弃用langgrpah更换deepagent

src/server/deep_agent/agents/init_llm.py

@@ -0,0 +1,23 @@
+from langchain_qwq import ChatQwen
+
+from src.core.config import settings
+
+llm = ChatQwen(
+    model="qwen3.5-flash",
+    max_tokens=3_000,
+    timeout=None,
+    max_retries=2,
+    enable_thinking=False,
+    api_key=settings.QWEN_API_KEY
+)
+
+title_llm = ChatQwen(
+    model="qwen-plus",
+    max_tokens=3_000,
+    timeout=None,
+    max_retries=2,
+    streaming=False,
+    temperature=0.1,
+    top_p=0.8,
+    api_key=settings.QWEN_API_KEY
+)

src/server/deep_agent/agents/main_agent.py

@@ -0,0 +1,51 @@
+from pathlib import Path
+
+from deepagents import create_deep_agent
+from deepagents.backends import FilesystemBackend
+from langchain.agents.middleware import SummarizationMiddleware
+from langgraph.checkpoint.mongodb import MongoDBSaver
+from langgraph.checkpoint.serde.jsonplus import JsonPlusSerializer
+from pymongo import MongoClient
+
+from src.core.config import MONGO_URI
+from src.server.deep_agent.agents.painter import painter_subagent
+from src.server.deep_agent.agents.researcher import research_subagent
+from src.server.deep_agent.agents.user_profile import user_profile_subagent
+from src.server.deep_agent.init_prompt import build_system_prompt
+from src.server.deep_agent.tools.report_generator_tool import llm
+
+TOOL_DIR = Path(__file__).resolve().parent
+PROJECT_ROOT = TOOL_DIR.parent
+client = MongoClient(MONGO_URI)
+checkpointer = MongoDBSaver(
+    client=client["furniture_agent_db"],
+    db_name="fida_agent_db",
+    collection_name="fida_agent_collection",
+    serde=JsonPlusSerializer(pickle_fallback=True),  # ← 關鍵這一行
+)
+subagents = [
+    painter_subagent,
+    research_subagent,
+    user_profile_subagent
+]
+
+
+def build_main_agent(use_report):
+    main_agent = create_deep_agent(
+        model=llm,
+        system_prompt=build_system_prompt(use_report=use_report),
+        subagents=subagents,
+        checkpointer=checkpointer,
+        backend=FilesystemBackend(
+            root_dir=str(PROJECT_ROOT / "agent_workspace"),
+            virtual_mode=False,  # 重要：關掉虛擬模式 → 真的寫硬碟
+        ),
+        middleware=[
+            SummarizationMiddleware(
+                model=llm,
+                trigger=("tokens", 3000),
+                keep=("messages", 100),
+            ),
+        ],
+    )
+    return main_agent

src/server/deep_agent/agents/painter.py

@@ -0,0 +1,22 @@
+from langchain.agents.middleware import wrap_tool_call
+
+from src.server.deep_agent.agents.init_llm import llm
+from src.server.deep_agent.init_prompt import build_painter_prompt
+from src.server.deep_agent.tools.generate_furniture_sketch import generate_furniture
+
+
+@wrap_tool_call
+async def log_tool_calls(request, handler):
+    """Intercept and log every tool call - demonstrates cross-cutting concern."""
+    print(request)
+    return handler(request)
+
+
+painter_subagent = {
+    "name": "painter_subagent",
+    "description": "理解用户意图，使用prompt,调用generate_furniture工具生成家具sketch草图.",
+    "system_prompt": build_painter_prompt(),
+    "tools": [generate_furniture],
+    "model": llm,
+    # "middleware": [log_tool_calls],
+}

src/server/deep_agent/agents/researcher.py

@@ -0,0 +1,21 @@
+from src.server.deep_agent.agents.init_llm import llm
+from src.server.deep_agent.init_prompt import build_researcher_prompt
+from src.server.deep_agent.tools.crawl_tool import crawl4ai_batch
+from src.server.deep_agent.tools.report_generator_tool import report_generator
+from src.server.deep_agent.tools.research_tool import topic_research
+from src.server.deep_agent.tools.structured_retrieval_tool import structured_retrieval
+from src.server.deep_agent.tools.user_persona_tool import query_report_profile
+
+research_subagent = {
+    "name": "research-agent",
+    "description": "通过网络搜索对家具设计开展深度研究并整合结论",
+    "system_prompt": build_researcher_prompt(),
+    "tools": [
+        query_report_profile,
+        topic_research,
+        crawl4ai_batch,
+        structured_retrieval,
+        report_generator
+    ],
+    "model": llm
+}

src/server/deep_agent/agents/user_profile.py

@@ -0,0 +1,15 @@
+from src.server.deep_agent.agents.init_llm import llm
+from src.server.deep_agent.init_prompt import build_user_persona_prompt
+from src.server.deep_agent.tools.user_persona_tool import query_report_profile, update_report_profile, check_profile_complete
+
+user_profile_subagent = {
+    "name": "user_profile_subagent",
+    "description": "收集用户报告画像并存储到MongoDB",
+    "system_prompt": build_user_persona_prompt(),
+    "model": llm,
+    "tools": [
+        query_report_profile,
+        update_report_profile,
+        check_profile_complete,
+    ],
+}