gap: 10px;
+}
+
+.history-list details {
+  background: rgba(10, 16, 30, 0.55);
+  border: 1px solid rgba(71, 85, 105, 0.35);
+  border-radius: 14px;
+  padding: 12px 16px;
+  color: rgba(226, 232, 240, 0.9);
+  transition: border-color 0.2s ease, background 0.2s ease;
+}
+
+.history-list details[open] {
+  background: rgba(15, 23, 42, 0.65);
+  border-color: rgba(96, 165, 250, 0.35);
+}
+
+.history-list summary {
+  cursor: pointer;
+  font-weight: 600;
+  outline: none;
+  list-style: none;
+  display: flex;
+  align-items: center;
+  justify-content: space-between;
+}
+
+.history-list summary::-webkit-details-marker {
+  display: none;
+}
+
+.history-list summary::after {
+  content: "▾";
+  margin-left: 6px;
+  font-size: 12px;
+  opacity: 0.7;
+  transition: transform 0.2s ease;
+}
+
+.history-list details[open] summary::after {
+  transform: rotate(180deg);
+}
+
+.block-highlight {
+  animation: glow 1.2s ease;
+}
+
+.sources-block h3,
+.summary-block h3 {
+  margin: 0 0 14px;
+  color: rgba(224, 231, 255, 0.92);
+  letter-spacing: 0.02em;
+}
+
+.sources-list {
+  list-style: none;
+  margin: 0;
+  padding: 0;
+  display: flex;
+  flex-direction: column;
+  gap: 10px;
+}
+
+.source-item {
+  position: relative;
+  display: inline-flex;
+  flex-direction: column;
+  gap: 6px;
+}
+
+.source-link {
+  color: #38bdf8;
+  text-decoration: none;
+  font-weight: 600;
+  letter-spacing: 0.01em;
+  transition: color 0.2s ease;
+}
+
+.source-link::after {
+  content: " ↗";
+  font-size: 12px;
+  opacity: 0.6;
+}
+
+.source-link:hover {
+  color: #fbbf24;
+}
+
+.source-tooltip {
+  display: none;
+  position: absolute;
+  bottom: calc(100% + 12px);
+  left: 50%;
+  transform: translateX(-50%);
+  background: rgba(8, 11, 20, 0.96);
+  color: #e2e8f0;
+  padding: 14px 16px;
+  border-radius: 16px;
+  box-shadow: 0 16px 36px rgba(8, 11, 20, 0.55);
+  width: min(420px, 90vw);
+  z-index: 20;
+  border: 1px solid rgba(148, 163, 184, 0.2);
+}
+
+.source-tooltip::after {
+  content: "";
+  position: absolute;
+  top: 100%;
+  left: 50%;
+  transform: translateX(-50%);
+  border-width: 10px;
+  border-style: solid;
+  border-color: rgba(8, 11, 20, 0.96) transparent transparent transparent;
+}
+
+.source-tooltip::before {
+  content: "";
+  position: absolute;
+  bottom: -12px;
+  left: 50%;
+  transform: translateX(-50%);
+  border-width: 12px 10px 0 10px;
+  border-style: solid;
+  border-color: rgba(8, 11, 20, 0.96) transparent transparent transparent;
+}
+
+.source-tooltip p {
+  margin: 0 0 8px;
+  font-size: 13px;
+  line-height: 1.6;
+}
+
+.source-tooltip p:last-child {
+  margin-bottom: 0;
+}
+
+.muted-text {
+  color: rgba(148, 163, 184, 0.8);
+}
+
+.source-item:hover .source-tooltip,
+.source-item:focus-within .source-tooltip {
+  display: block;
+}
+
+.hint.muted {
+  color: rgba(148, 163, 184, 0.74);
+}
+
+@keyframes float {
+  0% {
+    transform: translate3d(0, 0, 0) rotate(0deg);
+  }
+  50% {
+    transform: translate3d(10%, 6%, 0) rotate(3deg);
+  }
+  100% {
+    transform: translate3d(0, 0, 0) rotate(0deg);
+  }
+}
+
+@keyframes spin {
+  to {
+    transform: rotate(360deg);
+  }
+}
+
+@keyframes pulse {
+  0%,
+  100% {
+    transform: scale(1);
+    opacity: 1;
+  }
+  50% {
+    transform: scale(1.3);
+    opacity: 0.5;
+  }
+}
+
+@keyframes glow {
+  0% {
+    box-shadow: 0 0 0 rgba(59, 130, 246, 0.3);
+    border-color: rgba(59, 130, 246, 0.5);
+  }
+  100% {
+    box-shadow: inset 0 0 0 1px rgba(59, 130, 246, 0.12);
+    border-color: rgba(148, 163, 184, 0.2);
+  }
+}
+
+@media (max-width: 960px) {
+  .app-shell {
+    padding: 56px 16px;
+  }
+
+  .layout {
+    flex-direction: column;
+    align-items: stretch;
+  }
+
+  .panel {
+    padding: 22px;
+  }
+
+  .panel-form,
+  .panel-result {
+    max-width: none;
+  }
+
+  .status-bar {
+    flex-direction: column;
+    align-items: flex-start;
+  }
+
+  .status-main,
+  .status-controls {
+    width: 100%;
+  }
+
+  .status-controls {
+    justify-content: flex-start;
+  }
+}
+
+@media (max-width: 600px) {
+  .options {
+    flex-direction: column;
+  }
+
+  .status-meta {
+    font-size: 12px;
+  }
+
+  .panel-head {
+    flex-direction: column;
+    align-items: flex-start;
+  }
+
+  .panel-form h1 {
+    font-size: 24px;
+  }
+}
+</style>

code/chapter14/helloagents-deepresearch/frontend/src/env.d.ts

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

code/chapter14/helloagents-deepresearch/frontend/src/main.ts

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

code/chapter14/helloagents-deepresearch/frontend/src/services/api.ts

@@ -0,0 +1,96 @@
+const baseURL =
+  import.meta.env.VITE_API_BASE_URL || "http://localhost:8000";
+
+export interface ResearchRequest {
+  topic: string;
+  search_api?: string;
+}
+
+export interface ResearchStreamEvent {
+  type: string;
+  [key: string]: unknown;
+}
+
+export interface StreamOptions {
+  signal?: AbortSignal;
+}
+
+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;
+    }
+  }
+}

code/chapter14/helloagents-deepresearch/frontend/src/style.css

@@ -0,0 +1,18 @@
+:root {
+  color: #1a1a1a;
+  background-color: #f6f6f6;
+  font-family: "Helvetica Neue", Arial, sans-serif;
+  line-height: 1.5;
+}
+
+* {
+  box-sizing: border-box;
+}
+
+body {
+  margin: 0;
+}
+
+#app {
+  min-height: 100vh;
+}

code/chapter14/helloagents-deepresearch/frontend/tsconfig.json

@@ -0,0 +1,22 @@
+{
+  "compilerOptions": {
+    "target": "ESNext",
+    "useDefineForClassFields": true,
+    "module": "ESNext",
+    "moduleResolution": "Node",
+    "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" }]
+}

code/chapter14/helloagents-deepresearch/frontend/tsconfig.node.json

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

code/chapter14/helloagents-deepresearch/frontend/vite.config.ts

@@ -0,0 +1,9 @@
+import { defineConfig } from "vite";
+import vue from "@vitejs/plugin-vue";
+
+export default defineConfig({
+  plugins: [vue()],
+  server: {
+    port: 5174
+  }
+});

docs/chapter14/第十四章 自动化深度研究智能体.md

@@ -1,3 +1,600 @@
 # 第十四章 自动化深度研究智能体
 
-本章内容待补充...
+在第十三章的旅行助手项目中，我们体验了如何将HelloAgents应用于一个复杂但闭环的多智能体产品。本章我们继续向前，聚焦「知识密集型应用」：
+<strong>构建一个能够自动化执行深度研究任务的智能体体系。</strong>
+
+相比旅行规划，深度研究的难点在于信息的不断发散、事实的快速更新以及用户对引用来源的高要求。为了交付可信的研究报告，我们需要让智能体具备三个核心能力：
+
+<strong>（1）问题剖析</strong>：将用户的开放主题拆解为可检索的查询语句。
+
+<strong>（2）多轮信息采集</strong>：结合不同搜索API持续挖掘资料，并去重整合。
+
+<strong>（3）反思与总结</strong>：依据阶段结果识别知识空白，决定是否继续检索，并生成结构化总结。
+
+本章的目标是带你深入理解 `helloagents-deepresearch` 项目，掌握其架构、运行方式与扩展技巧，最终能够独立运营一套本地可控的研究助手。
+
+
+## 14.1 项目概述与场景定位
+
+### 14.1.1 为什么需要自动化深度研究智能体
+
+当话题复杂且信息分散时，单纯依赖搜索引擎难以获得结构化洞见。常见的痛点包括：
+
+- 信息碎片化：结果散落在不同站点，需要手动筛选、比对可靠性。
+- 维度不平衡：容易沉迷于同类观点，忽略实现细节、对比数据或最新进展。
+- 时间成本高：人工整理引用、撰写总结耗时，且难以复盘全过程。
+
+自动化深度研究智能体的优势在于它能够：
+
+- 以智能体循环执行「生成查询→检索→总结→反思」流程，保证主题覆盖度。
+- 在本地运行（Ollama、LMStudio），保护敏感主题，同时避免外部服务波动。
+- 输出带引用的Markdown报告，便于复盘、分享与二次编辑。
+
+### 14.1.2 整体能力与用户价值
+
+`helloagents-deepresearch` 项目将后端HelloAgents智能体、可配置搜索适配器与前端可视化界面结合，形成「输入主题→实时观察→获取总结」的闭环体验。整体亮点如下：
+
+- <strong>多提供者模型接入</strong>：支持 Ollama、LMStudio 或自定义 OpenAI 兼容服务，自主选择推理能力与成本。
+- <strong>多搜索源融合</strong>：内置 DuckDuckGo、Tavily、Perplexity、SearXNG 适配器，灵活切换，甚至可以按轮次混合使用。
+- <strong>流式反馈</strong>：后端通过 Server-Sent Events 推送各阶段结果，前端即时展示时间线、最新来源和逐字更新的总结。
+- <strong>配置优先级清晰</strong>：环境变量、代码默认值分层管理，方便调试与部署。
+系统采用经典的<strong>前后端分离架构</strong>，分为四个层次，如图14.1所示
+
+```mermaid
+graph LR
+  U((用户))
+  subgraph FE[前端（Vue 3 + TypeScript）]
+    UI[App.vue 研究界面]
+    FEAPI[services/api.ts\nrunResearchStream]
+  end
+  subgraph BE[后端（FastAPI DeepResearcher）]
+    API[/POST /research + /research/stream/]
+    Config[Configuration.from_env]
+    subgraph Workflow[DeepResearchAgent 工作流]
+      Planner{{PlanningService\n任务规划}}
+      Search{{dispatch_search\nprepare_research_context}}
+      Summarizer{{SummarizationService\n任务总结}}
+      Reporter{{ReportingService\n报告整合}}
+      Tracker[[ToolCallTracker\n工具事件]]
+    end
+  end
+  subgraph LLMTools[LLM + 工具层]
+    LLM[(HelloAgentsLLM)]
+    NoteTool[(NoteTool\nToolRegistry)]
+  end
+  subgraph External[外部依赖]
+    SearchAPI[(Tavily / Perplexity /\nDuckDuckGo / SearxNG / Advanced)]
+    Notes[(本地笔记\nnotes_workspace)]
+  end
+
+  U -->|交互| UI
+  UI -->|提交 topic/search_api| FEAPI
+  FEAPI -->|POST 请求| API
+  API -->|构建配置| Config
+  Config -->|实例化| Workflow
+  Workflow -->|加载模型| LLM
+  Workflow -->|注册工具| NoteTool
+  LLM --> Planner
+  LLM --> Summarizer
+  LLM --> Reporter
+  Planner -->|TODO 列表| Workflow
+  Workflow -->|SSE todo_list| API
+  Workflow --> Search
+  Search -->|调用| SearchAPI
+  SearchAPI --> Search
+  Search --> Summarizer
+  Summarizer -->|摘要片段| Workflow
+  Planner --> Tracker
+  Summarizer --> Tracker
+  Reporter --> Tracker
+  NoteTool --> Tracker
+  NoteTool -->|读写| Notes
+  Notes --> NoteTool
+  Workflow --> Reporter
+  Reporter -->|最终报告| Workflow
+  Workflow -->|final_report/done SSE| API
+  Tracker -->|tool_call SSE| API
+  API -->|Server-Sent Events| FEAPI
+  FEAPI -->|更新状态/日志| UI
+  UI -->|结果呈现| U
+```
+
+图14.1 整体架构图
+
+下一节我们会详细拆解技术架构与数据流转，理解智能体在每一轮循环中的决策与协作方式。
+
+## 14.2 技术架构与流程
+
+### 14.2.1 智能体循环的核心机制
+
+`helloagents-deepresearch` 的核心仍然是 `DeepResearchAgent`，但它不再让单个智能体“单线程”执行所有步骤，而是复用前几章学到的能力组合成一个协作团队：
+
+- <strong>研究规划专家</strong>（`PlanningService` 驱动的 `ToolAwareSimpleAgent`）——由第七章扩展过的 SimpleAgent 模板派生，专门负责把主题拆解成结构化任务。
+- <strong>任务总结专家</strong>（`SummarizationService`）——在每个任务的上下文就绪后撰写总结，并通过第九章的 `NoteTool` 进行笔记读写。
+- <strong>报告撰写专家</strong>（`ReportingService`）——整合所有任务笔记，产出最终报告并落盘。
+
+三位专家共享同一套工具注册表，其中既包括我们在第七章实现的多源搜索工具，也包括第九章整合的笔记管理能力。
+
+新版 `DeepResearchAgent` 的执行顺序如下：
+
+1. `PlanningService` 启动「研究规划专家」，由模型一次性列出 3~5 条任务，每条任务都会绑定唯一的 `task_id`、笔记标签与 `stream_token`。
+2. `run_stream` 为每条任务创建后台线程，线程内部依次完成搜索、总结、笔记写入，并通过队列把 SSE 事件推回主线程。
+3. `ReportingService` 等待所有任务完成后，再触发「报告撰写专家」，生成最终 Markdown 并固化到结论笔记中。
+
+以下代码片段（`backend/src/deep_researcher/app/agents/deep_research_agent.py:126` 起）展示了并行执行的核心结构：
+
+```python
+channel_map = {task.id: {"step": index, "token": f"task_{task.id}"}
+               for index, task in enumerate(state.todo_items, start=1)}
+
+def worker(task: TodoItem, step: int) -> None:
+    try:
+        enqueue({
+            "type": "task_status",
+            "task_id": task.id,
+            "status": "in_progress",
+            "title": task.title,
+        })
+        for event in self._execute_task(state, task, emit_stream=True, step=step):
+            enqueue(event)
+    finally:
+        enqueue({"type": "__task_done__", "task_id": task.id})
+
+threads = [Thread(target=worker, args=(task, channel_map[task.id]["step"]))
+           for task in state.todo_items]
+for thread in threads:
+    thread.start()
+```
+
+`enqueue` 会自动补全 `step`、`stream_token` 等字段，保证前端可以按任务维度渲染事件。得益于这种结构，多个任务可以同时检索、总结，再通过主线程串行输出，既不会丢事件，也避免了线程安全问题。
+
+下图给出了各智能体协同完成研究流程的职责分层与数据流向（图14.2）：
+
+```mermaid
+graph TD
+    TOPIC["研究主题输入"] --> DRA["DeepResearchAgent<br/>协调器"]
+    subgraph Shared["共享资源"]
+        CFG["Configuration"]
+        LLM["LLM 实例"]
+        NOTE["NoteTool / ToolRegistry"]
+        TRACKER["ToolCallTracker"]
+    end
+    CFG --> DRA
+    LLM --> DRA
+    NOTE -.注册.- DRA
+    TRACKER --> DRA
+    DRA --> STATE["SummaryState / TodoItem"]
+
+    subgraph Planning["规划阶段"]
+        PLAN_AGENT["研究规划专家<br/>ToolAwareSimpleAgent"]
+        PLAN_SERVICE["PlanningService"]
+    end
+    DRA --> PLAN_AGENT
+    PLAN_AGENT --> PLAN_SERVICE
+    PLAN_SERVICE -->|Todo 列表| STATE
+
+    subgraph Execution["任务执行"]
+        STATE --> TASK["TodoItem"]
+        TASK --> SEARCH["dispatch_search"]
+        SEARCH --> BACKENDS["搜索 API<br/>Tavily / Perplexity / DuckDuckGo / SearXNG / Advanced"]
+        SEARCH --> CONTEXT["prepare_research_context"]
+        CONTEXT --> SUMM_SERVICE["SummarizationService"]
+        DRA --> SUMM_AGENT["任务总结专家<br/>ToolAwareSimpleAgent"]
+        SUMM_AGENT --> SUMM_SERVICE
+        SUMM_SERVICE --> STATE
+        SUMM_SERVICE --> TRACKER
+    end
+
+    subgraph Reporting["报告阶段"]
+        REPORT_AGENT["报告撰写专家<br/>ToolAwareSimpleAgent"]
+        REPORT_SERVICE["ReportingService"]
+    end
+    DRA --> REPORT_AGENT
+    REPORT_AGENT --> REPORT_SERVICE
+    STATE --> REPORT_SERVICE
+    REPORT_SERVICE --> REPORT["最终报告 Markdown"]
+    REPORT --> STATE
+    REPORT --> NOTE
+    TRACKER --> STATE
+    REPORT -.SSE.-> OUTPUT["final_report / done"]
+    TRACKER -.SSE.-> OUTPUT
+```
+
+图14.2 Agents架构图
+
+
+### 14.2.2 后端组件与配置流
+
+深度研究智能体通过 FastAPI 暴露 `/research` 与 `/research/stream` 接口，主要模块如下（文件均位于 `backend/src/deep_researcher/app/` 下）：
+
+- `agents/deep_research_agent.py`：顶层协调者，负责任务规划、并行执行与报告沉淀。
+- `services/planner_service.py`、`summarization_service.py`、`reporting_service.py`：分别封装计划、总结、报告逻辑，内部都复用了 `ToolAwareSimpleAgent`。
+- `services/tool_events.py`：跟踪 `note` 工具调用，把事件转换成 SSE，可见第九章 `NoteTool` 的集成成果。
+- `services/search_service.py`：统一封装多源搜索结果，与第七章的自定义搜索工具同样遵循“结果字典”约定。
+- `configuration.py`、`api.py`：负责配置加载、HelloAgentsLLM 初始化与 HTTP 层的流式推送。
+
+数据流转顺序为：
+
+1. 前端提交主题、迭代次数、搜索 API（可选）。
+2. FastAPI 解析请求，合并配置并实例化 `DeepResearchAgent`。
+3. 智能体并行执行所有任务，并持续通过 SSE 推送 `tool_call`、`task_summary_chunk`、`sources`、`report_note` 等事件。
+4. 客户端结束时收到携带 `note_id` 的 `final_report` 与 `done` 事件，可直接展示、跳转或二次编辑。
+
+```python
+class Configuration(BaseModel):
+    max_web_research_loops: int = Field(default=3)
+    local_llm: str = Field(default="llama3.2")
+    llm_provider: str = Field(default="ollama")
+    search_api: SearchAPI = Field(default=SearchAPI.DUCKDUCKGO)
+    enable_notes: bool = Field(default=True)
+    notes_workspace: str = Field(default="./notes/deep_research")
+    fetch_full_page: bool = Field(default=True)
+    ollama_base_url: str = Field(default="http://localhost:11434")
+    lmstudio_base_url: str = Field(default="http://localhost:1234/v1")
+    strip_thinking_tokens: bool = Field(default=True)
+    use_tool_calling: bool = Field(default=False)
+    llm_api_key: Optional[str] = None
+    llm_base_url: Optional[str] = None
+    llm_model_id: Optional[str] = None
+
+    @classmethod
+    def from_env(cls, overrides: Optional[dict[str, Any]] = None) -> "Configuration":
+        raw_values: dict[str, Any] = {}
+
+        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]
+
+        env_aliases = {
+            "local_llm": os.getenv("LOCAL_LLM"),
+            "llm_provider": os.getenv("LLM_PROVIDER"),
+            "llm_api_key": os.getenv("LLM_API_KEY"),
+            "llm_model_id": os.getenv("LLM_MODEL_ID"),
+            "llm_base_url": os.getenv("LLM_BASE_URL"),
+            "lmstudio_base_url": os.getenv("LMSTUDIO_BASE_URL"),
+            "ollama_base_url": os.getenv("OLLAMA_BASE_URL"),
+            "max_web_research_loops": os.getenv("MAX_WEB_RESEARCH_LOOPS"),
+            "fetch_full_page": os.getenv("FETCH_FULL_PAGE"),
+            "strip_thinking_tokens": os.getenv("STRIP_THINKING_TOKENS"),
+            "use_tool_calling": os.getenv("USE_TOOL_CALLING"),
+            "search_api": os.getenv("SEARCH_API"),
+            "enable_notes": os.getenv("ENABLE_NOTES"),
+            "notes_workspace": os.getenv("NOTES_WORKSPACE"),
+        }
+
+        for key, value in env_aliases.items():
+            if value is not None:
+                raw_values.setdefault(key, value)
+
+        if overrides:
+            for key, value in overrides.items():
+                if value is not None:
+                    raw_values[key] = value
+
+        return cls(**raw_values)
+```
+
+配置解析逻辑（`backend/src/deep_researcher/configuration.py:18`）先读取所有大写环境变量，再应用显式别名，最后才合并 API 请求的覆盖值。实际运行时意味着：
+
+- `.env` 或系统环境变量拥有最高优先级，便于本地调试和生产部署。
+- LangGraph/UI 提交的临时参数通过 `overrides` 注入，不会修改全局环境。
+- 字段均由 Pydantic 校验，错误值会在启动阶段被捕获，避免无效配置导致的运行时故障。
+
+### 14.2.3 自定义多源搜索工具
+
+为了适应不同团队的检索与合规要求，`helloagents-deepresearch` 的搜索层设计成可插拔结构。除了内置的 DuckDuckGo、Tavily、Perplexity、SearXNG，我们还可以像第七章那样编写自己的多源工具，再通过配置切换到 `advanced` 模式使用。建议按如下步骤操作：
+
+1. **复用示例代码**：参考 `code/chapter7/my_advanced_search.py` 中的 `MyAdvancedSearchTool` 类和 `create_advanced_search_registry()` 工厂函数。该示例演示了如何根据环境变量自动检测 Tavily、SerpApi 等后端，并在执行时做降级提示。
+2. **本地验证**：运行 `code/chapter7/test_advanced_search.py` 可以快速检查自定义工具是否按预期返回结果；若未配置任何 API key，它会输出友好的诊断信息，便于调试。
+3. **接入深度研究项目**：在 `backend/src/deep_researcher/utils.py` 中新增一个适配函数（示例实现见函数 `advanced_search`），内部直接调用你自定义的工具，再将返回的文本整理为统一的 `{"results": [...], "notices": [...]}` 结构，方便后续的去重与摘要逻辑复用。
+4. **启用新工具**：将后端配置中的 `SEARCH_API` 设置为 `advanced`，或在前端表单里选择该选项；深度研究 Agent 会自动透传工具的降级提示与直接答案，并在前端时间线中显示，确保用户知晓检索来源和失败原因。
+
+通过这种方式，你可以把任意内部/垂直领域的检索能力接入到深度研究工作流里，同时保持与核心流程的高度解耦。
+
+> 提示：配置项 `ENABLE_NOTES` 默认为 `true`。当启用时，后端会为每个任务自动同步一份 Markdown 笔记（目录由 `NOTES_WORKSPACE` 指定），并把 `note` 工具挂载到所有 Agent，方便它们在需要时对笔记做增删改查。流式事件会附带 `note_id`，前端可据此展示或跳转对应笔记。
+
+### 14.2.4 前端交互与流式反馈
+
+前端基于 Vue 3 + Vite，核心页面是 `App.vue`，其职责包括：
+
+- 通过 `runResearchStream` 调用 `/research/stream`，逐条解析 SSE 数据，并依据 `task_id` / `stream_token` 把事件路由到对应任务卡片。
+- 监听 `tool_call` 与 `report_note` 事件，实时展示笔记更新与最终报告位置。
+- 支持用户在研究进行时主动取消请求（`AbortController`），确保界面响应及时。
+- 提供表单化配置入口（迭代次数、搜索引擎选择），与后端的覆盖策略一一对应。
+
+UI 设计强调“实时性”与“可追溯性”：时间线用于说明状态变化，来源列表/历史面板展示引用详情，摘要与报告区域则会在收到 `task_summary_chunk`、`report_note` 等事件时滚动高亮，提示用户关注最新内容。
+
+```typescript
+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) {
+          const event = JSON.parse(dataPayload) as ResearchStreamEvent;
+          onEvent(event);
+
+          if (event.type === "error" || event.type === "done") {
+            return;
+          }
+        }
+      }
+
+      boundary = buffer.indexOf("\n\n");
+    }
+
+    if (done) {
+      if (buffer.trim()) {
+        const rawEvent = buffer.trim();
+        if (rawEvent.startsWith("data:")) {
+          const event = JSON.parse(rawEvent.slice(5).trim()) as ResearchStreamEvent;
+          onEvent(event);
+        }
+      }
+      break;
+    }
+  }
+}
+```
+
+这段 TypeScript 实现（`frontend/src/services/api.ts:1`）通过原生 `ReadableStream` 持续解析 SSE：
+
+- 逐块累积缓冲区并按 `\n\n` 分割事件，兼容浏览器对流式分片的差异化处理。
+- 一旦收到 `error` 或 `done` 事件便主动收尾，防止前端挂起。
+- 支持传入 `AbortController`，与界面上的“取消研究”按钮形成闭环，用户体验更加可控。
+
+![14.3 UI交互图](docs/images/14-figures/14-3.png)
+图14.3 UI交互图
+
+## 14.3 快速上手指南
+
+为了在本地体验深度研究智能体，我们建议分三步完成：准备运行环境、配置模型与搜索服务、分别启动后端与前端。以下流程基于 `code/chapter14/helloagents-deepresearch/` 目录。
+
+### 14.3.1 环境要求与依赖安装
+
+- Python ≥ 3.11（后端使用 `pyproject.toml` 配合 `uv` 或 `pip` 安装依赖）
+- Node.js ≥ 18.0（建议 20+）与 npm ≥ 9.0
+- 至少一种本地大模型服务：Ollama 或 LMStudio或API供应商
+
+推荐使用虚拟环境隔离依赖：
+
+```bash
+cd code/chapter14/helloagents-deepresearch/backend
+python -m venv .venv
+source .venv/bin/activate  
+```
+
+随后安装 Python 依赖：
+
+```bash
+uv sync  # 若未安装 uv，可先使用pip install uv 来进行安装
+```
+
+前端部分进入 `frontend` 目录执行 `npm install` 即可。
+
+### 14.3.2 配置本地模型与搜索服务
+
+后端默认读取同目录下的 `.env` 文件。参考步骤：
+
+```bash
+cp .env.example .env
+```
+
+根据自身环境设置以下核心变量：
+
+- 模型提供者：`LLM_PROVIDER=ollama`、`LLM_PROVIDER=lmstudio` 或 `LLM_PROVIDER=custom`
+- 模型名称：`LOCAL_LLM=deepseek-r1:8b`（Ollama）/`LOCAL_LLM=qwen_qwq-32b`（LMStudio）或云端场景下的 `LLM_MODEL_ID`
+- 服务地址：`OLLAMA_BASE_URL`、`LMSTUDIO_BASE_URL` 或通用 `LLM_BASE_URL`
+- 是否开启工具调用：`USE_TOOL_CALLING=true`
+- 搜索服务：`SEARCH_API=duckduckgo`、`tavily`、`perplexity`、`searxng`
+
+若选择 Tavily、Perplexity 或自建 SearXNG，请额外填写对应的 `TAVILY_API_KEY`、`PERPLEXITY_API_KEY` 或 `SEARXNG_URL`。
+
+```bash
+# backend/.env 示例片段
+SEARCH_API='duckduckgo'
+LLM_PROVIDER=custom
+LLM_MODEL_ID=your-model-name
+LLM_API_KEY=your-api-key-here
+LLM_BASE_URL=your-api-base-url
+USE_TOOL_CALLING=true
+MAX_WEB_RESEARCH_LOOPS=3
+FETCH_FULL_PAGE=True
+# 切换至本地模型时，只需启用以下注释行：
+# LLM_PROVIDER=ollama
+# LOCAL_LLM=llama3.2
+# OLLAMA_BASE_URL=http://localhost:11434
+```
+
+如 `.env.example` 所示（`backend/.env.example`），云端、自建、本地三种配置都以注释形式保留。保持模板不删，有助于在不同部署模式间快速切换。
+以下是调用 SiliconFlow API 作为大模型服务的示例环境配置：
+![14.5 SiliconFlow 环境配置](docs/images/14-figures/14-5.png)
+
+### 14.3.3 启动后端服务
+
+后端以 FastAPI 形式提供 HTTP API 与 SSE 流接口。启动方式：
+
+```bash
+cd code/chapter14/helloagents-deepresearch/backend
+uv run uvicorn deep_researcher.api:app --reload
+```
+
+成功后可访问 `http://localhost:8000/docs` 查看自动生成的 OpenAPI 文档，验证 `/research` 与 `/research/stream` 接口是否可用。
+
+![14.4 FastApi页面](docs/images/14-figures/14-4.png)
+
+### 14.3.4 启动前端与联调
+
+前端基于 Vite，默认通过 `.env` 或命令行指定后端地址：
+
+```bash
+cd code/chapter14/helloagents-deepresearch/frontend
+echo "VITE_API_BASE_URL=http://localhost:8000" > .env.local  # 可选
+npm run dev
+```
+
+浏览器访问 `http://localhost:5173`，输入研究主题即可观察到流式时间线、来源列表和逐渐成型的总结。若需要部署或静态打包，可使用 `npm run build` 并将输出托管至任意静态服务器，注意同步配置 `VITE_API_BASE_URL`。
+
+完成以上步骤后，一个全流程的深度研究助手便可以在本地运行。接下来我们将深入拆解关键模块的实现细节，理解为何它能稳定输出结构化的研究结果。
+
+## 14.4 关键模块拆解
+
+从代码角度理解核心模块，有助于在后续扩展或排查问题时做到心中有数。本节按流程拆解查询生成、搜索适配、总结与反思逻辑，以及流式通信协议。
+
+### 14.4.1 任务规划：SimpleAgent 的再利用
+
+`PlanningService` 把第七章的 `SimpleAgent` 模板直接升级为多任务规划器。通过 `ToolAwareSimpleAgent` 的 `run` 方法，我们既继承了原有的 Prompt 能力，也让模型得以调用 `note` 工具记录任务框架。
+
+```python
+def plan_todo_list(self, state: SummaryState) -> List[TodoItem]:
+    prompt = todo_planner_instructions.format(
+        current_date=get_current_date(),
+        research_topic=state.research_topic,
+    )
+
+    response = self._agent.run(prompt)
+    self._agent.clear_history()
+
+    tasks_payload = self._extract_tasks(response)
+    todo_items: List[TodoItem] = []
+
+    for idx, item in enumerate(tasks_payload, start=1):
+        task = TodoItem(
+            id=idx,
+            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(),
+        )
+        todo_items.append(task)
+
+    state.todo_items = todo_items
+    return todo_items
+```
+
+（`backend/src/deep_researcher/app/services/planner_service.py:26`）
+
+`_extract_tasks` 优先解析 JSON，再兼容 `[TOOL_CALL:note:{...}]` 形式，这与我们在第九章使用 `NoteTool` 的方式完全一致：模型既可以一次输出结构化数组，也可以显式调用工具创建任务笔记。规划阶段只负责生成骨架，笔记的后续更新会在每个任务线程中完成。
+
+### 14.4.2 搜索执行与上下文整合
+
+`search_service.dispatch_search` 把第七章的高级搜索工具纳入统一接口。无论是内置的 DuckDuckGo/Tavily，还是我们自定义的 `advanced` 工具（复用 `code/chapter7/my_advanced_search.py`），最终都会被整理成 `{"results": [...], "notices": [...]}` 的结构。随后 `prepare_research_context` 会调用 `deduplicate_and_format_sources`，去除重复 URL 并裁剪正文长度，确保总结阶段的上下文既全面又精简。
+
+```python
+search_result, notices, answer_text, backend = dispatch_search(
+    task.query,
+    self.config,
+    state.research_loop_count,
+)
+
+sources_summary, context = prepare_research_context(
+    search_result,
+    answer_text,
+    self.config,
+)
+```
+
+（`backend/src/deep_researcher/app/agents/deep_research_agent.py:261`）
+
+正因为我们在第七章就把搜索工具封装成可复用的注册表，现在只需切换 `SEARCH_API=advanced`，`dispatch_search` 就会自动调用自定义工具，并把降级信息通过 `notices` 推送到前端，让用户随时了解当前轮次是否触发了兜底策略。
+
+### 14.4.3 并行总结与流式输出
+
+总结阶段同样依赖 `ToolAwareSimpleAgent`，但我们重写了 `stream_run` 以支持在流式输出中执行工具：
+
+```python
+for chunk in self.llm.stream_invoke(messages, **kwargs):
+    residual += chunk
+    for segment in process_residual():
+        if not segment:
+            continue
+        segments_this_round.append(segment)
+        final_segments.append(segment)
+        yield segment
+
+for call_text in tool_call_texts:
+    tool_calls.extend(self._parse_tool_calls(call_text))
+
+if tool_calls:
+    messages.append({"role": "assistant", "content": clean_response})
+    tool_results_text = "\n\n".join(self._execute_tool_call(...))
+    messages.append({"role": "user", "content": "工具执行结果：\n..."})
+```
+
+（`backend/src/deep_researcher/app/agents/tool_aware_agent.py:243`）
+
+`process_residual` 会截断尚未完整输出的 `[TOOL_CALL:...]` 片段，确保前端永远不会看到残缺的工具指令；一旦工具执行完成，新的上下文会立即写回消息栈，继续下一轮对话。配合 `SummarizationService.stream_task_summary`，我们可以在前端实时看到任务总结的增量内容，并且在总结结束后自动拿到最终文本。
+
+### 14.4.4 笔记同步与报告沉淀
+
+`ToolCallTracker` 记录所有笔记相关事件，并在任务线程之间同步 `note_id`。当所有任务完成后，`_persist_final_report` 会把最终 Markdown 写入结论笔记：若已有同名笔记则更新，否则创建新笔记并返回 `report_note` 事件。
+
+```python
+payload = {
+    "type": "report_note",
+    "note_id": note_id,
+    "title": note_title,
+    "content": content,
+    "note_path": str(note_path),
+}
+
+yield {
+    "type": "final_report",
+    "report": report,
+    "note_id": state.report_note_id,
+    "note_path": state.report_note_path,
+}
+```
+
+（`backend/src/deep_researcher/app/agents/deep_research_agent.py:248`）
+
+这意味着前端可以在报告区域直接显示笔记路径，甚至提供“一键打开 Markdown” 的快捷入口；而后端的数据结构中也会记录 `report_note_id`，便于二次加工或测试校验。
+
+### 14.4.5 SSE 事件协议升级
+
+随着并行化与笔记沉淀的引入，SSE 事件类型也进行了扩展：
+
+- `todo_list`：包含任务清单以及每个任务的 `stream_token`。
+- `task_status` / `task_summary_chunk` / `sources`：逐任务输出状态、总结增量与来源摘要。
+- `tool_call`：复用第九章的事件格式，记录模型执行 `note` 等工具的详情。
+- `report_note`：最终报告落盘后的通知，附带 `note_id`、`note_path` 与正文内容。
+- `final_report` / `done`：宣告流程结束，并把最终 Markdown 与笔记信息一起返回。
+
+前端 `runResearchStream` 只需在事件分发器中新增对 `report_note` 的处理，即可在 UI 中同步展示最新笔记。由于每条事件都包含 `task_id` 与 `stream_token`，即使多个任务并行输出，也能稳定位于正确的面板。
+
+掌握以上模块，对于定制 Prompt、扩展企业内部检索源或构建新的可视化界面都有直接帮助。下一节我们将讨论可能的扩展方向与实践建议。