[{"data":1,"prerenderedAt":1729},["ShallowReactive",2],{"header-counts":3,"footer-counts":6,"compare-dify-vs-flowise":8,"compare-a-dify":9,"compare-b-flowise":1170},{"tools":4,"reviews":5},98,27,{"tools":4,"reviews":5,"playbooks":7,"news":5},22,null,{"id":10,"title":11,"alternatives":12,"api_compatible":8,"body":17,"category":1101,"chinese_friendly":385,"cover":1102,"description":1103,"domestic":1104,"extension":1105,"faq":8,"free":1104,"github":331,"languages":1106,"lastVerified":8,"meta":1110,"models":8,"navigation":552,"notSuitable":8,"opensource":552,"path":1111,"pillar":1112,"platforms":1113,"priceTable":1117,"pricing":1134,"published":1135,"relatedPlaybooks":8,"relatedReviews":1136,"score":1141,"self_host":552,"seo":1142,"seoTitle":1143,"slug":1144,"sources":1145,"stem":1157,"suitable":8,"tagline":1158,"tags":1159,"updated":1167,"verdict":1168,"website":1043,"__hash__":1169},"tools\u002Ftools\u002Fagent\u002Fplatform\u002Fdify.md","Dify",[13,14,15,16],"agent\u002Fplatform\u002Fcoze","agent\u002Fplatform\u002Ffastgpt","agent\u002Fplatform\u002Fn8n","agent\u002Fplatform\u002Flangflow",{"type":18,"value":19,"toc":1079},"minimark",[20,25,53,59,62,67,70,145,148,152,155,171,190,194,197,208,212,220,232,236,239,242,246,255,316,323,327,335,405,408,428,432,435,495,501,505,600,603,632,635,776,794,833,836,909,913,916,936,939,968,971,1033,1036,1067,1075],[21,22,24],"h2",{"id":23},"tldr","TL;DR",[26,27,32,40],"div",{"className":28},[29,30,31],"card","p-5","my-4",[33,34,35,39],"p",{},[36,37,38],"strong",{},"一句话："," Dify 是开源 LLMOps 平台的事实标准。GitHub 13 万 star、累计 100 万+ 生产 app（据 chatforest.com 2026 评测引用 Dify 官方数据），把\"可视化工作流编排 + RAG 知识库 + Agent + MCP 协议\"打包成一个 Docker Compose 能跑起来的东西。",[33,41,42,43,46,47,52],{},"最大价值是 ",[36,44,45],{},"完全开源 + 模型不挑食","——同一个工作流里同时调 OpenAI、Anthropic、Ollama 本地、DeepSeek、Qwen 都行。代价是部署比 ",[48,49,51],"a",{"href":50},"\u002Fagent\u002Fplatform\u002Fcoze.html","Coze"," 折腾，新手得读 1-2 小时文档。",[54,55,56],"blockquote",{},[33,57,58],{},"来源说明：本文基于 docs.dify.ai 官方文档、langgenius\u002Fdify GitHub 仓库、第三方评测（besthub.dev \u002F chatforest.com \u002F joshuaopolko.com \u002F zhihu 知名专栏）综合归纳。版本号会变，部署要求请以官方最新文档为准。",[21,60,61],{"id":61},"核心特性",[63,64,66],"h3",{"id":65},"可视化工作流chatflow-workflow","可视化工作流（Chatflow + Workflow）",[33,68,69],{},"Dify 把 LLM 应用拆成两种\"应用类型\"：",[71,72,73,89],"table",{},[74,75,76],"thead",{},[77,78,79,83,86],"tr",{},[80,81,82],"th",{},"类型",[80,84,85],{},"适合场景",[80,87,88],{},"编排范式",[90,91,92,106,119,132],"tbody",{},[77,93,94,100,103],{},[95,96,97],"td",{},[36,98,99],{},"Chatbot",[95,101,102],{},"简单对话机器人",[95,104,105],{},"prompt + tools",[77,107,108,113,116],{},[95,109,110],{},[36,111,112],{},"Agent",[95,114,115],{},"自主多步任务",[95,117,118],{},"ReAct \u002F Function Calling",[77,120,121,126,129],{},[95,122,123],{},[36,124,125],{},"Chatflow",[95,127,128],{},"对话型工作流（多轮 + 分支）",[95,130,131],{},"节点 DAG，带聊天上下文",[77,133,134,139,142],{},[95,135,136],{},[36,137,138],{},"Workflow",[95,140,141],{},"单次输入→输出（API 模式）",[95,143,144],{},"节点 DAG，无对话状态",[33,146,147],{},"节点类型覆盖：LLM、知识检索、HTTP 请求、代码执行（Python \u002F JS）、条件分支、迭代、变量聚合、参数提取、问题分类——满足\"用拖拽实现可观测的 LLM pipeline\"。",[63,149,151],{"id":150},"rag-知识库","RAG 知识库",[33,153,154],{},"内置完整 RAG 链路：",[156,157,158,162,165,168],"ol",{},[159,160,161],"li",{},"上传文档（PDF \u002F Word \u002F Markdown \u002F 网页）",[159,163,164],{},"自动分块 + embedding（可配置分段策略和 embedding 模型）",[159,166,167],{},"混合检索（向量 + 全文 + 重排）",[159,169,170],{},"引用溯源（回答末尾自动附原文片段）",[33,172,173,174,180,181,184,185,189],{},"注意：根据 ",[48,175,179],{"href":176,"rel":177},"https:\u002F\u002Fzhuanlan.zhihu.com\u002Fp\u002F1887141987838309480",[178],"nofollow","知乎 LLM 实战笔记 2025-03 对比"," 的实测，Dify ",[36,182,183],{},"社区版默认是基础语义检索","，企业版才解锁多路召回 + 重排。RAG 极致精度场景仍推荐 ",[48,186,188],{"href":187},"\u002Fagent\u002Fplatform\u002Ffastgpt.html","FastGPT","（实测准确率高 10+ 个百分点），Dify 胜在工作流而非纯 RAG。",[63,191,193],{"id":192},"模型生态40-提供商","模型生态：40+ 提供商",[33,195,196],{},"Dify 通过插件市场接入主流模型——OpenAI、Anthropic、Google Gemini、Azure、AWS Bedrock、Cohere、xAI、DeepSeek、Qwen、智谱、文心、豆包、月之暗面、Ollama、LM Studio、Replicate、Together AI、OpenRouter……几乎你能数出来的 LLM 提供商都在。",[33,198,199,200,202,203,207],{},"国产模型原生支持（不像 ",[48,201,188],{"href":187}," 需要 ",[48,204,206],{"href":205},"\u002Fcoding\u002Fapi\u002Fone-api.html","OneAPI"," 中转），是 Dify 在国内 toB 场景流行的关键。",[63,209,211],{"id":210},"mcp-协议支持","MCP 协议支持",[33,213,214,215,219],{},"Dify 较早接入了 ",[48,216,218],{"href":217},"\u002Fwiki\u002Fmcp.html","MCP（Model Context Protocol）","，工作流可以直接调 MCP Server 暴露的 tools。意味着你可以让 Dify 工作流：",[221,222,223,226,229],"ul",{},[159,224,225],{},"通过 MCP 调本地 PostgreSQL \u002F SQLite",[159,227,228],{},"通过 MCP 调 GitHub \u002F Slack \u002F Linear",[159,230,231],{},"通过 MCP 调自家内部系统（写一个 MCP Server 即可）",[63,233,235],{"id":234},"api-first","API-first",[33,237,238],{},"每个 app 自动暴露 REST API，参数和返回结构自动生成 OpenAPI Schema。集成到自家产品里不需要写包装代码，给前端 \u002F 微信小程序 \u002F 飞书机器人调用都方便。",[21,240,241],{"id":241},"价格与运行成本",[63,243,245],{"id":244},"云版difyai","云版（dify.ai）",[33,247,248,249,254],{},"根据 ",[48,250,253],{"href":251,"rel":252},"https:\u002F\u002Fwww.tooljunction.io\u002Fai-tools\u002Fdify-ai",[178],"tooljunction.io 2026 评测"," 引用的官方定价：",[71,256,257,270],{},[74,258,259],{},[77,260,261,264,267],{},[80,262,263],{},"套餐",[80,265,266],{},"价格",[80,268,269],{},"主要限制",[90,271,272,283,294,305],{},[77,273,274,277,280],{},[95,275,276],{},"Sandbox",[95,278,279],{},"免费",[95,281,282],{},"200 次模型调用，1 app，5MB 知识库",[77,284,285,288,291],{},[95,286,287],{},"Professional",[95,289,290],{},"$59\u002F月起",[95,292,293],{},"5000 调用\u002F月，多 app，50MB 知识库",[77,295,296,299,302],{},[95,297,298],{},"Team",[95,300,301],{},"$159\u002F月起",[95,303,304],{},"团队协作、SSO",[77,306,307,310,313],{},[95,308,309],{},"Enterprise",[95,311,312],{},"联系销售",[95,314,315],{},"定制 SLA、私有云",[33,317,318,319,322],{},"注意：云版价格只是 Dify 平台费，",[36,320,321],{},"模型 API 费用另算","（自带 OpenAI \u002F Anthropic key）。",[63,324,326],{"id":325},"自托管推荐","自托管（推荐）",[33,328,329,334],{},[48,330,333],{"href":331,"rel":332},"https:\u002F\u002Fgithub.com\u002Flanggenius\u002Fdify",[178],"官方 GitHub 仓库"," 提供 Docker Compose 部署，社区版完全免费可商用：",[336,337,342],"pre",{"className":338,"code":339,"language":340,"meta":341,"style":341},"language-bash shiki shiki-themes github-light github-dark","git clone https:\u002F\u002Fgithub.com\u002Flanggenius\u002Fdify.git\ncd dify\u002Fdocker\ncp .env.example .env\ndocker compose up -d\n# 默认 http:\u002F\u002Flocalhost \u002F 端口可在 .env 调整\n","bash","",[343,344,345,361,371,383,398],"code",{"__ignoreMap":341},[346,347,350,354,358],"span",{"class":348,"line":349},"line",1,[346,351,353],{"class":352},"sScJk","git",[346,355,357],{"class":356},"sZZnC"," clone",[346,359,360],{"class":356}," https:\u002F\u002Fgithub.com\u002Flanggenius\u002Fdify.git\n",[346,362,364,368],{"class":348,"line":363},2,[346,365,367],{"class":366},"sj4cs","cd",[346,369,370],{"class":356}," dify\u002Fdocker\n",[346,372,374,377,380],{"class":348,"line":373},3,[346,375,376],{"class":352},"cp",[346,378,379],{"class":356}," .env.example",[346,381,382],{"class":356}," .env\n",[346,384,386,389,392,395],{"class":348,"line":385},4,[346,387,388],{"class":352},"docker",[346,390,391],{"class":356}," compose",[346,393,394],{"class":356}," up",[346,396,397],{"class":366}," -d\n",[346,399,401],{"class":348,"line":400},5,[346,402,404],{"class":403},"sJ8bj","# 默认 http:\u002F\u002Flocalhost \u002F 端口可在 .env 调整\n",[33,406,407],{},"硬件门槛（社区共识，非官方硬性要求）：",[221,409,410,416,422],{},[159,411,412,415],{},[36,413,414],{},"最低","：2 核 4G，纯外接 API 模式",[159,417,418,421],{},[36,419,420],{},"推荐","：4 核 8G + 至少 30GB 磁盘（向量数据 + 文件存储）",[159,423,424,427],{},[36,425,426],{},"企业","：8 核 16G+，单机日活上千",[63,429,431],{"id":430},"真实-tco","真实 TCO",[33,433,434],{},"按一家中小团队 3 年场景估算（基于上面引用的多份评测交叉对比）：",[71,436,437,450],{},[74,438,439],{},[77,440,441,444,447],{},[80,442,443],{},"成本项",[80,445,446],{},"云版 Professional",[80,448,449],{},"自托管",[90,451,452,463,473,484],{},[77,453,454,457,460],{},[95,455,456],{},"平台费",[95,458,459],{},"~$2,100（3 年）",[95,461,462],{},"$0",[77,464,465,468,470],{},[95,466,467],{},"服务器",[95,469,462],{},[95,471,472],{},"~$50\u002F月 × 36 = $1,800",[77,474,475,478,481],{},[95,476,477],{},"模型 API",[95,479,480],{},"与下同",[95,482,483],{},"与上同",[77,485,486,489,492],{},[95,487,488],{},"运维人力",[95,490,491],{},"0",[95,493,494],{},"约 0.2 人月",[33,496,497,500],{},[36,498,499],{},"结论","：日活 \u003C 100 用云版省心；> 500 或数据敏感场景自托管 ROI 更好。",[21,502,504],{"id":503},"上手-10-分钟","上手 10 分钟",[336,506,508],{"className":338,"code":507,"language":340,"meta":341,"style":341},"# 1. 自托管（社区版）\ngit clone https:\u002F\u002Fgithub.com\u002Flanggenius\u002Fdify.git\ncd dify\u002Fdocker\ncp .env.example .env\ndocker compose up -d\n\n# 2. 浏览器打开 http:\u002F\u002Flocalhost\n#    首次会让你创建 admin 账号\n\n# 3. 进入\"设置 → 模型供应商\"，配置 OpenAI \u002F 国产模型 API key\n\n# 4. 在主界面\"创建空白应用\"，选 Chatflow 或 Workflow\n# 5. 拖入\"开始 → LLM → 结束\"节点试一下基础 prompt\n# 6. 满意了点右上\"发布\"，自动生成 API endpoint\n",[343,509,510,515,523,529,537,547,554,560,566,571,577,582,588,594],{"__ignoreMap":341},[346,511,512],{"class":348,"line":349},[346,513,514],{"class":403},"# 1. 自托管（社区版）\n",[346,516,517,519,521],{"class":348,"line":363},[346,518,353],{"class":352},[346,520,357],{"class":356},[346,522,360],{"class":356},[346,524,525,527],{"class":348,"line":373},[346,526,367],{"class":366},[346,528,370],{"class":356},[346,530,531,533,535],{"class":348,"line":385},[346,532,376],{"class":352},[346,534,379],{"class":356},[346,536,382],{"class":356},[346,538,539,541,543,545],{"class":348,"line":400},[346,540,388],{"class":352},[346,542,391],{"class":356},[346,544,394],{"class":356},[346,546,397],{"class":366},[346,548,550],{"class":348,"line":549},6,[346,551,553],{"emptyLinePlaceholder":552},true,"\n",[346,555,557],{"class":348,"line":556},7,[346,558,559],{"class":403},"# 2. 浏览器打开 http:\u002F\u002Flocalhost\n",[346,561,563],{"class":348,"line":562},8,[346,564,565],{"class":403},"#    首次会让你创建 admin 账号\n",[346,567,569],{"class":348,"line":568},9,[346,570,553],{"emptyLinePlaceholder":552},[346,572,574],{"class":348,"line":573},10,[346,575,576],{"class":403},"# 3. 进入\"设置 → 模型供应商\"，配置 OpenAI \u002F 国产模型 API key\n",[346,578,580],{"class":348,"line":579},11,[346,581,553],{"emptyLinePlaceholder":552},[346,583,585],{"class":348,"line":584},12,[346,586,587],{"class":403},"# 4. 在主界面\"创建空白应用\"，选 Chatflow 或 Workflow\n",[346,589,591],{"class":348,"line":590},13,[346,592,593],{"class":403},"# 5. 拖入\"开始 → LLM → 结束\"节点试一下基础 prompt\n",[346,595,597],{"class":348,"line":596},14,[346,598,599],{"class":403},"# 6. 满意了点右上\"发布\"，自动生成 API endpoint\n",[21,601,602],{"id":602},"国内使用注意事项",[156,604,605,611,617,623],{},[159,606,607,610],{},[36,608,609],{},"云版 dify.ai 直连国内访问稳定但需要付款","——支持国际信用卡 \u002F Stripe",[159,612,613,616],{},[36,614,615],{},"自托管 + 国产模型"," = 完全国内闭环，是 Dify 在国内最大优势",[159,618,619,622],{},[36,620,621],{},"Docker 镜像拉取","：国内可能慢，建议配 Docker registry 镜像（阿里云 \u002F 网易）",[159,624,625,628,629,631],{},[36,626,627],{},"数据合规","：完全自托管时，数据零外泄；某些金融 \u002F 政府客户因此从 ",[48,630,51],{"href":50}," 迁到 Dify",[21,633,634],{"id":634},"与同类怎么选",[71,636,637,660],{},[74,638,639],{},[77,640,641,644,646,650,654],{},[80,642,643],{},"维度",[80,645,11],{},[80,647,648],{},[48,649,51],{"href":50},[80,651,652],{},[48,653,188],{"href":187},[80,655,656],{},[48,657,659],{"href":658},"\u002Fagent\u002Fplatform\u002Fn8n.html","n8n",[90,661,662,678,691,707,721,735,749,762],{},[77,663,664,667,670,673,675],{},[95,665,666],{},"开源",[95,668,669],{},"✅",[95,671,672],{},"❌",[95,674,669],{},[95,676,677],{},"✅（fair-code）",[77,679,680,683,685,687,689],{},[95,681,682],{},"私有部署",[95,684,669],{},[95,686,672],{},[95,688,669],{},[95,690,669],{},[77,692,693,696,699,702,704],{},[95,694,695],{},"上手难度",[95,697,698],{},"★★★☆☆",[95,700,701],{},"★★☆☆☆ 最简单",[95,703,698],{},[95,705,706],{},"★★★★☆",[77,708,709,712,715,717,719],{},[95,710,711],{},"工作流编排",[95,713,714],{},"★★★★★",[95,716,706],{},[95,718,698],{},[95,720,714],{},[77,722,723,726,728,730,732],{},[95,724,725],{},"RAG 精度",[95,727,706],{},[95,729,698],{},[95,731,714],{},[95,733,734],{},"★★☆☆☆",[77,736,737,740,742,744,747],{},[95,738,739],{},"模型生态",[95,741,714],{},[95,743,706],{},[95,745,746],{},"★★★☆☆（OneAPI 中转）",[95,748,706],{},[77,750,751,754,756,758,760],{},[95,752,753],{},"中文场景",[95,755,706],{},[95,757,714],{},[95,759,706],{},[95,761,698],{},[77,763,764,767,769,772,774],{},[95,765,766],{},"字节生态绑定",[95,768,672],{},[95,770,771],{},"✅（飞书\u002F抖音深度集成）",[95,773,672],{},[95,775,672],{},[33,777,778,781,782,787,788,793],{},[36,779,780],{},"怎么选","（基于 ",[48,783,786],{"href":784,"rel":785},"https:\u002F\u002Fwww.besthub.dev\u002Farticles\u002Fcoze-vs-dify-vs-fastgpt-which-ai-agent-platform-fits-your-needs-fa59cf97b798",[178],"BestHub 2025-07"," 和 ",[48,789,792],{"href":790,"rel":791},"https:\u002F\u002Fwww.cnblogs.com\u002Fuulucias\u002Fp\u002F19449008",[178],"博客园 2026-01"," 两份选型指南综合）：",[221,795,796,802,810,817,824],{},[159,797,798,801],{},[36,799,800],{},"数据必须不出内网 + 工作流复杂"," → Dify",[159,803,804,807,808],{},[36,805,806],{},"个人 \u002F 小团队 \u002F 快速原型 + 字节生态"," → ",[48,809,51],{"href":50},[159,811,812,807,815],{},[36,813,814],{},"核心场景就是企业知识库 QA",[48,816,188],{"href":187},[159,818,819,807,822],{},[36,820,821],{},"重点是连接外部 SaaS（Slack \u002F Notion \u002F 数据库）",[48,823,659],{"href":658},[159,825,826,807,829],{},[36,827,828],{},"要画图式表达 LangChain pipeline",[48,830,832],{"href":831},"\u002Fagent\u002Fplatform\u002Flangflow.html","Langflow",[21,834,835],{"id":835},"避坑清单",[221,837,838,844,861,872,885,891,897,903],{},[159,839,840,843],{},[36,841,842],{},"社区版与企业版差距比想象大","：多路召回 \u002F 重排序 \u002F 单点登录 \u002F 审计日志都在企业版。社区版做生产前心里要有数。",[159,845,846,852,853,856,857,860],{},[36,847,848,851],{},[343,849,850],{},".env"," 文件改完忘 restart","：",[343,854,855],{},"docker compose down && up -d","，不是 ",[343,858,859],{},"restart","——后者不重新加载 env。",[159,862,863,852,866,871],{},[36,864,865],{},"大版本升级会破坏数据库 schema",[48,867,870],{"href":868,"rel":869},"https:\u002F\u002Fdocs.dify.ai\u002Fzh-hans",[178],"官方升级文档"," 有详细 migration 步骤，跨大版本（如 0.x → 1.x）务必先备份 PostgreSQL 卷。生产环境强烈建议跑 staging 完整验证后再升。",[159,873,874,877,878,880,881,884],{},[36,875,876],{},"RAG 文件大小社区版默认 15MB","：根据上述知乎实测，超过会失败。改 ",[343,879,850],{}," 的 ",[343,882,883],{},"UPLOAD_FILE_SIZE_LIMIT"," 并重启容器。",[159,886,887,890],{},[36,888,889],{},"代码节点的 Sandbox 性能差","：内置代码执行节点跑在隔离容器里启动慢、内存小。生产高频用建议改成 HTTP 节点调外部服务。",[159,892,893,896],{},[36,894,895],{},"工作流\"迭代节点\"循环上限","：默认 10 次，复杂 ReAct agent 容易撞天花板，需要在节点设置里调高。",[159,898,899,902],{},[36,900,901],{},"Dify Plugin 系统是新东西","：1.0 后引入的 Plugin 体系替代了原来的 Tools\u002FModels 配置方式，老教程可能已过时——以最新官方文档为准。",[159,904,905,908],{},[36,906,907],{},"国内 Docker 拉取镜像慢","：先配国内 registry，否则首次 pull 可能要 30+ 分钟。",[21,910,912],{"id":911},"适合-不适合","适合 \u002F 不适合",[33,914,915],{},"✅ 适合：",[221,917,918,921,924,927,930,933],{},[159,919,920],{},"中大型企业 LLM 中台建设",[159,922,923],{},"需要私有化部署（金融 \u002F 医疗 \u002F 政府）",[159,925,926],{},"想做\"AI 工作流即产品\"的开发团队",[159,928,929],{},"同时需要 RAG + Agent + Workflow 三件套",[159,931,932],{},"想用国产模型 + 国际模型混合编排",[159,934,935],{},"已经接受 Docker + 一定运维投入",[33,937,938],{},"❌ 不适合：",[221,940,941,947,953,956,962],{},[159,942,943,944,946],{},"纯个人玩家做对话机器人（",[48,945,51],{"href":50}," 更快）",[159,948,949,950,952],{},"只想做企业知识库 QA（",[48,951,188],{"href":187}," RAG 更专）",[159,954,955],{},"团队完全没运维能力（云版还行，自托管会踩坑）",[159,957,958,959,961],{},"需要深度对接字节飞书 \u002F 抖音（",[48,960,51],{"href":50}," 原生）",[159,963,964,965,967],{},"工作流核心是连接 100+ SaaS（",[48,966,659],{"href":658}," 节点更全）",[21,969,970],{"id":970},"相关阅读",[221,972,973,985,1003,1022],{},[159,974,975,976,978,979,978,981,978,983],{},"同类对比：",[48,977,51],{"href":50}," \u002F ",[48,980,188],{"href":187},[48,982,659],{"href":658},[48,984,832],{"href":831},[159,986,987,988,978,992,978,996,978,999],{},"概念基础：",[48,989,991],{"href":990},"\u002Fwiki\u002Fai-agent.html","AI Agent",[48,993,995],{"href":994},"\u002Fwiki\u002Frag.html","RAG",[48,997,998],{"href":217},"MCP",[48,1000,1002],{"href":1001},"\u002Fwiki\u002Ffunction-calling.html","Function Calling",[159,1004,1005,1006,978,1010,978,1014,978,1018],{},"模型选型：",[48,1007,1009],{"href":1008},"\u002Fmodels\u002Fgpt-5.html","GPT-5",[48,1011,1013],{"href":1012},"\u002Fmodels\u002Fclaude-sonnet-4.html","Claude Sonnet 4",[48,1015,1017],{"href":1016},"\u002Fmodels\u002Fdeepseek-v3.html","DeepSeek-V3",[48,1019,1021],{"href":1020},"\u002Fmodels\u002Fglm-5.2.html","GLM-5.2",[159,1023,1024,1025,978,1029],{},"进阶：",[48,1026,1028],{"href":1027},"\u002Fwiki\u002Ffine-tuning-vs-rag.html","Fine-tuning vs RAG",[48,1030,1032],{"href":1031},"\u002Fwiki\u002Fcontext-engineering.html","Context Engineering",[21,1034,1035],{"id":1035},"来源",[221,1037,1038,1045,1051,1057,1064],{},[159,1039,1040,1041],{},"官网：",[48,1042,1043],{"href":1043,"rel":1044},"https:\u002F\u002Fdify.ai",[178],[159,1046,1047,1048],{},"中文文档：",[48,1049,868],{"href":868,"rel":1050},[178],[159,1052,1053,1054],{},"GitHub：",[48,1055,331],{"href":331,"rel":1056},[178],[159,1058,1059,1060],{},"官方定价：",[48,1061,1062],{"href":1062,"rel":1063},"https:\u002F\u002Fdify.ai\u002Fpricing",[178],[159,1065,1066],{},"第三方评测：tooljunction.io \u002F chatforest.com \u002F besthub.dev \u002F joshuaopolko.com \u002F 知乎 LLM 实战笔记",[33,1068,1069,1070,1074],{},"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；如发现版本号 \u002F 价格 \u002F 功能与最新官方信息不一致，请通过 ",[48,1071,1073],{"href":1072},"mailto:hello@aiho.net","反馈邮箱"," 反馈。",[1076,1077,1078],"style",{},"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: 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":341,"searchDepth":373,"depth":373,"links":1080},[1081,1082,1089,1094,1095,1096,1097,1098,1099,1100],{"id":23,"depth":363,"text":24},{"id":61,"depth":363,"text":61,"children":1083},[1084,1085,1086,1087,1088],{"id":65,"depth":373,"text":66},{"id":150,"depth":373,"text":151},{"id":192,"depth":373,"text":193},{"id":210,"depth":373,"text":211},{"id":234,"depth":373,"text":235},{"id":241,"depth":363,"text":241,"children":1090},[1091,1092,1093],{"id":244,"depth":373,"text":245},{"id":325,"depth":373,"text":326},{"id":430,"depth":373,"text":431},{"id":503,"depth":363,"text":504},{"id":602,"depth":363,"text":602},{"id":634,"depth":363,"text":634},{"id":835,"depth":363,"text":835},{"id":911,"depth":363,"text":912},{"id":970,"depth":363,"text":970},{"id":1035,"depth":363,"text":1035},"platform","\u002Fimg\u002Ftools\u002Fdify.webp","Dify 2026 真实评测：开源 LLMOps 与 AI Agent 平台，集工作流编排、RAG 知识库、Agent、MCP 和多模型接入于一体。本文对比 Coze、FastGPT、n8n，整理自托管部署、云版价格、适合团队和避坑建议。",false,"md",[1107,1108,1109],"zh","en","ja",{},"\u002Ftools\u002Fagent\u002Fplatform\u002Fdify","agent",[1114,1115,1116,388],"windows","macos","linux",[1118,1122,1126,1130],{"plan":1119,"price":462,"features":1120,"notes":1121},"Self-hosted（开源版）","Docker 一键部署 + 全部核心功能（工作流 \u002F RAG \u002F Agent \u002F MCP）+ 接任意模型 API","私有部署 \u002F 完全免费 \u002F Apache 2.0",{"plan":1123,"price":462,"features":1124,"notes":1125},"Cloud Sandbox（免费云）","官方托管试水档，含基础调用配额","免运维 \u002F 试水 POC",{"plan":1127,"price":290,"features":1128,"notes":1129},"Cloud Professional","更高调用额度 + 团队协作 + 商用支持","商用云首选",{"plan":1131,"price":1132,"features":1133,"notes":312},"Cloud Team \u002F Enterprise","Custom","更大配额 + SLA + 私有部署支持 + 合规","云版 SaaS（免费档 \u002F Professional $59\u002F月起） + 开源自托管完全免费","2026-06-18",[1137,1138,1139,1140],"coze-deep-review","coze-vs-dify","dify-deep-review","fastgpt-deep-review",{"power":400,"ux":385,"price":400,"cn_support":385,"stability":385},{"title":11,"description":1103},"Dify 评测 2026：开源 LLMOps 与 AI Agent 平台，自托管指南","agent\u002Fplatform\u002Fdify",[1146,1148,1150,1152,1154],{"title":1147,"url":868},"Dify 官方文档（中文）",{"title":1149,"url":331},"Dify GitHub",{"title":1151,"url":1062},"Dify 官方定价",{"title":1153,"url":784},"Coze vs Dify vs FastGPT 选型",{"title":1155,"url":1156},"Dify Self-Hosted Guide 2026","https:\u002F\u002Fjoshuaopolko.com\u002Fdify-self-hosted-guide","tools\u002Fagent\u002Fplatform\u002Fdify","开源 LLMOps 平台，私有部署 Agent 首选",[1160,1161,1162,1163,1164,1165,1166],"agent-platform","opensource","self-host","rag","workflow","llmops","mcp","2026-06-24","想私有部署、想接全球任意模型，Dify 是答案。比 Coze 工程化、上手陡一点；比 FastGPT 工作流强、RAG 略弱。","p5aiXfjt5rD0m3qxj903DVwZMIONVWKdagLa7niYhcE",{"id":1171,"title":1172,"alternatives":1173,"api_compatible":8,"body":1174,"category":1101,"chinese_friendly":373,"cover":1705,"description":1706,"domestic":1104,"extension":1105,"faq":8,"free":1104,"github":1689,"languages":1707,"lastVerified":1708,"meta":1709,"models":8,"navigation":552,"notSuitable":8,"opensource":552,"path":1710,"pillar":1112,"platforms":1711,"priceTable":8,"pricing":1712,"published":1713,"relatedPlaybooks":8,"relatedReviews":8,"score":1714,"self_host":1104,"seo":1715,"seoTitle":1716,"slug":1717,"sources":1718,"stem":1721,"suitable":8,"tagline":1722,"tags":1723,"updated":1708,"verdict":1727,"website":1682,"__hash__":1728},"tools\u002Ftools\u002Fagent\u002Fplatform\u002Fflowise.md","Flowise",[16,1144,15],{"type":18,"value":1175,"toc":1692},[1176,1178,1181,1184,1187,1243,1245,1302,1307,1311,1319,1343,1348,1371,1374,1402,1405,1539,1542,1586,1588,1620,1624,1630,1636,1646,1652,1654,1669,1671,1676],[21,1177,24],{"id":23},[33,1179,1180],{},"Flowise 是开源 LLM 流程编排平台（Apache 2.0），拖拽式可视化构建 AI 应用，底层基于 LangChain \u002F LlamaIndex 生态。节点包括 LLM \u002F Chat Model \u002F Embedding \u002F Vector Store \u002F Tool \u002F Agent \u002F Memory 等，连线编排成完整流程。Docker 自托管 + Cloud 云端双模式，导出为 API \u002F 嵌入式聊天组件 \u002F SDK。",[33,1182,1183],{},"适合：不写代码也要搭建 AI 应用的产品\u002F运营团队、快速验证 RAG \u002F Chatbot 原型、LangChain 生态用户可视化探索。不适合：复杂业务逻辑编排（用 n8n \u002F Dify）、大规模并发生产服务、深度定制需求（直接写 LangChain 代码）。",[21,1185,1186],{"id":1186},"核心能力",[221,1188,1189,1195,1201,1207,1213,1219,1225,1231,1237],{},[159,1190,1191,1194],{},[36,1192,1193],{},"可视化拖拽编排","：节点 + 连线构建 LLM 流程，实时预览",[159,1196,1197,1200],{},[36,1198,1199],{},"LangChain 生态","：直接使用 LangChain \u002F LlamaIndex 全部组件",[159,1202,1203,1206],{},[36,1204,1205],{},"丰富节点","：LLM \u002F Chat Model \u002F Embedding \u002F Vector Store \u002F Tool \u002F Agent \u002F Memory \u002F Chain",[159,1208,1209,1212],{},[36,1210,1211],{},"Agent 支持","：Conversational Agent \u002F Tool Calling Agent \u002F ReAct Agent",[159,1214,1215,1218],{},[36,1216,1217],{},"RAG 流程","：文档加载 → 切片 → 嵌入 → 向量存储 → 检索 → 生成，全可视化",[159,1220,1221,1224],{},[36,1222,1223],{},"多向量数据库","：Pinecone \u002F Qdrant \u002F Chroma \u002F Weaviate \u002F Supabase",[159,1226,1227,1230],{},[36,1228,1229],{},"多模型接入","：OpenAI \u002F Claude \u002F Gemini \u002F Azure \u002F Ollama \u002F HuggingFace",[159,1232,1233,1236],{},[36,1234,1235],{},"部署方式","：导出 REST API \u002F 嵌入式聊天组件 \u002F React SDK",[159,1238,1239,1242],{},[36,1240,1241],{},"凭据管理","：API Key 加密存储，支持环境变量",[21,1244,266],{"id":266},[71,1246,1247,1259],{},[74,1248,1249],{},[77,1250,1251,1254,1256],{},[80,1252,1253],{},"方案",[80,1255,266],{},[80,1257,1258],{},"核心功能",[90,1260,1261,1271,1282,1293],{},[77,1262,1263,1266,1268],{},[95,1264,1265],{},"开源版",[95,1267,462],{},[95,1269,1270],{},"完整功能，Apache 2.0，自托管",[77,1272,1273,1276,1279],{},[95,1274,1275],{},"Cloud Starter",[95,1277,1278],{},"$39\u002F月起",[95,1280,1281],{},"托管服务，1 个工作区",[77,1283,1284,1287,1290],{},[95,1285,1286],{},"Cloud Pro",[95,1288,1289],{},"$89\u002F月起",[95,1291,1292],{},"多工作区 + 团队协作 + 高并发",[77,1294,1295,1297,1299],{},[95,1296,309],{},[95,1298,312],{},[95,1300,1301],{},"私有部署 + SSO + 专属支持",[54,1303,1304],{},[33,1305,1306],{},"价格信息基于 2026-07 官网，可能调整。",[21,1308,1310],{"id":1309},"体验与评测资料整理","体验与评测（资料整理）",[54,1312,1313],{},[33,1314,1315,1316],{},"说明：本节基于官方文档与公开评测整理，非本站独立实测环境，具体数据请以官方为准。\n",[36,1317,1318],{},"亮点：",[221,1320,1321,1324,1327,1330,1337,1340],{},[159,1322,1323],{},"拖拽编排体验流畅，LangChain 组件全覆盖，不用写代码也能搭复杂流程",[159,1325,1326],{},"RAG 流程模板开箱即用，上传 PDF + 接 OpenAI 几分钟出问答机器人",[159,1328,1329],{},"嵌入式聊天组件方便，生成一段 JS 代码嵌入网页即可",[159,1331,1332,1333,1336],{},"Docker 部署简单，",[343,1334,1335],{},"docker-compose up"," 一条命令",[159,1338,1339],{},"节点参数可视化配置，temperature \u002F chunk size 等滑块调整直观",[159,1341,1342],{},"社区活跃，模板市场有不少现成的 Chatflow 可复用",[33,1344,1345],{},[36,1346,1347],{},"踩坑：",[221,1349,1350,1353,1356,1359,1362,1365,1368],{},[159,1351,1352],{},"流程复杂后画布混乱，连线交叉难以维护",[159,1354,1355],{},"性能一般——每个请求经过多个节点序列化\u002F反序列化，延迟偏高",[159,1357,1358],{},"错误信息不够友好，节点报错时定位问题费时",[159,1360,1361],{},"升级 LangChain 版本后部分节点可能不兼容",[159,1363,1364],{},"无法版本控制流程结构，团队协作时容易覆盖",[159,1366,1367],{},"并发能力有限，高并发场景需配合队列 + 负载均衡",[159,1369,1370],{},"深度定制仍需写代码——自定义节点门槛不低",[21,1372,1373],{"id":1373},"上手",[156,1375,1376,1383,1390,1393,1396,1399],{},[159,1377,1378,1379,1382],{},"Docker 部署：",[343,1380,1381],{},"docker-compose up -d","（官方提供 docker-compose.yml）",[159,1384,1385,1386,1389],{},"访问 ",[343,1387,1388],{},"http:\u002F\u002Flocalhost:3000","，创建管理员账号",[159,1391,1392],{},"配置 Credential：添加 OpenAI API Key 等凭据",[159,1394,1395],{},"新建 Chatflow → 从空白或模板开始",[159,1397,1398],{},"拖入节点：Chat OpenAI + Conversational Retrieval Chain + Vector Store",[159,1400,1401],{},"连线编排 → 右上角 Save → 测试对话 → 导出 API \u002F 嵌入组件",[21,1403,1404],{"id":1404},"对比",[71,1406,1407,1421],{},[74,1408,1409],{},[77,1410,1411,1413,1415,1417,1419],{},[80,1412,643],{},[80,1414,1172],{},[80,1416,832],{},[80,1418,11],{},[80,1420,659],{},[90,1422,1423,1440,1456,1470,1485,1497,1510,1524],{},[77,1424,1425,1428,1431,1434,1437],{},[95,1426,1427],{},"编排方式",[95,1429,1430],{},"拖拽 Chatflow",[95,1432,1433],{},"拖拽 Flow",[95,1435,1436],{},"拖拽 + YAML",[95,1438,1439],{},"拖拽 Workflow",[77,1441,1442,1445,1448,1450,1453],{},[95,1443,1444],{},"生态",[95,1446,1447],{},"LangChain",[95,1449,1447],{},[95,1451,1452],{},"自有",[95,1454,1455],{},"通用自动化",[77,1457,1458,1460,1463,1465,1467],{},[95,1459,1373],{},[95,1461,1462],{},"低",[95,1464,1462],{},[95,1466,1462],{},[95,1468,1469],{},"中",[77,1471,1472,1474,1477,1479,1482],{},[95,1473,995],{},[95,1475,1476],{},"✅ 模板丰富",[95,1478,669],{},[95,1480,1481],{},"✅ 强",[95,1483,1484],{},"需自建",[77,1486,1487,1489,1491,1493,1495],{},[95,1488,112],{},[95,1490,669],{},[95,1492,669],{},[95,1494,1481],{},[95,1496,669],{},[77,1498,1499,1502,1504,1506,1508],{},[95,1500,1501],{},"API 导出",[95,1503,669],{},[95,1505,669],{},[95,1507,669],{},[95,1509,669],{},[77,1511,1512,1515,1518,1520,1522],{},[95,1513,1514],{},"部署",[95,1516,1517],{},"Docker",[95,1519,1517],{},[95,1521,1517],{},[95,1523,1517],{},[77,1525,1526,1529,1532,1534,1537],{},[95,1527,1528],{},"适合",[95,1530,1531],{},"AI 应用原型",[95,1533,1531],{},[95,1535,1536],{},"生产 AI 应用",[95,1538,1455],{},[21,1540,1541],{"id":1541},"避坑",[221,1543,1544,1550,1556,1562,1568,1574,1580],{},[159,1545,1546,1549],{},[36,1547,1548],{},"流程别太复杂","：超过 15 个节点的 Chatflow 维护成本急升，拆分成多个",[159,1551,1552,1555],{},[36,1553,1554],{},"性能优化","：合并可合并的节点，减少序列化开销",[159,1557,1558,1561],{},[36,1559,1560],{},"版本管理","：定期导出 Chatflow JSON 备份，升级前测兼容性",[159,1563,1564,1567],{},[36,1565,1566],{},"凭据安全","：不要在流程中硬编码 API Key，统一走 Credential 管理",[159,1569,1570,1573],{},[36,1571,1572],{},"并发测试","：上线前压测，Flowise 单实例并发有限",[159,1575,1576,1579],{},[36,1577,1578],{},"LangChain 版本","：关注 Flowise 更新日志，LangChain 大版本升级可能有 breaking change",[159,1581,1582,1585],{},[36,1583,1584],{},"别替代生产框架","：复杂生产应用还是用 Dify 或直接写代码",[21,1587,912],{"id":911},[221,1589,1590,1593,1596,1599,1602,1605,1608,1611,1614,1617],{},[159,1591,1592],{},"✅ 不写代码搭建 RAG \u002F Chatbot 原型",[159,1594,1595],{},"✅ LangChain 生态用户可视化探索",[159,1597,1598],{},"✅ 快速验证 AI 应用概念",[159,1600,1601],{},"✅ 嵌入式聊天组件场景",[159,1603,1604],{},"✅ 教学 \u002F 演示 LLM 流程",[159,1606,1607],{},"❌ 复杂业务逻辑编排（用 n8n \u002F Dify）",[159,1609,1610],{},"❌ 大规模并发生产服务（性能有限）",[159,1612,1613],{},"❌ 深度定制需求（直接写 LangChain 代码）",[159,1615,1616],{},"❌ 需要版本控制 + 团队协作开发（能力有限）",[159,1618,1619],{},"❌ 非 LangChain 生态需求",[21,1621,1623],{"id":1622},"faq","FAQ",[33,1625,1626,1629],{},[36,1627,1628],{},"Q: Flowise 和 Langflow 怎么选？","\nA: 两者定位几乎相同——都是 LangChain 可视化编排。Flowise 界面更简洁、上手稍快、社区模板多。Langflow 由 DataStax 维护、与 LangChain 官方关系更近、组件更新更快。都试试选顺手的即可。",[33,1631,1632,1635],{},[36,1633,1634],{},"Q: Flowise 和 Dify 怎么选？","\nA: Flowise 专注 LLM 流程可视化编排，轻量、原型验证快。Dify 是完整 AI 应用开发平台——工作流 + Agent + RAG + API 管理 + 监控，功能更全更适合生产。做原型选 Flowise，做产品选 Dify。",[33,1637,1638,1641,1642,1645],{},[36,1639,1640],{},"Q: 可以接入本地模型吗？","\nA: 可以。Flowise 支持 Ollama \u002F HuggingFace 本地模型节点，配置 Ollama 地址（",[343,1643,1644],{},"http:\u002F\u002Flocalhost:11434","）即可在流程中使用本地 LLM 和 Embedding 模型。",[33,1647,1648,1651],{},[36,1649,1650],{},"Q: 生产环境能用吗？","\nA: 小规模可以（内部工具 \u002F 低并发场景）。高并发生产环境建议用 Dify 或直接写代码——Flowise 的节点序列化开销和单实例并发限制是瓶颈。如需生产部署，配合 Nginx 负载均衡 + 多实例 + Redis 队列。",[21,1653,970],{"id":970},[33,1655,1656,1660,1661,1660,1665],{},[48,1657,1659],{"href":1658},"\u002Fagent\u002Fplatform\u002Fautogen.html","AutoGen"," · ",[48,1662,1664],{"href":1663},"\u002Fagent\u002Fplatform\u002Fcrewai.html","CrewAI",[48,1666,1668],{"href":1667},"\u002Fagent\u002Fplatform\u002Fragflow.html","RAGFlow",[21,1670,1035],{"id":1035},[54,1672,1673],{},[33,1674,1675],{},"本文的价格、版本号与性能数据均参考以下官方渠道整理，可能随时间变动，请以官方实时信息为准。",[221,1677,1678,1685],{},[159,1679,1680],{},[48,1681,1684],{"href":1682,"rel":1683},"https:\u002F\u002Fflowiseai.com",[178],"官网",[159,1686,1687],{},[48,1688,1691],{"href":1689,"rel":1690},"https:\u002F\u002Fgithub.com\u002FFlowiseAI\u002FFlowise",[178],"GitHub",{"title":341,"searchDepth":373,"depth":373,"links":1693},[1694,1695,1696,1697,1698,1699,1700,1701,1702,1703,1704],{"id":23,"depth":363,"text":24},{"id":1186,"depth":363,"text":1186},{"id":266,"depth":363,"text":266},{"id":1309,"depth":363,"text":1310},{"id":1373,"depth":363,"text":1373},{"id":1404,"depth":363,"text":1404},{"id":1541,"depth":363,"text":1541},{"id":911,"depth":363,"text":912},{"id":1622,"depth":363,"text":1623},{"id":970,"depth":363,"text":970},{"id":1035,"depth":363,"text":1035},"\u002Fimg\u002Ftools\u002Fflowise.webp","Flowise 真实评测：开源 LLM 流程编排平台（Apache 2.0 协议），拖拽式可视化构建 AI 应用，基于 LangChain 生态。支持 Docker 自托管 + Cloud 云端，适合不写代码也能搭建 RAG\u002FAgent\u002FChatbot 流程的团队。",[1108],"2026-07-30",{},"\u002Ftools\u002Fagent\u002Fplatform\u002Fflowise",[1116,388],"Free \u002F 开源（Apache 2.0）\u002F Cloud","2026-07-05",{"power":373,"ux":385,"price":400,"cn_support":373,"stability":373},{"title":1172,"description":1706},"Flowise - 拖拽式 LLM 流程编排评测 | AIHO","agent\u002Fplatform\u002Fflowise",[1719,1720],{"title":1684,"url":1682},{"title":1691,"url":1689},"tools\u002Fagent\u002Fplatform\u002Fflowise","开源 LLM 流程编排，拖拽式构建 AI 应用",[1160,1161,1724,1725,1726],"flow","no-code","langchain","不写代码也要拖拽搭建 RAG \u002F Chatbot \u002F Agent 流程的团队首选，基于 LangChain 生态组件丰富、可视化编排直观，但复杂流程维护难、性能一般、深度定制仍需写代码。","T4ETS7kNZnBNcywZPndJ_ynLVGjoghi1D0jMxZ7sK1A",1785428441082]