{"id":121957,"date":"2026-11-04T02:24:50","date_gmt":"2026-11-03T18:24:50","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/121957.html"},"modified":"2026-11-04T02:24:50","modified_gmt":"2026-11-03T18:24:50","slug":"%e3%80%8ahello-agents%e3%80%8b%e7%ac%ac%e5%9b%9b%e8%87%b3%e5%85%ad%e7%ab%a0-%e6%a0%b8%e5%bf%83%e7%9f%a5%e8%af%86-%e6%a2%b3%e7%90%86%e6%80%bb%e7%bb%93","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/121957.html","title":{"rendered":"\u300aHello-Agents\u300b\u7b2c\u56db\u81f3\u516d\u7ae0 \u6838\u5fc3\u77e5\u8bc6 \u68b3\u7406\u603b\u7ed3"},"content":{"rendered":"

\u8303\u56f4：\u7b2c\u4e8c\u90e8\u5206\u300c\u667a\u80fd\u4f53\u6784\u5efa\u5b9e\u8df5\u300d｜PDF p82\u2013205（\u7b2c\u56db\u7ae0 p82\u2013114\u3001\u7b2c\u4e94\u7ae0 p115\u2013170\u3001\u7b2c\u516d\u7ae0 p171\u2013205）<\/p>\n

\n

\u5168\u5c40\u77e5\u8bc6\u5730\u56fe：\u8fd9\u4e09\u7ae0\u5728\u8bb2\u4ec0\u4e48<\/h3>\n\n\u7ae0\u56de\u7b54\u7684\u6838\u5fc3\u95ee\u9898\u4e0e\u524d\u540e\u7ae0\u8282\u7684\u5173\u7cfb<\/tr>\n\n\n\n\n
\u7b2c\u56db\u7ae0<\/td>\n\u4e09\u79cd\u7ecf\u5178\u8303\u5f0f\u600e\u4e48\u4ece\u96f6\u624b\u5199\u51fa\u6765<\/td>\n\u76f4\u63a5\u843d\u5730\u7b2c 1 \u7ae0\u7684 Agent Loop \u4e0e T-A-O \u534f\u8bae；\u662f\u7b2c 7 \u7ae0\u81ea\u5efa\u6846\u67b6\u3001\u7b2c 10 \u7ae0\u591a\u667a\u80fd\u4f53\u7684\u8303\u5f0f\u6765\u6e90<\/td>\n<\/tr>\n
\u7b2c\u4e94\u7ae0<\/td>\n\u4e0d\u5199\u4ee3\u7801\u80fd\u4e0d\u80fd\u66f4\u5feb\u642d\u51fa\u667a\u80fd\u4f53<\/td>\n\u4e0e\u7b2c\u56db\u7ae0\u6784\u6210"\u624b\u5199 vs \u5e73\u53f0"\u7684\u4e92\u8865\u89c6\u89d2；\u7ed3\u8bba\u662f\u6df7\u5408\u5f00\u53d1<\/td>\n<\/tr>\n
\u7b2c\u516d\u7ae0<\/td>\n\u7528\u6210\u719f\u6846\u67b6\u600e\u4e48\u89c4\u8303\u5730\u6784\u5efa<\/td>\n\u4e0e\u7b2c\u56db\u7ae0\u6784\u6210"\u624b\u5199 vs \u6846\u67b6"\u7684\u5bf9\u7167；\u76f4\u63a5\u94fa\u57ab\u7b2c 7 \u7ae0\u300c\u6784\u5efa\u4f60\u7684\u667a\u80fd\u4f53\u6846\u67b6\u300d<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\u4e00\u53e5\u8bdd\u4e32\u8054：\u5148\u624b\u5199\u4e09\u79cd\u8303\u5f0f\u641e\u61c2\u539f\u7406（\u7b2c\u56db\u7ae0）\u2192 \u518d\u7528\u4f4e\u4ee3\u7801\u5e73\u53f0\u63d0\u901f（\u7b2c\u4e94\u7ae0）\u2192 \u518d\u56de\u5230\u4e13\u4e1a\u6846\u67b6\u89e3\u51b3\u591a\u667a\u80fd\u4f53\u4e0e\u590d\u6742\u6d41\u7a0b（\u7b2c\u516d\u7ae0）\u2192 \u6700\u7ec8\u81ea\u5df1\u9020\u4e00\u4e2a\u6846\u67b6（\u7b2c\u4e03\u7ae0）\u3002<\/p>\n

\u8d2f\u7a7f\u4e09\u7ae0\u7684\u4e00\u6761\u6697\u7ebf：\u7b2c\u56db\u7ae0\u66b4\u9732\u7684\u5de5\u7a0b\u75db\u70b9（\u8f93\u51fa\u89e3\u6790\u3001\u5de5\u5177\u5931\u8d25\u91cd\u8bd5\u3001\u9632\u6b7b\u5faa\u73af\u3001\u72b6\u6001\u7ba1\u7406\u3001\u53ef\u89c2\u6d4b\u6027），\u6b63\u662f\u7b2c\u4e94\u7ae0\u5e73\u53f0\u4e0e\u7b2c\u516d\u7ae0\u6846\u67b6\u5b58\u5728\u7684\u7406\u7531\u3002<\/p>\n

\n

\u524d\u7f6e\u4e3b\u7ebf\u56de\u987e（\u8854\u63a5\u7b2c 1\u20133 \u7ae0）<\/h3>\n

\u7b2c\u56db\u7ae0\u662f\u524d\u4e09\u7ae0\u7684\u5b9e\u6218\u843d\u5730，\u8fdb\u5165\u524d\u9700\u8981\u5e26\u4e0a\u7684\u56db\u5757\u77e5\u8bc6：<\/p>\n

  • \u667a\u80fd\u4f53\u5b9a\u4e49\u4e0e\u5206\u7c7b：\u667a\u80fd\u4f53 = \u901a\u8fc7\u4f20\u611f\u5668\u611f\u77e5\u73af\u5883\u3001\u901a\u8fc7\u6267\u884c\u5668\u81ea\u4e3b\u884c\u52a8\u7684\u5b9e\u4f53；"\u667a\u80fd"\u6765\u81ea\u81ea\u4e3b\u6027\u800c\u975e\u7b97\u529b\u3002\u5206\u7c7b\u4e09\u7ef4\u5ea6\u2014\u2014\u51b3\u7b56\u67b6\u6784（\u53cd\u5c04 \/ \u57fa\u4e8e\u6a21\u578b \/ \u57fa\u4e8e\u76ee\u6807 \/ \u57fa\u4e8e\u6548\u7528 \/ \u5b66\u4e60\u578b）\u3001\u65f6\u95f4\u4e0e\u53cd\u5e94\u6027（\u53cd\u5e94\u5f0f \/ \u89c4\u5212\u5f0f \/ \u6df7\u5408\u5f0f）\u3001\u77e5\u8bc6\u8868\u793a（\u7b26\u53f7 \/ \u4e9a\u7b26\u53f7 \/ \u795e\u7ecf\u7b26\u53f7）\u3002\u73b0\u4ee3 LLM \u667a\u80fd\u4f53\u662f\u6df7\u5408\u5f0f + \u795e\u7ecf\u7b26\u53f7\u4e3b\u4e49\u7684\u5b9e\u8df5：\u795e\u7ecf\u7f51\u7edc\u63d0\u4f9b\u76f4\u89c9，\u7ed3\u6784\u5316\u7684 Thought\/Plan\/Tool Call \u63d0\u4f9b\u53ef\u8ffd\u6eaf\u7684\u7406\u6027\u3002<\/li>\n
  • \u6838\u5fc3\u7ec4\u4ef6\u4e0e\u8fd0\u884c\u5faa\u73af：Agent Loop = \u611f\u77e5 \u2192 \u601d\u8003（\u89c4\u5212 + \u5de5\u5177\u9009\u62e9）\u2192 \u884c\u52a8 \u2192 \u89c2\u5bdf \u2192 \u5faa\u73af；\u5bf9\u5916\u8868\u73b0\u4e3a Thought \/ Action \/ Observation \u4e09\u6bb5\u5f0f\u534f\u8bae\u3002<\/li>\n
  • LLM \u7684\u80fd\u529b\u8fb9\u754c\u4e0e\u5c40\u9650：\u672c\u8d28\u662f"\u9884\u6d4b\u4e0b\u4e00\u4e2a\u8bcd"\u7684\u81ea\u56de\u5f52\u6a21\u578b，\u65e0\u5185\u7f6e\u4e8b\u5b9e\u6838\u67e5\u6a21\u5757 \u2192 \u5e7b\u89c9；\u77e5\u8bc6\u6709\u65f6\u6548\u622a\u6b62；\u8fd8\u6709\u504f\u89c1\u3002\u8fd9\u51b3\u5b9a\u4e86\u5de5\u5177\u3001RAG\u3001Reflection \u4e09\u7c7b\u8865\u6551\u624b\u6bb5\u7684\u5fc5\u8981\u6027\u3002<\/li>\n
  • Prompt \u5de5\u7a0b\u5173\u952e\u539f\u5219（\u4e09\u7ae0\u53cd\u590d\u5728\u7528）：\u89d2\u8272\u8bbe\u5b9a \u2192 \u4efb\u52a1\u63cf\u8ff0 \u2192 \u683c\u5f0f\u89c4\u7ea6（\u5f3a\u5236\u7ed3\u6784\u5316\u8f93\u51fa）\u2192 \u52a8\u6001\u4e0a\u4e0b\u6587\u6ce8\u5165 \u2192 \u7ec8\u6b62\u6761\u4ef6；\u8c03\u8bd5\u65f6\u901a\u5e38\u628a temperature \u8bbe\u4e3a 0 \u4fdd\u8bc1\u786e\u5b9a\u6027；\u683c\u5f0f\u4e0d\u7a33\u5b9a\u65f6\u52a0 few-shot \u793a\u4f8b\u3002<\/li>\n
    \n

    \u7b2c\u56db\u7ae0 \u667a\u80fd\u4f53\u7ecf\u5178\u8303\u5f0f\u6784\u5efa（\u91cd\u70b9）<\/h2>\n

    4.1 \u73af\u5883\u51c6\u5907\u4e0e\u57fa\u7840\u5de5\u5177\u5b9a\u4e49<\/h3>\n

    4.1.1 \u73af\u5883\u4e0e\u5bc6\u94a5<\/h4>\n
      \n
    • \u4f9d\u8d56：openai（LLM \u8c03\u7528）+ python-dotenv（\u5bc6\u94a5\u7ba1\u7406）；Python 3.10+\u3002<\/li>\n
    • \u5bc6\u94a5\u4e0e\u6a21\u578b\u4fe1\u606f\u7edf\u4e00\u653e .env，\u4ee3\u7801\u81ea\u52a8\u52a0\u8f7d：LLM_API_KEY \/ LLM_MODEL_ID \/ LLM_BASE_URL\u3002<\/li>\n<\/ul>\n

      4.1.2 \u5c01\u88c5 LLM \u5ba2\u6237\u7aef：HelloAgentsLLM<\/h4>\n

      class<\/span> HelloAgentsLLM<\/span>:<\/span> # \u4f18\u5148\u7528\u6784\u9020\u53c2\u6570，\u7f3a\u5931\u5219\u56de\u843d\u5230\u73af\u5883\u53d8\u91cf<\/span>
      \n def<\/span> __init__<\/span>(<\/span>self,<\/span> model=<\/span>None<\/span>,<\/span> apiKey=<\/span>None<\/span>,<\/span> baseUrl=<\/span>None<\/span>,<\/span> timeout=<\/span>None<\/span>)<\/span>:<\/span> .<\/span>.<\/span>.<\/span>
      \n def<\/span> think<\/span>(<\/span>self,<\/span> messages:<\/span> List[<\/span>Dict[<\/span>str<\/span>,<\/span> str<\/span>]<\/span>]<\/span>,<\/span> temperature=<\/span>0<\/span>)<\/span> –<\/span>><\/span> str<\/span>:<\/span> .<\/span>.<\/span>.<\/span><\/p>\n

      \u8bbe\u8ba1\u8981\u70b9：<\/p>\n

        \n
      • \u7edf\u4e00\u5165\u53e3：\u6240\u6709\u8303\u5f0f\u53ea\u4f9d\u8d56 think(messages) -> str \u4e00\u4e2a\u65b9\u6cd5，\u6362\u6a21\u578b\/\u6362\u670d\u52a1\u5546\u90fd\u4e0d\u6539\u4e0a\u5c42\u903b\u8f91（\u4f9d\u8d56\u5012\u7f6e）\u3002<\/li>\n
      • \u9ed8\u8ba4\u6d41\u5f0f：stream=True \u9010 chunk \u6536\u96c6\u540e\u62fc\u63a5\u6210\u5b8c\u6574\u5b57\u7b26\u4e32；\u9519\u8bef\u65f6\u8fd4\u56de None \u800c\u975e\u629b\u5f02\u5e38，\u7531\u4e0a\u5c42\u51b3\u5b9a\u662f\u5426\u91cd\u8bd5\u6216\u964d\u7ea7\u3002<\/li>\n
      • \u5f3a\u6821\u9a8c：model \/ apiKey \/ baseUrl \u4e09\u8005\u7f3a\u4e00\u5373\u629b ValueError，\u4e0d\u8ba9\u7cfb\u7edf\u5e26\u75c5\u542f\u52a8\u3002<\/li>\n
      • \u9ed8\u8ba4 temperature=0：\u4fdd\u8bc1\u8f93\u51fa\u786e\u5b9a\u6027（\u5bf9\u5e94\u7b2c\u4e09\u7ae0\u91c7\u6837\u53c2\u6570\u4e09\u6863）\u3002<\/li>\n<\/ul>\n

        4.1.3 \u5de5\u5177\u7684\u5b9a\u4e49\u4e0e\u7ba1\u7406<\/h4>\n

        \u5de5\u5177\u7684\u4e09\u8981\u7d20（\u2b50 \u8fd9\u662f\u7b2c\u56db\u7ae0\u6700\u503c\u5f97\u8bb0\u4f4f\u7684\u4e00\u6761）：<\/p>\n\n\u8981\u7d20\u4f5c\u7528<\/tr>\n\n\n\n\n
        \u540d\u79f0（Name）<\/td>\n\u7b80\u6d01\u552f\u4e00\u7684\u6807\u8bc6\u7b26，\u4f9b Action \u8c03\u7528，\u5982 Search<\/td>\n<\/tr>\n
        \u63cf\u8ff0（Description）<\/td>\n\u81ea\u7136\u8bed\u8a00\u8bf4\u660e\u7528\u9014 \u2014\u2014 \u6574\u4e2a\u673a\u5236\u4e2d\u6700\u5173\u952e\u7684\u90e8\u5206，LLM \u9760\u5b83\u5224\u65ad\u4f55\u65f6\u8be5\u7528\u54ea\u4e2a\u5de5\u5177<\/td>\n<\/tr>\n
        \u6267\u884c\u903b\u8f91（Execution Logic）<\/td>\n\u771f\u6b63\u5e72\u6d3b\u7684\u51fd\u6570<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

        \u5de5\u5177\u6267\u884c\u5668 ToolExecutor：\u7ef4\u62a4 {\u5de5\u5177\u540d: {description, func}} \u5b57\u5178，\u63d0\u4f9b registerTool（\u6ce8\u518c，\u91cd\u540d\u8b66\u544a\u5e76\u8986\u76d6）\u3001getTool（\u6309\u540d\u53d6\u51fd\u6570）\u3001getAvailableTools（\u62fc\u6210\u683c\u5f0f\u5316\u5b57\u7b26\u4e32\u6ce8\u5165\u63d0\u793a\u8bcd）\u3002<\/p>\n

        \u672c\u7ae0\u7684\u5b9e\u6218\u5de5\u5177 search（\u57fa\u4e8e SerpApi） \u7684\u5de5\u7a0b\u7ec6\u8282，\u662f"\u5de5\u5177\u8be5\u600e\u4e48\u5199"\u7684\u8303\u672c：<\/p>\n

          \n
        • \u4f18\u5148\u89e3\u6790 answer_box \/ knowledge_graph \u7b49\u6700\u7cbe\u786e\u7684\u7ed3\u679c，\u6ca1\u6709\u624d\u9000\u56de\u524d 3 \u6761\u6709\u673a\u7ed3\u679c\u6458\u8981 \u2014\u2014 \u8fd9\u53eb\u667a\u80fd\u89e3\u6790，\u76f4\u63a5\u63d0\u9ad8\u5582\u7ed9 LLM \u7684\u4fe1\u606f\u8d28\u91cf\u3002<\/li>\n
        • \u5185\u90e8 try\/except \u515c\u4f4f\u5f02\u5e38，\u628a\u9519\u8bef\u8f6c\u6210\u5b57\u7b26\u4e32\u8fd4\u56de（\u5982 "\u641c\u7d22\u65f6\u53d1\u751f\u9519\u8bef: …"），\u56e0\u4e3a\u8fd4\u56de\u7684\u6587\u672c\u4f1a\u4f5c\u4e3a Observation \u8fdb\u5165\u4e0b\u4e00\u8f6e，\u8ba9\u667a\u80fd\u4f53\u81ea\u6211\u7ea0\u6b63\u3002<\/li>\n<\/ul>\n

          4.2 ReAct（Reasoning and Acting）<\/h3>\n

          ReAct（Yao et al., ICLR 2023）\u8303\u5f0f：\u662f\u6700\u7ecf\u5178\u7684\u4e00\u4e2a\u667a\u80fd\u4f53\u8303\u5f0f，\u628a**\u63a8\u7406（Reasoning）\u4e0e\u884c\u52a8（Acting）**\u663e\u5f0f\u7ed3\u5408，\u5f62\u6210"\u601d\u8003\u2014\u884c\u52a8\u2014\u89c2\u5bdf"\u5faa\u73af，\u8fb9\u60f3\u8fb9\u505a\u3001\u52a8\u6001\u8c03\u6574\u3002<\/p>\n

          4.2.1 \u4e3a\u4ec0\u4e48\u9700\u8981\u5b83<\/h4>\n
            \n
          • \u7eaf\u601d\u8003\u578b（\u5982\u601d\u7ef4\u94fe CoT）：\u80fd\u63a8\u7406，\u4f46\u65e0\u6cd5\u4e0e\u5916\u90e8\u4e16\u754c\u4ea4\u4e92 \u2192 \u5bb9\u6613\u4ea7\u751f\u4e8b\u5b9e\u5e7b\u89c9\u3002<\/li>\n
          • \u7eaf\u884c\u52a8\u578b：\u76f4\u63a5\u8f93\u51fa\u52a8\u4f5c，\u4f46\u7f3a\u4e4f\u89c4\u5212\u4e0e\u7ea0\u9519\u80fd\u529b\u3002<\/li>\n
          • ReAct \u7684\u6d1e\u89c1：\u601d\u8003\u4e0e\u884c\u52a8\u76f8\u8f85\u76f8\u6210 \u2014\u2014 \u601d\u8003\u6307\u5bfc\u884c\u52a8，\u884c\u52a8\u7684\u7ed3\u679c\u53cd\u8fc7\u6765\u4fee\u6b63\u601d\u8003\u3002<\/li>\n<\/ul>\n

            \u5f62\u5f0f\u5316\u8868\u8fbe（\u53ea\u8bb0\u542b\u4e49，\u4e0d\u8bb0\u7b26\u53f7）：\u5728\u7b2c tt<\/span><\/span>t<\/span><\/span><\/span><\/span><\/span> \u6b65，\u7b56\u7565 \u03c0\\\\pi<\/span><\/span>\u03c0<\/span><\/span><\/span><\/span><\/span>（\u5373 LLM）\u6839\u636e\u521d\u59cb\u95ee\u9898 qq<\/span><\/span>q<\/span><\/span><\/span><\/span><\/span> \u4e0e\u5386\u53f2\u6240\u6709（\u884c\u52a8, \u89c2\u5bdf）\u5bf9，\u751f\u6210\u672c\u8f6e\u7684\u601d\u8003\u548c\u884c\u52a8；\u5de5\u5177\u6267\u884c\u884c\u52a8\u540e\u8fd4\u56de\u65b0\u89c2\u5bdf；\u628a\u65b0\u7684（\u884c\u52a8, \u89c2\u5bdf）\u5bf9\u8ffd\u52a0\u8fdb\u5386\u53f2，\u76f4\u5230\u6a21\u578b\u5728\u601d\u8003\u4e2d\u5224\u65ad\u4efb\u52a1\u5b8c\u6210\u3002
            \n\"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\"<\/p>\n

            ReAct\u7684\u5de7\u5999\u4e4b\u5904\u5728\u4e8e，\u5b83\u8ba4\u8bc6\u5230\u601d\u8003\u4e0e\u884c\u52a8\u662f\u76f8\u8f85\u76f8\u6210\u7684\u3002\u601d\u8003\u6307\u5bfc\u884c\u52a8，\u800c\u884c\u52a8\u7684\u7ed3\u679c\u53c8\u53cd\u8fc7\u6765\u4fee\u6b63\u601d\u8003\u3002\u4e3a\u6b64，ReAct\u8303\u5f0f\u901a\u8fc7\u4e00\u79cd\u7279\u6b8a\u7684\u63d0\u793a\u5de5\u7a0b\u6765\u5f15\u5bfc\u6a21\u578b，\u4f7f\u5176\u6bcf\u4e00\u6b65\u7684\u8f93\u51fa\u90fd\u9075\u5faa\u4e00\u4e2a\u56fa\u5b9a\u7684\u8f68\u8ff9：<\/p>\n

            Thought (\u601d\u8003)： \u8fd9\u662f\u667a\u80fd\u4f53\u7684\u201c\u5185\u5fc3\u72ec\u767d\u201d\u3002\u5b83\u4f1a\u5206\u6790\u5f53\u524d\u60c5\u51b5\u3001\u5206\u89e3\u4efb\u52a1\u3001\u5236\u5b9a\u4e0b\u4e00\u6b65\u8ba1\u5212，\u6216\u8005\u53cd \u601d\u4e0a\u4e00\u6b65\u7684\u7ed3\u679c\u3002<\/p>\n

            Action (\u884c\u52a8)： \u8fd9\u662f\u667a\u80fd\u4f53\u51b3\u5b9a\u91c7\u53d6\u7684\u5177\u4f53\u52a8\u4f5c，\u901a\u5e38\u662f\u8c03\u7528\u4e00\u4e2a\u5916\u90e8\u5de5\u5177，\u4f8b\u5982 Search[\u2018\u534e\u4e3a\u6700\u65b0\u6b3e \u624b\u673a\u2019] \u3002<\/p>\n

            Observation (\u89c2\u5bdf)： \u8fd9\u662f\u6267\u884c Action \u540e\u4ece\u5916\u90e8\u5de5\u5177\u8fd4\u56de\u7684\u7ed3\u679c，\u4f8b\u5982\u641c\u7d22\u7ed3\u679c\u7684\u6458\u8981\u6216API\u7684\u8fd4\u56de \u503c\u3002<\/p>\n

            \u667a\u80fd\u4f53\u5c06\u4e0d\u65ad\u91cd\u590d\u8fd9\u4e2a Thought -> Action -> Observation \u7684\u5faa\u73af，\u5c06\u65b0\u7684\u89c2\u5bdf\u7ed3\u679c\u8ffd\u52a0\u5230\u5386\u53f2\u8bb0\u5f55\u4e2d， \u5f62\u6210\u4e00\u4e2a\u4e0d\u65ad\u589e\u957f\u7684\u4e0a\u4e0b\u6587，\u76f4\u5230\u5b83\u5728 Thought \u4e2d\u8ba4\u4e3a\u5df2\u7ecf\u627e\u5230\u4e86\u6700\u7ec8\u7b54\u6848，\u7136\u540e\u8f93\u51fa\u7ed3\u679c\u3002\u8fd9\u4e2a\u8fc7\u7a0b\u5f62\u6210 \u4e86\u4e00\u4e2a\u5f3a\u5927\u7684\u534f\u540c\u6548\u5e94：\u63a8\u7406\u4f7f\u5f97\u884c\u52a8\u66f4\u5177\u76ee\u7684\u6027，\u800c\u884c\u52a8\u5219\u4e3a\u63a8\u7406\u63d0\u4f9b\u4e86\u4e8b\u5b9e\u4f9d\u636e\u3002<\/p>\n

            ReAct\u9002\u7528\u573a\u666f：\u9700\u8981\u5916\u90e8\u77e5\u8bc6\u7684\u4efb\u52a1（\u5b9e\u65f6\u4fe1\u606f\u67e5\u8be2）\u3001\u9700\u8981\u7cbe\u786e\u8ba1\u7b97\u7684\u4efb\u52a1（\u4ea4\u7ed9\u8ba1\u7b97\u5668\u907f\u514d LLM \u7b97\u9519）\u3001\u9700\u8981\u4e0e API\/\u6570\u636e\u5e93\u4ea4\u4e92\u7684\u4efb\u52a1\u3002<\/p>\n

            4.2.2 \u63d0\u793a\u8bcd\u6a21\u677f（ReAct \u7684\u673a\u5236\u57fa\u77f3）<\/h4>\n

            \u8bf7\u6ce8\u610f，\u4f60\u662f\u4e00\u4e2a\u6709\u80fd\u529b\u8c03\u7528\u5916\u90e8\u5de5\u5177\u7684\u667a\u80fd\u52a9\u624b\u3002
            \n\u53ef\u7528\u5de5\u5177\u5982\u4e0b:<\/span>
            \n{<\/span>tools}<\/span>
            \n\u8bf7\u4e25\u683c\u6309\u7167\u4ee5\u4e0b\u683c\u5f0f\u8fdb\u884c\u56de\u5e94:<\/span>
            \nThought:<\/span> \u4f60\u7684\u601d\u8003\u8fc7\u7a0b，\u7528\u4e8e\u5206\u6790\u95ee\u9898\u3001\u62c6\u89e3\u4efb\u52a1\u548c\u89c4\u5212\u4e0b\u4e00\u6b65\u884c\u52a8\u3002
            \nAction:<\/span> \u4f60\u51b3\u5b9a\u91c7\u53d6\u7684\u884c\u52a8，\u5fc5\u987b\u662f\u4ee5\u4e0b\u683c\u5f0f\u4e4b\u4e00:<\/span>
            \n–<\/span> `{<\/span>tool_name}<\/span>[<\/span>{<\/span>tool_input}<\/span>]<\/span>`：\u8c03\u7528\u4e00\u4e2a\u53ef\u7528\u5de5\u5177\u3002
            \n–<\/span> `Finish[<\/span>\u6700\u7ec8\u7b54\u6848]<\/span>`：\u5f53\u4f60\u8ba4\u4e3a\u5df2\u7ecf\u83b7\u5f97\u6700\u7ec8\u7b54\u6848\u65f6\u3002
            \n\u73b0\u5728，\u8bf7\u5f00\u59cb\u89e3\u51b3\u4ee5\u4e0b\u95ee\u9898:<\/span>
            \nQuestion:<\/span> {<\/span>question}<\/span>
            \nHistory:<\/span> {<\/span>history}<\/span><\/p>\n

            \u56db\u90e8\u5206\u5404\u81ea\u7684\u4f5c\u7528：<\/p>\n\n\u90e8\u5206\u4f5c\u7528<\/tr>\n\n\n\n\n\n
            \u89d2\u8272\u5b9a\u4e49<\/td>\n\u201c\u6709\u80fd\u529b\u8c03\u7528\u5916\u90e8\u5de5\u5177\u7684\u667a\u80fd\u52a9\u624b\u201d，\u8bbe\u5b9a LLM \u7684\u89d2\u8272<\/td>\n<\/tr>\n
            \u5de5\u5177\u6e05\u5355 {tools}<\/td>\n\u544a\u8bc9 LLM \u5b83\u6709\u54ea\u4e9b"\u624b\u811a"<\/td>\n<\/tr>\n
            \u683c\u5f0f\u89c4\u7ea6（Thought\/Action）<\/td>\n\u2b50 \u6700\u91cd\u8981：\u5f3a\u5236\u8f93\u51fa\u7ed3\u6784\u5316，\u4ee3\u7801\u624d\u80fd\u7528\u6b63\u5219\u7cbe\u786e\u89e3\u6790\u610f\u56fe<\/td>\n<\/tr>\n
            \u52a8\u6001\u4e0a\u4e0b\u6587 {question} \/ {history}<\/td>\n\u6ce8\u5165\u539f\u59cb\u95ee\u9898\u4e0e\u4e0d\u65ad\u7d2f\u79ef\u7684\u4ea4\u4e92\u5386\u53f2<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

            4.2.3 \u6838\u5fc3\u5faa\u73af\u4e0e\u4e94\u4e2a\u5de5\u7a0b\u7ec6\u8282 \u2b50<\/h4>\n

            \u5faa\u73af\u4e3b\u5e72：\u683c\u5f0f\u5316\u63d0\u793a\u8bcd \u2192 \u8c03\u7528 LLM \u2192 \u89e3\u6790\u8f93\u51fa \u2192 \u6267\u884c\u5de5\u5177 \u2192 \u6574\u5408 Observation，\u76f4\u5230\u4efb\u52a1\u5b8c\u6210\u6216\u8fbe\u5230 max_steps\u3002<\/p>\n\n\u7ec6\u8282\u505a\u6cd5\u4e3a\u4ec0\u4e48<\/tr>\n\n\n\n\n\n\n
            \u2460 \u6700\u5927\u5faa\u73af\u6b21\u6570<\/td>\nwhile current_step < self.max_steps（\u9ed8\u8ba4 5）<\/td>\n\u2b50 \u9632\u6b7b\u5faa\u73af\u7684\u5b89\u5168\u9600，\u9632\u8d44\u6e90\u8017\u5c3d<\/td>\n<\/tr>\n
            \u2461 \u6b63\u5219\u89e3\u6790<\/td>\nThought:\\\\s*(.*?)(?=\\\\nAction:|$) \u4e0e Action:\\\\s*(.*?)$，Action \u518d\u7528 (\\\\w+)\\\\[(.*)\\\\] \u62c6\u51fa\u5de5\u5177\u540d\u4e0e\u8f93\u5165<\/td>\n\u4ece\u7eaf\u6587\u672c\u4e2d\u7cbe\u786e\u63d0\u53d6\u610f\u56fe<\/td>\n<\/tr>\n
            \u2462 \u622a\u65ad\u4e0e\u515c\u5e95<\/td>\n\u89e3\u6790\u4e0d\u51fa Action \u5c31\u6253\u5370\u8b66\u544a\u5e76\u7ec8\u6b62；\u5de5\u5177\u4e0d\u5b58\u5728\u8fd4\u56de "\u9519\u8bef:\u672a\u627e\u5230\u540d\u4e3a X \u7684\u5de5\u5177\u3002"<\/td>\n\u628a\u5931\u8d25\u8f6c\u6210\u53ef\u88ab\u6a21\u578b\u7406\u89e3\u7684\u6587\u672c<\/td>\n<\/tr>\n
            \u2463 \u5de5\u5177\u5206\u53d1<\/td>\ntool_executor.getTool(name) \u53d6\u51fd\u6570\u540e\u6267\u884c<\/td>\n\u5de5\u5177\u540d\u4e0e\u5b9e\u73b0\u89e3\u8026<\/td>\n<\/tr>\n
            \u2464 \u7ec8\u6b62\u6761\u4ef6<\/td>\n\u8bc6\u522b Finish[…] \u5e76 return \u6700\u7ec8\u7b54\u6848<\/td>\n\u660e\u786e"\u4ec0\u4e48\u65f6\u5019\u7b97\u505a\u5b8c"<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

            \u95ed\u73af\u7684\u5173\u952e\u4e00\u6b65：\u628a\u672c\u8f6e\u7684 Action: … \u4e0e Observation: … \u8ffd\u52a0\u8fdb self.history，\u4e0b\u4e00\u8f6e\u751f\u6210\u63d0\u793a\u8bcd\u65f6\u6a21\u578b\u5c31\u80fd"\u770b\u5230"\u4e0a\u4e00\u6b65\u7684\u7ed3\u679c \u2014\u2014 \u8fd9\u5c31\u662f\u7b2c 1 \u7ae0\u7684 Agent Loop \u5728\u4ee3\u7801\u91cc\u7684\u6837\u5b50\u3002<\/p>\n

            4.2.4 ReAct \u7684\u7279\u70b9\u3001\u5c40\u9650\u4e0e\u8c03\u8bd5<\/h4>\n

            \u4e09\u4e2a\u7279\u70b9：<\/p>\n

          • \u9ad8\u53ef\u89e3\u91ca\u6027：Thought \u94fe\u8ba9\u6bcf\u4e00\u6b65"\u5fc3\u8def\u5386\u7a0b"\u53ef\u89c1，\u4fbf\u4e8e\u7406\u89e3\u3001\u4fe1\u4efb\u4e0e\u8c03\u8bd5\u3002<\/li>\n
          • \u52a8\u6001\u89c4\u5212\u4e0e\u7ea0\u9519：\u8d70\u4e00\u6b65\u770b\u4e00\u6b65，\u641c\u7d22\u7ed3\u679c\u4e0d\u7406\u60f3\u5c31\u4e0b\u4e00\u6b65\u4fee\u6b63\u641c\u7d22\u8bcd\u3002<\/li>\n
          • \u5de5\u5177\u534f\u540c：LLM \u8fd0\u7b79\u5e37\u5e44（\u89c4\u5212\u63a8\u7406），\u5de5\u5177\u89e3\u51b3\u5177\u4f53\u95ee\u9898（\u641c\u7d22\u3001\u8ba1\u7b97），\u7a81\u7834\u5355\u4e00 LLM \u5728\u77e5\u8bc6\u65f6\u6548\u6027\u4e0e\u8ba1\u7b97\u51c6\u786e\u6027\u4e0a\u7684\u5c40\u9650\u3002<\/li>\n

            \u56db\u4e2a\u56fa\u6709\u5c40\u9650（\u9762\u8bd5\u5e38\u95ee）：<\/p>\n

          • \u5f3a\u4f9d\u8d56 LLM \u81ea\u8eab\u80fd\u529b：\u63a8\u7406\u3001\u6307\u4ee4\u9075\u5faa\u6216\u683c\u5f0f\u5316\u8f93\u51fa\u80fd\u529b\u4e0d\u8db3\u65f6，\u5bb9\u6613\u5728 Thought \u73af\u8282\u89c4\u5212\u9519\u8bef\u3001\u5728 Action \u73af\u8282\u683c\u5f0f\u8dd1\u504f\u3002<\/li>\n
          • \u6267\u884c\u6548\u7387：\u6bcf\u6b65\u90fd\u8981\u8c03 LLM，\u4e32\u884c\u5faa\u73af\u5e26\u6765\u9ad8\u5ef6\u8fdf\u4e0e\u9ad8\u8d39\u7528\u3002<\/li>\n
          • \u63d0\u793a\u8bcd\u8106\u5f31\u6027：\u6a21\u677f\u5fae\u5c0f\u6539\u52a8\u751a\u81f3\u7528\u8bcd\u5dee\u5f02\u90fd\u53ef\u80fd\u5f71\u54cd\u884c\u4e3a；\u5e76\u975e\u6240\u6709\u6a21\u578b\u90fd\u80fd\u7a33\u5b9a\u9075\u5faa\u683c\u5f0f\u3002<\/li>\n
          • \u53ef\u80fd\u9677\u5165\u5c40\u90e8\u6700\u4f18 \/ \u539f\u5730\u6253\u8f6c：\u6b65\u8fdb\u5f0f\u51b3\u7b56\u7f3a\u4e4f\u5168\u5c40\u957f\u8fdc\u89c4\u5212\u3002<\/li>\n

            \u4e94\u6761\u8c03\u8bd5\u6280\u5de7（\u5b9e\u7528）：<\/p>\n

          • \u8c03\u7528 LLM \u524d，\u6253\u5370\u5b8c\u6574\u63d0\u793a\u8bcd（\u542b\u6240\u6709\u5386\u53f2）\u2014\u2014\u8ffd\u6eaf\u51b3\u7b56\u6e90\u5934\u3002<\/li>\n
          • \u89e3\u6790\u5931\u8d25\u65f6，\u6253\u5370\u539f\u59cb\u672a\u5904\u7406\u6587\u672c\u2014\u2014\u533a\u5206\u662f\u6a21\u578b\u6ca1\u5b88\u683c\u5f0f\u8fd8\u662f\u6b63\u5219\u5199\u9519\u3002<\/li>\n
          • \u9a8c\u8bc1\u5de5\u5177\u7684\u8f93\u5165\u8f93\u51fa\u683c\u5f0f\u662f\u5426\u5339\u914d\u3002<\/li>\n
          • \u52a0 few-shot \u793a\u4f8b：\u5728\u63d0\u793a\u8bcd\u4e2d\u653e\u5165\u4e00\u4e24\u4e2a\u5b8c\u6574\u7684 T-A-O \u6210\u529f\u6848\u4f8b\u3002<\/li>\n
          • \u6362\u66f4\u5f3a\u6a21\u578b\u6216\u628a temperature \u8bbe\u4e3a 0\u3002<\/li>\n

            4.3 Plan-and-Solve（\u5148\u89c4\u5212，\u540e\u6267\u884c）<\/h3>\n

            Plan-and-Solve（Wang et al., 2023）\u8303\u5f0f：\u4e3a**\u89e3\u51b3\u601d\u7ef4\u94fe\u5728\u591a\u6b65\u590d\u6742\u95ee\u9898\u4e0a\u5bb9\u6613"\u504f\u79bb\u8f68\u9053"**\u800c\u751f\u3002\u5b83\u5c06\u4efb\u52a1\u5904\u7406\u660e\u786e\u5730\u5206\u4e3a\u4e24\u4e2a\u9636\u6bb5：\u5148\u89c4\u5212 (Plan)，\u540e\u6267\u884c (Solve)\u3002
            \n\u7c7b\u6bd4：ReAct \u50cf\u4fa6\u63a2，\u636e\u73b0\u573a\u7ebf\u7d22\u4e00\u6b65\u6b65\u63a8\u7406\u968f\u65f6\u8c03\u6574；Plan-and-Solve \u50cf\u5efa\u7b51\u5e08，\u5148\u7ed8\u84dd\u56fe\u518d\u4e25\u683c\u65bd\u5de5\u3002\u8bb8\u591a\u5927\u6a21\u578b\u7684 Agent \u6a21\u5f0f\u90fd\u878d\u5165\u4e86\u8fd9\u4e00\u8bbe\u8ba1\u3002<\/p>\n

            4.3.1 \u4e24\u9636\u6bb5\u5de5\u4f5c\u6d41<\/h4>\n

            \"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\"<\/p>\n\n\u9636\u6bb5\u505a\u4ec0\u4e48\u4ea7\u7269<\/tr>\n\n\n\n
            \u89c4\u5212\u9636\u6bb5（Planning）<\/td>\n\u63a5\u6536\u5b8c\u6574\u95ee\u9898，\u5148\u5206\u89e3\u95ee\u9898\u5e76\u5236\u5b9a\u5206\u6b65\u9aa4\u884c\u52a8\u8ba1\u5212（\u672c\u8eab\u4e5f\u662f\u4e00\u6b21 LLM \u8c03\u7528\u7684\u4ea7\u7269）<\/td>\n\u7ed3\u6784\u5316\u8ba1\u5212 P=(p1,p2,…,pn)P=(p_1, p_2, …, p_n)<\/span><\/span>P<\/span><\/span>=<\/span><\/span><\/span><\/span>(<\/span>p<\/span><\/span>1<\/span><\/span><\/span><\/span>\u200b<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span>,<\/span><\/span>p<\/span><\/span>2<\/span><\/span><\/span><\/span>\u200b<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span>,<\/span><\/span>…<\/span>,<\/span><\/span>p<\/span><\/span>n<\/span><\/span><\/span><\/span>\u200b<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span>)<\/span><\/span><\/span><\/span><\/span><\/td>\n<\/tr>\n
            \u6267\u884c\u9636\u6bb5（Solving）<\/td>\n\u4e25\u683c\u6309\u8ba1\u5212\u9010\u4e00\u6267\u884c，\u6bcf\u6b65\u53ef\u80fd\u662f\u4e00\u6b21\u72ec\u7acb LLM \u8c03\u7528<\/td>\n\u7b2c ii<\/span><\/span>i<\/span><\/span><\/span><\/span><\/span> \u6b65\u7ed3\u679c sis_i<\/span><\/span>s<\/span><\/span>i<\/span><\/span><\/span><\/span>\u200b<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span>，\u4f9d\u8d56 (\u539f\u59cb\u95ee\u9898, \u5b8c\u6574\u8ba1\u5212, \u524d\u9762\u6240\u6709\u6b65\u9aa4\u7ed3\u679c)；\u6700\u540e\u4e00\u6b65\u5373\u6700\u7ec8\u7b54\u6848<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

            \u6838\u5fc3\u6536\u76ca：\u4fdd\u6301\u76ee\u6807\u4e00\u81f4\u6027，\u907f\u514d\u5728\u4e2d\u95f4\u6b65\u9aa4\u8ff7\u5931\u65b9\u5411\u3002<\/p>\n

            \u9002\u7528：\u591a\u6b65\u6570\u5b66\u5e94\u7528\u9898\u3001\u9700\u6574\u5408\u591a\u4fe1\u606f\u6e90\u7684\u62a5\u544a\u64b0\u5199\u3001\u4ee3\u7801\u751f\u6210（\u5148\u6784\u601d\u7ed3\u6784\u518d\u9010\u4e2a\u5b9e\u73b0）\u2014\u2014 \u5373\u7ed3\u6784\u6027\u5f3a\u3001\u53ef\u6e05\u6670\u5206\u89e3\u7684\u4efb\u52a1\u3002<\/p>\n

            4.3.2 \u89c4\u5212\u5668 Planner<\/h4>\n

            \u63d0\u793a\u8bcd\u7684\u5173\u952e\u8bbe\u8ba1（\u4fdd\u8bc1\u8f93\u51fa\u53ef\u89e3\u6790）：<\/p>\n

              \n
            • \u89d2\u8272\u8bbe\u5b9a：\u201c\u4f60\u662f\u4e00\u4e2a\u9876\u7ea7\u7684 AI \u89c4\u5212\u4e13\u5bb6\u201d\u3002<\/li>\n
            • \u4efb\u52a1\u63cf\u8ff0：\u628a\u590d\u6742\u95ee\u9898\u5206\u89e3\u6210\u7b80\u5355\u6b65\u9aa4\u3002<\/li>\n
            • \u683c\u5f0f\u7ea6\u675f：\u26a0\ufe0f \u5f3a\u5236\u8f93\u51fa\u88ab python\u5305\u88f9\u7684 Python \u5217\u8868\u5b57\u7b26\u4e32，\u5982 ["\u6b65\u9aa41", "\u6b65\u9aa42", "\u6b65\u9aa43"] \u2014\u2014 \u6bd4\u89e3\u6790\u81ea\u7136\u8bed\u8a00\u7a33\u5b9a\u53ef\u9760\u5f97\u591a\u3002<\/li>\n<\/ul>\n

              \u89e3\u6790\u4fa7：\u5207\u51fa\u4ee3\u7801\u5757 \u2192 \u7528 ast.literal_eval \u5b89\u5168\u8f6c\u6210 Python \u5217\u8868（\u26a0\ufe0f \u4e0d\u7528 eval）\u2192 isinstance(plan, list) \u6821\u9a8c\u7c7b\u578b；ValueError \/ SyntaxError \/ IndexError \u5168\u90e8\u6355\u83b7\u5e76\u8fd4\u56de\u7a7a\u5217\u8868，\u540c\u65f6\u5728\u65e5\u5fd7\u91cc\u6253\u5370\u539f\u59cb\u54cd\u5e94\u3002<\/p>\n

              4.3.3 \u6267\u884c\u5668 Executor \u4e0e\u72b6\u6001\u7ba1\u7406<\/h4>\n

              \u6267\u884c\u5668\u7684\u63d0\u793a\u8bcd\u5fc5\u987b\u5305\u542b\u56db\u9879\u4fe1\u606f：\u539f\u59cb\u95ee\u9898（\u4e0d\u5fd8\u6700\u7ec8\u76ee\u6807）\u3001\u5b8c\u6574\u8ba1\u5212（\u77e5\u9053\u5f53\u524d\u6b65\u9aa4\u7684\u4f4d\u7f6e）\u3001\u5386\u53f2\u6b65\u9aa4\u4e0e\u7ed3\u679c（\u5f53\u524d\u6b65\u9aa4\u7684\u76f4\u63a5\u8f93\u5165）\u3001\u5f53\u524d\u6b65\u9aa4（\u660e\u786e\u73b0\u5728\u8981\u89e3\u51b3\u54ea\u4e00\u4e2a）\u3002\u5e76\u8981\u6c42\u4ec5\u8f93\u51fa\u8be5\u6b65\u9aa4\u7684\u7b54\u6848，\u4e0d\u8f93\u51fa\u89e3\u91ca\u3002<\/p>\n

              for<\/span> i,<\/span> step in<\/span> enumerate<\/span>(<\/span>plan)<\/span>:<\/span> # \u4e25\u683c\u6309\u5e8f\u6267\u884c<\/span>
              \n response_text =<\/span> llm.<\/span>think(<\/span>prompt(<\/span>question,<\/span> plan,<\/span> history,<\/span> step)<\/span>)<\/span>
              \n history +=<\/span> f"\u6b65\u9aa4 <\/span>{<\/span>i+<\/span>1<\/span>}<\/span><\/span>: <\/span>{<\/span>step}<\/span><\/span>\\\\n\u7ed3\u679c: <\/span>{<\/span>response_text}<\/span><\/span>\\\\n\\\\n"<\/span><\/span> # \u2b50 \u72b6\u6001\u5c31\u662f\u8fd9\u6bb5\u5386\u53f2\u6587\u672c<\/span>
              \nfinal_answer =<\/span> response_text # \u6700\u540e\u4e00\u6b65\u7684\u7ed3\u679c\u5373\u6700\u7ec8\u7b54\u6848<\/span><\/p>\n

              PlanAndSolveAgent \u7684\u8bbe\u8ba1：\u672c\u8eab\u4e0d\u542b\u590d\u6742\u903b\u8f91，\u53ea\u662f\u534f\u8c03\u8005（Orchestrator），\u5185\u90e8\u6301\u6709 Planner \u4e0e Executor \u2014\u2014 \u8fd9\u662f"\u7ec4\u5408\u4f18\u4e8e\u7ee7\u627f"\u539f\u5219\u7684\u6559\u79d1\u4e66\u793a\u4f8b\u3002<\/p>\n

              4.3.4 \u5c40\u9650\u4e0e\u5ef6\u4f38<\/h4>\n
                \n
              • \u8ba1\u5212\u662f\u9759\u6001\u7684：\u4e00\u6b21\u6027\u751f\u6210\u3001\u4e0d\u53ef\u4fee\u6539\u3002\u82e5\u6267\u884c\u4e2d\u53d1\u73b0\u67d0\u6b65\u65e0\u6cd5\u5b8c\u6210\u6216\u7ed3\u679c\u4e0d\u7b26\u9884\u671f，\u9700\u8981"\u52a8\u6001\u91cd\u89c4\u5212"\u673a\u5236（\u6559\u6750\u4e60\u9898 4 \u539f\u9898）\u3002<\/li>\n
              • \u4e0e ReAct \u7684\u53d6\u820d：\u4efb\u52a1\u8def\u5f84\u53ef\u9884\u5224 \u2192 Plan-and-Solve；\u8def\u5f84\u4f9d\u8d56\u5916\u90e8\u5b9e\u65f6\u53cd\u9988 \u2192 ReAct\u3002<\/li>\n
              • \u5206\u5c42\u89c4\u5212（\u4e60\u9898\u5ef6\u4f38）：\u5148\u751f\u6210\u9ad8\u5c42\u62bd\u8c61\u8ba1\u5212，\u518d\u4e3a\u6bcf\u4e2a\u9ad8\u5c42\u6b65\u9aa4\u751f\u6210\u8be6\u7ec6\u5b50\u8ba1\u5212 \u2014\u2014 \u517c\u987e\u5168\u5c40\u4e0e\u53ef\u6267\u884c\u6027\u3002<\/li>\n<\/ul>\n

                4.4 Reflection（\u81ea\u6211\u53cd\u601d\u4e0e\u8fed\u4ee3\u4f18\u5316）<\/h3>\n

                Reflection \u673a\u5236（\u6e90\u81ea Reflexion，Shinn et al., NeurIPS 2023）：\u4e3a\u667a\u80fd\u4f53\u5f15\u5165\u4e8b\u540e（post-hoc）\u81ea\u6211\u6821\u6b63\u5faa\u73af，\u8ba9\u5b83\u50cf\u4eba\u7c7b\u6821\u5bf9\u521d\u7a3f\u3001\u9a8c\u7b97\u6570\u5b66\u9898\u4e00\u6837\u5ba1\u89c6\u5e76\u4fee\u6b63\u81ea\u5df1\u7684\u8f93\u51fa\u3002<\/p>\n

                4.4.1 \u4e09\u6b65\u5faa\u73af \u2b50<\/h4>\n

                \"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\"<\/p>\n\n\u6b65\u9aa4\u8c01\u6765\u505a\u505a\u4ec0\u4e48<\/tr>\n\n\n\n\n
                \u2460 \u6267\u884c（Execution）<\/td>\n\u7528 ReAct \u6216 Plan-and-Solve \u5b8c\u6210\u4efb\u52a1<\/td>\n\u751f\u6210"\u521d\u7a3f"<\/td>\n<\/tr>\n
                \u2461 \u53cd\u601d（Reflection）<\/td>\n\u72ec\u7acb\u7684\u3001\u6216\u5e26\u7279\u6b8a\u63d0\u793a\u8bcd\u7684 LLM \u5b9e\u4f8b，\u626e\u6f14"\u8bc4\u5ba1\u5458"<\/td>\n\u6309\u591a\u7ef4\u5ea6\u8bc4\u4f30\u5e76\u751f\u6210\u7ed3\u6784\u5316\u53cd\u9988<\/td>\n<\/tr>\n
                \u2462 \u4f18\u5316（Refinement）<\/td>\nLLM \u628a"\u521d\u7a3f + \u53cd\u9988"\u4f5c\u4e3a\u65b0\u4e0a\u4e0b\u6587<\/td>\n\u751f\u6210"\u4fee\u8ba2\u7a3f"<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

                \u53cd\u601d\u7684\u56db\u4e2a\u8bc4\u4f30\u7ef4\u5ea6：\u4e8b\u5b9e\u6027\u9519\u8bef\u3001\u903b\u8f91\u6f0f\u6d1e\u3001\u6548\u7387\u95ee\u9898\u3001\u9057\u6f0f\u4fe1\u606f\u3002<\/p>\n

                \u5faa\u73af\u53ef\u91cd\u590d\u591a\u8f6e，\u76f4\u5230\u53cd\u601d\u8ba4\u4e3a\u65e0\u9700\u6539\u8fdb\u6216\u8fbe\u5230\u8fed\u4ee3\u6b21\u6570\u4e0a\u9650\u3002<\/p>\n

                \u4e09\u70b9\u4ef7\u503c：<\/p>\n

              • \u63d0\u4f9b\u5185\u90e8\u7ea0\u9519\u56de\u8def \u2014\u2014 \u4e0d\u518d\u53ea\u4f9d\u8d56\u5916\u90e8\u5de5\u5177\u7684\u53cd\u9988（ReAct \u7684 Observation），\u80fd\u4fee\u6b63\u66f4\u9ad8\u5c42\u7684\u903b\u8f91\u4e0e\u7b56\u7565\u9519\u8bef\u3002<\/li>\n
              • \u628a\u4e00\u6b21\u6027\u6267\u884c\u53d8\u6210\u6301\u7eed\u4f18\u5316\u8fc7\u7a0b，\u663e\u8457\u63d0\u5347\u590d\u6742\u4efb\u52a1\u6210\u529f\u7387\u4e0e\u7b54\u6848\u8d28\u91cf\u3002<\/li>\n
              • \u4e3a\u667a\u80fd\u4f53\u6784\u5efa\u4e34\u65f6\u7684\u77ed\u671f\u8bb0\u5fc6："\u6267\u884c\u2014\u53cd\u601d\u2014\u4f18\u5316"\u7684\u5b8c\u6574\u8f68\u8ff9\u672c\u8eab\u5c31\u662f\u5b9d\u8d35\u7ecf\u9a8c；\u4e14\u8bb0\u5fc6\u53ef\u4ee5\u662f\u591a\u6a21\u6001\u7684（\u4ee3\u7801\u3001\u56fe\u50cf\u7b49），\u4e3a\u591a\u6a21\u6001\u667a\u80fd\u4f53\u5960\u57fa\u3002<\/li>\n

                4.4.2 Memory \u6a21\u5757：\u8fed\u4ee3\u7684\u524d\u63d0<\/h4>\n

                Reflection \u7684\u6838\u5fc3\u5728\u4e8e\u8fed\u4ee3，\u800c\u8fed\u4ee3\u7684\u524d\u63d0\u662f\u80fd\u591f\u8bb0\u4f4f\u4e4b\u524d\u7684\u5c1d\u8bd5\u4e0e\u83b7\u5f97\u7684\u53cd\u9988\u3002<\/p>\n

                class<\/span> Memory<\/span>:<\/span>
                \n def<\/span> __init__<\/span>(<\/span>self)<\/span>:<\/span> self.<\/span>records =<\/span> [<\/span>]<\/span> # \u6309\u5e8f\u5b58\u653e\u6240\u6709\u8bb0\u5f55<\/span>
                \n def<\/span> add_record<\/span>(<\/span>self,<\/span> record_type,<\/span> content)<\/span>:<\/span> .<\/span>.<\/span>.<\/span> # 'execution' \u6216 'reflection'<\/span>
                \n def<\/span> get_trajectory<\/span>(<\/span>self)<\/span>:<\/span> .<\/span>.<\/span>.<\/span> # \u2b50 \u6838\u5fc3：\u628a\u8f68\u8ff9\u5e8f\u5217\u5316\u6210\u53ef\u63d2\u5165\u63d0\u793a\u8bcd\u7684\u6587\u672c<\/span>
                \n def<\/span> get_last_execution<\/span>(<\/span>self)<\/span>:<\/span> .<\/span>.<\/span>.<\/span> # \u53d6\u6700\u65b0"\u521d\u7a3f"\u4f9b\u53cd\u601d<\/span><\/p>\n

                \u8bbe\u8ba1\u8981\u70b9：get_trajectory() \u662f\u6838\u5fc3 \u2014\u2014 \u5b83\u628a\u8bb0\u5fc6\u5e8f\u5217\u5316\u4e3a"\u2014 \u4e0a\u4e00\u8f6e\u5c1d\u8bd5 (\u4ee3\u7801) \u2014\u201c\u4e0e\u201d\u2014 \u8bc4\u5ba1\u5458\u53cd\u9988 \u2014"\u4ea4\u66ff\u7684\u8fde\u8d2f\u6587\u672c，\u76f4\u63a5\u5d4c\u5165\u540e\u7eed\u63d0\u793a\u8bcd\u3002\u8fd9\u4e5f\u662f\u7b2c 8 \u7ae0\u8bb0\u5fc6\u7cfb\u7edf\u7684\u6700\u5c0f\u96cf\u5f62\u3002<\/p>\n

                4.4.3 \u4e09\u5957\u63d0\u793a\u8bcdPrompt（Reflection \u7684\u7075\u9b42）<\/h4>\n\n\u63d0\u793a\u8bcd\u89d2\u8272\u8bbe\u5b9a\u5173\u952e\u7ea6\u675f<\/tr>\n\n\n\n\n
                \u521d\u59cb\u6267\u884c<\/td>\n\u201c\u8d44\u6df1 Python \u7a0b\u5e8f\u5458\u201d<\/td>\n\u4ee3\u7801\u987b\u542b\u5b8c\u6574\u7b7e\u540d\u3001docstring\u3001\u9075\u5faa PEP 8；\u76f4\u63a5\u8f93\u51fa\u4ee3\u7801，\u4e0d\u8981\u89e3\u91ca<\/td>\n<\/tr>\n
                \u53cd\u601d<\/td>\n\u2b50 \u201c\u6781\u5176\u4e25\u683c\u7684\u4ee3\u7801\u8bc4\u5ba1\u4e13\u5bb6\u548c\u8d44\u6df1\u7b97\u6cd5\u5de5\u7a0b\u5e08，\u5bf9\u6027\u80fd\u6709\u6781\u81f4\u8981\u6c42\u201d<\/td>\n\u4e13\u6ce8\u627e\u7b97\u6cd5\u6548\u7387\u74f6\u9888；\u5206\u6790\u65f6\u95f4\u590d\u6742\u5ea6；\u53ea\u6709\u7b97\u6cd5\u5c42\u9762\u5df2\u6700\u4f18\u65f6\u624d\u5141\u8bb8\u56de\u7b54"\u65e0\u9700\u6539\u8fdb"<\/td>\n<\/tr>\n
                \u4f18\u5316<\/td>\n\u201c\u8d44\u6df1 Python \u7a0b\u5e8f\u5458\u201d<\/td>\n\u4e0a\u4e00\u8f6e\u4ee3\u7801 + \u8bc4\u5ba1\u53cd\u9988 \u2192 \u751f\u6210\u4f18\u5316\u7248；\u540c\u6837\u8981\u6c42\u7b7e\u540d\/docstring\/PEP 8\u3001\u4e0d\u8f93\u51fa\u89e3\u91ca<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

                \u2b50 \u89d2\u8272\u8bbe\u5b9a\u76f4\u63a5\u51b3\u5b9a\u4f18\u5316\u65b9\u5411：\u628a"\u6781\u81f4\u8981\u6c42\u6548\u7387"\u6362\u6210"\u6ce8\u91cd\u53ef\u8bfb\u6027\u7684\u5f00\u6e90\u9879\u76ee\u7ef4\u62a4\u8005"，\u4ea7\u51fa\u4f1a\u5b8c\u5168\u4e0d\u540c（\u6559\u6750\u4e60\u9898 6 \u539f\u9898）\u3002<\/p>\n

                4.4.4 \u7ec8\u6b62\u6761\u4ef6\u4e0e\u8fd0\u884c\u5b9e\u4f8b<\/h4>\n
                  \n
                • \u7ec8\u6b62\u6761\u4ef6：\u53cd\u9988\u4e2d\u51fa\u73b0"\u65e0\u9700\u6539\u8fdb"，\u6216\u8fbe\u5230 max_iterations（\u9ed8\u8ba4 3）\u3002<\/li>\n
                • \u5b9e\u4f8b（\u751f\u6210"\u627e\u51fa 1 \u5230 n \u4e4b\u95f4\u6240\u6709\u7d20\u6570"）：\u521d\u7248\u8bd5\u9664\u6cd5 O(nn)O(n\\\\sqrt{n})<\/span><\/span>O<\/span>(<\/span>n<\/span><\/span>n<\/span><\/span><\/span><\/span><\/span><\/span><\/span>\u200b<\/span><\/span><\/span><\/span><\/span><\/span><\/span>)<\/span><\/span><\/span><\/span><\/span> \u2192 \u53cd\u601d\u6307\u51fa\u74f6\u9888\u5e76\u5efa\u8bae\u57c3\u62c9\u6258\u65af\u7279\u5c3c\u7b5b\u6cd5 \u2192 \u4f18\u5316\u4e3a O(nlog\u2061log\u2061n)O(n \\\\log\\\\log n)<\/span><\/span>O<\/span>(<\/span>n<\/span><\/span>log<\/span><\/span><\/span>log<\/span><\/span><\/span>n<\/span>)<\/span><\/span><\/span><\/span><\/span> \u2192 \u7b2c\u4e8c\u8f6e\u53cd\u601d\u8ba4\u53ef\u6548\u7387\u5e76\u63d0\u53ca\u5206\u6bb5\u7b5b\u6cd5\u7b49\u8fdb\u9636\u65b9\u5411，\u4f46\u5224\u5b9a"\u4e00\u822c\u60c5\u51b5\u4e0b\u65e0\u9700\u6539\u8fdb" \u2192 \u6536\u655b\u7ec8\u6b62\u3002<\/li>\n
                • \u8fd9\u4e2a\u6848\u4f8b\u8bf4\u660e：Reflection \u7684\u4ef7\u503c\u4e0d\u4ec5\u662f\u4fee\u590d\u9519\u8bef，\u66f4\u662f\u9a71\u52a8\u65b9\u6848\u5728\u8d28\u91cf\u548c\u6548\u7387\u4e0a\u9636\u68af\u5f0f\u63d0\u5347\u3002<\/li>\n<\/ul>\n

                  4.4.5 \u6210\u672c\u6536\u76ca\u5206\u6790（\u5fc5\u8003）<\/h4>\n\n\u6210\u672c\u6536\u76ca<\/tr>\n\n\n\n\n
                  \u6a21\u578b\u8c03\u7528\u5f00\u9500：\u6bcf\u8f6e\u81f3\u5c11\u989d\u5916 2 \u6b21 LLM \u8c03\u7528（\u53cd\u601d + \u4f18\u5316），\u591a\u8f6e\u6210\u500d\u589e\u52a0<\/td>\n\u8d28\u91cf\u8dc3\u8fc1：\u628a"\u5408\u683c"\u7684\u521d\u7a3f\u8fed\u4ee3\u6210"\u4f18\u79c0"\u7684\u6700\u7ec8\u65b9\u6848<\/td>\n<\/tr>\n
                  \u4efb\u52a1\u5ef6\u8fdf\u663e\u8457\u63d0\u9ad8：\u4e32\u884c\u8fc7\u7a0b，\u5b9e\u65f6\u6027\u5dee<\/td>\n\u9c81\u68d2\u6027\u589e\u5f3a：\u81ea\u6211\u7ea0\u9519\u5faa\u73af\u80fd\u53d1\u73b0\u5e76\u4fee\u590d\u903b\u8f91\u6f0f\u6d1e\u3001\u4e8b\u5b9e\u9519\u8bef\u3001\u8fb9\u754c\u60c5\u51b5<\/td>\n<\/tr>\n
                  \u63d0\u793a\u5de5\u7a0b\u590d\u6742\u5ea6\u4e0a\u5347：\u8981\u4e3a\u6267\u884c\/\u53cd\u601d\/\u4f18\u5316\u4e09\u9636\u6bb5\u5206\u522b\u8bbe\u8ba1\u8c03\u8bd5\u63d0\u793a\u8bcd<\/td>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

                  \u7ed3\u8bba：Reflection \u662f\u5178\u578b\u7684"\u4ee5\u6210\u672c\u6362\u8d28\u91cf"\u7b56\u7565\u3002\u9002\u5408\u5bf9\u8d28\u91cf\/\u51c6\u786e\u6027\u8981\u6c42\u6781\u9ad8\u3001\u5bf9\u5b9e\u65f6\u6027\u8981\u6c42\u5bbd\u677e\u7684\u573a\u666f（\u5173\u952e\u4e1a\u52a1\u4ee3\u7801\u3001\u6280\u672f\u62a5\u544a\u3001\u79d1\u7814\u63a8\u6f14\u3001\u51b3\u7b56\u652f\u6301）；\u53cd\u4e4b\u5e94\u9009\u66f4\u8f7b\u91cf\u7684 ReAct \/ Plan-and-Solve\u3002<\/p>\n

                  4.5 \u4e09\u79cd\u8303\u5f0f\u5bf9\u6bd4\u4e0e\u9009\u578b \u2b50<\/h3>\n

                  \"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\"
                  \n\"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\"<\/p>\n\n\u7ef4\u5ea6ReActPlan-and-SolveReflection<\/tr>\n\n\n\n\n\n\n\n\n
                  \u7ec4\u7ec7\u65b9\u5f0f<\/td>\n\u601d\u8003\u4e0e\u884c\u52a8\u878d\u5408\u5728\u6bcf\u4e00\u6b65，\u8fb9\u60f3\u8fb9\u505a<\/td>\n\u601d\u8003\u4e0e\u884c\u52a8\u89e3\u8026\u4e3a\u4e24\u9636\u6bb5，\u5148\u8c0b\u540e\u52a8<\/td>\n\u5728\u4efb\u610f\u8303\u5f0f\u4e4b\u4e0a\u53e0\u52a0\u6267\u884c\u2014\u53cd\u601d\u2014\u4f18\u5316\u8fed\u4ee3<\/td>\n<\/tr>\n
                  \u5916\u90e8\u4ea4\u4e92<\/td>\n\u5f3a\u4f9d\u8d56（\u6bcf\u6b65\u90fd\u8981 Observation）<\/td>\n\u672c\u7ae0\u5b9e\u73b0\u4e2d\u4e0d\u4f7f\u7528\u5de5\u5177，\u7eaf\u63d0\u793a\u8bcd\u9a71\u52a8\u63a8\u7406<\/td>\n\u53ef\u9009；\u4f9d\u8d56\u521d\u7a3f\u6765\u6e90<\/td>\n<\/tr>\n
                  \u6838\u5fc3\u4f18\u52bf<\/td>\n\u73af\u5883\u9002\u5e94\u6027\u3001\u52a8\u6001\u7ea0\u9519<\/td>\n\u7ed3\u6784\u6027\u3001\u76ee\u6807\u4e00\u81f4\u6027（\u4e0d\u8ff7\u8def）<\/td>\n\u8d28\u91cf\u4e0e\u9c81\u68d2\u6027\u8dc3\u8fc1<\/td>\n<\/tr>\n
                  \u4e3b\u8981\u4ee3\u4ef7<\/td>\n\u4e32\u884c\u5faa\u73af \u2192 \u5ef6\u8fdf\u4e0e\u8d39\u7528\u9ad8<\/td>\n\u8ba1\u5212\u9759\u6001，\u9047\u963b\u9700\u91cd\u89c4\u5212<\/td>\n\u6bcf\u8f6e\u81f3\u5c11 +2 \u6b21\u8c03\u7528，\u5ef6\u8fdf\u9ad8<\/td>\n<\/tr>\n
                  \u5178\u578b\u4efb\u52a1<\/td>\n\u5b9e\u65f6\u4fe1\u606f\u67e5\u8be2\u3001API\/\u6570\u636e\u5e93\u64cd\u4f5c\u3001\u9700\u7cbe\u786e\u8ba1\u7b97<\/td>\n\u591a\u6b65\u6570\u5b66\u9898\u3001\u62a5\u544a\u64b0\u5199\u3001\u4ee3\u7801\u751f\u6210<\/td>\n\u5173\u952e\u4ee3\u7801\/\u62a5\u544a\u3001\u79d1\u7814\u63a8\u6f14<\/td>\n<\/tr>\n
                  \u7ec8\u6b62\u6761\u4ef6<\/td>\n\u8f93\u51fa Finish[…] \u6216\u8fbe max_steps<\/td>\n\u8ba1\u5212\u6267\u884c\u5b8c\u6bd5<\/td>\n\u53cd\u9988"\u65e0\u9700\u6539\u8fdb"\u6216\u8fbe max_iterations<\/td>\n<\/tr>\n
                  \u8bb0\u5fc6\u9700\u6c42<\/td>\n\u5386\u53f2\u6587\u672c（History）<\/td>\n\u5386\u53f2\u6b65\u9aa4\u4e0e\u7ed3\u679c<\/td>\n\u2b50 \u5fc5\u987b\u6709 Memory \u5b58\u50a8\u5b8c\u6574\u8f68\u8ff9<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

                  \u9009\u578b\u53e3\u8bc0：\u63a2\u7d22\u6027\u3001\u4f9d\u8d56\u5916\u90e8\u53cd\u9988 \u2192 ReAct；\u6b65\u9aa4\u53ef\u9884\u5224\u3001\u9700\u7a33\u5b9a\u6267\u884c \u2192 Plan-and-Solve；\u8f93\u51fa\u8d28\u91cf\u8981\u6c42\u9ad8\u3001\u53ef\u8fed\u4ee3 \u2192 Reflection\u3002<\/p>\n

                  \u53ef\u4ee5\u7ec4\u5408（\u4e60\u9898 1）：\u4f8b\u5982 Planner \u7528 Plan-and-Solve \u51fa\u8ba1\u5212 \u2192 Reader \u7528 ReAct \u6309\u6b65\u6267\u884c\u4e0e\u5916\u90e8\u4ea4\u4e92 \u2192 Reviewer \u7528 Reflection \u5ba1\u67e5\u4ea7\u51fa\u3002\u8fd9\u6b63\u662f\u5b66\u4e60\u8ba1\u5212\u4e2d RepoGuard \u9879\u76ee Planner\u2013Reader\u2013Reviewer\u2013Reporter \u56db\u667a\u80fd\u4f53\u7684\u8303\u5f0f\u5206\u914d\u601d\u8def\u3002<\/p>\n

                  \u7b2c\u56db\u7ae0\u5c0f\u7ed3<\/h4>\n

                  \u672c\u7ae0\u4ee5\u7b2c\u4e09\u7ae0\u7684 LLM \u77e5\u8bc6\u4e3a\u57fa，\u4eb2\u624b\u9020\u8f6e\u5b50\u5b9e\u73b0\u4e86\u4e09\u79cd\u7ecf\u5178\u8303\u5f0f\u3002<\/p>\n

                  ReAct \u7528"\u601d\u8003\u2014\u884c\u52a8\u2014\u89c2\u5bdf"\u7684\u52a8\u6001\u5faa\u73af\u8ba9\u667a\u80fd\u4f53\u80fd\u4e0e\u5916\u90e8\u4e16\u754c\u4ea4\u4e92，\u8d62\u5728\u73af\u5883\u9002\u5e94\u6027\u4e0e\u52a8\u6001\u7ea0\u9519，\u4ee3\u4ef7\u662f\u4e32\u884c\u4f4e\u6548\u4e0e\u53ef\u80fd\u9677\u5165\u5c40\u90e8\u6700\u4f18，\u5176\u5de5\u7a0b\u9aa8\u67b6\u662f\u63d0\u793a\u8bcd\u6a21\u677f + \u6b63\u5219\u89e3\u6790 + \u5de5\u5177\u5206\u53d1 + max_steps \u5b89\u5168\u9600\u3002<\/p>\n

                  Plan-and-Solve \u628a\u6d41\u7a0b\u89e3\u8026\u4e3a\u5148\u89c4\u5212\u540e\u6267\u884c，\u9760"\u8f93\u51fa Python \u5217\u8868 + ast.literal_eval \u89e3\u6790"\u6362\u53d6\u7ed3\u6784\u6027\u4e0e\u76ee\u6807\u4e00\u81f4\u6027，\u4ee3\u4ef7\u662f\u8ba1\u5212\u9759\u6001\u3001\u9700\u53e6\u914d\u52a8\u6001\u91cd\u89c4\u5212\u3002<\/p>\n

                  Reflection \u5728\u6b64\u4e4b\u4e0a\u53e0\u52a0"\u6267\u884c\u2014\u53cd\u601d\u2014\u4f18\u5316"\u5faa\u73af，\u7528Memory \u6a21\u5757\u8bb0\u4f4f\u5b8c\u6574\u8fed\u4ee3\u8f68\u8ff9\u3001\u7528\u4e09\u5957\u89d2\u8272\u5316\u63d0\u793a\u8bcd\u9a71\u52a8\u81ea\u6211\u6279\u5224，\u4ee5"\u6bcf\u8f6e +2 \u6b21\u8c03\u7528\u3001\u4e32\u884c\u5ef6\u8fdf"\u6362\u53d6\u8d28\u91cf\u4e0e\u9c81\u68d2\u6027\u7684\u9636\u68af\u5f0f\u63d0\u5347\u3002<\/p>\n

                  \u4e09\u8005\u7684\u53d6\u820d\u53ef\u6982\u62ec\u4e3a：ReAct \u8d62\u5728\u7075\u6d3b，PS \u8d62\u5728\u7a33\u5b9a，Reflection \u8d62\u5728\u8d28\u91cf；\u5de5\u7a0b\u4e0a\u5b83\u4eec\u4e0d\u662f\u4e92\u65a5\u9009\u9879，\u800c\u662f\u53ef\u4ee5\u7ec4\u5408\u6210\u591a\u667a\u80fd\u4f53\u6d41\u6c34\u7ebf\u7684\u4e09\u79cd\u57fa\u672c\u8282\u62cd\u3002<\/p>\n

                  \n

                  \u7b2c\u4e94\u7ae0 \u57fa\u4e8e\u4f4e\u4ee3\u7801\u5e73\u53f0\u7684\u667a\u80fd\u4f53\u642d\u5efa<\/h2>\n

                  \u672c\u7ae0\u5b9a\u4f4d：\u4e0e\u7b2c\u56db\u7ae0\u6784\u6210"\u624b\u5199 vs \u5e73\u53f0"\u7684\u5bf9\u7167\u3002\u53ea\u9700\u8bb0\u4f4f\u4e3a\u4ec0\u4e48\u9700\u8981\u5e73\u53f0\u3001\u56db\u5e73\u53f0\u5404\u81ea\u5b9a\u4f4d\u3001\u4ee5\u53ca"\u6df7\u5408\u5f00\u53d1"\u8fd9\u4e2a\u7ed3\u8bba\u3002<\/p>\n

                  5.1 \u4e3a\u4ec0\u4e48\u9700\u8981\u4f4e\u4ee3\u7801\u5e73\u53f0<\/h3>\n

                  \u56db\u6761\u4ef7\u503c：\u964d\u4f4e\u6280\u672f\u95e8\u69db（\u4e1a\u52a1\u4e13\u5bb6\u4e5f\u80fd\u53c2\u4e0e）\u3001\u63d0\u5347\u5f00\u53d1\u6548\u7387（\u6570\u5206\u949f\u9a8c\u8bc1\u539f\u578b）\u3001\u66f4\u4f18\u7684\u53ef\u89c6\u5316\u4e0e\u53ef\u89c2\u6d4b\u6027（\u8282\u70b9\u7ea7\u6570\u636e\u6d41\u4e0e\u8017\u65f6\u4e00\u76ee\u4e86\u7136）\u3001\u6807\u51c6\u5316\u4e0e\u6700\u4f73\u5b9e\u8df5\u6c89\u6dc0（\u9884\u8bbe ReAct \u6a21\u677f\u3001\u4f18\u5316\u8fc7\u7684\u68c0\u7d22\u5f15\u64ce）\u3002<\/p>\n

                  \u6838\u5fc3\u89c2\u70b9：\u4f4e\u4ee3\u7801\u5e73\u53f0\u5e76\u975e\u8981\u53d6\u4ee3\u4ee3\u7801，\u800c\u662f\u63d0\u4f9b\u66f4\u9ad8\u5c42\u6b21\u7684\u62bd\u8c61，\u8ba9\u4eba\u805a\u7126"\u601d\u8003\u4e0e\u884c\u52a8\u7684\u903b\u8f91"\u672c\u8eab\u3002<\/p>\n

                  5.2 \u56db\u4e2a\u5e73\u53f0\u7684\u5b9a\u4f4d\u5bf9\u6bd4 \u2b50<\/h3>\n\n\u5e73\u53f0\u6838\u5fc3\u5b9a\u4f4d\u7279\u70b9\u9002\u7528\u4eba\u7fa4<\/tr>\n\n\n\n\n\n
                  Coze（\u5b57\u8282）<\/td>\n\u96f6\u4ee3\u7801 \/ \u4f4e\u4ee3\u7801 Agent \u6784\u5efa<\/td>\n\u62d6\u62fd\u5f0f"\u642d\u79ef\u6728"；\u63d2\u4ef6\u5e93\u4e30\u5bcc；\u4e00\u952e\u53d1\u5e03\u5230\u6296\u97f3\/\u98de\u4e66\/\u5fae\u4fe1\u7b49\u5e73\u53f0<\/td>\nAI \u5165\u95e8\u7528\u6237\u3001\u4ea7\u54c1\u7ecf\u7406\u3001\u8fd0\u8425<\/td>\n<\/tr>\n
                  Dify<\/td>\n\u5f00\u6e90\u3001\u5168\u6808\u5f0f LLM \u5e94\u7528\u5f00\u53d1\u4e0e\u8fd0\u8425\u5e73\u53f0<\/td>\n\u878d\u5408\u540e\u7aef\u670d\u52a1\u4e0e\u6a21\u578b\u8fd0\u8425\u7406\u5ff5；\u652f\u6301 Agent \u5de5\u4f5c\u6d41\u3001RAG Pipeline\u3001\u6570\u636e\u6807\u6ce8\u4e0e\u5fae\u8c03；\u63d2\u4ef6\u5e02\u573a 8000+；\u53ef\u79c1\u6709\u5316\u90e8\u7f72<\/td>\n\u6709\u6280\u672f\u80cc\u666f\u7684\u5f00\u53d1\u8005\u3001\u4f01\u4e1a\u7ea7\u56e2\u961f<\/td>\n<\/tr>\n
                  FastGPT<\/td>\n\u5f00\u6e90\u77e5\u8bc6\u5e93\u95ee\u7b54\u5e73\u53f0 + Agent \u6784\u5efa\u5de5\u5177<\/td>\n\u2b50 \u5bf9 RAG \u77e5\u8bc6\u5e93\u573a\u666f\u6781\u81f4\u4f18\u5316（\u6570\u636e\u5bfc\u5165\u2192\u5206\u5757\u2192\u5411\u91cf\u5316\u2192\u68c0\u7d22\u5168\u94fe\u8def）；Flow \u53ef\u89c6\u5316\u7f16\u6392；\u6a21\u578b\u4e2d\u7acb；\u53ef\u5bf9\u63a5\u4f01\u5fae\/\u9489\u9489\/\u98de\u4e66<\/td>\n\u9700\u79c1\u6709\u77e5\u8bc6\u5e93\u95ee\u7b54\u3001\u667a\u80fd\u5ba2\u670d\u7684\u56e2\u961f<\/td>\n<\/tr>\n
                  n8n<\/td>\n\u5f00\u6e90\u5de5\u4f5c\u6d41\u81ea\u52a8\u5316\u5de5\u5177（\u975e\u7eaf LLM \u5e73\u53f0）<\/td>\n\u2b50 \u5f3a\u5728"\u8fde\u63a5"：\u6570\u767e\u4e2a\u9884\u7f6e\u8282\u70b9\u4e32\u8054 SaaS\/\u6570\u636e\u5e93\/API，\u5728\u5176\u4e2d\u5d4c\u5165 LLM \u8282\u70b9；\u5b66\u4e60\u66f2\u7ebf\u8f83\u9661<\/td>\n\u9700\u628a AI \u6df1\u5ea6\u5d4c\u5165\u73b0\u6709\u4e1a\u52a1\u6d41\u7a0b\u7684\u56e2\u961f<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

                  \u672c\u7ae0\u56db\u4e2a\u914d\u5957\u6848\u4f8b（\u77e5\u9053\u5373\u53ef）：Coze \u2192 \u6bcf\u65e5 AI \u7b80\u62a5；Dify \u2192 \u8d85\u7ea7\u667a\u80fd\u4f53\u4e2a\u4eba\u52a9\u624b（\u542b\u95ee\u9898\u5206\u7c7b\u5668\u505a\u591a\u667a\u80fd\u4f53\u8def\u7531\u3001MCP \u5de5\u5177\u96c6\u6210）；FastGPT \u2192 \u667a\u80fd\u6295\u987e\u52a9\u624b；n8n \u2192 \u667a\u80fd\u90ae\u4ef6\u52a9\u624b\u3002<\/p>\n

                  5.3 \u5404\u5e73\u53f0\u4e3b\u8981\u5c40\u9650<\/h3>\n
                    \n
                  • Coze：\u4e0d\u652f\u6301 MCP \u534f\u8bae，\u4e14\u65e0\u6cd5\u5bfc\u51fa\u6807\u51c6\u5316\u914d\u7f6e\u6587\u4ef6\u3002<\/li>\n
                  • Dify：\u5b66\u4e60\u66f2\u7ebf\u8f83\u9661，\u9ad8\u5e76\u53d1\u573a\u666f\u6709\u6027\u80fd\u6311\u6218\u3002<\/li>\n
                  • FastGPT：\u6a21\u677f\u751f\u6001\u76f8\u5bf9\u8584\u5f31，\u514d\u8d39\u7248\u989d\u5ea6\u6709\u9650\u3002<\/li>\n
                  • n8n：\u26a0\ufe0f \u5185\u7f6e\u5b58\u50a8\u975e\u6301\u4e45（Simple Memory \/ Simple Vector Store \u90fd\u5728\u5185\u5b58，\u670d\u52a1\u91cd\u542f\u5373\u4e22，\u751f\u4ea7\u5fc5\u987b\u6362 Redis\/Pinecone \u7b49\u5916\u90e8\u5b58\u50a8）；\u7248\u672c\u63a7\u5236\u4e0e\u591a\u4eba\u534f\u4f5c\u4e0d\u5982 git \u6210\u719f；\u8d85\u9ad8\u5e76\u53d1\u4e0b\u8282\u70b9\u8c03\u5ea6\u6709\u6027\u80fd\u5f00\u9500\u3002<\/li>\n<\/ul>\n

                    5.4 \u9009\u578b\u5efa\u8bae\u4e0e\u6df7\u5408\u5f00\u53d1<\/h3>\n
                      \n
                    • \u5feb\u901f\u539f\u578b\u9a8c\u8bc1\u3001\u975e\u6280\u672f\u7528\u6237 \u2192 Coze<\/li>\n
                    • \u4f01\u4e1a\u7ea7\u5e94\u7528\u3001\u590d\u6742\u4e1a\u52a1\u903b\u8f91\u3001\u591a\u6a21\u6001\u751f\u6210 \u2192 Dify<\/li>\n
                    • \u57fa\u4e8e\u79c1\u6709\u77e5\u8bc6\u5e93\u7684\u95ee\u7b54\u7cfb\u7edf\u3001\u667a\u80fd\u5ba2\u670d \u2192 FastGPT<\/li>\n
                    • \u6df1\u5ea6\u4e1a\u52a1\u96c6\u6210\u3001\u901a\u7528\u81ea\u52a8\u5316\u6d41\u7a0b \u2192 n8n<\/li>\n<\/ul>\n

                      \u2b50 \u7ed3\u8bba（\u539f\u6587）：\u4f4e\u4ee3\u7801\u5e73\u53f0\u4e0d\u53d6\u4ee3\u4ee3\u7801\u5f00\u53d1，\u800c\u662f\u4e92\u8865\u3002\u6df7\u5408\u5f00\u53d1\u624d\u662f\u667a\u80fd\u4f53\u5de5\u7a0b\u5316\u7684\u6700\u4f73\u5b9e\u8df5：\u7528\u4f4e\u4ee3\u7801\u5e73\u53f0\u5feb\u901f\u9a8c\u8bc1\u60f3\u6cd5，\u7528\u4ee3\u7801\u5b9e\u73b0\u7cbe\u7ec6\u5316\u63a7\u5236；\u7528\u5e73\u53f0\u5904\u7406\u6807\u51c6\u5316\u6d41\u7a0b，\u7528\u4ee3\u7801\u5904\u7406\u7279\u6b8a\u903b\u8f91\u3002<\/p>\n

                      \u7b2c\u4e94\u7ae0\u5c0f\u7ed3<\/h4>\n

                      \u7b2c\u4e94\u7ae0\u628a\u89c6\u89d2\u4ece"\u624b\u5199\u4ee3\u7801"\u5207\u5230"\u5e73\u53f0\u5316\u5f00\u53d1"，\u7528 Coze \/ Dify \/ FastGPT \/ n8n \u56db\u4e2a\u5e73\u53f0\u6f14\u793a\u4e86\u540c\u4e00\u4ef6\u4e8b\u7684\u53e6\u4e00\u6761\u8def\u5f84：\u628a API \u8c03\u7528\u3001\u72b6\u6001\u7ba1\u7406\u3001\u5e76\u53d1\u63a7\u5236\u5c01\u88c5\u6210\u53ef\u62d6\u62fd\u7684\u8282\u70b9，\u6362\u53d6\u95e8\u69db\u964d\u4f4e\u3001\u6548\u7387\u63d0\u5347\u4e0e\u7aef\u5230\u7aef\u53ef\u89c6\u5316\u3002<\/p>\n

                      \u56db\u8005\u5206\u5de5\u6e05\u6670\u2014\u2014Coze \u8d62\u5728\u96f6\u4ee3\u7801\u4e0e\u5206\u53d1\u751f\u6001，Dify \u8d62\u5728\u4f01\u4e1a\u7ea7\u5168\u6808\u80fd\u529b，FastGPT \u8d62\u5728 RAG \u77e5\u8bc6\u5e93\u7ec6\u8282，n8n \u8d62\u5728\u901a\u7528\u4e1a\u52a1"\u8fde\u63a5"\u3002<\/p>\n

                      \u5b83\u4eec\u5171\u540c\u7684\u77ed\u677f\u662f\u6301\u4e45\u5316\u3001\u9ad8\u5e76\u53d1\u4e0e\u7248\u672c\u63a7\u5236，\u56e0\u6b64\u672c\u7ae0\u7684\u6700\u7ec8\u7ed3\u8bba\u4e0d\u662f"\u9009\u5e73\u53f0\u8fd8\u662f\u9009\u4ee3\u7801"，\u800c\u662f\u6df7\u5408\u5f00\u53d1：\u5e73\u53f0\u8d1f\u8d23\u6807\u51c6\u5316\u6d41\u7a0b\u4e0e\u539f\u578b\u9a8c\u8bc1，\u4ee3\u7801\u8d1f\u8d23\u7279\u6b8a\u903b\u8f91\u4e0e\u7cbe\u7ec6\u63a7\u5236\u3002<\/p>\n

                      \n

                      \u7b2c\u516d\u7ae0 \u6846\u67b6\u5f00\u53d1\u5b9e\u8df5（\u91cd\u70b9）<\/h2>\n

                      6.1 \u4ece\u624b\u52a8\u5b9e\u73b0\u5230\u6846\u67b6\u5f00\u53d1<\/h3>\n

                      6.1.1 \u4e3a\u4ec0\u4e48\u9700\u8981\u6846\u67b6 \u2b50<\/h4>\n

                      \u7b2c\u56db\u7ae0\u7684\u4ee3\u7801\u4e3a\u6559\u5b66\u800c\u751f，\u80fd\u5b8c\u6210\u7279\u5b9a\u4efb\u52a1，\u4f46\u6784\u5efa\u591a\u4e2a\u590d\u6742\u667a\u80fd\u4f53\u65f6\u4f1a\u9047\u5230\u74f6\u9888\u3002\u6846\u67b6\u7684\u672c\u8d28\u662f\u63d0\u4f9b\u4e00\u5957\u7ecf\u8fc7\u9a8c\u8bc1\u7684"\u89c4\u8303"，\u628a\u6240\u6709\u667a\u80fd\u4f53\u5171\u6709\u7684\u91cd\u590d\u5de5\u4f5c\u62bd\u8c61\u5c01\u88c5：<\/p>\n

                    • \u63d0\u5347\u4ee3\u7801\u590d\u7528\u4e0e\u5f00\u53d1\u6548\u7387：\u901a\u7528 Agent \u57fa\u7c7b\/\u6267\u884c\u5668\u5c01\u88c5 Agent Loop，\u65e0\u8bba ReAct \u8fd8\u662f Plan-and-Solve \u90fd\u80fd\u57fa\u4e8e\u6807\u51c6\u7ec4\u4ef6\u5feb\u901f\u642d\u5efa\u3002<\/li>\n
                    • \u5b9e\u73b0\u6838\u5fc3\u7ec4\u4ef6\u7684\u89e3\u8026\u4e0e\u53ef\u6269\u5c55\u6027（\u6846\u67b6\u4f1a\u5f3a\u5236\u5206\u79bb\u5173\u6ce8\u70b9）：<\/li>\n\n\u5c42\u804c\u8d23\u53ef\u66ff\u6362\u6027<\/tr>\n\n\n\n\n
                      \u6a21\u578b\u5c42 Model Layer<\/td>\n\u4e0e LLM \u4ea4\u4e92<\/td>\n\u8f7b\u677e\u6362 OpenAI \/ Anthropic \/ \u672c\u5730\u6a21\u578b<\/td>\n<\/tr>\n
                      \u5de5\u5177\u5c42 Tool Layer<\/td>\n\u6807\u51c6\u5316\u7684\u5de5\u5177\u5b9a\u4e49\u3001\u6ce8\u518c\u3001\u6267\u884c\u63a5\u53e3<\/td>\n\u65b0\u589e\u5de5\u5177\u4e0d\u5f71\u54cd\u5176\u4ed6\u4ee3\u7801<\/td>\n<\/tr>\n
                      \u8bb0\u5fc6\u5c42 Memory Layer<\/td>\n\u77ed\u671f\u4e0e\u957f\u671f\u8bb0\u5fc6<\/td>\n\u53ef\u5207\u6362\u6ed1\u52a8\u7a97\u53e3 \/ \u6458\u8981\u8bb0\u5fc6\u7b49\u7b56\u7565<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
                    • \u6807\u51c6\u5316\u590d\u6742\u7684\u72b6\u6001\u7ba1\u7406：\u5904\u7406\u4e0a\u4e0b\u6587\u7a97\u53e3\u9650\u5236\u3001\u5386\u53f2\u6301\u4e45\u5316\u3001\u591a\u8f6e\u5bf9\u8bdd\u72b6\u6001\u8ddf\u8e2a \u2014\u2014 \u7b2c\u56db\u7ae0\u7684 Memory \u53ea\u662f\u6700\u7b80\u5355\u7684\u5f00\u59cb\u3002<\/li>\n
                    • \u7b80\u5316\u53ef\u89c2\u6d4b\u6027\u4e0e\u8c03\u8bd5：\u5185\u7f6e**\u4e8b\u4ef6\u56de\u8c03（Callbacks）**\u673a\u5236，\u5728 on_llm_start\u3001on_tool_end\u3001on_agent_finish \u7b49\u5173\u952e\u8282\u70b9\u81ea\u52a8\u8bb0\u5f55\u65e5\u5fd7\/\u4e0a\u62a5，\u8fdc\u4f18\u4e8e\u624b\u52a8 print\u3002<\/li>\n

                      6.1.2 \u56db\u6846\u67b6\u603b\u89c8 \u2b50<\/h4>\n

                      \"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\"<\/p>\n\n\u6846\u67b6\u6838\u5fc3\u9690\u55bb\u534f\u4f5c\u673a\u5236\u63a7\u5236\u65b9\u5f0f<\/tr>\n\n\n\n\n\n
                      AutoGen<\/td>\n\u7fa4\u804a \/ \u5706\u684c\u4f1a\u8bae<\/td>\n\u591a\u89d2\u8272"\u53ef\u5bf9\u8bdd"\u667a\u80fd\u4f53\u901a\u8fc7\u81ea\u52a8\u5316\u6d88\u606f\u4f20\u9012\u534f\u4f5c<\/td>\n\u6d8c\u73b0\u5f0f（\u5b9a\u4e49\u89d2\u8272\u4e0e\u89c4\u5219，\u6d41\u7a0b\u81ea\u7136\u53d1\u751f）<\/td>\n<\/tr>\n
                      AgentScope<\/td>\n\u667a\u80fd\u4f53\u64cd\u4f5c\u7cfb\u7edf<\/td>\n\u6d88\u606f\u9a71\u52a8，MsgHub \u4f5c\u6d88\u606f\u4e2d\u67a2<\/td>\n\u5de5\u7a0b\u5316 + \u5e76\u53d1 \/ \u5206\u5e03\u5f0f<\/td>\n<\/tr>\n
                      CAMEL<\/td>\n\u4e24\u4e2a\u4e13\u5bb6\u5bf9\u8c08<\/td>\n\u89d2\u8272\u626e\u6f14 + \u5f15\u5bfc\u6027\u63d0\u793a，\u53cc\u667a\u80fd\u4f53\u81ea\u4e3b\u5bf9\u8bdd<\/td>\n\u6d8c\u73b0\u5f0f（\u6700\u5c11\u4eba\u7c7b\u5e72\u9884）<\/td>\n<\/tr>\n
                      LangGraph<\/td>\n\u6709\u5411\u56fe \/ \u72b6\u6001\u673a<\/td>\n\u8282\u70b9\u4e0e\u8fb9\u6784\u6210\u5de5\u4f5c\u6d41<\/td>\n\u663e\u5f0f\u63a7\u5236（\u9884\u5b9a\u4e49\u6bcf\u6b65\u4e0e\u8df3\u8f6c\u6761\u4ef6）<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

                      6.2 AutoGen \u2014\u2014 \u4ee5\u5bf9\u8bdd\u9a71\u52a8\u534f\u4f5c<\/h3>\n

                      \u8bbe\u8ba1\u54f2\u5b66：\u628a\u590d\u6742\u4efb\u52a1\u89e3\u51b3\u6d41\u7a0b\u6620\u5c04\u4e3a\u4e0d\u540c\u89d2\u8272\u667a\u80fd\u4f53\u4e4b\u95f4\u7684\u4e00\u7cfb\u5217\u81ea\u52a8\u5316\u5bf9\u8bdd（Wu et al., COLM 2024）\u3002\u672c\u7ae0\u4ee5 0.7.4 \u7248\u672c\u4e3a\u4f8b，\u5b83\u4ee3\u8868\u4ece\u7c7b\u7ee7\u627f\u8bbe\u8ba1\u8f6c\u5411\u7ec4\u5408\u5f0f\u67b6\u6784\u7684\u4e00\u6b21\u91cd\u5927\u91cd\u6784\u3002<\/p>\n

                      \"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\"<\/p>\n

                      6.2.1 \u6838\u5fc3\u673a\u5236<\/h4>\n

                      \u2460 \u5206\u5c42 + \u5f02\u6b65\u4f18\u5148：<\/p>\n

                        \n
                      • autogen-core：\u5e95\u5c42\u57fa\u7840，\u5c01\u88c5 LLM \u4ea4\u4e92\u4e0e\u6d88\u606f\u4f20\u9012，\u4fdd\u8bc1\u7a33\u5b9a\u4e0e\u6269\u5c55\u6027\u3002<\/li>\n
                      • autogen-agentchat：\u57fa\u4e8e core \u7684\u9ad8\u7ea7\u63a5\u53e3，\u7b80\u5316\u5bf9\u8bdd\u5f0f\u5e94\u7528\u5f00\u53d1\u3002<\/li>\n
                      • \u5f02\u6b65\u4f18\u5148（async\/await）：\u591a\u667a\u80fd\u4f53\u573a\u666f\u4e2d\u7f51\u7edc\u8bf7\u6c42\u662f\u4e3b\u8981\u8017\u65f6，\u5f02\u6b65\u907f\u514d\u7ebf\u7a0b\u963b\u585e，\u663e\u8457\u63d0\u5347\u5e76\u53d1\u80fd\u529b\u3002<\/li>\n<\/ul>\n

                        \u2461 \u6838\u5fc3\u667a\u80fd\u4f53\u7ec4\u4ef6：<\/p>\n

                          \n
                        • AssistantAgent：\u4efb\u52a1\u4e3b\u8981\u89e3\u51b3\u8005，\u5185\u90e8\u5c01\u88c5 LLM，\u9760\u4e0d\u540c\u7cfb\u7edf\u6d88\u606f\u8d4b\u4e88\u4e0d\u540c"\u4e13\u5bb6"\u89d2\u8272\u3002<\/li>\n
                        • UserProxyAgent：\u53cc\u91cd\u89d2\u8272 \u2014\u2014 \u65e2\u662f\u4eba\u7c7b\u7684"\u4ee3\u8a00\u4eba"（\u53d1\u8d77\u4efb\u52a1\u3001\u4f20\u8fbe\u610f\u56fe），\u53c8\u662f\u53ef\u9760\u7684\u6267\u884c\u5668（\u6267\u884c\u4ee3\u7801\/\u8c03\u7528\u5de5\u5177）\u3002\u8fd9\u6e05\u6670\u5730\u533a\u5206\u4e86**\u201c\u601d\u8003\u201d（Assistant）\u4e0e"\u884c\u52a8"（UserProxy）**\u3002<\/li>\n<\/ul>\n

                          \u2462 \u4ece GroupChatManager \u5230 Team：\u65b0\u67b6\u6784\u5f15\u5165\u66f4\u7075\u6d3b\u7684 Team \u6982\u5ff5，\u5178\u578b\u5982 RoundRobinGroupChat（\u8f6e\u8be2\u7fa4\u804a）：\u6309\u9884\u5b9a\u4e49\u987a\u5e8f\u4f9d\u6b21\u6fc0\u6d3b\u667a\u80fd\u4f53\u53d1\u8a00，\u76f4\u5230\u6700\u5927\u5bf9\u8bdd\u8f6e\u6b21\u6216\u6ee1\u8db3\u7ec8\u6b62\u6761\u4ef6\u3002\u9002\u7528\u4e8e\u6d41\u7a0b\u56fa\u5b9a\u7684\u4efb\u52a1\u3002<\/p>\n

                          6.2.2 \u6848\u4f8b：\u6a21\u62df\u8f6f\u4ef6\u5f00\u53d1\u56e2\u961f（\u6bd4\u7279\u5e01\u4ef7\u683c\u663e\u793a\u5e94\u7528）<\/h4>\n

                          \u89d2\u8272\u5212\u5206（\u591a\u667a\u80fd\u4f53\u7cfb\u7edf\u8bbe\u8ba1\u7684\u5173\u952e\u4e00\u6b65）：<\/p>\n\n\u89d2\u8272\u804c\u8d23\u7cfb\u7edf\u6d88\u606f\u4e2d\u7684"\u4ea4\u63a5\u8bed"<\/tr>\n\n\n\n\n\n
                          ProductManager(\u4ea7\u54c1\u7ecf\u7406)<\/td>\n\u9700\u6c42\u5206\u6790\u3001\u6280\u672f\u89c4\u5212\u3001\u98ce\u9669\u8bc4\u4f30<\/td>\n\u5206\u6790\u5b8c\u6210\u540e\u8bf4"\u8bf7\u5de5\u7a0b\u5e08\u5f00\u59cb\u5b9e\u73b0"<\/td>\n<\/tr>\n
                          Engineer(\u5de5\u7a0b\u5e08)<\/td>\n\u6280\u672f\u5b9e\u73b0\u3001\u7f16\u5199\u4ee3\u7801\u3001\u5f02\u5e38\u5904\u7406<\/td>\n\u5b8c\u6210\u540e\u8bf4"\u8bf7\u4ee3\u7801\u5ba1\u67e5\u5458\u68c0\u67e5"<\/td>\n<\/tr>\n
                          CodeReviewer(\u4ee3\u7801\u5ba1\u67e5\u5458)<\/td>\n\u8d28\u91cf\u3001\u5b89\u5168\u6027\u3001\u6700\u4f73\u5b9e\u8df5\u5ba1\u67e5<\/td>\n\u5b8c\u6210\u540e\u8bf4"\u4ee3\u7801\u5ba1\u67e5\u5b8c\u6210，\u8bf7\u7528\u6237\u4ee3\u7406\u6d4b\u8bd5"<\/td>\n<\/tr>\n
                          UserProxy(\u7528\u6237\u4ee3\u7406)<\/td>\n\u4ee3\u8868\u7528\u6237\u53d1\u8d77\u4efb\u52a1\u3001\u6267\u884c\u9a8c\u8bc1<\/td>\n\u6d4b\u8bd5\u5b8c\u6210\u540e\u56de\u590d TERMINATE<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

                          \u534f\u4f5c\u4e09\u8981\u7d20：participants（\u5217\u8868\u987a\u5e8f = \u53d1\u8a00\u987a\u5e8f）\u3001termination_condition=TextMentionTermination("TERMINATE")（\u6d88\u606f\u542b\u5173\u952e\u8bcd\u5373\u7ec8\u6b62）\u3001max_turns=20（\u5b89\u5168\u9600）\u3002\u8fd0\u884c\u7528 await Console(team_chat.run_stream(task=task)) + asyncio.run(…)\u3002<\/p>\n

                          \u2b50 \u5173\u952e\u6d1e\u5bdf：\u5b9a\u4e49\u667a\u80fd\u4f53\u7684\u6838\u5fc3\u5728\u4e8e\u7f16\u5199\u9ad8\u8d28\u91cf\u7684\u7cfb\u7edf\u6d88\u606f（System Message） \u2014\u2014 \u5b83\u65e2\u662f"\u884c\u4e3a\u51c6\u5219"\u4e5f\u662f"\u4e13\u4e1a\u77e5\u8bc6\u5e93"，\u89c4\u5b9a\u4e86\u89d2\u8272\u3001\u804c\u8d23\u3001\u5de5\u4f5c\u6d41\u7a0b\u4e43\u81f3\u4e0e\u5176\u4ed6\u667a\u80fd\u4f53\u7684\u4ea4\u4e92\u65b9\u5f0f\u3002\u201c\u901a\u8fc7\u7cfb\u7edf\u6d88\u606f\u8fdb\u884c\u63d0\u793a\u5de5\u7a0b，\u662f\u8bbe\u8ba1\u9ad8\u6548\u591a\u667a\u80fd\u4f53\u5e94\u7528\u7684\u6838\u5fc3\u73af\u8282\u3002\u201d<\/p>\n

                          6.2.3 \u4f18\u52bf\u4e0e\u5c40\u9650<\/h4>\n

                          \u4f18\u52bf：\u2460 \u65e0\u9700\u8bbe\u8ba1\u590d\u6742\u72b6\u6001\u673a，\u628a\u5f00\u53d1\u6d41\u7a0b\u81ea\u7136\u6620\u5c04\u4e3a\u89d2\u8272\u5bf9\u8bdd，\u964d\u4f4e\u5efa\u6a21\u95e8\u69db\u2014\u2014\u5f00\u53d1\u8005\u53ea\u9700\u5173\u6ce8"\u8c01（\u89d2\u8272）\u201c\u4e0e"\u505a\u4ec0\u4e48（\u804c\u8d23）\u201d，\u800c\u975e"\u5982\u4f55\u505a（\u6d41\u7a0b\u63a7\u5236）"；\u2461 \u89d2\u8272\u9ad8\u5ea6\u4e13\u4e1a\u5316\u4e14\u53ef\u8de8\u9879\u76ee\u590d\u7528；\u2462 UserProxyAgent \u5929\u7136\u63d0\u4f9b"\u4eba\u7c7b\u5728\u73af（Human-in-the-loop）"\u63a5\u53e3，\u4fdd\u8bc1\u81ea\u52a8\u5316\u59cb\u7ec8\u53d7\u4eba\u7c7b\u76d1\u7763\u3002<\/p>\n

                          \u5c40\u9650：\u2460 \u57fa\u4e8e LLM \u7684\u5bf9\u8bdd\u672c\u8d28\u4e0d\u786e\u5b9a，\u53ef\u80fd\u504f\u79bb\u9884\u671f\u5206\u652f\u751a\u81f3\u9677\u5165\u5faa\u73af；\u2461 "\u5bf9\u8bdd\u5f0f\u8c03\u8bd5"\u96be\u9898 \u2014\u2014 \u51fa\u9519\u65f6\u5f97\u5230\u7684\u4e0d\u662f\u6e05\u6670\u9519\u8bef\u5806\u6808，\u800c\u662f\u4e00\u957f\u4e32\u5bf9\u8bdd\u5386\u53f2\u3002<\/p>\n

                          \u975e OpenAI \u6a21\u578b\u914d\u7f6e\u8865\u5145：\u9700\u5728 OpenAIChatCompletionClient \u4e2d\u4f20\u5165 model_info \u5b57\u5178，\u663e\u5f0f\u58f0\u660e function_calling \/ max_tokens \/ context_length \/ vision \/ json_output \/ structured_output \u7b49\u80fd\u529b\u9879 \u2014\u2014 \u544a\u8bc9\u6846\u67b6\u6a21\u578b\u7684\u80fd\u529b\u8fb9\u754c\u3002<\/p>\n

                          6.3 AgentScope \u2014\u2014 \u5de5\u7a0b\u5316\u4f18\u5148\u7684\u591a\u667a\u80fd\u4f53\u5e73\u53f0<\/h3>\n

                          \u7531\u963f\u91cc\u5df4\u5df4\u8fbe\u6469\u9662\u5f00\u53d1，\u9762\u5411\u5927\u89c4\u6a21\u3001\u9ad8\u53ef\u9760\u7684\u591a\u667a\u80fd\u4f53\u5e94\u7528，\u5185\u7f6e\u5206\u5e03\u5f0f\u90e8\u7f72\u3001\u5bb9\u9519\u6062\u590d\u3001\u53ef\u89c2\u6d4b\u6027\u7b49\u4f01\u4e1a\u7ea7\u7279\u6027\u3002
                          \n\u7c7b\u6bd4：AutoGen \u50cf\u7075\u6d3b\u7684"\u5bf9\u8bdd\u5de5\u4f5c\u5ba4"，AgentScope \u5219\u662f\u5b8c\u6574\u7684"\u667a\u80fd\u4f53\u64cd\u4f5c\u7cfb\u7edf"\u3002<\/p>\n

                          \"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\"<\/p>\n

                          6.3.1 \u8bbe\u8ba1：\u5206\u5c42\u67b6\u6784 + \u6d88\u606f\u9a71\u52a8<\/h4>\n

                          \u56db\u5c42\u67b6\u6784：<\/p>\n\n\u5c42\u5185\u5bb9<\/tr>\n\n\n\n\n\n
                          \u57fa\u7840\u7ec4\u4ef6\u5c42<\/td>\nMessage（\u7edf\u4e00\u6d88\u606f\u683c\u5f0f，\u652f\u6301\u591a\u6a21\u6001）\u3001Memory（\u77ed\u671f\/\u957f\u671f\u8bb0\u5fc6）\u3001Model API（\u62bd\u8c61\u4e0d\u540c LLM）\u3001Tool<\/td>\n<\/tr>\n
                          \u667a\u80fd\u4f53\u57fa\u7840\u8bbe\u65bd\u5c42<\/td>\n\u9884\u6784\u5efa\u667a\u80fd\u4f53（\u6d4f\u89c8\u5668\u3001\u6df1\u5ea6\u7814\u7a76\u7b49）\u3001ReAct \u8303\u5f0f\u3001\u667a\u80fd\u4f53\u94a9\u5b50\u3001\u5e76\u884c\u5de5\u5177\u8c03\u7528\u3001\u72b6\u6001\u7ba1\u7406；\u539f\u751f\u652f\u6301\u5f02\u6b65\u6267\u884c\u4e0e\u5b9e\u65f6\u63a7\u5236<\/td>\n<\/tr>\n
                          \u591a\u667a\u80fd\u4f53\u534f\u4f5c\u5c42<\/td>\n\u2b50 MsgHub（\u6d88\u606f\u4e2d\u5fc3，\u8d1f\u8d23\u8def\u7531\u4e0e\u72b6\u6001\u7ba1\u7406）+ Pipeline（\u987a\u5e8f\/\u5e76\u53d1\u7b49\u7f16\u6392\u6a21\u5f0f）<\/td>\n<\/tr>\n
                          \u5f00\u53d1\u4e0e\u90e8\u7f72\u5c42<\/td>\nAgentScope Runtime（\u751f\u4ea7\u7ea7\u8fd0\u884c\u65f6）+ AgentScope Studio（\u53ef\u89c6\u5316\u5f00\u53d1\u5de5\u5177\u94fe）<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

                          \u6d88\u606f\u9a71\u52a8\u7684\u56db\u4e2a\u4f18\u52bf：\u5f02\u6b65\u89e3\u8026（\u6536\u53d1\u53cc\u65b9\u65f6\u95f4\u4e0a\u89e3\u8026，\u5929\u7136\u9ad8\u5e76\u53d1）\u3001\u4f4d\u7f6e\u900f\u660e（\u4e0d\u5173\u5fc3\u5bf9\u65b9\u5728\u672c\u5730\u8fdb\u7a0b\u8fd8\u662f\u8fdc\u7a0b\u670d\u52a1\u5668，\u8def\u7531\u81ea\u52a8\u5904\u7406）\u3001\u53ef\u89c2\u6d4b\u6027（\u6bcf\u6761\u6d88\u606f\u53ef\u8bb0\u5f55\u8ffd\u8e2a\u5206\u6790）\u3001\u53ef\u9760\u6027（\u6d88\u606f\u53ef\u6301\u4e45\u5316\u4e0e\u91cd\u8bd5，\u6545\u969c\u540e\u4fdd\u8bc1\u6700\u7ec8\u4e00\u81f4\u6027）\u3002<\/p>\n

                          \u7edf\u4e00\u6d88\u606f\u7ed3\u6784：Msg(name=\u53d1\u9001\u8005, content=\u5185\u5bb9, role=\u89d2\u8272, metadata={timestamp, message_type, priority})\u3002<\/p>\n

                          \u667a\u80fd\u4f53\u751f\u547d\u5468\u671f：\u7edf\u4e00\u57fa\u7c7b AgentBase，\u5f00\u53d1\u8005\u53ea\u9700\u5b9e\u73b0\u6838\u5fc3\u7684 reply(x: Msg) -> Msg（\u601d\u8003\u4e0e\u56de\u5e94）\u4e0e\u53ef\u9009\u7684 observe(x: Msg)（\u89c2\u5bdf，\u901a\u5e38\u5199\u5165\u8bb0\u5fc6）\u2014\u2014 \u5206\u79bb\u4e86\u5185\u90e8\u903b\u8f91\u4e0e\u5916\u90e8\u901a\u4fe1\u3002<\/p>\n

                          MsgHub \u4e09\u5927\u80fd\u529b：\u7075\u6d3b\u8def\u7531（\u70b9\u5bf9\u70b9\/\u5e7f\u64ad\/\u7ec4\u64ad）\u3001\u6d88\u606f\u6301\u4e45\u5316（SQLite\/MongoDB，\u652f\u6301\u957f\u671f\u4efb\u52a1\u72b6\u6001\u6062\u590d）\u3001\u539f\u751f\u5206\u5e03\u5f0f（\u8de8\u8fdb\u7a0b\/\u8de8\u670d\u52a1\u5668\u90e8\u7f72\u65f6\u7531 RPC \u81ea\u52a8\u5904\u7406\u901a\u4fe1，\u5bf9\u5f00\u53d1\u8005\u900f\u660e）\u3002<\/p>\n

                          6.3.2 \u6848\u4f8b：\u4e09\u56fd\u72fc\u4eba\u6740（\u7406\u89e3\u6d88\u606f\u9a71\u52a8\u4e0e\u5e76\u53d1）<\/h4>\n

                          \u4e09\u5c42\u8bbe\u8ba1：\u6e38\u620f\u63a7\u5236\u5c42（ThreeKingdomsWerewolfGame \u7ef4\u62a4\u5168\u5c40\u72b6\u6001\u3001\u63a8\u8fdb\u9636\u6bb5\u3001\u88c1\u5b9a\u80dc\u8d1f）+ \u667a\u80fd\u4f53\u4ea4\u4e92\u5c42（\u5b8c\u5168\u7531 MsgHub \u9a71\u52a8）+ \u89d2\u8272\u5efa\u6a21\u5c42（\u6bcf\u4e2a\u73a9\u5bb6\u662f DialogAgent \u5b9e\u4f8b，\u63d0\u793a\u8bcd\u6ce8\u5165"\u6e38\u620f\u89d2\u8272"\u4e0e"\u4e09\u56fd\u4eba\u683c"\u53cc\u91cd\u8eab\u4efd）\u3002<\/p>\n

                          \u4e24\u4e2a\u6700\u503c\u5f97\u8bb0\u7684\u6280\u672f\u70b9：<\/p>\n

                        • \u7528\u6d88\u606f\u9891\u9053\u4ee3\u66ff\u72b6\u6001\u673a：\u72fc\u4eba\u9636\u6bb5\u901a\u8fc7 async with MsgHub(self.werewolves, enable_auto_broadcast=True, announcement=…) \u52a8\u6001\u521b\u5efa\u4e00\u4e2a\u4e34\u65f6\u79c1\u5bc6\u9891\u9053；\u8ba8\u8bba\u9636\u6bb5\u5e7f\u64ad\u534f\u5546，\u6295\u7968\u9636\u6bb5 set_auto_broadcast(False) \u540e\u7528 fanout_pipeline \u5e76\u884c\u6536\u96c6\u51b3\u7b56\u3002\u6e38\u620f\u903b\u8f91\u88ab\u8868\u8fbe\u4e3a"\u5728\u7279\u5b9a\u4e0a\u4e0b\u6587\u4e2d\u4ee5\u4f55\u79cd\u6a21\u5f0f\u4ea4\u6362\u6d88\u606f"，\u800c\u975e\u4e00\u8fde\u4e32\u50f5\u786c\u7684\u72b6\u6001\u8f6c\u6362\u3002<\/li>\n
                        • \u7528\u7ed3\u6784\u5316\u8f93\u51fa\u7ea6\u675f\u6e38\u620f\u89c4\u5219：\u4e3a\u6bcf\u4e2a\u884c\u4e3a\u5b9a\u4e49 Pydantic \u6a21\u578b（\u5982 DiscussionModelCN{reach_agreement, confidence_level(ge=1,le=10), key_evidence}\u3001WitchActionModelCN{use_antidote, use_poison, target_name}），\u628a\u6e38\u620f\u89c4\u5219\u76f4\u63a5\u8f6c\u5316\u4e3a\u4ee3\u7801\u5c42\u9762\u7684\u5b57\u6bb5\u7ea6\u675f\u4e0e\u9a8c\u8bc1（\u5973\u5deb\u4e0d\u80fd\u540c\u65f6\u5bf9\u540c\u4e00\u76ee\u6807\u7528\u89e3\u836f\u548c\u6bd2\u836f\u3001\u9884\u8a00\u5bb6\u6bcf\u665a\u53ea\u67e5\u4e00\u4eba）\u3002<\/li>\n

                          \u5e76\u53d1\u4e0e\u5bb9\u9519：fanout_pipeline(…) \u5e76\u884c\u5411\u6240\u6709\u667a\u80fd\u4f53\u53d1\u6d88\u606f\u5e76\u5f02\u6b65\u6536\u96c6\u54cd\u5e94（\u6a21\u62df"\u540c\u65f6\u6295\u7968"）；\u5173\u952e\u73af\u8282 try\/except \u6355\u83b7\u5f02\u5e38\u540e\u6784\u9020\u9ed8\u8ba4\u54cd\u5e94（\u5982 confidence_level=5）\u4fdd\u8bc1\u6d41\u7a0b\u4e0d\u4e2d\u65ad\u3002<\/p>\n

                          6.3.3 \u4f18\u52bf\u4e0e\u5c40\u9650<\/h4>\n

                          \u4f18\u52bf：\u6d88\u606f\u9a71\u52a8\u4f18\u96c5\u66ff\u4ee3\u50f5\u786c\u72b6\u6001\u673a；\u7ed3\u6784\u5316\u8f93\u51fa\u628a\u89c4\u5219\u53d8\u6210\u53ef\u9a8c\u8bc1\u7684\u4ee3\u7801\u7ea6\u675f；\u539f\u751f\u5e76\u53d1 + \u5355\u70b9\u5f02\u5e38\u4e0d\u5f71\u54cd\u6574\u4f53\u6d41\u7a0b\u3002
                          \n\u5c40\u9650：\u26a0\ufe0f \u67b6\u6784\u590d\u6742\u3001\u6280\u672f\u95e8\u69db\u9ad8（\u9700\u7406\u89e3\u5f02\u6b65\u7f16\u7a0b\u4e0e\u5206\u5e03\u5f0f\u901a\u4fe1）；\u7b80\u5355\u5bf9\u8bdd\u573a\u666f\u4e0b\u6709**\u201c\u8fc7\u5ea6\u5de5\u7a0b\u5316\u201d**\u98ce\u9669；\u4f5c\u4e3a\u8f83\u65b0\u7684\u6846\u67b6，\u751f\u6001\u4e0e\u793e\u533a\u8d44\u6e90\u6709\u5f85\u5b8c\u5584\u3002\u66f4\u9002\u5408\u751f\u4ea7\u7ea7\u5927\u89c4\u6a21\u7cfb\u7edf，\u5feb\u901f\u539f\u578b\u5e94\u9009\u66f4\u8f7b\u91cf\u7684\u6846\u67b6\u3002<\/p>\n

                          6.4 CAMEL \u2014\u2014 \u89d2\u8272\u626e\u6f14\u5f0f\u7684\u8f7b\u91cf\u534f\u4f5c<\/h3>\n

                          CAMEL\u521d\u59cb\u76ee\u6807：\u63a2\u7d22\u5982\u4f55\u5728\u6700\u5c11\u4eba\u7c7b\u5e72\u9884\u4e0b，\u8ba9\u4e24\u4e2a\u667a\u80fd\u4f53\u901a\u8fc7"\u89d2\u8272\u626e\u6f14"\u81ea\u4e3b\u534f\u4f5c\u89e3\u51b3\u590d\u6742\u4efb\u52a1（Li et al., NeurIPS 2023）\u3002<\/p>\n

                          6.4.1 \u4e24\u5927\u57fa\u77f3 \u2b50<\/h4>\n

                          \u2460 \u89d2\u8272\u626e\u6f14（Role-Playing）：\u4e24\u4e2a\u4e92\u8865\u89d2\u8272\u7684\u667a\u80fd\u4f53\u534f\u4f5c \u2014\u2014<\/p>\n

                            \n
                          • AI User\u7528\u6237：\u63d0\u51fa\u9700\u6c42\u3001\u4e0b\u8fbe\u6307\u4ee4\u3001\u6784\u601d\u4efb\u52a1\u6b65\u9aa4\u3002<\/li>\n
                          • AI Assistant\u52a9\u7406：\u6267\u884c\u5177\u4f53\u64cd\u4f5c\u3001\u63d0\u4f9b\u89e3\u51b3\u65b9\u6848\u3002<\/li>\n<\/ul>\n

                            \u5178\u578b\u8bbe\u5b9a（\u80a1\u7968\u4ea4\u6613\u7b56\u7565\u5206\u6790\u5de5\u5177）：AI User = \u61c2\u5e02\u573a\u4f46\u4e0d\u61c2\u7f16\u7a0b\u7684"\u8d44\u6df1\u80a1\u7968\u4ea4\u6613\u5458"；AI Assistant = \u7cbe\u901a\u7f16\u7a0b\u4f46\u4e0d\u61c2\u80a1\u7968\u7684"Python \u7a0b\u5e8f\u5458"\u3002\u4efb\u52a1\u89e3\u51b3\u8fc7\u7a0b\u88ab\u81ea\u7136\u8f6c\u5316\u4e3a\u4e24\u4f4d\u8de8\u9886\u57df\u4e13\u5bb6\u7684\u5bf9\u8bdd\u3002<\/p>\n

                            \"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\"<\/p>\n

                            \u2461 \u5f15\u5bfc\u6027\u63d0\u793a（Inception Prompting）：\u2b50 CAMEL \u6700\u6838\u5fc3\u7684\u6280\u672f \u2014\u2014 \u5bf9\u8bdd\u5f00\u59cb\u524d\u5206\u522b\u6ce8\u5165\u7ed9\u4e24\u4e2a\u667a\u80fd\u4f53\u7684\u7ed3\u6784\u5316\u521d\u59cb\u6307\u4ee4，\u786e\u4fdd\u5b83\u4eec"\u5f85\u5728\u81ea\u5df1\u7684\u89d2\u8272\u91cc"\u5e76\u671d\u5171\u540c\u76ee\u6807\u524d\u8fdb\u3002\u56db\u8981\u7d20：<\/p>\n

                          • \u660e\u786e\u81ea\u8eab\u89d2\u8272：\u201c\u4f60\u662f\u4e00\u4f4d\u8d44\u6df1\u7684\u80a1\u7968\u4ea4\u6613\u5458\u2026\u2026\u201d<\/li>\n
                          • \u544a\u77e5\u534f\u4f5c\u8005\u89d2\u8272：\u201c\u4f60\u6b63\u5728\u4e0e\u4e00\u4f4d\u4f18\u79c0\u7684 Python \u7a0b\u5e8f\u5458\u5408\u4f5c\u2026\u2026\u201d<\/li>\n
                          • \u5b9a\u4e49\u5171\u540c\u76ee\u6807：\u201c\u4f60\u4eec\u7684\u5171\u540c\u76ee\u6807\u662f\u5f00\u53d1\u4e00\u4e2a\u80a1\u7968\u4ea4\u6613\u7b56\u7565\u5206\u6790\u5de5\u5177\u3002\u201d<\/li>\n
                          • \u8bbe\u5b9a\u884c\u4e3a\u7ea6\u675f\u4e0e\u6c9f\u901a\u534f\u8bae（\u6700\u5173\u952e）：\u8981\u6c42 AI User"\u4e00\u6b21\u53ea\u63d0\u51fa\u4e00\u4e2a\u6e05\u6670\u5177\u4f53\u7684\u6b65\u9aa4"\u3001AI Assistant"\u5728\u5b8c\u6210\u4e0a\u4e00\u6b65\u4e4b\u524d\u4e0d\u8981\u8ffd\u95ee\u66f4\u591a\u7ec6\u8282"\u3001\u53cc\u65b9\u7528\u7279\u5b9a\u6807\u5fd7（\u5982 <SOLUTION> \/ <CAMEL_TASK_DONE>）\u6807\u8bc6\u5b8c\u6210\u3002<\/li>\n

                            6.4.2 \u6848\u4f8b：AI \u79d1\u666e\u7535\u5b50\u4e66（\u5fc3\u7406\u5b66\u5bb6 \u00d7 \u4f5c\u5bb6）<\/h4>\n
                              \n
                            • \u89d2\u8272\u5206\u914d：user_role_name="\u4f5c\u5bb6"（\u5bf9\u8bdd\u63a8\u52a8\u8005\u3001\u9700\u6c42\u65b9，\u8d1f\u8d23\u7ed3\u6784\u89c4\u5212\u4e0e\u53ef\u8bfb\u6027）\u3001assistant_role_name="\u5fc3\u7406\u5b66\u5bb6"（\u6267\u884c\u8005\u3001\u65b9\u6848\u63d0\u4f9b\u65b9，\u8d1f\u8d23\u4e13\u4e1a\u77e5\u8bc6）\u3002<\/li>\n
                            • API：RolePlaying(assistant_role_name, user_role_name, task_prompt, model=…)；init_chat() \u81ea\u52a8\u751f\u6210\u5f00\u573a（\u65e0\u9700\u4eba\u5de5\u5199），step(input_msg) \u9a71\u52a8\u4e00\u8f6e\u5b8c\u6574\u4ea4\u4e92（\u4f5c\u5bb6\u63d0\u9700\u6c42 + \u5fc3\u7406\u5b66\u5bb6\u7ed9\u5185\u5bb9），\u5e76\u628a\u4e0a\u4e00\u8f6e assistant \u7684\u8f93\u51fa\u4f5c\u4e3a\u4e0b\u4e00\u8f6e\u8f93\u5165\u3002<\/li>\n
                            • \u7ec8\u6b62：\u8fbe\u5230 chat_turn_limit（30 \u8f6e）\u6216\u4efb\u4e00\u65b9\u8f93\u51fa <CAMEL_TASK_DONE>\u3002<\/li>\n
                            • \u89c2\u5bdf\u5230\u7684\u56db\u4e2a\u81ea\u53d1\u9636\u6bb5：\u6846\u67b6\u642d\u5efa\u4e0e\u76ee\u6807\u5bf9\u9f50（1\u20135 \u8f6e）\u2192 \u6838\u5fc3\u5185\u5bb9\u751f\u6210\u4e0e\u77e5\u8bc6\u8f6c\u8bd1（6\u201320 \u8f6e，\u201c\u8bf7\u6c42\u2014\u54cd\u5e94"\u5faa\u73af，\u5fc3\u7406\u5b66\u5bb6\u63d0\u4f9b\u786c\u6838\u77e5\u8bc6\u3001\u4f5c\u5bb6\u505a"\u7ffb\u8bd1\u5b98\u201d）\u2192 \u8fed\u4ee3\u4f18\u5316\u4e0e\u8d28\u91cf\u4fdd\u8bc1（21\u201325 \u8f6e，\u4f5c\u5bb6\u770b\u6d41\u7545\u6027\u3001\u5fc3\u7406\u5b66\u5bb6\u505a\u4e8b\u5b9e\u6838\u67e5）\u2192 \u603b\u7ed3\u4e0e\u5347\u534e\u3002<\/li>\n<\/ul>\n

                              6.4.3 \u4f18\u52bf\u4e0e\u5c40\u9650<\/h4>\n

                              \u4f18\u52bf：\u2b50"\u8f7b\u67b6\u6784\u3001\u91cd\u63d0\u793a"\u2014\u2014 \u76f8\u6bd4 AutoGen \u7684\u590d\u6742\u5bf9\u8bdd\u7ba1\u7406\u4e0e AgentScope \u7684\u5206\u5e03\u5f0f\u67b6\u6784，\u9760\u7cbe\u5fc3\u8bbe\u8ba1\u7684\u521d\u59cb\u63d0\u793a\u5c31\u80fd\u5b9e\u73b0\u9ad8\u8d28\u91cf\u534f\u4f5c，\u81ea\u7136\u6d8c\u73b0\u7684\u534f\u4f5c\u5f80\u5f80\u6bd4\u786c\u7f16\u7801\u5de5\u4f5c\u6d41\u66f4\u7075\u6d3b\u9ad8\u6548\u3002\u73b0\u5df2\u6709\u591a\u6a21\u6001\u3001\u5de5\u5177\u5e93\u3001\u591a\u6a21\u578b\u540e\u7aef，\u5e76\u4e0e LangChain \/ CrewAI \/ AutoGen \u4e92\u64cd\u4f5c\u3002<\/p>\n

                              \u5c40\u9650：\u2460 \u5bf9\u63d0\u793a\u5de5\u7a0b\u9ad8\u5ea6\u4f9d\u8d56（\u8bbe\u8ba1\u95e8\u69db\u9ad8\u3001\u8c03\u8bd5\u96be\u5b9a\u4f4d\u3001\u4e0d\u540c LLM \u7406\u89e3\u4e0d\u4e00\u81f4）；\u2461 \u534f\u4f5c\u89c4\u6a21\u53d7\u9650（\u7f3a AutoGen \u5f0f\u5bf9\u8bdd\u8def\u7531\u3001\u7f3a AgentScope \u5f0f\u5206\u5e03\u5f0f\u72b6\u6001\u7ba1\u7406\u3001\u7f3a\u51b2\u7a81\u4ef2\u88c1）；\u2462 \u4efb\u52a1\u8fb9\u754c（\u4e25\u683c\u6d41\u7a0b\u63a7\u5236 \u2192 LangGraph \u66f4\u5408\u9002；\u5927\u89c4\u6a21\u5e76\u53d1 \u2192 AgentScope \u66f4\u4f18；\u590d\u6742\u591a\u65b9\u51b3\u7b56 \u2192 AutoGen \u7fa4\u804a\u66f4\u7075\u6d3b）\u3002<\/p>\n

                              6.5 LangGraph \u2014\u2014 \u7528\u72b6\u6001\u56fe\u7cbe\u786e\u63a7\u5236\u6d41\u7a0b（\u672c\u7ae0\u91cd\u70b9）<\/h3>\n

                              LangGraph \u662f LangChain \u751f\u6001\u7cfb\u7edf\u7684\u6269\u5c55\u3002LangGraph \u4e0e\u57fa\u4e8e"\u5bf9\u8bdd"\u7684\u6846\u67b6\u4e0d\u540c，\u5b83\u5c06\u667a\u80fd\u4f53\u7684\u6267\u884c\u6d41\u7a0b\u5efa\u6a21\u4e3a\u4e00\u79cd\u72b6\u6001\u673a （State Machine），\u5e76\u5c06\u5176\u8868\u793a\u4e3a\u6709\u5411\u56fe（Directed Graph）\u3002\u5728\u8fd9\u79cd\u8303\u5f0f\u4e2d，\u56fe\u7684**\u8282\u70b9（Nodes）\u4ee3\u8868\u4e00 \u4e2a\u5177\u4f53\u7684\u8ba1\u7b97\u6b65\u9aa4（\u5982\u8c03\u7528 LLM\u3001\u6267\u884c\u5de5\u5177），\u800c\u8fb9（Edges）**\u5219\u5b9a\u4e49\u4e86\u4ece\u4e00\u4e2a\u8282\u70b9\u5230\u53e6\u4e00\u4e2a\u8282\u70b9\u7684\u8df3\u8f6c\u903b \u8f91\u3002<\/p>\n

                              LangGraph\u8bbe\u8ba1\u7684\u9769\u547d\u6027\u4e4b\u5904\u5728\u4e8e\u5b83\u5929\u7136\u652f\u6301\u5faa\u73af，\u4f7f\u5f97\u6784\u5efa\u80fd\u591f\u8fdb\u884c\u8fed\u4ee3\u3001\u53cd\u601d\u548c\u81ea\u6211\u4fee\u6b63\u7684\u590d\u6742\u667a\u80fd\u4f53\u5de5\u4f5c \u6d41\u53d8\u5f97\u524d\u6240\u672a\u6709\u7684\u76f4\u89c2\u548c\u7b80\u5355\u3002<\/p>\n

                              6.5.1 \u4e09\u4e2a\u57fa\u672c\u6784\u6210\u8981\u7d20 \u2b50<\/h4>\n\n\u8981\u7d20\u5b9a\u4e49\u8981\u70b9<\/tr>\n\n\n\n\n
                              \u5168\u5c40\u72b6\u6001 State<\/td>\n\u7528 TypedDict \u5b9a\u4e49\u7684\u5171\u4eab\u72b6\u6001\u5bf9\u8c61（\u5bf9\u8bdd\u5386\u53f2\u3001\u4e2d\u95f4\u7ed3\u679c\u3001\u8fed\u4ee3\u6b21\u6570\u3001\u5f53\u524d\u6b65\u9aa4\u6807\u8bb0\u7b49）<\/td>\n\u6240\u6709\u8282\u70b9\u90fd\u80fd\u8bfb\u3001\u90fd\u80fd\u66f4\u65b0<\/td>\n<\/tr>\n
                              \u8282\u70b9 Node<\/td>\ndef node(state) -> dict，\u63a5\u6536\u72b6\u6001\u3001\u8fd4\u56de\u8981\u66f4\u65b0\u7684\u5b57\u6bb5\u589e\u91cf<\/td>\n\u6267\u884c\u5177\u4f53\u5de5\u4f5c\u7684\u5355\u5143；\u662f\u7eaf\u51fd\u6570，\u4fbf\u4e8e\u5355\u6d4b<\/td>\n<\/tr>\n
                              \u8fb9 Edge<\/td>\n\u8fde\u63a5\u8282\u70b9；\u542b\u5e38\u89c4\u8fb9\u4e0e\u6761\u4ef6\u8fb9（Conditional Edges）<\/td>\n\u2b50 \u6761\u4ef6\u8fb9\u901a\u8fc7\u51fd\u6570\u5224\u65ad\u72b6\u6001\u3001\u52a8\u6001\u51b3\u5b9a\u8df3\u5411\u54ea\u4e2a\u8282\u70b9 \u2014\u2014 \u5b9e\u73b0\u5faa\u73af\u4e0e\u5206\u652f\u7684\u5173\u952e<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

                              class<\/span> AgentState<\/span>(<\/span>TypedDict)<\/span>:<\/span> # \u2460 \u72b6\u6001<\/span>
                              \n messages:<\/span> List[<\/span>str<\/span>]<\/span>;<\/span> current_task:<\/span> str<\/span>;<\/span> final_answer:<\/span> str<\/span><\/p>\n

                              def<\/span> should_continue<\/span>(<\/span>state)<\/span> –<\/span>><\/span> str<\/span>:<\/span> # \u2462 \u6761\u4ef6\u8fb9\u51fd\u6570<\/span>
                              \n if<\/span> len<\/span>(<\/span>state[<\/span>"messages"<\/span>]<\/span>)<\/span> <<\/span> 3<\/span>:<\/span> return<\/span> "continue_to_planner"<\/span>
                              \n return<\/span> "end_workflow"<\/span><\/p>\n

                              workflow =<\/span> StateGraph(<\/span>AgentState)<\/span>
                              \nworkflow.<\/span>add_node(<\/span>"planner"<\/span>,<\/span> planner_node)<\/span> # \u2461 \u8282\u70b9<\/span>
                              \nworkflow.<\/span>set_entry_point(<\/span>"planner"<\/span>)<\/span>
                              \nworkflow.<\/span>add_edge(<\/span>"planner"<\/span>,<\/span> "executor"<\/span>)<\/span> # \u5e38\u89c4\u8fb9<\/span>
                              \nworkflow.<\/span>add_conditional_edges(<\/span>"executor"<\/span>,<\/span> should_continue,<\/span>
                              \n {<\/span>"continue_to_planner"<\/span>:<\/span> "planner"<\/span>,<\/span> "end_workflow"<\/span>:<\/span> END}<\/span>)<\/span> # \u2b50 \u6761\u4ef6\u8fb9 \u2192 \u5f62\u6210\u5faa\u73af<\/span>
                              \napp =<\/span> workflow.<\/span>compile<\/span>(<\/span>)<\/span>
                              \nfor<\/span> event in<\/span> app.<\/span>stream(<\/span>inputs)<\/span>:<\/span> print<\/span>(<\/span>event)<\/span> # \u8fd0\u884c<\/span><\/p>\n

                              6.5.2 \u6848\u4f8b：\u4e09\u6b65\u95ee\u7b54\u52a9\u624b（\u7406\u89e3 \u2192 \u641c\u7d22 \u2192 \u56de\u7b54）<\/h4>\n

                              \u72b6\u6001\u8bbe\u8ba1\u7684\u5173\u952e：SearchState \u540c\u65f6\u5305\u542b user_query（LLM \u7406\u89e3\u540e\u7684\u9700\u6c42\u603b\u7ed3）\u4e0e search_query（\u4f18\u5316\u540e\u7528\u4e8e\u641c\u7d22\u5f15\u64ce\u7684\u5173\u952e\u8bcd）\u2014\u2014 \u8ba9\u667a\u80fd\u4f53\u5148\u628a\u81ea\u7136\u8bed\u8a00\u63d0\u95ee\u4f18\u5316\u6210\u7cbe\u70bc\u5173\u952e\u8bcd，\u663e\u8457\u63d0\u5347\u641c\u7d22\u8d28\u91cf；\u53e6\u6709 step \u5b57\u6bb5\u6807\u8bb0\u5f53\u524d\u6b65\u9aa4，\u4f9b\u4e0b\u6e38\u8282\u70b9\u505a\u6761\u4ef6\u5224\u65ad\u3002<\/p>\n

                              \u4e09\u4e2a\u8282\u70b9：<\/p>\n\n\u8282\u70b9\u804c\u8d23\u5de5\u7a0b\u7ec6\u8282<\/tr>\n\n\n\n\n
                              understand<\/td>\n\u610f\u56fe\u7406\u89e3 + \u751f\u6210\u641c\u7d22\u5173\u952e\u8bcd<\/td>\n\u63d0\u793a\u8bcd\u8981\u6c42\u6309"\u7406\u89e3：\u2026 \/ \u641c\u7d22\u8bcd：\u2026"\u683c\u5f0f\u8f93\u51fa，\u518d\u6309\u5206\u9694\u7b26\u5207\u51fa\u5173\u952e\u8bcd<\/td>\n<\/tr>\n
                              search（Tavily）<\/td>\n\u771f\u5b9e\u8054\u7f51\u641c\u7d22<\/td>\n\u2b50 try\/except \u6355\u83b7\u5f02\u5e38，\u5931\u8d25\u65f6\u628a step \u7f6e\u4e3a "search_failed"（\u628a\u5931\u8d25\u5199\u8fdb\u72b6\u6001，\u4f9b\u4e0b\u6e38\u51b3\u7b56）<\/td>\n<\/tr>\n
                              answer<\/td>\n\u751f\u6210\u6700\u7ec8\u7b54\u6848<\/td>\n\u2b50 \u68c0\u67e5 state["step"]：\u5931\u8d25\u5219\u56de\u9000\u4e3a\u57fa\u4e8e LLM \u81ea\u8eab\u77e5\u8bc6\u56de\u7b54\u5e76\u544a\u77e5\u7528\u6237；\u6210\u529f\u5219\u7528\u5e26\u5b9e\u65f6\u7ed3\u679c\u7684\u63d0\u793a\u751f\u6210\u6709\u636e\u53ef\u4f9d\u7684\u7b54\u6848<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

                              \u56fe\u7ed3\u6784：START \u2192 understand \u2192 search \u2192 answer \u2192 END；\u7f16\u8bd1\u65f6\u53ef\u4f20\u5165 checkpointer=InMemorySaver() \u83b7\u5f97\u6301\u4e45\u5316\u4e0e\u591a\u8f6e\u4ea4\u4e92\u80fd\u529b\u3002<\/p>\n

                              6.5.3 \u4f18\u52bf\u4e0e\u5c40\u9650<\/h4>\n

                              \u4f18\u52bf：\u2460 \u2b50 \u9ad8\u5ea6\u53ef\u63a7\u4e0e\u53ef\u9884\u6d4b \u2014\u2014 \u53ef\u7cbe\u786e\u89c4\u5212\u6bcf\u4e00\u6b65\u884c\u4e3a，\u5bf9\u9ad8\u53ef\u9760\u6027\u3001\u53ef\u5ba1\u8ba1\u7684\u751f\u4ea7\u7ea7\u5e94\u7528\u81f3\u5173\u91cd\u8981；\u2461 \u5bf9\u5faa\u73af\u7684\u539f\u751f\u652f\u6301 \u2014\u2014 \u6761\u4ef6\u8fb9\u53ef\u8f7b\u677e\u6784\u5efa"\u53cd\u601d\u2014\u4fee\u6b63"\u4e0e"\u5931\u8d25\u56de\u9000"\u8def\u5f84；\u2462 \u6a21\u5757\u5316（\u6bcf\u4e2a\u8282\u70b9\u662f\u72ec\u7acb Python \u51fd\u6570）；\u2463 \u63d2\u5165"\u7b49\u5f85\u4eba\u5de5\u5ba1\u6838"\u8282\u70b9\u975e\u5e38\u76f4\u63a5 \u2192 \u5929\u7136\u7684 Human-in-the-loop\u3002<\/p>\n

                              \u5c40\u9650：\u2460 \u6837\u677f\u4ee3\u7801\u591a（Boilerplate），\u7b80\u5355\u4efb\u52a1\u663e\u5f97\u7e41\u7410；\u5f00\u53d1\u8005\u8981\u66f4\u591a\u601d\u8003"\u5982\u4f55\u63a7\u5236\u6d41\u7a0b（how）\u201c\u800c\u975e"\u505a\u4ec0\u4e48（what）\u201d；\u2461 \u2b50 \u7f3a\u5c11"\u6d8c\u73b0\u5f0f"\u7684\u52a8\u6001\u4ea4\u4e92 \u2014\u2014 \u5f3a\u9879\u662f\u6267\u884c\u786e\u5b9a\u7684\u53ef\u9760\u6d41\u7a0b，\u800c\u975e\u6a21\u62df\u5f00\u653e\u5f0f\u793e\u4f1a\u534f\u4f5c；\u2462 \u8c03\u8bd5\u8981\u6c42\u5bf9\u6574\u56fe\u6709\u5168\u5c40\u7406\u89e3（\u95ee\u9898\u53ef\u80fd\u5728\u8282\u70b9\u5185\u90e8\u903b\u8f91\u3001\u8282\u70b9\u95f4\u72b6\u6001\u5f02\u53d8\u3001\u6216\u8fb9\u6761\u4ef6\u5224\u65ad）\u3002<\/p>\n

                              6.6 \u56db\u6846\u67b6\u5bf9\u6bd4\u4e0e\u9009\u578b \u2b50<\/h3>\n

                              \"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\"<\/p>\n\n\u7ef4\u5ea6AutoGenAgentScopeCAMELLangGraph<\/tr>\n\n\n\n\n\n\n\n\n
                              \u534f\u4f5c\u6a21\u5f0f<\/td>\n\u7fa4\u804a（RoundRobin \/ Team）<\/td>\nMsgHub \u6d88\u606f\u4e2d\u5fc3 + Pipeline<\/td>\n\u53cc\u667a\u80fd\u4f53\u89d2\u8272\u626e\u6f14<\/td>\n\u6709\u5411\u56fe + \u72b6\u6001\u673a<\/td>\n<\/tr>\n
                              \u63a7\u5236\u65b9\u5f0f<\/td>\n\u6d8c\u73b0\u5f0f（\u5b9a\u89d2\u8272\u4e0e\u89c4\u5219）<\/td>\n\u6d88\u606f\u9a71\u52a8 + \u5de5\u7a0b\u5316<\/td>\n\u6d8c\u73b0\u5f0f（\u5f15\u5bfc\u6027\u63d0\u793a）<\/td>\n\u663e\u5f0f\u63a7\u5236（\u9884\u5b9a\u4e49\u8282\u70b9\u4e0e\u8fb9）<\/td>\n<\/tr>\n
                              \u5e76\u53d1\/\u5206\u5e03\u5f0f<\/td>\n\u5f02\u6b65\u4f18\u5148<\/td>\n\u2b50 \u539f\u751f\u5206\u5e03\u5f0f\u3001\u9ad8\u5e76\u53d1<\/td>\n\u5f31<\/td>\n\u4f9d\u8d56 LangChain \u751f\u6001<\/td>\n<\/tr>\n
                              \u5faa\u73af\u652f\u6301<\/td>\n\u95f4\u63a5（\u9760\u5bf9\u8bdd\u8f6e\u6b21）<\/td>\n\u9760\u6d88\u606f\u6a21\u5f0f<\/td>\n\u9760\u5bf9\u8bdd\u8f6e\u6b21<\/td>\n\u2b50 \u539f\u751f\u652f\u6301（\u6761\u4ef6\u8fb9）<\/td>\n<\/tr>\n
                              \u4eba\u7c7b\u5728\u73af<\/td>\n\u2b50 UserProxyAgent \u5929\u7136\u652f\u6301<\/td>\n\u652f\u6301<\/td>\n\u5f31<\/td>\n\u2b50 \u63d2\u5165\u4eba\u5de5\u5ba1\u6838\u8282\u70b9\u5373\u53ef<\/td>\n<\/tr>\n
                              \u4e0a\u624b\u6210\u672c<\/td>\n\u4e2d<\/td>\n\u9ad8（\u9700\u61c2\u5f02\u6b65\/\u5206\u5e03\u5f0f）<\/td>\n\u4f4e（\u63d0\u793a\u8bcd\u9a71\u52a8）<\/td>\n\u4e2d（\u6837\u677f\u4ee3\u7801\u591a）<\/td>\n<\/tr>\n
                              \u5178\u578b\u573a\u666f<\/td>\n\u6d41\u7a0b\u56fa\u5b9a\u7684\u591a\u4eba\u534f\u4f5c（\u8f6f\u4ef6\u5f00\u53d1\u56e2\u961f）<\/td>\n\u5927\u89c4\u6a21\u751f\u4ea7\u7ea7\u3001\u9ad8\u5e76\u53d1<\/td>\n\u521b\u610f\/\u6df1\u5ea6\u534f\u4f5c（\u5199\u4f5c\u3001\u7814\u7a76）<\/td>\n\u9700\u53ef\u5ba1\u8ba1\u3001\u53ef\u56de\u6eaf\u7684\u4e25\u8c28\u6d41\u7a0b<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

                              \u4e24\u6761\u8bbe\u8ba1\u6743\u8861\u4e3b\u7ebf（\u539f\u6587\u7ed3\u8bba）：<\/p>\n

                            • \u2b50 \u201c\u6d8c\u73b0\u5f0f\u534f\u4f5c\u201d vs \u201c\u663e\u5f0f\u63a7\u5236\u201d：AutoGen \u4e0e CAMEL \u901a\u8fc7\u5b9a\u4e49"\u89d2\u8272"\u4e0e"\u76ee\u6807"\u8ba9\u534f\u4f5c\u884c\u4e3a\u4ece\u7b80\u5355\u5bf9\u8bdd\u89c4\u5219\u4e2d\u6d8c\u73b0，\u8d34\u8fd1\u4eba\u7c7b\u4ea4\u4e92\u4f46\u96be\u9884\u6d4b\u3001\u96be\u8c03\u8bd5；LangGraph \u8981\u6c42\u660e\u786e\u5b9a\u4e49\u6bcf\u4e2a\u6b65\u9aa4\u4e0e\u8df3\u8f6c\u6761\u4ef6，\u727a\u7272\u4e86\u6d8c\u73b0\u7684\u60ca\u559c，\u6362\u6765\u53ef\u9760\u6027\u3001\u53ef\u63a7\u6027\u4e0e\u53ef\u89c2\u6d4b\u6027\u3002<\/li>\n
                            • \u2b50 \u5de5\u7a0b\u5316\u7ef4\u5ea6：AgentScope \u63ed\u793a\u4e86\u7b2c\u4e8c\u91cd\u6311\u6218 \u2014\u2014 \u65e0\u8bba\u9009\u54ea\u79cd\u534f\u4f5c\u8303\u5f0f，\u4ece\u5b9e\u9a8c\u539f\u578b\u63a8\u5411\u751f\u4ea7\u90fd\u5fc5\u987b\u9762\u5bf9\u5e76\u53d1\u3001\u5bb9\u9519\u3001\u5206\u5e03\u5f0f\u90e8\u7f72，\u8fd9\u4ee3\u8868"\u4ece\u80fd\u8fd0\u884c\u5230\u80fd\u7a33\u5b9a\u670d\u52a1"\u7684\u5173\u952e\u8de8\u8d8a\u3002<\/li>\n

                              \u9009\u578b\u901f\u7b54（\u5bf9\u5e94\u6559\u6750\u4e60\u9898 6）：<\/p>\n

                                \n
                              • \u9ad8\u5e76\u53d1\u5ba2\u670d（1000+ QPS\u30017\u00d724\u3001\u9700\u6c34\u5e73\u6269\u5c55）\u2192 AgentScope（\u539f\u751f\u5e76\u53d1\u4e0e\u5206\u5e03\u5f0f）\u3002<\/li>\n
                              • \u79d1\u7814\u8bba\u6587\u8f85\u52a9\u5199\u4f5c（\u4e24\u667a\u80fd\u4f53\u591a\u8f6e\u6df1\u5ea6\u8ba8\u8bba\u3001\u81ea\u4e3b\u63a8\u8fdb）\u2192 CAMEL（\u89d2\u8272\u626e\u6f14 + \u5f15\u5bfc\u6027\u63d0\u793a）\u3002<\/li>\n
                              • \u91d1\u878d\u98ce\u63a7\u5ba1\u6279（\u4e25\u683c\u6d41\u7a0b\u3001\u660e\u786e\u5206\u652f\u3001\u53ef\u8ffd\u6eaf\u53ef\u5ba1\u8ba1）\u2192 LangGraph（\u663e\u5f0f\u56fe\u7ed3\u6784 + \u6761\u4ef6\u8fb9）\u3002<\/li>\n<\/ul>\n

                                \u7b2c\u516d\u7ae0\u5c0f\u7ed3<\/h4>\n

                                \u672c\u7ae0\u7528\u56db\u4e2a\u6846\u67b6\u56de\u7b54\u4e86\u540c\u4e00\u4e2a\u95ee\u9898：\u591a\u667a\u80fd\u4f53\u4e0e\u590d\u6742\u6d41\u7a0b\u8be5\u600e\u4e48\u89c4\u8303\u5730\u6784\u5efa\u3002<\/p>\n

                                AutoGen \u628a\u534f\u4f5c\u62bd\u8c61\u6210\u4e00\u573a\u81ea\u52a8\u8fdb\u884c\u7684"\u7fa4\u804a"，\u8ba9\u5f00\u53d1\u8005\u53ea\u9700\u5b9a\u4e49\u89d2\u8272\u4e0e\u804c\u8d23，\u5176\u6838\u5fc3\u4ef7\u503c\u662f"\u7cfb\u7edf\u6d88\u606f\u5373\u63d0\u793a\u5de5\u7a0b"\u4e0e UserProxyAgent \u5e26\u6765\u7684\u4eba\u7c7b\u5728\u73af；<\/p>\n

                                AgentScope \u8d70\u5de5\u7a0b\u5316\u8def\u7ebf，\u7528\u7edf\u4e00\u7684 Msg \u6d88\u606f\u3001MsgHub \u6d88\u606f\u4e2d\u67a2\u4e0e AgentBase.reply \u751f\u547d\u5468\u671f\u628a\u5e76\u53d1\u3001\u5206\u5e03\u5f0f\u3001\u5bb9\u9519\u4e0e\u53ef\u89c2\u6d4b\u6027\u505a\u6210\u57fa\u7840\u8bbe\u65bd，\u5e76\u7528 Pydantic \u7ed3\u6784\u5316\u8f93\u51fa\u628a\u4e1a\u52a1\u89c4\u5219\u53d8\u6210\u53ef\u9a8c\u8bc1\u7ea6\u675f；<\/p>\n

                                CAMEL \u53cd\u5176\u9053\u800c\u884c，\u4ee5"\u8f7b\u67b6\u6784\u3001\u91cd\u63d0\u793a"\u7684\u89d2\u8272\u626e\u6f14 + \u5f15\u5bfc\u6027\u63d0\u793a，\u7528\u6700\u5c11\u4ee3\u7801\u6fc0\u53d1\u4e24\u4e2a\u4e13\u5bb6\u667a\u80fd\u4f53\u7684\u6df1\u5ea6\u81ea\u4e3b\u534f\u4f5c；<\/p>\n

                                LangGraph \u5219\u56de\u5230\u5e95\u5c42\u7684\u72b6\u6001\u673a\u6a21\u578b，\u4ee5 State \/ Node \/ Edge（\u5c24\u5176\u662f\u6761\u4ef6\u8fb9） \u4e09\u8981\u7d20\u8d4b\u4e88\u5f00\u53d1\u8005\u5bf9\u5de5\u4f5c\u6d41\u7684\u7cbe\u786e\u63a7\u5236，\u539f\u751f\u5faa\u73af\u4e3a\u53ef\u53cd\u601d\u3001\u53ef\u4fee\u6b63\u7684\u667a\u80fd\u4f53\u94fa\u5e73\u9053\u8def\u3002<\/p>\n

                                \u56db\u8005\u7684\u53d6\u820d\u6700\u7ec8\u6536\u655b\u5230\u4e24\u6761\u4e3b\u7ebf：\u4e00\u662f**"\u6d8c\u73b0\u5f0f\u534f\u4f5c"\u4e0e"\u663e\u5f0f\u63a7\u5236"\u7684\u6743\u8861**，\u4e8c\u662f\u4ece\u539f\u578b\u5230\u751f\u4ea7\u7684\u5de5\u7a0b\u5316\u8de8\u8d8a\u2014\u2014\u7406\u89e3\u8fd9\u4e24\u70b9，\u624d\u7b97\u771f\u6b63\u8bfb\u61c2\u6846\u67b6，\u4e5f\u624d\u80fd\u5728\u7b2c 7 \u7ae0\u9020\u51fa\u5c5e\u4e8e\u81ea\u5df1\u7684\u6846\u67b6\u3002<\/p>\n

                                \n

                                \u9644\u5f55：\u901f\u67e5\u8868<\/h2>\n\n\u6982\u5ff5\u4e00\u53e5\u8bdd<\/tr>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
                                ReAct<\/td>\n\u8fb9\u60f3\u8fb9\u505a：Thought \u2192 Action \u2192 Observation \u5faa\u73af，\u8d62\u5728\u52a8\u6001\u7ea0\u9519<\/td>\n<\/tr>\n
                                Plan-and-Solve<\/td>\n\u5148\u8c0b\u540e\u52a8：\u89c4\u5212\u5668\u51fa Python \u5217\u8868\u8ba1\u5212，\u6267\u884c\u5668\u6309\u6b65\u6267\u884c\u5e76\u4f20\u9012\u5386\u53f2\u7ed3\u679c<\/td>\n<\/tr>\n
                                Reflection<\/td>\n\u6267\u884c \u2192 \u53cd\u601d \u2192 \u4f18\u5316；\u9760 Memory \u8bb0\u8f68\u8ff9，\u4ee5\u6210\u672c\u6362\u8d28\u91cf<\/td>\n<\/tr>\n
                                \u5de5\u5177\u4e09\u8981\u7d20<\/td>\n\u540d\u79f0 \/ \u63cf\u8ff0（\u6700\u5173\u952e，\u51b3\u5b9a LLM \u9009\u8c01） \/ \u6267\u884c\u903b\u8f91<\/td>\n<\/tr>\n
                                max_steps \/ max_iterations \/ max_turns<\/td>\n\u9632\u6b7b\u5faa\u73af\u7684\u4e09\u9053\u5b89\u5168\u9600<\/td>\n<\/tr>\n
                                Finish[…] \/ \u201c\u65e0\u9700\u6539\u8fdb\u201d \/ TERMINATE<\/td>\n\u4e09\u79cd\u8303\u5f0f\u7684\u663e\u5f0f\u7ec8\u6b62\u4fe1\u53f7<\/td>\n<\/tr>\n
                                \u7ed3\u6784\u5316\u8f93\u51fa<\/td>\n\u5f3a\u5236 python\u5305\u88f9\u6216 Pydantic \u6a21\u578b；\u7528 ast.literal_eval \u89e3\u6790，\u7edd\u4e0d\u7528 eval<\/td>\n<\/tr>\n
                                \u7ec4\u5408\u4f18\u4e8e\u7ee7\u627f<\/td>\nAgent \u6301\u6709 LLM\/Planner\/Executor，\u800c\u975e\u7ee7\u627f\u5b83\u4eec<\/td>\n<\/tr>\n
                                \u6846\u67b6\u7684\u4e09\u4e2a\u89e3\u8026\u5c42<\/td>\n\u6a21\u578b\u5c42 \/ \u5de5\u5177\u5c42 \/ \u8bb0\u5fc6\u5c42<\/td>\n<\/tr>\n
                                AutoGen<\/td>\n\u7fa4\u804a\u9a71\u52a8；System Message \u5b9a\u4e49\u89d2\u8272；UserProxy = \u4eba\u7c7b\u5728\u73af + \u6267\u884c\u5668<\/td>\n<\/tr>\n
                                AgentScope<\/td>\n\u6d88\u606f\u9a71\u52a8；MsgHub \u8def\u7531 + \u6301\u4e45\u5316 + \u5206\u5e03\u5f0f；reply \u5b9a\u4e49\u667a\u80fd\u4f53<\/td>\n<\/tr>\n
                                CAMEL<\/td>\n\u89d2\u8272\u626e\u6f14 + \u5f15\u5bfc\u6027\u63d0\u793a；AI User \u63d0\u9700\u6c42\u3001AI Assistant \u7ed9\u65b9\u6848<\/td>\n<\/tr>\n
                                LangGraph<\/td>\nState + Node + Edge；\u6761\u4ef6\u8fb9\u5b9e\u73b0\u5faa\u73af；\u8282\u70b9\u662f\u7eaf\u51fd\u6570<\/td>\n<\/tr>\n
                                \u6d8c\u73b0 vs \u63a7\u5236<\/td>\nAutoGen\/CAMEL \u6d8c\u73b0\u5f0f；LangGraph \u663e\u5f0f\u63a7\u5236<\/td>\n<\/tr>\n
                                \u4f4e\u4ee3\u7801\u56db\u5e73\u53f0<\/td>\nCoze \u96f6\u4ee3\u7801\u5206\u53d1 \/ Dify \u4f01\u4e1a\u7ea7\u5168\u6808 \/ FastGPT \u6781\u81f4 RAG \/ n8n \u901a\u7528\u8fde\u63a5<\/td>\n<\/tr>\n
                                \u6df7\u5408\u5f00\u53d1<\/td>\n\u5e73\u53f0\u5feb\u901f\u9a8c\u8bc1 + \u4ee3\u7801\u7cbe\u7ec6\u63a7\u5236，\u624d\u662f\u6700\u4f73\u5b9e\u8df5<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n","protected":false},"excerpt":{"rendered":"

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