[{"data":1,"prerenderedAt":3531},["ShallowReactive",2],{"header-counts":3,"footer-counts":6,"review-openmanus-deep-review":9,"review-related-openmanus-deep-review":1289},{"tools":4,"reviews":5},77,25,{"tools":4,"reviews":5,"playbooks":7,"news":8},22,13,{"id":10,"title":11,"body":12,"cover":1270,"description":1271,"extension":1272,"meta":1273,"navigation":179,"path":1274,"published":1275,"relatedTools":1276,"seo":1280,"stem":1281,"tags":1282,"updated":1275,"verdict":1287,"__hash__":1288},"review\u002Freview\u002Fopenmanus-deep-review.md","OpenManus 深度评测：开源版 Manus 能替代原版吗",{"type":13,"value":14,"toc":1217},"minimark",[15,19,32,39,49,53,60,66,72,82,86,98,102,105,127,130,134,145,206,209,213,220,327,330,334,337,364,367,371,374,377,410,413,416,419,439,442,445,475,479,482,485,488,502,510,514,517,520,524,531,535,572,576,620,623,626,629,632,638,642,674,677,845,848,851,854,896,899,903,906,909,913,916,920,923,927,930,934,937,941,944,947,951,969,973,976,980,983,987,990,994,1003,1006,1009,1087,1096,1100,1104,1111,1115,1118,1122,1125,1129,1132,1136,1143,1153,1159,1173,1180,1183,1213],[16,17,18],"h2",{"id":18},"一句话结论",[20,21,22,23,27,28,31],"p",{},"如果你是",[24,25,26],"strong",{},"开发者 \u002F 研究者 \u002F 隐私敏感的用户","，想在自己的机器上复刻 Manus 风格的通用 Agent 体验——",[24,29,30],{},"OpenManus 在 2026 年是开源 Agent 里最完整的选择之一","。多 agent orchestration + Playwright 浏览器自动化 + MCP 工具协议 + DataAnalysis 内置模式 + OpenManus-RL 强化学习分支，52,000+ GitHub stars 印证了社区认同度。",[20,33,34,35,38],{},"但它",[24,36,37],{},"不是 Manus 的完整平替","：没有 GUI、没有云端异步执行、文档滞后新功能 1-2 个月、生产部署需要自己加监控和错误恢复。Manus 原版的\"提交任务 → 关掉浏览器 → 明早看结果\"体验，OpenManus 目前做不到——它是本地跑、实时盯、需要你自己管基础设施。",[40,41,46],"div",{"className":42},[43,44,45],"card","p-5","my-4",[20,47,48],{},"部署建议：先用 GPT-4o-mini 或 DeepSeek 跑 3-5 个简单任务验证基础能力，再逐步升级到复杂的多 agent + 浏览器 + 数据分析组合。不要一上来就跑生产级任务——项目演进快，breaking change 偶发，pin 住一个 commit 再用。",[16,50,52],{"id":51},"openmanus-真正在解决的问题","OpenManus 真正在解决的问题",[20,54,55,56,59],{},"社区讨论\"为什么 OpenManus 火\"经常聚焦在\"52k stars\"、\"MetaGPT 团队\"、\"开源版 Manus\"。但深一层看，OpenManus 是在解决",[24,57,58],{},"通用 Agent 领域的三个痛点","：",[20,61,62,65],{},[24,63,64],{},"第一个痛点：封闭生态与邀请码门槛。"," Manus 2025-03 首发时邀请码一度被炒到 ¥5 万-10 万（据维基百科引用 China Daily 报道），大量用户被挡在门外。OpenManus 的口号就是\"让所有人不靠邀请码就能用上类 Manus 能力\"——MIT 协议、git clone 即用、零订阅、零供应商绑定。这不是\"便宜版 Manus\"，而是\"人人可跑的 Manus 范式实现\"。",[20,67,68,71],{},[24,69,70],{},"第二个痛点：数据隐私。"," Manus 是云端 SaaS——你的任务、你的文件、你的数据全上 Butterfly Effect 的服务器。对隐私敏感场景（企业内部数据、医疗、金融、法律），这是不可接受的。OpenManus 完全自托管：数据留在你自己的机器上，LLM API 调用走你自己的 key，你可以接本地 Ollama 模型实现零数据外泄。",[20,73,74,77,78,81],{},[24,75,76],{},"第三个痛点：Agent 实现的学习成本。"," LangChain 是 building block 框架——你要从底层搭自己的 agent，门槛极高。AutoGPT 是早期通用 agent——架构陈旧，社区已不活跃。CrewAI 是多 agent 协作框架——更偏\"编排\"而非\"执行\"。OpenManus 填补了一个空白：",[24,79,80],{},"拉下来配 API 就能跑 Manus 风格任务的现成实现","，同时代码结构清晰（MetaGPT 团队的工程质量有保障），可以作为学习通用 Agent 架构的教材。",[16,83,85],{"id":84},"agent-能力多-agent-orchestration","Agent 能力：多 agent orchestration",[20,87,88,89,92,93,97],{},"OpenManus 的核心架构是",[24,90,91],{},"多 agent 编排","（",[94,95,96],"code",{},"run_flow.py","），这是它与单 agent 实现的根本差异。",[99,100,101],"h3",{"id":101},"三阶段引擎",[20,103,104],{},"OpenManus 的核心 agent 引擎分三个阶段：",[106,107,108,115,121],"ol",{},[109,110,111,114],"li",{},[24,112,113],{},"Reasoning（推理）","：理解任务目标，拆解成子步骤",[109,116,117,120],{},[24,118,119],{},"Planning（规划）","：为每个子步骤选择合适的工具和执行路径",[109,122,123,126],{},[24,124,125],{},"Execution（执行）","：调用具体工具（浏览器、代码、文件操作）完成任务",[20,128,129],{},"这个架构和 Manus 的多 sub-agent 并行理念相似，但实现方式不同。Manus 是云端 orchestrator 派发子 agent 到各自隔离环境并行跑；OpenManus 是本地 Python 进程编排，更偏串行流水线，但胜在可调试、可定制。",[99,131,133],{"id":132},"run_flowpy多-agent-协作","run_flow.py：多 agent 协作",[20,135,136,137,140,141,144],{},"单 agent 模式（",[94,138,139],{},"python main.py","）适合简单任务。复杂任务用多 agent 模式（",[94,142,143],{},"python run_flow.py","）：",[146,147,152],"pre",{"className":148,"code":149,"language":150,"meta":151,"style":151},"language-bash shiki shiki-themes github-light github-dark","# 多 agent 模式启动\npython run_flow.py\n\n# 试一个复杂任务\n> 帮我做一份「2026 开源 AI Agent 框架」竞品对比，\n> 含表格 + 引用 + 趋势分析，输出为 markdown 文件\n","bash","",[94,153,154,163,174,181,187,198],{"__ignoreMap":151},[155,156,159],"span",{"class":157,"line":158},"line",1,[155,160,162],{"class":161},"sJ8bj","# 多 agent 模式启动\n",[155,164,166,170],{"class":157,"line":165},2,[155,167,169],{"class":168},"sScJk","python",[155,171,173],{"class":172},"sZZnC"," run_flow.py\n",[155,175,177],{"class":157,"line":176},3,[155,178,180],{"emptyLinePlaceholder":179},true,"\n",[155,182,184],{"class":157,"line":183},4,[155,185,186],{"class":161},"# 试一个复杂任务\n",[155,188,190,194],{"class":157,"line":189},5,[155,191,193],{"class":192},"szBVR",">",[155,195,197],{"class":196},"sVt8B"," 帮我做一份「2026 开源 AI Agent 框架」竞品对比，\n",[155,199,201,203],{"class":157,"line":200},6,[155,202,193],{"class":192},[155,204,205],{"class":196}," 含表格 + 引用 + 趋势分析，输出为 markdown 文件\n",[20,207,208],{},"多 agent 模式下，OpenManus 会把任务拆给不同专门 agent：浏览 agent 负责搜索和抓取、分析 agent 负责处理数据、写作 agent 负责合成输出。每个 agent 有独立的 context，不会互相污染。",[99,210,212],{"id":211},"basetool自定义工具扩展","BaseTool：自定义工具扩展",[20,214,215,216,219],{},"OpenManus 提供了 ",[94,217,218],{},"BaseTool"," 基类，Python 继承即可快速添加新工具：",[146,221,224],{"className":222,"code":223,"language":169,"meta":151,"style":151},"language-python shiki shiki-themes github-light github-dark","from openmanus.tools.base import BaseTool\n\nclass MyCustomTool(BaseTool):\n    name: str = \"my_tool\"\n    description: str = \"描述这个工具做什么\"\n\n    async def execute(self, **kwargs):\n        # 你的工具逻辑\n        return result\n",[94,225,226,240,244,260,275,287,291,312,318],{"__ignoreMap":151},[155,227,228,231,234,237],{"class":157,"line":158},[155,229,230],{"class":192},"from",[155,232,233],{"class":196}," openmanus.tools.base ",[155,235,236],{"class":192},"import",[155,238,239],{"class":196}," BaseTool\n",[155,241,242],{"class":157,"line":165},[155,243,180],{"emptyLinePlaceholder":179},[155,245,246,249,252,255,257],{"class":157,"line":176},[155,247,248],{"class":192},"class",[155,250,251],{"class":168}," MyCustomTool",[155,253,254],{"class":196},"(",[155,256,218],{"class":168},[155,258,259],{"class":196},"):\n",[155,261,262,265,269,272],{"class":157,"line":183},[155,263,264],{"class":196},"    name: ",[155,266,268],{"class":267},"sj4cs","str",[155,270,271],{"class":192}," =",[155,273,274],{"class":172}," \"my_tool\"\n",[155,276,277,280,282,284],{"class":157,"line":189},[155,278,279],{"class":196},"    description: ",[155,281,268],{"class":267},[155,283,271],{"class":192},[155,285,286],{"class":172}," \"描述这个工具做什么\"\n",[155,288,289],{"class":157,"line":200},[155,290,180],{"emptyLinePlaceholder":179},[155,292,294,297,300,303,306,309],{"class":157,"line":293},7,[155,295,296],{"class":192},"    async",[155,298,299],{"class":192}," def",[155,301,302],{"class":168}," execute",[155,304,305],{"class":196},"(self, ",[155,307,308],{"class":192},"**",[155,310,311],{"class":196},"kwargs):\n",[155,313,315],{"class":157,"line":314},8,[155,316,317],{"class":161},"        # 你的工具逻辑\n",[155,319,321,324],{"class":157,"line":320},9,[155,322,323],{"class":192},"        return",[155,325,326],{"class":196}," result\n",[20,328,329],{},"这种可扩展性是开源项目的核心优势——Manus 用户只能等官方加功能，OpenManus 用户可以自己加。",[99,331,333],{"id":332},"mcp-协议支持","MCP 协议支持",[20,335,336],{},"OpenManus 支持 MCP（Model Context Protocol）——Anthropic 推出的工具协议标准。配置 MCP server 后，OpenManus 可以调用 filesystem、GitHub、Postgres 等外部工具：",[146,338,342],{"className":339,"code":340,"language":341,"meta":151,"style":151},"language-toml shiki shiki-themes github-light github-dark","# config.toml 里配 MCP server\n[mcp.filesystem]\ncommand = \"npx\"\nargs = [\"-y\", \"@modelcontextprotocol\u002Fserver-filesystem\", \"\u002Fpath\u002Fto\u002Fallowed\u002Fdir\"]\n","toml",[94,343,344,349,354,359],{"__ignoreMap":151},[155,345,346],{"class":157,"line":158},[155,347,348],{},"# config.toml 里配 MCP server\n",[155,350,351],{"class":157,"line":165},[155,352,353],{},"[mcp.filesystem]\n",[155,355,356],{"class":157,"line":176},[155,357,358],{},"command = \"npx\"\n",[155,360,361],{"class":157,"line":183},[155,362,363],{},"args = [\"-y\", \"@modelcontextprotocol\u002Fserver-filesystem\", \"\u002Fpath\u002Fto\u002Fallowed\u002Fdir\"]\n",[20,365,366],{},"MCP 支持打通了 Claude \u002F Cursor 生态——你在 Cursor 里用的 MCP server，OpenManus 也能用。这让它不只是\"Manus 替代\"，而是\"可以接入任何 MCP 工具的通用 Agent\"。",[16,368,370],{"id":369},"浏览器操控playwright-自动化","浏览器操控：Playwright 自动化",[20,372,373],{},"浏览器自动化是通用 Agent 的核心能力之一——Manus 火起来的 demo 就是浏览器操控。OpenManus 用 Playwright 实现了完整的浏览器自动化栈。",[99,375,376],{"id":376},"能做什么",[378,379,380,386,392,398,404],"ul",{},[109,381,382,385],{},[24,383,384],{},"截图 + DOM 操作","：AI 能\"看到\"网页并操作元素",[109,387,388,391],{},[24,389,390],{},"表单填写","：自动填搜索框、提交表单、翻页",[109,393,394,397],{},[24,395,396],{},"信息抓取","：从网页提取结构化数据",[109,399,400,403],{},[24,401,402],{},"多步导航","：点击 → 等待 → 再点击的复杂流程",[109,405,406,409],{},[24,407,408],{},"多模态回环","：截图 → 视觉模型分析 → 决定下一步操作",[99,411,412],{"id":412},"实际体验",[20,414,415],{},"浏览器自动化是 OpenManus 最完整的模块之一。MetaGPT 团队在 Playwright 集成上做了大量工程——截图回环、DOM 定位、等待策略都处理得比社区里大部分 Playwright agent 实现更稳。",[20,417,418],{},"但有明确边界：",[378,420,421,427,433],{},[109,422,423,426],{},[24,424,425],{},"CAPTCHA \u002F 反爬","：遇到验证码或 Cloudflare 防护会卡死。需要搭配 2captcha 等第三方服务或 human-in-loop",[109,428,429,432],{},[24,430,431],{},"动态 SPA","：重度动态加载的单页应用偶尔定位失败，需要调等待策略",[109,434,435,438],{},[24,436,437],{},"登录态","：需要自己管理 cookie \u002F session，不像商业产品有托管登录",[99,440,441],{"id":441},"首次安装注意",[20,443,444],{},"Playwright 首次安装要下 chromium，国内带宽慢：",[146,446,448],{"className":148,"code":447,"language":150,"meta":151,"style":151},"# 走镜像加速\nPLAYWRIGHT_DOWNLOAD_HOST=https:\u002F\u002Fnpmmirror.com\u002Fmirrors\u002Fplaywright playwright install chromium\n",[94,449,450,455],{"__ignoreMap":151},[155,451,452],{"class":157,"line":158},[155,453,454],{"class":161},"# 走镜像加速\n",[155,456,457,460,463,466,469,472],{"class":157,"line":165},[155,458,459],{"class":196},"PLAYWRIGHT_DOWNLOAD_HOST",[155,461,462],{"class":192},"=",[155,464,465],{"class":172},"https:\u002F\u002Fnpmmirror.com\u002Fmirrors\u002Fplaywright",[155,467,468],{"class":168}," playwright",[155,470,471],{"class":172}," install",[155,473,474],{"class":172}," chromium\n",[16,476,478],{"id":477},"代码执行dataanalysis-内置模式","代码执行：DataAnalysis 内置模式",[20,480,481],{},"OpenManus 内置了 DataAnalysis agent 模式——专门处理 CSV \u002F 数据分析任务。这是它对标 Manus 数据分析能力的关键模块。",[99,483,484],{"id":484},"工作方式",[20,486,487],{},"DataAnalysis agent 能：",[106,489,490,493,496,499],{},[109,491,492],{},"读取 CSV \u002F Excel 文件",[109,494,495],{},"用 Python（pandas \u002F matplotlib）分析数据",[109,497,498],{},"生成图表和统计报告",[109,500,501],{},"输出结构化的分析结果",[146,503,508],{"className":504,"code":506,"language":507},[505],"language-text","> 分析这份 sales_data.csv，回答：\n> 1. 哪个季度营收最高？\n> 2. Top 5 产品是什么？\n> 3. 有没有异常数据点？\n> 产出带图表的 markdown 报告\n","text",[94,509,506],{"__ignoreMap":151},[99,511,513],{"id":512},"与-manus-数据分析的差距","与 Manus 数据分析的差距",[20,515,516],{},"Manus 的数据分析跑在云端沙盒里，用户不用管环境。OpenManus 的代码执行跑在本地 Python 环境里——你需要自己确保依赖装好（pandas、matplotlib 等）。好处是数据处理完全本地、隐私安全；坏处是环境管理是你的责任。",[20,518,519],{},"代码执行也有安全考量：OpenManus 跑的 Python 代码有完整系统权限，不像 Manus 有沙盒隔离。在生产环境使用需要自己加沙盒（Docker 容器 \u002F restricted Python）。",[16,521,523],{"id":522},"与-manus-原版对比","与 Manus 原版对比",[20,525,526,527,530],{},"这是最核心的问题——OpenManus 能替代 Manus 吗？答案是",[24,528,529],{},"部分能，但不是完整替代","。",[99,532,534],{"id":533},"openmanus-能做到的","OpenManus 能做到的",[378,536,537,542,548,554,560,566],{},[109,538,539,541],{},[24,540,91],{},"：run_flow.py 的多 agent 协作理念与 Manus 的 sub-agent 并行相似",[109,543,544,547],{},[24,545,546],{},"浏览器自动化","：Playwright 集成完整度接近 Manus 的浏览器能力",[109,549,550,553],{},[24,551,552],{},"数据分析","：DataAnalysis 模式覆盖 CSV \u002F Excel 分析场景",[109,555,556,559],{},[24,557,558],{},"MCP 工具调用","：比 Manus 更开放，能接入任何 MCP server",[109,561,562,565],{},[24,563,564],{},"多模型支持","：GPT-4o \u002F Claude \u002F Qwen VL Plus \u002F DeepSeek \u002F Gemini \u002F 本地模型，比 Manus 的内部路由更自由",[109,567,568,571],{},[24,569,570],{},"自托管 + 隐私","：数据完全本地，这是 Manus 做不到的",[99,573,575],{"id":574},"openmanus-做不到的","OpenManus 做不到的",[378,577,578,584,590,596,602,608,614],{},[109,579,580,583],{},[24,581,582],{},"云端异步执行","：Manus 的核心体验是\"提交任务 → 关掉浏览器 → 明早看结果\"。OpenManus 是本地实时跑，你得开着终端盯着",[109,585,586,589],{},[24,587,588],{},"GAIA SOTA 准确率","：Manus 在 GAIA benchmark 上 >65%（SOTA），OpenManus 没有公开的 benchmark 数据，社区体感准确率约 Manus 的 60-70%",[109,591,592,595],{},[24,593,594],{},"引用密度","：Manus 有专门的引用检查子 agent，引用错配率明显低。OpenManus 没有这个机制，引用质量取决于底层模型",[109,597,598,601],{},[24,599,600],{},"Web App Builder","：Manus 有（虽然有 bug），OpenManus 没有",[109,603,604,607],{},[24,605,606],{},"开箱即用的 GUI","：Manus 有完整的 Web 界面，OpenManus 的 Web UI 监控在做但不完整",[109,609,610,613],{},[24,611,612],{},"任务可视化","：Manus 有步骤拆解 + 执行树可视化，OpenManus 的可视化有限",[109,615,616,619],{},[24,617,618],{},"稳定性和打磨","：Manus 是商业产品有 QA 团队，OpenManus 演进快、breaking change 偶发、文档滞后",[99,621,622],{"id":622},"准确率差距的根源",[20,624,625],{},"Manus 的准确率优势来自三点：多 sub-agent 真正并行（不是串行流水线）、自动模型路由（每步选最合适的模型）、引用检查子 agent。OpenManus 目前只有多 agent 编排，没有自动模型路由和引用检查。这意味着同样一个深度研究任务，Manus 的输出质量和引用密度会明显好于 OpenManus。",[20,627,628],{},"但 OpenManus 有 Manus 做不到的自由度——你可以自己加引用检查 agent、自己实现模型路由逻辑、自己定制 agent 行为。这是开源的价值。",[16,630,631],{"id":631},"部署门槛",[20,633,634,635,530],{},"诚实地说，OpenManus 的部署门槛",[24,636,637],{},"比 Manus 高一个数量级",[99,639,641],{"id":640},"硬件-环境要求","硬件 \u002F 环境要求",[378,643,644,650,656,662,668],{},[109,645,646,649],{},[24,647,648],{},"Python 3.12+","：版本要求严格，低版本会报错",[109,651,652,655],{},[24,653,654],{},"Playwright 依赖","：首次安装 chromium，约 300MB 下载",[109,657,658,661],{},[24,659,660],{},"LLM API key","：至少一个（OpenAI \u002F Anthropic \u002F DeepSeek）",[109,663,664,667],{},[24,665,666],{},"内存","：浏览器自动化 + 多 agent 建议 8GB+ 内存",[109,669,670,673],{},[24,671,672],{},"网络","：如果用 GPT-4o \u002F Claude 需要代理，DeepSeek \u002F Qwen 可直连",[99,675,676],{"id":676},"部署流程",[146,678,680],{"className":148,"code":679,"language":150,"meta":151,"style":151},"# 1. 克隆仓库\ngit clone https:\u002F\u002Fgithub.com\u002FFoundationAgents\u002FOpenManus.git\ncd OpenManus\n\n# 2. 创建虚拟环境\npython3.12 -m venv .venv\nsource .venv\u002Fbin\u002Factivate  # Windows: .venv\\Scripts\\activate\n\n# 3. 装依赖\npip install -r requirements.txt\n\n# 4. 装 Playwright 浏览器\nplaywright install chromium\n\n# 5. 配 LLM API\ncp config\u002Fconfig.example.toml config\u002Fconfig.toml\n# 编辑 config.toml 填 API key\n\n# 6. 跑\npython main.py        # 单 agent 模式\npython run_flow.py    # 多 agent 模式\n",[94,681,682,687,698,706,710,715,729,740,744,749,763,768,774,783,788,794,806,812,817,823,834],{"__ignoreMap":151},[155,683,684],{"class":157,"line":158},[155,685,686],{"class":161},"# 1. 克隆仓库\n",[155,688,689,692,695],{"class":157,"line":165},[155,690,691],{"class":168},"git",[155,693,694],{"class":172}," clone",[155,696,697],{"class":172}," https:\u002F\u002Fgithub.com\u002FFoundationAgents\u002FOpenManus.git\n",[155,699,700,703],{"class":157,"line":176},[155,701,702],{"class":267},"cd",[155,704,705],{"class":172}," OpenManus\n",[155,707,708],{"class":157,"line":183},[155,709,180],{"emptyLinePlaceholder":179},[155,711,712],{"class":157,"line":189},[155,713,714],{"class":161},"# 2. 创建虚拟环境\n",[155,716,717,720,723,726],{"class":157,"line":200},[155,718,719],{"class":168},"python3.12",[155,721,722],{"class":267}," -m",[155,724,725],{"class":172}," venv",[155,727,728],{"class":172}," .venv\n",[155,730,731,734,737],{"class":157,"line":293},[155,732,733],{"class":267},"source",[155,735,736],{"class":172}," .venv\u002Fbin\u002Factivate",[155,738,739],{"class":161},"  # Windows: .venv\\Scripts\\activate\n",[155,741,742],{"class":157,"line":314},[155,743,180],{"emptyLinePlaceholder":179},[155,745,746],{"class":157,"line":320},[155,747,748],{"class":161},"# 3. 装依赖\n",[155,750,752,755,757,760],{"class":157,"line":751},10,[155,753,754],{"class":168},"pip",[155,756,471],{"class":172},[155,758,759],{"class":267}," -r",[155,761,762],{"class":172}," requirements.txt\n",[155,764,766],{"class":157,"line":765},11,[155,767,180],{"emptyLinePlaceholder":179},[155,769,771],{"class":157,"line":770},12,[155,772,773],{"class":161},"# 4. 装 Playwright 浏览器\n",[155,775,776,779,781],{"class":157,"line":8},[155,777,778],{"class":168},"playwright",[155,780,471],{"class":172},[155,782,474],{"class":172},[155,784,786],{"class":157,"line":785},14,[155,787,180],{"emptyLinePlaceholder":179},[155,789,791],{"class":157,"line":790},15,[155,792,793],{"class":161},"# 5. 配 LLM API\n",[155,795,797,800,803],{"class":157,"line":796},16,[155,798,799],{"class":168},"cp",[155,801,802],{"class":172}," config\u002Fconfig.example.toml",[155,804,805],{"class":172}," config\u002Fconfig.toml\n",[155,807,809],{"class":157,"line":808},17,[155,810,811],{"class":161},"# 编辑 config.toml 填 API key\n",[155,813,815],{"class":157,"line":814},18,[155,816,180],{"emptyLinePlaceholder":179},[155,818,820],{"class":157,"line":819},19,[155,821,822],{"class":161},"# 6. 跑\n",[155,824,826,828,831],{"class":157,"line":825},20,[155,827,169],{"class":168},[155,829,830],{"class":172}," main.py",[155,832,833],{"class":161},"        # 单 agent 模式\n",[155,835,837,839,842],{"class":157,"line":836},21,[155,838,169],{"class":168},[155,840,841],{"class":172}," run_flow.py",[155,843,844],{"class":161},"    # 多 agent 模式\n",[20,846,847],{},"对比 Manus 的部署：打开 manus.im → 注册 → 提交任务。三步。",[99,849,850],{"id":850},"生产部署的额外工作",[20,852,853],{},"如果你要把 OpenManus 跑在生产环境（团队共享 \u002F 长期运行），还需要自己加：",[378,855,856,862,868,878,884,890],{},[109,857,858,861],{},[24,859,860],{},"监控","：prometheus + grafana 看任务状态和 token 消耗",[109,863,864,867],{},[24,865,866],{},"错误恢复","：agent 跑飞了要能自动重试或告警",[109,869,870,873,874,877],{},[24,871,872],{},"任务超时","：设 ",[94,875,876],{},"max_steps"," 防止失控烧 token",[109,879,880,883],{},[24,881,882],{},"日志","：记录每个 agent 的 reasoning \u002F planning \u002F execution 全流程",[109,885,886,889],{},[24,887,888],{},"沙盒","：代码执行加 Docker 隔离，防止误操作系统文件",[109,891,892,895],{},[24,893,894],{},"API key 管理","：多用户场景需要做 key 池 + 用量限制",[20,897,898],{},"这些 Manus 都内置了，OpenManus 需要你自己搭。这是\"自托管自由\"的代价。",[99,900,902],{"id":901},"openmanus-rl研究向分支","OpenManus-RL：研究向分支",[20,904,905],{},"OpenManus 项目下有一个强化学习分支——OpenManus-RL，提供 RL-based 微调方法优化 agent 性能。这对研究 \u002F 学术场景有价值：你可以用 RL 训练自己的 agent 策略，跑出比默认配置更好的任务完成率。普通用户主仓库已经够用，不需要碰 RL 分支。",[16,907,908],{"id":908},"适用场景",[99,910,912],{"id":911},"_1-自托管-manus-风格-agent","1. 自托管 Manus 风格 Agent",[20,914,915],{},"你是开发者，想在自己的机器上跑类 Manus 的通用 Agent，不想数据上云、不想等邀请码、不想付订阅费。OpenManus 是这个场景的最佳选择——MIT 协议、完整能力、社区活跃。",[99,917,919],{"id":918},"_2-研究-学术-教育","2. 研究 \u002F 学术 \u002F 教育",[20,921,922],{},"你在研究通用 Agent 架构、多 agent 协作、浏览器自动化、RL 微调。OpenManus 的代码结构清晰（MetaGPT 团队工程质量），可以作为学习教材和实验平台。OpenManus-RL 分支提供了 RL 微调的研究入口。",[99,924,926],{"id":925},"_3-隐私敏感场景","3. 隐私敏感场景",[20,928,929],{},"企业内部数据、医疗、金融、法律——数据不能上 Manus 商业云。OpenManus 完全自托管，可以接本地 Ollama 模型实现零数据外泄。这是 Manus 永远做不到的。",[99,931,933],{"id":932},"_4-中国大陆开发者","4. 中国大陆开发者",[20,935,936],{},"Manus 已官方屏蔽中国大陆访问。OpenManus 搭配 Qwen VL Plus \u002F DeepSeek 可以全本地化运行，中文支持自然，不受网络和账号限制。",[99,938,940],{"id":939},"_5-高度定制需求","5. 高度定制需求",[20,942,943],{},"你需要 Agent 做 Manus 不支持的事情：自定义工具、自定义 agent 策略、接入特定 MCP server、跑在特定硬件上。OpenManus 的 BaseTool 基类和模块化架构让定制成本远低于自己从零搭。",[16,945,946],{"id":946},"不推荐场景",[99,948,950],{"id":949},"_1-非开发者-不会-python","1. 非开发者 \u002F 不会 Python",[20,952,953,954,957,958,963,964,968],{},"OpenManus 没有 GUI（Web UI 监控在做但不完整），所有操作在终端里。你需要会 Python 基础、懂虚拟环境、能看 traceback 排错。如果你看到 ",[94,955,956],{},"pip install"," 就头疼，选 ",[959,960,962],"a",{"href":961},"\u002Fagent\u002Fgeneral\u002Fgenspark.html","Genspark"," 或 ",[959,965,967],{"href":966},"\u002Fagent\u002Fgeneral\u002Fflowith.html","Flowith"," 更合适。",[99,970,972],{"id":971},"_2-要开箱即用-上手即跑","2. 要开箱即用 \u002F 上手即跑",[20,974,975],{},"OpenManus 从 git clone 到跑通第一个任务，最快也要 20-30 分钟（装 Python、装依赖、装 Playwright、配 API key、试任务）。Manus 注册后 1 分钟就能提交任务。要\"开箱即用\"的体验，OpenManus 不是答案。",[99,977,979],{"id":978},"_3-生产级稳定","3. 生产级稳定",[20,981,982],{},"项目演进快，main 分支偶尔 break，文档滞后新功能 1-2 个月。生产部署必须 pin commit + 自己加监控 + 错误恢复。如果你的业务不能接受偶发的 breaking change，选商业产品。",[99,984,986],{"id":985},"_4-深度研究-高引用密度","4. 深度研究 \u002F 高引用密度",[20,988,989],{},"如果你需要 Manus 那种\"带 20+ 来源引用的完整 Markdown 报告\"的深度研究输出，OpenManus 目前的引用质量和密度还不够。它的多 agent 没有 Manus 的引用检查子 agent，输出质量取决于底层模型。严肃的研究报告产出，还是 Manus 更合适。",[99,991,993],{"id":992},"_5-团队协作-共享-workspace","5. 团队协作 \u002F 共享 workspace",[20,995,996,997,999,1000,1002],{},"OpenManus 是单机单用户的。没有共享 workspace、没有团队协作 UI、没有权限管理。团队场景用 ",[959,998,967],{"href":966}," Team \u002F ",[959,1001,962],{"href":961}," Team 更合适。",[16,1004,1005],{"id":1005},"价格与运行成本",[20,1007,1008],{},"OpenManus 本体完全免费（MIT 协议）。真实成本 = LLM API 调用费。",[1010,1011,1012,1028],"table",{},[1013,1014,1015],"thead",{},[1016,1017,1018,1022,1025],"tr",{},[1019,1020,1021],"th",{},"模型",[1019,1023,1024],{},"一次中等任务（10-20 步）",[1019,1026,1027],{},"月度估算（每天 2-3 任务）",[1029,1030,1031,1043,1054,1065,1076],"tbody",{},[1016,1032,1033,1037,1040],{},[1034,1035,1036],"td",{},"GPT-4o",[1034,1038,1039],{},"$0.10-0.50",[1034,1041,1042],{},"$6-45",[1016,1044,1045,1048,1051],{},[1034,1046,1047],{},"GPT-4o-mini",[1034,1049,1050],{},"$0.01-0.05",[1034,1052,1053],{},"$0.6-4.5",[1016,1055,1056,1059,1062],{},[1034,1057,1058],{},"Claude Sonnet 4",[1034,1060,1061],{},"$0.15-0.60",[1034,1063,1064],{},"$9-54",[1016,1066,1067,1070,1073],{},[1034,1068,1069],{},"DeepSeek-V3",[1034,1071,1072],{},"$0.02-0.10",[1034,1074,1075],{},"$1.2-9",[1016,1077,1078,1081,1084],{},[1034,1079,1080],{},"本地 Qwen2.5 32B（vLLM \u002F Ollama）",[1034,1082,1083],{},"$0",[1034,1085,1086],{},"$0（电费忽略）",[20,1088,1089,1092,1093,1095],{},[24,1090,1091],{},"省钱策略","：日常任务用 GPT-4o-mini 或 DeepSeek，复杂任务切 GPT-4o \u002F Claude Opus。设 ",[94,1094,876],{}," 防止 agent 失控烧 token。本地 Qwen2.5 32B + vLLM 可以实现全本地 + 零成本，但需要 16GB+ 显存。",[16,1097,1099],{"id":1098},"faq","FAQ",[99,1101,1103],{"id":1102},"openmanus-和-manus-是什么关系","OpenManus 和 Manus 是什么关系？",[20,1105,1106,1107,1110],{},"Manus 是 Butterfly Effect 出品的商业 \u002F 邀请制通用 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。",[24,1108,1109],{},"不是 Manus 官方出品","，是独立团队的开源实现。",[99,1112,1114],{"id":1113},"openmanus-能完全替代-manus-吗","OpenManus 能完全替代 Manus 吗？",[20,1116,1117],{},"不能。OpenManus 缺少 Manus 的三个核心优势：云端异步执行（关掉浏览器任务继续跑）、GAIA SOTA 准确率（>65% vs OpenManus 的 60-70% 体感）、引用检查子 agent。但 OpenManus 有 Manus 做不到的：自托管 + 数据隐私 + 任意模型 + 任意定制 + 零订阅费。两者是互补关系，不是替代关系。",[99,1119,1121],{"id":1120},"openmanus-和-langchain-autogpt-crewai-怎么定位","OpenManus 和 LangChain \u002F AutoGPT \u002F CrewAI 怎么定位？",[20,1123,1124],{},"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（架构陈旧）；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。",[99,1126,1128],{"id":1127},"生产部署要注意什么","生产部署要注意什么？",[20,1130,1131],{},"pin commit 用（main 分支偶尔 break）；加监控 + 错误重试 + 任务超时；代码执行加 Docker 沙盒；多用户场景做 API key 池 + 用量限制；Playwright 浏览器任务设 max_steps 防止失控烧 token。OpenManus 本身不提供这些生产级设施，需要你自己搭。",[16,1133,1135],{"id":1134},"aiho-推荐结论","AIHO 推荐结论",[20,1137,1138,1139,1142],{},"OpenManus 是 2026 年开源通用 Agent 里",[24,1140,1141],{},"最完整的现成实现","——52k stars 不是白来的。多 agent + Playwright + MCP + DataAnalysis + RL 分支，能力栈一站全开。MetaGPT 团队的工程背景保证了架构质量，模块化设计让定制和扩展成本可控。",[20,1144,34,1145,1148,1149,1152],{},[24,1146,1147],{},"不是 Manus 的免费平替","。Manus 的核心价值——云端异步执行、GAIA SOTA 准确率、引用检查子 agent、开箱即用的 Web 界面——这些 OpenManus 目前都还差一截。OpenManus 的价值在于",[24,1150,1151],{},"自由度和可控性","：自托管、数据本地、任意模型、任意定制、零订阅费。",[20,1154,1155,1158],{},[24,1156,1157],{},"选 OpenManus 如果","：你是开发者 \u002F 研究者，想自托管 Agent，隐私敏感，能接受部署门槛和运维成本。",[20,1160,1161,1164,1165,1167,1168,1172],{},[24,1162,1163],{},"别选 OpenManus 如果","：要开箱即用（去 ",[959,1166,962],{"href":961},"）、要深度研究引用密度（去 ",[959,1169,1171],{"href":1170},"\u002Fagent\u002Fgeneral\u002Fmanus.html","Manus","）、不会 Python（去 GUI 工具）、要生产级稳定（去商业产品）。",[20,1174,1175,1176,1179],{},"最好的用法是",[24,1177,1178],{},"两个都用","：Manus 做严肃的深度研究产出，OpenManus 做本地自托管的日常 Agent 任务和定制开发。两者不冲突，反而互补。",[16,1181,1182],{"id":1182},"相关阅读",[378,1184,1185,1191,1197,1203,1208],{},[109,1186,1187],{},[959,1188,1190],{"href":1189},"\u002Fagent\u002Fgeneral\u002Fopenmanus.html","OpenManus 工具卡：开源版 Manus",[109,1192,1193],{},[959,1194,1196],{"href":1195},"\u002Fcompare\u002Fmanus-vs-openmanus.html","Manus vs OpenManus：通用 Agent 原版 vs 开源版怎么选",[109,1198,1199],{},[959,1200,1202],{"href":1201},"\u002Freview\u002Fmanus-deep-review.html","Manus 深度评测：通用 Agent 天花板值不值 $20\u002F月",[109,1204,1205],{},[959,1206,1207],{"href":1170},"Manus 工具卡：通用 Agent 天花板",[109,1209,1210],{},[959,1211,1212],{"href":961},"Genspark 工具卡：AI 搜索 + Agent",[1214,1215,1216],"style",{},"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: 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深度评测：MetaGPT 核心团队 2025-03 推出的开源 Manus 替代方案，52k+ GitHub stars，MIT 协议。本文写它真正解决的问题、Agent 能力、浏览器操控、代码执行、与 Manus 原版的差距、部署门槛、适用场景和 5 类不推荐场景。AIHO 编辑部基于官方文档与社区反馈整理。","md",{},"\u002Freview\u002Fopenmanus-deep-review","2026-07-04",[1277,1278,1279],"agent\u002Fgeneral\u002Fopenmanus","agent\u002Fgeneral\u002Fmanus","agent\u002Fgeneral\u002Fgenspark",{"title":11,"description":1271},"review\u002Fopenmanus-deep-review",[1283,1284,1285,1286],"OpenManus","AI Agent","开源","深度评测","想自托管复刻 Manus 体验 + 不愿等邀请码 + 隐私敏感的开发者首选。52k stars 验证了社区认同度，多 agent + Playwright + MCP 工具栈一站全开。但无 GUI、文档滞后、生产级稳定不够——要上手即用选 Genspark，要深度研究选 Manus 原版。","7jQUdudOqnkwXNy7qBIxZx3GkiL__V9wzlP9bVFRjxA",[1290,1893,2855],{"id":1291,"title":962,"alternatives":1292,"api_compatible":1296,"body":1297,"category":1827,"chinese_friendly":183,"cover":1828,"description":1829,"domestic":1830,"extension":1272,"faq":1831,"free":1830,"github":1296,"languages":1844,"meta":1848,"models":1296,"navigation":179,"notSuitable":1296,"opensource":1830,"path":1849,"pillar":1850,"platforms":1851,"priceTable":1855,"pricing":1866,"published":1867,"relatedPlaybooks":1868,"relatedReviews":1296,"score":1870,"self_host":1830,"seo":1871,"seoTitle":1296,"slug":1279,"sources":1872,"stem":1882,"suitable":1296,"tagline":1883,"tags":1884,"updated":1875,"verdict":1890,"website":1891,"__hash__":1892},"tools\u002Ftools\u002Fagent\u002Fgeneral\u002Fgenspark.md",[1293,1294,1295],"agent\u002Fgeneral\u002Fflowith","agent\u002Fdesktop\u002Fclaude-desktop","agent\u002Fgeneral\u002Falice",null,{"type":13,"value":1298,"toc":1815},[1299,1303,1306,1309,1312,1386,1389,1415,1421,1425,1430,1453,1458,1484,1487,1507,1510,1670,1673,1729,1733,1759,1761,1781,1784],[16,1300,1302],{"id":1301},"tldr","TL;DR",[20,1304,1305],{},"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。",[20,1307,1308],{},"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）。",[16,1310,1311],{"id":1311},"核心能力",[378,1313,1314,1320,1326,1332,1338,1344,1350,1356,1362,1368,1374,1380],{},[109,1315,1316,1319],{},[24,1317,1318],{},"Super Agent","：自主规划 + 多步执行 + 工具调用",[109,1321,1322,1325],{},[24,1323,1324],{},"多 Agent 架构","：搜索 \u002F 研究 \u002F 内容 \u002F 调用 \u002F 数据分析 agent 协同",[109,1327,1328,1331],{},[24,1329,1330],{},"Sparkpage","：可视化研究输出（含图表 + 引用 + 可分享链接）",[109,1333,1334,1337],{},[24,1335,1336],{},"AI Slides","：10-15 张幻灯片含图表",[109,1339,1340,1343],{},[24,1341,1342],{},"AI Sites","：完整 landing page 一键生成",[109,1345,1346,1349],{},[24,1347,1348],{},"AI Video","：30-60s 短视频生成",[109,1351,1352,1355],{},[24,1353,1354],{},"AI Pods","：10 分钟 AI podcast 生成",[109,1357,1358,1361],{},[24,1359,1360],{},"Call For Me","：调用真实电话完成预订 \u002F 跟进",[109,1363,1364,1367],{},[24,1365,1366],{},"AI Sheets","：CSV 数据分析 + 自动图表",[109,1369,1370,1373],{},[24,1371,1372],{},"Plus 不限聊天","：o3-Pro \u002F Claude \u002F Gemini 顶级模型 chat 无限",[109,1375,1376,1379],{},[24,1377,1378],{},"iOS \u002F Android App","：移动端可用",[109,1381,1382,1385],{},[24,1383,1384],{},"企业 SSO \u002F API","（Pro+）：集成内部系统",[16,1387,1388],{"id":1388},"价格",[378,1390,1391,1397,1403,1409],{},[109,1392,1393,1396],{},[24,1394,1395],{},"Free","：100-200 daily credits（~3-8 简单任务）",[109,1398,1399,1402],{},[24,1400,1401],{},"Plus","：$25\u002F月（年付 $240，~$20\u002F月）；12,000 月度 credits + 顶级模型不限 chat",[109,1404,1405,1408],{},[24,1406,1407],{},"Pro","：$249\u002F月（年付 $2388，~$199\u002F月）；125,000 credits + 优先 + 早期 + 高端 agent",[109,1410,1411,1414],{},[24,1412,1413],{},"Extra credits","：仅 Pro 可买，$10 \u002F 5,000 credits",[1416,1417,1418],"blockquote",{},[20,1419,1420],{},"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。",[16,1422,1424],{"id":1423},"实测营销-销售-内容生产","实测（营销 \u002F 销售 \u002F 内容生产）",[20,1426,1427],{},[24,1428,1429],{},"亮点：",[378,1431,1432,1435,1438,1441,1444,1447,1450],{},[109,1433,1434],{},"一句话『做一份 B2B SaaS 竞品分析 + 5 张幻灯片 + landing page』全自动跑通",[109,1436,1437],{},"AI Sheets 处理 CSV 数据 + 自动出图 + 写入 Sparkpage 一步到位",[109,1439,1440],{},"Call For Me 跟进客户 \u002F 餐厅预订实际能用，时效感最强",[109,1442,1443],{},"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",[109,1445,1446],{},"多 Agent 在复杂任务上确实优于单 LLM 的 ChatGPT",[109,1448,1449],{},"Sparkpage 输出可直接分享 + 嵌入",[109,1451,1452],{},"ARR $250M 增长说明产品市场契合度（PMF）非常好",[20,1454,1455],{},[24,1456,1457],{},"踩坑：",[378,1459,1460,1463,1466,1469,1472,1475,1478,1481],{},[109,1461,1462],{},"credit 消耗不透明，复杂任务前难估算成本",[109,1464,1465],{},"用户反馈 customer support + refund 有问题",[109,1467,1468],{},"生成内容偶尔『AI 味』重，要人工编辑润色",[109,1470,1471],{},"引用 \u002F 来源不如 Perplexity 严谨，学术场景慎用",[109,1473,1474],{},"AI Sites \u002F Video 编辑灵活度低，定制要走外部工具",[109,1476,1477],{},"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",[109,1479,1480],{},"Call For Me 隐私 \u002F 合规要小心（GDPR \u002F 加州 CCPA \u002F 录音同意）",[109,1482,1483],{},"Pro $249 月价格陡，中端断档",[16,1485,1486],{"id":1486},"上手",[106,1488,1489,1492,1495,1498,1501,1504],{},[109,1490,1491],{},"genspark.ai 注册 → Free 拿 100-200 daily credits",[109,1493,1494],{},"Super Agent 试『一句话目标』：『做一份 AI Agent 2026 竞品分析 Sparkpage + 5 张 Slides』",[109,1496,1497],{},"等 5-10 分钟看 Sparkpage 输出 → 编辑 → 分享链接",[109,1499,1500],{},"AI Sheets 上传 CSV → 自动出图 → 集成进 Sparkpage",[109,1502,1503],{},"Call For Me 试一次小餐厅预订（自有号码 + 同意）",[109,1505,1506],{},"Plus $25 \u002F 月体验完整 → 决定 Pro",[16,1508,1509],{"id":1509},"对比",[1010,1511,1512,1529],{},[1013,1513,1514],{},[1016,1515,1516,1519,1521,1523,1526],{},[1019,1517,1518],{},"维度",[1019,1520,962],{},[1019,1522,967],{},[1019,1524,1525],{},"Perplexity Pro",[1019,1527,1528],{},"ChatGPT Plus",[1029,1530,1531,1547,1562,1576,1590,1606,1621,1637,1653],{},[1016,1532,1533,1535,1538,1541,1544],{},[1034,1534,1318],{},[1034,1536,1537],{},"✅ 多 Agent",[1034,1539,1540],{},"✅ Neo 自治",[1034,1542,1543],{},"–",[1034,1545,1546],{},"Operator",[1016,1548,1549,1552,1555,1558,1560],{},[1034,1550,1551],{},"可视化输出",[1034,1553,1554],{},"✅ Sparkpage",[1034,1556,1557],{},"✅ 画布",[1034,1559,1543],{},[1034,1561,1543],{},[1016,1563,1564,1567,1570,1572,1574],{},[1034,1565,1566],{},"真实电话调用",[1034,1568,1569],{},"✅ Call For Me",[1034,1571,1543],{},[1034,1573,1543],{},[1034,1575,1543],{},[1016,1577,1578,1581,1584,1586,1588],{},[1034,1579,1580],{},"一键建站",[1034,1582,1583],{},"✅ AI Sites",[1034,1585,1543],{},[1034,1587,1543],{},[1034,1589,1543],{},[1016,1591,1592,1595,1598,1601,1603],{},[1034,1593,1594],{},"视频 \u002F 音频生成",[1034,1596,1597],{},"✅ Video \u002F Pods",[1034,1599,1600],{},"部分",[1034,1602,1543],{},[1034,1604,1605],{},"Sora（独立）",[1016,1607,1608,1611,1614,1616,1619],{},[1034,1609,1610],{},"引用透明",[1034,1612,1613],{},"中",[1034,1615,1543],{},[1034,1617,1618],{},"✅ 顶级",[1034,1620,1600],{},[1016,1622,1623,1626,1629,1632,1635],{},[1034,1624,1625],{},"起价",[1034,1627,1628],{},"$25\u002F月（Plus）",[1034,1630,1631],{},"$19.90\u002F月",[1034,1633,1634],{},"$20\u002F月",[1034,1636,1634],{},[1016,1638,1639,1642,1645,1648,1650],{},[1034,1640,1641],{},"最贵",[1034,1643,1644],{},"$249\u002F月",[1034,1646,1647],{},"$499.90\u002F月",[1034,1649,1634],{},[1034,1651,1652],{},"$200\u002F月",[1016,1654,1655,1658,1661,1664,1667],{},[1034,1656,1657],{},"适合",[1034,1659,1660],{},"一站式 Super Agent",[1034,1662,1663],{},"画布深度",[1034,1665,1666],{},"搜索引用",[1034,1668,1669],{},"通用",[16,1671,1672],{"id":1672},"避坑",[378,1674,1675,1681,1687,1693,1699,1705,1711,1717,1723],{},[109,1676,1677,1680],{},[24,1678,1679],{},"Plus 先用一个月","：评估实际 credit 消耗再决定升 Pro",[109,1682,1683,1686],{},[24,1684,1685],{},"复杂任务前估算 credit","：AI Video \u002F Pods \u002F Sites 烧得快",[109,1688,1689,1692],{},[24,1690,1691],{},"Sparkpage 引用要核实","：学术场景不要直接用，把链接打开看原文",[109,1694,1695,1698],{},[24,1696,1697],{},"Call For Me 合规","：欧盟 \u002F 加州录音同意法规要遵守",[109,1700,1701,1704],{},[24,1702,1703],{},"生成内容人工润色","：AI 味需要花 10-20% 时间打磨",[109,1706,1707,1710],{},[24,1708,1709],{},"地域限制","：在受限国家用 VPN + 海外卡，企业合规要走法务",[109,1712,1713,1716],{},[24,1714,1715],{},"AI Sites 不可深度编辑","：要灵活定制走 Webflow \u002F Framer + AI 工具",[109,1718,1719,1722],{},[24,1720,1721],{},"Pro 不要直接上","：Plus 重度可能仍够用，$200 差价省下来",[109,1724,1725,1728],{},[24,1726,1727],{},"Extra credits 性价比低","：$10 \u002F 5k credits 不如直接升 Pro",[16,1730,1732],{"id":1731},"适合-不适合","适合 \u002F 不适合",[378,1734,1735,1738,1741,1744,1747,1750,1753,1756],{},[109,1736,1737],{},"✅ 营销 \u002F 销售 \u002F 内容创作者 + 要全栈输出",[109,1739,1740],{},"✅ Plus $25 重度多模型不限 chat 用户",[109,1742,1743],{},"✅ 要真实电话调用 \u002F 一键建站 \u002F 短视频",[109,1745,1746],{},"✅ 中级专业用户（自由职业 \u002F 小团队）",[109,1748,1749],{},"❌ 纯研究问答（Perplexity 便宜 + 引用强）",[109,1751,1752],{},"❌ 学术严谨场景",[109,1754,1755],{},"❌ 预算极紧",[109,1757,1758],{},"❌ 要画布 + 多线程深度（用 Flowith）",[16,1760,1182],{"id":1182},[378,1762,1763,1769,1775],{},[109,1764,1765],{},[959,1766,1768],{"href":1767},"\u002Ftools\u002Fagent\u002Fgeneral\u002Fflowith","Flowith 评测",[109,1770,1771],{},[959,1772,1774],{"href":1773},"\u002Ftools\u002Fagent\u002Fdesktop\u002Fclaude-desktop","Claude Desktop 评测",[109,1776,1777],{},[959,1778,1780],{"href":1779},"\u002Ftools\u002Fagent\u002Fgeneral\u002Falice","Alice 评测",[16,1782,1783],{"id":1783},"来源",[106,1785,1786,1794,1801,1808],{},[109,1787,1788,1789],{},"Genspark Pricing 官方 ",[959,1790,1791],{"href":1791,"rel":1792},"https:\u002F\u002Fgenspark.ai\u002Fpricing",[1793],"nofollow",[109,1795,1796,1797],{},"WebCraft — Genspark 2026 Review（Plus \u002F Pro \u002F credit 消耗）",[959,1798,1799],{"href":1799,"rel":1800},"https:\u002F\u002Fwebscraft.org\u002Fblog\u002Fgenspark-ai-oglyad-superagent-yakiy-avtonomno-stvoryuye-sayti-prezentatsiyi?lang=en",[1793],[109,1802,1803,1804],{},"Rimo — Genspark 2026 + ARR $250M 数据 ",[959,1805,1806],{"href":1806,"rel":1807},"https:\u002F\u002Frimo.app\u002Fen\u002Fblogs\u002Fgenspark-ai_en-US",[1793],[109,1809,1810,1811],{},"Lindy — Genspark Features 2026 Tested ",[959,1812,1813],{"href":1813,"rel":1814},"https:\u002F\u002Fwww.lindy.ai\u002Fblog\u002Fgenspark-ai-features",[1793],{"title":151,"searchDepth":176,"depth":176,"links":1816},[1817,1818,1819,1820,1821,1822,1823,1824,1825,1826],{"id":1301,"depth":165,"text":1302},{"id":1311,"depth":165,"text":1311},{"id":1388,"depth":165,"text":1388},{"id":1423,"depth":165,"text":1424},{"id":1486,"depth":165,"text":1486},{"id":1509,"depth":165,"text":1509},{"id":1672,"depth":165,"text":1672},{"id":1731,"depth":165,"text":1732},{"id":1182,"depth":165,"text":1182},{"id":1783,"depth":165,"text":1783},"general","\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。",false,[1832,1835,1838,1841],{"q":1833,"a":1834},"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":1836,"a":1837},"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":1839,"a":1840},"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":1842,"a":1843},"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。",[1845,1846,1847],"en","zh","multi",{},"\u002Ftools\u002Fagent\u002Fgeneral\u002Fgenspark","agent",[1852,1853,1854],"web","ios","android",[1856,1859,1863],{"plan":1395,"price":1083,"features":1857,"notes":1858},"100-200 daily credits + 基础模型 + Sparkpage \u002F 研究","试水",{"plan":1401,"price":1860,"features":1861,"notes":1862},"$25\u002F月","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":1407,"price":1644,"features":1864,"notes":1865},"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","2026-06-19",[1869],"onboarding\u002Fsuper-agent-workflow",{"power":189,"ux":183,"price":176,"cn_support":176,"stability":183},{"title":962,"description":1829},[1873,1876,1878,1880],{"name":1874,"url":1791,"accessed":1875},"Genspark 官网 + Pricing","2026-06-24",{"name":1877,"url":1799,"accessed":1875},"WebCraft — Genspark 2026 Review (Plus \u002F Pro \u002F Use Cases)",{"name":1879,"url":1806,"accessed":1875},"Rimo — Genspark 2026 + ARR $250M",{"name":1881,"url":1813,"accessed":1875},"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",[1885,1886,1887,1888,1889],"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","4z6BpXPUsDg0c4H6t-MNdIES6Gq2hbI5zP2Qe-fl4uU",{"id":1894,"title":1171,"alternatives":1895,"api_compatible":1899,"body":1900,"category":1827,"chinese_friendly":183,"cover":2781,"description":2782,"domestic":1830,"extension":1272,"faq":1296,"free":1830,"github":1296,"languages":2783,"meta":2784,"models":2785,"navigation":179,"notSuitable":2790,"opensource":1830,"path":2795,"pillar":1850,"platforms":2796,"priceTable":2800,"pricing":2815,"published":2816,"relatedPlaybooks":2817,"relatedReviews":2819,"score":2821,"self_host":1830,"seo":2822,"seoTitle":1296,"slug":1278,"sources":2823,"stem":2839,"suitable":2840,"tagline":2846,"tags":2847,"updated":1875,"verdict":2853,"website":2274,"__hash__":2854},"tools\u002Ftools\u002Fagent\u002Fgeneral\u002Fmanus.md",[1279,1896,1897,1898],"agent\u002Fgeneral\u002Felicit","agent\u002Fgeneral\u002Fdevin","agent\u002Fplatform\u002Fdify",[],{"type":13,"value":1901,"toc":2761},[1902,1904,1936,1941,1944,2008,2011,2015,2023,2028,2035,2039,2047,2112,2117,2121,2128,2131,2139,2144,2161,2164,2167,2170,2177,2179,2185,2240,2246,2252,2257,2263,2267,2308,2311,2348,2351,2523,2528,2545,2550,2570,2573,2639,2641,2644,2661,2664,2685,2687,2735,2737,2754],[16,1903,1302],{"id":1301},[40,1905,1907,1922],{"className":1906},[43,44,45],[20,1908,1909,1912,1913,92,1916,1921],{},[24,1910,1911],{},"一句话："," Butterfly Effect（中国创办、新加坡注册）2025-03-06 首发的通用 AI Agent，",[24,1914,1915],{},"邀请码一度被炒到 ¥5 万-10 万",[959,1917,1920],{"href":1918,"rel":1919},"https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FManus_%28AI_agent%29",[1793],"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。",[20,1923,1924,1925,92,1928,1932,1933,530],{},"2025-12 传被 ",[24,1926,1927],{},"Meta 以约 20-30 亿美元收购",[959,1929,1931],{"href":1918,"rel":1930},[1793],"Reuters \u002F AP 报道","），目前仍独立运营。最大价值是 ",[24,1934,1935],{},"deep research 类任务的引用密度和质量明显超过 ChatGPT Deep Research",[1416,1937,1938],{},[20,1939,1940],{},"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。",[16,1942,1943],{"id":1943},"背景与公司",[378,1945,1946,1957,1963,1969,1975,1986,1996,2002],{},[109,1947,1948,1951,1952,1956],{},[24,1949,1950],{},"公司","：Butterfly Effect Pte. Ltd.（",[959,1953,1955],{"href":1918,"rel":1954},[1793],"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）",[109,1958,1959,1962],{},[24,1960,1961],{},"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）",[109,1964,1965,1968],{},[24,1966,1967],{},"前作","：Monica，2023 年发布的浏览器扩展 AI 助手",[109,1970,1971,1974],{},[24,1972,1973],{},"历史融资","：2024 年字节跳动曾出价 ~3000 万美元收购被拒（据 36Kr）",[109,1976,1977,1980,1981,1985],{},[24,1978,1979],{},"Manus 启动","：2024-10 立项，灵感来自 ",[959,1982,1984],{"href":1983},"\u002Fcoding\u002Fide\u002Fcursor.html","Cursor","；名字来自 MIT 拉丁校训 \"Mens et Manus\"（手与脑）",[109,1987,1988,1991,1992],{},[24,1989,1990],{},"首发数据","：2025-03-06 邀请制 beta，7 天内 200 万人候补，",[959,1993,1995],{"href":1918,"rel":1994},[1793],"Demo 视频 20 小时百万播放",[109,1997,1998,2001],{},[24,1999,2000],{},"营收","：2025-08 ARR 约 9000 万美元 → 2025-12 升至 1.25 亿美元",[109,2003,2004,2007],{},[24,2005,2006],{},"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",[16,2009,2010],{"id":2010},"核心特性",[99,2012,2014],{"id":2013},"多-sub-agent-并行架构最大差异化","多 sub-agent 并行架构（最大差异化）",[20,2016,2017,2022],{},[959,2018,2021],{"href":2019,"rel":2020},"https:\u002F\u002Fpick-right.com\u002Ftools\u002Fmanus-ai",[1793],"Pick Right 2026-04 评测"," 描述的架构：",[1416,2024,2025],{},[20,2026,2027],{},"\"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.\"",[20,2029,2030,2031,2034],{},"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 ",[24,2032,2033],{},"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。",[99,2036,2038],{"id":2037},"通用-agent-模式","通用 Agent 模式",[20,2040,2041,2046],{},[959,2042,2045],{"href":2043,"rel":2044},"https:\u002F\u002Fwww.infotech.com\u002Fresearch\u002Fassessing-manus-the-future-of-agentic-ai",[1793],"Info-Tech 评测"," 列出的 GAIA 基准（General AI Assistants）：",[1010,2048,2049,2058],{},[1013,2050,2051],{},[1016,2052,2053,2055],{},[1019,2054,1021],{},[1019,2056,2057],{},"GAIA 准确率",[1029,2059,2060,2072,2080,2088,2096,2104],{},[1016,2061,2062,2067],{},[1034,2063,2064],{},[24,2065,2066],{},"Manus AI",[1034,2068,2069],{},[24,2070,2071],{},">65%（SOTA）",[1016,2073,2074,2077],{},[1034,2075,2076],{},"H2O.ai (h2oGPTe)",[1034,2078,2079],{},"65%",[1016,2081,2082,2085],{},[1034,2083,2084],{},"Google Langfun",[1034,2086,2087],{},"49%",[1016,2089,2090,2093],{},[1034,2091,2092],{},"Microsoft o1",[1034,2094,2095],{},"38%",[1016,2097,2098,2101],{},[1034,2099,2100],{},"OpenAI GPT-4o",[1034,2102,2103],{},"32%",[1016,2105,2106,2109],{},[1034,2107,2108],{},"OpenAI GPT-4 + Plugins",[1034,2110,2111],{},"15-30%",[1416,2113,2114],{},[20,2115,2116],{},"注：基准数据需第三方验证；Manus 官方公布数据，请审慎参考。",[99,2118,2120],{"id":2119},"后台执行-长任务","后台执行 + 长任务",[20,2122,2123,2124,2127],{},"最特别的体验：任务下达后",[24,2125,2126],{},"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。",[99,2129,600],{"id":2130},"web-app-builder",[20,2132,2133,2134,2138],{},"直接生成完整网站和应用，内置数据库 + Stripe 支付 + SEO。但 ",[959,2135,2137],{"href":2019,"rel":2136},[1793],"Pick Right 评测"," 直白警告：",[1416,2140,2141],{},[20,2142,2143],{},"\"Promising but buggy enough that I wouldn't ship to production from it yet.\"",[20,2145,2146,2147,2151,2152,2151,2156,2160],{},"复杂场景下出 bug 多，目前不建议生产部署，",[959,2148,2150],{"href":2149},"\u002Fcoding\u002Fbuilder\u002Fbolt-new.html","Bolt.new"," \u002F ",[959,2153,2155],{"href":2154},"\u002Fcoding\u002Fbuilder\u002Flovable.html","Lovable",[959,2157,2159],{"href":2158},"\u002Fcoding\u002Fbuilder\u002Fv0.html","v0"," 仍是 production app building 的更稳选项。",[99,2162,2163],{"id":2163},"桌面应用",[20,2165,2166],{},"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。",[99,2168,2169],{"id":2169},"多模型路由",[20,2171,2172,2173,2176],{},"Claude \u002F Qwen \u002F Manus 自研模型，",[24,2174,2175],{},"按任务步骤自动选","——这是 Manus 跟单一模型 agent 的根本差异。",[16,2178,1005],{"id":1005},[20,2180,2181,2184],{},[959,2182,2137],{"href":2019,"rel":2183},[1793]," 公开档位：",[1010,2186,2187,2199],{},[1013,2188,2189],{},[1016,2190,2191,2194,2196],{},[1019,2192,2193],{},"档位",[1019,2195,1388],{},[1019,2197,2198],{},"关键点",[1029,2200,2201,2210,2219,2229],{},[1016,2202,2203,2205,2207],{},[1034,2204,1395],{},[1034,2206,1083],{},[1034,2208,2209],{},"每日有限 credits，够 1 个高强度任务\u002F天",[1016,2211,2212,2214,2216],{},[1034,2213,1407],{},[1034,2215,1634],{},[1034,2217,2218],{},"大多数付费用户落点；中等 credit + 多模型",[1016,2220,2221,2223,2226],{},[1034,2222,1401],{},[1034,2224,2225],{},"$50\u002F月",[1034,2227,2228],{},"更高 credit + 优先队列，10+ 任务\u002F周",[1016,2230,2231,2234,2237],{},[1034,2232,2233],{},"Pro+ \u002F Team",[1034,2235,2236],{},"最高 $200\u002F月",[1034,2238,2239],{},"最大 credit + 团队空间（功能仍有限）",[20,2241,2242,2245],{},[24,2243,2244],{},"credit 经济学","：每个动作消耗 credits（浏览、代码运行、模型调用都计费）。",[20,2247,2248,59],{},[959,2249,2251],{"href":2019,"rel":2250},[1793],"Pick Right 真实使用反馈",[1416,2253,2254],{},[20,2255,2256],{},"\"Credits run out faster than the pricing page suggests. Heavy users routinely buy credit packs on top of subscriptions.\"",[20,2258,2259,2262],{},[24,2260,2261],{},"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。",[16,2264,2266],{"id":2265},"上手-5-分钟","上手 5 分钟",[106,2268,2269,2277,2280,2293,2296,2302,2305],{},[109,2270,2271,2272],{},"打开 ",[959,2273,2276],{"href":2274,"rel":2275},"https:\u002F\u002Fmanus.im",[1793],"manus.im",[109,2278,2279],{},"账号注册（Google \u002F Apple OAuth 最快，国内手机号注册受限）",[109,2281,2282,2283,2286,2287],{},"给一个完整任务描述（",[24,2284,2285],{},"关键","：不要碎片化指令，给全场景）\n",[146,2288,2291],{"className":2289,"code":2290,"language":507},[505],"\"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",[94,2292,2290],{"__ignoreMap":151},[109,2294,2295],{},"选模型路由（Auto 推荐）",[109,2297,2298,2299,2301],{},"提交后",[24,2300,2126],{},"——任务在云端跑",[109,2303,2304],{},"完成后邮件 \u002F 站内通知",[109,2306,2307],{},"看结果 \u002F 下载交付物（Markdown \u002F Excel \u002F Word \u002F 网页）",[16,2309,2310],{"id":2310},"国内使用注意事项",[106,2312,2313,2323,2329,2335],{},[109,2314,2315,59,2318,2322],{},[24,2316,2317],{},"大陆访问被屏蔽",[959,2319,2321],{"href":1918,"rel":2320},[1793],"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",[109,2324,2325,2328],{},[24,2326,2327],{},"访问需稳定代理","：日韩 \u002F 美国节点",[109,2330,2331,2334],{},[24,2332,2333],{},"账号 \u002F 支付","：海外信用卡（Visa \u002F MasterCard），第三方代付方案有限",[109,2336,2337,2340,2341,2343,2344,2347],{},[24,2338,2339],{},"替代路径","：国内可考虑 ",[959,2342,962],{"href":961}," \u002F Devv \u002F ",[959,2345,2346],{"href":966},"元宝 Yuanbao"," \u002F 秘塔 Metaso 等",[16,2349,2350],{"id":2350},"与同类怎么选",[1010,2352,2353,2377],{},[1013,2354,2355],{},[1016,2356,2357,2359,2361,2367,2371,2374],{},[1019,2358,1518],{},[1019,2360,1171],{},[1019,2362,2363],{},[959,2364,2366],{"href":2365},"\u002Fcoding\u002Fagent\u002Fdevin.html","Devin",[1019,2368,2369],{},[959,2370,962],{"href":961},[1019,2372,2373],{},"ChatGPT Deep Research",[1019,2375,2376],{},"GenAgent",[1029,2378,2379,2398,2418,2437,2451,2468,2485,2503],{},[1016,2380,2381,2384,2387,2390,2393,2396],{},[1034,2382,2383],{},"核心定位",[1034,2385,2386],{},"通用 Agent",[1034,2388,2389],{},"AI 程序员",[1034,2391,2392],{},"AI 搜索 + Agent",[1034,2394,2395],{},"LLM Deep Research",[1034,2397,1669],{},[1016,2399,2400,2403,2406,2409,2412,2415],{},[1034,2401,2402],{},"架构",[1034,2404,2405],{},"多 sub-agent 并行",[1034,2407,2408],{},"单 Agent + 沙盒",[1034,2410,2411],{},"多模型",[1034,2413,2414],{},"单模型",[1034,2416,2417],{},"—",[1016,2419,2420,2423,2426,2429,2432,2435],{},[1034,2421,2422],{},"长任务",[1034,2424,2425],{},"★★★★★ 小时级",[1034,2427,2428],{},"★★★★★",[1034,2430,2431],{},"★★★☆☆",[1034,2433,2434],{},"★★★★☆",[1034,2436,2431],{},[1016,2438,2439,2441,2443,2445,2447,2449],{},[1034,2440,594],{},[1034,2442,2428],{},[1034,2444,2431],{},[1034,2446,2434],{},[1034,2448,2431],{},[1034,2450,2431],{},[1016,2452,2453,2456,2459,2462,2464,2466],{},[1034,2454,2455],{},"App Builder",[1034,2457,2458],{},"⚠️ 有但 buggy",[1034,2460,2461],{},"❌",[1034,2463,2461],{},[1034,2465,2461],{},[1034,2467,2461],{},[1016,2469,2470,2473,2476,2479,2481,2483],{},[1034,2471,2472],{},"中文",[1034,2474,2475],{},"⚠️ 大陆屏蔽",[1034,2477,2478],{},"⚠️",[1034,2480,2428],{},[1034,2482,2478],{},[1034,2484,2434],{},[1016,2486,2487,2489,2492,2495,2498,2501],{},[1034,2488,1388],{},[1034,2490,2491],{},"$20-$200",[1034,2493,2494],{},"$500\u002F月",[1034,2496,2497],{},"$24.99\u002F月",[1034,2499,2500],{},"随 ChatGPT Plus",[1034,2502,2417],{},[1016,2504,2505,2508,2511,2514,2517,2520],{},[1034,2506,2507],{},"适合场景",[1034,2509,2510],{},"research \u002F 数据分析",[1034,2512,2513],{},"写代码 \u002F 修 bug",[1034,2515,2516],{},"信息检索",[1034,2518,2519],{},"单次深度研究",[1034,2521,2522],{},"综合",[20,2524,2525,59],{},[24,2526,2527],{},"选 Manus 如果你",[378,2529,2530,2536,2539,2542],{},[109,2531,2532,2533],{},"重视 research \u002F 数据分析任务的 ",[24,2534,2535],{},"引用密度和结构化输出",[109,2537,2538],{},"想试\"开任务下班、明早看结果\"的工作流",[109,2540,2541],{},"海外 \u002F 能解决账号网络问题",[109,2543,2544],{},"预算 $20-$50\u002F月，重度用户",[20,2546,2547,59],{},[24,2548,2549],{},"别选 Manus 如果你",[378,2551,2552,2561,2564,2567],{},[109,2553,2554,2555,2151,2557,2560],{},"国内裸用（",[959,2556,962],{"href":961},[959,2558,2559],{"href":966},"Yuanbao"," 更顺）",[109,2562,2563],{},"想生产部署 App（Web App Builder bug 多）",[109,2565,2566],{},"团队协作场景（功能不完善）",[109,2568,2569],{},"预算 \u003C $20\u002F月（Free 档够评估，付费档不一定划算）",[16,2571,2572],{"id":2572},"避坑清单",[378,2574,2575,2581,2591,2597,2603,2615,2621,2627],{},[109,2576,2577,2580],{},[24,2578,2579],{},"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",[109,2582,2583,59,2586,2590],{},[24,2584,2585],{},"credit 烧得比官方页面暗示的快",[959,2587,2589],{"href":2019,"rel":2588},[1793],"Pick Right 2026 评测"," 真实反馈，重度用户经常额外买 credit pack",[109,2592,2593,2596],{},[24,2594,2595],{},"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",[109,2598,2599,2602],{},[24,2600,2601],{},"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",[109,2604,2605,2608,2609,2614],{},[24,2606,2607],{},"每次任务从零开始","：没有持久 workspace，不像 ",[959,2610,2613],{"href":2611,"rel":2612},"https:\u002F\u002Fclaude.com",[1793],"Claude Projects"," \u002F ChatGPT Custom GPTs 能跨会话记忆",[109,2616,2617,2620],{},[24,2618,2619],{},"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",[109,2622,2623,2626],{},[24,2624,2625],{},"团队协作能力差","：单用户产品为主，无共享 workspace \u002F 评论 \u002F 审计",[109,2628,2629,2632,2633,2638],{},[24,2630,2631],{},"Meta 收购的不确定性","：Wikipedia 引用 ",[959,2634,2637],{"href":2635,"rel":2636},"https:\u002F\u002Fwww.nytimes.com\u002F2026\u002F03\u002F17\u002Ftechnology\u002Fchina-scrutiny-meta-manus.html",[1793],"纽时 2026-03"," 报道，中国审查 Meta 收购案，长期路线图待观察",[16,2640,1732],{"id":1731},[20,2642,2643],{},"✅ 适合：",[378,2645,2646,2649,2652,2655,2658],{},[109,2647,2648],{},"委托多步骤 research（\"调研 X，产出 Markdown 对比表\"）",[109,2650,2651],{},"CSV \u002F Excel 重的数据分析任务",[109,2653,2654],{},"长任务 + 不想盯着看（小时级）",[109,2656,2657],{},"单兵作战的咨询顾问 \u002F 分析师 \u002F 创业者",[109,2659,2660],{},"对\"通用 Agent 体感天花板\"感兴趣的测试者",[20,2662,2663],{},"❌ 不适合：",[378,2665,2666,2669,2672,2675,2678],{},[109,2667,2668],{},"生产级应用部署",[109,2670,2671],{},"团队协作工作流",[109,2673,2674],{},"预算极敏感（free 档非常受限）",[109,2676,2677],{},"大陆稳定访问需求",[109,2679,2680,2681,2684],{},"需要深度持久上下文（用 ",[959,2682,2613],{"href":2611,"rel":2683},[1793]," \u002F ChatGPT Custom GPT 等）",[16,2686,1182],{"id":1182},[378,2688,2689,2700,2710,2724],{},[109,2690,2691,2692,2151,2694,2696,2697,2699],{},"同类对比：",[959,2693,2366],{"href":2365},[959,2695,962],{"href":961}," \u002F Elicit \u002F ",[959,2698,2559],{"href":966}," \u002F Metaso",[109,2701,2702,2703,2151,2706,2709],{},"概念：",[959,2704,1284],{"href":2705},"\u002Fwiki\u002Fai-agent.html",[959,2707,2708],{"href":2705},"Multi-Agent"," \u002F Computer Use \u002F Deep Research",[109,2711,2712,2713,2151,2716,2151,2720],{},"模型：",[959,2714,1058],{"href":2715},"\u002Fmodels\u002Fclaude-sonnet-4.html",[959,2717,2719],{"href":2718},"\u002Fmodels\u002Fclaude-opus-4.html","Claude Opus 4",[959,2721,2723],{"href":2722},"\u002Fmodels\u002Fqwen-3.html","Qwen3",[109,2725,2726,2727,2151,2731],{},"进阶：",[959,2728,2730],{"href":2729},"\u002Fwiki\u002Fcontext-engineering.html","Context Engineering",[959,2732,2734],{"href":2733},"\u002Fwiki\u002Fprompt-engineering.html","Prompt Engineering",[16,2736,1783],{"id":1783},[378,2738,2739,2745,2748,2751],{},[109,2740,2741,2742],{},"官网：",[959,2743,2274],{"href":2274,"rel":2744},[1793],[109,2746,2747],{},"Wikipedia：\"Manus (AI agent)\" 条目",[109,2749,2750],{},"第三方评测：pick-right.com \u002F hyzenpro.com \u002F infotech.com \u002F weforum.org",[109,2752,2753],{},"媒体报道：Reuters \u002F AP \u002F China Daily \u002F 36Kr \u002F NYT",[20,2755,2756,2757,2760],{},"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 ",[959,2758,2759],{"href":2759},"\u002Fsubmit"," 反馈。",{"title":151,"searchDepth":176,"depth":176,"links":2762},[2763,2764,2765,2773,2774,2775,2776,2777,2778,2779,2780],{"id":1301,"depth":165,"text":1302},{"id":1943,"depth":165,"text":1943},{"id":2010,"depth":165,"text":2010,"children":2766},[2767,2768,2769,2770,2771,2772],{"id":2013,"depth":176,"text":2014},{"id":2037,"depth":176,"text":2038},{"id":2119,"depth":176,"text":2120},{"id":2130,"depth":176,"text":600},{"id":2163,"depth":176,"text":2163},{"id":2169,"depth":176,"text":2169},{"id":1005,"depth":165,"text":1005},{"id":2265,"depth":165,"text":2266},{"id":2310,"depth":165,"text":2310},{"id":2350,"depth":165,"text":2350},{"id":2572,"depth":165,"text":2572},{"id":1731,"depth":165,"text":1732},{"id":1182,"depth":165,"text":1182},{"id":1783,"depth":165,"text":1783},"\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。",[1846,1845],{},[2786,2787,2788,2789],"claude-sonnet-4","claude-opus-4","qwen-max","manus-internal",[2791,2792,2793,2794],"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",[1852,2797,2798,2799],"windows","macos","linux",[2801,2804,2808,2811],{"plan":1395,"price":1083,"limit":2802,"cn_pay":2417,"note":2803},"每日有限 credits，足够每天 1 个 demanding 任务","试水\u002F评估",{"plan":1407,"price":1634,"limit":2805,"cn_pay":2806,"note":2807},"更高 credit + 更长任务时长 + 多模型路由","⚠️ 需海外卡","个人主力档",{"plan":1401,"price":2225,"limit":2809,"cn_pay":2478,"note":2810},"更大 credit 池 + 优先队列","10+ 任务\u002F周",{"plan":2812,"price":1652,"limit":2813,"cn_pay":2478,"note":2814},"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",[2818],"onboarding\u002Fmanus-getting-started",[2820],"manus-deep-review",{"power":189,"ux":189,"price":176,"cn_support":183,"stability":176},{"title":1171,"description":2782},[2824,2826,2829,2831,2833,2836],{"title":2825,"url":2274},"Manus 官网",{"title":2827,"url":2828},"Manus 维基百科","https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FManus_(AI_agent)",{"title":2830,"url":2019},"Manus AI Review 2026 (Pick Right)",{"title":2832,"url":2043},"Info-Tech Manus Assessment",{"title":2834,"url":2835},"WEF Butterfly Effect Page","https:\u002F\u002Fwww.weforum.org\u002Forganizations\u002Fbutterfly-effect",{"title":2837,"url":2838},"HyzenPro 2026 Review","https:\u002F\u002Fhyzenpro.com\u002Fblog\u002Fmanus-ai-review","tools\u002Fagent\u002Fgeneral\u002Fmanus",[2841,2842,2843,2844,2845],"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",[2848,2849,2850,1886,2851,2852],"general-agent","autonomous","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。","AaSPUCWb2nxYWHxSC_BBzBy3OJG69bjyadYk9bISuwc",{"id":2856,"title":1283,"alternatives":2857,"api_compatible":1296,"body":2861,"category":1827,"chinese_friendly":183,"cover":3484,"description":3485,"domestic":1830,"extension":1272,"faq":3486,"free":1830,"github":1296,"languages":3498,"meta":3499,"models":1296,"navigation":179,"notSuitable":1296,"opensource":179,"path":3500,"pillar":1850,"platforms":3501,"priceTable":3503,"pricing":3507,"published":1867,"relatedPlaybooks":3508,"relatedReviews":1296,"score":3510,"self_host":179,"seo":3511,"seoTitle":1296,"slug":1277,"sources":3512,"stem":3521,"suitable":1296,"tagline":3522,"tags":3523,"updated":1875,"verdict":3529,"website":3446,"__hash__":3530},"tools\u002Ftools\u002Fagent\u002Fgeneral\u002Fopenmanus.md",[2858,2859,2860],"agent\u002Fplatform\u002Flangflow","agent\u002Fplatform\u002Fn8n","agent\u002Fprotocol\u002Fcomposio",{"type":13,"value":2862,"toc":3472},[2863,2865,2871,2874,2876,2950,2952,2969,2973,2977,3003,3007,3033,3035,3146,3149,3155,3157,3325,3327,3387,3389,3415,3417,3437,3439,3469],[16,2864,1302],{"id":1301},[20,2866,2867,2868,2870],{},"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（",[94,2869,96],{},"）+ 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。",[20,2872,2873],{},"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）。",[16,2875,1311],{"id":1311},[378,2877,2878,2886,2892,2897,2903,2909,2915,2921,2927,2933,2939,2944],{},[109,2879,2880,59,2883,2885],{},[24,2881,2882],{},"多 agent orchestration",[94,2884,96],{}," 编排多个专门 agent 协作",[109,2887,2888,2891],{},[24,2889,2890],{},"Playwright 浏览器自动化","：截图 + DOM 操作 + 表单填写 + 信息抓取",[109,2893,2894,2896],{},[24,2895,333],{},"：可调用 MCP server（filesystem \u002F GitHub \u002F Postgres）",[109,2898,2899,2902],{},[24,2900,2901],{},"DataAnalysis 模式","：内置 CSV \u002F 数据分析 agent",[109,2904,2905,2908],{},[24,2906,2907],{},"OpenManus-RL","：强化学习微调分支",[109,2910,2911,2914],{},[24,2912,2913],{},"BaseTool 自定义工具","：Python 继承基类快速添加新工具",[109,2916,2917,2920],{},[24,2918,2919],{},"多模态","：文本 + 视觉输入 + 浏览器截图回环",[109,2922,2923,2926],{},[24,2924,2925],{},"多 LLM provider","：GPT-4o \u002F Claude 3.5 \u002F Qwen VL Plus \u002F DeepSeek \u002F Gemini",[109,2928,2929,2932],{},[24,2930,2931],{},"核心 agent 引擎","：reasoning + planning + execution 三阶段",[109,2934,2935,2938],{},[24,2936,2937],{},"Web UI 监控","：实时看 AI thinking process",[109,2940,2941,2943],{},[24,2942,612],{},"：步骤拆解 + 执行树",[109,2945,2946,2949],{},[24,2947,2948],{},"MIT 协议","：个人 + 商用全免费",[16,2951,1388],{"id":1388},[378,2953,2954,2960,2963,2966],{},[109,2955,2956,2959],{},[24,2957,2958],{},"Free \u002F OSS","：$0；MIT 协议",[109,2961,2962],{},"真实成本 = LLM API（GPT-4o ~$5\u002FM input + $15\u002FM output \u002F Claude \u002F Qwen 等）",[109,2964,2965],{},"一次中等任务（10-20 步）API 费用 $0.05-0.5",[109,2967,2968],{},"本地 Qwen2.5 32B \u002F DeepSeek 走 vLLM \u002F Ollama 路径 $0",[16,2970,2972],{"id":2971},"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）",[20,2974,2975],{},[24,2976,1429],{},[378,2978,2979,2982,2985,2988,2991,2994,2997,3000],{},[109,2980,2981],{},"52k stars 印证社区认同度 + 活跃度",[109,2983,2984],{},"MetaGPT 团队背景保证架构质量",[109,2986,2987],{},"多 agent 协作场景比单 agent 实现稳得多",[109,2989,2990],{},"Playwright 浏览器自动化非常完整",[109,2992,2993],{},"MCP 协议接入打通 Claude \u002F Cursor 生态",[109,2995,2996],{},"DataAnalysis 内置 agent 模式开箱即用",[109,2998,2999],{},"中文支持自然（Qwen VL Plus 接入）",[109,3001,3002],{},"自托管 + 数据本地，隐私 \u002F 合规友好",[20,3004,3005],{},[24,3006,1457],{},[378,3008,3009,3012,3015,3018,3021,3024,3027,3030],{},[109,3010,3011],{},"项目演进快，breaking change 偶发（pin commit 跑生产）",[109,3013,3014],{},"文档滞后新功能 1-2 个月",[109,3016,3017],{},"无官方 GUI，监控 UI 在做但不完整",[109,3019,3020],{},"需要 Python 3.12+ + Playwright 依赖（首次安装 chromium 慢）",[109,3022,3023],{},"LLM API 配置非平凡（多 provider \u002F key \u002F 模型选择）",[109,3025,3026],{},"浏览器任务遇到 CAPTCHA \u002F 反爬偶尔卡死",[109,3028,3029],{},"中文 prompt 效果依赖底层模型",[109,3031,3032],{},"生产部署需自己加监控 \u002F 错误恢复 \u002F 重试",[16,3034,1486],{"id":1486},[146,3036,3038],{"className":148,"code":3037,"language":150,"meta":151,"style":151},"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",[94,3039,3040,3048,3054,3075,3085,3093,3097,3102,3110,3115,3119,3124,3131,3135,3140],{"__ignoreMap":151},[155,3041,3042,3044,3046],{"class":157,"line":158},[155,3043,691],{"class":168},[155,3045,694],{"class":172},[155,3047,697],{"class":172},[155,3049,3050,3052],{"class":157,"line":165},[155,3051,702],{"class":267},[155,3053,705],{"class":172},[155,3055,3056,3058,3060,3062,3065,3068,3070,3072],{"class":157,"line":176},[155,3057,719],{"class":168},[155,3059,722],{"class":267},[155,3061,725],{"class":172},[155,3063,3064],{"class":172}," .venv",[155,3066,3067],{"class":196}," && ",[155,3069,733],{"class":267},[155,3071,736],{"class":172},[155,3073,3074],{"class":161},"  # 或 .venv\\Scripts\\activate\n",[155,3076,3077,3079,3081,3083],{"class":157,"line":183},[155,3078,754],{"class":168},[155,3080,471],{"class":172},[155,3082,759],{"class":267},[155,3084,762],{"class":172},[155,3086,3087,3089,3091],{"class":157,"line":189},[155,3088,778],{"class":168},[155,3090,471],{"class":172},[155,3092,474],{"class":172},[155,3094,3095],{"class":157,"line":200},[155,3096,180],{"emptyLinePlaceholder":179},[155,3098,3099],{"class":157,"line":293},[155,3100,3101],{"class":161},"# 配 LLM API\n",[155,3103,3104,3106,3108],{"class":157,"line":314},[155,3105,799],{"class":168},[155,3107,802],{"class":172},[155,3109,805],{"class":172},[155,3111,3112],{"class":157,"line":320},[155,3113,3114],{"class":161},"# 编辑 config.toml 填 OPENAI \u002F ANTHROPIC \u002F DEEPSEEK key\n",[155,3116,3117],{"class":157,"line":751},[155,3118,180],{"emptyLinePlaceholder":179},[155,3120,3121],{"class":157,"line":765},[155,3122,3123],{"class":161},"# 单 agent 模式\n",[155,3125,3126,3128],{"class":157,"line":770},[155,3127,169],{"class":168},[155,3129,3130],{"class":172}," main.py\n",[155,3132,3133],{"class":157,"line":8},[155,3134,180],{"emptyLinePlaceholder":179},[155,3136,3137],{"class":157,"line":785},[155,3138,3139],{"class":161},"# 多 agent 模式\n",[155,3141,3142,3144],{"class":157,"line":790},[155,3143,169],{"class":168},[155,3145,173],{"class":172},[20,3147,3148],{},"试任务示例：",[146,3150,3153],{"className":3151,"code":3152,"language":507},[505],"> 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",[94,3154,3152],{"__ignoreMap":151},[16,3156,1509],{"id":1509},[1010,3158,3159,3176],{},[1013,3160,3161],{},[1016,3162,3163,3165,3167,3170,3173],{},[1019,3164,1518],{},[1019,3166,1283],{},[1019,3168,3169],{},"LangChain",[1019,3171,3172],{},"AutoGPT",[1019,3174,3175],{},"CrewAI",[1029,3177,3178,3195,3208,3222,3238,3251,3265,3279,3296,3309],{},[1016,3179,3180,3183,3186,3189,3192],{},[1034,3181,3182],{},"形态",[1034,3184,3185],{},"现成 agent 实现",[1034,3187,3188],{},"building blocks",[1034,3190,3191],{},"早期通用 agent",[1034,3193,3194],{},"多 agent 框架",[1016,3196,3197,3199,3202,3204,3206],{},[1034,3198,546],{},[1034,3200,3201],{},"✅ Playwright",[1034,3203,1543],{},[1034,3205,1600],{},[1034,3207,1543],{},[1016,3209,3210,3213,3216,3218,3220],{},[1034,3211,3212],{},"MCP",[1034,3214,3215],{},"✅",[1034,3217,1600],{},[1034,3219,1543],{},[1034,3221,1543],{},[1016,3223,3224,3227,3230,3233,3235],{},[1034,3225,3226],{},"多 agent",[1034,3228,3229],{},"✅ orchestration",[1034,3231,3232],{},"需自搭",[1034,3234,2461],{},[1034,3236,3237],{},"✅ 旗舰",[1016,3239,3240,3243,3245,3247,3249],{},[1034,3241,3242],{},"DataAnalysis 内置",[1034,3244,3215],{},[1034,3246,1543],{},[1034,3248,1543],{},[1034,3250,1543],{},[1016,3252,3253,3256,3259,3261,3263],{},[1034,3254,3255],{},"RL 微调",[1034,3257,3258],{},"✅ OpenManus-RL",[1034,3260,1543],{},[1034,3262,1543],{},[1034,3264,1543],{},[1016,3266,3267,3270,3273,3275,3277],{},[1034,3268,3269],{},"协议",[1034,3271,3272],{},"MIT",[1034,3274,3272],{},[1034,3276,3272],{},[1034,3278,3272],{},[1016,3280,3281,3284,3287,3290,3293],{},[1034,3282,3283],{},"Stars",[1034,3285,3286],{},"52k+",[1034,3288,3289],{},"100k+",[1034,3291,3292],{},"170k+",[1034,3294,3295],{},"30k+",[1016,3297,3298,3300,3302,3305,3307],{},[1034,3299,1486],{},[1034,3301,1613],{},[1034,3303,3304],{},"难",[1034,3306,1613],{},[1034,3308,1613],{},[1016,3310,3311,3313,3316,3319,3322],{},[1034,3312,1657],{},[1034,3314,3315],{},"自托管 Manus 复刻",[1034,3317,3318],{},"底层 framework",[1034,3320,3321],{},"学习经典",[1034,3323,3324],{},"多 agent 协作",[16,3326,1672],{"id":1672},[378,3328,3329,3335,3345,3351,3357,3363,3369,3375,3381],{},[109,3330,3331,3334],{},[24,3332,3333],{},"pin commit 用生产","：项目演进快，main 分支偶尔 break",[109,3336,3337,3340,3341,3344],{},[24,3338,3339],{},"Playwright 依赖大","：首次 ",[94,3342,3343],{},"playwright install"," 下 chromium 慢，国内走镜像",[109,3346,3347,3350],{},[24,3348,3349],{},"LLM 选择","：日常用 GPT-4o-mini \u002F DeepSeek 省钱，复杂任务切 GPT-4o \u002F Claude Opus",[109,3352,3353,3356],{},[24,3354,3355],{},"本地化中文","：Qwen2.5 VL 32B + vLLM 部署可全本地 + 零成本",[109,3358,3359,3362],{},[24,3360,3361],{},"监控自加","：生产部署要加 prometheus + 错误重试 + 任务超时",[109,3364,3365,3368],{},[24,3366,3367],{},"浏览器反爬","：CAPTCHA 场景搭配 2captcha \u002F human-in-loop",[109,3370,3371,3374],{},[24,3372,3373],{},"OpenManus-RL 分支","：研究场景才需要，普通用户主仓库就够",[109,3376,3377,3380],{},[24,3378,3379],{},"MCP server","：信任来源很重要，能访问的目录 \u002F 工具要审慎",[109,3382,3383,3386],{},[24,3384,3385],{},"多 agent runaway","：复杂任务设 max_steps 防止失控烧 token",[16,3388,1732],{"id":1731},[378,3390,3391,3394,3397,3400,3403,3406,3409,3412],{},[109,3392,3393],{},"✅ 开发者 + 想自托管 Manus 风格 agent",[109,3395,3396],{},"✅ 研究 \u002F 学术 \u002F 教育用通用 agent 学习",[109,3398,3399],{},"✅ 隐私敏感 + 不愿数据上商业云",[109,3401,3402],{},"✅ 中国大陆开发者（Qwen \u002F DeepSeek 本地化）",[109,3404,3405],{},"❌ 非开发者 \u002F 不会 Python + Playwright",[109,3407,3408],{},"❌ 要 GUI \u002F 上手即用",[109,3410,3411],{},"❌ 生产级稳定（文档滞后 + 演进快）",[109,3413,3414],{},"❌ 团队协作 + 共享 workspace（用 Flowith \u002F Genspark Team）",[16,3416,1182],{"id":1182},[378,3418,3419,3425,3431],{},[109,3420,3421],{},[959,3422,3424],{"href":3423},"\u002Ftools\u002Fagent\u002Fplatform\u002Flangflow","Langflow 评测",[109,3426,3427],{},[959,3428,3430],{"href":3429},"\u002Ftools\u002Fagent\u002Fplatform\u002Fn8n","n8n 评测",[109,3432,3433],{},[959,3434,3436],{"href":3435},"\u002Ftools\u002Fagent\u002Fprotocol\u002Fcomposio","Composio 评测",[16,3438,1783],{"id":1783},[106,3440,3441,3448,3455,3462],{},[109,3442,3443,3444],{},"OpenManus GitHub 主仓库 + Foundation Agents 组织 ",[959,3445,3446],{"href":3446,"rel":3447},"https:\u002F\u002Fgithub.com\u002FFoundationAgents\u002FOpenManus",[1793],[109,3449,3450,3451],{},"Foundation Agents — OpenManus 项目介绍 ",[959,3452,3453],{"href":3453,"rel":3454},"https:\u002F\u002Ffoundationagents.org\u002Fprojects\u002Fopenmanus\u002F",[1793],[109,3456,3457,3458],{},"Toolsverse — OpenManus 评测 + 52k stars ",[959,3459,3460],{"href":3460,"rel":3461},"https:\u002F\u002Fthetoolsverse.com\u002Ftools\u002Fopenmanus",[1793],[109,3463,3464,3465],{},"SoloSoft.dev — OpenManus 2026 Framework 综述 ",[959,3466,3467],{"href":3467,"rel":3468},"https:\u002F\u002Fwww.solosoft.dev\u002Fpost\u002Fopenmanus-agent-framework-2026\u002F",[1793],[1214,3470,3471],{},"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":151,"searchDepth":176,"depth":176,"links":3473},[3474,3475,3476,3477,3478,3479,3480,3481,3482,3483],{"id":1301,"depth":165,"text":1302},{"id":1311,"depth":165,"text":1311},{"id":1388,"depth":165,"text":1388},{"id":2971,"depth":165,"text":2972},{"id":1486,"depth":165,"text":1486},{"id":1509,"depth":165,"text":1509},{"id":1672,"depth":165,"text":1672},{"id":1731,"depth":165,"text":1732},{"id":1182,"depth":165,"text":1182},{"id":1783,"depth":165,"text":1783},"\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。",[3487,3489,3492,3495],{"q":1103,"a":3488},"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":3490,"a":3491},"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":3493,"a":3494},"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":3496,"a":3497},"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。",[1845,1846,1847],{},"\u002Ftools\u002Fagent\u002Fgeneral\u002Fopenmanus",[2799,2798,2797,3502],"docker",[3504],{"plan":2958,"price":1083,"features":3505,"notes":3506},"MIT 协议 + 全部功能 + 自托管 + 多 agent + 浏览器 + MCP + DataAnalysis + OpenManus-RL","LLM API 自付","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）",[3509],"onboarding\u002Fopen-source-general-agent",{"power":189,"ux":176,"price":189,"cn_support":183,"stability":176},{"title":1283,"description":3485},[3513,3515,3517,3519],{"name":3514,"url":3446,"accessed":1875},"OpenManus GitHub（FoundationAgents 组织）",{"name":3516,"url":3453,"accessed":1875},"Foundation Agents — OpenManus 项目介绍",{"name":3518,"url":3460,"accessed":1875},"Toolsverse — OpenManus 评测 + 52k stars",{"name":3520,"url":3467,"accessed":1875},"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",[3524,1886,3525,3526,3527,3528],"opensource","browser-automation","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 付费版。","12o_oYQnXHBozXE9Vv-Ii6enzsAMPUKrZZHDoUHQ6xM",1783173060781]