[{"data":1,"prerenderedAt":3704},["ShallowReactive",2],{"header-counts":3,"footer-counts":6,"alt-main-raga":8,"alt-list-raga":781},{"tools":4,"reviews":5},98,27,{"tools":4,"reviews":5,"playbooks":7,"news":5},23,{"id":9,"title":10,"alternatives":11,"api_compatible":16,"body":17,"category":711,"chinese_friendly":197,"cover":712,"description":713,"domestic":714,"extension":715,"faq":716,"free":237,"github":16,"languages":729,"lastVerified":731,"meta":732,"models":16,"navigation":237,"notSuitable":16,"opensource":237,"path":733,"pillar":734,"platforms":735,"priceTable":738,"pricing":751,"published":752,"relatedPlaybooks":753,"relatedReviews":16,"score":755,"self_host":237,"seo":756,"seoTitle":757,"slug":758,"sources":759,"stem":769,"suitable":16,"tagline":770,"tags":771,"updated":762,"verdict":778,"website":779,"__hash__":780},"tools\u002Ftools\u002Fcoding\u002Fagent\u002Fraga.md","RagaAI Catalyst",[12,13,14,15],"coding\u002Fapi\u002Fhelicone","coding\u002Fapi\u002Fportkey","coding\u002Fapi\u002Flitellm","agent\u002Fplatform\u002Flangflow",null,{"type":18,"value":19,"toc":699},"minimark",[20,25,29,32,35,94,97,117,123,127,132,152,157,180,183,209,373,376,547,550,600,604,630,633,660,663,695],[21,22,24],"h2",{"id":23},"tldr","TL;DR",[26,27,28],"p",{},"RagaAI Catalyst 是印度 RagaAI 出品的 AI agent \u002F RAG \u002F LLM 应用『测试 + 观测』专项平台——Python SDK + 云端，300+ 自动化测试库覆盖 hallucination \u002F bias \u002F RAG faithfulness \u002F agentic 协作，多 agent 系统 tracing + debug，主打『部署前发现生产风险减少 90%』。Catalyst SDK 开源（github.com\u002Fraga-ai-hub\u002FRagaAI-Catalyst），Cloud \u002F Enterprise 走销售定价。",[26,30,31],{},"适合：构建生产级 agentic 系统的中大型团队（金融 \u002F 医疗 \u002F 政企）需要 pre-prod 风险量化；RAG pipeline 需要 retrieval \u002F faithfulness 多维评测；多 agent 协作系统需要 trace + debug 工具链。不适合：单一 LLM 调用 + 简单观测（Helicone \u002F Langfuse 更轻）；预算紧的小团队（社区版 SDK 够但 Cloud 走企业销售）；中文 \u002F 国内合规为主（生态弱）。",[21,33,34],{"id":34},"核心能力",[36,37,38,46,52,58,64,70,76,82,88],"ul",{},[39,40,41,45],"li",{},[42,43,44],"strong",{},"300+ 自动化测试","：LLM（hallucination \u002F toxicity \u002F PII \u002F prompt injection）+ RAG（precision \u002F faithfulness \u002F relevance）+ Agentic（tool 正确性 \u002F 协作一致性 \u002F 任务完成率）",[39,47,48,51],{},[42,49,50],{},"多 agent tracing","：每次 LLM \u002F tool \u002F sub-agent 调用都可追溯 + 时序回放",[39,53,54,57],{},[42,55,56],{},"多 agent debug","：复杂 agent 失败时回放每个决策点 + 上下文",[39,59,60,63],{},[42,61,62],{},"Python SDK","：包装 LangChain \u002F LlamaIndex \u002F 自家框架自动埋点",[39,65,66,69],{},[42,67,68],{},"风险量化","：每个测试出风险分 + 影响面 + 修复建议",[39,71,72,75],{},[42,73,74],{},"数据集管理","：build eval dataset + 跑 regression",[39,77,78,81],{},[42,79,80],{},"报告 \u002F 仪表盘","：团队级风险仪表盘 + 趋势 + CI\u002FCD 集成",[39,83,84,87],{},[42,85,86],{},"自托管 SDK + Cloud 协同","：SDK 本地跑、Cloud 集中可视化",[39,89,90,93],{},[42,91,92],{},"合规友好","：私有部署 + SSO + 审计（Enterprise）",[21,95,96],{"id":96},"价格",[36,98,99,105,111],{},[39,100,101,104],{},[42,102,103],{},"Catalyst OSS Python SDK","：$0；功能含 tracing + 部分 eval",[39,106,107,110],{},[42,108,109],{},"Cloud","：Custom（联系销售）；含全 300+ 测试库 + 仪表盘 + 协作",[39,112,113,116],{},[42,114,115],{},"Enterprise","：Custom + SSO + 私有部署 + SLA",[118,119,120],"blockquote",{},[26,121,122],{},"真实场景：先用 OSS SDK 跑 trace 评估价值，进 PoC 后再谈 Cloud 价格。",[21,124,126],{"id":125},"实测中型-rag-agentic-产品-印度欧美客户","实测（中型 RAG \u002F agentic 产品 \u002F 印度欧美客户）",[26,128,129],{},[42,130,131],{},"亮点：",[36,133,134,137,140,143,146,149],{},[39,135,136],{},"300+ 测试库省去 reinvent 各种 eval metric 的工作",[39,138,139],{},"RAG faithfulness \u002F context precision 测试对反 hallucination 很有用",[39,141,142],{},"多 agent trace + 回放在调试复杂协作链时是救命工具",[39,144,145],{},"风险评分给业务方一个 quantitative 沟通口径",[39,147,148],{},"Catalyst SDK 开源，预算紧团队也能先用上",[39,150,151],{},"与 LangChain \u002F LlamaIndex 集成顺滑",[26,153,154],{},[42,155,156],{},"踩坑：",[36,158,159,162,165,168,171,174,177],{},[39,160,161],{},"文档对高级配置 + 自定义测试覆盖不足，社区反馈一致",[39,163,164],{},"Cloud 定价不透明，PoC 才能拿到报价",[39,166,167],{},"报告 \u002F UI 偏英文 + 印度产品风格，中文场景弱",[39,169,170],{},"多 agent trace 在超大调用图（>500 step）下渲染慢",[39,172,173],{},"测试结果质量依赖 dataset 质量，garbage in \u002F garbage out",[39,175,176],{},"比 Langfuse 更偏『测试』少偏『日常 observability』，两个工具有时要叠用",[39,178,179],{},"国内访问 Cloud 延迟 + 合规需要评估",[21,181,182],{"id":182},"上手",[184,185,190],"pre",{"className":186,"code":187,"language":188,"meta":189,"style":189},"language-bash shiki shiki-themes github-light github-dark","pip install ragaai-catalyst\n","bash","",[191,192,193],"code",{"__ignoreMap":189},[194,195,198,202,206],"span",{"class":196,"line":197},"line",1,[194,199,201],{"class":200},"sScJk","pip",[194,203,205],{"class":204},"sZZnC"," install",[194,207,208],{"class":204}," ragaai-catalyst\n",[184,210,214],{"className":211,"code":212,"language":213,"meta":189,"style":189},"language-python shiki shiki-themes github-light github-dark","from ragaai_catalyst import RagaAICatalyst, Tracer\n\ncatalyst = RagaAICatalyst(\n    access_key=\"...\",\n    secret_key=\"...\",\n    base_url=\"https:\u002F\u002Fcatalyst.raga.ai\"\n)\n\ntracer = Tracer(project_name=\"my-rag-app\", tracer_type=\"langchain\")\ntracer.start()\n\n# 你的 LangChain \u002F LlamaIndex \u002F 自家 agent 代码\n# tracer 自动捕获 LLM \u002F tool \u002F sub-agent 调用\n\ntracer.stop()\n# 登录 Catalyst Cloud 看 trace + 跑 300+ 测试\n","python",[191,215,216,232,239,251,266,278,289,295,300,332,338,343,350,356,361,367],{"__ignoreMap":189},[194,217,218,222,226,229],{"class":196,"line":197},[194,219,221],{"class":220},"szBVR","from",[194,223,225],{"class":224},"sVt8B"," ragaai_catalyst ",[194,227,228],{"class":220},"import",[194,230,231],{"class":224}," RagaAICatalyst, Tracer\n",[194,233,235],{"class":196,"line":234},2,[194,236,238],{"emptyLinePlaceholder":237},true,"\n",[194,240,242,245,248],{"class":196,"line":241},3,[194,243,244],{"class":224},"catalyst ",[194,246,247],{"class":220},"=",[194,249,250],{"class":224}," RagaAICatalyst(\n",[194,252,254,258,260,263],{"class":196,"line":253},4,[194,255,257],{"class":256},"s4XuR","    access_key",[194,259,247],{"class":220},[194,261,262],{"class":204},"\"...\"",[194,264,265],{"class":224},",\n",[194,267,269,272,274,276],{"class":196,"line":268},5,[194,270,271],{"class":256},"    secret_key",[194,273,247],{"class":220},[194,275,262],{"class":204},[194,277,265],{"class":224},[194,279,281,284,286],{"class":196,"line":280},6,[194,282,283],{"class":256},"    base_url",[194,285,247],{"class":220},[194,287,288],{"class":204},"\"https:\u002F\u002Fcatalyst.raga.ai\"\n",[194,290,292],{"class":196,"line":291},7,[194,293,294],{"class":224},")\n",[194,296,298],{"class":196,"line":297},8,[194,299,238],{"emptyLinePlaceholder":237},[194,301,303,306,308,311,314,316,319,322,325,327,330],{"class":196,"line":302},9,[194,304,305],{"class":224},"tracer ",[194,307,247],{"class":220},[194,309,310],{"class":224}," Tracer(",[194,312,313],{"class":256},"project_name",[194,315,247],{"class":220},[194,317,318],{"class":204},"\"my-rag-app\"",[194,320,321],{"class":224},", ",[194,323,324],{"class":256},"tracer_type",[194,326,247],{"class":220},[194,328,329],{"class":204},"\"langchain\"",[194,331,294],{"class":224},[194,333,335],{"class":196,"line":334},10,[194,336,337],{"class":224},"tracer.start()\n",[194,339,341],{"class":196,"line":340},11,[194,342,238],{"emptyLinePlaceholder":237},[194,344,346],{"class":196,"line":345},12,[194,347,349],{"class":348},"sJ8bj","# 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Custom",[406,522,523],{},"✅ $0\u002F$29\u002FEnterprise",[406,525,526],{},"✅ $0\u002F$79\u002F$799",[406,528,529],{},"✅ $0\u002F$49\u002F$799",[383,531,532,535,538,541,544],{},[406,533,534],{},"适合",[406,536,537],{},"pre-prod testing + agentic",[406,539,540],{},"日常 tracing + eval",[406,542,543],{},"改 URL 快上手",[406,545,546],{},"gateway + 治理",[21,548,549],{"id":549},"避坑",[36,551,552,558,564,570,576,582,588,594],{},[39,553,554,557],{},[42,555,556],{},"OSS SDK ≠ 完整 Cloud","：300+ 测试库主要在 Cloud，OSS 主要 tracing + 部分 eval",[39,559,560,563],{},[42,561,562],{},"dataset 质量决定测试质量","：建 eval dataset 时务必含 edge case \u002F adversarial 例",[39,565,566,569],{},[42,567,568],{},"大 trace 渲染慢","：>500 step 的 agent 用 filter \u002F sampling",[39,571,572,575],{},[42,573,574],{},"Cloud 定价 PoC 谈","：先 SDK 跑两月有数据再谈合同",[39,577,578,581],{},[42,579,580],{},"不替代日常 observability","：复杂场景叠 Langfuse \u002F Helicone",[39,583,584,587],{},[42,585,586],{},"中文场景弱","：报告 \u002F 文档 \u002F UI 均英文为主，国内项目要评估团队接受度",[39,589,590,593],{},[42,591,592],{},"Agentic 测试时间长","：300+ 测试跑一遍可能数小时，CI\u002FCD 集成要 schedule 而非每次 PR",[39,595,596,599],{},[42,597,598],{},"风险评分谨慎宣传","：『-90% 生产风险』是营销话术，实际依赖你的实施质量",[21,601,603],{"id":602},"适合-不适合","适合 \u002F 不适合",[36,605,606,609,612,615,618,621,624,627],{},[39,607,608],{},"✅ 生产级 agentic 系统 \u002F RAG pipeline 的中大型团队",[39,610,611],{},"✅ 金融 \u002F 医疗 \u002F 政企需要风险量化沟通",[39,613,614],{},"✅ 多 agent 协作系统需要 trace + debug",[39,616,617],{},"✅ 想把 LLM eval 从手工脚本升级成系统化平台",[39,619,620],{},"❌ 单一 LLM 调用 + 简单观测",[39,622,623],{},"❌ 预算紧 + 不要 Cloud（OSS SDK 部分能力够）",[39,625,626],{},"❌ 中文 \u002F 国内合规为主",[39,628,629],{},"❌ 不愿走企业销售流程",[21,631,632],{"id":632},"相关阅读",[36,634,635,642,648,654],{},[39,636,637],{},[638,639,641],"a",{"href":640},"\u002Ftools\u002Fcoding\u002Fapi\u002Fhelicone","Helicone 评测",[39,643,644],{},[638,645,647],{"href":646},"\u002Ftools\u002Fcoding\u002Fapi\u002Fportkey","Portkey 评测",[39,649,650],{},[638,651,653],{"href":652},"\u002Ftools\u002Fcoding\u002Fapi\u002Flitellm","LiteLLM 评测",[39,655,656],{},[638,657,659],{"href":658},"\u002Ftools\u002Fagent\u002Flangflow","Langflow 评测",[21,661,662],{"id":662},"来源",[664,665,666,674,681,688],"ol",{},[39,667,668,669],{},"F6S — RagaAI Catalyst 产品概览 ",[638,670,671],{"href":671,"rel":672},"https:\u002F\u002Fwww.f6s.com\u002Fsoftware\u002Fragaai-catalyst",[673],"nofollow",[39,675,676,677],{},"AIIndigo — RagaAI Catalyst 评测 + 定价 + 替代品 2026 ",[638,678,679],{"href":679,"rel":680},"https:\u002F\u002Faiindigo.com\u002Ftool\u002Fragaai-catalyst",[673],[39,682,683,684],{},"SwitchTools — RagaAI Inc 平台（300+ 测试 \u002F 90% 风险下降）",[638,685,686],{"href":686,"rel":687},"https:\u002F\u002Fwww.switchtools.io\u002Ftool\u002Fragaai-inc",[673],[39,689,690,691],{},"SoftwareSuggest — RagaAI Details \u002F Pricing 2026 ",[638,692,693],{"href":693,"rel":694},"https:\u002F\u002Fwww.softwaresuggest.com\u002Fragaai",[673],[696,697,698],"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 .default .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: 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.sJ8bj{--shiki-default:#6A737D;--shiki-dark:#6A737D}",{"title":189,"searchDepth":241,"depth":241,"links":700},[701,702,703,704,705,706,707,708,709,710],{"id":23,"depth":234,"text":24},{"id":34,"depth":234,"text":34},{"id":96,"depth":234,"text":96},{"id":125,"depth":234,"text":126},{"id":182,"depth":234,"text":182},{"id":375,"depth":234,"text":375},{"id":549,"depth":234,"text":549},{"id":602,"depth":234,"text":603},{"id":632,"depth":234,"text":632},{"id":662,"depth":234,"text":662},"agent","\u002Fimg\u002Ftools\u002Fraga.webp","RagaAI Catalyst 真实评测：印度 RagaAI 出品的 AI 测试 + 观测平台（Python SDK + 云端）。300+ 自动化测试覆盖 LLM \u002F RAG \u002F 多 agent，多 agent 系统 tracing + 调试，主打『部署前发现风险减少 90%』。F6S \u002F SoftwareSuggest \u002F SwitchTools 已收录。定价以企业销售为主，社区版 Python SDK 开源。",false,"md",[717,720,723,726],{"q":718,"a":719},"RagaAI 和 Langfuse \u002F Helicone 怎么选？","RagaAI Catalyst 把『测试』作为一等公民——300+ 自动化测试 + RAG \u002F Agent 风险量化，强调『部署前发现』。Langfuse 是 tracing + eval 双强，Helicone 是 proxy 观测 + gateway。要 agentic 系统的 pre-prod testing + 风险评分 → RagaAI；要日常 LLM trace \u002F 简单 eval → Langfuse；要改 baseURL 看 cost → Helicone。",{"q":721,"a":722},"300+ 测试覆盖什么？","覆盖三类系统：(1) LLM 应用——hallucination \u002F toxicity \u002F bias \u002F PII leakage \u002F prompt injection；(2) RAG pipeline——检索准确率 \u002F context precision \u002F answer relevance \u002F faithfulness；(3) Agentic 系统——tool use 正确性 \u002F 多 agent 协作一致性 \u002F 任务完成率 \u002F 安全围栏。",{"q":724,"a":725},"Catalyst SDK 怎么用？","pip install ragaai-catalyst → 包装 LangChain \u002F LlamaIndex \u002F 自家 agent 框架 → 自动 trace 每次 LLM \u002F tool \u002F sub-agent 调用 → 上传 Cloud 看仪表盘 \u002F 跑测试。SDK 本身 OSS，Cloud 部分付费。",{"q":727,"a":728},"国内能用吗？","Python SDK 自托管 OK。Cloud 服务器在海外，国内延迟 + 合规要评估。中文场景 + 中文文档极弱，prompt \u002F 报告均英文。国内类似定位推荐看 PromptLayer \u002F Helicone OSS 自托管，或自家 LangSmith \u002F Phoenix 组合。",[730],"en","2026-08-02",{},"\u002Ftools\u002Fcoding\u002Fagent\u002Fraga","coding",[213,736,737],"sdk","cloud",[739,744,748],{"plan":740,"price":741,"features":742,"notes":743},"Catalyst OSS","$0","Python SDK + agent \u002F LLM \u002F tool tracing + 多 agent debug + 部分 eval","github.com\u002Fraga-ai-hub\u002FRagaAI-Catalyst",{"plan":109,"price":745,"features":746,"notes":747},"Custom","300+ 自动化测试库 + RAG \u002F LLM \u002F Agentic 全景 + 报告 \u002F 仪表盘 + 协作","联系销售",{"plan":115,"price":745,"features":749,"notes":750},"SSO + 私有部署 + 合规 + SLA + 专属支持","大型 \u002F 受监管","Catalyst Python SDK 开源 \u002F Cloud Enterprise 定制","2026-06-19",[754],"onboarding\u002Frag-pipeline-build",{"power":268,"ux":241,"price":241,"cn_support":197,"stability":253},{"title":10,"description":713},"RagaAI Catalyst - AI Agent 测试平台评测 | AIHO","coding\u002Fagent\u002Fraga",[760,763,765,767],{"name":761,"url":671,"accessed":762},"F6S — RagaAI Catalyst 概览","2026-06-24",{"name":764,"url":679,"accessed":762},"AIIndigo — RagaAI Catalyst 评测 2026",{"name":766,"url":686,"accessed":762},"SwitchTools — RagaAI Inc 平台 + 90% 风险下降",{"name":768,"url":693,"accessed":762},"SoftwareSuggest — RagaAI Details 2026","tools\u002Fcoding\u002Fagent\u002Fraga","AI agent 测试 + 观测平台——300+ 自动化测试覆盖 LLM\u002FRAG\u002FAgentic，量化『生产风险 -90%』",[772,773,774,775,776,777],"agent-testing","llm-eval","rag-testing","observability","tracing","opensource","agentic AI 系统的『生产前测试 + 上线后观测』专项工具。要 300+ 测试库 + 多 agent debug 走 Raga；要简单 LLM trace 用 Langfuse \u002F Helicone；要网关 + 治理用 Portkey。中文 \u002F 国内场景生态弱。","https:\u002F\u002Fraga.ai","BjmvgLgqRDUh9oGaFrheYlvKgcs-v5Ie85syC3IC3hI",[782,1526,2337,3071],{"id":783,"title":396,"alternatives":784,"api_compatible":16,"body":787,"category":1461,"chinese_friendly":234,"cover":1462,"description":1463,"domestic":714,"extension":715,"faq":1464,"free":237,"github":1477,"languages":1478,"lastVerified":731,"meta":1479,"models":16,"navigation":237,"notSuitable":16,"opensource":237,"path":640,"pillar":734,"platforms":1480,"priceTable":1483,"pricing":1498,"published":752,"relatedPlaybooks":1499,"relatedReviews":16,"score":1500,"self_host":237,"seo":1501,"seoTitle":1502,"slug":12,"sources":1503,"stem":1514,"suitable":16,"tagline":1515,"tags":1516,"updated":762,"verdict":1523,"website":1524,"__hash__":1525},"tools\u002Ftools\u002Fcoding\u002Fapi\u002Fhelicone.md",[13,14,785,786],"coding\u002Fapi\u002Fopenrouter","coding\u002Fapi\u002Fone-api",{"type":18,"value":788,"toc":1449},[789,791,794,797,799,877,879,904,909,913,917,940,944,970,972,1091,1135,1137,1300,1302,1352,1354,1383,1385,1407,1409,1446],[21,790,24],{"id":23},[26,792,793],{},"Helicone 是 YC W23 出身的开源 LLM 观测平台，主打『一行代码接入』：改 baseURL，立刻看到 cost \u002F latency \u002F errors \u002F token 详情。背后还有 Rust 写的 AI Gateway，路由 100+ 模型 + 缓存 + fallback + 限流。2026-03-03 被 Mintlify 收购后转维护模式（安全 patch + 新模型 + bug fix 继续，主动新功能停）。Hobby 免费 10K req\u002F月，Pro $79，Team $799（SOC2 + HIPAA），Enterprise 定制。SOC2 + GDPR 合规，可 Docker \u002F Kubernetes 自托管。",[26,795,796],{},"适合：要 5 分钟接入 LLM 观测的小团队 \u002F 早期产品；改 baseURL 模式想看 cost \u002F latency 即可；预算紧 → Hobby 10K 免费 + 自托管 OSS。不适合：复杂 agent 调试需要 nested span（Langfuse）；要 250+ 模型 \u002F MCP Gateway \u002F 治理（Portkey）；担心维护模式带来的路线图风险（Mintlify 收购后主动开发结束）。",[21,798,34],{"id":34},[36,800,801,807,813,819,825,830,836,842,847,853,859,865,871],{},[39,802,803,806],{},[42,804,805],{},"Proxy 模式","：改 baseURL，零 SDK 改造",[39,808,809,812],{},[42,810,811],{},"Async 模式","：SDK 异步上报，0 延迟 + 容错",[39,814,815,818],{},[42,816,817],{},"Request logging","：每条请求 + 响应 + cost + token + latency",[39,820,821,824],{},[42,822,823],{},"Custom properties + sessions","：把多步工作流分组看",[39,826,827],{},[42,828,829],{},"Prompt management + playground",[39,831,832,835],{},[42,833,834],{},"Custom scoring","：基础打分 + 数据集",[39,837,838,841],{},[42,839,840],{},"AI Gateway","（Rust 写）：100+ 模型 + caching + fallback + rate limit",[39,843,844],{},[42,845,846],{},"OpenAI \u002F Anthropic \u002F Azure \u002F LiteLLM \u002F Anyscale \u002F Together \u002F OpenRouter 集成",[39,848,849,852],{},[42,850,851],{},"Trace API","：手动 POST 多步 trace 数据（弥补 proxy 看不到 agent 内部）",[39,854,855,858],{},[42,856,857],{},"Dashboard API","：查 dashboard 数据",[39,860,861,864],{},[42,862,863],{},"Self-host","：Docker \u002F Kubernetes，开源 MIT",[39,866,867,870],{},[42,868,869],{},"合规","：SOC 2 + GDPR；Team+ HIPAA",[39,872,873,876],{},[42,874,875],{},"导出","：webhook + 外部 reporting",[21,878,96],{"id":96},[36,880,881,887,893,899],{},[39,882,883,886],{},[42,884,885],{},"Hobby","：$0 \u002F 10K req\u002F月 \u002F 7 天 retention \u002F 1 seat \u002F 1GB 存储",[39,888,889,892],{},[42,890,891],{},"Pro","：$79\u002F月 \u002F 10K 起 + usage \u002F 1 月 retention \u002F unlimited seats \u002F alerts \u002F HQL",[39,894,895,898],{},[42,896,897],{},"Team","：$799\u002F月 \u002F 10K 起 + usage \u002F 3 月 retention \u002F SOC 2 + HIPAA \u002F 5 orgs",[39,900,901,903],{},[42,902,115],{},"：Custom \u002F 自定义 retention \u002F 永久存储 \u002F SSO \u002F on-prem",[118,905,906],{},[26,907,908],{},"自托管版（OSS）= $0 + 无限 logs，但运维 \u002F Postgres \u002F Clickhouse 全要自己搞。",[21,910,912],{"id":911},"实测早期-saas-5-人团队","实测（早期 SaaS \u002F 5 人团队）",[26,914,915],{},[42,916,131],{},[36,918,919,922,925,928,931,934,937],{},[39,920,921],{},"5 分钟接入，OpenAI SDK 改 baseURL + 加 Helicone-Auth header",[39,923,924],{},"Dashboard 看 cost \u002F latency \u002F error 第一天就帮发现 prompt 失控",[39,926,927],{},"Custom properties 给每个 user \u002F feature 打标，分摊成本清晰",[39,929,930],{},"Sessions 把 multi-step workflow 串起来（不像 Langfuse 是真 trace，但够小项目用）",[39,932,933],{},"AI Gateway 给 fallback \u002F caching 一处接入",[39,935,936],{},"开源自托管对预算紧团队是真救命",[39,938,939],{},"与 LiteLLM 集成顺滑",[26,941,942],{},[42,943,156],{},[36,945,946,949,952,955,958,961,964,967],{},[39,947,948],{},"2026-03 被 Mintlify 收购转维护模式后，主动功能开发停，长期路线图不确定",[39,950,951],{},"Proxy 模式 5–20ms 延迟在高 QPS 场景叠加可观",[39,953,954],{},"Proxy 看不到 agent 内部 tool \u002F sub-agent \u002F retries——复杂 agent 调试不够",[39,956,957],{},"Custom scoring 浅，不如 Langfuse 的 LLM-as-judge + 数据集 eval 体系完整",[39,959,960],{},"Free tier 10K req 小型生产几天就用完",[39,962,963],{},"文档自 Mintlify 收购后部分整合到 Mintlify Docs，导航变化",[39,965,966],{},"中文场景体验有限",[39,968,969],{},"Prompt caching 需要 cache-aware prompt design（含 timestamp \u002F nonce 命中率为 0）",[21,971,182],{"id":182},[184,973,975],{"className":211,"code":974,"language":213,"meta":189,"style":189},"# Proxy 模式\nimport openai\nclient = openai.OpenAI(\n    base_url=\"https:\u002F\u002Foai.helicone.ai\u002Fv1\",\n    api_key=OPENAI_KEY,\n    default_headers={\n        \"Helicone-Auth\": f\"Bearer {HELICONE_KEY}\",\n        \"Helicone-User-Id\": \"user_123\",\n        \"Helicone-Property-Feature\": \"summarize\"\n    }\n)\n# 立刻在 https:\u002F\u002Fus.helicone.ai 看到 cost \u002F latency \u002F log\n",[191,976,977,982,989,999,1010,1023,1033,1055,1067,1077,1082,1086],{"__ignoreMap":189},[194,978,979],{"class":196,"line":197},[194,980,981],{"class":348},"# Proxy 模式\n",[194,983,984,986],{"class":196,"line":234},[194,985,228],{"class":220},[194,987,988],{"class":224}," openai\n",[194,990,991,994,996],{"class":196,"line":241},[194,992,993],{"class":224},"client ",[194,995,247],{"class":220},[194,997,998],{"class":224}," openai.OpenAI(\n",[194,1000,1001,1003,1005,1008],{"class":196,"line":253},[194,1002,283],{"class":256},[194,1004,247],{"class":220},[194,1006,1007],{"class":204},"\"https:\u002F\u002Foai.helicone.ai\u002Fv1\"",[194,1009,265],{"class":224},[194,1011,1012,1015,1017,1021],{"class":196,"line":268},[194,1013,1014],{"class":256},"    api_key",[194,1016,247],{"class":220},[194,1018,1020],{"class":1019},"sj4cs","OPENAI_KEY",[194,1022,265],{"class":224},[194,1024,1025,1028,1030],{"class":196,"line":280},[194,1026,1027],{"class":256},"    default_headers",[194,1029,247],{"class":220},[194,1031,1032],{"class":224},"{\n",[194,1034,1035,1038,1041,1044,1047,1050,1053],{"class":196,"line":291},[194,1036,1037],{"class":204},"        \"Helicone-Auth\"",[194,1039,1040],{"class":224},": ",[194,1042,1043],{"class":220},"f",[194,1045,1046],{"class":204},"\"Bearer ",[194,1048,1049],{"class":1019},"{HELICONE_KEY}",[194,1051,1052],{"class":204},"\"",[194,1054,265],{"class":224},[194,1056,1057,1060,1062,1065],{"class":196,"line":297},[194,1058,1059],{"class":204},"        \"Helicone-User-Id\"",[194,1061,1040],{"class":224},[194,1063,1064],{"class":204},"\"user_123\"",[194,1066,265],{"class":224},[194,1068,1069,1072,1074],{"class":196,"line":302},[194,1070,1071],{"class":204},"        \"Helicone-Property-Feature\"",[194,1073,1040],{"class":224},[194,1075,1076],{"class":204},"\"summarize\"\n",[194,1078,1079],{"class":196,"line":334},[194,1080,1081],{"class":224},"    }\n",[194,1083,1084],{"class":196,"line":340},[194,1085,294],{"class":224},[194,1087,1088],{"class":196,"line":345},[194,1089,1090],{"class":348},"# 立刻在 https:\u002F\u002Fus.helicone.ai 看到 cost \u002F latency \u002F log\n",[184,1092,1094],{"className":186,"code":1093,"language":188,"meta":189,"style":189},"# 自托管\ngit clone https:\u002F\u002Fgithub.com\u002FHelicone\u002Fhelicone\ncd helicone && docker compose up -d\n",[191,1095,1096,1101,1112],{"__ignoreMap":189},[194,1097,1098],{"class":196,"line":197},[194,1099,1100],{"class":348},"# 自托管\n",[194,1102,1103,1106,1109],{"class":196,"line":234},[194,1104,1105],{"class":200},"git",[194,1107,1108],{"class":204}," clone",[194,1110,1111],{"class":204}," https:\u002F\u002Fgithub.com\u002FHelicone\u002Fhelicone\n",[194,1113,1114,1117,1120,1123,1126,1129,1132],{"class":196,"line":241},[194,1115,1116],{"class":1019},"cd",[194,1118,1119],{"class":204}," helicone",[194,1121,1122],{"class":224}," && ",[194,1124,1125],{"class":200},"docker",[194,1127,1128],{"class":204}," compose",[194,1130,1131],{"class":204}," up",[194,1133,1134],{"class":1019}," -d\n",[21,1136,375],{"id":375},[377,1138,1139,1151],{},[380,1140,1141],{},[383,1142,1143,1145,1147,1149],{},[386,1144,388],{},[386,1146,396],{},[386,1148,393],{},[386,1150,399],{},[401,1152,1153,1167,1180,1193,1205,1219,1233,1245,1258,1272,1286],{},[383,1154,1155,1158,1161,1164],{},[406,1156,1157],{},"架构",[406,1159,1160],{},"Proxy \u002F Async",[406,1162,1163],{},"SDK",[406,1165,1166],{},"Gateway",[383,1168,1169,1172,1175,1178],{},[406,1170,1171],{},"接入时间",[406,1173,1174],{},"分钟（改 URL）",[406,1176,1177],{},"小时（代码改造）",[406,1179,1174],{},[383,1181,1182,1185,1188,1191],{},[406,1183,1184],{},"Tracing 深度",[406,1186,1187],{},"浅（请求级）",[406,1189,1190],{},"✅ 深 nested",[406,1192,452],{},[383,1194,1195,1198,1201,1203],{},[406,1196,1197],{},"Multi-provider routing",[406,1199,1200],{},"✅ 基础 fallback",[406,1202,475],{},[406,1204,483],{},[383,1206,1207,1210,1213,1216],{},[406,1208,1209],{},"Prompt mgmt",[406,1211,1212],{},"playground + 基础版本",[406,1214,1215],{},"✅ 版本 + playground",[406,1217,1218],{},"模板 + 管理",[383,1220,1221,1224,1227,1230],{},[406,1222,1223],{},"Eval",[406,1225,1226],{},"基础 scoring",[406,1228,1229],{},"✅ LLM-as-judge + datasets",[406,1231,1232],{},"基础",[383,1234,1235,1237,1240,1243],{},[406,1236,863],{},[406,1238,1239],{},"✅ OSS 完整",[406,1241,1242],{},"✅ MIT 19K+ stars",[406,1244,499],{},[383,1246,1247,1249,1252,1255],{},[406,1248,869],{},[406,1250,1251],{},"SOC2 + HIPAA (Team+)",[406,1253,1254],{},"SOC2 (Enterprise)",[406,1256,1257],{},"SOC2 + HIPAA + ISO27001",[383,1259,1260,1263,1266,1269],{},[406,1261,1262],{},"Free tier",[406,1264,1265],{},"10K req\u002F月",[406,1267,1268],{},"50K events\u002F月",[406,1270,1271],{},"10K logs\u002F月",[383,1273,1274,1277,1280,1283],{},[406,1275,1276],{},"Paid 起价",[406,1278,1279],{},"$79",[406,1281,1282],{},"$29",[406,1284,1285],{},"$49",[383,1287,1288,1291,1294,1297],{},[406,1289,1290],{},"路线图",[406,1292,1293],{},"⚠️ 维护模式",[406,1295,1296],{},"✅ 活跃",[406,1298,1299],{},"✅ 活跃（PANW 收购）",[21,1301,549],{"id":549},[36,1303,1304,1310,1316,1322,1328,1334,1340,1346],{},[39,1305,1306,1309],{},[42,1307,1308],{},"维护模式风险","：被 Mintlify 收购后主动开发停，新项目长期演进考虑 Langfuse \u002F Portkey",[39,1311,1312,1315],{},[42,1313,1314],{},"Proxy 延迟 + SPOF","：Helicone 挂了，业务也挂；敏感链路用 async",[39,1317,1318,1321],{},[42,1319,1320],{},"Proxy 看不到 agent 内部","：tool call \u002F sub-agent \u002F retries 不可见，复杂 agent 用 Trace API 或转 Langfuse",[39,1323,1324,1327],{},[42,1325,1326],{},"Hobby 10K 跑不久","：小型生产几天用完，预算够直接上 Pro",[39,1329,1330,1333],{},[42,1331,1332],{},"Cache-aware prompt 设计","：prompt 含 timestamp \u002F random nonce 命中率 = 0",[39,1335,1336,1339],{},[42,1337,1338],{},"Custom scoring 浅","：复杂 eval 用 Langfuse 数据集 + LLM-as-judge",[39,1341,1342,1345],{},[42,1343,1344],{},"Air-gapped 价格","：自托管时部分模型价格表要手动维护",[39,1347,1348,1351],{},[42,1349,1350],{},"PII 路径敏感","：所有 prompt 经过 Helicone，要 review PII 处理 + retention + 自托管选项",[21,1353,603],{"id":602},[36,1355,1356,1359,1362,1365,1368,1371,1374,1377,1380],{},[39,1357,1358],{},"✅ 早期产品 + 想 5 分钟接入观测",[39,1360,1361],{},"✅ 改 baseURL 模式偏好 \u002F 不想 SDK 改造",[39,1363,1364],{},"✅ 预算紧 + 自托管接受",[39,1366,1367],{},"✅ 想用 Rust AI Gateway 做 fallback + caching",[39,1369,1370],{},"✅ SOC2 \u002F HIPAA Team+ 合规",[39,1372,1373],{},"❌ 复杂 agent 多步骤调试（用 Langfuse）",[39,1375,1376],{},"❌ 要 250+ 模型 + MCP + 强治理（用 Portkey）",[39,1378,1379],{},"❌ 长期路线图敏感（维护模式 risk）",[39,1381,1382],{},"❌ 中文运营（社区 \u002F 文档 \u002F UI 均英文）",[21,1384,632],{"id":632},[36,1386,1387,1391,1395,1401],{},[39,1388,1389],{},[638,1390,647],{"href":646},[39,1392,1393],{},[638,1394,653],{"href":652},[39,1396,1397],{},[638,1398,1400],{"href":1399},"\u002Ftools\u002Fcoding\u002Fapi\u002Fopenrouter","OpenRouter 评测",[39,1402,1403],{},[638,1404,1406],{"href":1405},"\u002Ftools\u002Fcoding\u002Fapi\u002Fone-api","One-API 评测",[21,1408,662],{"id":662},[664,1410,1411,1418,1425,1432,1439],{},[39,1412,1413,1414],{},"Inference.net — Helicone Pricing & Alternatives 2026（含 Mintlify 收购 + 维护模式细节）",[638,1415,1416],{"href":1416,"rel":1417},"https:\u002F\u002Finference.net\u002Fcontent\u002Fhelicone-pricing-alternatives",[673],[39,1419,1420,1421],{},"Helicone 官网 ",[638,1422,1423],{"href":1423,"rel":1424},"https:\u002F\u002Fwww.helicone.ai\u002F",[673],[39,1426,1427,1428],{},"QASkills — Helicone LLM Monitoring Complete Guide 2026 ",[638,1429,1430],{"href":1430,"rel":1431},"https:\u002F\u002Fqaskills.sh\u002Fblog\u002Fhelicone-llm-monitoring-complete-guide",[673],[39,1433,1434,1435],{},"AiPedia — Helicone Features Pricing & Failure Modes ",[638,1436,1437],{"href":1437,"rel":1438},"https:\u002F\u002Fwww.aipedia.wiki\u002Ftools\u002Fhelicone\u002F",[673],[39,1440,1441,1442],{},"BuildMVPFast — Langfuse vs Helicone vs Portkey ",[638,1443,1444],{"href":1444,"rel":1445},"https:\u002F\u002Fwww.buildmvpfast.com\u002Fblog\u002Fllm-observability-stack-langfuse-helicone-portkey-2026",[673],[696,1447,1448],{},"html pre.shiki code .sJ8bj, html code.shiki .sJ8bj{--shiki-default:#6A737D;--shiki-dark:#6A737D}html pre.shiki code .szBVR, html code.shiki .szBVR{--shiki-default:#D73A49;--shiki-dark:#F97583}html pre.shiki code .sVt8B, html code.shiki .sVt8B{--shiki-default:#24292E;--shiki-dark:#E1E4E8}html pre.shiki code .s4XuR, html code.shiki .s4XuR{--shiki-default:#E36209;--shiki-dark:#FFAB70}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);}html pre.shiki code .sScJk, html code.shiki .sScJk{--shiki-default:#6F42C1;--shiki-dark:#B392F0}",{"title":189,"searchDepth":241,"depth":241,"links":1450},[1451,1452,1453,1454,1455,1456,1457,1458,1459,1460],{"id":23,"depth":234,"text":24},{"id":34,"depth":234,"text":34},{"id":96,"depth":234,"text":96},{"id":911,"depth":234,"text":912},{"id":182,"depth":234,"text":182},{"id":375,"depth":234,"text":375},{"id":549,"depth":234,"text":549},{"id":602,"depth":234,"text":603},{"id":632,"depth":234,"text":632},{"id":662,"depth":234,"text":662},"api","\u002Fimg\u002Ftools\u002Fhelicone.webp","Helicone 真实评测：YC W23 项目，开源 LLM 观测平台 + Rust 写的 AI Gateway。2026-03-03 被 Mintlify 收购后转维护模式（安全更新 + 新模型 + bug fix 继续，主动新功能停）。免费 10K req\u002F月 + Pro $79 + Team $799（SOC2 + HIPAA）+ Enterprise 定制。零代码 SDK 改造、改 baseURL 即用，是接入最快的 LLM 观测之一。",[1465,1468,1471,1474],{"q":1466,"a":1467},"Helicone 被收购后还能用吗？","2026-03-03 Mintlify 收购 Helicone，产品转维护模式：安全 patch + 新模型支持 + bug fix 继续发，但主动新功能开发结束。现存生产部署仍稳定，但选型时要权衡：路线图不动 \u002F 不期待新观测形态 \u002F 接受工具被 Mintlify 整合或归档的风险。新项目长期演进建议看 Langfuse 或 Portkey。",{"q":1469,"a":1470},"Proxy 和 async 模式哪个好？","Proxy（改 baseURL，所有请求走 Helicone）= 接入快 + 完整捕获，但加 ~5–20ms 延迟、Helicone 挂了请求也挂。Async（SDK 异步上报）= 0 延迟 + Helicone 故障不影响业务，但失败窗口可能漏 log。生产敏感链路用 async，开发 \u002F 内部用 proxy。",{"q":1472,"a":1473},"Proxy 能看到 agent 内部工具调用吗？","不能——proxy 只能看到穿过它的 LLM 请求，agent 框架内的 tool execution \u002F sub-agent \u002F retries 都不可见。复杂 agent 调试需要 trace 级深度，要 Langfuse 的 nested span 或 Helicone Trace API（手动 POST）。",{"q":1475,"a":1476},"和 Langfuse \u002F Portkey 怎么选？","Helicone = proxy-based，URL 一改分钟上手，请求级日志 + 基础 gateway 路由。Langfuse = SDK-based，代码改造小时级，但 nested span 深度 tracing + LLM-as-judge eval 一等。Portkey = gateway-first，250+ 模型 + MCP + 强合规但定价复杂。简单 + 快 → Helicone；agent 调试 → Langfuse；网关 + 治理 + 合规 → Portkey。","https:\u002F\u002Fgithub.com\u002FHelicone\u002Fhelicone",[730],{},[1481,1482,1461,736],"saas","self-host",[1484,1487,1491,1495],{"plan":885,"price":741,"features":1485,"notes":1486},"10K req\u002F月 + 7 天 retention + 1 seat + 1GB storage + 基础 dashboards","Free trial \u002F hobby \u002F 验证",{"plan":891,"price":1488,"features":1489,"notes":1490},"$79\u002F月","10K + usage + 1 月 retention + unlimited seats + alerts + HQL + prompt management","中小生产",{"plan":897,"price":1492,"features":1493,"notes":1494},"$799\u002F月","10K + usage + 3 月 retention + SOC-2 + HIPAA + 5 orgs","中大型 + 合规",{"plan":115,"price":745,"features":1496,"notes":1497},"自定义 retention + 永久存储 + SSO + on-prem + 专属支持","大型 \u002F 政企","Hobby 免费 10K req\u002F月 \u002F Pro $79\u002F月 \u002F Team $799\u002F月 \u002F Enterprise 定制",[754],{"power":253,"ux":268,"price":253,"cn_support":234,"stability":241},{"title":396,"description":1463},"Helicone - 开源 LLM 观测网关评测 | AIHO",[1504,1506,1508,1510,1512],{"name":1505,"url":1416,"accessed":762},"Inference.net — Helicone Pricing & Alternatives (Jun 2026)",{"name":1507,"url":1423,"accessed":762},"Helicone 官网",{"name":1509,"url":1430,"accessed":762},"QASkills — Helicone LLM Monitoring Guide 2026",{"name":1511,"url":1437,"accessed":762},"AiPedia — Helicone 评测 + 失败模式",{"name":1513,"url":1444,"accessed":762},"BuildMVPFast — Langfuse vs Helicone vs Portkey","tools\u002Fcoding\u002Fapi\u002Fhelicone","一行代码 LLM 观测——开源 Proxy + AI Gateway，2026-03 被 Mintlify 收购、维护模式运行",[1517,1518,1519,777,1520,1521,1522],"llm-observability","proxy","ai-gateway","ycombinator","soc2","hipaa","最容易接入的 LLM 观测，改 baseURL 几分钟看到 cost\u002Flatency\u002Ferrors。被 Mintlify 收购转维护模式后路线图不确定——做新项目权衡：要快上手 + 不期待新功能 OK；要 nested span + 持续演进走 Langfuse。","https:\u002F\u002Fwww.helicone.ai","8JJIpaMuzoQlpfxMpxjgaihSjvMbFMn2TFQ3tFomDRc",{"id":1527,"title":399,"alternatives":1528,"api_compatible":1529,"body":1545,"category":1461,"chinese_friendly":234,"cover":2278,"description":2279,"domestic":714,"extension":715,"faq":2280,"free":237,"github":2293,"languages":2294,"lastVerified":731,"meta":2295,"models":16,"navigation":237,"notSuitable":16,"opensource":237,"path":646,"pillar":734,"platforms":2296,"priceTable":2297,"pricing":2312,"published":752,"relatedPlaybooks":2313,"relatedReviews":2314,"score":2316,"self_host":237,"seo":2317,"seoTitle":2318,"slug":13,"sources":2319,"stem":2327,"suitable":16,"tagline":2328,"tags":2329,"updated":762,"verdict":2334,"website":2335,"__hash__":2336},"tools\u002Ftools\u002Fcoding\u002Fapi\u002Fportkey.md",[12,14,785,786],[1530,1531,1532,1533,1534,1535,1536,1537,1538,1539,1540,1541,1542,1543,1544],"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":18,"value":1546,"toc":2266},[1547,1549,1552,1555,1557,1651,1653,1682,1687,1691,1695,1715,1719,1741,1743,1945,1947,2129,2131,2181,2183,2212,2214,2232,2234,2263],[21,1548,24],{"id":23},[26,1550,1551],{},"Portkey 把 AI 网关（250+ 模型 + fallback + 路由 + 缓存）和 LLM 全栈观测（40+ 维度 + 成本归因 + tracing + auto-instrumentation）做成同一个 SaaS，再加 MCP Gateway 让 AI agent 工具调用可追溯——目标是中大型团队从 PoC 走向生产的『AI 控制面板』。2026 年 Palo Alto Networks 完成收购，附带 SOC2 Type 2 + HIPAA + GDPR + ISO 27001 合规背书。3000+ GenAI 团队使用，1T tokens\u002F天里程碑。",[26,1553,1554],{},"适合：从 PoC 走向生产的中大型团队；要 SOC2\u002FHIPAA\u002FHIPAA\u002FGDPR 强合规；多团队 RBAC + budgets + 治理；大规模 AI agent 部署需要 MCP 工具调用可追溯。不适合：深度 nested span tracing（用 Langfuse）；纯网关不要观测（用 LiteLLM）；中文支付 \u002F 中文 UI（用 one-api \u002F new-api）；预算极紧的小项目（10K log 免费 + Pro $79 但深度有限，Helicone 同档更轻）。",[21,1556,34],{"id":34},[36,1558,1559,1565,1571,1577,1583,1589,1595,1601,1607,1612,1618,1623,1628,1633,1639,1645],{},[39,1560,1561,1564],{},[42,1562,1563],{},"250+ 模型 unified API","：OpenAI \u002F Anthropic \u002F Google \u002F AWS Bedrock \u002F Azure \u002F Cohere \u002F 等",[39,1566,1567,1570],{},[42,1568,1569],{},"Fallback \u002F Load Balancing \u002F Conditional Routing","：跨 provider 高可用",[39,1572,1573,1576],{},[42,1574,1575],{},"Semantic + simple caching","：减延迟 + 降成本",[39,1578,1579,1582],{},[42,1580,1581],{},"Retries \u002F circuit breakers","：完整 SRE 配套",[39,1584,1585,1588],{},[42,1586,1587],{},"Full-stack Observability","：40+ 维度 logs \u002F traces \u002F metrics",[39,1590,1591,1594],{},[42,1592,1593],{},"OpenTelemetry 兼容","：导出到现有 APM",[39,1596,1597,1600],{},[42,1598,1599],{},"Tracing","：跨 LLM call + tool use 统一时序视图",[39,1602,1603,1606],{},[42,1604,1605],{},"Auto-instrumentation","：自动埋点多个 LLM \u002F agent 框架",[39,1608,1609],{},[42,1610,1611],{},"Prompt management + templates",[39,1613,1614,1617],{},[42,1615,1616],{},"MCP Gateway（GA）","：AI agent 工具调用统一访问 + 审计",[39,1619,1620],{},[42,1621,1622],{},"RBAC + SSO\u002FSAML + Audit",[39,1624,1625],{},[42,1626,1627],{},"Hierarchical budgets + rate limits",[39,1629,1630],{},[42,1631,1632],{},"Guardrails",[39,1634,1635,1638],{},[42,1636,1637],{},"Compliance","：SOC2 Type 2 \u002F HIPAA \u002F GDPR \u002F ISO 27001（Enterprise）",[39,1640,1641,1644],{},[42,1642,1643],{},"部署","：SaaS \u002F 私有云 \u002F VPC \u002F on-prem（Enterprise）",[39,1646,1647,1650],{},[42,1648,1649],{},"Open Source Gateway","：MIT 协议，可自托管纯路由层",[21,1652,96],{"id":96},[36,1654,1655,1661,1667,1672,1677],{},[39,1656,1657,1660],{},[42,1658,1659],{},"Developer Free","：10K logs\u002F月 + 3 天 retention + 基础功能",[39,1662,1663,1666],{},[42,1664,1665],{},"Production","：$49\u002F月（早期媒体引用 $79 已下调）+ 100K logs + $9\u002F100K 超量（最高 3M）+ 30 天 retention + 语义缓存 + RBAC + Guardrails",[39,1668,1669,1671],{},[42,1670,897],{},"：联系销售（更高 log 配额 + 团队治理 + 更长 retention）",[39,1673,1674,1676],{},[42,1675,115],{},"：Custom（业界估 $5K–$10K+\u002F月）+ 10M+ logs + 自定义 retention + 全套合规 + SSO + 私有云",[39,1678,1679,1681],{},[42,1680,1649],{},"：$0 自托管 MIT",[118,1683,1684],{},[26,1685,1686],{},"真实成本陷阱：超过 Production 100K logs 后 $9\u002F100K 累计快，月 1M 请求 ≈ $49 + 9 × 9 ≈ $130。",[21,1688,1690],{"id":1689},"实测中型-saas-series-b-团队","实测（中型 SaaS \u002F Series B 团队）",[26,1692,1693],{},[42,1694,131],{},[36,1696,1697,1700,1703,1706,1709,1712],{},[39,1698,1699],{},"2 分钟改 baseURL 接入，立即看到所有 LLM 调用",[39,1701,1702],{},"40+ 维度 dashboard 让 FinOps 团队第一次能拍预算",[39,1704,1705],{},"conditional routing 让『便宜模型先试 + 失败 fallback 贵模型』容易实现",[39,1707,1708],{},"MCP Gateway 给团队的 agent 工具调用上了治理",[39,1710,1711],{},"SOC 2 Type 2 + HIPAA 让合规过审快",[39,1713,1714],{},"Auto-instrumentation 帮 LangChain \u002F LlamaIndex 应用零代码改造",[26,1716,1717],{},[42,1718,156],{},[36,1720,1721,1724,1727,1730,1733,1735,1738],{},[39,1722,1723],{},"按 recorded logs 计费让用量突增时账单失控",[39,1725,1726],{},"Tracing 深度不如 Langfuse 的 nested span（复杂 agent 调试不够）",[39,1728,1729],{},"模型价格表对部分模型不全 \u002F air-gapped 部署需手动维护",[39,1731,1732],{},"文档对高级配置存在 gap，社区反馈一致",[39,1734,966],{},[39,1736,1737],{},"log retention 30 天上限不动 Enterprise 解决",[39,1739,1740],{},"PANW 收购后路线图 \u002F 定价变动是评估风险",[21,1742,182],{"id":182},[184,1744,1746],{"className":211,"code":1745,"language":213,"meta":189,"style":189},"from portkey_ai import Portkey\n\nclient = Portkey(\n    api_key=\"YOUR_PORTKEY_KEY\",\n    virtual_key=\"OPENAI_VIRTUAL_KEY\",\n    config={\n        \"strategy\": {\"mode\": \"fallback\"},\n        \"targets\": [\n            {\"virtual_key\": \"OPENAI_VK\"},\n            {\"virtual_key\": \"ANTHROPIC_VK\"}\n        ],\n        \"cache\": {\"mode\": \"semantic\"}\n    }\n)\n\nresp = client.chat.completions.create(\n    model=\"gpt-5.4\",\n    messages=[...]\n)\n# Portkey dashboard 自动看到完整 trace + cost + latency\n",[191,1747,1748,1760,1764,1773,1784,1796,1805,1824,1832,1847,1861,1866,1882,1886,1890,1894,1904,1917,1934,1939],{"__ignoreMap":189},[194,1749,1750,1752,1755,1757],{"class":196,"line":197},[194,1751,221],{"class":220},[194,1753,1754],{"class":224}," portkey_ai ",[194,1756,228],{"class":220},[194,1758,1759],{"class":224}," Portkey\n",[194,1761,1762],{"class":196,"line":234},[194,1763,238],{"emptyLinePlaceholder":237},[194,1765,1766,1768,1770],{"class":196,"line":241},[194,1767,993],{"class":224},[194,1769,247],{"class":220},[194,1771,1772],{"class":224}," Portkey(\n",[194,1774,1775,1777,1779,1782],{"class":196,"line":253},[194,1776,1014],{"class":256},[194,1778,247],{"class":220},[194,1780,1781],{"class":204},"\"YOUR_PORTKEY_KEY\"",[194,1783,265],{"class":224},[194,1785,1786,1789,1791,1794],{"class":196,"line":268},[194,1787,1788],{"class":256},"    virtual_key",[194,1790,247],{"class":220},[194,1792,1793],{"class":204},"\"OPENAI_VIRTUAL_KEY\"",[194,1795,265],{"class":224},[194,1797,1798,1801,1803],{"class":196,"line":280},[194,1799,1800],{"class":256},"    config",[194,1802,247],{"class":220},[194,1804,1032],{"class":224},[194,1806,1807,1810,1813,1816,1818,1821],{"class":196,"line":291},[194,1808,1809],{"class":204},"        \"strategy\"",[194,1811,1812],{"class":224},": {",[194,1814,1815],{"class":204},"\"mode\"",[194,1817,1040],{"class":224},[194,1819,1820],{"class":204},"\"fallback\"",[194,1822,1823],{"class":224},"},\n",[194,1825,1826,1829],{"class":196,"line":297},[194,1827,1828],{"class":204},"        \"targets\"",[194,1830,1831],{"class":224},": [\n",[194,1833,1834,1837,1840,1842,1845],{"class":196,"line":302},[194,1835,1836],{"class":224},"            {",[194,1838,1839],{"class":204},"\"virtual_key\"",[194,1841,1040],{"class":224},[194,1843,1844],{"class":204},"\"OPENAI_VK\"",[194,1846,1823],{"class":224},[194,1848,1849,1851,1853,1855,1858],{"class":196,"line":334},[194,1850,1836],{"class":224},[194,1852,1839],{"class":204},[194,1854,1040],{"class":224},[194,1856,1857],{"class":204},"\"ANTHROPIC_VK\"",[194,1859,1860],{"class":224},"}\n",[194,1862,1863],{"class":196,"line":340},[194,1864,1865],{"class":224},"        ],\n",[194,1867,1868,1871,1873,1875,1877,1880],{"class":196,"line":345},[194,1869,1870],{"class":204},"        \"cache\"",[194,1872,1812],{"class":224},[194,1874,1815],{"class":204},[194,1876,1040],{"class":224},[194,1878,1879],{"class":204},"\"semantic\"",[194,1881,1860],{"class":224},[194,1883,1884],{"class":196,"line":352},[194,1885,1081],{"class":224},[194,1887,1888],{"class":196,"line":358},[194,1889,294],{"class":224},[194,1891,1892],{"class":196,"line":363},[194,1893,238],{"emptyLinePlaceholder":237},[194,1895,1896,1899,1901],{"class":196,"line":369},[194,1897,1898],{"class":224},"resp ",[194,1900,247],{"class":220},[194,1902,1903],{"class":224}," client.chat.completions.create(\n",[194,1905,1907,1910,1912,1915],{"class":196,"line":1906},17,[194,1908,1909],{"class":256},"    model",[194,1911,247],{"class":220},[194,1913,1914],{"class":204},"\"gpt-5.4\"",[194,1916,265],{"class":224},[194,1918,1920,1923,1925,1928,1931],{"class":196,"line":1919},18,[194,1921,1922],{"class":256},"    messages",[194,1924,247],{"class":220},[194,1926,1927],{"class":224},"[",[194,1929,1930],{"class":1019},"...",[194,1932,1933],{"class":224},"]\n",[194,1935,1937],{"class":196,"line":1936},19,[194,1938,294],{"class":224},[194,1940,1942],{"class":196,"line":1941},20,[194,1943,1944],{"class":348},"# Portkey dashboard 自动看到完整 trace + cost + latency\n",[21,1946,375],{"id":375},[377,1948,1949,1964],{},[380,1950,1951],{},[383,1952,1953,1955,1957,1959,1961],{},[386,1954,388],{},[386,1956,399],{},[386,1958,396],{},[386,1960,393],{},[386,1962,1963],{},"LiteLLM",[401,1965,1966,1983,1999,2012,2024,2039,2054,2068,2083,2098,2113],{},[383,1967,1968,1971,1974,1977,1980],{},[406,1969,1970],{},"形态",[406,1972,1973],{},"Gateway+Obs SaaS",[406,1975,1976],{},"Proxy Obs",[406,1978,1979],{},"SDK Tracing",[406,1981,1982],{},"OSS Gateway",[383,1984,1985,1988,1991,1993,1996],{},[406,1986,1987],{},"集成",[406,1989,1990],{},"改 baseURL（分钟）",[406,1992,1990],{},[406,1994,1995],{},"代码改造（小时）",[406,1997,1998],{},"自托管（小时）",[383,2000,2001,2003,2005,2007,2009],{},[406,2002,1184],{},[406,2004,452],{},[406,2006,434],{},[406,2008,1190],{},[406,2010,2011],{},"–",[383,2013,2014,2016,2018,2020,2022],{},[406,2015,1197],{},[406,2017,483],{},[406,2019,475],{},[406,2021,475],{},[406,2023,480],{},[383,2025,2026,2029,2032,2035,2037],{},[406,2027,2028],{},"Auto fallback",[406,2030,2031],{},"✅ chains",[406,2033,2034],{},"✅ 基础",[406,2036,475],{},[406,2038,480],{},[383,2040,2041,2044,2046,2049,2051],{},[406,2042,2043],{},"Semantic caching",[406,2045,480],{},[406,2047,2048],{},"proxy 级",[406,2050,475],{},[406,2052,2053],{},"Redis",[383,2055,2056,2059,2062,2064,2066],{},[406,2057,2058],{},"MCP Gateway",[406,2060,2061],{},"✅ GA",[406,2063,475],{},[406,2065,475],{},[406,2067,475],{},[383,2069,2070,2072,2075,2078,2081],{},[406,2071,863],{},[406,2073,2074],{},"Enterprise \u002F OSS Gateway",[406,2076,2077],{},"Enterprise（已收购）",[406,2079,2080],{},"✅ 免费无限",[406,2082,480],{},[383,2084,2085,2087,2090,2093,2096],{},[406,2086,869],{},[406,2088,2089],{},"SOC2 + HIPAA + GDPR + ISO27001",[406,2091,2092],{},"SOC2 + HIPAA",[406,2094,2095],{},"SOC2",[406,2097,480],{},[383,2099,2100,2103,2106,2108,2111],{},[406,2101,2102],{},"起价",[406,2104,2105],{},"$49\u002F月",[406,2107,1488],{},[406,2109,2110],{},"$29\u002F月",[406,2112,741],{},[383,2114,2115,2118,2121,2124,2127],{},[406,2116,2117],{},"免费配额",[406,2119,2120],{},"10K logs",[406,2122,2123],{},"10K req",[406,2125,2126],{},"50K events",[406,2128,2011],{},[21,2130,549],{"id":549},[36,2132,2133,2139,2145,2151,2157,2163,2169,2175],{},[39,2134,2135,2138],{},[42,2136,2137],{},"按 logs 计费要设报警","：超量 $9\u002F100K 容易爆账单",[39,2140,2141,2144],{},[42,2142,2143],{},"PANW 收购变量","：评估时把定价 \u002F 路线图 \u002F API 变动写进风险",[39,2146,2147,2150],{},[42,2148,2149],{},"Tracing 不深够用就好","：复杂 agent 调试要叠 Langfuse",[39,2152,2153,2156],{},[42,2154,2155],{},"Production tier retention 30 天","：超长归档要 Enterprise",[39,2158,2159,2162],{},[42,2160,2161],{},"Air-gapped 价格表","：部分模型成本要手动维护",[39,2164,2165,2168],{},[42,2166,2167],{},"Virtual Key 别裸暴露","：客户端调用务必走 server-side proxy",[39,2170,2171,2174],{},[42,2172,2173],{},"Conditional routing 别写太复杂","：3+ 层条件路由调试痛苦",[39,2176,2177,2180],{},[42,2178,2179],{},"Open Source Gateway != 企业版","：自托管开源版缺 governance \u002F dashboards \u002F evals",[21,2182,603],{"id":602},[36,2184,2185,2188,2191,2194,2197,2200,2203,2206,2209],{},[39,2186,2187],{},"✅ 中大型团队从 PoC 走向生产",[39,2189,2190],{},"✅ 强合规（SOC2 Type 2 + HIPAA + GDPR + ISO27001）",[39,2192,2193],{},"✅ 多团队 RBAC + 预算治理",[39,2195,2196],{},"✅ 大规模 AI agent 部署 + MCP 工具治理",[39,2198,2199],{},"✅ 想要『网关 + 观测』一体化 SaaS",[39,2201,2202],{},"❌ 深度 nested span 调试（用 Langfuse）",[39,2204,2205],{},"❌ 纯网关不要观测（用 LiteLLM）",[39,2207,2208],{},"❌ 中文运营 \u002F 中文支付（用 one-api \u002F new-api）",[39,2210,2211],{},"❌ 极小预算项目（Helicone Hobby \u002F Langfuse Free 更划算）",[21,2213,632],{"id":632},[36,2215,2216,2220,2224,2228],{},[39,2217,2218],{},[638,2219,641],{"href":640},[39,2221,2222],{},[638,2223,653],{"href":652},[39,2225,2226],{},[638,2227,1400],{"href":1399},[39,2229,2230],{},[638,2231,1406],{"href":1405},[21,2233,662],{"id":662},[664,2235,2236,2243,2250,2256],{},[39,2237,2238,2239],{},"Portkey 官网 — Observability 全栈观测 + Palo Alto Networks 收购公告 ",[638,2240,2241],{"href":2241,"rel":2242},"https:\u002F\u002Fportkey.ai\u002Ffeatures\u002Fobservability",[673],[39,2244,2245,2246],{},"TrueFoundry — Portkey AI Gateway Pricing Guide 2026 ",[638,2247,2248],{"href":2248,"rel":2249},"https:\u002F\u002Fwww.truefoundry.com\u002Fblog\u002Fportkey-pricing-guide",[673],[39,2251,2252,2253],{},"BuildMVPFast — Langfuse vs Helicone vs Portkey 对比 ",[638,2254,1444],{"href":1444,"rel":2255},[673],[39,2257,2258,2259],{},"DevTune — Portkey AI 评测 + 定价 + Gartner Cool Vendor ",[638,2260,2261],{"href":2261,"rel":2262},"https:\u002F\u002Fdevtune.ai\u002Fverticals\u002Fllm-observability-evals-gateways\u002Fportkey",[673],[696,2264,2265],{},"html pre.shiki code .szBVR, html code.shiki .szBVR{--shiki-default:#D73A49;--shiki-dark:#F97583}html pre.shiki code .sVt8B, html code.shiki .sVt8B{--shiki-default:#24292E;--shiki-dark:#E1E4E8}html pre.shiki code .s4XuR, html code.shiki .s4XuR{--shiki-default:#E36209;--shiki-dark:#FFAB70}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":189,"searchDepth":241,"depth":241,"links":2267},[2268,2269,2270,2271,2272,2273,2274,2275,2276,2277],{"id":23,"depth":234,"text":24},{"id":34,"depth":234,"text":34},{"id":96,"depth":234,"text":96},{"id":1689,"depth":234,"text":1690},{"id":182,"depth":234,"text":182},{"id":375,"depth":234,"text":375},{"id":549,"depth":234,"text":549},{"id":602,"depth":234,"text":603},{"id":632,"depth":234,"text":632},{"id":662,"depth":234,"text":662},"\u002Fimg\u002Ftools\u002Fportkey.webp","Portkey 真实评测：250+ 模型统一 AI 网关 + 全栈观测 + MCP Gateway。2026 被 Palo Alto Networks 收购。免费 10K logs\u002F月，Production $49\u002F月（100K logs + 30 天 retention），Team 大体量，Enterprise SOC2 Type 2 + HIPAA + GDPR + ISO 27001 + 私有云 \u002F VPC + 1T tokens\u002F天里程碑。",[2281,2284,2287,2290],{"q":2282,"a":2283},"Portkey 收购了？","2026 年 Palo Alto Networks 完成收购 Portkey，作为其『保护 AI agent 崛起』战略的一部分。产品继续运营 + 1T tokens\u002F天里程碑达成；对企业买家是利好（背靠 Palo Alto 安全 + 合规背书），但社区担忧定价 \u002F 路线图变化——评估时要把『被 PANW 整合』作为风险项。",{"q":2285,"a":2286},"Recorded logs 是什么计量？","Portkey 不按请求 \u002F token 计费，而是按『记录到观测系统的日志条数』。一次 API 调用 = 一条 log（不含 retries）。100K logs 是『被采集 100K 次请求』的容量，不是『LLM 100K 调用配额』——LLM 费用走你自己付。",{"q":2288,"a":2289},"自托管选项？","Portkey 开源了核心 AI Gateway（GitHub Star 10K+，MIT），可以自托管做纯路由 + 基础观测；但企业级功能（SOC2 \u002F 自动 evals \u002F 团队治理 \u002F governance dashboard）只在 SaaS 或 Enterprise on-prem 提供。",{"q":2291,"a":2292},"MCP Gateway 是什么？","面向 AI agent 的 MCP（Model Context Protocol）统一访问层，已 GA。每次 agent 调工具都自动记录工具名 \u002F 参数 \u002F 响应 \u002F 用户 \u002F 团队 \u002F 延迟 \u002F 状态，是大规模 agent 部署里『谁的 agent 在做什么』可追溯的关键基础设施。","https:\u002F\u002Fgithub.com\u002FPortkey-AI\u002Fportkey-ai",[730],{},[1481,1461,736],[2298,2302,2305,2308],{"plan":2299,"price":741,"features":2300,"notes":2301},"Developer","10K recorded logs\u002F月 + 3 天 retention + 社区支持 + 基础 fallback","PoC \u002F hobby \u002F 小项目",{"plan":1665,"price":2105,"features":2303,"notes":2304},"100K logs\u002F月 + $9\u002F100K 超量（最高 3M）+ 30 天 retention + 语义缓存 + RBAC + Guardrails + 生产支持","中小生产应用",{"plan":897,"price":747,"features":2306,"notes":2307},"更高 log 配额 + 团队治理 + 更长 retention","中型组织",{"plan":115,"price":2309,"features":2310,"notes":2311},"Custom（$5K–$10K+\u002F月）","10M+ logs + 自定义 retention + SSO\u002FSAML + 私有云\u002FVPC + SOC2 Type 2 + HIPAA + GDPR + ISO 27001 + 单租户","大企业 \u002F 强合规","Developer 免费 10K logs\u002F月 \u002F Production $49\u002F月 100K logs \u002F Team 大体量 \u002F Enterprise 定制",[754],[2315],"llm-gateway-comparison",{"power":268,"ux":253,"price":241,"cn_support":234,"stability":253},{"title":399,"description":2279},"Portkey 评测 2026：AI 网关 + 全栈观测，被 Palo Alto 收购",[2320,2322,2324,2325],{"name":2321,"url":2241,"accessed":762},"Portkey 官网 — Observability 功能",{"name":2323,"url":2248,"accessed":762},"TrueFoundry — Portkey Pricing Guide 2026",{"name":1513,"url":1444,"accessed":762},{"name":2326,"url":2261,"accessed":762},"DevTune — Portkey AI 评测 + 定价","tools\u002Fcoding\u002Fapi\u002Fportkey","Control Panel for Production AI——AI 网关 + 全栈观测 + MCP 网关，2026 被 Palo Alto Networks 收购",[2330,775,2331,2332,2333,1521,1522],"llm-gateway","mcp","governance","enterprise","网关 + 观测一体化最完整的 SaaS。中大型团队从 PoC 走向生产、要 SOC2\u002FHIPAA\u002FMCP\u002F治理走 Portkey。纯观测 → Helicone\u002FLangfuse；自托管控制成本 → LiteLLM + Langfuse。","https:\u002F\u002Fportkey.ai","9Mhkjcqkyj4_ZuP7nvoUjWvqhUYDS_-lrh0pnW4Dq3s",{"id":2338,"title":1963,"alternatives":2339,"api_compatible":2340,"body":2341,"category":1461,"chinese_friendly":241,"cover":3018,"description":3019,"domestic":714,"extension":715,"faq":3020,"free":237,"github":3001,"languages":3033,"lastVerified":731,"meta":3034,"models":16,"navigation":237,"notSuitable":16,"opensource":237,"path":652,"pillar":734,"platforms":3035,"priceTable":3037,"pricing":3046,"published":752,"relatedPlaybooks":3047,"relatedReviews":3048,"score":3049,"self_host":237,"seo":3050,"seoTitle":3051,"slug":14,"sources":3052,"stem":3061,"suitable":16,"tagline":3062,"tags":3063,"updated":762,"verdict":3068,"website":3069,"__hash__":3070},"tools\u002Ftools\u002Fcoding\u002Fapi\u002Flitellm.md",[785,786,13,12],[1530,1531,1532,1533,1534,1535,1536,1537,1538,1539,1540,1541,1542,1543,1544],{"type":18,"value":2342,"toc":3006},[2343,2345,2352,2355,2357,2451,2453,2469,2474,2478,2482,2505,2509,2534,2538,2704,2706,2857,2859,2923,2925,2951,2953,2971,2973,3003],[21,2344,24],{"id":23},[26,2346,2347,2348,2351],{},"LiteLLM 是开源 LLM 网关里事实标准：MIT 协议，BerriAI 维护，双形态——SDK（",[191,2349,2350],{},"pip install","，单进程嵌入）+ Proxy（Docker Compose + Postgres，团队级网关）。100+ 厂商统一 OpenAI 接口，虚拟 Key + 团队预算 + 三类 fallback（错误\u002F政策\u002F上下文）+ 成本追踪 + 内置 admin UI。2026-03 供应链事件后 v1.83+ 强化镜像验证，生产固定签名版本。",[26,2353,2354],{},"适合：中大型团队 \u002F 合规 \u002F 想完全控数据 + BYOK；微服务架构需要语言无关的 OpenAI 网关；要把成本治理 + 观测做到自建。不适合：不想运维（SaaS 走 OpenRouter \u002F Portkey）；中文支付 \u002F 业务运营（走 one-api \u002F new-api）；纯个人项目（pip 装 SDK 已够，不需要 Proxy）。",[21,2356,34],{"id":34},[36,2358,2359,2365,2374,2384,2393,2402,2408,2413,2422,2428,2433,2439,2445],{},[39,2360,2361,2364],{},[42,2362,2363],{},"100+ providers","：OpenAI \u002F Anthropic \u002F Google \u002F AWS Bedrock \u002F Azure \u002F vLLM \u002F Ollama \u002F Together \u002F HuggingFace 等",[39,2366,2367,2370,2371],{},[42,2368,2369],{},"统一 OpenAI 接口","：所有模型走 ",[191,2372,2373],{},"\u002Fv1\u002Fchat\u002Fcompletions",[39,2375,2376,2379,2380,2383],{},[42,2377,2378],{},"SDK 模式","：",[191,2381,2382],{},"from litellm import completion","，适合嵌入",[39,2385,2386,2388,2389,2392],{},[42,2387,805],{},"：HTTP 服务 + Postgres + admin UI（",[191,2390,2391],{},"\u002Fui","）",[39,2394,2395,2379,2398,2401],{},[42,2396,2397],{},"虚拟 Key",[191,2399,2400],{},"\u002Fkey\u002Fgenerate"," 给团队 \u002F 服务签发独立 Key + 预算 + 模型白名单",[39,2403,2404,2407],{},[42,2405,2406],{},"三类 fallback","：错误 \u002F 内容政策 \u002F context window",[39,2409,2410,1582],{},[42,2411,2412],{},"重试 + 超时 + cooldown",[39,2414,2415,2379,2418,2421],{},[42,2416,2417],{},"Cost tracking",[191,2419,2420],{},"\u002Fglobal\u002Fspend\u002Freport"," + admin UI 看每团队 \u002F Key \u002F 模型成本",[39,2423,2424,2427],{},[42,2425,2426],{},"回调","：Langfuse \u002F Prometheus \u002F Slack 一键挂载",[39,2429,2430,2432],{},[42,2431,1632],{},"：Presidio PII masking \u002F 自定义内容检查",[39,2434,2435,2438],{},[42,2436,2437],{},"缓存","：Redis 内置 + 语义缓存",[39,2440,2441,2444],{},[42,2442,2443],{},"Routing strategy","：cost-based \u002F latency-based \u002F round-robin",[39,2446,2447,2450],{},[42,2448,2449],{},"MIT 协议","：完全自由商用 + 修改",[21,2452,96],{"id":96},[36,2454,2455,2461,2466],{},[39,2456,2457,2460],{},[42,2458,2459],{},"OSS","：$0；自托管成本 = 1 台 Postgres + 1 台 LiteLLM container（~2GB RAM）",[39,2462,2463,2465],{},[42,2464,115],{},"：Custom；SSO \u002F SAML \u002F 审计 \u002F SLA \u002F on-prem 部署支持",[39,2467,2468],{},"模型成本走各厂商直接结算（BYOK）",[118,2470,2471],{},[26,2472,2473],{},"小规模总成本：1 台 2 核 4G VPS 跑 Postgres + LiteLLM 月 $20–30，团队 10 人完全够。",[21,2475,2477],{"id":2476},"实测10-人-saas-微服务架构","实测（10 人 SaaS \u002F 微服务架构）",[26,2479,2480],{},[42,2481,131],{},[36,2483,2484,2487,2490,2493,2496,2499,2502],{},[39,2485,2486],{},"Docker Compose 40 分钟拉起完整生产栈",[39,2488,2489],{},"虚拟 Key + 预算让微服务团队成本归因清晰",[39,2491,2492],{},"三类 fallback 配齐后可用率从 99.2% → 99.8%",[39,2494,2495],{},"admin UI 看每团队每天成本省了一堆自研 dashboard",[39,2497,2498],{},"Postgres + master key 模式，密钥 + 配置一致性高",[39,2500,2501],{},"与 Langfuse 集成做 trace + cost 双视角",[39,2503,2504],{},"多语言客户端（Python \u002F Node \u002F Go \u002F Rust）走同一 endpoint 体验一致",[26,2506,2507],{},[42,2508,156],{},[36,2510,2511,2518,2521,2524,2531],{},[39,2512,2513,2514,2517],{},"2026-03 供应链事件让团队对 ",[191,2515,2516],{},":latest"," tag 警惕，生产必须固定签名版本（如 v1.85.0）",[39,2519,2520],{},"Postgres salt key 一旦生成不能轮换，初始化前要谨慎备份",[39,2522,2523],{},"admin UI 早期版本英文为主，中文文档少",[39,2525,2526,2527,2530],{},"模型 ID 命名复杂（",[191,2528,2529],{},"provider\u002Fmodel-name","），各厂商命名规则不一",[39,2532,2533],{},"自托管 = 自付运维（Postgres backup \u002F 升级 \u002F 监控）",[21,2535,2537],{"id":2536},"上手proxy-模式","上手（Proxy 模式）",[184,2539,2541],{"className":186,"code":2540,"language":188,"meta":189,"style":189},"mkdir llm-gateway && cd llm-gateway\nopenssl rand -hex 32 > .master_key\nopenssl rand -hex 32 > .salt_key\n\n# docker-compose.yml 拉 ghcr.io\u002Fberriai\u002Flitellm:v1.85.0\n# config.yaml 写 model_list \u002F fallbacks \u002F litellm_settings\n\ndocker compose up -d\n\n# 生成虚拟 Key\ncurl -X POST http:\u002F\u002Flocalhost:4000\u002Fkey\u002Fgenerate \\\n  -H \"Authorization: Bearer $LITELLM_MASTER_KEY\" \\\n  -d '{\"models\":[\"gpt-5.4\",\"claude-sonnet-4.6\"],\"max_budget\":100}'\n\n# 业务方调用\ncurl http:\u002F\u002Flocalhost:4000\u002Fv1\u002Fchat\u002Fcompletions \\\n  -H \"Authorization: Bearer sk-xxx\" \\\n  -d '{\"model\":\"gpt-5.4\",\"messages\":[...]}'\n",[191,2542,2543,2558,2578,2593,2597,2602,2607,2611,2621,2625,2630,2647,2662,2670,2674,2679,2688,2697],{"__ignoreMap":189},[194,2544,2545,2548,2551,2553,2555],{"class":196,"line":197},[194,2546,2547],{"class":200},"mkdir",[194,2549,2550],{"class":204}," llm-gateway",[194,2552,1122],{"class":224},[194,2554,1116],{"class":1019},[194,2556,2557],{"class":204}," llm-gateway\n",[194,2559,2560,2563,2566,2569,2572,2575],{"class":196,"line":234},[194,2561,2562],{"class":200},"openssl",[194,2564,2565],{"class":204}," rand",[194,2567,2568],{"class":1019}," -hex",[194,2570,2571],{"class":1019}," 32",[194,2573,2574],{"class":220}," >",[194,2576,2577],{"class":204}," .master_key\n",[194,2579,2580,2582,2584,2586,2588,2590],{"class":196,"line":241},[194,2581,2562],{"class":200},[194,2583,2565],{"class":204},[194,2585,2568],{"class":1019},[194,2587,2571],{"class":1019},[194,2589,2574],{"class":220},[194,2591,2592],{"class":204}," .salt_key\n",[194,2594,2595],{"class":196,"line":253},[194,2596,238],{"emptyLinePlaceholder":237},[194,2598,2599],{"class":196,"line":268},[194,2600,2601],{"class":348},"# docker-compose.yml 拉 ghcr.io\u002Fberriai\u002Flitellm:v1.85.0\n",[194,2603,2604],{"class":196,"line":280},[194,2605,2606],{"class":348},"# config.yaml 写 model_list \u002F fallbacks \u002F litellm_settings\n",[194,2608,2609],{"class":196,"line":291},[194,2610,238],{"emptyLinePlaceholder":237},[194,2612,2613,2615,2617,2619],{"class":196,"line":297},[194,2614,1125],{"class":200},[194,2616,1128],{"class":204},[194,2618,1131],{"class":204},[194,2620,1134],{"class":1019},[194,2622,2623],{"class":196,"line":302},[194,2624,238],{"emptyLinePlaceholder":237},[194,2626,2627],{"class":196,"line":334},[194,2628,2629],{"class":348},"# 生成虚拟 Key\n",[194,2631,2632,2635,2638,2641,2644],{"class":196,"line":340},[194,2633,2634],{"class":200},"curl",[194,2636,2637],{"class":1019}," -X",[194,2639,2640],{"class":204}," POST",[194,2642,2643],{"class":204}," http:\u002F\u002Flocalhost:4000\u002Fkey\u002Fgenerate",[194,2645,2646],{"class":1019}," \\\n",[194,2648,2649,2652,2655,2658,2660],{"class":196,"line":345},[194,2650,2651],{"class":1019},"  -H",[194,2653,2654],{"class":204}," \"Authorization: Bearer ",[194,2656,2657],{"class":224},"$LITELLM_MASTER_KEY",[194,2659,1052],{"class":204},[194,2661,2646],{"class":1019},[194,2663,2664,2667],{"class":196,"line":352},[194,2665,2666],{"class":1019},"  -d",[194,2668,2669],{"class":204}," '{\"models\":[\"gpt-5.4\",\"claude-sonnet-4.6\"],\"max_budget\":100}'\n",[194,2671,2672],{"class":196,"line":358},[194,2673,238],{"emptyLinePlaceholder":237},[194,2675,2676],{"class":196,"line":363},[194,2677,2678],{"class":348},"# 业务方调用\n",[194,2680,2681,2683,2686],{"class":196,"line":369},[194,2682,2634],{"class":200},[194,2684,2685],{"class":204}," http:\u002F\u002Flocalhost:4000\u002Fv1\u002Fchat\u002Fcompletions",[194,2687,2646],{"class":1019},[194,2689,2690,2692,2695],{"class":196,"line":1906},[194,2691,2651],{"class":1019},[194,2693,2694],{"class":204}," \"Authorization: Bearer sk-xxx\"",[194,2696,2646],{"class":1019},[194,2698,2699,2701],{"class":196,"line":1919},[194,2700,2666],{"class":1019},[194,2702,2703],{"class":204}," '{\"model\":\"gpt-5.4\",\"messages\":[...]}'\n",[21,2705,375],{"id":375},[377,2707,2708,2724],{},[380,2709,2710],{},[383,2711,2712,2714,2716,2719,2722],{},[386,2713,388],{},[386,2715,1963],{},[386,2717,2718],{},"OpenRouter",[386,2720,2721],{},"One-API",[386,2723,399],{},[401,2725,2726,2740,2754,2767,2784,2797,2811,2827,2841],{},[383,2727,2728,2730,2733,2736,2738],{},[406,2729,1970],{},[406,2731,2732],{},"OSS + Enterprise",[406,2734,2735],{},"SaaS",[406,2737,2459],{},[406,2739,2735],{},[383,2741,2742,2745,2748,2750,2752],{},[406,2743,2744],{},"协议",[406,2746,2747],{},"MIT",[406,2749,2011],{},[406,2751,2747],{},[406,2753,2011],{},[383,2755,2756,2759,2761,2763,2765],{},[406,2757,2758],{},"自托管",[406,2760,480],{},[406,2762,475],{},[406,2764,480],{},[406,2766,115],{},[383,2768,2769,2772,2775,2778,2781],{},[406,2770,2771],{},"模型数",[406,2773,2774],{},"100+",[406,2776,2777],{},"300+",[406,2779,2780],{},"30+",[406,2782,2783],{},"250+",[383,2785,2786,2789,2791,2793,2795],{},[406,2787,2788],{},"虚拟 Key + 预算",[406,2790,480],{},[406,2792,2011],{},[406,2794,480],{},[406,2796,480],{},[383,2798,2799,2802,2805,2807,2809],{},[406,2800,2801],{},"Fallback",[406,2803,2804],{},"✅ 三类",[406,2806,480],{},[406,2808,480],{},[406,2810,480],{},[383,2812,2813,2816,2819,2822,2825],{},[406,2814,2815],{},"admin UI",[406,2817,2818],{},"✅ 内置",[406,2820,2821],{},"dashboard",[406,2823,2824],{},"✅ 中文 UI",[406,2826,480],{},[383,2828,2829,2832,2834,2836,2839],{},[406,2830,2831],{},"中文支付",[406,2833,475],{},[406,2835,475],{},[406,2837,2838],{},"✅ EPay 内置",[406,2840,475],{},[383,2842,2843,2846,2849,2851,2854],{},[406,2844,2845],{},"集成观测",[406,2847,2848],{},"Langfuse\u002FProm",[406,2850,2821],{},[406,2852,2853],{},"仪表盘",[406,2855,2856],{},"原生",[21,2858,549],{"id":549},[36,2860,2861,2869,2875,2881,2887,2897,2903,2917],{},[39,2862,2863,2379,2866,2868],{},[42,2864,2865],{},"必固定版本号",[191,2867,2516],{}," \u002F 滚动 tag 在 2026-03 供应链事件后已是禁忌；用带签名验证的具体版本（如 v1.85.0）",[39,2870,2871,2874],{},[42,2872,2873],{},"salt_key 不能轮换","：初始化前生成 + 加密备份",[39,2876,2877,2880],{},[42,2878,2879],{},"Postgres 备份","：所有虚拟 Key + 预算都在 DB，必须定期备份",[39,2882,2883,2886],{},[42,2884,2885],{},"fallback 链别堆超 3 个","：失败叠加延迟 + 计费混乱",[39,2888,2889,2892,2893,2896],{},[42,2890,2891],{},"drop_params 谨慎开","：开 ",[191,2894,2895],{},"drop_params: true"," 会静默丢不兼容字段，调试时容易 confused",[39,2898,2899,2902],{},[42,2900,2901],{},"PII guardrail 不是 0 延迟","：Presidio 调用增 50–100ms，敏感场景再用",[39,2904,2905,2908,2909,2912,2913,2916],{},[42,2906,2907],{},"monorepo + workspace 模型映射","：业务方调 ",[191,2910,2911],{},"gpt-4"," → config 映射到 ",[191,2914,2915],{},"openai\u002Fgpt-5.4-mini","，要规划稳定映射表",[39,2918,2919,2922],{},[42,2920,2921],{},"国内自托管","：BYOK Key 走海外 API 仍然受网络影响，需要部署在能直连厂商的节点",[21,2924,603],{"id":602},[36,2926,2927,2930,2933,2936,2939,2942,2945,2948],{},[39,2928,2929],{},"✅ 中大型团队 \u002F 微服务架构",[39,2931,2932],{},"✅ 合规 \u002F 数据主权要求",[39,2934,2935],{},"✅ 多团队预算治理",[39,2937,2938],{},"✅ 想叠加 Langfuse \u002F Helicone \u002F Prometheus 观测",[39,2940,2941],{},"✅ BYOK 模式跨多厂商",[39,2943,2944],{},"❌ 不想运维（走 OpenRouter \u002F Portkey）",[39,2946,2947],{},"❌ 国内业务支付 + 中文运营（走 one-api \u002F new-api）",[39,2949,2950],{},"❌ 纯个人项目（SDK 足够，不需要 Proxy）",[21,2952,632],{"id":632},[36,2954,2955,2959,2963,2967],{},[39,2956,2957],{},[638,2958,1400],{"href":1399},[39,2960,2961],{},[638,2962,1406],{"href":1405},[39,2964,2965],{},[638,2966,647],{"href":646},[39,2968,2969],{},[638,2970,641],{"href":640},[21,2972,662],{"id":662},[664,2974,2975,2982,2989,2996],{},[39,2976,2977,2978],{},"NerdLevelTech — LiteLLM Proxy Production Tutorial 2026（含 v1.85.0 + 供应链事件细节）",[638,2979,2980],{"href":2980,"rel":2981},"https:\u002F\u002Fnerdleveltech.com\u002Flitellm-proxy-production-llm-gateway-tutorial",[673],[39,2983,2984,2985],{},"LiteLLM 官方文档 — Fallbacks \u002F Retries \u002F Cooldowns ",[638,2986,2987],{"href":2987,"rel":2988},"https:\u002F\u002Fdocs.litellm.ai\u002Fdocs\u002Fproxy\u002Freliability",[673],[39,2990,2991,2992],{},"Youngju.dev — LiteLLM Complete Guide 2026（SDK vs Proxy \u002F cost-based routing）",[638,2993,2994],{"href":2994,"rel":2995},"https:\u002F\u002Fwww.youngju.dev\u002Fblog\u002Fculture\u002F2026-03-25-litellm-unified-llm-api-proxy-guide-2025.en",[673],[39,2997,2998,2999],{},"GitHub — BerriAI\u002Flitellm README ",[638,3000,3001],{"href":3001,"rel":3002},"https:\u002F\u002Fgithub.com\u002FBerriAI\u002Flitellm",[673],[696,3004,3005],{},"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 .sVt8B, html code.shiki .sVt8B{--shiki-default:#24292E;--shiki-dark:#E1E4E8}html pre.shiki code .sj4cs, html code.shiki .sj4cs{--shiki-default:#005CC5;--shiki-dark:#79B8FF}html pre.shiki code .szBVR, html code.shiki .szBVR{--shiki-default:#D73A49;--shiki-dark:#F97583}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":189,"searchDepth":241,"depth":241,"links":3007},[3008,3009,3010,3011,3012,3013,3014,3015,3016,3017],{"id":23,"depth":234,"text":24},{"id":34,"depth":234,"text":34},{"id":96,"depth":234,"text":96},{"id":2476,"depth":234,"text":2477},{"id":2536,"depth":234,"text":2537},{"id":375,"depth":234,"text":375},{"id":549,"depth":234,"text":549},{"id":602,"depth":234,"text":603},{"id":632,"depth":234,"text":632},{"id":662,"depth":234,"text":662},"\u002Fimg\u002Ftools\u002Flitellm.webp","LiteLLM 真实评测：MIT 开源 LLM 网关，BerriAI 维护。SDK 模式（pip install litellm）+ Proxy 模式（Docker Compose 自托管）双形态，100+ 厂商统一 OpenAI 接口，虚拟 Key + 团队预算 + 三类 fallback + 成本追踪 + 内置 UI。2026-03 经历供应链事件后 v1.83+ 强化签名 + 镜像验证，生产请固定版本号。",[3021,3024,3027,3030],{"q":3022,"a":3023},"SDK 和 Proxy 模式区别？","SDK 模式（`from litellm import completion`）直接在 Python 里调，适合个人项目 \u002F 单服务。Proxy 模式启动一个 HTTP 服务（默认 4000 端口）+ Postgres，所有应用通过 OpenAI 兼容 endpoint 调用，支持虚拟 Key \u002F 团队 \u002F 预算 \u002F 配额 \u002F admin UI——团队 \u002F 多语言客户端 \u002F 生产建议走 Proxy。",{"q":3025,"a":3026},"Fallback 有几种？","三类：(1) general fallback——5xx\u002F429 错误切到备用模型；(2) content policy fallback——内容审查拒绝时切；(3) context window fallback——输入超 context 切大窗口模型（比如超 GPT-4 32k 自动切 Claude 200k）。可叠加 num_retries \u002F cooldown \u002F timeout 做完整 SRE 策略。",{"q":3028,"a":3029},"2026-03 供应链事件是什么？","2026-03-24 PyPI 上的 v1.82.7 \u002F v1.82.8 在被替换约 40 分钟内可能植入 Trivy CI token 外泄代码。BerriAI 在 v1.83+ 强化签名 + 镜像验证，生产部署务必固定到带签名验证的版本（如 v1.85.0），不要用 `:latest` tag。",{"q":3031,"a":3032},"和 OpenRouter 怎么选？","OpenRouter = SaaS（不用自己运维 + 直接付钱）；LiteLLM = OSS 自托管（自付服务器 + 完全控制数据 + BYOK 所有 Key）。中大型团队 \u002F 合规 \u002F 长期成本敏感 \u002F 自建 → LiteLLM Proxy；小团队 \u002F 不想运维 \u002F 快速迭代 → OpenRouter。",[730],{},[736,1518,1125,3036],"kubernetes",[3038,3043],{"plan":3039,"price":3040,"features":3041,"notes":3042},"Open Source","$0（MIT）","SDK + Proxy + Postgres 虚拟 Key + 预算 + fallback + admin UI + 100+ providers","完全自托管，模型 \u002F 平台费走自付",{"plan":115,"price":745,"features":3044,"notes":3045},"SSO \u002F SAML + Audit + JWT auth + 高级路由 + SLA + on-prem 部署支持","联系 BerriAI 销售","MIT 开源免费 \u002F Enterprise SaaS 定制",[754],[2315],{"power":268,"ux":253,"price":268,"cn_support":241,"stability":253},{"title":1963,"description":3019},"LiteLLM 评测 2026：开源 LLM 网关，100+ 厂商统一 OpenAI 接口",[3053,3055,3057,3059],{"name":3054,"url":2980,"accessed":762},"NerdLevelTech — LiteLLM Proxy 生产教程 2026",{"name":3056,"url":2987,"accessed":762},"LiteLLM 官方文档 — Fallbacks",{"name":3058,"url":2994,"accessed":762},"Youngju.dev — LiteLLM Complete Guide 2026",{"name":3060,"url":3001,"accessed":762},"GitHub — BerriAI\u002Flitellm","tools\u002Fcoding\u002Fapi\u002Flitellm","MIT 开源 LLM 网关——SDK + Proxy 双形态，100+ 厂商统一 OpenAI 接口 + 虚拟 Key + 团队预算",[2330,1518,736,777,3064,3065,3066,3067],"mit","fallback","virtual-keys","byok","想要『SaaS 体验 + 完全自托管』的开源 LLM 网关首选。组合『LiteLLM 网关 + Helicone\u002FLangfuse 观测』在中大型 dev org 是黄金组合。中文中转 + 业务支付走 one-api。","https:\u002F\u002Fwww.litellm.ai","gajiT_SY7TiVCCCUElYHDjw0I5C_FDozvhWdbofF-EY",{"id":3072,"title":3073,"alternatives":3074,"api_compatible":3078,"body":3079,"category":3642,"chinese_friendly":241,"cover":3643,"description":3644,"domestic":714,"extension":715,"faq":3645,"free":237,"github":3658,"languages":3659,"lastVerified":731,"meta":3661,"models":16,"navigation":237,"notSuitable":16,"opensource":237,"path":3662,"pillar":711,"platforms":3663,"priceTable":3665,"pricing":3679,"published":752,"relatedPlaybooks":3680,"relatedReviews":16,"score":3682,"self_host":237,"seo":3683,"seoTitle":3684,"slug":15,"sources":3685,"stem":3695,"suitable":16,"tagline":3696,"tags":3697,"updated":762,"verdict":3702,"website":3688,"__hash__":3703},"tools\u002Ftools\u002Fagent\u002Fplatform\u002Flangflow.md","Langflow",[3075,3076,3077],"agent\u002Fplatform\u002Fn8n","agent\u002Fprotocol\u002Fcomposio","agent\u002Fgeneral\u002Fopenmanus",[1530,1531,1537,1536,1539,1541],{"type":18,"value":3080,"toc":3630},[3081,3083,3086,3089,3091,3165,3167,3192,3197,3201,3205,3231,3235,3261,3263,3317,3320,3343,3345,3487,3489,3545,3547,3573,3575,3595,3597,3627],[21,3082,24],{"id":23},[26,3084,3085],{},"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。",[26,3087,3088],{},"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）。",[21,3090,34],{"id":34},[36,3092,3093,3099,3105,3111,3117,3123,3129,3135,3141,3147,3153,3159],{},[39,3094,3095,3098],{},[42,3096,3097],{},"可视化 LangChain 画布","：节点 = primitive，连线 = 数据流",[39,3100,3101,3104],{},[42,3102,3103],{},"RAG pipeline 原生组件","：Chunking \u002F Embedding \u002F VectorStore \u002F Retriever",[39,3106,3107,3110],{},[42,3108,3109],{},"多 agent 工作流","：编排多 agent 协作",[39,3112,3113,3116],{},[42,3114,3115],{},"Python 下钻","：任意节点可写 custom Python",[39,3118,3119,3122],{},[42,3120,3121],{},"向量库适配","：Astra DB \u002F Pinecone \u002F pgvector \u002F Weaviate \u002F Chroma",[39,3124,3125,3128],{},[42,3126,3127],{},"API 部署","：流程一键导出为 REST API",[39,3130,3131,3134],{},[42,3132,3133],{},"Real-time collaboration","：多用户同 project",[39,3136,3137,3140],{},[42,3138,3139],{},"版本控制","：内置 versioning + revert",[39,3142,3143,3146],{},[42,3144,3145],{},"数据可视化","：node output \u002F data flow 可视化调试",[39,3148,3149,3152],{},[42,3150,3151],{},"角色权限","：user auth + RBAC",[39,3154,3155,3158],{},[42,3156,3157],{},"Docker \u002F pip 安装","：5 分钟启动",[39,3160,3161,3164],{},[42,3162,3163],{},"Astra-hosted cloud","：DataStax 托管选项",[21,3166,96],{"id":96},[36,3168,3169,3175,3181,3187],{},[39,3170,3171,3174],{},[42,3172,3173],{},"Self-Host (OSS)","：$0；MIT 完全免费，自付 LLM API + 服务器",[39,3176,3177,3180],{},[42,3178,3179],{},"Cloud Free","：$0；DataStax Astra-hosted 小流量",[39,3182,3183,3186],{},[42,3184,3185],{},"Cloud Paid","：~$25\u002F月起；Astra DB + 更高额度 + 团队协作",[39,3188,3189,3191],{},[42,3190,115],{},"：联系销售；SSO + audit + 私有部署 + SLA",[118,3193,3194],{},[26,3195,3196],{},"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。",[21,3198,3200],{"id":3199},"实测rag-应用-内部工具搭建","实测（RAG 应用 + 内部工具搭建）",[26,3202,3203],{},[42,3204,131],{},[36,3206,3207,3210,3213,3216,3219,3222,3225,3228],{},[39,3208,3209],{},"画布直观，比纯写 LangChain 协作效率高 5x",[39,3211,3212],{},"节点下钻到 Python 让灵活度不被画布限制",[39,3214,3215],{},"Astra DB 集成省了配 vector store 时间",[39,3217,3218],{},"4.4\u002F5 用户评分（Propicked \u002F Tooliverse）",[39,3220,3221],{},"开源 + 自托管 + 数据驻留满足合规",[39,3223,3224],{},"多 agent 编排比裸 LangChain 调试容易",[39,3226,3227],{},"RAG pipeline 模板一键起 demo",[39,3229,3230],{},"与 DataStax 长期支持降低 abandon ware 风险",[26,3232,3233],{},[42,3234,156],{},[36,3236,3237,3240,3243,3246,3249,3252,3255,3258],{},[39,3238,3239],{},"稳定性偶发：复杂流大流量下 node 偶尔失联（SelectHub 反馈）",[39,3241,3242],{},"第三方 API 依赖：external API 失败时错误处理弱",[39,3244,3245],{},"production readiness 不算 mission-critical（要自加 observability）",[39,3247,3248],{},"LangChain 升级偶尔 break 旧 flow",[39,3250,3251],{},"文档对新组件滞后 1-2 月",[39,3253,3254],{},"中文 UI 不完整，业务侧用户上手陡",[39,3256,3257],{},"大型 flow（100+ 节点）画布卡顿",[39,3259,3260],{},"多人协作偶发同步冲突",[21,3262,182],{"id":182},[184,3264,3266],{"className":186,"code":3265,"language":188,"meta":189,"style":189},"# 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",[191,3267,3268,3273,3282,3293,3297,3302],{"__ignoreMap":189},[194,3269,3270],{"class":196,"line":197},[194,3271,3272],{"class":348},"# pip 安装\n",[194,3274,3275,3277,3279],{"class":196,"line":234},[194,3276,201],{"class":200},[194,3278,205],{"class":204},[194,3280,3281],{"class":204}," langflow\n",[194,3283,3284,3287,3290],{"class":196,"line":241},[194,3285,3286],{"class":200},"langflow",[194,3288,3289],{"class":204}," run",[194,3291,3292],{"class":348},"  # http:\u002F\u002Flocalhost:7860\n",[194,3294,3295],{"class":196,"line":253},[194,3296,238],{"emptyLinePlaceholder":237},[194,3298,3299],{"class":196,"line":268},[194,3300,3301],{"class":348},"# 或 Docker\n",[194,3303,3304,3306,3308,3311,3314],{"class":196,"line":280},[194,3305,1125],{"class":200},[194,3307,3289],{"class":204},[194,3309,3310],{"class":1019}," -p",[194,3312,3313],{"class":204}," 7860:7860",[194,3315,3316],{"class":204}," langflowai\u002Flangflow:latest\n",[26,3318,3319],{},"试 RAG 流：",[664,3321,3322,3325,3328,3331,3334,3337,3340],{},[39,3323,3324],{},"新建 flow → 选 Document QA 模板",[39,3326,3327],{},"Document Loader 节点 → 上传 PDF",[39,3329,3330],{},"Splitter → Embedder（OpenAI 或本地）",[39,3332,3333],{},"VectorStore（Astra \u002F Chroma）",[39,3335,3336],{},"Retriever + ChatOpenAI → Chat Output",[39,3338,3339],{},"部署为 API → 拿到 endpoint",[39,3341,3342],{},"复杂场景下钻节点写 Python 自定义",[21,3344,375],{"id":375},[377,3346,3347,3364],{},[380,3348,3349],{},[383,3350,3351,3353,3355,3358,3361],{},[386,3352,388],{},[386,3354,3073],{},[386,3356,3357],{},"Dify",[386,3359,3360],{},"n8n",[386,3362,3363],{},"Flowise",[401,3365,3366,3383,3399,3413,3427,3442,3455,3471],{},[383,3367,3368,3371,3374,3377,3380],{},[406,3369,3370],{},"中心",[406,3372,3373],{},"LangChain primitive",[406,3375,3376],{},"LLMOps 全平台",[406,3378,3379],{},"通用 workflow",[406,3381,3382],{},"LangChain（JS）",[383,3384,3385,3388,3391,3394,3397],{},[406,3386,3387],{},"开源",[406,3389,3390],{},"✅ MIT",[406,3392,3393],{},"✅ AGPL",[406,3395,3396],{},"✅ Sustainable",[406,3398,3390],{},[383,3400,3401,3403,3406,3409,3411],{},[406,3402,2758],{},[406,3404,3405],{},"✅ pip\u002FDocker",[406,3407,3408],{},"✅ Docker",[406,3410,3408],{},[406,3412,480],{},[383,3414,3415,3418,3421,3423,3425],{},[406,3416,3417],{},"可视化",[406,3419,3420],{},"✅ 旗舰",[406,3422,480],{},[406,3424,480],{},[406,3426,480],{},[383,3428,3429,3432,3435,3437,3440],{},[406,3430,3431],{},"代码下钻",[406,3433,3434],{},"✅ Python",[406,3436,431],{},[406,3438,3439],{},"✅ JS",[406,3441,3439],{},[383,3443,3444,3447,3449,3451,3453],{},[406,3445,3446],{},"RAG 内置",[406,3448,480],{},[406,3450,480],{},[406,3452,431],{},[406,3454,480],{},[383,3456,3457,3460,3463,3466,3469],{},[406,3458,3459],{},"起价（云）",[406,3461,3462],{},"$25\u002F月",[406,3464,3465],{},"$59\u002F月（Team）",[406,3467,3468],{},"自托管 $0",[406,3470,2011],{},[383,3472,3473,3475,3478,3481,3484],{},[406,3474,534],{},[406,3476,3477],{},"工程 + LangChain",[406,3479,3480],{},"业务 + LLMOps",[406,3482,3483],{},"通用自动化",[406,3485,3486],{},"JS 生态",[21,3488,549],{"id":549},[36,3490,3491,3497,3503,3509,3515,3521,3527,3533,3539],{},[39,3492,3493,3496],{},[42,3494,3495],{},"自托管推荐 Docker","：pip 版本依赖冲突难调",[39,3498,3499,3502],{},[42,3500,3501],{},"生产加 observability","：langsmith \u002F sentry \u002F prometheus 必装",[39,3504,3505,3508],{},[42,3506,3507],{},"Astra DB cloud free","：起步够用，付费版起步前算清成本",[39,3510,3511,3514],{},[42,3512,3513],{},"复杂 flow 拆模块","：100+ 节点画布卡顿，拆成子 flow",[39,3516,3517,3520],{},[42,3518,3519],{},"LangChain 版本 pin","：Langflow 升级前测试 flow 兼容性",[39,3522,3523,3526],{},[42,3524,3525],{},"第三方 API 加重试","：custom Python 节点写 retry + fallback",[39,3528,3529,3532],{},[42,3530,3531],{},"多人协作 lock","：同时编辑 flow 容易冲突，加 lock 流程",[39,3534,3535,3538],{},[42,3536,3537],{},"中文场景","：UI 英文为主，业务侧用户先培训",[39,3540,3541,3544],{},[42,3542,3543],{},"Self-host vs Cloud","：合规要 self-host，省心要 Cloud",[21,3546,603],{"id":602},[36,3548,3549,3552,3555,3558,3561,3564,3567,3570],{},[39,3550,3551],{},"✅ 工程团队要可视化建 LangChain 流",[39,3553,3554],{},"✅ 合规 \u002F 数据驻留要求自托管",[39,3556,3557],{},"✅ 要 Astra DB 一站式 RAG",[39,3559,3560],{},"✅ Python 团队 + 想画布 + 想下钻代码",[39,3562,3563],{},"❌ 业务侧 + 非工程师（用 Dify \u002F Coze）",[39,3565,3566],{},"❌ 纯无代码偏好",[39,3568,3569],{},"❌ 轻量场景 + 直接写 LangChain 更快",[39,3571,3572],{},"❌ JS 生态优先（用 Flowise）",[21,3574,632],{"id":632},[36,3576,3577,3583,3589],{},[39,3578,3579],{},[638,3580,3582],{"href":3581},"\u002Ftools\u002Fagent\u002Fplatform\u002Fn8n","n8n 评测",[39,3584,3585],{},[638,3586,3588],{"href":3587},"\u002Ftools\u002Fagent\u002Fprotocol\u002Fcomposio","Composio 评测",[39,3590,3591],{},[638,3592,3594],{"href":3593},"\u002Ftools\u002Fagent\u002Fgeneral\u002Fopenmanus","OpenManus 评测",[21,3596,662],{"id":662},[664,3598,3599,3606,3613,3620],{},[39,3600,3601,3602],{},"Langflow 官网 + 定价 ",[638,3603,3604],{"href":3604,"rel":3605},"https:\u002F\u002Fwww.langflow.org\u002Fpricing",[673],[39,3607,3608,3609],{},"AutomationAtlas — Langflow $25\u002Fmo 2026 Comparison ",[638,3610,3611],{"href":3611,"rel":3612},"https:\u002F\u002Fautomationatlas.io\u002Ftools\u002Flangflow",[673],[39,3614,3615,3616],{},"xpay — LangFlow 2026 Visual AI Agent Builder（MIT + DataStax）",[638,3617,3618],{"href":3618,"rel":3619},"https:\u002F\u002Fwww.xpay.sh\u002Fresources\u002Fagentic-frameworks\u002Flangflow",[673],[39,3621,3622,3623],{},"SelectHub — LangFlow Reviews 2026（稳定性 + 生产 readiness）",[638,3624,3625],{"href":3625,"rel":3626},"https:\u002F\u002Fwww.selecthub.com\u002Fp\u002Fai-agent-builder-software\u002Flangflow\u002F",[673],[696,3628,3629],{},"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":189,"searchDepth":241,"depth":241,"links":3631},[3632,3633,3634,3635,3636,3637,3638,3639,3640,3641],{"id":23,"depth":234,"text":24},{"id":34,"depth":234,"text":34},{"id":96,"depth":234,"text":96},{"id":3199,"depth":234,"text":3200},{"id":182,"depth":234,"text":182},{"id":375,"depth":234,"text":375},{"id":549,"depth":234,"text":549},{"id":602,"depth":234,"text":603},{"id":632,"depth":234,"text":632},{"id":662,"depth":234,"text":662},"platform","\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。",[3646,3649,3652,3655],{"q":3647,"a":3648},"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":3650,"a":3651},"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":3653,"a":3654},"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":3656,"a":3657},"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",[730,3660],"multi",{},"\u002Ftools\u002Fagent\u002Fplatform\u002Flangflow",[1482,737,1125,3664],"web",[3666,3669,3672,3676],{"plan":3173,"price":741,"features":3667,"notes":3668},"MIT + pip\u002FDocker + 全部组件 + Pinecone\u002Fpgvector\u002FAstra 适配","自付 LLM API + 服务器",{"plan":3179,"price":741,"features":3670,"notes":3671},"DataStax 托管 + 小流量","试水",{"plan":3185,"price":3673,"features":3674,"notes":3675},"~$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":115,"price":747,"features":3677,"notes":3678},"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）",[3681],"onboarding\u002Frag-app-workflow",{"power":253,"ux":268,"price":268,"cn_support":241,"stability":253},{"title":3073,"description":3644},"Langflow 评测 2026：可视化 AI 工作流构建工具，LangChain 低代码平台",[3686,3689,3691,3693],{"name":3687,"url":3688,"accessed":762},"Langflow 官网","https:\u002F\u002Fwww.langflow.org",{"name":3690,"url":3611,"accessed":762},"AutomationAtlas — Langflow $25\u002Fmo 2026 Comparison",{"name":3692,"url":3618,"accessed":762},"xpay — LangFlow 2026 Visual AI Agent Builder",{"name":3694,"url":3625,"accessed":762},"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",[777,3698,3699,3700,3701,3286],"visual-builder","langchain","rag","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",1785660639645]