[{"data":1,"prerenderedAt":1688},["ShallowReactive",2],{"header-counts":3,"footer-counts":6,"compare-anythingllm-vs-dify":8,"compare-a-anythingllm":9,"compare-b-dify":583},{"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":16,"category":545,"chinese_friendly":531,"cover":546,"description":547,"domestic":548,"extension":549,"faq":8,"free":548,"github":526,"languages":550,"lastVerified":552,"meta":553,"models":8,"navigation":554,"notSuitable":8,"opensource":554,"path":555,"pillar":556,"platforms":557,"priceTable":8,"pricing":562,"published":563,"relatedPlaybooks":8,"relatedReviews":8,"score":564,"self_host":548,"seo":567,"seoTitle":568,"slug":569,"sources":570,"stem":573,"suitable":8,"tagline":574,"tags":575,"updated":552,"verdict":581,"website":518,"__hash__":582},"tools\u002Ftools\u002Fagent\u002Fplatform\u002Fanythingllm.md","AnythingLLM",[13,14,15],"agent\u002Fplatform\u002Fdify","agent\u002Fplatform\u002Ffastgpt","agent\u002Fplatform\u002Flangflow",{"type":17,"value":18,"toc":529},"minimark",[19,24,28,31,34,93,96,151,157,161,169,194,199,219,222,251,254,380,383,424,428,457,461,467,473,479,485,488,504,507,512],[20,21,23],"h2",{"id":22},"tldr","TL;DR",[25,26,27],"p",{},"AnythingLLM 是 Mintplex Labs 出品的开源私有部署 LLM 平台，MIT 协议，主打\"一站式 RAG 知识库 + Agent + 多用户权限管理\"。桌面应用 \u002F Docker 双部署模式，接入 OpenAI \u002F Claude \u002F Ollama \u002F Azure 等任意模型，Workspaces 隔离不同知识库，内置向量数据库。适合需要私有化部署 AI 知识库且不写代码的团队。",[25,29,30],{},"适合：企业内网知识库、团队共享 AI 助手、需要多用户权限控制的私有化场景。不适合：需要复杂 Agent 编排（用 Dify \u002F Langflow）、需要极致 RAG 召回精度（用 RAGFlow \u002F FastGPT）、需要大规模并发生产服务。",[20,32,33],{"id":33},"核心能力",[35,36,37,45,51,57,63,69,75,81,87],"ul",{},[38,39,40,44],"li",{},[41,42,43],"strong",{},"私有化部署","：Docker \u002F 桌面应用（Win\u002FMac\u002FLinux），数据完全在内网",[38,46,47,50],{},[41,48,49],{},"Workspaces 知识库隔离","：不同工作区独立向量库 + 文档 + 对话历史",[38,52,53,56],{},[41,54,55],{},"多用户权限管理","：管理员 \u002F 用户 \u002F 多工作区角色分配，适合团队使用",[38,58,59,62],{},[41,60,61],{},"任意模型接入","：OpenAI \u002F Claude \u002F Azure \u002F Ollama \u002F LM Studio \u002F 本地模型",[38,64,65,68],{},[41,66,67],{},"多向量数据库","：内置 LanceDB，可选 Chroma \u002F Pinecone \u002F Weaviate \u002F Qdrant",[38,70,71,74],{},[41,72,73],{},"文档处理","：PDF \u002F Word \u002F Excel \u002F TXT \u002F Markdown \u002F 网页链接，自动切片 + 向量化",[38,76,77,80],{},[41,78,79],{},"Agent 能力","：内置 Web 搜索 \u002F RAG 搜索 \u002F SQL 查询等工具调用",[38,82,83,86],{},[41,84,85],{},"嵌入向量","：支持自定义 embedding 模型，兼容 OpenAI \u002F 本地嵌入",[38,88,89,92],{},[41,90,91],{},"API 接口","：提供完整 REST API，可集成到外部系统",[20,94,95],{"id":95},"价格",[97,98,99,114],"table",{},[100,101,102],"thead",{},[103,104,105,109,111],"tr",{},[106,107,108],"th",{},"方案",[106,110,95],{},[106,112,113],{},"核心功能",[115,116,117,129,140],"tbody",{},[103,118,119,123,126],{},[120,121,122],"td",{},"开源版",[120,124,125],{},"$0",[120,127,128],{},"完整功能，MIT 协议，自托管",[103,130,131,134,137],{},[120,132,133],{},"Cloud",[120,135,136],{},"$30\u002F月起",[120,138,139],{},"托管服务，免去运维，含团队协作",[103,141,142,145,148],{},[120,143,144],{},"Enterprise",[120,146,147],{},"联系销售",[120,149,150],{},"SSO \u002F 审计日志 \u002F 私有部署支持",[152,153,154],"blockquote",{},[25,155,156],{},"价格信息基于 2026-07 官网，可能调整。",[20,158,160],{"id":159},"体验与评测资料整理","体验与评测（资料整理）",[152,162,163],{},[25,164,165,166],{},"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",[41,167,168],{},"亮点：",[35,170,171,179,182,185,188,191],{},[38,172,173,174,178],{},"Docker 部署极快，一条 ",[175,176,177],"code",{},"docker-compose up"," 起来就能用",[38,180,181],{},"Workspaces 隔离设计实用，不同部门知识库互不干扰",[38,183,184],{},"接 Ollama 本地模型完全离线运行，数据不出内网",[38,186,187],{},"桌面应用适合个人用户，安装即用零配置",[38,189,190],{},"文档上传后自动切片 + 向量化，问答效果在通用场景下可接受",[38,192,193],{},"多用户权限管理是开源 RAG 平台中少有的完整实现",[25,195,196],{},[41,197,198],{},"踩坑：",[35,200,201,204,207,210,213,216],{},[38,202,203],{},"文档切片策略偏简单（固定长度），复杂表格 \u002F 图文混排召回效果一般",[38,205,206],{},"大文件（100MB+ PDF）处理偶尔超时，需调超时参数",[38,208,209],{},"Agent 能力有限，复杂工具链编排不如 Dify",[38,211,212],{},"向量库默认 LanceDB 在数据量大时查询变慢，建议切 Qdrant \u002F Chroma",[38,214,215],{},"UI 偶有卡顿，文档列表加载慢",[38,217,218],{},"中文文档的 OCR 需要额外配置，默认对扫描件支持有限",[20,220,221],{"id":221},"上手",[223,224,225,232,239,242,245,248],"ol",{},[38,226,227,228,231],{},"Docker 部署：",[175,229,230],{},"docker-compose up -d","（官方提供 docker-compose.yml）",[38,233,234,235,238],{},"首次访问 ",[175,236,237],{},"http:\u002F\u002Flocalhost:3001","，创建管理员账号",[38,240,241],{},"Settings → LLM Provider 配置模型（OpenAI API Key 或 Ollama 地址）",[38,243,244],{},"创建 Workspace → 上传文档（PDF\u002FWord\u002FTXT）",[38,246,247],{},"等待文档向量化完成，在 Chat 中开始问答",[38,249,250],{},"Settings → Users 添加团队成员并分配工作区权限",[20,252,253],{"id":253},"对比",[97,255,256,274],{},[100,257,258],{},[103,259,260,263,265,268,271],{},[106,261,262],{},"维度",[106,264,11],{},[106,266,267],{},"Dify",[106,269,270],{},"FastGPT",[106,272,273],{},"Langflow",[115,275,276,291,307,321,337,351,364],{},[103,277,278,281,284,287,289],{},[120,279,280],{},"部署门槛",[120,282,283],{},"极低",[120,285,286],{},"中",[120,288,286],{},[120,290,286],{},[103,292,293,296,299,302,304],{},[120,294,295],{},"多用户权限",[120,297,298],{},"✅ 完整",[120,300,301],{},"✅",[120,303,301],{},[120,305,306],{},"❌",[103,308,309,312,314,317,319],{},[120,310,311],{},"RAG 精度",[120,313,286],{},[120,315,316],{},"高",[120,318,316],{},[120,320,286],{},[103,322,323,326,329,332,334],{},[120,324,325],{},"Agent 编排",[120,327,328],{},"基础",[120,330,331],{},"强",[120,333,286],{},[120,335,336],{},"强（可视化）",[103,338,339,342,345,347,349],{},[120,340,341],{},"模型接入",[120,343,344],{},"丰富",[120,346,344],{},[120,348,344],{},[120,350,344],{},[103,352,353,356,358,360,362],{},[120,354,355],{},"桌面应用",[120,357,301],{},[120,359,306],{},[120,361,306],{},[120,363,306],{},[103,365,366,369,372,375,378],{},[120,367,368],{},"协议",[120,370,371],{},"MIT",[120,373,374],{},"Apache 2.0",[120,376,377],{},"FastGPT Open",[120,379,371],{},[20,381,382],{"id":382},"避坑",[35,384,385,391,397,403,412,418],{},[38,386,387,390],{},[41,388,389],{},"切片策略默认偏简单","：对结构化文档（表格\u002F代码）效果差，可调 chunk size",[38,392,393,396],{},[41,394,395],{},"LanceDB 大数据量变慢","：文档超过 1 万条建议切 Qdrant 或 Chroma",[38,398,399,402],{},[41,400,401],{},"大文件超时","：调整 Docker 超时配置，或拆分文档上传",[38,404,405,408,409],{},[41,406,407],{},"Ollama 连接","：Docker 内访问宿主机 Ollama 需用 ",[175,410,411],{},"host.docker.internal",[38,413,414,417],{},[41,415,416],{},"embedding 模型选择","：中文场景建议用 bge-large-zh 而非默认 OpenAI embedding",[38,419,420,423],{},[41,421,422],{},"不要当生产级 Agent 平台用","：Agent 能力是辅助，复杂编排上 Dify",[20,425,427],{"id":426},"适合-不适合","适合 \u002F 不适合",[35,429,430,433,436,439,442,445,448,451,454],{},[38,431,432],{},"✅ 企业内网私有化 AI 知识库",[38,434,435],{},"✅ 团队共享 AI 助手 + 多用户权限管理",[38,437,438],{},"✅ 接 Ollama 完全离线运行",[38,440,441],{},"✅ 个人桌面端快速体验 RAG",[38,443,444],{},"✅ 需要快速验证 RAG 概念的原型项目",[38,446,447],{},"❌ 需要复杂 Agent 工作流编排（用 Dify \u002F Langflow）",[38,449,450],{},"❌ 需要极致 RAG 召回精度（用 RAGFlow \u002F FastGPT）",[38,452,453],{},"❌ 大规模并发生产服务（架构未做高可用）",[38,455,456],{},"❌ 需要深度文档解析（复杂表格\u002F公式\u002F扫描件）",[20,458,460],{"id":459},"faq","FAQ",[25,462,463,466],{},[41,464,465],{},"Q: AnythingLLM 和 Dify 怎么选？","\nA: AnythingLLM 更轻量，部署快、有桌面应用、多用户权限开箱即用，适合快速搭建团队知识库。Dify 功能更全面，Agent 编排、工作流、API 发布能力更强，适合需要构建复杂 AI 应用的团队。简单知识库选 AnythingLLM，复杂应用选 Dify。",[25,468,469,472],{},[41,470,471],{},"Q: 可以完全离线使用吗？","\nA: 可以。接 Ollama 本地模型 + 用本地 embedding 模型（如 bge-large-zh）+ 内置 LanceDB 向量库，整个系统完全离线运行，数据不出内网。适合数据敏感的企业场景。",[25,474,475,478],{},[41,476,477],{},"Q: 免费开源版有什么限制？","\nA: MIT 协议开源版功能完整，无用户数 \u002F 文档数 \u002F API 调用限制。Cloud 版和 Enterprise 版主要是托管服务和企业管理功能（SSO \u002F 审计日志），功能层面开源版已够用。",[25,480,481,484],{},[41,482,483],{},"Q: 支持中文文档吗？","\nA: 支持，但效果取决于 embedding 模型。默认 OpenAI embedding 对中文尚可，追求精度建议切换 bge-large-zh 或 m3e 模型。OCR 扫描件需额外配置 Tesseract 或接入外部 OCR 服务。",[20,486,487],{"id":487},"相关阅读",[25,489,490,495,496,495,500],{},[491,492,494],"a",{"href":493},"\u002Fagent\u002Fplatform\u002Fragflow.html","RAGFlow"," · ",[491,497,499],{"href":498},"\u002Fagent\u002Fplatform\u002Fflowise.html","Flowise",[491,501,503],{"href":502},"\u002Fagent\u002Fgeneral\u002Fperplexity.html","Perplexity",[20,505,506],{"id":506},"来源",[152,508,509],{},[25,510,511],{},"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。",[35,513,514,522],{},[38,515,516],{},[491,517,521],{"href":518,"rel":519},"https:\u002F\u002Fuseanything.com",[520],"nofollow","官网",[38,523,524],{},[491,525,528],{"href":526,"rel":527},"https:\u002F\u002Fgithub.com\u002FMintplex-Labs\u002Fanything-llm",[520],"GitHub",{"title":530,"searchDepth":531,"depth":531,"links":532},"",3,[533,535,536,537,538,539,540,541,542,543,544],{"id":22,"depth":534,"text":23},2,{"id":33,"depth":534,"text":33},{"id":95,"depth":534,"text":95},{"id":159,"depth":534,"text":160},{"id":221,"depth":534,"text":221},{"id":253,"depth":534,"text":253},{"id":382,"depth":534,"text":382},{"id":426,"depth":534,"text":427},{"id":459,"depth":534,"text":460},{"id":487,"depth":534,"text":487},{"id":506,"depth":534,"text":506},"platform","\u002Fimg\u002Ftools\u002Fanythingllm.webp","AnythingLLM 真实评测：Mintplex Labs 出品的开源私有部署 LLM 平台（MIT 协议），一站式 RAG 知识库 + Agent + 多用户权限管理。支持 Docker\u002F桌面部署，接入 OpenAI\u002FClaude\u002FOllama 等任意模型，适合企业内网私有化 AI 知识库场景。",false,"md",[551],"en","2026-07-30",{},true,"\u002Ftools\u002Fagent\u002Fplatform\u002Fanythingllm","agent",[558,559,560,561],"windows","macos","linux","docker","Free \u002F 开源（MIT）\u002F Cloud","2026-07-05",{"power":531,"ux":565,"price":566,"cn_support":531,"stability":531},4,5,{"title":11,"description":547},"AnythingLLM - 开源私有部署 LLM 平台评测 | AIHO","agent\u002Fplatform\u002Fanythingllm",[571,572],{"title":521,"url":518},{"title":528,"url":526},"tools\u002Fagent\u002Fplatform\u002Fanythingllm","开源私有部署 LLM 平台，一站式 RAG + Agent + 多用户",[576,577,578,579,580],"agent-platform","opensource","self-host","rag","multi-user","需要快速搭建私有化 AI 知识库且要求多用户权限管理的企业团队首选，MIT 协议 + 桌面\u002FDocker 双模式 + 任意模型接入降低了部署门槛，但 RAG 精度和 Agent 编排能力不及 Dify\u002FFastGPT 等专业平台。","iKAMhkQImqK_QZGWLaE4i_9IjAMMpCeSwojp4L34T6A",{"id":584,"title":267,"alternatives":585,"api_compatible":8,"body":588,"category":545,"chinese_friendly":565,"cover":1632,"description":1633,"domestic":548,"extension":549,"faq":8,"free":548,"github":880,"languages":1634,"lastVerified":8,"meta":1637,"models":8,"navigation":554,"notSuitable":8,"opensource":554,"path":1638,"pillar":556,"platforms":1639,"priceTable":1640,"pricing":1657,"published":1658,"relatedPlaybooks":8,"relatedReviews":1659,"score":1664,"self_host":554,"seo":1665,"seoTitle":1666,"slug":13,"sources":1667,"stem":1679,"suitable":8,"tagline":1680,"tags":1681,"updated":1685,"verdict":1686,"website":1574,"__hash__":1687},"tools\u002Ftools\u002Fagent\u002Fplatform\u002Fdify.md",[586,14,587,15],"agent\u002Fplatform\u002Fcoze","agent\u002Fplatform\u002Fn8n",{"type":17,"value":589,"toc":1610},[590,592,617,622,625,630,633,702,705,709,712,726,743,747,750,761,765,773,784,788,791,794,798,807,865,872,876,884,947,950,970,974,977,1036,1042,1046,1140,1143,1172,1175,1312,1330,1368,1371,1444,1446,1449,1469,1472,1501,1503,1565,1567,1598,1606],[20,591,23],{"id":22},[593,594,599,605],"div",{"className":595},[596,597,598],"card","p-5","my-4",[25,600,601,604],{},[41,602,603],{},"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。",[25,606,607,608,611,612,616],{},"最大价值是 ",[41,609,610],{},"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 ",[491,613,615],{"href":614},"\u002Fagent\u002Fplatform\u002Fcoze.html","Coze"," 折腾，新手得读 1-2 小时文档。",[152,618,619],{},[25,620,621],{},"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。",[20,623,624],{"id":624},"核心特性",[626,627,629],"h3",{"id":628},"可视化工作流chatflow-workflow","可视化工作流（Chatflow + Workflow）",[25,631,632],{},"Dify 把 LLM 应用拆成两种\"应用类型\"：",[97,634,635,648],{},[100,636,637],{},[103,638,639,642,645],{},[106,640,641],{},"类型",[106,643,644],{},"适合场景",[106,646,647],{},"编排范式",[115,649,650,663,676,689],{},[103,651,652,657,660],{},[120,653,654],{},[41,655,656],{},"Chatbot",[120,658,659],{},"简单对话机器人",[120,661,662],{},"prompt + tools",[103,664,665,670,673],{},[120,666,667],{},[41,668,669],{},"Agent",[120,671,672],{},"自主多步任务",[120,674,675],{},"ReAct \u002F Function Calling",[103,677,678,683,686],{},[120,679,680],{},[41,681,682],{},"Chatflow",[120,684,685],{},"对话型工作流（多轮 + 分支）",[120,687,688],{},"节点 DAG，带聊天上下文",[103,690,691,696,699],{},[120,692,693],{},[41,694,695],{},"Workflow",[120,697,698],{},"单次输入→输出（API 模式）",[120,700,701],{},"节点 DAG，无对话状态",[25,703,704],{},"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\"。",[626,706,708],{"id":707},"rag-知识库","RAG 知识库",[25,710,711],{},"内置完整 RAG 链路：",[223,713,714,717,720,723],{},[38,715,716],{},"上传文档（PDF \u002F Word \u002F Markdown \u002F 网页）",[38,718,719],{},"自动分块 + embedding（可配置分段策略和 embedding 模型）",[38,721,722],{},"混合检索（向量 + 全文 + 重排）",[38,724,725],{},"引用溯源（回答末尾自动附原文片段）",[25,727,728,729,734,735,738,739,742],{},"注意：根据 ",[491,730,733],{"href":731,"rel":732},"https:\u002F\u002Fzhuanlan.zhihu.com\u002Fp\u002F1887141987838309480",[520],"知乎 LLM 实战笔记 2025-03 对比"," 的实测，Dify ",[41,736,737],{},"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 ",[491,740,270],{"href":741},"\u002Fagent\u002Fplatform\u002Ffastgpt.html","（实测准确率高 10+ 个百分点），Dify 胜在工作流而非纯 RAG。",[626,744,746],{"id":745},"模型生态40-提供商","模型生态：40+ 提供商",[25,748,749],{},"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。",[25,751,752,753,755,756,760],{},"国产模型原生支持（不像 ",[491,754,270],{"href":741}," 需要 ",[491,757,759],{"href":758},"\u002Fcoding\u002Fapi\u002Fone-api.html","OneAPI"," 中转），是 Dify 在国内 toB 场景流行的关键。",[626,762,764],{"id":763},"mcp-协议支持","MCP 协议支持",[25,766,767,768,772],{},"Dify 较早接入了 ",[491,769,771],{"href":770},"\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：",[35,774,775,778,781],{},[38,776,777],{},"通过 MCP 调本地 PostgreSQL \u002F SQLite",[38,779,780],{},"通过 MCP 调 GitHub \u002F Slack \u002F Linear",[38,782,783],{},"通过 MCP 调自家内部系统（写一个 MCP Server 即可）",[626,785,787],{"id":786},"api-first","API-first",[25,789,790],{},"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。",[20,792,793],{"id":793},"价格与运行成本",[626,795,797],{"id":796},"云版difyai","云版（dify.ai）",[25,799,800,801,806],{},"根据 ",[491,802,805],{"href":803,"rel":804},"https:\u002F\u002Fwww.tooljunction.io\u002Fai-tools\u002Fdify-ai",[520],"tooljunction.io 2026 评测"," 引用的官方定价：",[97,808,809,821],{},[100,810,811],{},[103,812,813,816,818],{},[106,814,815],{},"套餐",[106,817,95],{},[106,819,820],{},"主要限制",[115,822,823,834,845,856],{},[103,824,825,828,831],{},[120,826,827],{},"Sandbox",[120,829,830],{},"免费",[120,832,833],{},"200 次模型调用，1 app，5MB 知识库",[103,835,836,839,842],{},[120,837,838],{},"Professional",[120,840,841],{},"$59\u002F月起",[120,843,844],{},"5000 调用\u002F月，多 app，50MB 知识库",[103,846,847,850,853],{},[120,848,849],{},"Team",[120,851,852],{},"$159\u002F月起",[120,854,855],{},"团队协作、SSO",[103,857,858,860,862],{},[120,859,144],{},[120,861,147],{},[120,863,864],{},"定制 SLA、私有云",[25,866,867,868,871],{},"注意：云版价格只是 Dify 平台费，",[41,869,870],{},"模型 API 费用另算","（自带 OpenAI \u002F Anthropic key）。",[626,873,875],{"id":874},"自托管推荐","自托管（推荐）",[25,877,878,883],{},[491,879,882],{"href":880,"rel":881},"https:\u002F\u002Fgithub.com\u002Flanggenius\u002Fdify",[520],"官方 GitHub 仓库"," 提供 Docker Compose 部署，社区版完全免费可商用：",[885,886,890],"pre",{"className":887,"code":888,"language":889,"meta":530,"style":530},"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",[175,891,892,908,917,928,941],{"__ignoreMap":530},[893,894,897,901,905],"span",{"class":895,"line":896},"line",1,[893,898,900],{"class":899},"sScJk","git",[893,902,904],{"class":903},"sZZnC"," clone",[893,906,907],{"class":903}," https:\u002F\u002Fgithub.com\u002Flanggenius\u002Fdify.git\n",[893,909,910,914],{"class":895,"line":534},[893,911,913],{"class":912},"sj4cs","cd",[893,915,916],{"class":903}," dify\u002Fdocker\n",[893,918,919,922,925],{"class":895,"line":531},[893,920,921],{"class":899},"cp",[893,923,924],{"class":903}," .env.example",[893,926,927],{"class":903}," .env\n",[893,929,930,932,935,938],{"class":895,"line":565},[893,931,561],{"class":899},[893,933,934],{"class":903}," compose",[893,936,937],{"class":903}," up",[893,939,940],{"class":912}," -d\n",[893,942,943],{"class":895,"line":566},[893,944,946],{"class":945},"sJ8bj","# 默认 http:\u002F\u002Flocalhost \u002F 端口可在 .env 调整\n",[25,948,949],{},"硬件门槛（社区共识，非官方硬性要求）：",[35,951,952,958,964],{},[38,953,954,957],{},[41,955,956],{},"最低","：2 核 4G，纯外接 API 模式",[38,959,960,963],{},[41,961,962],{},"推荐","：4 核 8G + 至少 30GB 磁盘（向量数据 + 文件存储）",[38,965,966,969],{},[41,967,968],{},"企业","：8 核 16G+，单机日活上千",[626,971,973],{"id":972},"真实-tco","真实 TCO",[25,975,976],{},"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）：",[97,978,979,992],{},[100,980,981],{},[103,982,983,986,989],{},[106,984,985],{},"成本项",[106,987,988],{},"云版 Professional",[106,990,991],{},"自托管",[115,993,994,1004,1014,1025],{},[103,995,996,999,1002],{},[120,997,998],{},"平台费",[120,1000,1001],{},"~$2,100（3 年）",[120,1003,125],{},[103,1005,1006,1009,1011],{},[120,1007,1008],{},"服务器",[120,1010,125],{},[120,1012,1013],{},"~$50\u002F月 × 36 = $1,800",[103,1015,1016,1019,1022],{},[120,1017,1018],{},"模型 API",[120,1020,1021],{},"与下同",[120,1023,1024],{},"与上同",[103,1026,1027,1030,1033],{},[120,1028,1029],{},"运维人力",[120,1031,1032],{},"0",[120,1034,1035],{},"约 0.2 人月",[25,1037,1038,1041],{},[41,1039,1040],{},"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。",[20,1043,1045],{"id":1044},"上手-10-分钟","上手 10 分钟",[885,1047,1049],{"className":887,"code":1048,"language":889,"meta":530,"style":530},"# 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",[175,1050,1051,1056,1064,1070,1078,1088,1094,1100,1106,1111,1117,1122,1128,1134],{"__ignoreMap":530},[893,1052,1053],{"class":895,"line":896},[893,1054,1055],{"class":945},"# 1. 自托管（社区版）\n",[893,1057,1058,1060,1062],{"class":895,"line":534},[893,1059,900],{"class":899},[893,1061,904],{"class":903},[893,1063,907],{"class":903},[893,1065,1066,1068],{"class":895,"line":531},[893,1067,913],{"class":912},[893,1069,916],{"class":903},[893,1071,1072,1074,1076],{"class":895,"line":565},[893,1073,921],{"class":899},[893,1075,924],{"class":903},[893,1077,927],{"class":903},[893,1079,1080,1082,1084,1086],{"class":895,"line":566},[893,1081,561],{"class":899},[893,1083,934],{"class":903},[893,1085,937],{"class":903},[893,1087,940],{"class":912},[893,1089,1091],{"class":895,"line":1090},6,[893,1092,1093],{"emptyLinePlaceholder":554},"\n",[893,1095,1097],{"class":895,"line":1096},7,[893,1098,1099],{"class":945},"# 2. 浏览器打开 http:\u002F\u002Flocalhost\n",[893,1101,1103],{"class":895,"line":1102},8,[893,1104,1105],{"class":945},"#    首次会让你创建 admin 账号\n",[893,1107,1109],{"class":895,"line":1108},9,[893,1110,1093],{"emptyLinePlaceholder":554},[893,1112,1114],{"class":895,"line":1113},10,[893,1115,1116],{"class":945},"# 3. 进入\"设置 → 模型供应商\"，配置 OpenAI \u002F 国产模型 API key\n",[893,1118,1120],{"class":895,"line":1119},11,[893,1121,1093],{"emptyLinePlaceholder":554},[893,1123,1125],{"class":895,"line":1124},12,[893,1126,1127],{"class":945},"# 4. 在主界面\"创建空白应用\"，选 Chatflow 或 Workflow\n",[893,1129,1131],{"class":895,"line":1130},13,[893,1132,1133],{"class":945},"# 5. 拖入\"开始 → LLM → 结束\"节点试一下基础 prompt\n",[893,1135,1137],{"class":895,"line":1136},14,[893,1138,1139],{"class":945},"# 6. 满意了点右上\"发布\"，自动生成 API endpoint\n",[20,1141,1142],{"id":1142},"国内使用注意事项",[223,1144,1145,1151,1157,1163],{},[38,1146,1147,1150],{},[41,1148,1149],{},"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",[38,1152,1153,1156],{},[41,1154,1155],{},"自托管 + 国产模型"," = 完全国内闭环，是 Dify 在国内最大优势",[38,1158,1159,1162],{},[41,1160,1161],{},"Docker 镜像拉取","：国内可能慢，建议配 Docker registry 镜像（阿里云 \u002F 网易）",[38,1164,1165,1168,1169,1171],{},[41,1166,1167],{},"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 ",[491,1170,615],{"href":614}," 迁到 Dify",[20,1173,1174],{"id":1174},"与同类怎么选",[97,1176,1177,1199],{},[100,1178,1179],{},[103,1180,1181,1183,1185,1189,1193],{},[106,1182,262],{},[106,1184,267],{},[106,1186,1187],{},[491,1188,615],{"href":614},[106,1190,1191],{},[491,1192,270],{"href":741},[106,1194,1195],{},[491,1196,1198],{"href":1197},"\u002Fagent\u002Fplatform\u002Fn8n.html","n8n",[115,1200,1201,1215,1228,1244,1258,1271,1285,1298],{},[103,1202,1203,1206,1208,1210,1212],{},[120,1204,1205],{},"开源",[120,1207,301],{},[120,1209,306],{},[120,1211,301],{},[120,1213,1214],{},"✅（fair-code）",[103,1216,1217,1220,1222,1224,1226],{},[120,1218,1219],{},"私有部署",[120,1221,301],{},[120,1223,306],{},[120,1225,301],{},[120,1227,301],{},[103,1229,1230,1233,1236,1239,1241],{},[120,1231,1232],{},"上手难度",[120,1234,1235],{},"★★★☆☆",[120,1237,1238],{},"★★☆☆☆ 最简单",[120,1240,1235],{},[120,1242,1243],{},"★★★★☆",[103,1245,1246,1249,1252,1254,1256],{},[120,1247,1248],{},"工作流编排",[120,1250,1251],{},"★★★★★",[120,1253,1243],{},[120,1255,1235],{},[120,1257,1251],{},[103,1259,1260,1262,1264,1266,1268],{},[120,1261,311],{},[120,1263,1243],{},[120,1265,1235],{},[120,1267,1251],{},[120,1269,1270],{},"★★☆☆☆",[103,1272,1273,1276,1278,1280,1283],{},[120,1274,1275],{},"模型生态",[120,1277,1251],{},[120,1279,1243],{},[120,1281,1282],{},"★★★☆☆（OneAPI 中转）",[120,1284,1243],{},[103,1286,1287,1290,1292,1294,1296],{},[120,1288,1289],{},"中文场景",[120,1291,1243],{},[120,1293,1251],{},[120,1295,1243],{},[120,1297,1235],{},[103,1299,1300,1303,1305,1308,1310],{},[120,1301,1302],{},"字节生态绑定",[120,1304,306],{},[120,1306,1307],{},"✅（飞书\u002F抖音深度集成）",[120,1309,306],{},[120,1311,306],{},[25,1313,1314,1317,1318,1323,1324,1329],{},[41,1315,1316],{},"怎么选","（基于 ",[491,1319,1322],{"href":1320,"rel":1321},"https:\u002F\u002Fwww.besthub.dev\u002Farticles\u002Fcoze-vs-dify-vs-fastgpt-which-ai-agent-platform-fits-your-needs-fa59cf97b798",[520],"BestHub 2025-07"," 和 ",[491,1325,1328],{"href":1326,"rel":1327},"https:\u002F\u002Fwww.cnblogs.com\u002Fuulucias\u002Fp\u002F19449008",[520],"博客园 2026-01"," 两份选型指南综合）：",[35,1331,1332,1338,1346,1353,1360],{},[38,1333,1334,1337],{},[41,1335,1336],{},"数据必须不出内网 + 工作流复杂"," → Dify",[38,1339,1340,1343,1344],{},[41,1341,1342],{},"个人 \u002F 小团队 \u002F 快速原型 + 字节生态"," → ",[491,1345,615],{"href":614},[38,1347,1348,1343,1351],{},[41,1349,1350],{},"核心场景就是企业知识库 QA",[491,1352,270],{"href":741},[38,1354,1355,1343,1358],{},[41,1356,1357],{},"重点是连接外部 SaaS（Slack \u002F Notion \u002F 数据库）",[491,1359,1198],{"href":1197},[38,1361,1362,1343,1365],{},[41,1363,1364],{},"要画图式表达 LangChain pipeline",[491,1366,273],{"href":1367},"\u002Fagent\u002Fplatform\u002Flangflow.html",[20,1369,1370],{"id":1370},"避坑清单",[35,1372,1373,1379,1396,1407,1420,1426,1432,1438],{},[38,1374,1375,1378],{},[41,1376,1377],{},"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。",[38,1380,1381,1387,1388,1391,1392,1395],{},[41,1382,1383,1386],{},[175,1384,1385],{},".env"," 文件改完忘 restart","：",[175,1389,1390],{},"docker compose down && up -d","，不是 ",[175,1393,1394],{},"restart","——后者不重新加载 env。",[38,1397,1398,1387,1401,1406],{},[41,1399,1400],{},"大版本升级会破坏数据库 schema",[491,1402,1405],{"href":1403,"rel":1404},"https:\u002F\u002Fdocs.dify.ai\u002Fzh-hans",[520],"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。",[38,1408,1409,1412,1413,1415,1416,1419],{},[41,1410,1411],{},"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 ",[175,1414,1385],{}," 的 ",[175,1417,1418],{},"UPLOAD_FILE_SIZE_LIMIT"," 并重启容器。",[38,1421,1422,1425],{},[41,1423,1424],{},"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。",[38,1427,1428,1431],{},[41,1429,1430],{},"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。",[38,1433,1434,1437],{},[41,1435,1436],{},"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。",[38,1439,1440,1443],{},[41,1441,1442],{},"国内 Docker 拉取镜像慢","：先配国内 registry，否则首次 pull 可能要 30+ 分钟。",[20,1445,427],{"id":426},[25,1447,1448],{},"✅ 适合：",[35,1450,1451,1454,1457,1460,1463,1466],{},[38,1452,1453],{},"中大型企业 LLM 中台建设",[38,1455,1456],{},"需要私有化部署（金融 \u002F 医疗 \u002F 政府）",[38,1458,1459],{},"想做\"AI 工作流即产品\"的开发团队",[38,1461,1462],{},"同时需要 RAG + Agent + Workflow 三件套",[38,1464,1465],{},"想用国产模型 + 国际模型混合编排",[38,1467,1468],{},"已经接受 Docker + 一定运维投入",[25,1470,1471],{},"❌ 不适合：",[35,1473,1474,1480,1486,1489,1495],{},[38,1475,1476,1477,1479],{},"纯个人玩家做对话机器人（",[491,1478,615],{"href":614}," 更快）",[38,1481,1482,1483,1485],{},"只想做企业知识库 QA（",[491,1484,270],{"href":741}," RAG 更专）",[38,1487,1488],{},"团队完全没运维能力（云版还行，自托管会踩坑）",[38,1490,1491,1492,1494],{},"需要深度对接字节飞书 \u002F 抖音（",[491,1493,615],{"href":614}," 原生）",[38,1496,1497,1498,1500],{},"工作流核心是连接 100+ SaaS（",[491,1499,1198],{"href":1197}," 节点更全）",[20,1502,487],{"id":487},[35,1504,1505,1517,1535,1554],{},[38,1506,1507,1508,1510,1511,1510,1513,1510,1515],{},"同类对比：",[491,1509,615],{"href":614}," \u002F ",[491,1512,270],{"href":741},[491,1514,1198],{"href":1197},[491,1516,273],{"href":1367},[38,1518,1519,1520,1510,1524,1510,1528,1510,1531],{},"概念基础：",[491,1521,1523],{"href":1522},"\u002Fwiki\u002Fai-agent.html","AI Agent",[491,1525,1527],{"href":1526},"\u002Fwiki\u002Frag.html","RAG",[491,1529,1530],{"href":770},"MCP",[491,1532,1534],{"href":1533},"\u002Fwiki\u002Ffunction-calling.html","Function Calling",[38,1536,1537,1538,1510,1542,1510,1546,1510,1550],{},"模型选型：",[491,1539,1541],{"href":1540},"\u002Fmodels\u002Fgpt-5.html","GPT-5",[491,1543,1545],{"href":1544},"\u002Fmodels\u002Fclaude-sonnet-4.html","Claude Sonnet 4",[491,1547,1549],{"href":1548},"\u002Fmodels\u002Fdeepseek-v3.html","DeepSeek-V3",[491,1551,1553],{"href":1552},"\u002Fmodels\u002Fglm-5.2.html","GLM-5.2",[38,1555,1556,1557,1510,1561],{},"进阶：",[491,1558,1560],{"href":1559},"\u002Fwiki\u002Ffine-tuning-vs-rag.html","Fine-tuning vs RAG",[491,1562,1564],{"href":1563},"\u002Fwiki\u002Fcontext-engineering.html","Context Engineering",[20,1566,506],{"id":506},[35,1568,1569,1576,1582,1588,1595],{},[38,1570,1571,1572],{},"官网：",[491,1573,1574],{"href":1574,"rel":1575},"https:\u002F\u002Fdify.ai",[520],[38,1577,1578,1579],{},"中文文档：",[491,1580,1403],{"href":1403,"rel":1581},[520],[38,1583,1584,1585],{},"GitHub：",[491,1586,880],{"href":880,"rel":1587},[520],[38,1589,1590,1591],{},"官方定价：",[491,1592,1593],{"href":1593,"rel":1594},"https:\u002F\u002Fdify.ai\u002Fpricing",[520],[38,1596,1597],{},"第三方评测：tooljunction.io \u002F chatforest.com \u002F besthub.dev \u002F joshuaopolko.com \u002F 知乎 LLM 实战笔记",[25,1599,1600,1601,1605],{},"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 ",[491,1602,1604],{"href":1603},"mailto:hello@aiho.net","反馈邮箱"," 反馈。",[1607,1608,1609],"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":530,"searchDepth":531,"depth":531,"links":1611},[1612,1613,1620,1625,1626,1627,1628,1629,1630,1631],{"id":22,"depth":534,"text":23},{"id":624,"depth":534,"text":624,"children":1614},[1615,1616,1617,1618,1619],{"id":628,"depth":531,"text":629},{"id":707,"depth":531,"text":708},{"id":745,"depth":531,"text":746},{"id":763,"depth":531,"text":764},{"id":786,"depth":531,"text":787},{"id":793,"depth":534,"text":793,"children":1621},[1622,1623,1624],{"id":796,"depth":531,"text":797},{"id":874,"depth":531,"text":875},{"id":972,"depth":531,"text":973},{"id":1044,"depth":534,"text":1045},{"id":1142,"depth":534,"text":1142},{"id":1174,"depth":534,"text":1174},{"id":1370,"depth":534,"text":1370},{"id":426,"depth":534,"text":427},{"id":487,"depth":534,"text":487},{"id":506,"depth":534,"text":506},"\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。",[1635,551,1636],"zh","ja",{},"\u002Ftools\u002Fagent\u002Fplatform\u002Fdify",[558,559,560,561],[1641,1645,1649,1653],{"plan":1642,"price":125,"features":1643,"notes":1644},"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":1646,"price":125,"features":1647,"notes":1648},"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":1650,"price":841,"features":1651,"notes":1652},"Cloud Professional","更高调用额度 + 团队协作 + 商用支持","商用云首选",{"plan":1654,"price":1655,"features":1656,"notes":147},"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",[1660,1661,1662,1663],"coze-deep-review","coze-vs-dify","dify-deep-review","fastgpt-deep-review",{"power":566,"ux":565,"price":566,"cn_support":565,"stability":565},{"title":267,"description":1633},"Dify 评测 2026：开源 LLMOps 与 AI Agent 平台，自托管指南",[1668,1670,1672,1674,1676],{"title":1669,"url":1403},"Dify 官方文档（中文）",{"title":1671,"url":880},"Dify GitHub",{"title":1673,"url":1593},"Dify 官方定价",{"title":1675,"url":1320},"Coze vs Dify vs FastGPT 选型",{"title":1677,"url":1678},"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",[576,577,578,579,1682,1683,1684],"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",1785428440959]