[{"data":1,"prerenderedAt":3431},["ShallowReactive",2],{"header-counts":3,"footer-counts":6,"alt-main-anythingllm":8,"alt-list-anythingllm":599},{"tools":4,"reviews":5},98,27,{"tools":4,"reviews":5,"playbooks":7,"news":5},23,{"id":9,"title":10,"alternatives":11,"api_compatible":15,"body":31,"category":560,"chinese_friendly":546,"cover":561,"description":562,"domestic":563,"extension":564,"faq":565,"free":566,"github":541,"languages":567,"lastVerified":569,"meta":570,"models":565,"navigation":566,"notSuitable":565,"opensource":566,"path":571,"pillar":572,"platforms":573,"priceTable":565,"pricing":578,"published":579,"relatedPlaybooks":565,"relatedReviews":565,"score":580,"self_host":563,"seo":583,"seoTitle":584,"slug":585,"sources":586,"stem":589,"suitable":565,"tagline":590,"tags":591,"updated":569,"verdict":597,"website":533,"__hash__":598},"tools\u002Ftools\u002Fagent\u002Fplatform\u002Fanythingllm.md","AnythingLLM",[12,13,14],"agent\u002Fplatform\u002Fdify","agent\u002Fplatform\u002Ffastgpt","agent\u002Fplatform\u002Flangflow",[16,17,18,19,20,21,22,23,24,25,26,27,28,29,30],"OpenAI","Anthropic","Google","Grok","Mistral","Cohere","阿里通义","百度文心","腾讯混元","Moonshot Kimi","字节豆包","DeepSeek","智谱 GLM","Ollama","Hugging Face",{"type":32,"value":33,"toc":544},"minimark",[34,39,43,46,49,108,111,166,172,176,184,209,214,234,237,266,269,395,398,439,443,472,476,482,488,494,500,503,519,522,527],[35,36,38],"h2",{"id":37},"tldr","TL;DR",[40,41,42],"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。",[40,44,45],{},"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。",[35,47,48],{"id":48},"核心能力",[50,51,52,60,66,72,78,84,90,96,102],"ul",{},[53,54,55,59],"li",{},[56,57,58],"strong",{},"私有化部署","：Docker \u002F 桌面应用（Win\u002FMac\u002FLinux），数据完全在内网",[53,61,62,65],{},[56,63,64],{},"Workspaces 知识库隔离","：不同工作区独立向量库 + 文档 + 对话历史",[53,67,68,71],{},[56,69,70],{},"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",[53,73,74,77],{},[56,75,76],{},"任意模型接入","：OpenAI \u002F Claude \u002F Azure \u002F Ollama \u002F LM Studio \u002F 本地模型",[53,79,80,83],{},[56,81,82],{},"多向量数据库","：内置 LanceDB，可选 Chroma \u002F Pinecone \u002F Weaviate \u002F Qdrant",[53,85,86,89],{},[56,87,88],{},"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",[53,91,92,95],{},[56,93,94],{},"Agent 能力","：内置 Web 搜索 \u002F RAG 搜索 \u002F SQL 查询等工具调用",[53,97,98,101],{},[56,99,100],{},"嵌入向量","：支持自定义 embedding 模型，兼容 OpenAI \u002F 本地嵌入",[53,103,104,107],{},[56,105,106],{},"API 接口","：提供完整 REST API，可集成到外部系统",[35,109,110],{"id":110},"价格",[112,113,114,129],"table",{},[115,116,117],"thead",{},[118,119,120,124,126],"tr",{},[121,122,123],"th",{},"方案",[121,125,110],{},[121,127,128],{},"核心功能",[130,131,132,144,155],"tbody",{},[118,133,134,138,141],{},[135,136,137],"td",{},"开源版",[135,139,140],{},"$0",[135,142,143],{},"完整功能，MIT 协议，自托管",[118,145,146,149,152],{},[135,147,148],{},"Cloud",[135,150,151],{},"$30\u002F月起",[135,153,154],{},"托管服务，免去运维，含团队协作",[118,156,157,160,163],{},[135,158,159],{},"Enterprise",[135,161,162],{},"联系销售",[135,164,165],{},"SSO \u002F 审计日志 \u002F 私有部署支持",[167,168,169],"blockquote",{},[40,170,171],{},"价格信息基于 2026-07 官网，可能调整。",[35,173,175],{"id":174},"体验与评测资料整理","体验与评测（资料整理）",[167,177,178],{},[40,179,180,181],{},"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",[56,182,183],{},"亮点：",[50,185,186,194,197,200,203,206],{},[53,187,188,189,193],{},"Docker 部署极快，一条 ",[190,191,192],"code",{},"docker-compose up"," 起来就能用",[53,195,196],{},"Workspaces 隔离设计实用，不同部门知识库互不干扰",[53,198,199],{},"接 Ollama 本地模型完全离线运行，数据不出内网",[53,201,202],{},"桌面应用适合个人用户，安装即用零配置",[53,204,205],{},"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",[53,207,208],{},"多用户权限管理是开源 RAG 平台中少有的完整实现",[40,210,211],{},[56,212,213],{},"踩坑：",[50,215,216,219,222,225,228,231],{},[53,217,218],{},"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",[53,220,221],{},"大文件（100MB+ PDF）处理偶尔超时，需调超时参数",[53,223,224],{},"Agent 能力有限，复杂工具链编排不如 Dify",[53,226,227],{},"向量库默认 LanceDB 在数据量大时查询变慢，建议切 Qdrant \u002F Chroma",[53,229,230],{},"UI 偶有卡顿，文档列表加载慢",[53,232,233],{},"中文文档的 OCR 需要额外配置，默认对扫描件支持有限",[35,235,236],{"id":236},"上手",[238,239,240,247,254,257,260,263],"ol",{},[53,241,242,243,246],{},"Docker 部署：",[190,244,245],{},"docker-compose up -d","（官方提供 docker-compose.yml）",[53,248,249,250,253],{},"首次访问 ",[190,251,252],{},"http:\u002F\u002Flocalhost:3001","，创建管理员账号",[53,255,256],{},"Settings → LLM Provider 配置模型（OpenAI API Key 或 Ollama 地址）",[53,258,259],{},"创建 Workspace → 上传文档（PDF\u002FWord\u002FTXT）",[53,261,262],{},"等待文档向量化完成，在 Chat 中开始问答",[53,264,265],{},"Settings → Users 添加团队成员并分配工作区权限",[35,267,268],{"id":268},"对比",[112,270,271,289],{},[115,272,273],{},[118,274,275,278,280,283,286],{},[121,276,277],{},"维度",[121,279,10],{},[121,281,282],{},"Dify",[121,284,285],{},"FastGPT",[121,287,288],{},"Langflow",[130,290,291,306,322,336,352,366,379],{},[118,292,293,296,299,302,304],{},[135,294,295],{},"部署门槛",[135,297,298],{},"极低",[135,300,301],{},"中",[135,303,301],{},[135,305,301],{},[118,307,308,311,314,317,319],{},[135,309,310],{},"多用户权限",[135,312,313],{},"✅ 完整",[135,315,316],{},"✅",[135,318,316],{},[135,320,321],{},"❌",[118,323,324,327,329,332,334],{},[135,325,326],{},"RAG 精度",[135,328,301],{},[135,330,331],{},"高",[135,333,331],{},[135,335,301],{},[118,337,338,341,344,347,349],{},[135,339,340],{},"Agent 编排",[135,342,343],{},"基础",[135,345,346],{},"强",[135,348,301],{},[135,350,351],{},"强（可视化）",[118,353,354,357,360,362,364],{},[135,355,356],{},"模型接入",[135,358,359],{},"丰富",[135,361,359],{},[135,363,359],{},[135,365,359],{},[118,367,368,371,373,375,377],{},[135,369,370],{},"桌面应用",[135,372,316],{},[135,374,321],{},[135,376,321],{},[135,378,321],{},[118,380,381,384,387,390,393],{},[135,382,383],{},"协议",[135,385,386],{},"MIT",[135,388,389],{},"Apache 2.0",[135,391,392],{},"FastGPT Open",[135,394,386],{},[35,396,397],{"id":397},"避坑",[50,399,400,406,412,418,427,433],{},[53,401,402,405],{},[56,403,404],{},"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",[53,407,408,411],{},[56,409,410],{},"LanceDB 大数据量变慢","：文档超过 1 万条建议切 Qdrant 或 Chroma",[53,413,414,417],{},[56,415,416],{},"大文件超时","：调整 Docker 超时配置，或拆分文档上传",[53,419,420,423,424],{},[56,421,422],{},"Ollama 连接","：Docker 内访问宿主机 Ollama 需用 ",[190,425,426],{},"host.docker.internal",[53,428,429,432],{},[56,430,431],{},"embedding 模型选择","：中文场景建议用 bge-large-zh 而非默认 OpenAI embedding",[53,434,435,438],{},[56,436,437],{},"不要当生产级 Agent 平台用","：Agent 能力是辅助，复杂编排上 Dify",[35,440,442],{"id":441},"适合-不适合","适合 \u002F 不适合",[50,444,445,448,451,454,457,460,463,466,469],{},[53,446,447],{},"✅ 企业内网私有化 AI 知识库",[53,449,450],{},"✅ 团队共享 AI 助手 + 多用户权限管理",[53,452,453],{},"✅ 接 Ollama 完全离线运行",[53,455,456],{},"✅ 个人桌面端快速体验 RAG",[53,458,459],{},"✅ 需要快速验证 RAG 概念的原型项目",[53,461,462],{},"❌ 需要复杂 Agent 工作流编排（用 Dify \u002F Langflow）",[53,464,465],{},"❌ 需要极致 RAG 召回精度（用 RAGFlow \u002F FastGPT）",[53,467,468],{},"❌ 大规模并发生产服务（架构未做高可用）",[53,470,471],{},"❌ 需要深度文档解析（复杂表格\u002F公式\u002F扫描件）",[35,473,475],{"id":474},"faq","FAQ",[40,477,478,481],{},[56,479,480],{},"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。",[40,483,484,487],{},[56,485,486],{},"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。",[40,489,490,493],{},[56,491,492],{},"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。",[40,495,496,499],{},[56,497,498],{},"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。",[35,501,502],{"id":502},"相关阅读",[40,504,505,510,511,510,515],{},[506,507,509],"a",{"href":508},"\u002Fagent\u002Fplatform\u002Fragflow.html","RAGFlow"," · ",[506,512,514],{"href":513},"\u002Fagent\u002Fplatform\u002Fflowise.html","Flowise",[506,516,518],{"href":517},"\u002Fagent\u002Fgeneral\u002Fperplexity.html","Perplexity",[35,520,521],{"id":521},"来源",[167,523,524],{},[40,525,526],{},"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。",[50,528,529,537],{},[53,530,531],{},[506,532,536],{"href":533,"rel":534},"https:\u002F\u002Fuseanything.com",[535],"nofollow","官网",[53,538,539],{},[506,540,543],{"href":541,"rel":542},"https:\u002F\u002Fgithub.com\u002FMintplex-Labs\u002Fanything-llm",[535],"GitHub",{"title":545,"searchDepth":546,"depth":546,"links":547},"",3,[548,550,551,552,553,554,555,556,557,558,559],{"id":37,"depth":549,"text":38},2,{"id":48,"depth":549,"text":48},{"id":110,"depth":549,"text":110},{"id":174,"depth":549,"text":175},{"id":236,"depth":549,"text":236},{"id":268,"depth":549,"text":268},{"id":397,"depth":549,"text":397},{"id":441,"depth":549,"text":442},{"id":474,"depth":549,"text":475},{"id":502,"depth":549,"text":502},{"id":521,"depth":549,"text":521},"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",null,true,[568],"en","2026-07-30",{},"\u002Ftools\u002Fagent\u002Fplatform\u002Fanythingllm","agent",[574,575,576,577],"windows","macos","linux","docker","Free \u002F 开源（MIT）\u002F Cloud","2026-07-05",{"power":546,"ux":581,"price":582,"cn_support":546,"stability":546},4,5,{"title":10,"description":562},"AnythingLLM - 开源私有部署 LLM 平台评测 | AIHO","agent\u002Fplatform\u002Fanythingllm",[587,588],{"title":536,"url":533},{"title":543,"url":541},"tools\u002Fagent\u002Fplatform\u002Fanythingllm","开源私有部署 LLM 平台，一站式 RAG + Agent + 多用户",[592,593,594,595,596],"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。","w0FYQb9Q4nM6-GQPZyzHTVhhjdZKUXmEmR920GlQFQ4",[600,1707,2803],{"id":601,"title":282,"alternatives":602,"api_compatible":605,"body":606,"category":560,"chinese_friendly":581,"cover":1650,"description":1651,"domestic":566,"extension":564,"faq":565,"free":566,"github":898,"languages":1652,"lastVerified":1655,"meta":1656,"models":565,"navigation":566,"notSuitable":565,"opensource":566,"path":1657,"pillar":572,"platforms":1658,"priceTable":1659,"pricing":1676,"published":1677,"relatedPlaybooks":565,"relatedReviews":1678,"score":1683,"self_host":566,"seo":1684,"seoTitle":1685,"slug":12,"sources":1686,"stem":1698,"suitable":565,"tagline":1699,"tags":1700,"updated":1704,"verdict":1705,"website":1592,"__hash__":1706},"tools\u002Ftools\u002Fagent\u002Fplatform\u002Fdify.md",[603,13,604,14],"agent\u002Fplatform\u002Fcoze","agent\u002Fplatform\u002Fn8n",[16,17,23,22,25,27],{"type":32,"value":607,"toc":1628},[608,610,635,640,643,648,651,720,723,727,730,744,761,765,768,779,783,791,802,806,809,812,816,825,883,890,894,902,965,968,988,992,995,1054,1060,1064,1158,1161,1190,1193,1330,1348,1386,1389,1462,1464,1467,1487,1490,1519,1521,1583,1585,1616,1624],[35,609,38],{"id":37},[611,612,617,623],"div",{"className":613},[614,615,616],"card","p-5","my-4",[40,618,619,622],{},[56,620,621],{},"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。",[40,624,625,626,629,630,634],{},"最大价值是 ",[56,627,628],{},"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 ",[506,631,633],{"href":632},"\u002Fagent\u002Fplatform\u002Fcoze.html","Coze"," 折腾，新手得读 1-2 小时文档。",[167,636,637],{},[40,638,639],{},"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。",[35,641,642],{"id":642},"核心特性",[644,645,647],"h3",{"id":646},"可视化工作流chatflow-workflow","可视化工作流（Chatflow + Workflow）",[40,649,650],{},"Dify 把 LLM 应用拆成两种\"应用类型\"：",[112,652,653,666],{},[115,654,655],{},[118,656,657,660,663],{},[121,658,659],{},"类型",[121,661,662],{},"适合场景",[121,664,665],{},"编排范式",[130,667,668,681,694,707],{},[118,669,670,675,678],{},[135,671,672],{},[56,673,674],{},"Chatbot",[135,676,677],{},"简单对话机器人",[135,679,680],{},"prompt + tools",[118,682,683,688,691],{},[135,684,685],{},[56,686,687],{},"Agent",[135,689,690],{},"自主多步任务",[135,692,693],{},"ReAct \u002F Function Calling",[118,695,696,701,704],{},[135,697,698],{},[56,699,700],{},"Chatflow",[135,702,703],{},"对话型工作流（多轮 + 分支）",[135,705,706],{},"节点 DAG，带聊天上下文",[118,708,709,714,717],{},[135,710,711],{},[56,712,713],{},"Workflow",[135,715,716],{},"单次输入→输出（API 模式）",[135,718,719],{},"节点 DAG，无对话状态",[40,721,722],{},"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\"。",[644,724,726],{"id":725},"rag-知识库","RAG 知识库",[40,728,729],{},"内置完整 RAG 链路：",[238,731,732,735,738,741],{},[53,733,734],{},"上传文档（PDF \u002F Word \u002F Markdown \u002F 网页）",[53,736,737],{},"自动分块 + embedding（可配置分段策略和 embedding 模型）",[53,739,740],{},"混合检索（向量 + 全文 + 重排）",[53,742,743],{},"引用溯源（回答末尾自动附原文片段）",[40,745,746,747,752,753,756,757,760],{},"注意：根据 ",[506,748,751],{"href":749,"rel":750},"https:\u002F\u002Fzhuanlan.zhihu.com\u002Fp\u002F1887141987838309480",[535],"知乎 LLM 实战笔记 2025-03 对比"," 的实测，Dify ",[56,754,755],{},"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 ",[506,758,285],{"href":759},"\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。",[644,762,764],{"id":763},"模型生态40-提供商","模型生态：40+ 提供商",[40,766,767],{},"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。",[40,769,770,771,773,774,778],{},"国产模型原生支持（不像 ",[506,772,285],{"href":759}," 需要 ",[506,775,777],{"href":776},"\u002Fcoding\u002Fapi\u002Fone-api.html","OneAPI"," 中转），是 Dify 在国内 toB 场景流行的关键。",[644,780,782],{"id":781},"mcp-协议支持","MCP 协议支持",[40,784,785,786,790],{},"Dify 较早接入了 ",[506,787,789],{"href":788},"\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：",[50,792,793,796,799],{},[53,794,795],{},"通过 MCP 调本地 PostgreSQL \u002F SQLite",[53,797,798],{},"通过 MCP 调 GitHub \u002F Slack \u002F Linear",[53,800,801],{},"通过 MCP 调自家内部系统（写一个 MCP Server 即可）",[644,803,805],{"id":804},"api-first","API-first",[40,807,808],{},"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。",[35,810,811],{"id":811},"价格与运行成本",[644,813,815],{"id":814},"云版difyai","云版（dify.ai）",[40,817,818,819,824],{},"根据 ",[506,820,823],{"href":821,"rel":822},"https:\u002F\u002Fwww.tooljunction.io\u002Fai-tools\u002Fdify-ai",[535],"tooljunction.io 2026 评测"," 引用的官方定价：",[112,826,827,839],{},[115,828,829],{},[118,830,831,834,836],{},[121,832,833],{},"套餐",[121,835,110],{},[121,837,838],{},"主要限制",[130,840,841,852,863,874],{},[118,842,843,846,849],{},[135,844,845],{},"Sandbox",[135,847,848],{},"免费",[135,850,851],{},"200 次模型调用，1 app，5MB 知识库",[118,853,854,857,860],{},[135,855,856],{},"Professional",[135,858,859],{},"$59\u002F月起",[135,861,862],{},"5000 调用\u002F月，多 app，50MB 知识库",[118,864,865,868,871],{},[135,866,867],{},"Team",[135,869,870],{},"$159\u002F月起",[135,872,873],{},"团队协作、SSO",[118,875,876,878,880],{},[135,877,159],{},[135,879,162],{},[135,881,882],{},"定制 SLA、私有云",[40,884,885,886,889],{},"注意：云版价格只是 Dify 平台费，",[56,887,888],{},"模型 API 费用另算","（自带 OpenAI \u002F Anthropic key）。",[644,891,893],{"id":892},"自托管推荐","自托管（推荐）",[40,895,896,901],{},[506,897,900],{"href":898,"rel":899},"https:\u002F\u002Fgithub.com\u002Flanggenius\u002Fdify",[535],"官方 GitHub 仓库"," 提供 Docker Compose 部署，社区版完全免费可商用：",[903,904,908],"pre",{"className":905,"code":906,"language":907,"meta":545,"style":545},"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",[190,909,910,926,935,946,959],{"__ignoreMap":545},[911,912,915,919,923],"span",{"class":913,"line":914},"line",1,[911,916,918],{"class":917},"sScJk","git",[911,920,922],{"class":921},"sZZnC"," clone",[911,924,925],{"class":921}," https:\u002F\u002Fgithub.com\u002Flanggenius\u002Fdify.git\n",[911,927,928,932],{"class":913,"line":549},[911,929,931],{"class":930},"sj4cs","cd",[911,933,934],{"class":921}," dify\u002Fdocker\n",[911,936,937,940,943],{"class":913,"line":546},[911,938,939],{"class":917},"cp",[911,941,942],{"class":921}," .env.example",[911,944,945],{"class":921}," .env\n",[911,947,948,950,953,956],{"class":913,"line":581},[911,949,577],{"class":917},[911,951,952],{"class":921}," compose",[911,954,955],{"class":921}," up",[911,957,958],{"class":930}," -d\n",[911,960,961],{"class":913,"line":582},[911,962,964],{"class":963},"sJ8bj","# 默认 http:\u002F\u002Flocalhost \u002F 端口可在 .env 调整\n",[40,966,967],{},"硬件门槛（社区共识，非官方硬性要求）：",[50,969,970,976,982],{},[53,971,972,975],{},[56,973,974],{},"最低","：2 核 4G，纯外接 API 模式",[53,977,978,981],{},[56,979,980],{},"推荐","：4 核 8G + 至少 30GB 磁盘（向量数据 + 文件存储）",[53,983,984,987],{},[56,985,986],{},"企业","：8 核 16G+，单机日活上千",[644,989,991],{"id":990},"真实-tco","真实 TCO",[40,993,994],{},"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）：",[112,996,997,1010],{},[115,998,999],{},[118,1000,1001,1004,1007],{},[121,1002,1003],{},"成本项",[121,1005,1006],{},"云版 Professional",[121,1008,1009],{},"自托管",[130,1011,1012,1022,1032,1043],{},[118,1013,1014,1017,1020],{},[135,1015,1016],{},"平台费",[135,1018,1019],{},"~$2,100（3 年）",[135,1021,140],{},[118,1023,1024,1027,1029],{},[135,1025,1026],{},"服务器",[135,1028,140],{},[135,1030,1031],{},"~$50\u002F月 × 36 = $1,800",[118,1033,1034,1037,1040],{},[135,1035,1036],{},"模型 API",[135,1038,1039],{},"与下同",[135,1041,1042],{},"与上同",[118,1044,1045,1048,1051],{},[135,1046,1047],{},"运维人力",[135,1049,1050],{},"0",[135,1052,1053],{},"约 0.2 人月",[40,1055,1056,1059],{},[56,1057,1058],{},"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。",[35,1061,1063],{"id":1062},"上手-10-分钟","上手 10 分钟",[903,1065,1067],{"className":905,"code":1066,"language":907,"meta":545,"style":545},"# 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",[190,1068,1069,1074,1082,1088,1096,1106,1112,1118,1124,1129,1135,1140,1146,1152],{"__ignoreMap":545},[911,1070,1071],{"class":913,"line":914},[911,1072,1073],{"class":963},"# 1. 自托管（社区版）\n",[911,1075,1076,1078,1080],{"class":913,"line":549},[911,1077,918],{"class":917},[911,1079,922],{"class":921},[911,1081,925],{"class":921},[911,1083,1084,1086],{"class":913,"line":546},[911,1085,931],{"class":930},[911,1087,934],{"class":921},[911,1089,1090,1092,1094],{"class":913,"line":581},[911,1091,939],{"class":917},[911,1093,942],{"class":921},[911,1095,945],{"class":921},[911,1097,1098,1100,1102,1104],{"class":913,"line":582},[911,1099,577],{"class":917},[911,1101,952],{"class":921},[911,1103,955],{"class":921},[911,1105,958],{"class":930},[911,1107,1109],{"class":913,"line":1108},6,[911,1110,1111],{"emptyLinePlaceholder":566},"\n",[911,1113,1115],{"class":913,"line":1114},7,[911,1116,1117],{"class":963},"# 2. 浏览器打开 http:\u002F\u002Flocalhost\n",[911,1119,1121],{"class":913,"line":1120},8,[911,1122,1123],{"class":963},"#    首次会让你创建 admin 账号\n",[911,1125,1127],{"class":913,"line":1126},9,[911,1128,1111],{"emptyLinePlaceholder":566},[911,1130,1132],{"class":913,"line":1131},10,[911,1133,1134],{"class":963},"# 3. 进入\"设置 → 模型供应商\"，配置 OpenAI \u002F 国产模型 API key\n",[911,1136,1138],{"class":913,"line":1137},11,[911,1139,1111],{"emptyLinePlaceholder":566},[911,1141,1143],{"class":913,"line":1142},12,[911,1144,1145],{"class":963},"# 4. 在主界面\"创建空白应用\"，选 Chatflow 或 Workflow\n",[911,1147,1149],{"class":913,"line":1148},13,[911,1150,1151],{"class":963},"# 5. 拖入\"开始 → LLM → 结束\"节点试一下基础 prompt\n",[911,1153,1155],{"class":913,"line":1154},14,[911,1156,1157],{"class":963},"# 6. 满意了点右上\"发布\"，自动生成 API endpoint\n",[35,1159,1160],{"id":1160},"国内使用注意事项",[238,1162,1163,1169,1175,1181],{},[53,1164,1165,1168],{},[56,1166,1167],{},"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",[53,1170,1171,1174],{},[56,1172,1173],{},"自托管 + 国产模型"," = 完全国内闭环，是 Dify 在国内最大优势",[53,1176,1177,1180],{},[56,1178,1179],{},"Docker 镜像拉取","：国内可能慢，建议配 Docker registry 镜像（阿里云 \u002F 网易）",[53,1182,1183,1186,1187,1189],{},[56,1184,1185],{},"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 ",[506,1188,633],{"href":632}," 迁到 Dify",[35,1191,1192],{"id":1192},"与同类怎么选",[112,1194,1195,1217],{},[115,1196,1197],{},[118,1198,1199,1201,1203,1207,1211],{},[121,1200,277],{},[121,1202,282],{},[121,1204,1205],{},[506,1206,633],{"href":632},[121,1208,1209],{},[506,1210,285],{"href":759},[121,1212,1213],{},[506,1214,1216],{"href":1215},"\u002Fagent\u002Fplatform\u002Fn8n.html","n8n",[130,1218,1219,1233,1246,1262,1276,1289,1303,1316],{},[118,1220,1221,1224,1226,1228,1230],{},[135,1222,1223],{},"开源",[135,1225,316],{},[135,1227,321],{},[135,1229,316],{},[135,1231,1232],{},"✅（fair-code）",[118,1234,1235,1238,1240,1242,1244],{},[135,1236,1237],{},"私有部署",[135,1239,316],{},[135,1241,321],{},[135,1243,316],{},[135,1245,316],{},[118,1247,1248,1251,1254,1257,1259],{},[135,1249,1250],{},"上手难度",[135,1252,1253],{},"★★★☆☆",[135,1255,1256],{},"★★☆☆☆ 最简单",[135,1258,1253],{},[135,1260,1261],{},"★★★★☆",[118,1263,1264,1267,1270,1272,1274],{},[135,1265,1266],{},"工作流编排",[135,1268,1269],{},"★★★★★",[135,1271,1261],{},[135,1273,1253],{},[135,1275,1269],{},[118,1277,1278,1280,1282,1284,1286],{},[135,1279,326],{},[135,1281,1261],{},[135,1283,1253],{},[135,1285,1269],{},[135,1287,1288],{},"★★☆☆☆",[118,1290,1291,1294,1296,1298,1301],{},[135,1292,1293],{},"模型生态",[135,1295,1269],{},[135,1297,1261],{},[135,1299,1300],{},"★★★☆☆（OneAPI 中转）",[135,1302,1261],{},[118,1304,1305,1308,1310,1312,1314],{},[135,1306,1307],{},"中文场景",[135,1309,1261],{},[135,1311,1269],{},[135,1313,1261],{},[135,1315,1253],{},[118,1317,1318,1321,1323,1326,1328],{},[135,1319,1320],{},"字节生态绑定",[135,1322,321],{},[135,1324,1325],{},"✅（飞书\u002F抖音深度集成）",[135,1327,321],{},[135,1329,321],{},[40,1331,1332,1335,1336,1341,1342,1347],{},[56,1333,1334],{},"怎么选","（基于 ",[506,1337,1340],{"href":1338,"rel":1339},"https:\u002F\u002Fwww.besthub.dev\u002Farticles\u002Fcoze-vs-dify-vs-fastgpt-which-ai-agent-platform-fits-your-needs-fa59cf97b798",[535],"BestHub 2025-07"," 和 ",[506,1343,1346],{"href":1344,"rel":1345},"https:\u002F\u002Fwww.cnblogs.com\u002Fuulucias\u002Fp\u002F19449008",[535],"博客园 2026-01"," 两份选型指南综合）：",[50,1349,1350,1356,1364,1371,1378],{},[53,1351,1352,1355],{},[56,1353,1354],{},"数据必须不出内网 + 工作流复杂"," → Dify",[53,1357,1358,1361,1362],{},[56,1359,1360],{},"个人 \u002F 小团队 \u002F 快速原型 + 字节生态"," → ",[506,1363,633],{"href":632},[53,1365,1366,1361,1369],{},[56,1367,1368],{},"核心场景就是企业知识库 QA",[506,1370,285],{"href":759},[53,1372,1373,1361,1376],{},[56,1374,1375],{},"重点是连接外部 SaaS（Slack \u002F Notion \u002F 数据库）",[506,1377,1216],{"href":1215},[53,1379,1380,1361,1383],{},[56,1381,1382],{},"要画图式表达 LangChain pipeline",[506,1384,288],{"href":1385},"\u002Fagent\u002Fplatform\u002Flangflow.html",[35,1387,1388],{"id":1388},"避坑清单",[50,1390,1391,1397,1414,1425,1438,1444,1450,1456],{},[53,1392,1393,1396],{},[56,1394,1395],{},"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。",[53,1398,1399,1405,1406,1409,1410,1413],{},[56,1400,1401,1404],{},[190,1402,1403],{},".env"," 文件改完忘 restart","：",[190,1407,1408],{},"docker compose down && up -d","，不是 ",[190,1411,1412],{},"restart","——后者不重新加载 env。",[53,1415,1416,1405,1419,1424],{},[56,1417,1418],{},"大版本升级会破坏数据库 schema",[506,1420,1423],{"href":1421,"rel":1422},"https:\u002F\u002Fdocs.dify.ai\u002Fzh-hans",[535],"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。",[53,1426,1427,1430,1431,1433,1434,1437],{},[56,1428,1429],{},"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 ",[190,1432,1403],{}," 的 ",[190,1435,1436],{},"UPLOAD_FILE_SIZE_LIMIT"," 并重启容器。",[53,1439,1440,1443],{},[56,1441,1442],{},"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。",[53,1445,1446,1449],{},[56,1447,1448],{},"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。",[53,1451,1452,1455],{},[56,1453,1454],{},"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。",[53,1457,1458,1461],{},[56,1459,1460],{},"国内 Docker 拉取镜像慢","：先配国内 registry，否则首次 pull 可能要 30+ 分钟。",[35,1463,442],{"id":441},[40,1465,1466],{},"✅ 适合：",[50,1468,1469,1472,1475,1478,1481,1484],{},[53,1470,1471],{},"中大型企业 LLM 中台建设",[53,1473,1474],{},"需要私有化部署（金融 \u002F 医疗 \u002F 政府）",[53,1476,1477],{},"想做\"AI 工作流即产品\"的开发团队",[53,1479,1480],{},"同时需要 RAG + Agent + Workflow 三件套",[53,1482,1483],{},"想用国产模型 + 国际模型混合编排",[53,1485,1486],{},"已经接受 Docker + 一定运维投入",[40,1488,1489],{},"❌ 不适合：",[50,1491,1492,1498,1504,1507,1513],{},[53,1493,1494,1495,1497],{},"纯个人玩家做对话机器人（",[506,1496,633],{"href":632}," 更快）",[53,1499,1500,1501,1503],{},"只想做企业知识库 QA（",[506,1502,285],{"href":759}," RAG 更专）",[53,1505,1506],{},"团队完全没运维能力（云版还行，自托管会踩坑）",[53,1508,1509,1510,1512],{},"需要深度对接字节飞书 \u002F 抖音（",[506,1511,633],{"href":632}," 原生）",[53,1514,1515,1516,1518],{},"工作流核心是连接 100+ SaaS（",[506,1517,1216],{"href":1215}," 节点更全）",[35,1520,502],{"id":502},[50,1522,1523,1535,1553,1572],{},[53,1524,1525,1526,1528,1529,1528,1531,1528,1533],{},"同类对比：",[506,1527,633],{"href":632}," \u002F ",[506,1530,285],{"href":759},[506,1532,1216],{"href":1215},[506,1534,288],{"href":1385},[53,1536,1537,1538,1528,1542,1528,1546,1528,1549],{},"概念基础：",[506,1539,1541],{"href":1540},"\u002Fwiki\u002Fai-agent.html","AI Agent",[506,1543,1545],{"href":1544},"\u002Fwiki\u002Frag.html","RAG",[506,1547,1548],{"href":788},"MCP",[506,1550,1552],{"href":1551},"\u002Fwiki\u002Ffunction-calling.html","Function Calling",[53,1554,1555,1556,1528,1560,1528,1564,1528,1568],{},"模型选型：",[506,1557,1559],{"href":1558},"\u002Fmodels\u002Fgpt-5.html","GPT-5",[506,1561,1563],{"href":1562},"\u002Fmodels\u002Fclaude-sonnet-4.html","Claude Sonnet 4",[506,1565,1567],{"href":1566},"\u002Fmodels\u002Fdeepseek-v3.html","DeepSeek-V3",[506,1569,1571],{"href":1570},"\u002Fmodels\u002Fglm-5.2.html","GLM-5.2",[53,1573,1574,1575,1528,1579],{},"进阶：",[506,1576,1578],{"href":1577},"\u002Fwiki\u002Ffine-tuning-vs-rag.html","Fine-tuning vs RAG",[506,1580,1582],{"href":1581},"\u002Fwiki\u002Fcontext-engineering.html","Context Engineering",[35,1584,521],{"id":521},[50,1586,1587,1594,1600,1606,1613],{},[53,1588,1589,1590],{},"官网：",[506,1591,1592],{"href":1592,"rel":1593},"https:\u002F\u002Fdify.ai",[535],[53,1595,1596,1597],{},"中文文档：",[506,1598,1421],{"href":1421,"rel":1599},[535],[53,1601,1602,1603],{},"GitHub：",[506,1604,898],{"href":898,"rel":1605},[535],[53,1607,1608,1609],{},"官方定价：",[506,1610,1611],{"href":1611,"rel":1612},"https:\u002F\u002Fdify.ai\u002Fpricing",[535],[53,1614,1615],{},"第三方评测：tooljunction.io \u002F chatforest.com \u002F besthub.dev \u002F joshuaopolko.com \u002F 知乎 LLM 实战笔记",[40,1617,1618,1619,1623],{},"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 ",[506,1620,1622],{"href":1621},"mailto:hello@aiho.net","反馈邮箱"," 反馈。",[1625,1626,1627],"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":545,"searchDepth":546,"depth":546,"links":1629},[1630,1631,1638,1643,1644,1645,1646,1647,1648,1649],{"id":37,"depth":549,"text":38},{"id":642,"depth":549,"text":642,"children":1632},[1633,1634,1635,1636,1637],{"id":646,"depth":546,"text":647},{"id":725,"depth":546,"text":726},{"id":763,"depth":546,"text":764},{"id":781,"depth":546,"text":782},{"id":804,"depth":546,"text":805},{"id":811,"depth":549,"text":811,"children":1639},[1640,1641,1642],{"id":814,"depth":546,"text":815},{"id":892,"depth":546,"text":893},{"id":990,"depth":546,"text":991},{"id":1062,"depth":549,"text":1063},{"id":1160,"depth":549,"text":1160},{"id":1192,"depth":549,"text":1192},{"id":1388,"depth":549,"text":1388},{"id":441,"depth":549,"text":442},{"id":502,"depth":549,"text":502},{"id":521,"depth":549,"text":521},"\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。",[1653,568,1654],"zh","ja","2026-08-02",{},"\u002Ftools\u002Fagent\u002Fplatform\u002Fdify",[574,575,576,577],[1660,1664,1668,1672],{"plan":1661,"price":140,"features":1662,"notes":1663},"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":1665,"price":140,"features":1666,"notes":1667},"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":1669,"price":859,"features":1670,"notes":1671},"Cloud Professional","更高调用额度 + 团队协作 + 商用支持","商用云首选",{"plan":1673,"price":1674,"features":1675,"notes":162},"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",[1679,1680,1681,1682],"coze-deep-review","coze-vs-dify","dify-deep-review","fastgpt-deep-review",{"power":582,"ux":581,"price":582,"cn_support":581,"stability":581},{"title":282,"description":1651},"Dify 评测 2026：开源 LLMOps 与 AI Agent 平台，自托管指南",[1687,1689,1691,1693,1695],{"title":1688,"url":1421},"Dify 官方文档（中文）",{"title":1690,"url":898},"Dify GitHub",{"title":1692,"url":1611},"Dify 官方定价",{"title":1694,"url":1338},"Coze vs Dify vs FastGPT 选型",{"title":1696,"url":1697},"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",[592,593,594,595,1701,1702,1703],"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。","q61l3oA5zdTKrp-66KGGh7wde1ZGUvjMulHa4GKaOXE",{"id":1708,"title":285,"alternatives":1709,"api_compatible":1710,"body":1712,"category":560,"chinese_friendly":582,"cover":2730,"description":2731,"domestic":566,"extension":564,"faq":565,"free":566,"github":2693,"languages":2732,"lastVerified":1655,"meta":2733,"models":2734,"navigation":566,"notSuitable":2741,"opensource":566,"path":2745,"pillar":572,"platforms":2746,"priceTable":2747,"pricing":2771,"published":1677,"relatedPlaybooks":2772,"relatedReviews":2774,"score":2775,"self_host":566,"seo":2776,"seoTitle":2777,"slug":13,"sources":2778,"stem":2789,"suitable":2790,"tagline":2796,"tags":2797,"updated":1704,"verdict":2801,"website":2687,"__hash__":2802},"tools\u002Ftools\u002Fagent\u002Fplatform\u002Ffastgpt.md",[12,603,14,604],[1711],"openai",{"type":32,"value":1713,"toc":2712},[1714,1716,1745,1756,1758,1762,1765,1779,1782,1808,1812,1819,1851,1858,1862,1915,1922,1925,1928,1931,1938,1984,1991,1995,2026,2030,2136,2139,2142,2146,2154,2277,2292,2294,2451,2461,2495,2501,2503,2578,2580,2582,2602,2604,2622,2624,2679,2681,2704,2709],[35,1715,38],{"id":37},[611,1717,1719,1734],{"className":1718},[614,615,616],[40,1720,1721,1723,1724,1729,1730,1733],{},[56,1722,621],{}," labring 团队开源的 LLM 知识库 RAG 平台，27k+ GitHub star（截至 2026-03 数据，",[506,1725,1728],{"href":1726,"rel":1727},"https:\u002F\u002Fcloud.tencent.com\u002Fdeveloper\u002Farticle\u002F2632669",[535],"腾讯云 2026-03 教程"," 引用），Apache 2.0 许可证可商用。",[56,1731,1732],{},"核心优势是 RAG 链路工程做得极细","——问题预处理、混合检索、重排序、上下文组装、答案生成每一步都可视化调参。",[40,1735,1736,1737,1740,1741,1744],{},"最大价值在 ",[56,1738,1739],{},"国内企业知识库 + 私有部署"," 场景。代价是 ",[56,1742,1743],{},"配置门槛","：docker 基础 + 网络知识 + 一定运维能力。",[167,1746,1747],{},[40,1748,1749,1750,1755],{},"来源说明：本文基于 fastgpt.io 官方页面、github.com\u002Flabring\u002FFastGPT 仓库、",[506,1751,1754],{"href":1752,"rel":1753},"https:\u002F\u002Fwww.nanhuantech.com\u002Fzh\u002Fai-reviews\u002Ffastgpt-2025-review",[535],"南环 AI 2026-05 评测","、腾讯云开发者社区 2026-03 部署教程综合整理。版本迭代较快，命令和价格请以最新官方文档为准。",[35,1757,642],{"id":642},[644,1759,1761],{"id":1760},"知识库管理核心能力","知识库管理（核心能力）",[40,1763,1764],{},"支持文件类型：",[50,1766,1767,1770,1773,1776],{},[53,1768,1769],{},"文档：PDF \u002F Word \u002F Markdown \u002F TXT \u002F HTML",[53,1771,1772],{},"表格：Excel \u002F CSV",[53,1774,1775],{},"网页：URL 抓取 + 定时同步",[53,1777,1778],{},"API：通过接口推送内容",[40,1780,1781],{},"处理流程：上传 → 文本切分 → 向量化 → 存储 → 可用于问答。支持：",[50,1783,1784,1790,1796,1802],{},[53,1785,1786,1789],{},[56,1787,1788],{},"文件夹分组","：不同主题 \u002F 部门分类",[53,1791,1792,1795],{},[56,1793,1794],{},"多种分块策略","：默认按段落 \u002F 按 token 数 \u002F 自定义",[53,1797,1798,1801],{},[56,1799,1800],{},"批量导入","：脚本化大批量同步",[53,1803,1804,1807],{},[56,1805,1806],{},"定时同步","：网页源自动更新",[644,1809,1811],{"id":1810},"rag-流程编排最强卖点","RAG 流程编排（最强卖点）",[40,1813,1814,1818],{},[506,1815,1817],{"href":1752,"rel":1816},[535],"南环 AI 2026 评测"," 总结的 FastGPT RAG 链路：",[238,1820,1821,1827,1833,1839,1845],{},[53,1822,1823,1826],{},[56,1824,1825],{},"问题预处理","：改写 \u002F 扩展 \u002F 错词纠正（提升召回率）",[53,1828,1829,1832],{},[56,1830,1831],{},"检索策略","：语义检索 \u002F 关键词 BM25 \u002F 混合检索，可调相似度阈值",[53,1834,1835,1838],{},[56,1836,1837],{},"重排序（Rerank）","：对初步检索结果二次排序，提升相关性",[53,1840,1841,1844],{},[56,1842,1843],{},"上下文组装","：最优 chunk + 问题 → prompt",[53,1846,1847,1850],{},[56,1848,1849],{},"答案生成","：调大模型基于检索结果回答 + 引用标注",[40,1852,1853,1854,1857],{},"每一步都可视化调参，这是 FastGPT 比 Coze \u002F Dify 在 ",[56,1855,1856],{},"纯知识库 QA 精度","上更高的原因。",[644,1859,1861],{"id":1860},"多模型支持不绑定厂商","多模型支持（不绑定厂商）",[112,1863,1864,1874],{},[115,1865,1866],{},[118,1867,1868,1871],{},[121,1869,1870],{},"模型类别",[121,1872,1873],{},"支持",[130,1875,1876,1884,1891,1899,1907],{},[118,1877,1878,1881],{},[135,1879,1880],{},"国产闭源",[135,1882,1883],{},"豆包 \u002F 通义千问 \u002F 文心一言 \u002F 智谱 GLM \u002F Moonshot Kimi \u002F MiniMax",[118,1885,1886,1888],{},[135,1887,1223],{},[135,1889,1890],{},"LLaMA \u002F Qwen \u002F ChatGLM \u002F DeepSeek 等可自部署",[118,1892,1893,1896],{},[135,1894,1895],{},"OpenAI 系",[135,1897,1898],{},"GPT-5 \u002F GPT-5 mini \u002F o3",[118,1900,1901,1904],{},[135,1902,1903],{},"Claude 系",[135,1905,1906],{},"Sonnet 4 \u002F Opus 4 \u002F Haiku",[118,1908,1909,1912],{},[135,1910,1911],{},"嵌入 \u002F 重排",[135,1913,1914],{},"BGE \u002F m3e \u002F OpenAI text-embedding-3",[40,1916,1917,1918,1921],{},"可以在 ",[56,1919,1920],{},"应用级别","为不同知识库 \u002F 不同场景配置不同模型，做\"低成本 embedding + 高质量 LLM 生成\"组合。",[644,1923,1924],{"id":1924},"工作流与高级编排",[40,1926,1927],{},"新版本（v4.14.x）支持类似 Dify 的工作流节点编排——条件分支、循环、HTTP 调用、代码节点。能做\"分类 → 路由到不同子知识库 → 不同模型回答\"这类复杂场景。",[644,1929,1930],{"id":1930},"多向量库选择",[40,1932,1933,1937],{},[506,1934,1936],{"href":1726,"rel":1935},[535],"腾讯云教程"," 公开的 4 种向量后端：",[112,1939,1940,1950],{},[115,1941,1942],{},[118,1943,1944,1947],{},[121,1945,1946],{},"后端",[121,1948,1949],{},"适用",[130,1951,1952,1960,1968,1976],{},[118,1953,1954,1957],{},[135,1955,1956],{},"PgVector",[135,1958,1959],{},"5000 万索引以下，新手 \u002F 小规模",[118,1961,1962,1965],{},[135,1963,1964],{},"Milvus",[135,1966,1967],{},"亿级以上，高性能",[118,1969,1970,1973],{},[135,1971,1972],{},"Zilliz Cloud",[135,1974,1975],{},"Milvus 全托管 SaaS",[118,1977,1978,1981],{},[135,1979,1980],{},"SeekDB \u002F OceanBase",[135,1982,1983],{},"企业级国产化",[40,1985,1986,1987,1990],{},"部署时选对应 ",[190,1988,1989],{},"docker-compose.{pgvector|milvus|...}.yml","。",[644,1992,1994],{"id":1993},"api-与-mcp","API 与 MCP",[50,1996,1997,2003,2009,2020],{},[53,1998,1999,2002],{},[56,2000,2001],{},"对话 API","：流式 \u002F 非流式 HTTP，OpenAI 兼容",[53,2004,2005,2008],{},[56,2006,2007],{},"知识库检索 API","：单独调检索（不走生成）做 hybrid pipeline",[53,2010,2011,2014,2015,2019],{},[56,2012,2013],{},"MCP Server","：3005 端口暴露 MCP SSE 服务，可被 ",[506,2016,2018],{"href":2017},"\u002Fcoding\u002Fcli\u002Fclaude-code.html","Claude Code"," 等客户端直接接入",[53,2021,2022,2025],{},[56,2023,2024],{},"Webhook","：回调通知",[35,2027,2029],{"id":2028},"部署-10-分钟docker","部署 10 分钟（Docker）",[903,2031,2033],{"className":905,"code":2032,"language":907,"meta":545,"style":545},"# 克隆代码\ngit clone https:\u002F\u002Fgithub.com\u002Flabring\u002FFastGPT.git\ncd FastGPT\n\n# 切到最新稳定版（参考 GitHub releases）\ngit switch -c 4.14.7.2\n\n# 选向量库版本（个人 \u002F 小规模选 pg）\ncd deploy\u002Fdocker\u002Fcn\nwget https:\u002F\u002Fdoc.fastgpt.cn\u002Fdeploy\u002Fconfig\u002Fconfig.json\n\n# 启动\ndocker-compose -f docker-compose.pg.yml up -d\n\n# 访问 http:\u002F\u002F\u003Cip>:3000，默认账号 root \u002F 1234\n",[190,2034,2035,2040,2049,2056,2060,2065,2078,2082,2087,2094,2102,2106,2111,2126,2130],{"__ignoreMap":545},[911,2036,2037],{"class":913,"line":914},[911,2038,2039],{"class":963},"# 克隆代码\n",[911,2041,2042,2044,2046],{"class":913,"line":549},[911,2043,918],{"class":917},[911,2045,922],{"class":921},[911,2047,2048],{"class":921}," https:\u002F\u002Fgithub.com\u002Flabring\u002FFastGPT.git\n",[911,2050,2051,2053],{"class":913,"line":546},[911,2052,931],{"class":930},[911,2054,2055],{"class":921}," FastGPT\n",[911,2057,2058],{"class":913,"line":581},[911,2059,1111],{"emptyLinePlaceholder":566},[911,2061,2062],{"class":913,"line":582},[911,2063,2064],{"class":963},"# 切到最新稳定版（参考 GitHub releases）\n",[911,2066,2067,2069,2072,2075],{"class":913,"line":1108},[911,2068,918],{"class":917},[911,2070,2071],{"class":921}," switch",[911,2073,2074],{"class":930}," -c",[911,2076,2077],{"class":930}," 4.14.7.2\n",[911,2079,2080],{"class":913,"line":1114},[911,2081,1111],{"emptyLinePlaceholder":566},[911,2083,2084],{"class":913,"line":1120},[911,2085,2086],{"class":963},"# 选向量库版本（个人 \u002F 小规模选 pg）\n",[911,2088,2089,2091],{"class":913,"line":1126},[911,2090,931],{"class":930},[911,2092,2093],{"class":921}," deploy\u002Fdocker\u002Fcn\n",[911,2095,2096,2099],{"class":913,"line":1131},[911,2097,2098],{"class":917},"wget",[911,2100,2101],{"class":921}," https:\u002F\u002Fdoc.fastgpt.cn\u002Fdeploy\u002Fconfig\u002Fconfig.json\n",[911,2103,2104],{"class":913,"line":1137},[911,2105,1111],{"emptyLinePlaceholder":566},[911,2107,2108],{"class":913,"line":1142},[911,2109,2110],{"class":963},"# 启动\n",[911,2112,2113,2116,2119,2122,2124],{"class":913,"line":1148},[911,2114,2115],{"class":917},"docker-compose",[911,2117,2118],{"class":930}," -f",[911,2120,2121],{"class":921}," docker-compose.pg.yml",[911,2123,955],{"class":921},[911,2125,958],{"class":930},[911,2127,2128],{"class":913,"line":1154},[911,2129,1111],{"emptyLinePlaceholder":566},[911,2131,2133],{"class":913,"line":2132},15,[911,2134,2135],{"class":963},"# 访问 http:\u002F\u002F\u003Cip>:3000，默认账号 root \u002F 1234\n",[40,2137,2138],{},"最低配置：2C4G + 20GB 硬盘 + Docker 28+ + Docker Compose 2.20+。",[40,2140,2141],{},"进入后台 → 账号 → 模型提供商 → 配置至少 1 个对话模型 + 1 个嵌入模型 → 即可开始建知识库。",[35,2143,2145],{"id":2144},"云版-vs-自托管对比","云版 vs 自托管对比",[40,2147,2148,2153],{},[506,2149,2152],{"href":2150,"rel":2151},"https:\u002F\u002Ffastgpt.io\u002Fzh\u002Fprice",[535],"fastgpt.io 官方定价"," 公开数据：",[112,2155,2156,2181],{},[115,2157,2158],{},[118,2159,2160,2162,2164,2167,2170,2173,2175,2178],{},[121,2161,833],{},[121,2163,110],{},[121,2165,2166],{},"AI 积分",[121,2168,2169],{},"知识库索引",[121,2171,2172],{},"团队",[121,2174,687],{},[121,2176,2177],{},"知识库",[121,2179,2180],{},"QPM",[130,2182,2183,2208,2232,2256],{},[118,2184,2185,2187,2190,2193,2196,2199,2202,2205],{},[135,2186,848],{},[135,2188,2189],{},"¥0",[135,2191,2192],{},"100",[135,2194,2195],{},"600",[135,2197,2198],{},"1",[135,2200,2201],{},"10",[135,2203,2204],{},"3",[135,2206,2207],{},"30",[118,2209,2210,2212,2215,2218,2221,2224,2227,2229],{},[135,2211,343],{},[135,2213,2214],{},"¥99\u002F月",[135,2216,2217],{},"4000",[135,2219,2220],{},"6000",[135,2222,2223],{},"5",[135,2225,2226],{},"50",[135,2228,2207],{},[135,2230,2231],{},"300",[118,2233,2234,2237,2240,2243,2246,2248,2251,2253],{},[135,2235,2236],{},"高级",[135,2238,2239],{},"¥599\u002F月",[135,2241,2242],{},"25000",[135,2244,2245],{},"36000",[135,2247,2226],{},[135,2249,2250],{},"200",[135,2252,2192],{},[135,2254,2255],{},"1500",[118,2257,2258,2261,2264,2267,2269,2271,2273,2275],{},[135,2259,2260],{},"定制",[135,2262,2263],{},"议价",[135,2265,2266],{},"弹性",[135,2268,2266],{},[135,2270,2266],{},[135,2272,2266],{},[135,2274,2266],{},[135,2276,2266],{},[40,2278,2279,2282,2283,2286,2287,2291],{},[56,2280,2281],{},"云版适合","：不想运维、量小、要快速上线\n",[56,2284,2285],{},"自托管适合","：量大（10 万+ 日问答）、数据敏感、要深度定制——按 ",[506,2288,2290],{"href":1752,"rel":2289},[535],"南环评测"," 估算：\"日均 10 万次问答的企业场景，商业 SaaS 年费数十万，自建 FastGPT + 开源模型只需数万硬件投入\"",[35,2293,1192],{"id":1192},[112,2295,2296,2317],{},[115,2297,2298],{},[118,2299,2300,2302,2304,2309,2313,2315],{},[121,2301,277],{},[121,2303,285],{},[121,2305,2306],{},[506,2307,282],{"href":2308},"\u002Fagent\u002Fplatform\u002Fdify.html",[121,2310,2311],{},[506,2312,633],{"href":632},[121,2314,509],{},[121,2316,10],{},[130,2318,2319,2339,2355,2371,2388,2403,2419,2434],{},[118,2320,2321,2324,2327,2330,2333,2336],{},[135,2322,2323],{},"核心定位",[135,2325,2326],{},"知识库 QA",[135,2328,2329],{},"综合 LLMOps",[135,2331,2332],{},"Bot + 工作流",[135,2334,2335],{},"文档解析+RAG",[135,2337,2338],{},"桌面级 KB",[118,2340,2341,2343,2346,2348,2350,2352],{},[135,2342,1223],{},[135,2344,2345],{},"✅ Apache 2.0",[135,2347,2345],{},[135,2349,321],{},[135,2351,2345],{},[135,2353,2354],{},"✅ MIT",[118,2356,2357,2359,2362,2364,2367,2369],{},[135,2358,1237],{},[135,2360,2361],{},"★★★★★ docker",[135,2363,1269],{},[135,2365,2366],{},"⚠️ 仅企业版",[135,2368,1261],{},[135,2370,1269],{},[118,2372,2373,2376,2379,2381,2383,2386],{},[135,2374,2375],{},"RAG 深度",[135,2377,2378],{},"★★★★★ 最细",[135,2380,1261],{},[135,2382,1253],{},[135,2384,2385],{},"★★★★★ 文档解析最强",[135,2387,1253],{},[118,2389,2390,2393,2395,2397,2399,2401],{},[135,2391,2392],{},"工作流",[135,2394,1261],{},[135,2396,1269],{},[135,2398,1261],{},[135,2400,1253],{},[135,2402,1288],{},[118,2404,2405,2407,2410,2412,2415,2417],{},[135,2406,236],{},[135,2408,2409],{},"★★★☆☆ 需 docker",[135,2411,1261],{},[135,2413,2414],{},"★★★★★ 最简单",[135,2416,1253],{},[135,2418,1261],{},[118,2420,2421,2424,2426,2428,2430,2432],{},[135,2422,2423],{},"中文优化",[135,2425,1269],{},[135,2427,1261],{},[135,2429,1269],{},[135,2431,1261],{},[135,2433,1253],{},[118,2435,2436,2439,2442,2444,2446,2449],{},[135,2437,2438],{},"多平台发布",[135,2440,2441],{},"⚠️ API 为主",[135,2443,1261],{},[135,2445,1269],{},[135,2447,2448],{},"⚠️",[135,2450,2448],{},[40,2452,2453,2455,2456,2460],{},[56,2454,1334],{},"（综合 ",[506,2457,2459],{"href":1752,"rel":2458},[535],"南环 AI 评测","）：",[50,2462,2463,2469,2476,2483,2489],{},[53,2464,2465,2468],{},[56,2466,2467],{},"核心需求是 RAG 精度"," → FastGPT",[53,2470,2471,1361,2474],{},[56,2472,2473],{},"需要丰富插件 + 复杂工作流 + 多平台发布",[506,2475,282],{"href":2308},[53,2477,2478,1361,2481],{},[56,2479,2480],{},"零代码、快速发布到飞书 \u002F 微信",[506,2482,633],{"href":632},[53,2484,2485,2488],{},[56,2486,2487],{},"文档解析（含 OCR \u002F 表格 \u002F 公式）是瓶颈"," → RAGFlow",[53,2490,2491,2494],{},[56,2492,2493],{},"桌面 \u002F 单机使用"," → AnythingLLM",[40,2496,2497,2500],{},[56,2498,2499],{},"很多企业同时用","：FastGPT 做知识库底座 + Coze 做前端 Bot 发布 \u002F 工作流编排。",[35,2502,1388],{"id":1388},[50,2504,2505,2518,2527,2533,2543,2555,2561,2567],{},[53,2506,2507,1405,2510,2513,2514,2517],{},[56,2508,2509],{},"docker-compose 镜像 tag 不一致",[506,2511,1936],{"href":1726,"rel":2512},[535]," 实测的坑——某些版本编排文件的 image tag 与最新 release 不一致，启动报\"镜像找不到\"，手动改 ",[190,2515,2516],{},"image:"," 行为正确版本即可",[53,2519,2520,2523,2524,2526],{},[56,2521,2522],{},"3000 端口冲突","：默认占用 3000（主服务）\u002F 9000（S3 \u002F MinIO）\u002F 3005（MCP）；改 ",[190,2525,2115],{}," 的 ports 映射端口",[53,2528,2529,2532],{},[56,2530,2531],{},"PostgreSQL pgvector 不够用就换 Milvus","：单库索引超 5000 万时 pgvector 查询性能下降，切 Milvus",[53,2534,2535,2538,2539,2542],{},[56,2536,2537],{},"向量库选错代价大","：先评估索引量再选向量后端，迁移要重新 embedding 整库，按 ",[506,2540,2290],{"href":1752,"rel":2541},[535],"：\"新手 \u002F 小规模 PgVector，中大规模 Milvus，企业 \u002F 国产 OceanBase\"",[53,2544,2545,1405,2548,2551,2552],{},[56,2546,2547],{},"MinIO 默认密码",[190,2549,2550],{},"minioadmin\u002Fminioadmin","，",[56,2553,2554],{},"部署到公网前必须改",[53,2556,2557,2560],{},[56,2558,2559],{},"分段策略影响巨大","：默认分段对长法律 \u002F 医疗文档不友好，需调\"按章节\"或\"自定义\"",[53,2562,2563,2566],{},[56,2564,2565],{},"嵌入模型 ≠ 对话模型","：经常有人只配 GPT-4 没配 embedding 模型，知识库无法索引——必须同时配两类",[53,2568,2569,2572,2573,2577],{},[56,2570,2571],{},"云版 AI 积分会过期","：未用完不能跨月累积（按 ",[506,2574,2576],{"href":2150,"rel":2575},[535],"fastgpt.io 定价 FAQ","）",[35,2579,442],{"id":441},[40,2581,1466],{},[50,2583,2584,2587,2590,2593,2596,2599],{},[53,2585,2586],{},"企业内部知识库（员工手册 \u002F 制度 \u002F 流程）",[53,2588,2589],{},"产品 FAQ \u002F 用户手册问答",[53,2591,2592],{},"医疗 \u002F 法律 \u002F 金融垂直领域知识系统",[53,2594,2595],{},"数据严格不出网 + Apache 2.0 商用",[53,2597,2598],{},"有 docker 运维基础的技术团队",[53,2600,2601],{},"需要把 RAG 当后端服务的开发者（API 接入业务系统）",[40,2603,1489],{},[50,2605,2606,2611,2616,2619],{},[53,2607,2608,2609,2577],{},"完全非技术用户（去 ",[506,2610,633],{"href":632},[53,2612,2613,2614,2577],{},"主要需求是工作流 + 插件集成（去 ",[506,2615,282],{"href":2308},[53,2617,2618],{},"文档解析 \u002F OCR 是首要痛点（RAGFlow）",[53,2620,2621],{},"不想自己运维 + 量很小（FastGPT 云免费版起步即可）",[35,2623,502],{"id":502},[50,2625,2626,2635,2652,2671],{},[53,2627,1525,2628,1528,2630,2632,2633],{},[506,2629,282],{"href":2308},[506,2631,633],{"href":632}," \u002F RAGFlow \u002F AnythingLLM \u002F ",[506,2634,1216],{"href":1215},[53,2636,2637,2638,1528,2640,1528,2644,1528,2647,1528,2650],{},"概念：",[506,2639,1545],{"href":1544},[506,2641,2643],{"href":2642},"\u002Fwiki\u002Fembedding.html","Embedding",[506,2645,2646],{"href":2642},"Vector Database",[506,2648,2649],{"href":1544},"Reranker",[506,2651,1541],{"href":1540},[53,2653,2654,2655,1528,2657,1528,2661,1528,2663,1528,2667],{},"模型：",[506,2656,1567],{"href":1566},[506,2658,2660],{"href":2659},"\u002Fmodels\u002Fqwen-3.html","Qwen3",[506,2662,1571],{"href":1570},[506,2664,2666],{"href":2665},"\u002Fmodels\u002Fkimi-k2.html","Kimi K2",[506,2668,2670],{"href":2669},"\u002Fmodels\u002Fdoubao-1-5-pro.html","豆包 Doubao",[53,2672,1574,2673,1528,2675],{},[506,2674,1582],{"href":1581},[506,2676,2678],{"href":2677},"\u002Fwiki\u002Fprompt-engineering.html","Prompt Engineering",[35,2680,521],{"id":521},[50,2682,2683,2689,2695,2701],{},[53,2684,1589,2685],{},[506,2686,2687],{"href":2687,"rel":2688},"https:\u002F\u002Ffastgpt.io",[535],[53,2690,1602,2691],{},[506,2692,2693],{"href":2693,"rel":2694},"https:\u002F\u002Fgithub.com\u002Flabring\u002FFastGPT",[535],[53,2696,2697,2698],{},"定价：",[506,2699,2150],{"href":2150,"rel":2700},[535],[53,2702,2703],{},"第三方评测：南环 AI \u002F 腾讯云开发者社区 \u002F 飞书 AGI 掘金知识库",[40,2705,2706,2707,1623],{},"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 命令 \u002F 功能与最新官方信息不一致，请通过 ",[506,2708,1622],{"href":1621},[1625,2710,2711],{},"html pre.shiki code .sJ8bj, html code.shiki .sJ8bj{--shiki-default:#6A737D;--shiki-dark:#6A737D}html pre.shiki code .sScJk, html code.shiki .sScJk{--shiki-default:#6F42C1;--shiki-dark:#B392F0}html pre.shiki code .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 .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":545,"searchDepth":546,"depth":546,"links":2713},[2714,2715,2723,2724,2725,2726,2727,2728,2729],{"id":37,"depth":549,"text":38},{"id":642,"depth":549,"text":642,"children":2716},[2717,2718,2719,2720,2721,2722],{"id":1760,"depth":546,"text":1761},{"id":1810,"depth":546,"text":1811},{"id":1860,"depth":546,"text":1861},{"id":1924,"depth":546,"text":1924},{"id":1930,"depth":546,"text":1930},{"id":1993,"depth":546,"text":1994},{"id":2028,"depth":549,"text":2029},{"id":2144,"depth":549,"text":2145},{"id":1192,"depth":549,"text":1192},{"id":1388,"depth":549,"text":1388},{"id":441,"depth":549,"text":442},{"id":502,"depth":549,"text":502},{"id":521,"depth":549,"text":521},"\u002Fimg\u002Ftools\u002Ffastgpt.webp","FastGPT 真实评测：开源 LLM 知识库 RAG 平台，labring 团队出品，27k+ GitHub star。一键 docker-compose 部署、RAG 流程编排可视化、多向量库支持。AIHO 编辑部基于官方文档与社区资料整理，含与 Dify\u002FCoze 对比、避坑指南。",[1653,568],{},[2735,2736,2737,2738,2739,2740],"deepseek-v3","qwen-max","doubao-pro","gpt-4o","claude-sonnet-4","kimi",[2742,2743,2744],"完全零代码 \u002F 不懂 docker 的用户（去 Coze）","Bot 多平台一键发布场景（Coze 强项）","插件 \u002F 工作流复杂集成（去 Dify）","\u002Ftools\u002Fagent\u002Fplatform\u002Ffastgpt",[574,575,576],[2748,2753,2758,2763,2767],{"plan":2749,"price":848,"limit":2750,"cn_pay":2751,"note":2752},"Self-host 开源","全功能 + 全数据本地","—","Apache 2.0 可商用",{"plan":2754,"price":2755,"limit":2756,"cn_pay":2751,"note":2757},"云免费版","¥0\u002F月","100 AI 积分 + 600 索引 + 3 知识库","试水",{"plan":2759,"price":2214,"limit":2760,"cn_pay":2761,"note":2762},"云基础版","4000 积分 + 6000 索引 + 50 Agent","✅ 微信\u002F支付宝","中小团队 SaaS",{"plan":2764,"price":2239,"limit":2765,"cn_pay":316,"note":2766},"云高级版","25000 积分 + 36000 索引 + 50 成员 + 200 Agent + 1500 QPM","企业级生产",{"plan":2768,"price":2263,"limit":2769,"cn_pay":316,"note":2770},"云定制版","弹性资源 + 深度技术支持 + 专属客户经理","中大型企业","自托管开源免费 \u002F 云版 ¥0-¥599\u002F月",[2773],"onboarding\u002Ffastgpt-getting-started",[1682,1679,1680,1681],{"power":581,"ux":581,"price":582,"cn_support":582,"stability":581},{"title":285,"description":2731},"FastGPT 评测 2026：开源知识库问答平台，AI 工作流引擎，对比 Dify",[2779,2781,2783,2785,2787],{"title":2780,"url":2687},"FastGPT 官网",{"title":2782,"url":2693},"FastGPT GitHub",{"title":2784,"url":2150},"FastGPT 定价页",{"title":2786,"url":1752},"FastGPT 2025 测评（南环 AI）",{"title":2788,"url":1726},"FastGPT 部署教程（腾讯云）","tools\u002Fagent\u002Fplatform\u002Ffastgpt",[2791,2792,2793,2794,2795],"企业内部知识库（员工手册、规章、流程）","产品文档智能问答（FAQ \u002F 用户手册）","垂直领域知识库（医疗、法律、金融）","数据严格不出网的合规场景","需要精细 RAG 流程编排（重排序、混合检索、阈值调节）","开源知识库问答系统，国内私有部署友好",[592,593,594,595,2798,2799,2800],"china","knowledge-base","labring","国内企业知识库私有化首选。RAG 召回工程做得很细，可视化调试好用，docker-compose 一键部署。生态插件不如 Dify 丰富。","3k7sqE3ueQ8CMIEFNqawWzvX4hGkRy7UVY9-HChmMJM",{"id":2804,"title":288,"alternatives":2805,"api_compatible":2808,"body":2809,"category":560,"chinese_friendly":546,"cover":3370,"description":3371,"domestic":563,"extension":564,"faq":3372,"free":566,"github":3385,"languages":3386,"lastVerified":1655,"meta":3388,"models":565,"navigation":566,"notSuitable":565,"opensource":566,"path":3389,"pillar":572,"platforms":3390,"priceTable":3393,"pricing":3406,"published":3407,"relatedPlaybooks":3408,"relatedReviews":565,"score":3410,"self_host":566,"seo":3411,"seoTitle":3412,"slug":14,"sources":3413,"stem":3423,"suitable":565,"tagline":3424,"tags":3425,"updated":1704,"verdict":3429,"website":3416,"__hash__":3430},"tools\u002Ftools\u002Fagent\u002Fplatform\u002Flangflow.md",[604,2806,2807],"agent\u002Fprotocol\u002Fcomposio","agent\u002Fgeneral\u002Fopenmanus",[16,17,23,22,25,27],{"type":32,"value":2810,"toc":3358},[2811,2813,2816,2819,2821,2895,2897,2922,2927,2931,2935,2961,2965,2991,2993,3049,3052,3075,3077,3217,3219,3274,3276,3302,3304,3324,3326,3356],[35,2812,38],{"id":37},[40,2814,2815],{},"Langflow 是 2023 开源、2024 被 DataStax 收购的可视化 LangChain 画布。20,000+ GitHub stars、MIT 协议、Python 实现、与 Astra DB 向量库深度集成。差异点：拖拽式画布把 LangChain primitive 映射成节点 + RAG pipeline 原生组件 + 多 agent 工作流 + 节点可下钻到 Python 代码 + 自托管 \u002F Docker \u002F DataStax Astra 云托管 + Pinecone \u002F pgvector \u002F 主流向量库适配 + Visual GUI for building LangChain pipelines。Self-host 免费 \u002F Cloud Free + Paid ~$25\u002F月起。",[40,2817,2818],{},"适合：工程团队要可视化建 LangChain 流 + 不被 SaaS 锁住；合规 \u002F 数据驻留要求自托管；要 Astra DB 一站式 RAG；想用 LangChain 但讨厌纯代码协作。不适合：业务侧 + 非工程师（用 Dify \u002F Coze）；纯无代码偏好（Langflow 仍要懂 LangChain 概念）；轻量场景 + 不需要画布（直接写 LangChain 代码）。",[35,2820,48],{"id":48},[50,2822,2823,2829,2835,2841,2847,2853,2859,2865,2871,2877,2883,2889],{},[53,2824,2825,2828],{},[56,2826,2827],{},"可视化 LangChain 画布","：节点 = primitive，连线 = 数据流",[53,2830,2831,2834],{},[56,2832,2833],{},"RAG pipeline 原生组件","：Chunking \u002F Embedding \u002F VectorStore \u002F Retriever",[53,2836,2837,2840],{},[56,2838,2839],{},"多 agent 工作流","：编排多 agent 协作",[53,2842,2843,2846],{},[56,2844,2845],{},"Python 下钻","：任意节点可写 custom Python",[53,2848,2849,2852],{},[56,2850,2851],{},"向量库适配","：Astra DB \u002F Pinecone \u002F pgvector \u002F Weaviate \u002F Chroma",[53,2854,2855,2858],{},[56,2856,2857],{},"API 部署","：流程一键导出为 REST API",[53,2860,2861,2864],{},[56,2862,2863],{},"Real-time collaboration","：多用户同 project",[53,2866,2867,2870],{},[56,2868,2869],{},"版本控制","：内置 versioning + revert",[53,2872,2873,2876],{},[56,2874,2875],{},"数据可视化","：node output \u002F data flow 可视化调试",[53,2878,2879,2882],{},[56,2880,2881],{},"角色权限","：user auth + RBAC",[53,2884,2885,2888],{},[56,2886,2887],{},"Docker \u002F pip 安装","：5 分钟启动",[53,2890,2891,2894],{},[56,2892,2893],{},"Astra-hosted cloud","：DataStax 托管选项",[35,2896,110],{"id":110},[50,2898,2899,2905,2911,2917],{},[53,2900,2901,2904],{},[56,2902,2903],{},"Self-Host (OSS)","：$0；MIT 完全免费，自付 LLM API + 服务器",[53,2906,2907,2910],{},[56,2908,2909],{},"Cloud Free","：$0；DataStax Astra-hosted 小流量",[53,2912,2913,2916],{},[56,2914,2915],{},"Cloud Paid","：~$25\u002F月起；Astra DB + 更高额度 + 团队协作",[53,2918,2919,2921],{},[56,2920,159],{},"：联系销售；SSO + audit + 私有部署 + SLA",[167,2923,2924],{},[40,2925,2926],{},"自托管最低成本：$5-10\u002F月 VPS + LLM API token。Cloud Paid $25\u002F月适合不想运维的小团队。",[35,2928,2930],{"id":2929},"实测rag-应用-内部工具搭建","实测（RAG 应用 + 内部工具搭建）",[40,2932,2933],{},[56,2934,183],{},[50,2936,2937,2940,2943,2946,2949,2952,2955,2958],{},[53,2938,2939],{},"画布直观，比纯写 LangChain 协作效率高 5x",[53,2941,2942],{},"节点下钻到 Python 让灵活度不被画布限制",[53,2944,2945],{},"Astra DB 集成省了配 vector store 时间",[53,2947,2948],{},"4.4\u002F5 用户评分（Propicked \u002F Tooliverse）",[53,2950,2951],{},"开源 + 自托管 + 数据驻留满足合规",[53,2953,2954],{},"多 agent 编排比裸 LangChain 调试容易",[53,2956,2957],{},"RAG pipeline 模板一键起 demo",[53,2959,2960],{},"与 DataStax 长期支持降低 abandon ware 风险",[40,2962,2963],{},[56,2964,213],{},[50,2966,2967,2970,2973,2976,2979,2982,2985,2988],{},[53,2968,2969],{},"稳定性偶发：复杂流大流量下 node 偶尔失联（SelectHub 反馈）",[53,2971,2972],{},"第三方 API 依赖：external API 失败时错误处理弱",[53,2974,2975],{},"production readiness 不算 mission-critical（要自加 observability）",[53,2977,2978],{},"LangChain 升级偶尔 break 旧 flow",[53,2980,2981],{},"文档对新组件滞后 1-2 月",[53,2983,2984],{},"中文 UI 不完整，业务侧用户上手陡",[53,2986,2987],{},"大型 flow（100+ 节点）画布卡顿",[53,2989,2990],{},"多人协作偶发同步冲突",[35,2992,236],{"id":236},[903,2994,2996],{"className":905,"code":2995,"language":907,"meta":545,"style":545},"# pip 安装\npip install langflow\nlangflow run  # http:\u002F\u002Flocalhost:7860\n\n# 或 Docker\ndocker run -p 7860:7860 langflowai\u002Flangflow:latest\n",[190,2997,2998,3003,3014,3025,3029,3034],{"__ignoreMap":545},[911,2999,3000],{"class":913,"line":914},[911,3001,3002],{"class":963},"# pip 安装\n",[911,3004,3005,3008,3011],{"class":913,"line":549},[911,3006,3007],{"class":917},"pip",[911,3009,3010],{"class":921}," install",[911,3012,3013],{"class":921}," langflow\n",[911,3015,3016,3019,3022],{"class":913,"line":546},[911,3017,3018],{"class":917},"langflow",[911,3020,3021],{"class":921}," run",[911,3023,3024],{"class":963},"  # http:\u002F\u002Flocalhost:7860\n",[911,3026,3027],{"class":913,"line":581},[911,3028,1111],{"emptyLinePlaceholder":566},[911,3030,3031],{"class":913,"line":582},[911,3032,3033],{"class":963},"# 或 Docker\n",[911,3035,3036,3038,3040,3043,3046],{"class":913,"line":1108},[911,3037,577],{"class":917},[911,3039,3021],{"class":921},[911,3041,3042],{"class":930}," -p",[911,3044,3045],{"class":921}," 7860:7860",[911,3047,3048],{"class":921}," langflowai\u002Flangflow:latest\n",[40,3050,3051],{},"试 RAG 流：",[238,3053,3054,3057,3060,3063,3066,3069,3072],{},[53,3055,3056],{},"新建 flow → 选 Document QA 模板",[53,3058,3059],{},"Document Loader 节点 → 上传 PDF",[53,3061,3062],{},"Splitter → Embedder（OpenAI 或本地）",[53,3064,3065],{},"VectorStore（Astra \u002F Chroma）",[53,3067,3068],{},"Retriever + ChatOpenAI → Chat Output",[53,3070,3071],{},"部署为 API → 拿到 endpoint",[53,3073,3074],{},"复杂场景下钻节点写 Python 自定义",[35,3076,268],{"id":268},[112,3078,3079,3093],{},[115,3080,3081],{},[118,3082,3083,3085,3087,3089,3091],{},[121,3084,277],{},[121,3086,288],{},[121,3088,282],{},[121,3090,1216],{},[121,3092,514],{},[130,3094,3095,3112,3126,3140,3154,3170,3183,3200],{},[118,3096,3097,3100,3103,3106,3109],{},[135,3098,3099],{},"中心",[135,3101,3102],{},"LangChain primitive",[135,3104,3105],{},"LLMOps 全平台",[135,3107,3108],{},"通用 workflow",[135,3110,3111],{},"LangChain（JS）",[118,3113,3114,3116,3118,3121,3124],{},[135,3115,1223],{},[135,3117,2354],{},[135,3119,3120],{},"✅ AGPL",[135,3122,3123],{},"✅ Sustainable",[135,3125,2354],{},[118,3127,3128,3130,3133,3136,3138],{},[135,3129,1009],{},[135,3131,3132],{},"✅ pip\u002FDocker",[135,3134,3135],{},"✅ Docker",[135,3137,3135],{},[135,3139,316],{},[118,3141,3142,3145,3148,3150,3152],{},[135,3143,3144],{},"可视化",[135,3146,3147],{},"✅ 旗舰",[135,3149,316],{},[135,3151,316],{},[135,3153,316],{},[118,3155,3156,3159,3162,3165,3168],{},[135,3157,3158],{},"代码下钻",[135,3160,3161],{},"✅ Python",[135,3163,3164],{},"部分",[135,3166,3167],{},"✅ JS",[135,3169,3167],{},[118,3171,3172,3175,3177,3179,3181],{},[135,3173,3174],{},"RAG 内置",[135,3176,316],{},[135,3178,316],{},[135,3180,3164],{},[135,3182,316],{},[118,3184,3185,3188,3191,3194,3197],{},[135,3186,3187],{},"起价（云）",[135,3189,3190],{},"$25\u002F月",[135,3192,3193],{},"$59\u002F月（Team）",[135,3195,3196],{},"自托管 $0",[135,3198,3199],{},"–",[118,3201,3202,3205,3208,3211,3214],{},[135,3203,3204],{},"适合",[135,3206,3207],{},"工程 + LangChain",[135,3209,3210],{},"业务 + LLMOps",[135,3212,3213],{},"通用自动化",[135,3215,3216],{},"JS 生态",[35,3218,397],{"id":397},[50,3220,3221,3227,3233,3239,3245,3251,3257,3263,3268],{},[53,3222,3223,3226],{},[56,3224,3225],{},"自托管推荐 Docker","：pip 版本依赖冲突难调",[53,3228,3229,3232],{},[56,3230,3231],{},"生产加 observability","：langsmith \u002F sentry \u002F prometheus 必装",[53,3234,3235,3238],{},[56,3236,3237],{},"Astra DB cloud free","：起步够用，付费版起步前算清成本",[53,3240,3241,3244],{},[56,3242,3243],{},"复杂 flow 拆模块","：100+ 节点画布卡顿，拆成子 flow",[53,3246,3247,3250],{},[56,3248,3249],{},"LangChain 版本 pin","：Langflow 升级前测试 flow 兼容性",[53,3252,3253,3256],{},[56,3254,3255],{},"第三方 API 加重试","：custom Python 节点写 retry + fallback",[53,3258,3259,3262],{},[56,3260,3261],{},"多人协作 lock","：同时编辑 flow 容易冲突，加 lock 流程",[53,3264,3265,3267],{},[56,3266,1307],{},"：UI 英文为主，业务侧用户先培训",[53,3269,3270,3273],{},[56,3271,3272],{},"Self-host vs Cloud","：合规要 self-host，省心要 Cloud",[35,3275,442],{"id":441},[50,3277,3278,3281,3284,3287,3290,3293,3296,3299],{},[53,3279,3280],{},"✅ 工程团队要可视化建 LangChain 流",[53,3282,3283],{},"✅ 合规 \u002F 数据驻留要求自托管",[53,3285,3286],{},"✅ 要 Astra DB 一站式 RAG",[53,3288,3289],{},"✅ Python 团队 + 想画布 + 想下钻代码",[53,3291,3292],{},"❌ 业务侧 + 非工程师（用 Dify \u002F Coze）",[53,3294,3295],{},"❌ 纯无代码偏好",[53,3297,3298],{},"❌ 轻量场景 + 直接写 LangChain 更快",[53,3300,3301],{},"❌ JS 生态优先（用 Flowise）",[35,3303,502],{"id":502},[50,3305,3306,3312,3318],{},[53,3307,3308],{},[506,3309,3311],{"href":3310},"\u002Ftools\u002Fagent\u002Fplatform\u002Fn8n","n8n 评测",[53,3313,3314],{},[506,3315,3317],{"href":3316},"\u002Ftools\u002Fagent\u002Fprotocol\u002Fcomposio","Composio 评测",[53,3319,3320],{},[506,3321,3323],{"href":3322},"\u002Ftools\u002Fagent\u002Fgeneral\u002Fopenmanus","OpenManus 评测",[35,3325,521],{"id":521},[238,3327,3328,3335,3342,3349],{},[53,3329,3330,3331],{},"Langflow 官网 + 定价 ",[506,3332,3333],{"href":3333,"rel":3334},"https:\u002F\u002Fwww.langflow.org\u002Fpricing",[535],[53,3336,3337,3338],{},"AutomationAtlas — Langflow $25\u002Fmo 2026 Comparison ",[506,3339,3340],{"href":3340,"rel":3341},"https:\u002F\u002Fautomationatlas.io\u002Ftools\u002Flangflow",[535],[53,3343,3344,3345],{},"xpay — LangFlow 2026 Visual AI Agent Builder（MIT + DataStax）",[506,3346,3347],{"href":3347,"rel":3348},"https:\u002F\u002Fwww.xpay.sh\u002Fresources\u002Fagentic-frameworks\u002Flangflow",[535],[53,3350,3351,3352],{},"SelectHub — LangFlow Reviews 2026（稳定性 + 生产 readiness）",[506,3353,3354],{"href":3354,"rel":3355},"https:\u002F\u002Fwww.selecthub.com\u002Fp\u002Fai-agent-builder-software\u002Flangflow\u002F",[535],[1625,3357,2711],{},{"title":545,"searchDepth":546,"depth":546,"links":3359},[3360,3361,3362,3363,3364,3365,3366,3367,3368,3369],{"id":37,"depth":549,"text":38},{"id":48,"depth":549,"text":48},{"id":110,"depth":549,"text":110},{"id":2929,"depth":549,"text":2930},{"id":236,"depth":549,"text":236},{"id":268,"depth":549,"text":268},{"id":397,"depth":549,"text":397},{"id":441,"depth":549,"text":442},{"id":502,"depth":549,"text":502},{"id":521,"depth":549,"text":521},"\u002Fimg\u002Ftools\u002Flangflow.webp","Langflow 真实评测：2023 开源项目，2024 被 DataStax 收购，与 Astra DB 向量数据库平台深度集成。20,000+ GitHub stars、MIT 协议。差异点：拖拽式可视化画布映射 LangChain 原语 + RAG pipeline 原生组件 + 多 agent 工作流 + Python + 自托管（pip \u002F Docker）+ DataStax Astra 托管云 + Pinecone \u002F pgvector \u002F 主流向量库适配。Cloud 免费档 + 约 $25\u002F月起。",[3373,3376,3379,3382],{"q":3374,"a":3375},"Langflow 和 Dify \u002F n8n \u002F Flowise 怎么选？","Langflow 强『可视化 LangChain 原语 + Python 代码可下钻 + 自托管 + Astra DB 集成』，工程导向。Dify 是 LLMOps 全平台（含 dataset \u002F app \u002F observability），业务侧更友好。n8n 是通用 workflow（非 LangChain 中心），70+ LangChain 节点是后加的。Flowise 是 Langflow 的同类竞品（JS 生态）。工程团队 + Python + 合规自托管 → Langflow；业务 + 完整 LLMOps → Dify；通用自动化 → n8n。",{"q":3377,"a":3378},"为什么被 DataStax 收购？","DataStax 是 Cassandra 商业公司 + Astra DB 向量数据库厂商。收购 Langflow 是为了把『可视化 LangChain builder』和『生产级 vector store』捆成一站式 RAG 解决方案。Langflow 主仓仍是 MIT 开源，但 cloud \u002F 企业版深度依赖 Astra。",{"q":3380,"a":3381},"可视化画布会不会限制灵活度？","Langflow 节点对应 LangChain primitive，可在任意节点下钻到 Python 代码 + 自定义。所以可视化层是『脚手架 + 协作工具』，不是『纯无代码黑盒』。复杂逻辑、custom tool、retriever 都能写代码扩展。",{"q":3383,"a":3384},"production readiness 如何？","用户反馈：原型 + 内部工具非常顺；大流量 \u002F 关键业务要自行加 observability \u002F 错误处理 \u002F 缓存。SelectHub 评测列出『稳定性偶发 + 第三方 API 依赖 + 非完全 production-ready』。生产部署建议 Astra-hosted cloud 或自托管 + 加 sentry \u002F langsmith \u002F prometheus。","https:\u002F\u002Fgithub.com\u002Flangflow-ai\u002Flangflow",[568,3387],"multi",{},"\u002Ftools\u002Fagent\u002Fplatform\u002Flangflow",[594,3391,577,3392],"cloud","web",[3394,3397,3399,3403],{"plan":2903,"price":140,"features":3395,"notes":3396},"MIT + pip\u002FDocker + 全部组件 + Pinecone\u002Fpgvector\u002FAstra 适配","自付 LLM API + 服务器",{"plan":2909,"price":140,"features":3398,"notes":2757},"DataStax 托管 + 小流量",{"plan":2915,"price":3400,"features":3401,"notes":3402},"~$25\u002F月起","Astra DB + 更高额度 + 更多并发 + 团队协作","按使用量阶梯",{"plan":159,"price":162,"features":3404,"notes":3405},"SSO + audit + 私有部署 + SLA + 数据驻留","合规 \u002F 大客户","Self-host 完全免费 MIT \u002F Cloud Free + 付费起 ~$25·月（Astra-hosted）","2026-06-19",[3409],"onboarding\u002Frag-app-workflow",{"power":581,"ux":582,"price":582,"cn_support":546,"stability":581},{"title":288,"description":3371},"Langflow 评测 2026：可视化 AI 工作流构建工具，LangChain 低代码平台",[3414,3417,3419,3421],{"name":3415,"url":3416,"accessed":1704},"Langflow 官网","https:\u002F\u002Fwww.langflow.org",{"name":3418,"url":3340,"accessed":1704},"AutomationAtlas — Langflow $25\u002Fmo 2026 Comparison",{"name":3420,"url":3347,"accessed":1704},"xpay — LangFlow 2026 Visual AI Agent Builder",{"name":3422,"url":3354,"accessed":1704},"SelectHub — LangFlow Reviews 2026","tools\u002Fagent\u002Fplatform\u002Flangflow","DataStax 出品的可视化 LangChain 画布——MIT 开源 + 20k+ stars + 自托管 + Astra DB 云托管",[593,3426,3427,595,3428,3018],"visual-builder","langchain","datastax","工程团队要可视化建 LangChain 流 + 不被 SaaS 锁住 + 合规要求自托管的最佳选择。要纯无代码 + 业务侧 → Dify \u002F Coze；要纯代码 → 直接写 LangChain \u002F LlamaIndex。","PNaJCu7eJDsH8LhAlO7CJ5JmktlB6hCq0vbBojxlTeM",1785660639195]