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var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}html.dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}html pre.shiki code .szBVR, html code.shiki .szBVR{--shiki-default:#D73A49;--shiki-dark:#F97583}html pre.shiki code .sj4cs, html code.shiki .sj4cs{--shiki-default:#005CC5;--shiki-dark:#79B8FF}html pre.shiki code .sJ8bj, html code.shiki .sJ8bj{--shiki-default:#6A737D;--shiki-dark:#6A737D}",{"title":226,"searchDepth":325,"depth":325,"links":848},[849,850,851,852,853,858,859,860,861],{"id":27,"depth":318,"text":28},{"id":51,"depth":318,"text":51},{"id":131,"depth":318,"text":131},{"id":148,"depth":318,"text":149},{"id":214,"depth":318,"text":214,"children":854},[855,856,857],{"id":218,"depth":325,"text":219},{"id":274,"depth":325,"text":275},{"id":450,"depth":325,"text":451},{"id":603,"depth":318,"text":603},{"id":763,"depth":318,"text":764},{"id":799,"depth":318,"text":799},{"id":817,"depth":318,"text":817},"general","\u002Fimg\u002Ftools\u002Fpageagent.webp","PageAgent 是阿里巴巴开源的纯前端 JavaScript GUI Agent 框架，采用 MIT 协议。核心创新在于：不依赖截图和多模态模型，而是通过 DOM 脱水（DOM Dehydration）技术将页面结构序列化为文本，让 LLM 理解并操作页面元素。仅需一行 `\u003Cscript>` 标签即可嵌入任何网页，实现自然语言驱动的点击、输入、导航等操作。","md",[867,870,873,876,879],{"q":868,"a":869},"PageAgent 和传统浏览器自动化（Playwright \u002F Selenium）有什么区别？","PageAgent 是『页面内嵌』方案，直接在浏览器中作为 JavaScript 运行，天然继承用户登录态，无需无头浏览器和 Python 后端。Playwright 等是『外部驱动』方案，通过 CDP 协议远程控制浏览器，适合大规模测试和跨网站爬取。PageAgent 更适合为你的 Web 应用添加 AI 操作能力。",{"q":871,"a":872},"PageAgent 需要 Chrome 扩展吗？","不需要。PageAgent.js 作为独立脚本在单页面内即可工作。Chrome 扩展是可选的『增强包』，仅在需要跨标签页或多页面任务时才需要安装。",{"q":874,"a":875},"支持哪些 LLM？","支持任何兼容 OpenAI `\u002Fchat\u002Fcompletions` 接口的模型，包括 OpenAI GPT-4o、Claude 3.5、Qwen3.5、DeepSeek、Gemini、Grok、Kimi、GLM，以及通过 Ollama 运行的本地开源模型。用户提供自己的 API Key。",{"q":877,"a":878},"PageAgent 是国产的吗？","是的，由阿里巴巴开源，MIT 协议，TypeScript 编写。对中文场景有天然优化，支持中文指令和中文界面操作。",{"q":880,"a":881},"PageAgent 的 DOM 脱水（DOM Dehydration）是什么？","DOM 脱水是将页面 DOM 树压缩为精简文本的技术。它会移除样式信息、冗余容器和隐藏元素，只保留交互元素（按钮、输入框、链接等）及其索引，发送给 LLM 处理。相比截图方案，成本低一个数量级，速度更快（0.5-1s vs 2-5s）。",[883,884,885],"en","zh","multi",null,{},[889,890,891,892,893],"GPT-4o","Claude 3.5 Sonnet","Qwen3.5-plus","DeepSeek-V3","Gemini 2.5 Pro","\u002Ftools\u002Fagent\u002Fgeneral\u002Fpageagent","agent",[897],"web",[899],{"plan":138,"price":900,"features":901,"notes":902},"$0","MIT 协议 + 全部功能 + 自托管 + DOM Dehydration + 任意 LLM 接入 + Chrome 扩展 + MCP Server","LLM API 自付","MIT 完全免费开源 \u002F 用户自付 LLM API（支持 OpenAI \u002F Claude \u002F Qwen \u002F DeepSeek 等任意兼容模型）","2026-07-28",[906],"pageagent-deep-review",{"power":347,"ux":347,"price":359,"cn_support":359,"stability":325},{"title":10,"description":864},"agent\u002Fgeneral\u002Fpageagent",[911,913,915],{"name":912,"url":826,"accessed":904},"PageAgent 官方文档",{"name":914,"url":834,"accessed":904},"PageAgent GitHub（alibaba 组织）",{"name":916,"url":841,"accessed":904},"PageAgent 官网","tools\u002Fagent\u002Fgeneral\u002Fpageagent","阿里巴巴开源纯前端 GUI Agent——一行 script 标签让 AI 自然语言控制你的 Web 页面",[920,921,922,923,924,925,926],"opensource","gui-agent","browser-automation","dom-dehydration","alibaba","javascript","frontend","想为你的 Web 应用添加 AI 操作能力、又不想搭后端和无头浏览器的开发者首选——一行代码嵌入，纯文本 DOM 分析，接入任意 LLM 即可。需要跨网站爬取\u002F大规模测试场景建议 Playwright 或 browser-use。","A41K5d-SHNiZQg3H8Pc4tTzdGO6g7xo7XIXylo1Ifyg",[930,1513,2057,2614,3378,4345,4927,5530,6204],{"id":931,"title":932,"alternatives":933,"api_compatible":886,"body":937,"category":862,"chinese_friendly":325,"cover":1450,"description":1451,"domestic":1452,"extension":865,"faq":1453,"free":1452,"github":886,"languages":1466,"lastVerified":886,"meta":1467,"models":886,"navigation":321,"notSuitable":886,"opensource":1452,"path":1468,"pillar":895,"platforms":1469,"priceTable":1473,"pricing":1486,"published":1487,"relatedPlaybooks":1488,"relatedReviews":886,"score":1490,"self_host":1452,"seo":1491,"seoTitle":886,"slug":1492,"sources":1493,"stem":1502,"suitable":886,"tagline":1503,"tags":1504,"updated":1495,"verdict":1510,"website":1511,"__hash__":1512},"tools\u002Ftools\u002Fagent\u002Fgeneral\u002Falice.md","Alice",[934,935,936],"agent\u002Fdesktop\u002Fclaude-desktop","agent\u002Fdesktop\u002Fopenclaw","agent\u002Fgeneral\u002Fflowith",{"type":22,"value":938,"toc":1438},[939,941,944,947,949,1017,1019,1042,1048,1052,1056,1079,1083,1109,1111,1134,1136,1297,1300,1356,1358,1384,1386,1406,1408],[25,940,28],{"id":27},[30,942,943],{},"Alice（heyalice.app）是 Greg Rog \u002F Techsistence 团队 macOS \u002F Windows \u002F Linux 原生桌面 AI 助手，2026-02 版本 5.0 发布，8000+ 创作者使用。差异点：OS 级全局快捷键唤起 + 自带 API Key（OpenAI \u002F Claude \u002F Gemini \u002F Grok \u002F Groq）+ 数据全本地存储零云依赖 + 50+ ready-to-use Assistants + 200+ Skills 社区库 + MCP \u002F Zapier \u002F Make \u002F n8n 集成。无月费订阅，3 周免费试用 + 一次买断 ~$59-99。",[30,945,946],{},"适合：macOS \u002F Windows 党 + 想全局快捷键唤起 AI；多模型用户不想被单 vendor 锁定；隐私敏感 + 不愿数据上云；自由职业 \u002F 个人开发者 + 不喜欢月费订阅。不适合：纯网页党（无 web 版）；移动设备主力（无 iOS \u002F Android）；团队协作 + 共享 workspace；不愿管理 API Key 的纯用户。",[25,948,51],{"id":51},[53,950,951,957,963,969,975,981,987,993,999,1005,1011],{},[56,952,953,956],{},[34,954,955],{},"全局快捷键唤起","：任何应用按 hotkey 召唤 AI",[56,958,959,962],{},[34,960,961],{},"BYO API Key","：OpenAI \u002F Anthropic \u002F Gemini \u002F xAI Grok \u002F Groq \u002F OpenRouter",[56,964,965,968],{},[34,966,967],{},"50+ Assistants","：'Deal Closer' \u002F 'Bug Hunter' \u002F 'Meeting Genius' 等开箱即用角色",[56,970,971,974],{},[34,972,973],{},"200+ Skills","：power user 社区贡献的能力包",[56,976,977,980],{},[34,978,979],{},"MCP 集成","：可调用 MCP server 工具",[56,982,983,986],{},[34,984,985],{},"自动化对接","：Zapier \u002F Make \u002F n8n + 无代码工作流",[56,988,989,992],{},[34,990,991],{},"Memory & Documents","：项目文档 + 偏好记忆，无需重复输入",[56,994,995,998],{},[34,996,997],{},"数据本地","：所有对话 \u002F 设置 \u002F 偏好仅本地存储",[56,1000,1001,1004],{},[34,1002,1003],{},"多模型在一窗口","：GPT-5.x \u002F Claude \u002F Gemini \u002F Grok \u002F Llama 切换",[56,1006,1007,1010],{},[34,1008,1009],{},"CSV \u002F 邮件 \u002F 日程","：直接读本地 CSV \u002F Calendar \u002F 起草邮件 \u002F Todo",[56,1012,1013,1016],{},[34,1014,1015],{},"3 周免费试用","：体验完整再决策",[25,1018,131],{"id":131},[53,1020,1021,1027,1033,1039],{},[56,1022,1023,1026],{},[34,1024,1025],{},"Free Trial","：3 周完整功能，无信用卡",[56,1028,1029,1032],{},[34,1030,1031],{},"Personal","：~$59 一次买断（永久授权 + 永久更新）",[56,1034,1035,1038],{},[34,1036,1037],{},"Business \u002F Pro","：~$99 一次买断 + 优先支持 + 商用授权",[56,1040,1041],{},"API 费用单算（OpenAI \u002F Anthropic 按 token 计费）",[1043,1044,1045],"blockquote",{},[30,1046,1047],{},"真实成本预估：重度日用 GPT-5.x \u002F Claude 一个月 $30-80 API 费 + 一次买断 ~$59 = 第一年总投入约 $400-500，长期省于 ChatGPT Pro 月费。",[25,1049,1051],{"id":1050},"实测macos-主力-自由职业-多模型混用","实测（macOS 主力 \u002F 自由职业 \u002F 多模型混用）",[30,1053,1054],{},[34,1055,154],{},[53,1057,1058,1061,1064,1067,1070,1073,1076],{},[56,1059,1060],{},"全局 hotkey 召唤体验在 macOS 上非常顺，比开浏览器 + 切 tab 快 10 倍",[56,1062,1063],{},"多模型对比：同问题同时跑 GPT-5 + Claude Opus + Gemini 看差异",[56,1065,1066],{},"50+ Assistants 节省 prompt engineering 时间",[56,1068,1069],{},"数据本地 + 无 telemetry，隐私党非常友好",[56,1071,1072],{},"一次买断 vs 多家月费的累计性价比高",[56,1074,1075],{},"BYO API 让账单透明，不会有平台抽成",[56,1077,1078],{},"MCP 让本地工具可被 AI 调用，文件系统 \u002F DB \u002F Shell",[30,1080,1081],{},[34,1082,188],{},[53,1084,1085,1088,1091,1094,1097,1100,1103,1106],{},[56,1086,1087],{},"API Key 管理复杂：每家都要单独申请 + 充值 + 监控配额",[56,1089,1090],{},"使用量爆冷热不均，BYO 模型 API 费偶尔月底惊喜",[56,1092,1093],{},"移动端缺失，出门用不上",[56,1095,1096],{},"Linux 版本更新偶尔比 macOS 慢",[56,1098,1099],{},"中文 prompt 效果依赖底层模型，UI 英文为主",[56,1101,1102],{},"50+ Assistants 质量参差，要挑高赞的用",[56,1104,1105],{},"团队协作 \u002F 云同步不是产品重点",[56,1107,1108],{},"学习曲线：自定义 Assistant + Skill 体系花时间",[25,1110,214],{"id":214},[819,1112,1113,1116,1119,1122,1125,1128,1131],{},[56,1114,1115],{},"heyalice.app → 下载 macOS \u002F Windows \u002F Linux 安装包",[56,1117,1118],{},"启动 → 3 周免费试用激活",[56,1120,1121],{},"Settings → 配 API Key（最少配一家，推荐 OpenRouter 统一）",[56,1123,1124],{},"试 Assistant Marketplace 装 'Meeting Genius' \u002F 'Bug Hunter'",[56,1126,1127],{},"自定义全局 hotkey（默认 Cmd+Shift+Space）",[56,1129,1130],{},"任何 App 按 hotkey → 输入 prompt → 选 Assistant + 模型 → 干活",[56,1132,1133],{},"满意后选 Personal $59 \u002F Business $99 买断",[25,1135,603],{"id":603},[605,1137,1138,1155],{},[608,1139,1140],{},[611,1141,1142,1144,1146,1149,1152],{},[614,1143,616],{},[614,1145,932],{},[614,1147,1148],{},"Raycast AI",[614,1150,1151],{},"Claude Desktop",[614,1153,1154],{},"ChatGPT Desktop",[629,1156,1157,1173,1190,1206,1221,1236,1250,1266,1280],{},[611,1158,1159,1162,1165,1168,1171],{},[634,1160,1161],{},"全局 hotkey",[634,1163,1164],{},"✅ 顶级",[634,1166,1167],{},"✅ Raycast",[634,1169,1170],{},"弱",[634,1172,1170],{},[611,1174,1175,1178,1181,1184,1187],{},[634,1176,1177],{},"BYO API",[634,1179,1180],{},"✅",[634,1182,1183],{},"❌ 内置",[634,1185,1186],{},"❌ Anthropic",[634,1188,1189],{},"❌ OpenAI",[611,1191,1192,1195,1198,1201,1204],{},[634,1193,1194],{},"一次买断",[634,1196,1197],{},"✅ $59-99",[634,1199,1200],{},"$10\u002F月",[634,1202,1203],{},"$20\u002F月",[634,1205,1203],{},[611,1207,1208,1211,1213,1215,1218],{},[634,1209,1210],{},"多模型一窗",[634,1212,1180],{},[634,1214,1180],{},[634,1216,1217],{},"Claude only",[634,1219,1220],{},"OpenAI only",[611,1222,1223,1226,1228,1231,1234],{},[634,1224,1225],{},"MCP",[634,1227,1180],{},[634,1229,1230],{},"部分",[634,1232,1233],{},"✅ 旗舰",[634,1235,1230],{},[611,1237,1238,1240,1243,1246,1248],{},[634,1239,997],{},[634,1241,1242],{},"✅ 零云",[634,1244,1245],{},"–",[634,1247,1245],{},[634,1249,1245],{},[611,1251,1252,1255,1258,1261,1263],{},[634,1253,1254],{},"平台",[634,1256,1257],{},"macOS\u002FWin\u002FLinux",[634,1259,1260],{},"macOS only",[634,1262,1257],{},[634,1264,1265],{},"macOS\u002FWin",[611,1267,1268,1271,1274,1276,1278],{},[634,1269,1270],{},"移动端",[634,1272,1273],{},"❌",[634,1275,1273],{},[634,1277,1245],{},[634,1279,1180],{},[611,1281,1282,1285,1288,1291,1294],{},[634,1283,1284],{},"适合",[634,1286,1287],{},"全局 AI + 多模型 + 买断",[634,1289,1290],{},"Mac 启动器 + AI",[634,1292,1293],{},"MCP 生态",[634,1295,1296],{},"OpenAI 全家桶",[25,1298,1299],{"id":1299},"避坑",[53,1301,1302,1308,1314,1320,1326,1332,1338,1344,1350],{},[56,1303,1304,1307],{},[34,1305,1306],{},"API Key 用 OpenRouter 统一","：避免管理 5 家账号 \u002F 5 个充值",[56,1309,1310,1313],{},[34,1311,1312],{},"API 预算 cap","：每家 API console 设月度 cap，避免月底炸账单",[56,1315,1316,1319],{},[34,1317,1318],{},"Assistant 不要装太多","：50+ 装一半就乱，按当前项目装 3-5 个",[56,1321,1322,1325],{},[34,1323,1324],{},"Linux 版本滞后","：macOS \u002F Windows 体验更稳",[56,1327,1328,1331],{},[34,1329,1330],{},"隐私是核心卖点","：装第三方 Skill \u002F Assistant 前看 source \u002F 权限声明",[56,1333,1334,1337],{},[34,1335,1336],{},"快捷键冲突","：默认 Cmd+Shift+Space 和 Spotlight 冲突，改为 Cmd+\u002F",[56,1339,1340,1343],{},[34,1341,1342],{},"中文场景","：自带 API 接 DeepSeek \u002F Qwen \u002F GLM 走 OpenRouter，prompt 用中文 OK",[56,1345,1346,1349],{},[34,1347,1348],{},"不要一次升级到最新版","：5.x 改动大，pin minor 版本观察社区反馈",[56,1351,1352,1355],{},[34,1353,1354],{},"MCP server","：信任来源很重要，能访问的目录 \u002F 工具要审慎",[25,1357,764],{"id":763},[53,1359,1360,1363,1366,1369,1372,1375,1378,1381],{},[56,1361,1362],{},"✅ macOS \u002F Windows 主力 + 想全局 hotkey 唤起 AI",[56,1364,1365],{},"✅ 多模型用户不想被单 vendor 锁定",[56,1367,1368],{},"✅ 隐私敏感 + 数据全本地",[56,1370,1371],{},"✅ 自由职业 \u002F 个人开发者 + 不愿月费",[56,1373,1374],{},"❌ 纯网页党 \u002F iPad \u002F 移动主力",[56,1376,1377],{},"❌ 团队协作 + 共享 workspace（用 Claude Team \u002F ChatGPT Team）",[56,1379,1380],{},"❌ 不想管理 API Key 的纯用户",[56,1382,1383],{},"❌ 中国大陆海外卡 + API 不可得",[25,1385,799],{"id":799},[53,1387,1388,1394,1400],{},[56,1389,1390],{},[805,1391,1393],{"href":1392},"\u002Ftools\u002Fagent\u002Fdesktop\u002Fclaude-desktop","Claude Desktop 评测",[56,1395,1396],{},[805,1397,1399],{"href":1398},"\u002Ftools\u002Fagent\u002Fdesktop\u002Fopenclaw","OpenClaw 评测",[56,1401,1402],{},[805,1403,1405],{"href":1404},"\u002Ftools\u002Fagent\u002Fgeneral\u002Fflowith","Flowith 评测",[25,1407,817],{"id":817},[819,1409,1410,1417,1424,1431],{},[56,1411,1412,1413],{},"Alice 官网（heyalice.app）",[805,1414,1415],{"href":1415,"rel":1416},"https:\u002F\u002Fheyalice.app\u002F",[828],[56,1418,1419,1420],{},"Comparateur-IA — Alice AI Review 2026（BYO API \u002F 3 周试用 \u002F 50+ Assistant）",[805,1421,1422],{"href":1422,"rel":1423},"https:\u002F\u002Fcomparateur-ia.com\u002Fen\u002Freviews\u002Falice-ai",[828],[56,1425,1426,1427],{},"AI Tools Cafe — Alice Pricing & Features 2026 ",[805,1428,1429],{"href":1429,"rel":1430},"https:\u002F\u002Faitoolscafe.com\u002Ftool\u002Falice",[828],[56,1432,1433,1434],{},"Skywork — Alice AI Ultimate 2026 Guide ",[805,1435,1436],{"href":1436,"rel":1437},"https:\u002F\u002Fskywork.ai\u002Fskypage\u002Fen\u002Falice-ai-bot-guide\u002F2028644172050882560",[828],{"title":226,"searchDepth":325,"depth":325,"links":1439},[1440,1441,1442,1443,1444,1445,1446,1447,1448,1449],{"id":27,"depth":318,"text":28},{"id":51,"depth":318,"text":51},{"id":131,"depth":318,"text":131},{"id":1050,"depth":318,"text":1051},{"id":214,"depth":318,"text":214},{"id":603,"depth":318,"text":603},{"id":1299,"depth":318,"text":1299},{"id":763,"depth":318,"text":764},{"id":799,"depth":318,"text":799},{"id":817,"depth":318,"text":817},"\u002Fimg\u002Ftools\u002Falice.webp","Alice 真实评测：Greg Rog \u002F Techsistence 团队出品的 macOS \u002F Windows \u002F Linux 原生桌面 AI 助手（heyalice.app）。差异点：OS 级全局快捷键唤起 + 自带 API Key（OpenAI \u002F Claude \u002F Gemini \u002F Grok \u002F Groq）+ 数据全本地存储 + 50+ ready-to-use Assistants + 200+ Skills 社区库 + MCP \u002F Zapier \u002F Make \u002F n8n 集成。版本 5.0 已发布，8000+ 创作者使用。3 周免费试用，无月费订阅，付费一次买断。",false,[1454,1457,1460,1463],{"q":1455,"a":1456},"Alice 和 Raycast AI \u002F Claude Desktop \u002F ChatGPT Desktop 怎么选？","Alice 强在『全局快捷键 + BYO API + 一次买断 + 隐私本地』——多模型用户 \u002F 自由职业 \u002F 隐私敏感党最划算。Raycast AI 是 Raycast 启动器内置 AI，深度集成快捷指令但限 Raycast 生态。Claude Desktop 强 MCP + Anthropic 模型。ChatGPT Desktop 强 OpenAI 全家桶。要 Mac 全局 hotkey + 多模型 + 不订阅 → Alice；要工具栏启动器 + 简单 AI → Raycast AI；要 MCP 生态 → Claude Desktop。",{"q":1458,"a":1459},"为什么要自带 API Key？","Alice 自身不卖 LLM 服务，让你直接用 OpenAI \u002F Anthropic \u002F Google \u002F xAI \u002F Groq 的 API。优点：透明计费 + 同一 key 多工具复用 + 不依赖 Alice 商业稳定。缺点：要自己申请 + 充值多家 API + 重度用户成本估算难。OpenRouter 可以一个 key 接所有模型。",{"q":1461,"a":1462},"Skills 和 Assistants 区别？","Assistant 是预配置 system prompt + 工具 + 模型的『角色』（如 'Deal Closer' \u002F 'Bug Hunter' \u002F 'Meeting Genius'），打开就是这个角色；Skill 是 Assistant 内部可调用的『能力包』（搜索 \u002F 摘要 \u002F 翻译 \u002F 数据分析等）。50+ Assistants + 200+ Skills 由 power user 社区贡献，免费可装。",{"q":1464,"a":1465},"中国大陆能用吗？","App 本身可下载 + 本地运行。海外信用卡买断 + 海外 LLM API Key 是门槛。OpenRouter 中转可接国产模型（智谱 \u002F DeepSeek \u002F Qwen），跑国内场景。隐私上数据全本地存储，无国外服务器同步。",[883],{},"\u002Ftools\u002Fagent\u002Fgeneral\u002Falice",[1470,1471,1472],"macos","windows","linux",[1474,1478,1482],{"plan":1025,"price":1475,"features":1476,"notes":1477},"$0 · 3 周","完整功能体验","无信用卡",{"plan":1031,"price":1479,"features":1480,"notes":1481},"~$59 一次","永久授权 + 50+ Assistants + 200+ Skills + MCP\u002FZapier\u002FMake\u002Fn8n","个人买断",{"plan":1037,"price":1483,"features":1484,"notes":1485},"~$99 一次","Personal + 优先支持 + 商用授权 + 更新永久免费","商业 \u002F 团队","$59-99 一次买断 + 3 周免费试用 \u002F 自带 API Key 走 LLM 计费","2026-06-19",[1489],"onboarding\u002Fmacos-global-ai-workflow",{"power":347,"ux":359,"price":347,"cn_support":325,"stability":347},{"title":932,"description":1451},"agent\u002Fgeneral\u002Falice",[1494,1496,1498,1500],{"name":1412,"url":1415,"accessed":1495},"2026-06-24",{"name":1497,"url":1422,"accessed":1495},"Comparateur-IA — Alice AI Review 2026",{"name":1499,"url":1429,"accessed":1495},"AI Tools Cafe — Alice Pricing & Features 2026",{"name":1501,"url":1436,"accessed":1495},"Skywork — Alice AI Ultimate 2026 Guide","tools\u002Fagent\u002Fgeneral\u002Falice","heyalice.app 桌面 AI 助手——OS 级全局快捷键 + 多模型 + 50+ Assistants \u002F 200+ Skills",[1505,1506,1507,1508,1509,1470],"desktop-assistant","multi-model","byo-api","privacy","hotkey","桌面 AI 助手里最像 Raycast 的存在——全局快捷键 + 一次买断 + 自带 API。重度多模型用户 + macOS 党 + 隐私敏感首选。要团队协作 \u002F 云同步走 Claude Desktop \u002F ChatGPT Team。","https:\u002F\u002Fheyalice.app","-juFVoVCOe2IMWYOktV5tVOtNYMShjUQt6rV32PxbPE",{"id":1514,"title":1515,"alternatives":1516,"api_compatible":886,"body":1518,"category":862,"chinese_friendly":347,"cover":1996,"description":1997,"domestic":1452,"extension":865,"faq":1998,"free":1452,"github":886,"languages":2011,"lastVerified":886,"meta":2012,"models":886,"navigation":321,"notSuitable":886,"opensource":1452,"path":1404,"pillar":895,"platforms":2013,"priceTable":2016,"pricing":2032,"published":1487,"relatedPlaybooks":2033,"relatedReviews":2035,"score":2037,"self_host":1452,"seo":2038,"seoTitle":886,"slug":936,"sources":2039,"stem":2048,"suitable":886,"tagline":2049,"tags":2050,"updated":1495,"verdict":2055,"website":1961,"__hash__":2056},"tools\u002Ftools\u002Fagent\u002Fgeneral\u002Fflowith.md","Flowith",[1517,1492,934],"agent\u002Fgeneral\u002Fgenspark",{"type":22,"value":1519,"toc":1984},[1520,1522,1525,1528,1530,1592,1594,1620,1625,1629,1633,1656,1660,1686,1688,1708,1710,1842,1844,1905,1907,1933,1935,1952,1954],[25,1521,28],{"id":27},[30,1523,1524],{},"Flowith 是新加坡公司 Flowith Technologies Pte. Ltd.（2023 创立）打造的画布式 AI 工作空间。差异点：无限 2D 画布把每次 AI 交互变成可分支可合并的节点 + Agent Neo（10M token 上下文 \u002F 1000+ 自治步骤）+ Knowledge Garden（文档原子化成 Seeds）+ 40+ AI 模型一站接入 + FlowithOS 早期预览。2024-08 Product Hunt 当日 #1，2026 已有 100 万+ 用户，背后有 Stripe \u002F NVIDIA Inception \u002F Google for Startups \u002F Microsoft \u002F AWS。",[30,1526,1527],{},"适合：研究 \u002F 内容生产 \u002F 网站搭建 \u002F 长期项目 + 多线程并行；要 10M token 超长上下文做深度分析；想画布一眼看整个项目结构；多模型对比 + 长任务自治需求。不适合：纯简单问答（杀鸡用牛刀）；预算紧（credit 消耗快）；线性思维偏好用户（画布反而增加心智负担）；不愿学习曲线的纯小白。",[25,1529,51],{"id":51},[53,1531,1532,1538,1544,1550,1556,1562,1568,1574,1580,1586],{},[56,1533,1534,1537],{},[34,1535,1536],{},"无限 2D 画布","：每次 AI 交互 = 节点，可分支 \u002F 合并 \u002F 并行",[56,1539,1540,1543],{},[34,1541,1542],{},"Agent Neo","：10M token 上下文 + 1000+ 步自治 + 动态适应中间结果",[56,1545,1546,1549],{},[34,1547,1548],{},"Knowledge Garden","：文档拆解成 Seeds 知识单元，按需调用",[56,1551,1552,1555],{},[34,1553,1554],{},"40+ AI 模型","：GPT-5 \u002F Claude \u002F Gemini \u002F DeepSeek \u002F 国产模型并存",[56,1557,1558,1561],{},[34,1559,1560],{},"多线程并行","：同时跑多个 thread，画布一览",[56,1563,1564,1567],{},[34,1565,1566],{},"FlowithOS","：下一代 AI Agent OS（Pro+ 早期预览）",[56,1569,1570,1573],{},[34,1571,1572],{},"iOS App","：2026 上线，移动 workspace 访问",[56,1575,1576,1579],{},[34,1577,1578],{},"图像 \u002F 视频批量","：Pro+ 解锁批处理",[56,1581,1582,1585],{},[34,1583,1584],{},"商业授权","：Pro+ 包含商业使用权",[56,1587,1588,1591],{},[34,1589,1590],{},"Web Control UI","：浏览器原生，多设备同步",[25,1593,131],{"id":131},[53,1595,1596,1602,1608,1614],{},[56,1597,1598,1601],{},[34,1599,1600],{},"Free","：$0；300 一次性 credits（试水级）",[56,1603,1604,1607],{},[34,1605,1606],{},"Pro","：$19.90\u002F月（年付 $15.32）；22,000 月度 credits",[56,1609,1610,1613],{},[34,1611,1612],{},"Ultimate","：$49.90\u002F月（年付 $39.92）；85,000 credits + 优先队列",[56,1615,1616,1619],{},[34,1617,1618],{},"Infinite","：$499.90\u002F月（年付 $459.90）；1,000,000 credits + 创始人直通",[1043,1621,1622],{},[30,1623,1624],{},"真实成本：跑 Agent Neo 长任务一次 5k-20k credits，Pro 一月 1-3 个完整 Neo 任务 + 日常多线程对话；重度建议 Ultimate 起。",[25,1626,1628],{"id":1627},"实测深度研究-多线程内容生产","实测（深度研究 \u002F 多线程内容生产）",[30,1630,1631],{},[34,1632,154],{},[53,1634,1635,1638,1641,1644,1647,1650,1653],{},[56,1636,1637],{},"画布让整个项目结构一眼可见，比 ChatGPT 翻 thread 高效得多",[56,1639,1640],{},"Agent Neo 跑 10M token 长上下文任务时表现确实超线性 chat",[56,1642,1643],{},"Knowledge Garden 的 Seeds 设计精妙，文档复用无重复 token 成本",[56,1645,1646],{},"40+ 模型一窗体内切换 + 对比",[56,1648,1649],{},"多设备同步流畅",[56,1651,1652],{},"中文支持良好（接入 DeepSeek \u002F GLM 等国产模型）",[56,1654,1655],{},"学术 \u002F 战略 \u002F 内容创作场景非常顺手",[30,1657,1658],{},[34,1659,188],{},[53,1661,1662,1665,1668,1671,1674,1677,1680,1683],{},[56,1663,1664],{},"credit 消耗变化大，前沿模型一条 prompt 烧 5k+ credit",[56,1666,1667],{},"学习曲线明显，画布操作 + 节点连线要花时间",[56,1669,1670],{},"简单问答场景 Flowith 反而慢于 ChatGPT",[56,1672,1673],{},"Free 300 credits 不够体验完整流程",[56,1675,1676],{},"Agent Neo 偶尔『过度自治』产出不可控",[56,1678,1679],{},"移动 iOS 体验不如桌面 Web",[56,1681,1682],{},"商业授权要 Pro 起步，Free \u002F 试用产物商用合规要看条款",[56,1684,1685],{},"价格阶梯陡：Pro $20 → Ultimate $50 → Infinite $500，中端断档",[25,1687,214],{"id":214},[819,1689,1690,1693,1696,1699,1702,1705],{},[56,1691,1692],{},"flowith.io → Free 注册 → 拿 300 credits",[56,1694,1695],{},"新建画布 → 试 Quick Start 模板（research \u002F writing \u002F website）",[56,1697,1698],{},"画布上 + 添加节点 → 选模型 → 输入 prompt → 节点输出 → 拖出新节点延展",[56,1700,1701],{},"Knowledge Garden → 上传 3-5 个 PDF \u002F 笔记 → Seeds 自动生成",[56,1703,1704],{},"Agent Neo → 给一个完整目标（\"做一份 AI Agent 2026 竞品分析报告 + 网站\"）→ 等 30 分钟看结果",[56,1706,1707],{},"看 credit 消耗 → 觉得划算升 Pro \u002F Ultimate",[25,1709,603],{"id":603},[605,1711,1712,1729],{},[608,1713,1714],{},[611,1715,1716,1718,1720,1723,1726],{},[614,1717,616],{},[614,1719,1515],{},[614,1721,1722],{},"Genspark",[614,1724,1725],{},"ChatGPT",[614,1727,1728],{},"Claude",[629,1730,1731,1747,1764,1781,1796,1811,1826],{},[611,1732,1733,1736,1739,1742,1745],{},[634,1734,1735],{},"形态",[634,1737,1738],{},"无限画布 + 节点",[634,1740,1741],{},"多 Agent + Sparkpage",[634,1743,1744],{},"线性 chat",[634,1746,1744],{},[611,1748,1749,1752,1755,1758,1761],{},[634,1750,1751],{},"自治 agent",[634,1753,1754],{},"✅ Neo 1000+ 步",[634,1756,1757],{},"✅ Super Agent",[634,1759,1760],{},"✅ Operator",[634,1762,1763],{},"❌（API 走）",[611,1765,1766,1769,1772,1775,1778],{},[634,1767,1768],{},"上下文",[634,1770,1771],{},"10M token",[634,1773,1774],{},"200K",[634,1776,1777],{},"1M（GPT-5）",[634,1779,1780],{},"1M（Claude）",[611,1782,1783,1786,1789,1792,1794],{},[634,1784,1785],{},"模型数",[634,1787,1788],{},"40+",[634,1790,1791],{},"多家",[634,1793,15],{},[634,1795,16],{},[611,1797,1798,1801,1804,1806,1809],{},[634,1799,1800],{},"Knowledge base",[634,1802,1803],{},"✅ Seeds",[634,1805,1245],{},[634,1807,1808],{},"Projects",[634,1810,1808],{},[611,1812,1813,1816,1819,1822,1824],{},[634,1814,1815],{},"起价",[634,1817,1818],{},"$19.90\u002F月",[634,1820,1821],{},"$25\u002F月",[634,1823,1203],{},[634,1825,1203],{},[611,1827,1828,1830,1833,1836,1839],{},[634,1829,1284],{},[634,1831,1832],{},"深度多线程 + 长任务",[634,1834,1835],{},"全能 Super Agent",[634,1837,1838],{},"通用",[634,1840,1841],{},"Coding \u002F 长文",[25,1843,1299],{"id":1299},[53,1845,1846,1852,1858,1864,1870,1876,1882,1888,1893,1899],{},[56,1847,1848,1851],{},[34,1849,1850],{},"从 Pro 起","：Free 300 credits 玩不出深度",[56,1853,1854,1857],{},[34,1855,1856],{},"Knowledge Garden 文档先放进去","：所有任务再调用，省 token",[56,1859,1860,1863],{},[34,1861,1862],{},"Agent Neo 任务先小后大","：先 100 步任务感受效果，再上 1000 步",[56,1865,1866,1869],{},[34,1867,1868],{},"credit 监控","：Settings 看每日消耗，前沿模型烧得快",[56,1871,1872,1875],{},[34,1873,1874],{},"画布命名清晰","：节点 \u002F 分支 \u002F 项目名字要规范，否则一周后看不懂自己画的",[56,1877,1878,1881],{},[34,1879,1880],{},"年付折扣","：Pro 年付 $15.32 vs 月付 $19.90 一年省 $55",[56,1883,1884,1887],{},[34,1885,1886],{},"商业产物","：Free 试用产物商用前看条款，Pro+ 才有 commercial license",[56,1889,1890,1892],{},[34,1891,1270],{},"：iOS App 用于查看 \u002F 复用，深度编辑回桌面 Web",[56,1894,1895,1898],{},[34,1896,1897],{},"不要把 Flowith 当 ChatGPT 用","：简单问答用 ChatGPT，复杂多分支用 Flowith",[56,1900,1901,1904],{},[34,1902,1903],{},"Seeds 数量","：单 Knowledge Garden 不要塞太多，分主题建多个 Garden",[25,1906,764],{"id":763},[53,1908,1909,1912,1915,1918,1921,1924,1927,1930],{},[56,1910,1911],{},"✅ 深度研究 \u002F 长篇内容 \u002F 长期项目",[56,1913,1914],{},"✅ 要 10M token 超长上下文做综合分析",[56,1916,1917],{},"✅ 多模型 + 多线程并行 + 画布化思考者",[56,1919,1920],{},"✅ 自治 agent 跑长任务（Neo 1000+ 步）",[56,1922,1923],{},"❌ 纯简单问答（用 ChatGPT \u002F Claude）",[56,1925,1926],{},"❌ 预算极紧 + 不愿学习曲线",[56,1928,1929],{},"❌ 线性思维 + 单线程偏好",[56,1931,1932],{},"❌ 严格自托管 \u002F 数据零云（Flowith 是 SaaS）",[25,1934,799],{"id":799},[53,1936,1937,1943,1948],{},[56,1938,1939],{},[805,1940,1942],{"href":1941},"\u002Ftools\u002Fagent\u002Fgeneral\u002Fgenspark","Genspark 评测",[56,1944,1945],{},[805,1946,1947],{"href":1468},"Alice 评测",[56,1949,1950],{},[805,1951,1393],{"href":1392},[25,1953,817],{"id":817},[819,1955,1956,1963,1970,1977],{},[56,1957,1958,1959],{},"Flowith 官网 + Pricing ",[805,1960,1961],{"href":1961,"rel":1962},"https:\u002F\u002Fflowith.io",[828],[56,1964,1965,1966],{},"Toolworthy — Flowith AI Review 2026（Agent Neo \u002F Knowledge Garden）",[805,1967,1968],{"href":1968,"rel":1969},"https:\u002F\u002Fwww.toolworthy.ai\u002Ftool\u002Fflowith-ai",[828],[56,1971,1972,1973],{},"BlockSentient — Flowith 详细评测 + 价格 + FAQ ",[805,1974,1975],{"href":1975,"rel":1976},"https:\u002F\u002Fblocksentient.com\u002Freview\u002Fflowith",[828],[56,1978,1979,1980],{},"OpenTools — Flowith Review May 2026 ",[805,1981,1982],{"href":1982,"rel":1983},"https:\u002F\u002Fopentools.ai\u002Ftools\u002Fflowith",[828],{"title":226,"searchDepth":325,"depth":325,"links":1985},[1986,1987,1988,1989,1990,1991,1992,1993,1994,1995],{"id":27,"depth":318,"text":28},{"id":51,"depth":318,"text":51},{"id":131,"depth":318,"text":131},{"id":1627,"depth":318,"text":1628},{"id":214,"depth":318,"text":214},{"id":603,"depth":318,"text":603},{"id":1299,"depth":318,"text":1299},{"id":763,"depth":318,"text":764},{"id":799,"depth":318,"text":799},{"id":817,"depth":318,"text":817},"\u002Fimg\u002Ftools\u002Fflowith.webp","Flowith 真实评测：Flowith Technologies Pte. Ltd.（新加坡，2023 创立，Yichen Wu + Derek Nee 联合创办）打造的画布式 AI 工作空间。2024-08 Product Hunt #1。差异点：无限 2D 画布把每次 AI 交互变成可连可分支的节点 + Agent Neo（号称『世界首个无限 agent』10M token 上下文 \u002F 1000+ 步骤）+ Knowledge Garden（文档拆解成 Seeds 知识单元）+ 40+ AI 模型一站接入 + FlowithOS 早期预览 + iOS App。Free \u002F Pro $19.90 \u002F Ultimate $49.90 \u002F Infinite $499.90。",[1999,2002,2005,2008],{"q":2000,"a":2001},"Agent Neo 是什么？","Flowith 的自治 agent，10M token 上下文窗口（约 7500 页）+ 1000+ 推理步骤。给一个目标（研究 \u002F 内容 \u002F 建站 \u002F 竞争分析），它自主规划子任务、网络搜索、文档分析、综合结论、迭代输出。和单回合 ChatGPT 区别在于持续运行直到任务完成，可自动建网站 \u002F 写报告 \u002F 做策略。",{"q":2003,"a":2004},"Knowledge Garden 怎么用？","上传 PDF \u002F 笔记 \u002F 文件后，Flowith 把它们『原子化』成离散的 Seeds（知识单元）。任何 canvas 节点可调用相关 Seeds 做 RAG，无需手动复制粘贴。和 NotebookLM 类似但深度集成画布工作流。",{"q":2006,"a":2007},"credit 怎么算？","Credit-based 模型按 token + 任务类型计费。文本：基础模型 100 \u002F 百万 token，GPT-5 等前沿模型 15,000 \u002F 百万 token。图像 20-190 credits\u002F张，视频 475-6000 credits\u002F条。Free 一次性 300 credits 只够试水；Pro 22k credits 中等用户够；重度跑 Agent Neo 长任务建议 Ultimate 起。建议先 Pro 试一个月看消耗再升级。",{"q":2009,"a":2010},"和 ChatGPT \u002F Claude 怎么选？","ChatGPT \u002F Claude 适合线性快问快答，每次一条 thread。Flowith 适合多分支并行 + 项目级长期上下文 + 自治长任务，画布让整个项目一眼可见。简单问答用 ChatGPT；多线程深度研究 \u002F 内容生产 + 自治 agent → Flowith。两者互补。",[883,884,885],{},[897,2014,2015],"ios","macos-flowithos",[2017,2021,2024,2028],{"plan":2018,"price":900,"features":2019,"notes":2020},"Starter (Free)","300 一次性 credits + 标准模型 + 5 并发 + 2 设备 + 标准速度","试水",{"plan":1606,"price":1818,"features":2022,"notes":2023},"22,000 月度 credits + 40+ 模型 + 50 并发 + 5 设备 + 图像\u002F视频批量 + FlowithOS 早期","年付 $15.32\u002F月",{"plan":1612,"price":2025,"features":2026,"notes":2027},"$49.90\u002F月","85,000 月度 credits + 100 并发 + 高速处理 + 优先队列","年付 $39.92\u002F月",{"plan":1618,"price":2029,"features":2030,"notes":2031},"$499.90\u002F月","1,000,000 月度 credits + 不限并发 + 最高速 + 优先支持 + 创始人直通 + 1-on-1 onboard","年付 $459.90\u002F月","Free (300 一次性 credits) \u002F Pro $19.90·月 \u002F Ultimate $49.90·月 \u002F Infinite $499.90·月（年付 8 折）",[2034],"onboarding\u002Fcanvas-ai-research-workflow",[2036],"manus-deep-review",{"power":359,"ux":347,"price":325,"cn_support":347,"stability":347},{"title":1515,"description":1997},[2040,2042,2044,2046],{"name":2041,"url":1961,"accessed":1495},"Flowith 官网",{"name":2043,"url":1968,"accessed":1495},"Toolworthy — Flowith Review 2026",{"name":2045,"url":1975,"accessed":1495},"BlockSentient — Flowith 2026 详细评测",{"name":2047,"url":1982,"accessed":1495},"OpenTools — Flowith Review May 2026","tools\u002Fagent\u002Fgeneral\u002Fflowith","无限画布 AI 工作空间——Agent Neo 10M token 上下文 + 1000+ 步自治 + 40+ 模型 + Knowledge Garden",[2051,1506,2052,2053,2054],"canvas","agentic","knowledge-base","flowith","线性 chat 装不下的复杂深度工作专用——研究 \u002F 报告 \u002F 网站搭建 \u002F 长期项目首选画布。简单问答 \u002F 短任务用 ChatGPT \u002F Claude 即可，Flowith 学习曲线 + credit 消耗都偏高。","iWJm7mD7oy-36SwSo0c6C21SoSEqDllx19rybeIJxHY",{"id":2058,"title":1722,"alternatives":2059,"api_compatible":886,"body":2060,"category":862,"chinese_friendly":347,"cover":2558,"description":2559,"domestic":1452,"extension":865,"faq":2560,"free":1452,"github":886,"languages":2573,"lastVerified":886,"meta":2574,"models":886,"navigation":321,"notSuitable":886,"opensource":1452,"path":1941,"pillar":895,"platforms":2575,"priceTable":2577,"pricing":2586,"published":1487,"relatedPlaybooks":2587,"relatedReviews":2589,"score":2592,"self_host":1452,"seo":2593,"seoTitle":886,"slug":1517,"sources":2594,"stem":2603,"suitable":886,"tagline":2604,"tags":2605,"updated":1495,"verdict":2611,"website":2612,"__hash__":2613},"tools\u002Ftools\u002Fagent\u002Fgeneral\u002Fgenspark.md",[936,934,1492],{"type":22,"value":2061,"toc":2546},[2062,2064,2067,2070,2072,2146,2148,2172,2177,2181,2185,2208,2212,2238,2240,2260,2262,2412,2414,2470,2472,2498,2500,2514,2516],[25,2063,28],{"id":27},[30,2065,2066],{},"Genspark 是 2024 年创立于 Palo Alto 的 AI Super Agent 公司，CEO Eric Jing（前 Microsoft Bing \u002F 小冰），团队来自 Microsoft + Google。2026-04 ARR 突破 $250M（12 个月内）。差异点：『多 Agent 架构』路由不同 agent + 模型组合，而非单 LLM + Super Agent 自治执行覆盖研究 \u002F 内容 \u002F 调用 \u002F 建站 \u002F 视频 \u002F 数据分析 \u002F 真实电话 + Sparkpage 可视化输出。Free 100-200 daily credits \u002F Plus $25 月度 12k credits \u002F Pro $249 月度 125k credits。",[30,2068,2069],{},"适合：要一站式 Super Agent 完成完整工作流（研究 → 文档 → 演示 → 网站）；重度内容创作者 + 营销 \u002F 销售（Call For Me 跟进）；中级专业用户（Plus $25 \u002F 月划算）。不适合：纯研究问答（Perplexity \u002F ChatGPT 更便宜）；预算极紧（credit 消耗快）；需要透明引用 \u002F 学术严谨（Sparkpage 引用不如 Perplexity 清晰）；要画布多线程（用 Flowith）。",[25,2071,51],{"id":51},[53,2073,2074,2080,2086,2092,2098,2104,2110,2116,2122,2128,2134,2140],{},[56,2075,2076,2079],{},[34,2077,2078],{},"Super Agent","：自主规划 + 多步执行 + 工具调用",[56,2081,2082,2085],{},[34,2083,2084],{},"多 Agent 架构","：搜索 \u002F 研究 \u002F 内容 \u002F 调用 \u002F 数据分析 agent 协同",[56,2087,2088,2091],{},[34,2089,2090],{},"Sparkpage","：可视化研究输出（含图表 + 引用 + 可分享链接）",[56,2093,2094,2097],{},[34,2095,2096],{},"AI Slides","：10-15 张幻灯片含图表",[56,2099,2100,2103],{},[34,2101,2102],{},"AI Sites","：完整 landing page 一键生成",[56,2105,2106,2109],{},[34,2107,2108],{},"AI Video","：30-60s 短视频生成",[56,2111,2112,2115],{},[34,2113,2114],{},"AI Pods","：10 分钟 AI podcast 生成",[56,2117,2118,2121],{},[34,2119,2120],{},"Call For Me","：调用真实电话完成预订 \u002F 跟进",[56,2123,2124,2127],{},[34,2125,2126],{},"AI Sheets","：CSV 数据分析 + 自动图表",[56,2129,2130,2133],{},[34,2131,2132],{},"Plus 不限聊天","：o3-Pro \u002F Claude \u002F Gemini 顶级模型 chat 无限",[56,2135,2136,2139],{},[34,2137,2138],{},"iOS \u002F Android App","：移动端可用",[56,2141,2142,2145],{},[34,2143,2144],{},"企业 SSO \u002F API","（Pro+）：集成内部系统",[25,2147,131],{"id":131},[53,2149,2150,2155,2161,2166],{},[56,2151,2152,2154],{},[34,2153,1600],{},"：100-200 daily credits（~3-8 简单任务）",[56,2156,2157,2160],{},[34,2158,2159],{},"Plus","：$25\u002F月（年付 $240，~$20\u002F月）；12,000 月度 credits + 顶级模型不限 chat",[56,2162,2163,2165],{},[34,2164,1606],{},"：$249\u002F月（年付 $2388，~$199\u002F月）；125,000 credits + 优先 + 早期 + 高端 agent",[56,2167,2168,2171],{},[34,2169,2170],{},"Extra credits","：仅 Pro 可买，$10 \u002F 5,000 credits",[1043,2173,2174],{},[30,2175,2176],{},"Plus 一月做 10-15 个严肃任务（slides \u002F 研究 \u002F sites）；重度做视频 \u002F 通话 \u002F 多 sites 建议 Pro。",[25,2178,2180],{"id":2179},"实测营销-销售-内容生产","实测（营销 \u002F 销售 \u002F 内容生产）",[30,2182,2183],{},[34,2184,154],{},[53,2186,2187,2190,2193,2196,2199,2202,2205],{},[56,2188,2189],{},"一句话『做一份 B2B SaaS 竞品分析 + 5 张幻灯片 + landing page』全自动跑通",[56,2191,2192],{},"AI Sheets 处理 CSV 数据 + 自动出图 + 写入 Sparkpage 一步到位",[56,2194,2195],{},"Call For Me 跟进客户 \u002F 餐厅预订实际能用，时效感最强",[56,2197,2198],{},"Plus 顶级模型不限 chat 性价比超高，比 ChatGPT Plus $20 + Claude Pro $20 + Perplexity Pro $20 全订便宜",[56,2200,2201],{},"多 Agent 在复杂任务上确实优于单 LLM 的 ChatGPT",[56,2203,2204],{},"Sparkpage 输出可直接分享 + 嵌入",[56,2206,2207],{},"ARR $250M 增长说明产品市场契合度（PMF）非常好",[30,2209,2210],{},[34,2211,188],{},[53,2213,2214,2217,2220,2223,2226,2229,2232,2235],{},[56,2215,2216],{},"credit 消耗不透明，复杂任务前难估算成本",[56,2218,2219],{},"用户反馈 customer support + refund 有问题",[56,2221,2222],{},"生成内容偶尔『AI 味』重，要人工编辑润色",[56,2224,2225],{},"引用 \u002F 来源不如 Perplexity 严谨，学术场景慎用",[56,2227,2228],{},"AI Sites \u002F Video 编辑灵活度低，定制要走外部工具",[56,2230,2231],{},"地域限制：印度 \u002F 巴西 \u002F 巴基斯坦 \u002F 尼日利亚 \u002F 印尼等部分国家 Plus \u002F Pro 不可用",[56,2233,2234],{},"Call For Me 隐私 \u002F 合规要小心（GDPR \u002F 加州 CCPA \u002F 录音同意）",[56,2236,2237],{},"Pro $249 月价格陡，中端断档",[25,2239,214],{"id":214},[819,2241,2242,2245,2248,2251,2254,2257],{},[56,2243,2244],{},"genspark.ai 注册 → Free 拿 100-200 daily credits",[56,2246,2247],{},"Super Agent 试『一句话目标』：『做一份 AI Agent 2026 竞品分析 Sparkpage + 5 张 Slides』",[56,2249,2250],{},"等 5-10 分钟看 Sparkpage 输出 → 编辑 → 分享链接",[56,2252,2253],{},"AI Sheets 上传 CSV → 自动出图 → 集成进 Sparkpage",[56,2255,2256],{},"Call For Me 试一次小餐厅预订（自有号码 + 同意）",[56,2258,2259],{},"Plus $25 \u002F 月体验完整 → 决定 Pro",[25,2261,603],{"id":603},[605,2263,2264,2280],{},[608,2265,2266],{},[611,2267,2268,2270,2272,2274,2277],{},[614,2269,616],{},[614,2271,1722],{},[614,2273,1515],{},[614,2275,2276],{},"Perplexity Pro",[614,2278,2279],{},"ChatGPT Plus",[629,2281,2282,2297,2312,2326,2340,2355,2369,2382,2397],{},[611,2283,2284,2286,2289,2292,2294],{},[634,2285,2078],{},[634,2287,2288],{},"✅ 多 Agent",[634,2290,2291],{},"✅ Neo 自治",[634,2293,1245],{},[634,2295,2296],{},"Operator",[611,2298,2299,2302,2305,2308,2310],{},[634,2300,2301],{},"可视化输出",[634,2303,2304],{},"✅ Sparkpage",[634,2306,2307],{},"✅ 画布",[634,2309,1245],{},[634,2311,1245],{},[611,2313,2314,2317,2320,2322,2324],{},[634,2315,2316],{},"真实电话调用",[634,2318,2319],{},"✅ Call For Me",[634,2321,1245],{},[634,2323,1245],{},[634,2325,1245],{},[611,2327,2328,2331,2334,2336,2338],{},[634,2329,2330],{},"一键建站",[634,2332,2333],{},"✅ AI Sites",[634,2335,1245],{},[634,2337,1245],{},[634,2339,1245],{},[611,2341,2342,2345,2348,2350,2352],{},[634,2343,2344],{},"视频 \u002F 音频生成",[634,2346,2347],{},"✅ Video \u002F Pods",[634,2349,1230],{},[634,2351,1245],{},[634,2353,2354],{},"Sora（独立）",[611,2356,2357,2360,2363,2365,2367],{},[634,2358,2359],{},"引用透明",[634,2361,2362],{},"中",[634,2364,1245],{},[634,2366,1164],{},[634,2368,1230],{},[611,2370,2371,2373,2376,2378,2380],{},[634,2372,1815],{},[634,2374,2375],{},"$25\u002F月（Plus）",[634,2377,1818],{},[634,2379,1203],{},[634,2381,1203],{},[611,2383,2384,2387,2390,2392,2394],{},[634,2385,2386],{},"最贵",[634,2388,2389],{},"$249\u002F月",[634,2391,2029],{},[634,2393,1203],{},[634,2395,2396],{},"$200\u002F月",[611,2398,2399,2401,2404,2407,2410],{},[634,2400,1284],{},[634,2402,2403],{},"一站式 Super Agent",[634,2405,2406],{},"画布深度",[634,2408,2409],{},"搜索引用",[634,2411,1838],{},[25,2413,1299],{"id":1299},[53,2415,2416,2422,2428,2434,2440,2446,2452,2458,2464],{},[56,2417,2418,2421],{},[34,2419,2420],{},"Plus 先用一个月","：评估实际 credit 消耗再决定升 Pro",[56,2423,2424,2427],{},[34,2425,2426],{},"复杂任务前估算 credit","：AI Video \u002F Pods \u002F Sites 烧得快",[56,2429,2430,2433],{},[34,2431,2432],{},"Sparkpage 引用要核实","：学术场景不要直接用，把链接打开看原文",[56,2435,2436,2439],{},[34,2437,2438],{},"Call For Me 合规","：欧盟 \u002F 加州录音同意法规要遵守",[56,2441,2442,2445],{},[34,2443,2444],{},"生成内容人工润色","：AI 味需要花 10-20% 时间打磨",[56,2447,2448,2451],{},[34,2449,2450],{},"地域限制","：在受限国家用 VPN + 海外卡，企业合规要走法务",[56,2453,2454,2457],{},[34,2455,2456],{},"AI Sites 不可深度编辑","：要灵活定制走 Webflow \u002F Framer + AI 工具",[56,2459,2460,2463],{},[34,2461,2462],{},"Pro 不要直接上","：Plus 重度可能仍够用，$200 差价省下来",[56,2465,2466,2469],{},[34,2467,2468],{},"Extra credits 性价比低","：$10 \u002F 5k credits 不如直接升 Pro",[25,2471,764],{"id":763},[53,2473,2474,2477,2480,2483,2486,2489,2492,2495],{},[56,2475,2476],{},"✅ 营销 \u002F 销售 \u002F 内容创作者 + 要全栈输出",[56,2478,2479],{},"✅ Plus $25 重度多模型不限 chat 用户",[56,2481,2482],{},"✅ 要真实电话调用 \u002F 一键建站 \u002F 短视频",[56,2484,2485],{},"✅ 中级专业用户（自由职业 \u002F 小团队）",[56,2487,2488],{},"❌ 纯研究问答（Perplexity 便宜 + 引用强）",[56,2490,2491],{},"❌ 学术严谨场景",[56,2493,2494],{},"❌ 预算极紧",[56,2496,2497],{},"❌ 要画布 + 多线程深度（用 Flowith）",[25,2499,799],{"id":799},[53,2501,2502,2506,2510],{},[56,2503,2504],{},[805,2505,1405],{"href":1404},[56,2507,2508],{},[805,2509,1393],{"href":1392},[56,2511,2512],{},[805,2513,1947],{"href":1468},[25,2515,817],{"id":817},[819,2517,2518,2525,2532,2539],{},[56,2519,2520,2521],{},"Genspark Pricing 官方 ",[805,2522,2523],{"href":2523,"rel":2524},"https:\u002F\u002Fgenspark.ai\u002Fpricing",[828],[56,2526,2527,2528],{},"WebCraft — Genspark 2026 Review（Plus \u002F Pro \u002F credit 消耗）",[805,2529,2530],{"href":2530,"rel":2531},"https:\u002F\u002Fwebscraft.org\u002Fblog\u002Fgenspark-ai-oglyad-superagent-yakiy-avtonomno-stvoryuye-sayti-prezentatsiyi?lang=en",[828],[56,2533,2534,2535],{},"Rimo — Genspark 2026 + ARR $250M 数据 ",[805,2536,2537],{"href":2537,"rel":2538},"https:\u002F\u002Frimo.app\u002Fen\u002Fblogs\u002Fgenspark-ai_en-US",[828],[56,2540,2541,2542],{},"Lindy — Genspark Features 2026 Tested ",[805,2543,2544],{"href":2544,"rel":2545},"https:\u002F\u002Fwww.lindy.ai\u002Fblog\u002Fgenspark-ai-features",[828],{"title":226,"searchDepth":325,"depth":325,"links":2547},[2548,2549,2550,2551,2552,2553,2554,2555,2556,2557],{"id":27,"depth":318,"text":28},{"id":51,"depth":318,"text":51},{"id":131,"depth":318,"text":131},{"id":2179,"depth":318,"text":2180},{"id":214,"depth":318,"text":214},{"id":603,"depth":318,"text":603},{"id":1299,"depth":318,"text":1299},{"id":763,"depth":318,"text":764},{"id":799,"depth":318,"text":799},{"id":817,"depth":318,"text":817},"\u002Fimg\u002Ftools\u002Fgenspark.webp","Genspark 真实评测：2024 年创立于 Palo Alto，CEO Eric Jing（前 Microsoft Bing \u002F 小冰），团队来自 Microsoft + Google。差异点：『多 Agent 架构』而非单 LLM + Super Agent 自治执行（研究 \u002F 内容 \u002F 调用 \u002F 建站 \u002F 视频 \u002F 数据分析）+ Sparkpage 可视化输出 + Call For Me 真实电话调度。2026-04 ARR $250M（12 个月内达成）。Free 100-200 daily credits \u002F Plus $25 月度 12k credits \u002F Pro $249 月度 125k credits。",[2561,2564,2567,2570],{"q":2562,"a":2563},"credit 消耗如何？","Simple Sparkpage \u002F 研究 30-80 credits；AI Slides（10-15 张 + 图表）250-450；AI Sites（完整 landing）600-1200；AI Video（30-60s）800-2000；Call For Me（2-4 分钟）400-900；AI Pods（10 分钟 podcast）1000-1800。Plus 12k credits 大约一个月做 10-15 个严肃任务（不重度跑视频 \u002F 通话）。",{"q":2565,"a":2566},"Call For Me 真的能打电话？","对，调用真实电话 API（背后用 Bland AI \u002F Vapi 类供应商），帮你预订餐厅 \u002F 跟进客户 \u002F 查信息。需要明确隐私 + 合规：欧盟 \u002F 加州的呼叫录音 \u002F 同意法规要遵守，敏感场景慎用。",{"q":2568,"a":2569},"多 Agent 架构和单 LLM 区别？","Genspark 不依赖单一 LLM，而是按任务类型路由到不同 agent + 模型组合（搜索 agent \u002F 研究 agent \u002F 内容 agent \u002F 调用 agent 等）。优势：每类任务用最适合的工具 + 大型搜索 + 验证流水线；劣势：黑盒程度高 + credit 消耗模型让用量不可预测。",{"q":2571,"a":2572},"和 Flowith \u002F ChatGPT \u002F Perplexity 怎么选？","Genspark 强在『全栈 Super Agent + 真实调用（电话 \u002F 搜索 \u002F 建站）+ Sparkpage 可视化』。Flowith 强在『画布 + 长任务自治 + Knowledge Garden』。Perplexity 强在『搜索 + 引用透明 + 价格便宜』。ChatGPT 强在『通用 + 生态成熟』。一站式 Super Agent → Genspark；画布多线程 → Flowith；搜索问答 → Perplexity。",[883,884,885],{},[897,2014,2576],"android",[2578,2580,2583],{"plan":1600,"price":900,"features":2579,"notes":2020},"100-200 daily credits + 基础模型 + Sparkpage \u002F 研究",{"plan":2159,"price":1821,"features":2581,"notes":2582},"12,000 月度 credits + 顶级模型不限聊天（o3-Pro \u002F Claude \u002F Gemini）+ Slides \u002F 研究","年付 $240",{"plan":1606,"price":2389,"features":2584,"notes":2585},"125,000 月度 credits + 优先速度 + 早期功能 + AI Sites \u002F Video \u002F Call","年付 $2388","Free (100-200 daily) \u002F Plus $25·月 (12k credits) \u002F Pro $249·月 (125k credits) \u002F 年付有折扣",[2588],"onboarding\u002Fsuper-agent-workflow",[2590,2036,2591],"autoglm-deep-review","openmanus-deep-review",{"power":359,"ux":347,"price":325,"cn_support":325,"stability":347},{"title":1722,"description":2559},[2595,2597,2599,2601],{"name":2596,"url":2523,"accessed":1495},"Genspark 官网 + Pricing",{"name":2598,"url":2530,"accessed":1495},"WebCraft — Genspark 2026 Review (Plus \u002F Pro \u002F Use Cases)",{"name":2600,"url":2537,"accessed":1495},"Rimo — Genspark 2026 + ARR $250M",{"name":2602,"url":2544,"accessed":1495},"Lindy — Genspark Features Tested 2026","tools\u002Fagent\u002Fgeneral\u002Fgenspark","Palo Alto 出品的 AI Super Agent——多 Agent 架构 + Sparkpage \u002F Slides \u002F Sites \u002F Video \u002F Call For Me 全栈",[2606,2607,2608,2609,2610],"super-agent","multi-agent","sparkpage","call-agent","genspark","Super Agent 类目里目前最完整的一站式产品——研究 \u002F 内容 \u002F 建站 \u002F 视频 \u002F 电话调度全包。Plus $25 \u002F 月在重度场景非常划算。要纯研究问答用 Perplexity \u002F ChatGPT 更便宜。","https:\u002F\u002Fgenspark.ai","8VbyAz5Oo2nCM445dUtrQuYglNvB7HnGEg1uQuSVnOo",{"id":2615,"title":2616,"alternatives":2617,"api_compatible":2619,"body":2623,"category":862,"chinese_friendly":318,"cover":3309,"description":3310,"domestic":1452,"extension":865,"faq":886,"free":1452,"github":3311,"languages":3312,"lastVerified":886,"meta":3313,"models":3314,"navigation":321,"notSuitable":3319,"opensource":321,"path":3324,"pillar":895,"platforms":3325,"priceTable":3326,"pricing":3338,"published":3339,"relatedPlaybooks":886,"relatedReviews":3340,"score":3342,"self_host":321,"seo":3343,"seoTitle":3344,"slug":3345,"sources":3346,"stem":3361,"suitable":3362,"tagline":3368,"tags":3369,"updated":3339,"verdict":3376,"website":3311,"__hash__":3377},"tools\u002Ftools\u002Fagent\u002Fgeneral\u002Fhermes-agent.md","Hermes Agent",[2618,12,935],"agent\u002Fgeneral\u002Fmanus",[2620,2621,2622],"openai","anthropic","local",{"type":22,"value":2624,"toc":3287},[2625,2628,2635,2641,2651,2655,2659,2666,2692,2699,2703,2710,2724,2727,2731,2737,2816,2822,2824,2827,2833,2851,2854,2857,2860,2880,2883,2886,2889,2903,2906,2909,2973,2988,2991,2997,3011,3014,3017,3043,3045,3099,3105,3108,3216,3220,3226,3232,3238,3244,3250,3252,3284],[25,2626,2627],{"id":2627},"一句话结论",[30,2629,2630,2631,2634],{},"如果你想要一个",[34,2632,2633],{},"完全开源、能自我进化、有长期记忆、可以部署在十几个消息平台上的 AI Agent","——Hermes Agent 在 2026 年是最成熟的选择。GitHub 10 万 Star，2 月开源到 5 月就翻了一倍。",[30,2636,2637,2638],{},"但它的门槛比 Manus 高一个量级：没有漂亮的 Web 界面，需要自己装 Python 环境、配置 LLM API、管理记忆数据库。",[34,2639,2640],{},"它面向的是\"想拥有自己 Agent 基础设施\"的极客，不是\"想要一个好用 AI 助手\"的普通用户。",[2642,2643,2645],"callout",{"type":2644},"info",[30,2646,2647,2650],{},[34,2648,2649],{},"定位区分","：Manus 是\"帮你干完事的云端 Agent\"，OpenClaw 是\"常驻你电脑的 AI 操作系统\"，Hermes Agent 是\"你能完全掌控和改造的自进化 Agent\"。三者目标不同，选型看你需要的是\"用\"还是\"拥有\"。",[25,2652,2654],{"id":2653},"hermes-agent-解决的核心问题","Hermes Agent 解决的核心问题",[216,2656,2658],{"id":2657},"问题-1agent-没有长期记忆","问题 1：Agent 没有\"长期记忆\"",[30,2660,2661,2662,2665],{},"大多数 AI Agent 的记忆只有当前会话的上下文窗口。你昨天让它做的事、偏好、上下文，今天全忘了。Hermes Agent 内置了",[34,2663,2664],{},"分层记忆系统","：",[53,2667,2668,2674,2680,2686],{},[56,2669,2670,2673],{},[34,2671,2672],{},"工作记忆","：当前会话上下文，类似人类短期记忆",[56,2675,2676,2679],{},[34,2677,2678],{},"情景记忆","：记录每次交互的时间、事件、结果，支持按时间线回溯",[56,2681,2682,2685],{},[34,2683,2684],{},"语义记忆","：从交互中抽取知识（你告诉它的偏好、事实、规则），长期保存",[56,2687,2688,2691],{},[34,2689,2690],{},"技能记忆","：Agent 自动总结\"怎么做某件事\"的步骤，下次直接调用",[30,2693,2694,2695,2698],{},"这意味着 Hermes Agent 是",[34,2696,2697],{},"越用越好用","的——它会记住你的工作习惯、项目上下文、你纠正过的错误。",[216,2700,2702],{"id":2701},"问题-2单个模型能力有天花板","问题 2：单个模型能力有天花板",[30,2704,2705,2706,2709],{},"2026 年 6 月，Nous Research 给 Hermes Agent 加了 ",[34,2707,2708],{},"MoA（Mixture of Agents）"," 功能：",[53,2711,2712,2715,2718,2721],{},[56,2713,2714],{},"多个 Agent 实例并行处理同一个任务",[56,2716,2717],{},"每个 Agent 可以用不同模型（Claude \u002F GPT \u002F Hermes \u002F 本地模型）",[56,2719,2720],{},"结果由一个\"聚合 Agent\"合并去重、交叉验证",[56,2722,2723],{},"最终输出质量 > 任何单个模型",[30,2725,2726],{},"实际效果：用 3 个中档模型做 MoA，输出质量可以接近 1 个顶级模型，但成本只有 1\u002F3。",[216,2728,2730],{"id":2729},"问题-3agent-只能在一个地方用","问题 3：Agent 只能在一个地方用",[30,2732,2733,2734,2665],{},"Hermes Agent 支持 ",[34,2735,2736],{},"14 个消息渠道",[605,2738,2739,2748],{},[608,2740,2741],{},[611,2742,2743,2745],{},[614,2744,1254],{},[614,2746,2747],{},"状态",[629,2749,2750,2758,2765,2772,2779,2786,2793,2800,2808],{},[611,2751,2752,2755],{},[634,2753,2754],{},"Telegram",[634,2756,2757],{},"✅ 推荐，最稳定",[611,2759,2760,2763],{},[634,2761,2762],{},"Discord",[634,2764,1180],{},[611,2766,2767,2770],{},[634,2768,2769],{},"Slack",[634,2771,1180],{},[611,2773,2774,2777],{},[634,2775,2776],{},"WhatsApp",[634,2778,1180],{},[611,2780,2781,2784],{},[634,2782,2783],{},"Signal",[634,2785,1180],{},[611,2787,2788,2791],{},[634,2789,2790],{},"Email",[634,2792,1180],{},[611,2794,2795,2798],{},[634,2796,2797],{},"CLI（终端）",[634,2799,1180],{},[611,2801,2802,2805],{},[634,2803,2804],{},"Web UI",[634,2806,2807],{},"✅ 基础版",[611,2809,2810,2813],{},[634,2811,2812],{},"微信",[634,2814,2815],{},"⚠️ 非官方，不稳定",[30,2817,2818,2819],{},"你可以在 Telegram 上给它发消息让它做事，结果推送到 Discord；或者在 CLI 里开发时让它监听 Git 提交自动跑测试。",[34,2820,2821],{},"一个 Agent 实例，多个入口。",[25,2823,51],{"id":51},[216,2825,2826],{"id":2826},"自我进化",[30,2828,2829,2830,2665],{},"Hermes Agent 最独特的能力是 ",[34,2831,2832],{},"Profile 系统",[53,2834,2835,2838,2841,2848],{},[56,2836,2837],{},"每个 Profile 是一个\"人格 + 技能包\"的组合",[56,2839,2840],{},"你可以创建多个 Profile（如\"代码助手\"、\"研究助手\"、\"项目经理\"）",[56,2842,2843,2844,2847],{},"Agent 在执行任务后会",[34,2845,2846],{},"自动总结经验","，更新 Profile 的技能库",[56,2849,2850],{},"下次遇到类似任务，直接调用已有技能，不用从零开始",[30,2852,2853],{},"这意味着 Hermes Agent 不是\"每次都从零开始的 ChatBot\"，而是\"会积累经验的数字员工\"。",[216,2855,2856],{"id":2856},"工具调用",[30,2858,2859],{},"Hermes Agent 支持自定义工具（function calling）：",[53,2861,2862,2865,2868,2871,2874,2877],{},[56,2863,2864],{},"搜索引擎（Google \u002F Bing \u002F SearXNG）",[56,2866,2867],{},"代码执行（Python sandbox）",[56,2869,2870],{},"文件读写",[56,2872,2873],{},"Web 浏览（Playwright）",[56,2875,2876],{},"自定义 API 调用",[56,2878,2879],{},"数据库查询",[30,2881,2882],{},"工具配置是 JSON 格式，添加新工具只需写一个 function 定义。",[216,2884,2885],{"id":2885},"记忆管理",[30,2887,2888],{},"记忆系统基于向量数据库（默认 ChromaDB）：",[53,2890,2891,2894,2897,2900],{},[56,2892,2893],{},"自动从对话中提取关键信息存入语义记忆",[56,2895,2896],{},"支持手动\"forget\"删除特定记忆",[56,2898,2899],{},"记忆有 TTL（过期时间），避免无限膨胀",[56,2901,2902],{},"支持记忆导出\u002F导入（JSON 格式），方便迁移",[25,2904,2905],{"id":2905},"使用体验",[216,2907,2908],{"id":2908},"安装部署",[221,2910,2912],{"className":278,"code":2911,"language":280,"meta":226,"style":226},"git clone https:\u002F\u002Fgithub.com\u002FNousResearch\u002Fhermes-agent\ncd hermes-agent\npip install -r requirements.txt\ncp .env.example .env\n# 配置 LLM API key、消息平台 token\npython -m hermes.agent\n",[39,2913,2914,2925,2933,2946,2957,2962],{"__ignoreMap":226},[230,2915,2916,2919,2922],{"class":232,"line":233},[230,2917,2918],{"class":244},"git",[230,2920,2921],{"class":251}," clone",[230,2923,2924],{"class":251}," https:\u002F\u002Fgithub.com\u002FNousResearch\u002Fhermes-agent\n",[230,2926,2927,2930],{"class":232,"line":318},[230,2928,2929],{"class":331},"cd",[230,2931,2932],{"class":251}," hermes-agent\n",[230,2934,2935,2938,2940,2943],{"class":232,"line":325},[230,2936,2937],{"class":244},"pip",[230,2939,290],{"class":251},[230,2941,2942],{"class":331}," -r",[230,2944,2945],{"class":251}," requirements.txt\n",[230,2947,2948,2951,2954],{"class":232,"line":347},[230,2949,2950],{"class":244},"cp",[230,2952,2953],{"class":251}," .env.example",[230,2955,2956],{"class":251}," .env\n",[230,2958,2959],{"class":232,"line":359},[230,2960,2961],{"class":406},"# 配置 LLM API key、消息平台 token\n",[230,2963,2964,2967,2970],{"class":232,"line":370},[230,2965,2966],{"class":244},"python",[230,2968,2969],{"class":331}," -m",[230,2971,2972],{"class":251}," hermes.agent\n",[30,2974,2975,2976,2979,2980,2983,2984,2987],{},"部署需要 ",[34,2977,2978],{},"Python 3.11+","、",[34,2981,2982],{},"至少 8GB RAM","（跑本地模型需要更多）、",[34,2985,2986],{},"向量数据库","（默认 ChromaDB，可选 Qdrant）。",[216,2989,2990],{"id":2990},"日常使用",[30,2992,2993,2994,2665],{},"最顺的使用方式是 ",[34,2995,2996],{},"Telegram + Claude API",[819,2998,2999,3002,3005,3008],{},[56,3000,3001],{},"在 Telegram 上给 Agent 发消息",[56,3003,3004],{},"Agent 读取记忆、规划任务、调用工具",[56,3006,3007],{},"执行过程中实时推送进度",[56,3009,3010],{},"完成后推送结果 + 自动更新记忆",[30,3012,3013],{},"体感类似\"有一个 7×24 小时在线的助手\"，但它不是即问即答——复杂任务可能需要 2-5 分钟。",[216,3015,3016],{"id":3016},"短板",[53,3018,3019,3025,3031,3037],{},[56,3020,3021,3024],{},[34,3022,3023],{},"文档偏英文","：几乎没有中文文档，国内用户上手门槛高",[56,3026,3027,3030],{},[34,3028,3029],{},"稳定性","：项目迭代极快（每周多个 commit），偶尔有 breaking change",[56,3032,3033,3036],{},[34,3034,3035],{},"资源消耗","：记忆系统 + 多 Agent 会占用较多内存和 API token",[56,3038,3039,3042],{},[34,3040,3041],{},"UI 简陋","：Web UI 只是基础版，不如 Manus \u002F OpenClaw 精致",[25,3044,131],{"id":131},[605,3046,3047,3057],{},[608,3048,3049],{},[611,3050,3051,3054],{},[614,3052,3053],{},"项目",[614,3055,3056],{},"成本",[629,3058,3059,3067,3075,3083,3091],{},[611,3060,3061,3064],{},[634,3062,3063],{},"Hermes Agent 本体",[634,3065,3066],{},"免费（开源）",[611,3068,3069,3072],{},[634,3070,3071],{},"LLM API",[634,3073,3074],{},"BYOK，用 Claude\u002FGPT 按各自 API 计费",[611,3076,3077,3080],{},[634,3078,3079],{},"本地模型",[634,3081,3082],{},"免费但需要 GPU（70B 模型需 ~48GB VRAM）",[611,3084,3085,3088],{},[634,3086,3087],{},"服务器",[634,3089,3090],{},"自托管需要一台 VPS（推荐 4 核 16GB 起步）",[611,3092,3093,3096],{},[634,3094,3095],{},"消息平台",[634,3097,3098],{},"Telegram\u002FDiscord 等均免费",[30,3100,3101,3104],{},[34,3102,3103],{},"最低成本","：一台 $5\u002F月 VPS + Claude API 按量付费 ≈ $10-20\u002F月可跑日常任务。",[25,3106,3107],{"id":3107},"与同类对比",[605,3109,3110,3124],{},[608,3111,3112],{},[611,3113,3114,3116,3118,3121],{},[614,3115,616],{},[614,3117,2616],{},[614,3119,3120],{},"Manus",[614,3122,3123],{},"OpenClaw",[629,3125,3126,3137,3149,3162,3176,3190,3202],{},[611,3127,3128,3131,3133,3135],{},[634,3129,3130],{},"开源",[634,3132,1180],{},[634,3134,1273],{},[634,3136,1180],{},[611,3138,3139,3141,3144,3146],{},[634,3140,2826],{},[634,3142,3143],{},"✅ Profile 系统",[634,3145,1273],{},[634,3147,3148],{},"⚠️ 有限",[611,3150,3151,3154,3157,3160],{},[634,3152,3153],{},"长期记忆",[634,3155,3156],{},"✅ 分层记忆",[634,3158,3159],{},"❌ 单会话",[634,3161,1180],{},[611,3163,3164,3167,3170,3173],{},[634,3165,3166],{},"跨平台部署",[634,3168,3169],{},"✅ 14 渠道",[634,3171,3172],{},"❌ Web only",[634,3174,3175],{},"⚠️ 桌面+CLI",[611,3177,3178,3181,3184,3187],{},[634,3179,3180],{},"上手难度",[634,3182,3183],{},"★★★★☆",[634,3185,3186],{},"★☆☆☆☆",[634,3188,3189],{},"★★★☆☆",[611,3191,3192,3195,3198,3200],{},[634,3193,3194],{},"中文体验",[634,3196,3197],{},"★★☆☆☆",[634,3199,3183],{},[634,3201,3189],{},[611,3203,3204,3207,3210,3213],{},[634,3205,3206],{},"适合人群",[634,3208,3209],{},"极客\u002F研究者",[634,3211,3212],{},"普通用户",[634,3214,3215],{},"开发者",[25,3217,3219],{"id":3218},"faq","FAQ",[30,3221,3222,3225],{},[34,3223,3224],{},"Q：Hermes Agent 能用中文交互吗？","\n能，但体验一般。Hermes 4 模型的中文能力不如 Claude\u002FGPT，且文档和社区以英文为主。建议用 Claude\u002FGPT 作为后端模型，中文交互质量会好很多。",[30,3227,3228,3231],{},[34,3229,3230],{},"Q：和 OpenManus 有什么区别？","\nOpenManus 是 Manus 的开源复刻版，定位是\"通用 Agent 执行器\"。Hermes Agent 更侧重\"长期陪伴+自我进化+多平台部署\"。OpenManus 更轻量，Hermes 功能更全但更重。",[30,3233,3234,3237],{},[34,3235,3236],{},"Q：需要什么硬件？","\n纯 API 模式（用 Claude\u002FGPT）只需一台普通 VPS。跑本地 Hermes 4 70B 需要 ~48GB VRAM，405B 需要 ~240GB VRAM（多卡服务器）。",[30,3239,3240,3243],{},[34,3241,3242],{},"Q：记忆数据存在哪？","\n默认存在本地 ChromaDB（SQLite + 向量索引）。可以配置为 Qdrant、Weaviate 等远程向量数据库。数据完全自主，不会上传到任何第三方。",[30,3245,3246,3249],{},[34,3247,3248],{},"Q：能同时跑多个 Profile 吗？","\n可以。每个 Profile 是独立的记忆+技能库，可以并行运行。比如同时让\"代码助手\"Profile 审查代码、\"研究助手\"Profile 做市场调研。",[25,3251,799],{"id":799},[53,3253,3254,3260,3266,3272,3278],{},[56,3255,3256],{},[805,3257,3259],{"href":3258},"\u002Fagent\u002Fgeneral\u002Fmanus.html","Manus 工具卡",[56,3261,3262],{},[805,3263,3265],{"href":3264},"\u002Fagent\u002Fgeneral\u002Fopenmanus.html","OpenManus 工具卡",[56,3267,3268],{},[805,3269,3271],{"href":3270},"\u002Fagent\u002Fdesktop\u002Fopenclaw.html","OpenClaw 工具卡",[56,3273,3274],{},[805,3275,3277],{"href":3276},"\u002Freview\u002Fmanus-deep-review.html","Manus 深度评测",[56,3279,3280],{},[805,3281,3283],{"href":3282},"\u002Fcompare\u002Fmanus-vs-genspark.html","Manus vs Genspark 对比",[844,3285,3286],{},"html pre.shiki code .sScJk, html code.shiki .sScJk{--shiki-default:#6F42C1;--shiki-dark:#B392F0}html pre.shiki code .sZZnC, html code.shiki .sZZnC{--shiki-default:#032F62;--shiki-dark:#9ECBFF}html pre.shiki code .sj4cs, html code.shiki .sj4cs{--shiki-default:#005CC5;--shiki-dark:#79B8FF}html pre.shiki code .sJ8bj, html code.shiki .sJ8bj{--shiki-default:#6A737D;--shiki-dark:#6A737D}html .default .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: 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var(--shiki-dark-text-decoration);}",{"title":226,"searchDepth":325,"depth":325,"links":3288},[3289,3290,3295,3300,3305,3306,3307,3308],{"id":2627,"depth":318,"text":2627},{"id":2653,"depth":318,"text":2654,"children":3291},[3292,3293,3294],{"id":2657,"depth":325,"text":2658},{"id":2701,"depth":325,"text":2702},{"id":2729,"depth":325,"text":2730},{"id":51,"depth":318,"text":51,"children":3296},[3297,3298,3299],{"id":2826,"depth":325,"text":2826},{"id":2856,"depth":325,"text":2856},{"id":2885,"depth":325,"text":2885},{"id":2905,"depth":318,"text":2905,"children":3301},[3302,3303,3304],{"id":2908,"depth":325,"text":2908},{"id":2990,"depth":325,"text":2990},{"id":3016,"depth":325,"text":3016},{"id":131,"depth":318,"text":131},{"id":3107,"depth":318,"text":3107},{"id":3218,"depth":318,"text":3219},{"id":799,"depth":318,"text":799},"\u002Fimg\u002Ftools\u002Fhermes-agent.webp","Hermes Agent 真实评测：Nous Research 出品的开源自进化 AI Agent，2026 年 2 月开源即获 10 万 GitHub Star。MoA 混合智能体架构、记忆系统、技能自动创建、跨平台部署（Telegram\u002FDiscord\u002FSlack\u002FWhatsApp\u002FCLI）。本文整理核心能力、使用体验、与 Manus\u002FOpenClaw 对比、适用场景。","https:\u002F\u002Fgithub.com\u002FNousResearch\u002Fhermes-agent",[883],{},[3315,3316,3317,3318],"hermes-4-405b","hermes-4-70b","claude-sonnet-4","gpt-5",[3320,3321,3322,3323],"需要中文为主交互的玩家（文档和界面均为英文）","不想折腾部署和配置的个人用户","需要生产级稳定性（项目仍在快速迭代）","预算有限且没有 GPU 服务器的用户","\u002Ftools\u002Fagent\u002Fgeneral\u002Fhermes-agent",[1472,1470,1471],[3327,3332],{"plan":3328,"price":900,"limit":3329,"cn_pay":3330,"note":3331},"开源版","完整功能，自托管","—","BYOK 模式",{"plan":3333,"price":3334,"limit":3335,"cn_pay":3336,"note":3337},"API（Hermes 4）","按 token 计费","405B 模型按量付费","⚠️ 需海外卡","不想自托管时","免费（开源，BYOK）","2026-07-04",[3341],"openhuman-deep-review",{"power":347,"ux":325,"price":359,"cn_support":318,"stability":325},{"title":2616,"description":3310},"Hermes Agent 评测 2026：Nous Research 开源自进化 AI Agent，10 万 Star","agent\u002Fgeneral\u002Fhermes-agent",[3347,3349,3352,3355,3358],{"title":3348,"url":3311},"Hermes Agent GitHub",{"title":3350,"url":3351},"Nous Research 官网","https:\u002F\u002Fnousresearch.com",{"title":3353,"url":3354},"Hermes Agent 安装教程","https:\u002F\u002Fblog.csdn.net\u002Fyweng18\u002Farticle\u002Fdetails\u002F161148047",{"title":3356,"url":3357},"Hermes MoA 体验报告","https:\u002F\u002Fm.toutiao.com\u002Fgroup\u002F7657411078791365171\u002F",{"title":3359,"url":3360},"Hermes vs OpenCode 对比","https:\u002F\u002Fm.toutiao.com\u002Fgroup\u002F7645892543409816064\u002F","tools\u002Fagent\u002Fgeneral\u002Fhermes-agent",[3363,3364,3365,3366,3367],"需要自托管、数据完全自主的 AI Agent","对 Agent 自我进化 \u002F 记忆系统有研究兴趣","需要跨平台部署（Telegram \u002F Discord \u002F Slack 等多通道）","有 GPU 服务器可以跑 Hermes 4 模型","想要一个长期陪伴型个人 Agent","Nous Research 开源自进化 AI Agent，10 万 Star，MoA 混合智能体",[3370,3371,920,3372,3373,3374,3375],"general-agent","autonomous","self-evolving","memory","nous-research","moa","2026 最火开源 Agent。MoA 混合智能体+自我进化+记忆系统+跨平台部署，GitHub 10 万 Star。适合长期陪伴型任务和自托管 Agent 研究，但中文支持和文档偏弱。","3gxu2kQEY9RgT_Ir0N2dPCaoLfjN4jFfYElq7IKZGhA",{"id":3379,"title":3120,"alternatives":3380,"api_compatible":3383,"body":3384,"category":862,"chinese_friendly":347,"cover":4278,"description":4279,"domestic":1452,"extension":865,"faq":886,"free":1452,"github":886,"languages":4280,"lastVerified":886,"meta":4281,"models":4282,"navigation":321,"notSuitable":4286,"opensource":1452,"path":4291,"pillar":895,"platforms":4292,"priceTable":4293,"pricing":4307,"published":4308,"relatedPlaybooks":4309,"relatedReviews":4311,"score":4312,"self_host":1452,"seo":4313,"seoTitle":4314,"slug":2618,"sources":4315,"stem":4331,"suitable":4332,"tagline":4338,"tags":4339,"updated":1495,"verdict":4343,"website":3767,"__hash__":4344},"tools\u002Ftools\u002Fagent\u002Fgeneral\u002Fmanus.md",[1517,3381,3382],"coding\u002Fagent\u002Fdevin","agent\u002Fplatform\u002Fdify",[],{"type":22,"value":3385,"toc":4258},[3386,3388,3426,3431,3434,3498,3501,3505,3513,3518,3525,3529,3537,3603,3608,3612,3619,3623,3631,3636,3653,3656,3659,3662,3669,3672,3678,3733,3739,3745,3750,3756,3760,3803,3806,3845,3848,4017,4022,4039,4044,4064,4067,4133,4135,4138,4155,4158,4179,4181,4231,4233,4250],[25,3387,28],{"id":27},[3389,3390,3395,3411],"div",{"className":3391},[3392,3393,3394],"card","p-5","my-4",[30,3396,3397,3400,3401,3404,3405,3410],{},[34,3398,3399],{},"一句话："," Butterfly Effect（中国创办、新加坡注册）2025-03-06 首发的通用 AI Agent，",[34,3402,3403],{},"邀请码一度被炒到 ¥5 万-10 万","（",[805,3406,3409],{"href":3407,"rel":3408},"https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FManus_%28AI_agent%29",[828],"据维基百科引用 China Daily 报道","）。多 sub-agent 并行架构——浏览、数据分析、代码执行、写作各自专门子 agent，主 orchestrator 自动路由到 Claude \u002F Qwen \u002F 自研模型，结果合成交付。",[30,3412,3413,3414,3404,3417,3421,3422,3425],{},"2025-12 传被 ",[34,3415,3416],{},"Meta 以约 20-30 亿美元收购",[805,3418,3420],{"href":3407,"rel":3419},[828],"Reuters \u002F AP 报道","），目前仍独立运营。最大价值是 ",[34,3423,3424],{},"deep research 类任务的引用密度和质量明显超过 ChatGPT Deep Research","。",[1043,3427,3428],{},[30,3429,3430],{},"来源说明：本文基于 manus.im 官方页面、Wikipedia \"Manus (AI agent)\" 条目、第三方评测（Pick Right \u002F HyzenPro \u002F Info-Tech Research Group \u002F 36Kr \u002F 世界经济论坛企业页）综合整理。Meta 收购案细节、产品路线图仍在变化中，请以最新官方公告为准。",[25,3432,3433],{"id":3433},"背景与公司",[53,3435,3436,3447,3453,3459,3465,3476,3486,3492],{},[56,3437,3438,3441,3442,3446],{},[34,3439,3440],{},"公司","：Butterfly Effect Pte. Ltd.（",[805,3443,3445],{"href":3407,"rel":3444},[828],"蝴蝶效应","），创始人 Xiao Hong（季逸超 Ji Yichao 为 Manus 联合创始人 + 首席科学家）",[56,3448,3449,3452],{},[34,3450,3451],{},"公司位置","：办公室在北京 + 武汉 + 新加坡，目标市场北美 \u002F 日本 \u002F 韩国（不主打中国大陆）",[56,3454,3455,3458],{},[34,3456,3457],{},"前作","：Monica，2023 年发布的浏览器扩展 AI 助手",[56,3460,3461,3464],{},[34,3462,3463],{},"历史融资","：2024 年字节跳动曾出价 ~3000 万美元收购被拒（据 36Kr）",[56,3466,3467,3470,3471,3475],{},[34,3468,3469],{},"Manus 启动","：2024-10 立项，灵感来自 ",[805,3472,3474],{"href":3473},"\u002Fcoding\u002Fide\u002Fcursor.html","Cursor","；名字来自 MIT 拉丁校训 \"Mens et Manus\"（手与脑）",[56,3477,3478,3481,3482],{},[34,3479,3480],{},"首发数据","：2025-03-06 邀请制 beta，7 天内 200 万人候补，",[805,3483,3485],{"href":3407,"rel":3484},[828],"Demo 视频 20 小时百万播放",[56,3487,3488,3491],{},[34,3489,3490],{},"营收","：2025-08 ARR 约 9000 万美元 → 2025-12 升至 1.25 亿美元",[56,3493,3494,3497],{},[34,3495,3496],{},"Meta 收购","：2025-12-29 宣布，估值 20-30 亿美元，目前仍独立运营，但中国大陆已被屏蔽访问 + 关闭中文社交账号",[25,3499,3500],{"id":3500},"核心特性",[216,3502,3504],{"id":3503},"多-sub-agent-并行架构最大差异化","多 sub-agent 并行架构（最大差异化）",[30,3506,3507,3512],{},[805,3508,3511],{"href":3509,"rel":3510},"https:\u002F\u002Fpick-right.com\u002Ftools\u002Fmanus-ai",[828],"Pick Right 2026-04 评测"," 描述的架构：",[1043,3514,3515],{},[30,3516,3517],{},"\"Where most general-purpose AI products use one model end-to-end, Manus runs multiple specialized sub-agents in parallel: one handles web browsing, one handles data analysis, one handles code execution, one handles synthesis and writing.\"",[30,3519,3520,3521,3524],{},"主 orchestrator 给每一步选最合适的模型（Claude \u002F Qwen \u002F 自研），结果合成。这是 Manus 研究输出 ",[34,3522,3523],{},"引用密度更高、幻觉更少","的工程根源——分工 + 并行让每个 sub-agent 只做自己最擅长的部分。",[216,3526,3528],{"id":3527},"通用-agent-模式","通用 Agent 模式",[30,3530,3531,3536],{},[805,3532,3535],{"href":3533,"rel":3534},"https:\u002F\u002Fwww.infotech.com\u002Fresearch\u002Fassessing-manus-the-future-of-agentic-ai",[828],"Info-Tech 评测"," 列出的 GAIA 基准（General AI Assistants）：",[605,3538,3539,3549],{},[608,3540,3541],{},[611,3542,3543,3546],{},[614,3544,3545],{},"模型",[614,3547,3548],{},"GAIA 准确率",[629,3550,3551,3563,3571,3579,3587,3595],{},[611,3552,3553,3558],{},[634,3554,3555],{},[34,3556,3557],{},"Manus AI",[634,3559,3560],{},[34,3561,3562],{},">65%（SOTA）",[611,3564,3565,3568],{},[634,3566,3567],{},"H2O.ai (h2oGPTe)",[634,3569,3570],{},"65%",[611,3572,3573,3576],{},[634,3574,3575],{},"Google Langfun",[634,3577,3578],{},"49%",[611,3580,3581,3584],{},[634,3582,3583],{},"Microsoft o1",[634,3585,3586],{},"38%",[611,3588,3589,3592],{},[634,3590,3591],{},"OpenAI GPT-4o",[634,3593,3594],{},"32%",[611,3596,3597,3600],{},[634,3598,3599],{},"OpenAI GPT-4 + Plugins",[634,3601,3602],{},"15-30%",[1043,3604,3605],{},[30,3606,3607],{},"注：基准数据需第三方验证；Manus 官方公布数据，请审慎参考。",[216,3609,3611],{"id":3610},"后台执行-长任务","后台执行 + 长任务",[30,3613,3614,3615,3618],{},"最特别的体验：任务下达后",[34,3616,3617],{},"可以关掉浏览器","，Manus 在云端继续跑（小时级），完成时通知。这种\"开着任务下班\"的模式是它 viral 的关键。",[216,3620,3622],{"id":3621},"web-app-builder","Web App Builder",[30,3624,3625,3626,3630],{},"直接生成完整网站和应用，内置数据库 + Stripe 支付 + SEO。但 ",[805,3627,3629],{"href":3509,"rel":3628},[828],"Pick Right 评测"," 直白警告：",[1043,3632,3633],{},[30,3634,3635],{},"\"Promising but buggy enough that I wouldn't ship to production from it yet.\"",[30,3637,3638,3639,3643,3644,3643,3648,3652],{},"复杂场景下出 bug 多，目前不建议生产部署，",[805,3640,3642],{"href":3641},"\u002Fcoding\u002Fbuilder\u002Fbolt-new.html","Bolt.new"," \u002F ",[805,3645,3647],{"href":3646},"\u002Fcoding\u002Fbuilder\u002Flovable.html","Lovable",[805,3649,3651],{"href":3650},"\u002Fcoding\u002Fbuilder\u002Fv0.html","v0"," 仍是 production app building 的更稳选项。",[216,3654,3655],{"id":3655},"桌面应用",[30,3657,3658],{},"提供 desktop app，能读本地文件 + 集成你的机器，不仅限于浏览器内任务。",[216,3660,3661],{"id":3661},"多模型路由",[30,3663,3664,3665,3668],{},"Claude \u002F Qwen \u002F Manus 自研模型，",[34,3666,3667],{},"按任务步骤自动选","——这是 Manus 跟单一模型 agent 的根本差异。",[25,3670,3671],{"id":3671},"价格与运行成本",[30,3673,3674,3677],{},[805,3675,3629],{"href":3509,"rel":3676},[828]," 公开档位：",[605,3679,3680,3692],{},[608,3681,3682],{},[611,3683,3684,3687,3689],{},[614,3685,3686],{},"档位",[614,3688,131],{},[614,3690,3691],{},"关键点",[629,3693,3694,3703,3712,3722],{},[611,3695,3696,3698,3700],{},[634,3697,1600],{},[634,3699,900],{},[634,3701,3702],{},"每日有限 credits，够 1 个高强度任务\u002F天",[611,3704,3705,3707,3709],{},[634,3706,1606],{},[634,3708,1203],{},[634,3710,3711],{},"大多数付费用户落点；中等 credit + 多模型",[611,3713,3714,3716,3719],{},[634,3715,2159],{},[634,3717,3718],{},"$50\u002F月",[634,3720,3721],{},"更高 credit + 优先队列，10+ 任务\u002F周",[611,3723,3724,3727,3730],{},[634,3725,3726],{},"Pro+ \u002F Team",[634,3728,3729],{},"最高 $200\u002F月",[634,3731,3732],{},"最大 credit + 团队空间（功能仍有限）",[30,3734,3735,3738],{},[34,3736,3737],{},"credit 经济学","：每个动作消耗 credits（浏览、代码运行、模型调用都计费）。",[30,3740,3741,2665],{},[805,3742,3744],{"href":3509,"rel":3743},[828],"Pick Right 真实使用反馈",[1043,3746,3747],{},[30,3748,3749],{},"\"Credits run out faster than the pricing page suggests. Heavy users routinely buy credit packs on top of subscriptions.\"",[30,3751,3752,3755],{},[34,3753,3754],{},"预算建议","：先用 Free 跑 3-5 个真实任务评估消耗速度，再决定档位。",[25,3757,3759],{"id":3758},"上手-5-分钟","上手 5 分钟",[819,3761,3762,3770,3773,3788,3791,3797,3800],{},[56,3763,3764,3765],{},"打开 ",[805,3766,3769],{"href":3767,"rel":3768},"https:\u002F\u002Fmanus.im",[828],"manus.im",[56,3771,3772],{},"账号注册（Google \u002F Apple OAuth 最快，国内手机号注册受限）",[56,3774,3775,3776,3779,3780],{},"给一个完整任务描述（",[34,3777,3778],{},"关键","：不要碎片化指令，给全场景）\n",[221,3781,3786],{"className":3782,"code":3784,"language":3785},[3783],"language-text","\"调研欧洲前 10 大 EV 充电网络（覆盖率、价格、可靠性、充电速度），\n产出带引用的 Markdown 对比表\"\n","text",[39,3787,3784],{"__ignoreMap":226},[56,3789,3790],{},"选模型路由（Auto 推荐）",[56,3792,3793,3794,3796],{},"提交后",[34,3795,3617],{},"——任务在云端跑",[56,3798,3799],{},"完成后邮件 \u002F 站内通知",[56,3801,3802],{},"看结果 \u002F 下载交付物（Markdown \u002F Excel \u002F Word \u002F 网页）",[25,3804,3805],{"id":3805},"国内使用注意事项",[819,3807,3808,3818,3824,3830],{},[56,3809,3810,2665,3813,3817],{},[34,3811,3812],{},"大陆访问被屏蔽",[805,3814,3816],{"href":3407,"rel":3815},[828],"Wikipedia 引用 36Kr 报道","，Butterfly Effect 已关闭中文社交账号、阻断中国大陆访问，原 Alibaba Qwen 合作版\"中文版 Manus\"已搁置",[56,3819,3820,3823],{},[34,3821,3822],{},"访问需稳定代理","：日韩 \u002F 美国节点",[56,3825,3826,3829],{},[34,3827,3828],{},"账号 \u002F 支付","：海外信用卡（Visa \u002F MasterCard），第三方代付方案有限",[56,3831,3832,3835,3836,3839,3840,3844],{},[34,3833,3834],{},"替代路径","：国内可考虑 ",[805,3837,1722],{"href":3838},"\u002Fagent\u002Fgeneral\u002Fgenspark.html"," \u002F Devv \u002F ",[805,3841,3843],{"href":3842},"\u002Fagent\u002Fgeneral\u002Fflowith.html","元宝 Yuanbao"," \u002F 秘塔 Metaso 等",[25,3846,3847],{"id":3847},"与同类怎么选",[605,3849,3850,3874],{},[608,3851,3852],{},[611,3853,3854,3856,3858,3864,3868,3871],{},[614,3855,616],{},[614,3857,3120],{},[614,3859,3860],{},[805,3861,3863],{"href":3862},"\u002Fcoding\u002Fagent\u002Fdevin.html","Devin",[614,3865,3866],{},[805,3867,1722],{"href":3838},[614,3869,3870],{},"ChatGPT Deep Research",[614,3872,3873],{},"GenAgent",[629,3875,3876,3895,3914,3931,3946,3962,3979,3997],{},[611,3877,3878,3881,3884,3887,3890,3893],{},[634,3879,3880],{},"核心定位",[634,3882,3883],{},"通用 Agent",[634,3885,3886],{},"AI 程序员",[634,3888,3889],{},"AI 搜索 + Agent",[634,3891,3892],{},"LLM Deep Research",[634,3894,1838],{},[611,3896,3897,3900,3903,3906,3909,3912],{},[634,3898,3899],{},"架构",[634,3901,3902],{},"多 sub-agent 并行",[634,3904,3905],{},"单 Agent + 沙盒",[634,3907,3908],{},"多模型",[634,3910,3911],{},"单模型",[634,3913,3330],{},[611,3915,3916,3919,3922,3925,3927,3929],{},[634,3917,3918],{},"长任务",[634,3920,3921],{},"★★★★★ 小时级",[634,3923,3924],{},"★★★★★",[634,3926,3189],{},[634,3928,3183],{},[634,3930,3189],{},[611,3932,3933,3936,3938,3940,3942,3944],{},[634,3934,3935],{},"引用密度",[634,3937,3924],{},[634,3939,3189],{},[634,3941,3183],{},[634,3943,3189],{},[634,3945,3189],{},[611,3947,3948,3951,3954,3956,3958,3960],{},[634,3949,3950],{},"App Builder",[634,3952,3953],{},"⚠️ 有但 buggy",[634,3955,1273],{},[634,3957,1273],{},[634,3959,1273],{},[634,3961,1273],{},[611,3963,3964,3967,3970,3973,3975,3977],{},[634,3965,3966],{},"中文",[634,3968,3969],{},"⚠️ 大陆屏蔽",[634,3971,3972],{},"⚠️",[634,3974,3924],{},[634,3976,3972],{},[634,3978,3183],{},[611,3980,3981,3983,3986,3989,3992,3995],{},[634,3982,131],{},[634,3984,3985],{},"$20-$200",[634,3987,3988],{},"$500\u002F月",[634,3990,3991],{},"$24.99\u002F月",[634,3993,3994],{},"随 ChatGPT Plus",[634,3996,3330],{},[611,3998,3999,4002,4005,4008,4011,4014],{},[634,4000,4001],{},"适合场景",[634,4003,4004],{},"research \u002F 数据分析",[634,4006,4007],{},"写代码 \u002F 修 bug",[634,4009,4010],{},"信息检索",[634,4012,4013],{},"单次深度研究",[634,4015,4016],{},"综合",[30,4018,4019,2665],{},[34,4020,4021],{},"选 Manus 如果你",[53,4023,4024,4030,4033,4036],{},[56,4025,4026,4027],{},"重视 research \u002F 数据分析任务的 ",[34,4028,4029],{},"引用密度和结构化输出",[56,4031,4032],{},"想试\"开任务下班、明早看结果\"的工作流",[56,4034,4035],{},"海外 \u002F 能解决账号网络问题",[56,4037,4038],{},"预算 $20-$50\u002F月，重度用户",[30,4040,4041,2665],{},[34,4042,4043],{},"别选 Manus 如果你",[53,4045,4046,4055,4058,4061],{},[56,4047,4048,4049,3643,4051,4054],{},"国内裸用（",[805,4050,1722],{"href":3838},[805,4052,4053],{"href":3842},"Yuanbao"," 更顺）",[56,4056,4057],{},"想生产部署 App（Web App Builder bug 多）",[56,4059,4060],{},"团队协作场景（功能不完善）",[56,4062,4063],{},"预算 \u003C $20\u002F月（Free 档够评估，付费档不一定划算）",[25,4065,4066],{"id":4066},"避坑清单",[53,4068,4069,4075,4085,4091,4097,4109,4115,4121],{},[56,4070,4071,4074],{},[34,4072,4073],{},"大陆访问已被官方屏蔽","：2025 年起阻断中国大陆访问，原\"中文版 Manus\"项目搁置",[56,4076,4077,2665,4080,4084],{},[34,4078,4079],{},"credit 烧得比官方页面暗示的快",[805,4081,4083],{"href":3509,"rel":4082},[828],"Pick Right 2026 评测"," 真实反馈，重度用户经常额外买 credit pack",[56,4086,4087,4090],{},[34,4088,4089],{},"任务一启动无法控预算","：开始跑后只能 cancel 或等结束，credits 会一直消耗",[56,4092,4093,4096],{},[34,4094,4095],{},"Web App Builder 别上生产","：demo 漂亮，复杂场景翻车，Bolt.new \u002F Lovable \u002F v0 仍是生产部署更稳选项",[56,4098,4099,4102,4103,4108],{},[34,4100,4101],{},"每次任务从零开始","：没有持久 workspace，不像 ",[805,4104,4107],{"href":4105,"rel":4106},"https:\u002F\u002Fclaude.com",[828],"Claude Projects"," \u002F ChatGPT Custom GPTs 能跨会话记忆",[56,4110,4111,4114],{},[34,4112,4113],{},"没有 HubSpot \u002F Salesforce \u002F Notion \u002F Slack 集成","：拿到结果后要自己手动搬到工具栈",[56,4116,4117,4120],{},[34,4118,4119],{},"团队协作能力差","：单用户产品为主，无共享 workspace \u002F 评论 \u002F 审计",[56,4122,4123,4126,4127,4132],{},[34,4124,4125],{},"Meta 收购的不确定性","：Wikipedia 引用 ",[805,4128,4131],{"href":4129,"rel":4130},"https:\u002F\u002Fwww.nytimes.com\u002F2026\u002F03\u002F17\u002Ftechnology\u002Fchina-scrutiny-meta-manus.html",[828],"纽时 2026-03"," 报道，中国审查 Meta 收购案，长期路线图待观察",[25,4134,764],{"id":763},[30,4136,4137],{},"✅ 适合：",[53,4139,4140,4143,4146,4149,4152],{},[56,4141,4142],{},"委托多步骤 research（\"调研 X，产出 Markdown 对比表\"）",[56,4144,4145],{},"CSV \u002F Excel 重的数据分析任务",[56,4147,4148],{},"长任务 + 不想盯着看（小时级）",[56,4150,4151],{},"单兵作战的咨询顾问 \u002F 分析师 \u002F 创业者",[56,4153,4154],{},"对\"通用 Agent 体感天花板\"感兴趣的测试者",[30,4156,4157],{},"❌ 不适合：",[53,4159,4160,4163,4166,4169,4172],{},[56,4161,4162],{},"生产级应用部署",[56,4164,4165],{},"团队协作工作流",[56,4167,4168],{},"预算极敏感（free 档非常受限）",[56,4170,4171],{},"大陆稳定访问需求",[56,4173,4174,4175,4178],{},"需要深度持久上下文（用 ",[805,4176,4107],{"href":4105,"rel":4177},[828]," \u002F ChatGPT Custom GPT 等）",[25,4180,799],{"id":799},[53,4182,4183,4194,4205,4220],{},[56,4184,4185,4186,3643,4188,4190,4191,4193],{},"同类对比：",[805,4187,3863],{"href":3862},[805,4189,1722],{"href":3838}," \u002F Elicit \u002F ",[805,4192,4053],{"href":3842}," \u002F Metaso",[56,4195,4196,4197,3643,4201,4204],{},"概念：",[805,4198,4200],{"href":4199},"\u002Fwiki\u002Fai-agent.html","AI Agent",[805,4202,4203],{"href":4199},"Multi-Agent"," \u002F Computer Use \u002F Deep Research",[56,4206,4207,4208,3643,4212,3643,4216],{},"模型：",[805,4209,4211],{"href":4210},"\u002Fmodels\u002Fclaude-sonnet-4.html","Claude Sonnet 4",[805,4213,4215],{"href":4214},"\u002Fmodels\u002Fclaude-opus-4.html","Claude Opus 4",[805,4217,4219],{"href":4218},"\u002Fmodels\u002Fqwen-3.html","Qwen3",[56,4221,4222,4223,3643,4227],{},"进阶：",[805,4224,4226],{"href":4225},"\u002Fwiki\u002Fcontext-engineering.html","Context Engineering",[805,4228,4230],{"href":4229},"\u002Fwiki\u002Fprompt-engineering.html","Prompt Engineering",[25,4232,817],{"id":817},[53,4234,4235,4241,4244,4247],{},[56,4236,4237,4238],{},"官网：",[805,4239,3767],{"href":3767,"rel":4240},[828],[56,4242,4243],{},"Wikipedia：\"Manus (AI agent)\" 条目",[56,4245,4246],{},"第三方评测：pick-right.com \u002F hyzenpro.com \u002F infotech.com \u002F weforum.org",[56,4248,4249],{},"媒体报道：Reuters \u002F AP \u002F China Daily \u002F 36Kr \u002F NYT",[30,4251,4252,4253,4257],{},"本卡片由 AIHO 编辑部根据官方公开资料与第三方评测整理。所有事实点均标注来源；如发现价格 \u002F 功能 \u002F 公司状态与最新官方信息不一致，请通过 ",[805,4254,4256],{"href":4255},"mailto:hello@aiho.net","反馈邮箱"," 反馈。",{"title":226,"searchDepth":325,"depth":325,"links":4259},[4260,4261,4262,4270,4271,4272,4273,4274,4275,4276,4277],{"id":27,"depth":318,"text":28},{"id":3433,"depth":318,"text":3433},{"id":3500,"depth":318,"text":3500,"children":4263},[4264,4265,4266,4267,4268,4269],{"id":3503,"depth":325,"text":3504},{"id":3527,"depth":325,"text":3528},{"id":3610,"depth":325,"text":3611},{"id":3621,"depth":325,"text":3622},{"id":3655,"depth":325,"text":3655},{"id":3661,"depth":325,"text":3661},{"id":3671,"depth":318,"text":3671},{"id":3758,"depth":318,"text":3759},{"id":3805,"depth":318,"text":3805},{"id":3847,"depth":318,"text":3847},{"id":4066,"depth":318,"text":4066},{"id":763,"depth":318,"text":764},{"id":799,"depth":318,"text":799},{"id":817,"depth":318,"text":817},"\u002Fimg\u002Ftools\u002Fmanus.webp","Manus 真实评测：Butterfly Effect（蝴蝶效应）出品的通用 AI Agent，多 sub-agent 并行架构 + 多模型路由（Claude \u002F Qwen \u002F 自研）。2025-03 首发即引爆，2025-12 传被 Meta 以约 20 亿美元收购。AIHO 编辑部基于官方文档与多份评测整理。",[884,883],{},[3317,4283,4284,4285],"claude-opus-4","qwen-max","manus-internal",[4287,4288,4289,4290],"生产级 Web App 部署（Web App Builder 仍有 bug）","团队协作场景（功能未完善）","需要持久工作空间 \u002F 跨会话上下文","对成本极敏感（credits 烧得快）","\u002Ftools\u002Fagent\u002Fgeneral\u002Fmanus",[897,1471,1470,1472],[4294,4297,4300,4303],{"plan":1600,"price":900,"limit":4295,"cn_pay":3330,"note":4296},"每日有限 credits，足够每天 1 个 demanding 任务","试水\u002F评估",{"plan":1606,"price":1203,"limit":4298,"cn_pay":3336,"note":4299},"更高 credit + 更长任务时长 + 多模型路由","个人主力档",{"plan":2159,"price":3718,"limit":4301,"cn_pay":3972,"note":4302},"更大 credit 池 + 优先队列","10+ 任务\u002F周",{"plan":4304,"price":2396,"limit":4305,"cn_pay":3972,"note":4306},"Pro+\u002FTeam","最大 credit + 团队工作空间（功能受限）","团队功能仍有限","Free \u002F Pro $20\u002Fmo \u002F Plus $50\u002Fmo \u002F Pro+ Team 最高 $200\u002Fmo","2026-06-18",[4310],"onboarding\u002Fmanus-getting-started",[2036,2590,2591],{"power":359,"ux":359,"price":325,"cn_support":347,"stability":325},{"title":3120,"description":4279},"Manus AI Agent 评测 2026：通用 AI 智能体，自主完成任务，对比 Devin",[4316,4318,4321,4323,4325,4328],{"title":4317,"url":3767},"Manus 官网",{"title":4319,"url":4320},"Manus 维基百科","https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FManus_(AI_agent)",{"title":4322,"url":3509},"Manus AI Review 2026 (Pick Right)",{"title":4324,"url":3533},"Info-Tech Manus Assessment",{"title":4326,"url":4327},"WEF Butterfly Effect Page","https:\u002F\u002Fwww.weforum.org\u002Forganizations\u002Fbutterfly-effect",{"title":4329,"url":4330},"HyzenPro 2026 Review","https:\u002F\u002Fhyzenpro.com\u002Fblog\u002Fmanus-ai-review","tools\u002Fagent\u002Fgeneral\u002Fmanus",[4333,4334,4335,4336,4337],"需要委托多步骤研究 \u002F 报告（带引用密度的 deep research）","数据分析（CSV \u002F Excel 重的任务）","需要","调研类工作（市场调研、竞品对比、文献综述）","对 Computer Use 类 Agent 实践有兴趣的人","通用 Agent 体感天花板，自主完成复杂多步骤任务",[3370,3371,4340,2607,4341,4342],"computer-use","butterfly-effect","monica","通用 Agent 体感天花板代表。多 sub-agent 并行 + 自动模型路由让 research \u002F 数据分析输出明显比 ChatGPT Deep Research 引用密度更高。慢、credit 烧得快、Web App Builder 仍有 bug，但任务跑通时确实让人惊艳。","D_3USoIRX1i-tNsMzyFhKfWICosFY8LK0cepfSlF8_U",{"id":4346,"title":4347,"alternatives":4348,"api_compatible":886,"body":4352,"category":862,"chinese_friendly":325,"cover":4870,"description":4871,"domestic":1452,"extension":865,"faq":4872,"free":1452,"github":886,"languages":4884,"lastVerified":886,"meta":4885,"models":886,"navigation":321,"notSuitable":886,"opensource":1452,"path":4886,"pillar":895,"platforms":4887,"priceTable":4888,"pricing":4904,"published":1487,"relatedPlaybooks":4905,"relatedReviews":886,"score":4907,"self_host":321,"seo":4908,"seoTitle":886,"slug":4909,"sources":4910,"stem":4919,"suitable":886,"tagline":4920,"tags":4921,"updated":1495,"verdict":4924,"website":4925,"__hash__":4926},"tools\u002Ftools\u002Fagent\u002Fgeneral\u002Fmsty.md","Msty",[4349,4350,4351],"coding\u002Flocal\u002Follama","coding\u002Flocal\u002Flm-studio","coding\u002Flocal\u002Fopen-webui",{"type":22,"value":4353,"toc":4858},[4354,4356,4359,4362,4364,4438,4440,4465,4470,4474,4478,4504,4508,4534,4536,4559,4561,4719,4721,4776,4778,4804,4806,4826,4828],[25,4355,28],{"id":27},[30,4357,4358],{},"Msty 是 privacy-first 桌面 AI 工作站，macOS \u002F Windows \u002F Linux 原生 app + 浏览器，独立开发者出品。差异点：内置 MLX (Apple) \u002F llama.cpp \u002F Ollama 三种本地推理引擎，无需 CLI + Hosted Models（OpenAI \u002F Anthropic \u002F Gemini）一窗体并存 + Split Chats 同问题多模型并行 + Knowledge Stack（per-conversation RAG）+ Prompt \u002F Persona \u002F Skills 三个 Studios + Agent Mode 多步执行。Free 本地无限 \u002F Aurum $149·年 \u002F Lifetime $349 \u002F Enterprise $300\u002Fuser·年。",[30,4360,4361],{},"适合：隐私敏感 + 不愿数据上云；Mac mini \u002F Linux box 当私人 AI 服务器；想避开 Ollama CLI 的非工程师；多模型对比决策场景。不适合：硬件不行（7B+ 本地跑不动）；要 BYO API + 极简（用 Typing Mind）；要 mobile（无 iOS \u002F Android 移动 app）；团队协作（更适合 Claude Team）。",[25,4363,51],{"id":51},[53,4365,4366,4372,4378,4384,4390,4396,4402,4408,4414,4420,4426,4432],{},[56,4367,4368,4371],{},[34,4369,4370],{},"三引擎本地推理","：MLX（Apple）\u002F llama.cpp \u002F Ollama 开箱即用",[56,4373,4374,4377],{},[34,4375,4376],{},"Hosted Models","：OpenAI \u002F Anthropic \u002F Gemini 一窗体接入",[56,4379,4380,4383],{},[34,4381,4382],{},"Split Chats","：同问题同时跑多模型 + side-by-side 对比",[56,4385,4386,4389],{},[34,4387,4388],{},"Knowledge Stack","：per-conversation RAG，文档 \u002F URL \u002F Obsidian vault \u002F YouTube transcript",[56,4391,4392,4395],{},[34,4393,4394],{},"Prompt Studio","：变量 + 模板 + 测试",[56,4397,4398,4401],{},[34,4399,4400],{},"Persona Studio","：自定义角色 + 工具 + context",[56,4403,4404,4407],{},[34,4405,4406],{},"Skills Studio","：可复用能力包",[56,4409,4410,4413],{},[34,4411,4412],{},"Agent Mode","：多步执行 + 工具调用",[56,4415,4416,4419],{},[34,4417,4418],{},"Flowchart 对话","：分支可视化",[56,4421,4422,4425],{},[34,4423,4424],{},"Real-time 数据","：实时 web fetch",[56,4427,4428,4431],{},[34,4429,4430],{},"Offline-first","：零账号 \u002F 零 telemetry \u002F 零云依赖（Free）",[56,4433,4434,4437],{},[34,4435,4436],{},"Cloud Sync","（Aurum）：跨设备同步对话",[25,4439,131],{"id":131},[53,4441,4442,4447,4453,4459],{},[56,4443,4444,4446],{},[34,4445,1600],{},"：$0 永久；本地全功能 + 云 API 接入 + Split Chats + Knowledge Stack",[56,4448,4449,4452],{},[34,4450,4451],{},"Aurum","：$149\u002F年；cloud sync + 高级 Knowledge + Studio Desktop alpha + 优先支持",[56,4454,4455,4458],{},[34,4456,4457],{},"Lifetime","：$349 一次；Aurum 全部功能 + 终身更新",[56,4460,4461,4464],{},[34,4462,4463],{},"Enterprise","：$300\u002Fuser·年；SSO + 私有部署 + 团队管理",[1043,4466,4467],{},[30,4468,4469],{},"Lifetime 在 2 年用回本，重度用户首选。Free 已经足够个人 90% 场景。",[25,4471,4473],{"id":4472},"实测macos-mac-mini-私人-ai-服务器","实测（macOS + Mac mini 私人 AI 服务器）",[30,4475,4476],{},[34,4477,154],{},[53,4479,4480,4483,4486,4489,4492,4495,4498,4501],{},[56,4481,4482],{},"装完立刻能用，无需 Ollama \u002F llama.cpp \u002F MLX 任何 CLI",[56,4484,4485],{},"Split Chats 对比 GPT-5 + Claude + Qwen2.5 + Llama 3.3 一目了然",[56,4487,4488],{},"Knowledge Stack 的 per-conversation 设计完美：每项目独立 RAG context",[56,4490,4491],{},"Prompt \u002F Persona \u002F Skills 三 Studio 解决重复 prompt 痛点",[56,4493,4494],{},"MLX 在 M1\u002FM2\u002FM3\u002FM4 上跑得快",[56,4496,4497],{},"中文 Qwen2.5 \u002F DeepSeek 走本地路径无外网依赖",[56,4499,4500],{},"Free 永久免费 + 本地无限 = 真正 zero-cost 路径",[56,4502,4503],{},"Lifetime $349 比 ChatGPT Plus 18 个月便宜",[30,4505,4506],{},[34,4507,188],{},[53,4509,4510,4513,4516,4519,4522,4525,4528,4531],{},[56,4511,4512],{},"本地性能受硬件限制：M1 Air 跑 7B 慢，M3 Max \u002F Mac Studio \u002F 高端 GPU 更适合",[56,4514,4515],{},"插件 \u002F Skill 生态比 Typing Mind \u002F LM Studio 弱",[56,4517,4518],{},"桌面 only，无 iOS \u002F Android",[56,4520,4521],{},"llama.cpp 更新滞后官方上游 1-2 版本",[56,4523,4524],{},"Knowledge Stack 大文档（>100MB）切分偶尔失败",[56,4526,4527],{},"Agent Mode 仍在打磨，复杂任务稳定性不如 Claude",[56,4529,4530],{},"Studio Desktop（Aurum alpha）测试中，bug 偶发",[56,4532,4533],{},"中文 UI 不完整，部分功能仍是英文",[25,4535,214],{"id":214},[819,4537,4538,4541,4544,4547,4550,4553,4556],{},[56,4539,4540],{},"msty.ai → 下载 macOS \u002F Windows \u002F Linux → 安装",[56,4542,4543],{},"Settings → Model Providers → Ollama 连本地（或直接装 Msty 自带 llama.cpp \u002F MLX）",[56,4545,4546],{},"装 1-2 个本地模型：Qwen2.5 7B（中文）+ Llama 3.3 8B（英文）",[56,4548,4549],{},"加云模型：OpenAI Key + Anthropic Key",[56,4551,4552],{},"新建 chat → 试 Split Chats：+ 第二个模型 → 同问题并行",[56,4554,4555],{},"Knowledge Stack → 上传项目文档 → 附加到 conversation",[56,4557,4558],{},"满意后 Free 用着，重度需要同步上 Lifetime $349",[25,4560,603],{"id":603},[605,4562,4563,4579],{},[608,4564,4565],{},[611,4566,4567,4569,4571,4573,4576],{},[614,4568,616],{},[614,4570,4347],{},[614,4572,20],{},[614,4574,4575],{},"LM Studio",[614,4577,4578],{},"Jan",[629,4580,4581,4596,4611,4624,4636,4649,4663,4677,4689,4703],{},[611,4582,4583,4586,4589,4592,4594],{},[634,4584,4585],{},"GUI",[634,4587,4588],{},"✅ 颜值高",[634,4590,4591],{},"❌ CLI",[634,4593,1180],{},[634,4595,1180],{},[611,4597,4598,4601,4604,4606,4609],{},[634,4599,4600],{},"本地引擎",[634,4602,4603],{},"MLX\u002Fllama.cpp\u002FOllama",[634,4605,20],{},[634,4607,4608],{},"llama.cpp",[634,4610,4608],{},[611,4612,4613,4616,4618,4620,4622],{},[634,4614,4615],{},"云模型",[634,4617,1180],{},[634,4619,1273],{},[634,4621,1273],{},[634,4623,1230],{},[611,4625,4626,4628,4630,4632,4634],{},[634,4627,4382],{},[634,4629,1233],{},[634,4631,1273],{},[634,4633,1230],{},[634,4635,1245],{},[611,4637,4638,4640,4643,4645,4647],{},[634,4639,4388],{},[634,4641,4642],{},"✅ per-conv",[634,4644,1273],{},[634,4646,1273],{},[634,4648,1230],{},[611,4650,4651,4654,4657,4659,4661],{},[634,4652,4653],{},"Studios",[634,4655,4656],{},"✅ Prompt\u002FPersona\u002FSkills",[634,4658,1245],{},[634,4660,1245],{},[634,4662,1245],{},[611,4664,4665,4667,4669,4672,4674],{},[634,4666,3130],{},[634,4668,1273],{},[634,4670,4671],{},"✅ MIT",[634,4673,1273],{},[634,4675,4676],{},"✅ Apache 2.0",[611,4678,4679,4681,4683,4685,4687],{},[634,4680,1815],{},[634,4682,900],{},[634,4684,900],{},[634,4686,900],{},[634,4688,900],{},[611,4690,4691,4694,4697,4699,4701],{},[634,4692,4693],{},"终身",[634,4695,4696],{},"$349",[634,4698,1245],{},[634,4700,1245],{},[634,4702,1245],{},[611,4704,4705,4707,4710,4713,4716],{},[634,4706,1284],{},[634,4708,4709],{},"桌面颜值 + 多模型",[634,4711,4712],{},"CLI \u002F Server",[634,4714,4715],{},"模型市场",[634,4717,4718],{},"严格开源",[25,4720,1299],{"id":1299},[53,4722,4723,4729,4735,4741,4747,4753,4759,4765,4771],{},[56,4724,4725,4728],{},[34,4726,4727],{},"硬件评估","：M1 Air 8GB 只跑 3B-7B，M3 Pro \u002F Max 跑 13B-30B 流畅",[56,4730,4731,4734],{},[34,4732,4733],{},"Ollama 已装就连","：避免重复下模型，连本地 Ollama 复用 model library",[56,4736,4737,4740],{},[34,4738,4739],{},"Knowledge Stack 文档","：单文档 \u003C50MB 最稳，大文件先切分",[56,4742,4743,4746],{},[34,4744,4745],{},"Persona vs Skill","：Persona 是角色（完整 system + 模型）；Skill 是能力包；不要混用",[56,4748,4749,4752],{},[34,4750,4751],{},"Split Chats 三个模型够","：4 个起每问 token 烧得快",[56,4754,4755,4758],{},[34,4756,4757],{},"Aurum cloud sync 谨慎","：隐私敏感场景仍用 Free 本地",[56,4760,4761,4764],{},[34,4762,4763],{},"Studio Desktop alpha","：稳定性不如 main 版本，重要工作不要全押",[56,4766,4767,4770],{},[34,4768,4769],{},"本地中文模型","：Qwen2.5 7B \u002F DeepSeek 7B 中文最优，Llama 3.3 8B 英文最优",[56,4772,4773,4775],{},[34,4774,1354],{},"：当前不如 Claude Desktop 强，要 MCP 重度场景考虑 Claude Desktop \u002F Crush",[25,4777,764],{"id":763},[53,4779,4780,4783,4786,4789,4792,4795,4798,4801],{},[56,4781,4782],{},"✅ 隐私敏感 + 数据零云",[56,4784,4785],{},"✅ Mac mini \u002F Linux box 私人 AI 服务器",[56,4787,4788],{},"✅ 不愿学 Ollama CLI 的非工程师",[56,4790,4791],{},"✅ 多模型对比决策场景",[56,4793,4794],{},"❌ 硬件不行（8GB RAM）跑不动 7B+",[56,4796,4797],{},"❌ 要 mobile 移动主力",[56,4799,4800],{},"❌ 团队协作 + 共享 workspace",[56,4802,4803],{},"❌ 要 MCP 工具栈深度（用 Claude Desktop \u002F Crush）",[25,4805,799],{"id":799},[53,4807,4808,4814,4820],{},[56,4809,4810],{},[805,4811,4813],{"href":4812},"\u002Ftools\u002Fcoding\u002Flocal\u002Follama","Ollama 评测",[56,4815,4816],{},[805,4817,4819],{"href":4818},"\u002Ftools\u002Fcoding\u002Flocal\u002Flm-studio","LM Studio 评测",[56,4821,4822],{},[805,4823,4825],{"href":4824},"\u002Ftools\u002Fcoding\u002Flocal\u002Fopen-webui","Open WebUI 评测",[25,4827,817],{"id":817},[819,4829,4830,4837,4844,4851],{},[56,4831,4832,4833],{},"Msty 官网 + Features（MLX \u002F llama.cpp \u002F Ollama \u002F Studios）",[805,4834,4835],{"href":4835,"rel":4836},"https:\u002F\u002Fmsty.ai\u002Fstudio\u002Ffeatures",[828],[56,4838,4839,4840],{},"AI Chat Daily — Msty Review 2026（4.3\u002F5 评分 + Lifetime）",[805,4841,4842],{"href":4842,"rel":4843},"https:\u002F\u002Fwww.aichatdaily.com\u002Ftools\u002Fmsty",[828],[56,4845,4846,4847],{},"ML Journey — Msty Multi-model Comparison Guide ",[805,4848,4849],{"href":4849,"rel":4850},"https:\u002F\u002Fmljourney.com\u002Fmsty-the-local-llm-app-that-lets-you-compare-models-side-by-side",[828],[56,4852,4853,4854],{},"AISO Tools — Msty Pricing 2026 ",[805,4855,4856],{"href":4856,"rel":4857},"https:\u002F\u002Faisotools.com\u002Fpricing\u002Fmsty",[828],{"title":226,"searchDepth":325,"depth":325,"links":4859},[4860,4861,4862,4863,4864,4865,4866,4867,4868,4869],{"id":27,"depth":318,"text":28},{"id":51,"depth":318,"text":51},{"id":131,"depth":318,"text":131},{"id":4472,"depth":318,"text":4473},{"id":214,"depth":318,"text":214},{"id":603,"depth":318,"text":603},{"id":1299,"depth":318,"text":1299},{"id":763,"depth":318,"text":764},{"id":799,"depth":318,"text":799},{"id":817,"depth":318,"text":817},"\u002Fimg\u002Ftools\u002Fmsty.webp","Msty 真实评测：privacy-first 桌面 AI 工作站，macOS \u002F Windows \u002F Linux 原生 app + 浏览器。差异点：内置 MLX (Apple) \u002F llama.cpp \u002F Ollama 三种本地推理引擎 + Hosted Models（OpenAI \u002F Anthropic \u002F Gemini）一窗体管理 + Split Chats 多模型同时跑同问题 + Knowledge Stack（per-conversation RAG，区别于 AnythingLLM workspace）+ Prompt \u002F Persona \u002F Skills 三个 Studios + Agent Mode 多步执行。Free 本地全功能 \u002F Aurum $5-149 \u002F Lifetime $349 \u002F Enterprise $300\u002Fuser·年。",[4873,4876,4879,4882],{"q":4874,"a":4875},"和 Ollama \u002F LM Studio \u002F Jan \u002F AnythingLLM 怎么选？","Msty 强在『多模型 split chat 对比 + Knowledge Stack 灵活 per-conversation + UI 颜值』。Ollama 是 CLI + server，无 GUI 适合开发者。LM Studio 强在『模型市场 + 性能 profiling』。Jan 开源 + Apache 2.0 协议自由度高。AnythingLLM 强在 workspace + RAG agent。要桌面颜值 + 多模型对比 + 简单 RAG → Msty；要 CLI \u002F server → Ollama；要模型市场 → LM Studio；要严格开源 → Jan。",{"q":4877,"a":4878},"Knowledge Stack 怎么用？","上传文档 \u002F URL \u002F 文本到 Knowledge collection，per-conversation 附加。和 AnythingLLM 的 workspace 区别：Msty 是 conversation 级，每对话独立 context，不会跨对话泄露。多项目并行场景非常顺。Aurum 解锁更大 \u002F 更高级 Knowledge。",{"q":4880,"a":4881},"Split Chats 真的实用吗？","对，多模型决策场景非常有用：决定哪个模型适合任务（同问题看 GPT-5 \u002F Claude \u002F Llama 3 输出）；本地 vs 云模型质量评估；事实问题模型分歧检测（多个模型给同样答案 = 更可信）。日常使用确实降低选错模型成本。",{"q":1464,"a":4883},"本地模式完全离线可用（Ollama \u002F llama.cpp \u002F MLX 本地模型 + Qwen \u002F DeepSeek 中文模型）。云模式接 OpenAI \u002F Anthropic 需要海外网络 + 卡。Aurum 订阅需海外支付。最佳路径：本地 Free + Ollama + Qwen2.5\u002FDeepSeek 中文，零订阅 + 零外网依赖。",[883,885],{},"\u002Ftools\u002Fagent\u002Fgeneral\u002Fmsty",[1470,1471,1472,897],[4889,4892,4896,4900],{"plan":1600,"price":900,"features":4890,"notes":4891},"本地模型 + 云 API 接入 + Split Chats + Knowledge Stack（无限）+ 无账号","永久免费",{"plan":4451,"price":4893,"features":4894,"notes":4895},"$149\u002F年","Free 全部 + cloud sync + 高级 Knowledge + 优先支持 + Studio Desktop alpha","每用户",{"plan":4457,"price":4897,"features":4898,"notes":4899},"$349 一次","Aurum 终身授权","永久 + 所有更新",{"plan":4463,"price":4901,"features":4902,"notes":4903},"$300\u002Fuser·年","SSO + 团队管理 + 私有部署支持","需联系销售","Free 本地无限 \u002F Aurum $149·年（cloud sync）\u002F Lifetime $349 一次买断 \u002F Enterprise $300·user·年",[4906],"onboarding\u002Flocal-ai-workstation",{"power":347,"ux":359,"price":359,"cn_support":325,"stability":347},{"title":4347,"description":4871},"agent\u002Fgeneral\u002Fmsty",[4911,4913,4915,4917],{"name":4912,"url":4835,"accessed":1495},"Msty 官网 + Features",{"name":4914,"url":4842,"accessed":1495},"AI Chat Daily — Msty Review 2026 4.3\u002F5",{"name":4916,"url":4849,"accessed":1495},"ML Journey — Msty Local LLM Comparison Guide",{"name":4918,"url":4856,"accessed":1495},"AISO Tools — Msty Pricing 2026","tools\u002Fagent\u002Fgeneral\u002Fmsty","本地优先 + 多模型并行的桌面 AI——Split Chats \u002F Knowledge Stack \u002F Agent Mode 三件套",[4922,1506,1508,2053,4923],"local-first","msty","Ollama \u002F LM Studio 的『精品桌面应用版』——拒绝 CLI + 拒绝云上传 + 多模型同窗对比的最佳选择。Mac mini \u002F Linux 私人 AI 服务器场景神器。要团队协作 + 云同步建议 Claude Team \u002F ChatGPT Team。","https:\u002F\u002Fmsty.ai","niNqR8c2HfHsRBNs01ip9Crt9cywkIdrVITosmvDYyA",{"id":4928,"title":4929,"alternatives":4930,"api_compatible":886,"body":4932,"category":862,"chinese_friendly":325,"cover":5475,"description":5476,"domestic":1452,"extension":865,"faq":5477,"free":1452,"github":886,"languages":5493,"lastVerified":886,"meta":5494,"models":886,"navigation":321,"notSuitable":886,"opensource":321,"path":5495,"pillar":895,"platforms":5496,"priceTable":5497,"pricing":5505,"published":5506,"relatedPlaybooks":5507,"relatedReviews":5509,"score":5510,"self_host":321,"seo":5511,"seoTitle":886,"slug":5512,"sources":5513,"stem":5520,"suitable":886,"tagline":5521,"tags":5522,"updated":5506,"verdict":5528,"website":5163,"__hash__":5529},"tools\u002Ftools\u002Fagent\u002Fgeneral\u002Fopenhuman.md","OpenHuman",[12,3345,4931],"agent\u002Fplatform\u002Fcoze",{"type":22,"value":4933,"toc":5463},[4934,4936,4939,4942,4944,5030,5032,5049,5053,5057,5080,5084,5107,5109,5158,5165,5168,5179,5181,5331,5333,5377,5379,5408,5410,5429,5431,5460],[25,4935,28],{"id":27},[30,4937,4938],{},"OpenHuman 是 TinyHumans 团队 2026-05 推出的开源个人 AI 超级智能助手，7.8k+ GitHub stars。差异点：Rust（Tauri）桌面优先架构 + 118+ 第三方 OAuth 集成 + Memory Tree 长期记忆系统（10 亿 token 容量）+ TokenJuice 智能压缩（省 80% token）+ 智能模型路由 + 桌面吉祥物 + Google Meet 参会 + 语音交互 + Obsidian 知识库兼容。20 分钟自动同步你的数字生活，构建本地私有的个人记忆库。",[30,4940,4941],{},"适合：追求个人 AI 助手真正了解你的知识工作者；Obsidian 用户；跨越多个工具的协作场景；关注数据隐私的用户。不适合：追求极致轻量单功能工具；需要完全离线运行（OAuth 和模型调用需网络）；无法接受 Early Beta 产品的不稳定。",[25,4943,51],{"id":51},[53,4945,4946,4952,4958,4964,4970,4976,4982,4988,4994,5000,5006,5012,5018,5024],{},[56,4947,4948,4951],{},[34,4949,4950],{},"Memory Tree 长期记忆系统","：自动抓取邮件、文档、聊天记录 → 智能评分 → 层级摘要树 → 本地 SQLite 存储 + Obsidian 兼容 .md 文件",[56,4953,4954,4957],{},[34,4955,4956],{},"118+ 第三方 OAuth 集成","：Gmail \u002F Outlook \u002F Notion \u002F GitHub \u002F Slack \u002F Google Calendar \u002F Stripe \u002F Linear \u002F Jira 等",[56,4959,4960,4963],{},[34,4961,4962],{},"TokenJuice 智能压缩","：HTML→Markdown \u002F URL 缩短 \u002F 去重，最高省 80% token",[56,4965,4966,4969],{},[34,4967,4968],{},"智能模型路由","：统一订阅，自动分配推理 \u002F 快速 \u002F 多模态 \u002F 本地模型",[56,4971,4972,4975],{},[34,4973,4974],{},"桌面吉祥物","：有表情、会说话的桌面小伙伴，响应环境变化",[56,4977,4978,4981],{},[34,4979,4980],{},"Google Meet 参会","：以真实参与者身份加入线上会议",[56,4983,4984,4987],{},[34,4985,4986],{},"语音交互","：STT 语音输入 + ElevenLabs TTS 语音输出 + 唇形同步",[56,4989,4990,4993],{},[34,4991,4992],{},"后台持续思考","：即使不主动交互，也在后台分析和整理信息",[56,4995,4996,4999],{},[34,4997,4998],{},"自动同步","：每 20 分钟自动遍历所有活跃连接，拉取最新数据",[56,5001,5002,5005],{},[34,5003,5004],{},"Obsidian 兼容","：记忆以 Markdown 形式存储，可直接用 Obsidian 查看\u002F编辑",[56,5007,5008,5011],{},[34,5009,5010],{},"本地优先","：核心数据存储在本地 SQLite，不上传云端",[56,5013,5014,5017],{},[34,5015,5016],{},"本地加密","：数据在设备端加密存储",[56,5019,5020,5023],{},[34,5021,5022],{},"Ollama 支持","：可运行本地 LLM，敏感任务完全不上云",[56,5025,5026,5029],{},[34,5027,5028],{},"GPL-3.0 开源","：完整源码可审计、可定制",[25,5031,131],{"id":131},[53,5033,5034,5040,5046],{},[56,5035,5036,5039],{},[34,5037,5038],{},"统一订阅","：价格待定（Early Beta）；一价全包所有模型 + 自动路由",[56,5041,5042,5045],{},[34,5043,5044],{},"自托管","：$0；GPL-3.0 协议，需自备 LLM API 或本地 Ollama 模型",[56,5047,5048],{},"自托管一次中等任务 API 费用 $0.02-0.5（取决于模型选择）",[25,5050,5052],{"id":5051},"实测个人-ai-助手场景","实测（个人 AI 助手场景）",[30,5054,5055],{},[34,5056,154],{},[53,5058,5059,5062,5065,5068,5071,5074,5077],{},[56,5060,5061],{},"Memory Tree 让 AI 真正拥有\"长期记忆\"，不是每次对话从零开始",[56,5063,5064],{},"118+ 集成开箱即用，OAuth 一键连接，无需手动配置 API",[56,5066,5067],{},"TokenJuice 压缩效果显著，实测 token 消耗降低 60-80%",[56,5069,5070],{},"桌面 UI 设计精美，吉祥物交互有趣，不是命令行工具",[56,5072,5073],{},"自动同步机制省心，不用手动通知 AI 新信息",[56,5075,5076],{},"Obsidian 兼容让记忆透明可读，用户完全掌控自己的数据",[56,5078,5079],{},"智能模型路由省去手动切换模型的麻烦",[30,5081,5082],{},[34,5083,188],{},[53,5085,5086,5089,5092,5095,5098,5101,5104],{},[56,5087,5088],{},"Early Beta 阶段，功能迭代快，偶尔有 breaking change",[56,5090,5091],{},"中文支持依赖底层模型，部分场景效果不如英文",[56,5093,5094],{},"OAuth 连接器稳定性参差不齐，部分服务偶尔断连",[56,5096,5097],{},"桌面吉祥物 CPU\u002F内存占用不低，低配机器略卡",[56,5099,5100],{},"文档尚在完善中，部分功能缺少详细说明",[56,5102,5103],{},"自动同步频率固定 20 分钟，无法手动触发即时同步",[56,5105,5106],{},"隐私边界需关注：虽然数据本地存储，但 OAuth 连接和模型调用涉及网络",[25,5108,214],{"id":214},[221,5110,5112],{"className":278,"code":5111,"language":280,"meta":226,"style":226},"# macOS \u002F Linux\ncurl -fsSL https:\u002F\u002Fraw.githubusercontent.com\u002Ftinyhumansai\u002Fopenhuman\u002Fmain\u002Fscripts\u002Finstall.sh | bash\n\n# Windows (PowerShell)\nirm https:\u002F\u002Fraw.githubusercontent.com\u002Ftinyhumansai\u002Fopenhuman\u002Fmain\u002Fscripts\u002Finstall.ps1 | iex\n",[39,5113,5114,5119,5136,5140,5145],{"__ignoreMap":226},[230,5115,5116],{"class":232,"line":233},[230,5117,5118],{"class":406},"# macOS \u002F Linux\n",[230,5120,5121,5124,5127,5130,5133],{"class":232,"line":318},[230,5122,5123],{"class":244},"curl",[230,5125,5126],{"class":331}," -fsSL",[230,5128,5129],{"class":251}," https:\u002F\u002Fraw.githubusercontent.com\u002Ftinyhumansai\u002Fopenhuman\u002Fmain\u002Fscripts\u002Finstall.sh",[230,5131,5132],{"class":305}," |",[230,5134,5135],{"class":244}," bash\n",[230,5137,5138],{"class":232,"line":325},[230,5139,322],{"emptyLinePlaceholder":321},[230,5141,5142],{"class":232,"line":347},[230,5143,5144],{"class":406},"# Windows (PowerShell)\n",[230,5146,5147,5150,5153,5155],{"class":232,"line":359},[230,5148,5149],{"class":244},"irm",[230,5151,5152],{"class":251}," https:\u002F\u002Fraw.githubusercontent.com\u002Ftinyhumansai\u002Fopenhuman\u002Fmain\u002Fscripts\u002Finstall.ps1",[230,5154,5132],{"class":305},[230,5156,5157],{"class":244}," iex\n",[30,5159,5160,5161],{},"或从官网下载安装包：",[805,5162,5163],{"href":5163,"rel":5164},"https:\u002F\u002Ftinyhumans.ai\u002Fopenhuman",[828],[30,5166,5167],{},"首次启动后：",[819,5169,5170,5173,5176],{},[56,5171,5172],{},"完成 OAuth 授权连接你的邮箱 \u002F 日历 \u002F 文档 \u002F 代码仓库",[56,5174,5175],{},"等待 2-5 分钟初始数据同步（Memory Tree 自动构建）",[56,5177,5178],{},"开始对话——AI 已经知道你的工作背景和习惯",[25,5180,603],{"id":603},[605,5182,5183,5198],{},[608,5184,5185],{},[611,5186,5187,5189,5191,5194,5196],{},[614,5188,616],{},[614,5190,4929],{},[614,5192,5193],{},"Claude Cowork",[614,5195,3123],{},[614,5197,2616],{},[629,5199,5200,5214,5228,5243,5259,5275,5288,5304,5316],{},[611,5201,5202,5204,5206,5209,5212],{},[634,5203,1735],{},[634,5205,3655],{},[634,5207,5208],{},"桌面+CLI",[634,5210,5211],{},"终端",[634,5213,5211],{},[611,5215,5216,5218,5221,5224,5226],{},[634,5217,3130],{},[634,5219,5220],{},"✅ GPL-3.0",[634,5222,5223],{},"❌ 专有",[634,5225,4671],{},[634,5227,4671],{},[611,5229,5230,5232,5235,5238,5241],{},[634,5231,3180],{},[634,5233,5234],{},"✅ UI 优先，几分钟",[634,5236,5237],{},"✅ 桌面",[634,5239,5240],{},"⚠️ 终端",[634,5242,5240],{},[611,5244,5245,5247,5250,5253,5256],{},[634,5246,3153],{},[634,5248,5249],{},"✅ Memory Tree",[634,5251,5252],{},"⚠️ 对话级",[634,5254,5255],{},"⚠️ 依赖插件",[634,5257,5258],{},"✅ 自学习",[611,5260,5261,5264,5267,5270,5273],{},[634,5262,5263],{},"集成数量",[634,5265,5266],{},"118+ OAuth",[634,5268,5269],{},"少",[634,5271,5272],{},"需自建",[634,5274,5272],{},[611,5276,5277,5279,5282,5284,5286],{},[634,5278,4998],{},[634,5280,5281],{},"✅ 20分钟",[634,5283,1273],{},[634,5285,1273],{},[634,5287,1273],{},[611,5289,5290,5293,5296,5299,5302],{},[634,5291,5292],{},"模型路由",[634,5294,5295],{},"✅ 内置智能",[634,5297,5298],{},"❌ 单一",[634,5300,5301],{},"⚠️ 手动",[634,5303,5301],{},[611,5305,5306,5308,5310,5312,5314],{},[634,5307,4974],{},[634,5309,1180],{},[634,5311,1273],{},[634,5313,1273],{},[634,5315,1273],{},[611,5317,5318,5321,5323,5326,5329],{},[634,5319,5320],{},"隐私",[634,5322,5010],{},[634,5324,5325],{},"云端",[634,5327,5328],{},"本地",[634,5330,5328],{},[25,5332,1299],{"id":1299},[53,5334,5335,5341,5347,5353,5359,5365,5371],{},[56,5336,5337,5340],{},[34,5338,5339],{},"Early Beta 不稳定","：功能迭代快，关键工作建议备份记忆数据",[56,5342,5343,5346],{},[34,5344,5345],{},"中文效果","：依赖底层模型，GPT-4o \u002F Claude 中文效果好，本地模型待验证",[56,5348,5349,5352],{},[34,5350,5351],{},"OAuth 断连","：部分服务 token 会过期，需定期检查连接状态",[56,5354,5355,5358],{},[34,5356,5357],{},"TokenJuice 精度","：压缩虽好，处理合同\u002F账单等敏感内容需注意细节丢失",[56,5360,5361,5364],{},[34,5362,5363],{},"资源占用","：桌面吉祥物 + 后台同步建议 8GB+ 内存",[56,5366,5367,5370],{},[34,5368,5369],{},"网络依赖","：OAuth 连接和远程模型调用需要稳定网络",[56,5372,5373,5376],{},[34,5374,5375],{},"隐私边界","：虽然数据本地存储，但 OAuth 连接意味着服务商可知你的连接状态",[25,5378,764],{"id":763},[53,5380,5381,5384,5387,5390,5393,5396,5399,5402,5405],{},[56,5382,5383],{},"✅ 追求个人 AI 助手真正了解你的知识工作者",[56,5385,5386],{},"✅ Obsidian 用户，希望 AI 自动维护知识库",[56,5388,5389],{},"✅ 工作流跨越多个工具（Gmail \u002F Slack \u002F Notion \u002F GitHub）",[56,5391,5392],{},"✅ 关注数据隐私，希望敏感信息保留在本地",[56,5394,5395],{},"✅ 愿意尝试 Early Beta，能接受快速迭代",[56,5397,5398],{},"❌ 追求极致轻量，不需要那么多集成功能",[56,5400,5401],{},"❌ 对隐私要求极高，希望完全离线运行",[56,5403,5404],{},"❌ 无法接受 Beta 产品的粗糙边缘和偶发 bug",[56,5406,5407],{},"❌ 需要中文深度优化（当前英文体验最佳）",[25,5409,799],{"id":799},[53,5411,5412,5417,5423],{},[56,5413,5414],{},[805,5415,5416],{"href":3264},"OpenManus 工具卡：开源版 Manus",[56,5418,5419],{},[805,5420,5422],{"href":5421},"\u002Fagent\u002Fgeneral\u002Fhermes-agent.html","Hermes Agent 工具卡：自学习 AI Agent",[56,5424,5425],{},[805,5426,5428],{"href":5427},"\u002Fagent\u002Fplatform\u002Fcoze.html","Coze 平台评测",[25,5430,817],{"id":817},[819,5432,5433,5440,5446,5453],{},[56,5434,5435,5436],{},"OpenHuman GitHub 主仓库 + TinyHumans 组织 ",[805,5437,5438],{"href":5438,"rel":5439},"https:\u002F\u002Fgithub.com\u002Ftinyhumansai\u002Fopenhuman",[828],[56,5441,5442,5443],{},"OpenHuman 官网 ",[805,5444,5163],{"href":5163,"rel":5445},[828],[56,5447,5448,5449],{},"OpenHuman 文档 ",[805,5450,5451],{"href":5451,"rel":5452},"https:\u002F\u002Ftinyhumans.gitbook.io\u002Fopenhuman",[828],[56,5454,5455,5456],{},"OpenHuman Discord ",[805,5457,5458],{"href":5458,"rel":5459},"https:\u002F\u002Fdiscord.tinyhumans.ai\u002F",[828],[844,5461,5462],{},"html pre.shiki code .sJ8bj, html code.shiki .sJ8bj{--shiki-default:#6A737D;--shiki-dark:#6A737D}html pre.shiki code .sScJk, html code.shiki .sScJk{--shiki-default:#6F42C1;--shiki-dark:#B392F0}html pre.shiki code .sj4cs, html code.shiki .sj4cs{--shiki-default:#005CC5;--shiki-dark:#79B8FF}html pre.shiki code .sZZnC, html code.shiki .sZZnC{--shiki-default:#032F62;--shiki-dark:#9ECBFF}html pre.shiki code .szBVR, html code.shiki .szBVR{--shiki-default:#D73A49;--shiki-dark:#F97583}html .default .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: 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var(--shiki-dark-text-decoration);}",{"title":226,"searchDepth":325,"depth":325,"links":5464},[5465,5466,5467,5468,5469,5470,5471,5472,5473,5474],{"id":27,"depth":318,"text":28},{"id":51,"depth":318,"text":51},{"id":131,"depth":318,"text":131},{"id":5051,"depth":318,"text":5052},{"id":214,"depth":318,"text":214},{"id":603,"depth":318,"text":603},{"id":1299,"depth":318,"text":1299},{"id":763,"depth":318,"text":764},{"id":799,"depth":318,"text":799},{"id":817,"depth":318,"text":817},"\u002Fimg\u002Ftools\u002Fopenhuman.svg","OpenHuman 真实评测：TinyHumans 团队 2026-05 开源的个人 AI 超级智能助手，7.8k+ GitHub stars。GPL-3.0 协议 + Rust（Tauri）架构 + 桌面 UI 优先 + 118+ 第三方 OAuth 集成 + Memory Tree 长期记忆系统（10亿 token 容量）+ TokenJuice 智能压缩（省 80% token）+ 智能模型路由 + 桌面吉祥物 + Google Meet 参会 + 语音交互 + Obsidian 知识库兼容。20 分钟自动同步你的邮箱、日历、文档、代码仓库，构建本地私有的个人记忆库。",[5478,5481,5484,5487,5490],{"q":5479,"a":5480},"OpenHuman 和 ChatGPT \u002F Claude 有什么不同？","ChatGPT \u002F Claude 是通用对话 AI，每次对话从零开始，没有你的长期记忆。OpenHuman 通过 Memory Tree 系统持续学习你的邮件、日历、文档、代码仓库，构建一个持续更新的个人记忆库。它知道『你是谁』而非只是『你问了什么』。",{"q":5482,"a":5483},"OpenHuman 的数据安全如何？","核心记忆数据存储在本地 SQLite 数据库，不上传云端。数据在设备端加密存储。可通过 Ollama 运行本地 LLM 实现敏感任务完全离线。但 OAuth 连接和远程模型调用仍涉及网络传输。",{"q":5485,"a":5486},"Memory Tree 是什么？","OpenHuman 的核心记忆系统。所有接入数据（邮件、文档、聊天记录）被转化为 ≤3000 token 的 Markdown 块，经智能评分后折叠成层级摘要树，存入本地 SQLite。同时生成 .md 文件同步到 Obsidian 知识库。记忆容量可达 10 亿 token。",{"q":5488,"a":5489},"TokenJuice 有什么用？","TokenJuice 是 OpenHuman 的智能压缩层，在数据进入 LLM 前进行预处理——HTML 转 Markdown、长 URL 缩短、重复内容去重等，最高可降低 80% 的 token 消耗，大幅降低 API 成本和响应延迟。",{"q":5491,"a":5492},"支持哪些第三方服务？","118+ 第三方 OAuth 集成，涵盖：Gmail \u002F Outlook（邮件）、Notion \u002F Google Docs \u002F Obsidian（文档）、GitHub \u002F GitLab（代码）、Slack \u002F Discord（沟通）、Google Calendar \u002F Outlook Calendar（日历）、Google Drive \u002F Dropbox（存储）、Stripe（支付）、Linear \u002F Jira（项目）等。",[883,885],{},"\u002Ftools\u002Fagent\u002Fgeneral\u002Fopenhuman",[1472,1470,1471],[5498,5502],{"plan":5038,"price":5499,"features":5500,"notes":5501},"待定","全部模型 + 自动路由 + 118+ 集成 + Memory Tree + TokenJuice","Early Beta 定价可能调整",{"plan":5044,"price":900,"features":5503,"notes":5504},"GPL-3.0 协议 + 全部功能 + 可接 Ollama 本地模型","需自备 LLM API","统一订阅制（一价全包，自动模型路由）\u002F 自备 LLM API 可选","2026-07-17",[5508],"onboarding\u002Fpersonal-ai-agent",[3341],{"power":347,"ux":359,"price":347,"cn_support":325,"stability":325},{"title":4929,"description":5476},"agent\u002Fgeneral\u002Fopenhuman",[5514,5516,5518],{"name":5515,"url":5438,"accessed":5506},"OpenHuman GitHub（TinyHumans 组织）",{"name":5517,"url":5163,"accessed":5506},"OpenHuman 官网",{"name":5519,"url":5451,"accessed":5506},"OpenHuman 文档","tools\u002Fagent\u002Fgeneral\u002Fopenhuman","TinyHumans 团队开源个人 AI 超级智能——118+ 集成 \u002F Memory Tree \u002F TokenJuice 压缩 \u002F 桌面优先",[920,5523,5524,895,5525,5526,5527],"personal-ai","memory-tree","desktop","obsidian","rust","想要一个真正懂你、记得你、能自动学习的个人 AI 助手——OpenHuman 是目前开源社区最完整的选择。Memory Tree + 118+ 集成 + TokenJuice 压缩 + 桌面 UI 设计，让 AI 从『聊天工具』升级为『数字分身』。适合注重隐私、使用多平台工具的知识工作者。Early Beta 阶段，功能迭代快，适合愿意尝鲜的用户。","04_vvWTbB6SIvLrj3ZPMYuag3r930_UxuzpUgV7DQjI",{"id":5531,"title":627,"alternatives":5532,"api_compatible":886,"body":5535,"category":862,"chinese_friendly":347,"cover":6159,"description":6160,"domestic":1452,"extension":865,"faq":6161,"free":1452,"github":886,"languages":6174,"lastVerified":886,"meta":6175,"models":886,"navigation":321,"notSuitable":886,"opensource":321,"path":807,"pillar":895,"platforms":6176,"priceTable":6178,"pricing":6181,"published":1487,"relatedPlaybooks":6182,"relatedReviews":6184,"score":6185,"self_host":321,"seo":6186,"seoTitle":886,"slug":12,"sources":6187,"stem":6196,"suitable":886,"tagline":6197,"tags":6198,"updated":1495,"verdict":6202,"website":6121,"__hash__":6203},"tools\u002Ftools\u002Fagent\u002Fgeneral\u002Fopenmanus.md",[5533,5534,13],"agent\u002Fplatform\u002Flangflow","agent\u002Fplatform\u002Fn8n",{"type":22,"value":5536,"toc":6147},[5537,5539,5546,5549,5551,5627,5629,5644,5648,5652,5678,5682,5708,5710,5833,5836,5842,5844,6004,6006,6064,6066,6092,6094,6112,6114,6144],[25,5538,28],{"id":27},[30,5540,5541,5542,5545],{},"OpenManus 是 MetaGPT 核心团队 2025-03 推出的开源 Manus 替代方案，52,000+ GitHub stars。差异点：MIT 协议 + Python 模块化架构 + 多 agent orchestration（",[39,5543,5544],{},"run_flow.py","）+ Playwright 浏览器自动化 + MCP 工具协议支持 + DataAnalysis 内置模式 + OpenManus-RL 强化学习分支 + 自定义工具基类（BaseTool）+ 多模型（GPT-4o \u002F Claude \u002F Qwen VL Plus）。零邀请码、零订阅、零供应商绑定。",[30,5547,5548],{},"适合：想自托管复刻 Manus 体验的开发者；研究 \u002F 学术 \u002F 教育用通用 agent 实现学习；隐私敏感 + 不愿数据上 Manus 商业云；预算紧（只付 LLM API）；中国大陆开发者（搭配 Qwen \u002F DeepSeek 本地化）。不适合：非开发者 \u002F 不会折腾 Python + Playwright；要 GUI \u002F 上手即用；生产级稳定（项目演进快，文档滞后）。",[25,5550,51],{"id":51},[53,5552,5553,5561,5567,5573,5579,5585,5591,5597,5603,5609,5615,5621],{},[56,5554,5555,2665,5558,5560],{},[34,5556,5557],{},"多 agent orchestration",[39,5559,5544],{}," 编排多个专门 agent 协作",[56,5562,5563,5566],{},[34,5564,5565],{},"Playwright 浏览器自动化","：截图 + DOM 操作 + 表单填写 + 信息抓取",[56,5568,5569,5572],{},[34,5570,5571],{},"MCP 协议支持","：可调用 MCP server（filesystem \u002F GitHub \u002F Postgres）",[56,5574,5575,5578],{},[34,5576,5577],{},"DataAnalysis 模式","：内置 CSV \u002F 数据分析 agent",[56,5580,5581,5584],{},[34,5582,5583],{},"OpenManus-RL","：强化学习微调分支",[56,5586,5587,5590],{},[34,5588,5589],{},"BaseTool 自定义工具","：Python 继承基类快速添加新工具",[56,5592,5593,5596],{},[34,5594,5595],{},"多模态","：文本 + 视觉输入 + 浏览器截图回环",[56,5598,5599,5602],{},[34,5600,5601],{},"多 LLM provider","：GPT-4o \u002F Claude 3.5 \u002F Qwen VL Plus \u002F DeepSeek \u002F Gemini",[56,5604,5605,5608],{},[34,5606,5607],{},"核心 agent 引擎","：reasoning + planning + execution 三阶段",[56,5610,5611,5614],{},[34,5612,5613],{},"Web UI 监控","：实时看 AI thinking process",[56,5616,5617,5620],{},[34,5618,5619],{},"任务可视化","：步骤拆解 + 执行树",[56,5622,5623,5626],{},[34,5624,5625],{},"MIT 协议","：个人 + 商用全免费",[25,5628,131],{"id":131},[53,5630,5631,5635,5638,5641],{},[56,5632,5633,139],{},[34,5634,138],{},[56,5636,5637],{},"真实成本 = LLM API（GPT-4o ~$5\u002FM input + $15\u002FM output \u002F Claude \u002F Qwen 等）",[56,5639,5640],{},"一次中等任务（10-20 步）API 费用 $0.05-0.5",[56,5642,5643],{},"本地 Qwen2.5 32B \u002F DeepSeek 走 vLLM \u002F Ollama 路径 $0",[25,5645,5647],{"id":5646},"实测开发者-自托管-研究场景","实测（开发者 \u002F 自托管 \u002F 研究场景）",[30,5649,5650],{},[34,5651,154],{},[53,5653,5654,5657,5660,5663,5666,5669,5672,5675],{},[56,5655,5656],{},"52k stars 印证社区认同度 + 活跃度",[56,5658,5659],{},"MetaGPT 团队背景保证架构质量",[56,5661,5662],{},"多 agent 协作场景比单 agent 实现稳得多",[56,5664,5665],{},"Playwright 浏览器自动化非常完整",[56,5667,5668],{},"MCP 协议接入打通 Claude \u002F Cursor 生态",[56,5670,5671],{},"DataAnalysis 内置 agent 模式开箱即用",[56,5673,5674],{},"中文支持自然（Qwen VL Plus 接入）",[56,5676,5677],{},"自托管 + 数据本地，隐私 \u002F 合规友好",[30,5679,5680],{},[34,5681,188],{},[53,5683,5684,5687,5690,5693,5696,5699,5702,5705],{},[56,5685,5686],{},"项目演进快，breaking change 偶发（pin commit 跑生产）",[56,5688,5689],{},"文档滞后新功能 1-2 个月",[56,5691,5692],{},"无官方 GUI，监控 UI 在做但不完整",[56,5694,5695],{},"需要 Python 3.12+ + Playwright 依赖（首次安装 chromium 慢）",[56,5697,5698],{},"LLM API 配置非平凡（多 provider \u002F key \u002F 模型选择）",[56,5700,5701],{},"浏览器任务遇到 CAPTCHA \u002F 反爬偶尔卡死",[56,5703,5704],{},"中文 prompt 效果依赖底层模型",[56,5706,5707],{},"生产部署需自己加监控 \u002F 错误恢复 \u002F 重试",[25,5709,214],{"id":214},[221,5711,5713],{"className":278,"code":5712,"language":280,"meta":226,"style":226},"git clone https:\u002F\u002Fgithub.com\u002FFoundationAgents\u002FOpenManus.git\ncd OpenManus\npython3.12 -m venv .venv && source .venv\u002Fbin\u002Factivate  # 或 .venv\\Scripts\\activate\npip install -r requirements.txt\nplaywright install chromium\n\n# 配 LLM API\ncp config\u002Fconfig.example.toml config\u002Fconfig.toml\n# 编辑 config.toml 填 OPENAI \u002F ANTHROPIC \u002F DEEPSEEK key\n\n# 单 agent 模式\npython main.py\n\n# 多 agent 模式\npython run_flow.py\n",[39,5714,5715,5724,5731,5756,5766,5776,5780,5785,5795,5800,5804,5809,5816,5820,5825],{"__ignoreMap":226},[230,5716,5717,5719,5721],{"class":232,"line":233},[230,5718,2918],{"class":244},[230,5720,2921],{"class":251},[230,5722,5723],{"class":251}," https:\u002F\u002Fgithub.com\u002FFoundationAgents\u002FOpenManus.git\n",[230,5725,5726,5728],{"class":232,"line":318},[230,5727,2929],{"class":331},[230,5729,5730],{"class":251}," OpenManus\n",[230,5732,5733,5736,5738,5741,5744,5747,5750,5753],{"class":232,"line":325},[230,5734,5735],{"class":244},"python3.12",[230,5737,2969],{"class":331},[230,5739,5740],{"class":251}," venv",[230,5742,5743],{"class":251}," .venv",[230,5745,5746],{"class":236}," && ",[230,5748,5749],{"class":331},"source",[230,5751,5752],{"class":251}," .venv\u002Fbin\u002Factivate",[230,5754,5755],{"class":406},"  # 或 .venv\\Scripts\\activate\n",[230,5757,5758,5760,5762,5764],{"class":232,"line":347},[230,5759,2937],{"class":244},[230,5761,290],{"class":251},[230,5763,2942],{"class":331},[230,5765,2945],{"class":251},[230,5767,5768,5771,5773],{"class":232,"line":359},[230,5769,5770],{"class":244},"playwright",[230,5772,290],{"class":251},[230,5774,5775],{"class":251}," chromium\n",[230,5777,5778],{"class":232,"line":370},[230,5779,322],{"emptyLinePlaceholder":321},[230,5781,5782],{"class":232,"line":381},[230,5783,5784],{"class":406},"# 配 LLM API\n",[230,5786,5787,5789,5792],{"class":232,"line":392},[230,5788,2950],{"class":244},[230,5790,5791],{"class":251}," config\u002Fconfig.example.toml",[230,5793,5794],{"class":251}," config\u002Fconfig.toml\n",[230,5796,5797],{"class":232,"line":398},[230,5798,5799],{"class":406},"# 编辑 config.toml 填 OPENAI \u002F ANTHROPIC \u002F DEEPSEEK key\n",[230,5801,5802],{"class":232,"line":403},[230,5803,322],{"emptyLinePlaceholder":321},[230,5805,5806],{"class":232,"line":410},[230,5807,5808],{"class":406},"# 单 agent 模式\n",[230,5810,5811,5813],{"class":232,"line":438},[230,5812,2966],{"class":244},[230,5814,5815],{"class":251}," main.py\n",[230,5817,5818],{"class":232,"line":581},[230,5819,322],{"emptyLinePlaceholder":321},[230,5821,5822],{"class":232,"line":586},[230,5823,5824],{"class":406},"# 多 agent 模式\n",[230,5826,5828,5830],{"class":232,"line":5827},15,[230,5829,2966],{"class":244},[230,5831,5832],{"class":251}," run_flow.py\n",[30,5834,5835],{},"试任务示例：",[221,5837,5840],{"className":5838,"code":5839,"language":3785},[3783],"> 帮我做一份『2026 开源 AI Agent 框架』竞品对比，含表格 + 引用 + 趋势分析，输出为 markdown 文件\n",[39,5841,5839],{"__ignoreMap":226},[25,5843,603],{"id":603},[605,5845,5846,5863],{},[608,5847,5848],{},[611,5849,5850,5852,5854,5857,5860],{},[614,5851,616],{},[614,5853,627],{},[614,5855,5856],{},"LangChain",[614,5858,5859],{},"AutoGPT",[614,5861,5862],{},"CrewAI",[629,5864,5865,5881,5894,5906,5921,5934,5948,5960,5975,5988],{},[611,5866,5867,5869,5872,5875,5878],{},[634,5868,1735],{},[634,5870,5871],{},"现成 agent 实现",[634,5873,5874],{},"building blocks",[634,5876,5877],{},"早期通用 agent",[634,5879,5880],{},"多 agent 框架",[611,5882,5883,5886,5888,5890,5892],{},[634,5884,5885],{},"浏览器自动化",[634,5887,711],{},[634,5889,1245],{},[634,5891,1230],{},[634,5893,1245],{},[611,5895,5896,5898,5900,5902,5904],{},[634,5897,1225],{},[634,5899,1180],{},[634,5901,1230],{},[634,5903,1245],{},[634,5905,1245],{},[611,5907,5908,5911,5914,5917,5919],{},[634,5909,5910],{},"多 agent",[634,5912,5913],{},"✅ orchestration",[634,5915,5916],{},"需自搭",[634,5918,1273],{},[634,5920,1233],{},[611,5922,5923,5926,5928,5930,5932],{},[634,5924,5925],{},"DataAnalysis 内置",[634,5927,1180],{},[634,5929,1245],{},[634,5931,1245],{},[634,5933,1245],{},[611,5935,5936,5939,5942,5944,5946],{},[634,5937,5938],{},"RL 微调",[634,5940,5941],{},"✅ OpenManus-RL",[634,5943,1245],{},[634,5945,1245],{},[634,5947,1245],{},[611,5949,5950,5952,5954,5956,5958],{},[634,5951,733],{},[634,5953,736],{},[634,5955,736],{},[634,5957,736],{},[634,5959,736],{},[611,5961,5962,5964,5966,5969,5972],{},[634,5963,748],{},[634,5965,760],{},[634,5967,5968],{},"100k+",[634,5970,5971],{},"170k+",[634,5973,5974],{},"30k+",[611,5976,5977,5979,5981,5984,5986],{},[634,5978,214],{},[634,5980,2362],{},[634,5982,5983],{},"难",[634,5985,2362],{},[634,5987,2362],{},[611,5989,5990,5992,5995,5998,6001],{},[634,5991,1284],{},[634,5993,5994],{},"自托管 Manus 复刻",[634,5996,5997],{},"底层 framework",[634,5999,6000],{},"学习经典",[634,6002,6003],{},"多 agent 协作",[25,6005,1299],{"id":1299},[53,6007,6008,6014,6024,6030,6036,6042,6048,6054,6058],{},[56,6009,6010,6013],{},[34,6011,6012],{},"pin commit 用生产","：项目演进快，main 分支偶尔 break",[56,6015,6016,6019,6020,6023],{},[34,6017,6018],{},"Playwright 依赖大","：首次 ",[39,6021,6022],{},"playwright install"," 下 chromium 慢，国内走镜像",[56,6025,6026,6029],{},[34,6027,6028],{},"LLM 选择","：日常用 GPT-4o-mini \u002F DeepSeek 省钱，复杂任务切 GPT-4o \u002F Claude Opus",[56,6031,6032,6035],{},[34,6033,6034],{},"本地化中文","：Qwen2.5 VL 32B + vLLM 部署可全本地 + 零成本",[56,6037,6038,6041],{},[34,6039,6040],{},"监控自加","：生产部署要加 prometheus + 错误重试 + 任务超时",[56,6043,6044,6047],{},[34,6045,6046],{},"浏览器反爬","：CAPTCHA 场景搭配 2captcha \u002F human-in-loop",[56,6049,6050,6053],{},[34,6051,6052],{},"OpenManus-RL 分支","：研究场景才需要，普通用户主仓库就够",[56,6055,6056,1355],{},[34,6057,1354],{},[56,6059,6060,6063],{},[34,6061,6062],{},"多 agent runaway","：复杂任务设 max_steps 防止失控烧 token",[25,6065,764],{"id":763},[53,6067,6068,6071,6074,6077,6080,6083,6086,6089],{},[56,6069,6070],{},"✅ 开发者 + 想自托管 Manus 风格 agent",[56,6072,6073],{},"✅ 研究 \u002F 学术 \u002F 教育用通用 agent 学习",[56,6075,6076],{},"✅ 隐私敏感 + 不愿数据上商业云",[56,6078,6079],{},"✅ 中国大陆开发者（Qwen \u002F DeepSeek 本地化）",[56,6081,6082],{},"❌ 非开发者 \u002F 不会 Python + Playwright",[56,6084,6085],{},"❌ 要 GUI \u002F 上手即用",[56,6087,6088],{},"❌ 生产级稳定（文档滞后 + 演进快）",[56,6090,6091],{},"❌ 团队协作 + 共享 workspace（用 Flowith \u002F Genspark Team）",[25,6093,799],{"id":799},[53,6095,6096,6102,6108],{},[56,6097,6098],{},[805,6099,6101],{"href":6100},"\u002Ftools\u002Fagent\u002Fplatform\u002Flangflow","Langflow 评测",[56,6103,6104],{},[805,6105,6107],{"href":6106},"\u002Ftools\u002Fagent\u002Fplatform\u002Fn8n","n8n 评测",[56,6109,6110],{},[805,6111,814],{"href":813},[25,6113,817],{"id":817},[819,6115,6116,6123,6130,6137],{},[56,6117,6118,6119],{},"OpenManus GitHub 主仓库 + Foundation Agents 组织 ",[805,6120,6121],{"href":6121,"rel":6122},"https:\u002F\u002Fgithub.com\u002FFoundationAgents\u002FOpenManus",[828],[56,6124,6125,6126],{},"Foundation Agents — OpenManus 项目介绍 ",[805,6127,6128],{"href":6128,"rel":6129},"https:\u002F\u002Ffoundationagents.org\u002Fprojects\u002Fopenmanus\u002F",[828],[56,6131,6132,6133],{},"Toolsverse — OpenManus 评测 + 52k stars ",[805,6134,6135],{"href":6135,"rel":6136},"https:\u002F\u002Fthetoolsverse.com\u002Ftools\u002Fopenmanus",[828],[56,6138,6139,6140],{},"SoloSoft.dev — OpenManus 2026 Framework 综述 ",[805,6141,6142],{"href":6142,"rel":6143},"https:\u002F\u002Fwww.solosoft.dev\u002Fpost\u002Fopenmanus-agent-framework-2026\u002F",[828],[844,6145,6146],{},"html pre.shiki code .sScJk, html code.shiki .sScJk{--shiki-default:#6F42C1;--shiki-dark:#B392F0}html pre.shiki code .sZZnC, html code.shiki .sZZnC{--shiki-default:#032F62;--shiki-dark:#9ECBFF}html pre.shiki code .sj4cs, html code.shiki .sj4cs{--shiki-default:#005CC5;--shiki-dark:#79B8FF}html pre.shiki code .sVt8B, html code.shiki .sVt8B{--shiki-default:#24292E;--shiki-dark:#E1E4E8}html pre.shiki code .sJ8bj, html code.shiki .sJ8bj{--shiki-default:#6A737D;--shiki-dark:#6A737D}html .default .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}html.dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}",{"title":226,"searchDepth":325,"depth":325,"links":6148},[6149,6150,6151,6152,6153,6154,6155,6156,6157,6158],{"id":27,"depth":318,"text":28},{"id":51,"depth":318,"text":51},{"id":131,"depth":318,"text":131},{"id":5646,"depth":318,"text":5647},{"id":214,"depth":318,"text":214},{"id":603,"depth":318,"text":603},{"id":1299,"depth":318,"text":1299},{"id":763,"depth":318,"text":764},{"id":799,"depth":318,"text":799},{"id":817,"depth":318,"text":817},"\u002Fimg\u002Ftools\u002Fopenmanus.webp","OpenManus 真实评测：MetaGPT 核心成员 Xinbin Liang \u002F Jinyu Xiang \u002F Zhaoyang Yu \u002F Jiayi Zhang \u002F Sirui Hong 在 2025-03 推出的开源 Manus 替代方案，52,000+ GitHub stars。MIT 协议 + Python 模块化架构 + 多 agent orchestration（run_flow.py）+ Playwright 浏览器自动化 + MCP 工具支持 + DataAnalysis 内置模式 + OpenManus-RL 强化学习微调分支 + 自定义工具基类（BaseTool）。零邀请码、零订阅、零供应商绑定，唯一成本 = LLM API。",[6162,6165,6168,6171],{"q":6163,"a":6164},"OpenManus 和 Manus 是什么关系？","Manus 是商业 \u002F 邀请制的通用 AI agent 产品。OpenManus 是 MetaGPT 核心团队 2025-03 推出的开源复刻版，目标是『让所有人不靠邀请码就能用上类 Manus 能力』。功能覆盖：研究 \u002F 浏览器 \u002F 数据分析 \u002F 文件操作 \u002F 多步 reasoning。不是 Manus 官方出品。",{"q":6166,"a":6167},"和 LangChain \u002F AutoGPT \u002F CrewAI 怎么定位？","OpenManus 不是 framework，更像『可直接跑的通用 agent 实现』。LangChain 是 building block 框架；AutoGPT 是早期通用 agent；CrewAI 是多 agent 协作 framework。要『拉下来配 API 就能跑 Manus 风格任务』→ OpenManus；要『从底层搭自己的 agent』→ LangChain \u002F CrewAI；要『历史经典 + 学习』→ AutoGPT。OpenManus 内部用 LangChain-like 模块，可视为『现成实现』。",{"q":6169,"a":6170},"OpenManus-RL 是什么？","OpenManus 项目下的强化学习分支，提供 RL-based 微调方法优化 agent 性能。对研究 \u002F 高定制场景有价值，普通用户主仓库已经够用。",{"q":6172,"a":6173},"上手门槛？","需要 Python 3.12+ + 熟悉终端 + 自配 LLM API。无 GUI（虽然 web 监控界面在做）。documentation 偶尔滞后。非开发者建议先试 GUI 工具（Flowith \u002F Genspark），开发者 \u002F 研究者 + 想自托管 + 隐私敏感 → OpenManus。",[883,884,885],{},[1472,1470,1471,6177],"docker",[6179],{"plan":138,"price":900,"features":6180,"notes":902},"MIT 协议 + 全部功能 + 自托管 + 多 agent + 浏览器 + MCP + DataAnalysis + OpenManus-RL","MIT 完全免费开源 \u002F 用户自付 LLM API（GPT-4o \u002F Claude 3.5 \u002F Qwen VL Plus 任选）",[6183],"onboarding\u002Fopen-source-general-agent",[3341,2591],{"power":359,"ux":325,"price":359,"cn_support":347,"stability":325},{"title":627,"description":6160},[6188,6190,6192,6194],{"name":6189,"url":6121,"accessed":1495},"OpenManus GitHub（FoundationAgents 组织）",{"name":6191,"url":6128,"accessed":1495},"Foundation Agents — OpenManus 项目介绍",{"name":6193,"url":6135,"accessed":1495},"Toolsverse — OpenManus 评测 + 52k stars",{"name":6195,"url":6142,"accessed":1495},"SoloSoft.dev — OpenManus 2026 Framework 综述","tools\u002Fagent\u002Fgeneral\u002Fopenmanus","MetaGPT 团队开源版 Manus——52k+ stars \u002F MIT \u002F 多 agent + 浏览器自动化 + MCP + DataAnalysis",[920,2607,922,6199,6200,6201],"mcp","metagpt","openmanus","想自托管复刻 Manus 全能 agent 体验 + 不愿等邀请码的开发者首选——浏览器 + 数据分析 + MCP 工具栈一站全。要 GUI \u002F 上手即用 \u002F 生产级稳定建议 Genspark \u002F Flowith 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是 AI agent 集成基础设施层，把『agent 接入 1000-10,000+ 真实工具』的复杂度封装为一个 SDK。差异点：1000+ pre-built 集成（GitHub \u002F Slack \u002F Gmail \u002F Salesforce \u002F Notion \u002F Linear \u002F Jira \u002F Google Workspace）+ 托管 OAuth（含自动 token refresh）+ 沙箱执行 + 细粒度 action-level 权限 + 完整审计日志 + 框架无关（MCP \u002F LangChain \u002F CrewAI \u002F Autogen \u002F OpenAI Agents SDK \u002F Claude \u002F ChatGPT 都能用）+ Python \u002F TypeScript SDK + AWS \u002F Zoom \u002F Glean 等企业客户。Tooliverse 评分 8.56\u002F10，295 条多平台评测交叉验证。",[30,7348,7349],{},"适合：要造 agent 接多个 SaaS 工具的团队；OAuth \u002F token refresh \u002F 错误重试不想自己写；多框架兼容（不锁定 LangChain）；企业 + 合规 + audit log；已有 MCP server 但想要更广覆盖。不适合：单一工具 high-frequency 场景（自接 API 更便宜）；轻量 hobby project（LangChain native tool 够用）；要 1000% 控制集成代码（用开源 MCP server）；数据零云 + 完全自托管（Composio 是 SaaS）。",[25,7351,51],{"id":51},[53,7353,7354,7360,7366,7372,7378,7384,7390,7396,7401,7407,7413,7419],{},[56,7355,7356,7359],{},[34,7357,7358],{},"1000+ pre-built 集成","：GitHub \u002F Slack \u002F Gmail \u002F Salesforce \u002F Notion \u002F Linear \u002F Jira \u002F Google Workspace 等",[56,7361,7362,7365],{},[34,7363,7364],{},"托管 OAuth","：OAuth flow + token refresh + multi-user 全自动",[56,7367,7368,7371],{},[34,7369,7370],{},"沙箱执行","：tool call 在隔离环境跑，安全隔离",[56,7373,7374,7377],{},[34,7375,7376],{},"action-level 权限","：可精确控制 agent 能做什么（read-only \u002F write \u002F admin）",[56,7379,7380,7383],{},[34,7381,7382],{},"审计日志","：每次 tool call 完整记录（timestamp \u002F user \u002F args \u002F result）",[56,7385,7386,7389],{},[34,7387,7388],{},"框架无关","：MCP \u002F LangChain \u002F CrewAI \u002F Autogen \u002F OpenAI Agents SDK \u002F Claude \u002F ChatGPT",[56,7391,7392,7395],{},[34,7393,7394],{},"Python + TypeScript SDK","：主流 agent 栈都能用",[56,7397,7398,7400],{},[34,7399,1354],{},"：1000+ 工具统一 MCP 入口",[56,7402,7403,7406],{},[34,7404,7405],{},"多用户 session","：per-user OAuth + 隔离 workspace",[56,7408,7409,7412],{},[34,7410,7411],{},"自定义工具","：BaseTool 扩展 + JSON schema 验证",[56,7414,7415,7418],{},[34,7416,7417],{},"企业能力","：SSO \u002F audit \u002F SLA \u002F 私有部署",[56,7420,7421,7424],{},[34,7422,7423],{},"AWS \u002F Zoom \u002F Glean 客户","：production 验证",[25,7426,131],{"id":131},[53,7428,7429,7434,7440],{},[56,7430,7431,7433],{},[34,7432,1600],{},"：$0；全部 1000+ 集成 + 限月度 action + Python\u002FTS SDK + MCP server",[56,7435,7436,7439],{},[34,7437,7438],{},"Growth","：按 action 量阶梯；更高额度 + 团队",[56,7441,7442,7444],{},[34,7443,4463],{},"：联系销售；SSO + audit + 私有部署 + SLA",[1043,7446,7447],{},[30,7448,7449],{},"真实成本：低频 + 多样工具（每天几百次 action）Free 档够用；high-frequency（每分钟轮询 \u002F 大量 batch）成本陡升，要算清单 action 价格。",[25,7451,7453],{"id":7452},"实测agent-接入多-saas","实测（agent 接入多 SaaS）",[30,7455,7456],{},[34,7457,154],{},[53,7459,7460,7463,7466,7469,7472,7475,7478,7481],{},[56,7461,7462],{},"集成宽度（1000+）业内最广",[56,7464,7465],{},"OAuth 全托管省 50% 工程量",[56,7467,7468],{},"一份 SDK 跑通 CrewAI \u002F LangChain \u002F OpenAI Agents SDK",[56,7470,7471],{},"沙箱执行 + audit log 让企业合规过关",[56,7473,7474],{},"AWS \u002F Zoom \u002F Glean 等大客户 = production-grade 信号",[56,7476,7477],{},"Tooliverse 8.56\u002F10、Toolradar 4.9\u002F5（295 reviews）",[56,7479,7480],{},"5 分钟可让 CrewAI agent 在 GitHub 上 star 一个 repo",[56,7482,7483],{},"self-healing tool execution（自动重试 + 错误处理）",[30,7485,7486],{},[34,7487,188],{},[53,7489,7490,7493,7496,7499,7502,7505,7508,7511],{},[56,7491,7492],{},"文档『can lag behind rapid feature releases』",[56,7494,7495],{},"pricing scales steeply for high-frequency use cases",[56,7497,7498],{},"自定义工具要『master schema validation that documentation doesn't fully demystify』",[56,7500,7501],{},"冷门集成 action coverage 浅，只支持基础 CRUD",[56,7503,7504],{},"数据完全 SaaS 化，零云需求不能用",[56,7506,7507],{},"国内访问偶发慢（CDN 在海外）",[56,7509,7510],{},"Free 档 action 限额对生产 hobby 也容易撞顶",[56,7512,7513],{},"vendor lock-in：迁移要重写 OAuth 层",[25,7515,7517],{"id":7516},"上手crewai-5-分钟","上手（CrewAI 5 分钟）",[221,7519,7521],{"className":278,"code":7520,"language":280,"meta":226,"style":226},"pip install composio composio-crewai crewai\nexport COMPOSIO_API_KEY=***\n",[39,7522,7523,7538],{"__ignoreMap":226},[230,7524,7525,7527,7529,7532,7535],{"class":232,"line":233},[230,7526,2937],{"class":244},[230,7528,290],{"class":251},[230,7530,7531],{"class":251}," composio",[230,7533,7534],{"class":251}," composio-crewai",[230,7536,7537],{"class":251}," crewai\n",[230,7539,7540,7543,7546],{"class":232,"line":318},[230,7541,7542],{"class":305},"export",[230,7544,7545],{"class":236}," COMPOSIO_API_KEY",[230,7547,7548],{"class":305},"=***\n",[221,7550,7553],{"className":7551,"code":7552,"language":2966,"meta":226,"style":226},"language-python shiki shiki-themes github-light github-dark","from composio_crewai import ComposioProvider\nfrom composio import Composio\nfrom crewai import Agent, Task, Crew\n\ncomposio = Composio(provider=ComposioProvider())\nsession = composio.create(user_id=\"alice\", toolkits=[\"github\", \"gmail\"])\ntools = session.tools()\n\n# 手动授权（首次）\nauth = session.authorize(\"github\")\nprint(f\"Visit: {auth.redirect_url}\")\n\nagent = Agent(\n    role=\"GitHub Agent\",\n    goal=\"Star repos on behalf of users\",\n    tools=tools, llm=...\n)\ntask = Task(description=\"Star composiohq\u002Fcomposio\", agent=agent, expected_output=\"done\")\nCrew(agents=[agent], tasks=[task]).kickoff()\n",[39,7554,7555,7567,7579,7591,7595,7614,7654,7664,7668,7673,7687,7714,7718,7728,7740,7752,7771,7776,7814],{"__ignoreMap":226},[230,7556,7557,7559,7562,7564],{"class":232,"line":233},[230,7558,312],{"class":305},[230,7560,7561],{"class":236}," composio_crewai ",[230,7563,306],{"class":305},[230,7565,7566],{"class":236}," ComposioProvider\n",[230,7568,7569,7571,7574,7576],{"class":232,"line":318},[230,7570,312],{"class":305},[230,7572,7573],{"class":236}," composio ",[230,7575,306],{"class":305},[230,7577,7578],{"class":236}," Composio\n",[230,7580,7581,7583,7586,7588],{"class":232,"line":325},[230,7582,312],{"class":305},[230,7584,7585],{"class":236}," crewai ",[230,7587,306],{"class":305},[230,7589,7590],{"class":236}," Agent, Task, Crew\n",[230,7592,7593],{"class":232,"line":347},[230,7594,322],{"emptyLinePlaceholder":321},[230,7596,7597,7600,7602,7605,7609,7611],{"class":232,"line":359},[230,7598,7599],{"class":236},"composio ",[230,7601,248],{"class":305},[230,7603,7604],{"class":236}," Composio(",[230,7606,7608],{"class":7607},"s4XuR","provider",[230,7610,248],{"class":305},[230,7612,7613],{"class":236},"ComposioProvider())\n",[230,7615,7616,7619,7621,7624,7627,7629,7632,7635,7638,7640,7643,7646,7648,7651],{"class":232,"line":370},[230,7617,7618],{"class":236},"session ",[230,7620,248],{"class":305},[230,7622,7623],{"class":236}," composio.create(",[230,7625,7626],{"class":7607},"user_id",[230,7628,248],{"class":305},[230,7630,7631],{"class":251},"\"alice\"",[230,7633,7634],{"class":236},", ",[230,7636,7637],{"class":7607},"toolkits",[230,7639,248],{"class":305},[230,7641,7642],{"class":236},"[",[230,7644,7645],{"class":251},"\"github\"",[230,7647,7634],{"class":236},[230,7649,7650],{"class":251},"\"gmail\"",[230,7652,7653],{"class":236},"])\n",[230,7655,7656,7659,7661],{"class":232,"line":381},[230,7657,7658],{"class":236},"tools ",[230,7660,248],{"class":305},[230,7662,7663],{"class":236}," session.tools()\n",[230,7665,7666],{"class":232,"line":392},[230,7667,322],{"emptyLinePlaceholder":321},[230,7669,7670],{"class":232,"line":398},[230,7671,7672],{"class":406},"# 手动授权（首次）\n",[230,7674,7675,7678,7680,7683,7685],{"class":232,"line":403},[230,7676,7677],{"class":236},"auth ",[230,7679,248],{"class":305},[230,7681,7682],{"class":236}," session.authorize(",[230,7684,7645],{"class":251},[230,7686,435],{"class":236},[230,7688,7689,7692,7694,7697,7700,7703,7706,7709,7712],{"class":232,"line":410},[230,7690,7691],{"class":331},"print",[230,7693,429],{"class":236},[230,7695,7696],{"class":305},"f",[230,7698,7699],{"class":251},"\"Visit: ",[230,7701,7702],{"class":331},"{",[230,7704,7705],{"class":236},"auth.redirect_url",[230,7707,7708],{"class":331},"}",[230,7710,7711],{"class":251},"\"",[230,7713,435],{"class":236},[230,7715,7716],{"class":232,"line":438},[230,7717,322],{"emptyLinePlaceholder":321},[230,7719,7720,7723,7725],{"class":232,"line":581},[230,7721,7722],{"class":236},"agent ",[230,7724,248],{"class":305},[230,7726,7727],{"class":236}," Agent(\n",[230,7729,7730,7733,7735,7738],{"class":232,"line":586},[230,7731,7732],{"class":7607},"    role",[230,7734,248],{"class":305},[230,7736,7737],{"class":251},"\"GitHub Agent\"",[230,7739,356],{"class":236},[230,7741,7742,7745,7747,7750],{"class":232,"line":5827},[230,7743,7744],{"class":7607},"    goal",[230,7746,248],{"class":305},[230,7748,7749],{"class":251},"\"Star repos on behalf of users\"",[230,7751,356],{"class":236},[230,7753,7755,7758,7760,7763,7766,7768],{"class":232,"line":7754},16,[230,7756,7757],{"class":7607},"    tools",[230,7759,248],{"class":305},[230,7761,7762],{"class":236},"tools, ",[230,7764,7765],{"class":7607},"llm",[230,7767,248],{"class":305},[230,7769,7770],{"class":331},"...\n",[230,7772,7774],{"class":232,"line":7773},17,[230,7775,435],{"class":236},[230,7777,7779,7782,7784,7787,7790,7792,7795,7797,7799,7801,7804,7807,7809,7812],{"class":232,"line":7778},18,[230,7780,7781],{"class":236},"task ",[230,7783,248],{"class":305},[230,7785,7786],{"class":236}," Task(",[230,7788,7789],{"class":7607},"description",[230,7791,248],{"class":305},[230,7793,7794],{"class":251},"\"Star composiohq\u002Fcomposio\"",[230,7796,7634],{"class":236},[230,7798,895],{"class":7607},[230,7800,248],{"class":305},[230,7802,7803],{"class":236},"agent, ",[230,7805,7806],{"class":7607},"expected_output",[230,7808,248],{"class":305},[230,7810,7811],{"class":251},"\"done\"",[230,7813,435],{"class":236},[230,7815,7817,7820,7823,7825,7828,7831,7833],{"class":232,"line":7816},19,[230,7818,7819],{"class":236},"Crew(",[230,7821,7822],{"class":7607},"agents",[230,7824,248],{"class":305},[230,7826,7827],{"class":236},"[agent], ",[230,7829,7830],{"class":7607},"tasks",[230,7832,248],{"class":305},[230,7834,7835],{"class":236},"[task]).kickoff()\n",[25,7837,603],{"id":603},[605,7839,7840,7857],{},[608,7841,7842],{},[611,7843,7844,7846,7848,7851,7854],{},[614,7845,616],{},[614,7847,7336],{},[614,7849,7850],{},"Smithery",[614,7852,7853],{},"MCP Toolbox",[614,7855,7856],{},"LangChain Tools",[629,7858,7859,7876,7891,7907,7919,7932,7945,7957,7971],{},[611,7860,7861,7864,7867,7870,7873],{},[634,7862,7863],{},"集成数",[634,7865,7866],{},"1000+",[634,7868,7869],{},"MCP 注册中心",[634,7871,7872],{},"Google MCP 集合",[634,7874,7875],{},"~200",[611,7877,7878,7881,7883,7886,7888],{},[634,7879,7880],{},"OAuth 托管",[634,7882,1233],{},[634,7884,7885],{},"❌ 各 server 自管",[634,7887,1273],{},[634,7889,7890],{},"自己写",[611,7892,7893,7896,7899,7902,7904],{},[634,7894,7895],{},"框架兼容",[634,7897,7898],{},"MCP + LC + CrewAI + Autogen + OpenAI",[634,7900,7901],{},"MCP only",[634,7903,7901],{},[634,7905,7906],{},"LangChain only",[611,7908,7909,7911,7913,7915,7917],{},[634,7910,7370],{},[634,7912,1180],{},[634,7914,1273],{},[634,7916,1273],{},[634,7918,1273],{},[611,7920,7921,7924,7926,7928,7930],{},[634,7922,7923],{},"audit log",[634,7925,1180],{},[634,7927,1273],{},[634,7929,1230],{},[634,7931,1273],{},[611,7933,7934,7937,7939,7941,7943],{},[634,7935,7936],{},"企业 SSO",[634,7938,1180],{},[634,7940,1273],{},[634,7942,1273],{},[634,7944,1273],{},[611,7946,7947,7949,7951,7953,7955],{},[634,7948,5044],{},[634,7950,1273],{},[634,7952,1230],{},[634,7954,4676],{},[634,7956,4671],{},[611,7958,7959,7961,7964,7966,7969],{},[634,7960,1815],{},[634,7962,7963],{},"Free + 按 action",[634,7965,1600],{},[634,7967,7968],{},"$0 OSS",[634,7970,900],{},[611,7972,7973,7975,7978,7981,7984],{},[634,7974,1284],{},[634,7976,7977],{},"多 SaaS + 多框架",[634,7979,7980],{},"MCP server 发现",[634,7982,7983],{},"Google Cloud",[634,7985,7906],{},[25,7987,1299],{"id":1299},[53,7989,7990,7996,8002,8011,8016,8022,8028,8034,8040,8046],{},[56,7991,7992,7995],{},[34,7993,7994],{},"action 预算","：先估月度 action 量 + 选档，high-frequency 不要无脑用 Free",[56,7997,7998,8001],{},[34,7999,8000],{},"OAuth scope 最小化","：只授权必需 scope，audit log 也更干净",[56,8003,8004,8007,8008,8010],{},[34,8005,8006],{},"每用户 session","：multi-tenant 用 ",[39,8009,7626],{}," 隔离 OAuth + workspace",[56,8012,8013,8015],{},[34,8014,7411],{},"：先看 JSON schema 文档 + 跑 unit test 验证",[56,8017,8018,8021],{},[34,8019,8020],{},"冷门集成","：先 try Free 跑 demo 确认 action coverage 够",[56,8023,8024,8027],{},[34,8025,8026],{},"fallback","：Composio 不可用要有 fallback（重试 + 备用 SDK）",[56,8029,8030,8033],{},[34,8031,8032],{},"数据零云需求","：用 MCP server 自托管，不要用 Composio",[56,8035,8036,8039],{},[34,8037,8038],{},"国内访问","：CDN 海外，国内 production 用 proxy \u002F 反代",[56,8041,8042,8045],{},[34,8043,8044],{},"rate limit","：每个 SaaS 还有自己的 rate limit，Composio 不替你解决",[56,8047,8048,8051],{},[34,8049,8050],{},"vendor lock","：业务核心要做迁移预案（abstract OAuth 层）",[25,8053,764],{"id":763},[53,8055,8056,8059,8062,8065,8068,8071,8074,8077,8080],{},[56,8057,8058],{},"✅ 要造 agent 接多 SaaS 工具",[56,8060,8061],{},"✅ OAuth \u002F token refresh \u002F 错误重试不想自写",[56,8063,8064],{},"✅ 多框架兼容（不锁定）",[56,8066,8067],{},"✅ 企业 + 合规 + audit log",[56,8069,8070],{},"✅ 已有 MCP server 但想要更广覆盖",[56,8072,8073],{},"❌ 单一工具 high-frequency（自接 API 更便宜）",[56,8075,8076],{},"❌ 轻量 hobby project（LangChain 够用）",[56,8078,8079],{},"❌ 完全自托管 \u002F 数据零云",[56,8081,8082],{},"❌ 要 1000% 控制集成代码",[25,8084,799],{"id":799},[53,8086,8087,8093,8099],{},[56,8088,8089],{},[805,8090,8092],{"href":8091},"\u002Ftools\u002Fagent\u002Fprotocol\u002Fsmithery","Smithery 评测",[56,8094,8095],{},[805,8096,8098],{"href":8097},"\u002Ftools\u002Fagent\u002Fprotocol\u002Fmcp-toolbox","MCP Toolbox 评测",[56,8100,8101],{},[805,8102,6101],{"href":6100},[25,8104,817],{"id":817},[819,8106,8107,8114,8121,8128,8135],{},[56,8108,8109,8110],{},"Composio 官网 + Pricing ",[805,8111,8112],{"href":8112,"rel":8113},"https:\u002F\u002Fcomposio.dev",[828],[56,8115,8116,8117],{},"Tooliverse — Composio Review 2026 + 8.56\u002F10 + 295 reviews ",[805,8118,8119],{"href":8119,"rel":8120},"https:\u002F\u002Ftooliverse.ai\u002Ftools\u002Fcomposio",[828],[56,8122,8123,8124],{},"Sift AI — Composio Review 2026: Integration Platform for AI Agents ",[805,8125,8126],{"href":8126,"rel":8127},"https:\u002F\u002Fsiftaitools.com\u002Freviews\u002Fcomposio",[828],[56,8129,8130,8131],{},"Toolradar — Composio Reviews + 4.9\u002F5 ",[805,8132,8133],{"href":8133,"rel":8134},"https:\u002F\u002Ftoolradar.com\u002Ftools\u002Fcomposio",[828],[56,8136,8137,8138],{},"CrewAI Docs — Composio Tool 集成示例 + 250+ tools ",[805,8139,8140],{"href":8140,"rel":8141},"https:\u002F\u002Fdocs.crewai.com\u002Fen\u002Ftools\u002Fautomation\u002Fcomposiotool",[828],[844,8143,8144],{},"html pre.shiki code .sScJk, html code.shiki .sScJk{--shiki-default:#6F42C1;--shiki-dark:#B392F0}html pre.shiki code .sZZnC, html code.shiki .sZZnC{--shiki-default:#032F62;--shiki-dark:#9ECBFF}html pre.shiki code .szBVR, html code.shiki .szBVR{--shiki-default:#D73A49;--shiki-dark:#F97583}html pre.shiki code .sVt8B, html code.shiki .sVt8B{--shiki-default:#24292E;--shiki-dark:#E1E4E8}html .default .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}html.dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}html pre.shiki code .s4XuR, html code.shiki .s4XuR{--shiki-default:#E36209;--shiki-dark:#FFAB70}html pre.shiki code .sJ8bj, html code.shiki .sJ8bj{--shiki-default:#6A737D;--shiki-dark:#6A737D}html pre.shiki code .sj4cs, html code.shiki .sj4cs{--shiki-default:#005CC5;--shiki-dark:#79B8FF}",{"title":226,"searchDepth":325,"depth":325,"links":8146},[8147,8148,8149,8150,8151,8152,8153,8154,8155,8156],{"id":27,"depth":318,"text":28},{"id":51,"depth":318,"text":51},{"id":131,"depth":318,"text":131},{"id":7452,"depth":318,"text":7453},{"id":7516,"depth":318,"text":7517},{"id":603,"depth":318,"text":603},{"id":1299,"depth":318,"text":1299},{"id":763,"depth":318,"text":764},{"id":799,"depth":318,"text":799},{"id":817,"depth":318,"text":817},"protocol","\u002Fimg\u002Ftools\u002Fcomposio.webp","Composio（composio.dev）真实评测：AI agent 集成基础设施层，把『agent 接入 1000-10,000+ 真实工具』的复杂度封装为一个 SDK。差异点：1000+ pre-built 集成（GitHub \u002F Slack \u002F Gmail \u002F Salesforce \u002F Notion \u002F Linear 等）+ 托管 OAuth（含自动 token refresh）+ 沙箱执行 + 细粒度权限 + 完整审计日志 + 框架无关（MCP \u002F LangChain \u002F CrewAI \u002F Autogen \u002F OpenAI Agents SDK \u002F Claude \u002F ChatGPT 都能用）+ Python \u002F TypeScript SDK + AWS \u002F Zoom \u002F Glean 等企业客户背书。",[8161,8164,8167,8170],{"q":8162,"a":8163},"Composio 解决什么问题？","AI agent 在 production 撞墙最常见原因 = 『集成 + 认证基础设施』。每接一个工具（GitHub \u002F Slack \u002F Gmail \u002F Salesforce）要：写 OAuth flow + 保存 \u002F 刷新 token + 处理 rate limit + 写 JSON schema 给 LLM + 沙箱执行 + 错误重试。这些代码占了 agent 项目 50%+ 工程量。Composio 把这部分托管化，开发者只 import SDK 就能用 1000+ 工具，重心回到『agent 智能本身』。",{"q":8165,"a":8166},"和 MCP 是什么关系？","Composio 同时是 MCP server 提供方 + 自有 SDK。MCP 是 Anthropic 推出的开放协议，但 MCP server 仍需有人维护（GitHub MCP \u002F Slack MCP 等）。Composio 提供了 1000+ 工具的『大集合 MCP server』+ 还兼容 LangChain \u002F CrewAI \u002F OpenAI Agents SDK 等非 MCP 框架。所以可视为『MCP 协议 + 多框架原生 SDK』的双轨集成层。",{"q":8168,"a":8169},"和 LangChain native tools 怎么选？","LangChain 自带 ~200 tool，但很多是社区维护、OAuth 要自己处理、production 稳定性参差。Composio 用 1000+ 商业维护的集成 + 托管 OAuth + 企业级权限审计换更高费用。Hobby \u002F 学习项目 → LangChain native；production \u002F 多用户 \u002F 多 OAuth scope → Composio。",{"q":8171,"a":8172},"实际用起来贵不贵？","免费档对开发 \u002F 验证够用。生产部署 high-frequency 用例（如每分钟轮询 Gmail）会快速消耗 action 额度 → Growth\u002FEnterprise 起步。Tooliverse 评测明确指出『pricing scales steeply for high-frequency use cases』。低频 + 多样工具 → Composio 性价比高；高频 + 单工具 → 自接 API 更便宜。",[883],{},[8176,8177,6199],"api","sdk",[8179,8182,8186],{"plan":1600,"price":900,"features":8180,"notes":8181},"全部 1000+ 集成 + 限月度 action + Python\u002FTS SDK + MCP server","开发 \u002F 试水",{"plan":7438,"price":8183,"features":8184,"notes":8185},"按 action","更高 action 限额 + 更多并发 + 细粒度权限 + 团队","生产部署",{"plan":4463,"price":8187,"features":8188,"notes":8189},"联系销售","SSO + audit + 私有部署 + SLA + 自定义工具开发 + 专属支持","大客户","Free（有月度 action 限额）\u002F Growth 按 action 量阶梯 \u002F Enterprise 联系销售",[8192],"onboarding\u002Fagent-integration-layer",[8194],"mcp-ecosystem-review",{"power":359,"ux":347,"price":347,"cn_support":325,"stability":347},{"title":7336,"description":8159},[8198,8200,8202,8204,8206],{"name":8199,"url":8112,"accessed":1495},"Composio 官网",{"name":8201,"url":8119,"accessed":1495},"Tooliverse — Composio Review 2026 Agent Tool Execution",{"name":8203,"url":8126,"accessed":1495},"Sift AI — Composio Review 2026: Integration Platform",{"name":8205,"url":8133,"accessed":1495},"Toolradar — Composio Reviews + 4.9\u002F5",{"name":8207,"url":8140,"accessed":1495},"CrewAI Docs — Composio Tool 集成示例","tools\u002Fagent\u002Fprotocol\u002Fcomposio","AI Agent 集成中间件——1000+ 工具 + 托管 OAuth + 框架无关 + MCP + 沙箱执行",[8211,8212,6199,8213,8214],"agent-infra","integrations","oauth","composio","Agent 开发的『集成中间件层』——免自己写 1000 个 OAuth + token refresh + JSON schema。AWS \u002F Zoom \u002F Glean 在用。要省钱 \u002F 自托管全部用 MCP server 自己接；要简单 LangChain 工具用 LangChain native tools。","vtYi8gv6tOz3yaPGR6QFD6enNUVQMS5KYZ27p5SUD8g",[8218],{"id":8219,"title":8220,"body":8221,"cover":9014,"description":9015,"extension":865,"lastVerified":886,"meta":9016,"navigation":321,"path":9017,"published":904,"relatedTools":9018,"seo":9019,"seoTitle":9020,"stem":9021,"tags":9022,"updated":904,"verdict":9027,"__hash__":9028},"review\u002Freview\u002Fpageagent-deep-review.md","PageAgent 深度评测：阿里巴巴开源纯前端 GUI Agent，一行代码让 AI 操控你的网页",{"type":22,"value":8222,"toc":8985},[8223,8225,8231,8238,8241,8244,8250,8256,8262,8354,8358,8364,8368,8371,8391,8395,8402,8405,8411,8414,8420,8431,8434,8437,8443,8449,8459,8546,8550,8553,8559,8562,8586,8589,8596,8600,8604,8609,8612,8615,8619,8624,8627,8630,8634,8638,8641,8644,8648,8652,8655,8658,8661,8665,8668,8671,8681,8685,8688,8693,8766,8771,8775,8778,8781,8784,8787,8793,8799,8805,8810,8817,8820,8826,8832,8838,8844,8847,8850,8856,8859,8876,8879,8895,8897,8914,8916,8982],[25,8224,2627],{"id":2627},[30,8226,8227,8230],{},[34,8228,8229],{},"PageAgent 是 2026 年最值得关注的纯前端 GUI Agent 方案之一。"," 阿里巴巴开源、MIT 协议、一行代码嵌入、纯文本 DOM 分析——这些特性加在一起，让它成为为 Web 应用添加 AI 操作能力的最轻量选择。",[30,8232,8233,8234,8237],{},"但它的应用场景有明确的边界：",[34,8235,8236],{},"适合嵌进你的 Web 应用里用，不适合从外部控制别人的网站。"," 理解这个边界，比评价它的功能更重要。",[25,8239,8240],{"id":8240},"浏览器自动化的第三次范式转移",[30,8242,8243],{},"在评估 PageAgent 之前，先看一个更大的背景：浏览器自动化正在经历第三次范式转移。",[30,8245,8246,8249],{},[34,8247,8248],{},"第一代：脚本驱动（2010s）。"," Selenium、Playwright、Puppeteer 为代表。开发者编写确定性的选择器和操作步骤，浏览器机械执行。优势是稳定、可预测、成熟。劣势是页面结构一变就断，无法理解语义。",[30,8251,8252,8255],{},[34,8253,8254],{},"第二代：视觉驱动（2024-2025）。"," browser-use、Stagehand 为代表。AI Agent 通过截图 + 多模态模型理解页面，再执行操作。优势是能适应页面变化，不需要固定的选择器。劣势是需要多模态模型（贵）、需要无头浏览器（复杂）、推理速度慢（2-5s\u002F步）。",[30,8257,8258,8261],{},[34,8259,8260],{},"第三代：DOM 内嵌（2025-2026）。"," PageAgent 为代表。AI Agent 直接作为 JavaScript 运行在页面内部，通过文本化 DOM 理解页面结构。优势是零后端、纯文本 LLM（便宜）、速度快（0.5-1s\u002F步）、天然继承登录态。劣势是依赖 DOM 质量、只能操作当前页面。",[605,8263,8264,8286],{},[608,8265,8266],{},[611,8267,8268,8271,8274,8277,8280,8283],{},[614,8269,8270],{},"代际",[614,8272,8273],{},"代表",[614,8275,8276],{},"核心原理",[614,8278,8279],{},"部署成本",[614,8281,8282],{},"每步成本",[614,8284,8285],{},"适用场景",[629,8287,8288,8307,8325],{},[611,8289,8290,8293,8295,8298,8301,8304],{},[634,8291,8292],{},"第一代",[634,8294,621],{},[634,8296,8297],{},"选择器 + 指令",[634,8299,8300],{},"高（Python + 浏览器）",[634,8302,8303],{},"接近 0",[634,8305,8306],{},"测试 \u002F 爬虫",[611,8308,8309,8312,8314,8317,8320,8323],{},[634,8310,8311],{},"第二代",[634,8313,624],{},[634,8315,8316],{},"截图 + 多模态",[634,8318,8319],{},"很高（GPU + 多模态 API）",[634,8321,8322],{},"高",[634,8324,3883],{},[611,8326,8327,8330,8334,8339,8344,8349],{},[634,8328,8329],{},"第三代",[634,8331,8332],{},[34,8333,10],{},[634,8335,8336],{},[34,8337,8338],{},"DOM 文本 + LLM",[634,8340,8341],{},[34,8342,8343],{},"极低（一行 script）",[634,8345,8346],{},[34,8347,8348],{},"低",[634,8350,8351],{},[34,8352,8353],{},"Web 应用内嵌 AI",[25,8355,8357],{"id":8356},"pageagent-的核心架构dom-脱水","PageAgent 的核心架构：DOM 脱水",[30,8359,8360,8361,8363],{},"PageAgent 最核心的技术创新是 ",[34,8362,60],{},"。理解这个技术，就理解了 PageAgent 的整条产品逻辑。",[216,8365,8367],{"id":8366},"为什么不用截图","为什么不用截图？",[30,8369,8370],{},"2024-2025 年的浏览器 Agent 方案几乎都走同一条路：截屏 → 多模态 LLM 识别页面元素 → 返回坐标\u002F操作 → 执行。这条路线有两个根本问题：",[819,8372,8373,8379,8385],{},[56,8374,8375,8378],{},[34,8376,8377],{},"贵","：多模态模型的 Token 价格是纯文本模型的 5-10 倍。GPT-4o 的视觉定价是 $5\u002F1M 输入 Token，而纯文本模型 DeepSeek 只要 $0.5\u002F1M。",[56,8380,8381,8384],{},[34,8382,8383],{},"慢","：截图传输 + 图像编码 + 多模态推理，每一步都要 2-5 秒。复杂任务累积下来，一次 20 步的操作要等 40-100 秒。",[56,8386,8387,8390],{},[34,8388,8389],{},"不精确","：像素级别的截图里，按钮文字可能模糊、重叠元素可能识别错误。",[216,8392,8394],{"id":8393},"dom-脱水怎么工作","DOM 脱水怎么工作？",[30,8396,8397,8398,8401],{},"PageAgent 的答案是：",[34,8399,8400],{},"浏览器的 DOM 本身就是最精确的页面描述。"," 不需要截图，把 DOM 树里的交互元素提取出来、编上索引、压缩成文本，直接发给 LLM。",[30,8403,8404],{},"具体流程：",[221,8406,8409],{"className":8407,"code":8408,"language":3785},[3783],"用户指令 → 扫描 DOM 树 → 提取交互元素 → 分配给索引 → 压缩为精简文本 → 发送给 LLM\n  ↓\nLLM 返回工具调用（如 click_element_by_index: 3）→ 直接在页面内执行 → 观察反馈 → 下一步\n",[39,8410,8408],{"__ignoreMap":226},[30,8412,8413],{},"一个典型的脱水 DOM 输出长这样：",[221,8415,8418],{"className":8416,"code":8417,"language":3785},[3783],"[1] button \"登录\"\n[2] input \"用户名\" placeholder=\"请输入\"\n[3] input \"密码\" type=\"password\"\n[4] checkbox \"记住我\" checked=false\n[5] link \"忘记密码\"\n[6] button \"注册新账号\"\n",[39,8419,8417],{"__ignoreMap":226},[30,8421,8422,8423,8426,8427,8430],{},"这个文本表示通常只有 ",[34,8424,8425],{},"1-5k Token","，而一张截图经过 Base64 编码后通常要 ",[34,8428,8429],{},"50-100k Token","。成本差距是一个数量级。",[216,8432,8433],{"id":8433},"技术细节",[30,8435,8436],{},"PageAgent 的架构是标准的 ReAct（Reasoning + Acting）循环，但多了两个关键设计：",[30,8438,8439,8442],{},[34,8440,8441],{},"Reflection-Before-Action（行动前反思）","：每一步执行前，Agent 先评估上一步的执行效果，再决定下一步。这听起来简单，但实际效果显著——减少了错误累积，尤其是在长任务链中。",[30,8444,8445,8448],{},[34,8446,8447],{},"FlatDomTree 压缩","：现代网页可能有几千个 DOM 节点，大部分是样式容器和隐藏元素。PageAgent 把 DOM 树拍平（flatten），只保留交互元素（按钮、输入框、链接、表单等），并给每个元素分配一个索引。LLM 看到的不是原始的 HTML 结构，而是一个精简的、带索引的交互元素列表。",[30,8450,8451,8454,8455,8458],{},[34,8452,8453],{},"PageController 抽象","：实际操作由 ",[39,8456,8457],{},"PageController"," 负责，它封装了 DOM 提取、元素索引、点击、输入、滚动等操作。核心方法：",[221,8460,8462],{"className":296,"code":8461,"language":298,"meta":226,"style":226},"await this.pageController.updateTree()    \u002F\u002F 更新 DOM 树\nawait this.pageController.clickElement(index)    \u002F\u002F 点击索引元素\nawait this.pageController.inputText(index, text) \u002F\u002F 输入文本\nawait this.pageController.scroll({ down: true, numPages: 1 }) \u002F\u002F 滚动\n",[39,8463,8464,8483,8500,8517],{"__ignoreMap":226},[230,8465,8466,8468,8471,8474,8477,8480],{"class":232,"line":233},[230,8467,589],{"class":305},[230,8469,8470],{"class":331}," this",[230,8472,8473],{"class":236},".pageController.",[230,8475,8476],{"class":244},"updateTree",[230,8478,8479],{"class":236},"()    ",[230,8481,8482],{"class":406},"\u002F\u002F 更新 DOM 树\n",[230,8484,8485,8487,8489,8491,8494,8497],{"class":232,"line":318},[230,8486,589],{"class":305},[230,8488,8470],{"class":331},[230,8490,8473],{"class":236},[230,8492,8493],{"class":244},"clickElement",[230,8495,8496],{"class":236},"(index)    ",[230,8498,8499],{"class":406},"\u002F\u002F 点击索引元素\n",[230,8501,8502,8504,8506,8508,8511,8514],{"class":232,"line":325},[230,8503,589],{"class":305},[230,8505,8470],{"class":331},[230,8507,8473],{"class":236},[230,8509,8510],{"class":244},"inputText",[230,8512,8513],{"class":236},"(index, text) ",[230,8515,8516],{"class":406},"\u002F\u002F 输入文本\n",[230,8518,8519,8521,8523,8525,8528,8531,8534,8537,8540,8543],{"class":232,"line":347},[230,8520,589],{"class":305},[230,8522,8470],{"class":331},[230,8524,8473],{"class":236},[230,8526,8527],{"class":244},"scroll",[230,8529,8530],{"class":236},"({ down: ",[230,8532,8533],{"class":331},"true",[230,8535,8536],{"class":236},", numPages: ",[230,8538,8539],{"class":331},"1",[230,8541,8542],{"class":236}," }) ",[230,8544,8545],{"class":406},"\u002F\u002F 滚动\n",[25,8547,8549],{"id":8548},"部署体验从-10-分钟到-10-秒","部署体验：从 10 分钟到 10 秒",[30,8551,8552],{},"传统浏览器自动化的部署路径：",[221,8554,8557],{"className":8555,"code":8556,"language":3785},[3783],"安装 Python → 安装 Playwright → 下载 Chromium → 编写脚本 → 处理 Cookie\u002F登录态 → 调试 → 上线\n（估算：30 分钟到 2 小时）\n",[39,8558,8556],{"__ignoreMap":226},[30,8560,8561],{},"PageAgent 的部署路径：",[221,8563,8565],{"className":223,"code":8564,"language":225,"meta":226,"style":226},"\u003Cscript src=\"page-agent.js\">\u003C\u002Fscript>\n",[39,8566,8567],{"__ignoreMap":226},[230,8568,8569,8571,8573,8575,8577,8580,8582,8584],{"class":232,"line":233},[230,8570,237],{"class":236},[230,8572,241],{"class":240},[230,8574,245],{"class":244},[230,8576,248],{"class":236},[230,8578,8579],{"class":251},"\"page-agent.js\"",[230,8581,263],{"class":236},[230,8583,241],{"class":240},[230,8585,268],{"class":236},[30,8587,8588],{},"就这一行。10 秒。",[30,8590,8591,8592,8595],{},"从 30 分钟到 10 秒，本质上是",[34,8593,8594],{},"把部署复杂度从「运维问题」变成了「前端问题」","。对于前端团队来说，这意味着不需要申请服务器资源、不需要配置 Python 环境、不需要处理无头浏览器的兼容性——只需要在现有的前端工程里加一行代码。",[25,8597,8599],{"id":8598},"四种场景四种判断","四种场景，四种判断",[216,8601,8603],{"id":8602},"场景一saas-ai-copilot","场景一：SaaS AI Copilot",[30,8605,8606],{},[34,8607,8608],{},"适合度：⭐⭐⭐⭐⭐",[30,8610,8611],{},"这是 PageAgent 最完美的场景。假设你在做一个项目管理 SaaS，想给用户加一个 AI 助手，让用户说\"帮我创建一个新项目，叫 Q4 营销计划，邀请张三和李四加入\"——传统的做法是写后端 API、定义工具函数、处理状态管理，至少 2-3 周。用 PageAgent，几行代码就能实现，不需要后端改动。",[30,8613,8614],{},"因为 PageAgent 运行在页面内部，它看到的 DOM 和用户看到的一模一样，所有 UI 验证规则和权限控制天然生效。",[216,8616,8618],{"id":8617},"场景二传统-erp-现代化改造","场景二：传统 ERP 现代化改造",[30,8620,8621],{},[34,8622,8623],{},"适合度：⭐⭐⭐⭐",[30,8625,8626],{},"很多企业的 ERP 系统是 5-10 年前开发的，界面复杂、交互繁琐。用 PageAgent 可以给这些老旧系统加一个 \"AI 操作层\"，用户说\"提交周五的差旅报销\"——Agent 自动导航到报销模块、填写表单、上传附件、提交审批。",[30,8628,8629],{},"这比重新开发 ERP 前端或者写 RPA 脚本要便宜得多。但需要注意：ERP 系统的 DOM 结构可能非常复杂，嵌套表格、自定义控件、IFrame 等场景需要额外处理。",[216,8631,8633],{"id":8632},"场景三客服机器人升级","场景三：客服机器人升级",[30,8635,8636],{},[34,8637,8623],{},[30,8639,8640],{},"传统客服机器人只能\"告诉用户怎么操作\"，用户还得自己一步步点。接入 PageAgent 后，机器人可以直接操作页面——\"我现在帮您配置，请稍等\"——然后自动完成所有操作步骤。",[30,8642,8643],{},"难点在于：客服对话通常发生在聊天窗口，而操作发生在产品页面，这需要跨页面通信。PageAgent 的 Chrome 扩展和 MCP Server 可以解决，但增加了复杂度。",[216,8645,8647],{"id":8646},"场景四无障碍增强","场景四：无障碍增强",[30,8649,8650],{},[34,8651,8608],{},[30,8653,8654],{},"这是 PageAgent 被低估的潜力场景。中国有超过 1700 万视障人士，但绝大多数 Web 应用的无障碍支持停留在\"能过合规检查\"的水平。用 PageAgent，可以给任意 Web 应用添加自然语言操作能力——用户说\"打开上个月的报表\"或\"把字体调大\"，Agent 就能执行。",[30,8656,8657],{},"这比改造整个应用的无障碍架构要务实得多。而且 PageAgent 天然支持中文，对国内无障碍场景很有价值。",[25,8659,8660],{"id":8660},"与竞品的对比",[216,8662,8664],{"id":8663},"vs-playwright","vs. Playwright",[30,8666,8667],{},"Playwright 是确定性自动化工具，不需要 LLM，执行速度快（\u003C100ms\u002F步），适合大规模测试和 CI\u002FCD 场景。但它需要固定的选择器，页面结构一变就断。",[30,8669,8670],{},"PageAgent 是 LLM 驱动，能自适应页面变化，但依赖 LLM 意味着有 Token 成本、推理延迟、以及 LLM 固有的不确定性。",[30,8672,8673,8676,8677,8680],{},[34,8674,8675],{},"选型建议","：做测试\u002F爬虫 → Playwright。做 AI Copilot → PageAgent。",[34,8678,8679],{},"两者可以互补","——用 Playwright 跑回归测试，用 PageAgent 做 AI 交互。",[216,8682,8684],{"id":8683},"vs-browser-use","vs. browser-use",[30,8686,8687],{},"browser-use（101k+ GitHub stars）是 PageAgent 的主要对标对象。browser-use 走的是\"Python 后端 + 截图 + 多模态\"路线，而 PageAgent 走的是\"纯前端 + DOM 文本 + 纯文本 LLM\"路线。",[30,8689,8690,2665],{},[34,8691,8692],{},"关键差异",[605,8694,8695,8705],{},[608,8696,8697],{},[611,8698,8699,8701,8703],{},[614,8700,616],{},[614,8702,624],{},[614,8704,10],{},[629,8706,8707,8718,8727,8735,8746,8756],{},[611,8708,8709,8712,8715],{},[634,8710,8711],{},"部署位置",[634,8713,8714],{},"服务器端",[634,8716,8717],{},"浏览器端",[611,8719,8720,8722,8724],{},[634,8721,652],{},[634,8723,661],{},[634,8725,8726],{},"DOM 脱水文本",[611,8728,8729,8731,8733],{},[634,8730,668],{},[634,8732,677],{},[634,8734,671],{},[611,8736,8737,8740,8743],{},[634,8738,8739],{},"跨网站",[634,8741,8742],{},"✅ 原生",[634,8744,8745],{},"❌ 需扩展",[611,8747,8748,8750,8753],{},[634,8749,3056],{},[634,8751,8752],{},"高（多模态 + 服务器）",[634,8754,8755],{},"低（纯文本 + 零后端）",[611,8757,8758,8760,8763],{},[634,8759,3180],{},[634,8761,8762],{},"中（Python + 依赖）",[634,8764,8765],{},"低（一行 script）",[30,8767,8768,8770],{},[34,8769,8675],{},"：需要跨网站自动化、爬虫、数据采集 → browser-use。需要给自己的 Web 应用加 AI 能力 → PageAgent。",[216,8772,8774],{"id":8773},"vs-openmanus","vs. OpenManus",[30,8776,8777],{},"OpenManus（52k+ stars）是通用 Agent 实现，目标是\"拉下来配置 API 就能跑 Manus 风格任务\"。它做的事情比 PageAgent 更\"重\"——多 Agent 编排、浏览器自动化、数据分析等。",[30,8779,8780],{},"PageAgent 更\"轻\"、更\"专\"——它只做一件事：在网页内部操作 DOM。但它的集成方式更简单（前端脚本 vs. Python 项目），适合的场景更聚焦（Web 应用内嵌 vs. 通用 Agent）。",[25,8782,8783],{"id":8783},"安全与隐私",[30,8785,8786],{},"PageAgent 的安全模型有几个值得关注的设计：",[30,8788,8789,8792],{},[34,8790,8791],{},"操作白名单","：开发者可以定义 Agent 允许执行的操作类型（如只允许点击、不允许读取输入框内容）。如果 Agent 尝试执行未授权的操作，系统会阻止。",[30,8794,8795,8798],{},[34,8796,8797],{},"数据脱敏","：可以标记某些字段（如密码框、身份证号输入框）为敏感字段，DOM 脱水时自动替换为占位符，LLM 永远不会看到真实内容。",[30,8800,8801,8804],{},[34,8802,8803],{},"BYOK（Bring Your Own Key）","：用户数据直接从浏览器发往自己配置的 LLM 端点，PageAgent 自身不托管任何后端服务，数据隐私风险可归入模型端的安全审计范围。",[30,8806,8807,8809],{},[34,8808,85],{},"：Agent 执行每一步操作前都会在侧边面板展示思考过程，用户可以在任意步骤中断、修改或确认操作。",[30,8811,8812,8813,8816],{},"但需要注意：",[34,8814,8815],{},"安全边界在页面内，不在页面外。"," PageAgent 和页面上的其他 JavaScript 共享同一安全上下文，如果页面本身有 XSS 漏洞，PageAgent 不会提供额外保护。在敏感操作（如支付、权限变更）场景，建议在后端再加一层验证。",[25,8818,8819],{"id":8819},"局限与风险",[30,8821,8822,8825],{},[34,8823,8824],{},"DOM 依赖","：PageAgent 的核心假设是\"DOM 能精确描述页面\"。但有些场景 DOM 不一定可靠——Canvas 渲染的内容、WebGL 图形、CodeMirror\u002FMonaco 等自定义编辑器（这些编辑器通常用 contenteditable 或 canvas 实现，DOM 里没有直观的\"输入框\"元素）。CSDN 的富文本编辑器和掘金的 CodeMirror 编辑器就是典型案例，纯 DOM 自动化在这些场景会遇到困难。",[30,8827,8828,8831],{},[34,8829,8830],{},"版本迭代风险","：截至 2026 年 7 月，PageAgent 最新版本为 v1.8.2，仍处于快速迭代期。这意味着 API 可能变化，文档可能滞后，生产环境需要 pin 版本。",[30,8833,8834,8837],{},[34,8835,8836],{},"社区生态","：18k+ GitHub stars 说明社区关注度不错，但相比 Playwright 的 70k+ 和 browser-use 的 101k+，生态成熟度还有差距。第三方插件、教程、最佳实践相对较少。",[30,8839,8840,8843],{},[34,8841,8842],{},"不是测试工具","：PageAgent 的定位不是 Playwright 的替代品。它不适合跑 CI\u002FCD 测试、不适合做性能测试、不适合做大规模爬虫。用对工具做对事。",[25,8845,8846],{"id":8846},"结论",[30,8848,8849],{},"PageAgent 是 2026 年浏览器自动化领域最值得关注的创新之一。它的 DOM 脱水技术巧妙地绕过了截图方案的昂贵和复杂，纯前端架构让部署成本降到极限。对于想为自己的 Web 应用添加 AI 操作能力的团队，它是目前最轻量的选择。",[30,8851,8852,8853,8237],{},"但它的定位不是 Playwright 的替代品，而是互补品——",[34,8854,8855],{},"适合「嵌进去用」，不适合「从外面控制」。",[216,8857,8858],{"id":8858},"谁适合用",[53,8860,8861,8864,8867,8870,8873],{},[56,8862,8863],{},"✅ 前端开发者想为产品添加 AI Copilot",[56,8865,8866],{},"✅ SaaS 团队想降低 AI 功能集成成本",[56,8868,8869],{},"✅ 需要 AI 无障碍增强的 Web 应用",[56,8871,8872],{},"✅ 阿里巴巴\u002F阿里云技术栈的用户",[56,8874,8875],{},"✅ 想用纯前端方案实现 AI 操作的团队",[216,8877,8878],{"id":8878},"谁不适合用",[53,8880,8881,8884,8887,8890,8893],{},[56,8882,8883],{},"❌ 需要大规模跨网站爬虫",[56,8885,8886],{},"❌ CI\u002FCD 自动化测试",[56,8888,8889],{},"❌ 非技术人员的无代码自动化",[56,8891,8892],{},"❌ Canvas\u002FWebGL 密集的页面操作",[56,8894,796],{},[25,8896,799],{"id":799},[53,8898,8899,8904,8909],{},[56,8900,8901],{},[805,8902,8903],{"href":894},"PageAgent 工具卡：阿里巴巴开源纯前端 GUI Agent",[56,8905,8906],{},[805,8907,8908],{"href":807},"OpenManus 评测：开源通用 Agent 实现",[56,8910,8911],{},[805,8912,8913],{"href":813},"Composio 评测：MCP 工具协议",[25,8915,817],{"id":817},[819,8917,8918,8923,8928,8933,8940,8947,8954,8961,8968,8975],{},[56,8919,823,8920],{},[805,8921,826],{"href":826,"rel":8922},[828],[56,8924,831,8925],{},[805,8926,834],{"href":834,"rel":8927},[828],[56,8929,838,8930],{},[805,8931,841],{"href":841,"rel":8932},[828],[56,8934,8935,8936],{},"CSDN — 阿里开源纯前端浏览器自动化 PageAgent ",[805,8937,8938],{"href":8938,"rel":8939},"https:\u002F\u002Fblog.csdn.net\u002Fm0_55049655\u002Farticle\u002Fdetails\u002F159350982",[828],[56,8941,8942,8943],{},"CoddyKit — Page-Agent: Alibaba's Open-Source JavaScript Library ",[805,8944,8945],{"href":8945,"rel":8946},"https:\u002F\u002Fwww.coddykit.com\u002Fpages\u002Fblog-detail?id=512893",[828],[56,8948,8949,8950],{},"掘金 — page-agent: 纯 JS 的网页 GUI Agent ",[805,8951,8952],{"href":8952,"rel":8953},"https:\u002F\u002Fjuejin.cn\u002Fpost\u002F7655611059512983604",[828],[56,8955,8956,8957],{},"Meta AI Labs — Meet Alibaba's Page Agent ",[805,8958,8959],{"href":8959,"rel":8960},"https:\u002F\u002Fmetaailabs.com\u002Fmeet-alibabas-page-agent-a-javascript-in-page-gui-agent-that-controls-web-interfaces-with-natural-language-through-the-dom\u002F",[828],[56,8962,8963,8964],{},"AI Tools Atlas — PageAgent Review 2026 ",[805,8965,8966],{"href":8966,"rel":8967},"https:\u002F\u002Faitoolsatlas.ai\u002Ftools\u002Fpageagent\u002Freview",[828],[56,8969,8970,8971],{},"AI Tool Net — page-agent ",[805,8972,8973],{"href":8973,"rel":8974},"https:\u002F\u002Fwww.aitoolnet.com\u002Fpageagent",[828],[56,8976,8977,8978],{},"MCPgee — Page Agent ",[805,8979,8980],{"href":8980,"rel":8981},"https:\u002F\u002Fwww.mcpgee.com\u002Fservers\u002Fpage-agent",[828],[844,8983,8984],{},"html pre.shiki code .szBVR, html code.shiki .szBVR{--shiki-default:#D73A49;--shiki-dark:#F97583}html pre.shiki code .sj4cs, html code.shiki .sj4cs{--shiki-default:#005CC5;--shiki-dark:#79B8FF}html pre.shiki code .sVt8B, html code.shiki .sVt8B{--shiki-default:#24292E;--shiki-dark:#E1E4E8}html pre.shiki code .sScJk, html code.shiki .sScJk{--shiki-default:#6F42C1;--shiki-dark:#B392F0}html pre.shiki code .sJ8bj, html code.shiki .sJ8bj{--shiki-default:#6A737D;--shiki-dark:#6A737D}html .default .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}html.dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}html pre.shiki code .s9eBZ, html code.shiki .s9eBZ{--shiki-default:#22863A;--shiki-dark:#85E89D}html pre.shiki code .sZZnC, html code.shiki .sZZnC{--shiki-default:#032F62;--shiki-dark:#9ECBFF}",{"title":226,"searchDepth":325,"depth":325,"links":8986},[8987,8988,8989,8994,8995,9001,9006,9007,9008,9012,9013],{"id":2627,"depth":318,"text":2627},{"id":8240,"depth":318,"text":8240},{"id":8356,"depth":318,"text":8357,"children":8990},[8991,8992,8993],{"id":8366,"depth":325,"text":8367},{"id":8393,"depth":325,"text":8394},{"id":8433,"depth":325,"text":8433},{"id":8548,"depth":318,"text":8549},{"id":8598,"depth":318,"text":8599,"children":8996},[8997,8998,8999,9000],{"id":8602,"depth":325,"text":8603},{"id":8617,"depth":325,"text":8618},{"id":8632,"depth":325,"text":8633},{"id":8646,"depth":325,"text":8647},{"id":8660,"depth":318,"text":8660,"children":9002},[9003,9004,9005],{"id":8663,"depth":325,"text":8664},{"id":8683,"depth":325,"text":8684},{"id":8773,"depth":325,"text":8774},{"id":8783,"depth":318,"text":8783},{"id":8819,"depth":318,"text":8819},{"id":8846,"depth":318,"text":8846,"children":9009},[9010,9011],{"id":8858,"depth":325,"text":8858},{"id":8878,"depth":325,"text":8878},{"id":799,"depth":318,"text":799},{"id":817,"depth":318,"text":817},"\u002Fog\u002Freview\u002Fpageagent.svg","PageAgent 深度评测：阿里巴巴开源的纯前端 JavaScript GUI Agent 框架，一行 script 标签嵌入任意网页，自然语言操控 UI——无需截图、无需多模态、无需后端。本文从架构原理、DOM 脱水技术、与 Playwright\u002Fbrowser-use 的对比、4 个真实场景和 5 类不推荐场景全面分析。",{},"\u002Freview\u002Fpageagent-deep-review",[909,12,13],{"title":8220,"description":9015},"PageAgent 评测 2026：阿里巴巴开源 GUI Agent，DOM 脱水技术，自然语言操控网页","review\u002Fpageagent-deep-review",[10,9023,9024,9025,5885,3130,4200,9026],"阿里巴巴","GUI Agent","DOM Dehydration","深度评测","PageAgent 代表的不是又一个浏览器自动化工具，而是『浏览器自动化』到『网页内 AI 操作』的范式转移。它用 DOM 脱水技术绕过了截图方案的昂贵和复杂，用纯前端架构消除了后端基建成本。对于想为自己的 Web 应用添加 AI 操作能力的团队，它是目前最轻量的选择。但它的定位不是 Playwright 的替代品，而是互补品——适合『嵌进去用』，不适合『从外面控制』。","rQFrSTcw7iybADjUU2VB-gUz_hkKTtwmk85D8f1pPpk",1785253926167]