diff --git a/src/server/deep_agent/tools/research_tool.py b/src/server/deep_agent/tools/research_tool.py
index 49fa09c..0e63eea 100755
--- a/src/server/deep_agent/tools/research_tool.py
+++ b/src/server/deep_agent/tools/research_tool.py
@@ -1,67 +1,152 @@
+# import asyncio
+# import json
+# from datetime import datetime
+# from typing import List, Set, Optional
+# from langchain_core.tools import tool
+# from tavily import TavilyClient
+#
+# from src.core.config import settings
+#
+# # 模拟配置加载
+# TAVILY_API_KEY = settings.TAVILY_API_KEY
+#
+#
+# @tool
+# async def topic_research(topic: list[str], max_urls: int = 5) -> str:
+#     """
+#     深度调研工具。该工具会利用 Tavily 搜索引擎针对特定主题进行多维度搜索。
+#     它会自动生成针对性的搜索词（包含年份和趋势），并返回去重后的高质量 URL 列表。
+#     """
+#     if not TAVILY_API_KEY:
+#         return "❌ 错误: 未配置 TAVILY_API_KEY。"
+#
+#     client = TavilyClient(api_key=TAVILY_API_KEY)
+#
+#     # 1. 自动生成多维度搜索词 (在工具内部快速生成)
+#
+#     # 2. 并行执行搜索
+#     async def perform_search(q: str):
+#         # 使用 asyncio.to_thread 运行同步的 Tavily SDK
+#         def sync_search():
+#             try:
+#                 response = client.search(
+#                     query=q,
+#                     search_depth="advanced",
+#                     max_results=5,
+#                     include_answer=False
+#                 )
+#                 return response.get('results', [])
+#             except Exception as e:
+#                 print(f"Search error: {e}")
+#                 return []
+#
+#         return await asyncio.to_thread(sync_search)
+#
+#     search_tasks = [perform_search(q) for q in topic]
+#     search_results_list = await asyncio.gather(*search_tasks)
+#
+#     # 3. 结果去重与过滤
+#     seen_urls: Set[str] = set()
+#     final_urls = []
+#
+#     # 常见的非内容页面过滤
+#     skip_extensions = ('.pdf', '.jpg', '.png', '.zip', '.exe')
+#
+#     for results in search_results_list:
+#         for item in results:
+#             url = item.get('url')
+#             if url and url not in seen_urls:
+#                 if not url.lower().endswith(skip_extensions):
+#                     seen_urls.add(url)
+#                     final_urls.append(url)
+#
+#     # 4. 结果截断
+#     selected_urls = final_urls[:max_urls]
+#
+#     # 返回 JSON 字符串，便于 Agent 下一步调用批量爬虫 (Crawl4ai)
+#     return json.dumps(selected_urls, ensure_ascii=False)
+#
+
+
 import asyncio
 import json
-from datetime import datetime
-from typing import List, Set, Optional
-from langchain_core.tools import tool
-from tavily import TavilyClient
+from typing import List, Set
 
-from src.core.config import settings
-
-# 模拟配置加载
-TAVILY_API_KEY = settings.TAVILY_API_KEY
+from ddgs import DDGS
+from langchain.tools import tool
 
 
 @tool
-async def topic_research(topic: list[str], max_urls: int = 5) -> str:
+async def topic_research(topic: List[str], max_urls: int = 5) -> str:
     """
-    深度调研工具。该工具会利用 Tavily 搜索引擎针对特定主题进行多维度搜索。
-    它会自动生成针对性的搜索词（包含年份和趋势），并返回去重后的高质量 URL 列表。
+    深度调研工具（DuckDuckGo版本）。
+    根据多个主题关键词进行搜索，返回去重后的高质量 URL 列表（JSON字符串）。
     """
-    if not TAVILY_API_KEY:
-        return "❌ 错误: 未配置 TAVILY_API_KEY。"
 
-    client = TavilyClient(api_key=TAVILY_API_KEY)
-
-    # 1. 自动生成多维度搜索词 (在工具内部快速生成)
-
-    # 2. 并行执行搜索
-    async def perform_search(q: str):
-        # 使用 asyncio.to_thread 运行同步的 Tavily SDK
-        def sync_search():
-            try:
-                response = client.search(
-                    query=q,
-                    search_depth="advanced",
-                    max_results=5,
-                    include_answer=False
+    # DuckDuckGo 是同步库，需要丢到线程池
+    def sync_search(query: str):
+        try:
+            with DDGS() as ddgs:
+                results = ddgs.text(
+                    query,
+                    max_results=8  # 稍微多一点，后面会过滤
                 )
-                return response.get('results', [])
-            except Exception as e:
-                print(f"Search error: {e}")
-                return []
+                return [r.get("href") for r in results if r.get("href")]
+        except Exception as e:
+            print(f"Search error: {e}")
+            return []
 
-        return await asyncio.to_thread(sync_search)
+    async def perform_search(q: str):
+        return await asyncio.to_thread(sync_search, q)
 
+    # 并发执行多个 query
     search_tasks = [perform_search(q) for q in topic]
     search_results_list = await asyncio.gather(*search_tasks)
 
-    # 3. 结果去重与过滤
+    # ========================
+    # 去重 + 过滤
+    # ========================
     seen_urls: Set[str] = set()
     final_urls = []
 
-    # 常见的非内容页面过滤
     skip_extensions = ('.pdf', '.jpg', '.png', '.zip', '.exe')
 
     for results in search_results_list:
-        for item in results:
-            url = item.get('url')
-            if url and url not in seen_urls:
-                if not url.lower().endswith(skip_extensions):
-                    seen_urls.add(url)
-                    final_urls.append(url)
+        for url in results:
+            if not url:
+                continue
 
-    # 4. 结果截断
+            if url not in seen_urls and not url.lower().endswith(skip_extensions):
+                seen_urls.add(url)
+                final_urls.append(url)
+
+    # ========================
+    # 截断结果
+    # ========================
     selected_urls = final_urls[:max_urls]
-
-    # 返回 JSON 字符串，便于 Agent 下一步调用批量爬虫 (Crawl4ai)
+    print(f" topic research !!!!!!!!!!!!!!!!!!!!! {selected_urls}")
     return json.dumps(selected_urls, ensure_ascii=False)
+
+
+# import asyncio
+#
+#
+# # 假设你已经定义了 topic_research
+#
+# async def test():
+#     topics = [
+#         "modern furniture design trends 2025",
+#         "scandinavian furniture materials",
+#         "minimalist living room furniture ideas"
+#     ]
+#
+#     result = await topic_research.ainvoke({
+#         "topic": topics,
+#         "max_urls": 5
+#     })
+#
+#     print("结果👇")
+#     print(result)
+#
+#
+# asyncio.run(test())