[{"data":1,"prerenderedAt":13392},["ShallowReactive",2],{"header-counts":3,"home-counts":6,"footer-counts":5221,"home-hot-tools":5222,"home-reviews":5848,"home-playbooks":8329,"home-news":12152},{"tools":4,"reviews":5},77,25,{"tools":4,"reviews":5,"playbooks":7,"news":8,"codingCount":9,"agentCount":10,"newTools":11,"latestNews":5000},22,13,54,23,[12,829,1394,2283,3133,4198],{"id":13,"title":14,"alternatives":15,"api_compatible":19,"body":23,"category":750,"chinese_friendly":353,"cover":751,"description":752,"domestic":753,"extension":754,"faq":755,"free":753,"github":756,"languages":757,"meta":759,"models":760,"navigation":765,"notSuitable":766,"opensource":765,"path":771,"pillar":772,"platforms":773,"priceTable":777,"pricing":790,"published":791,"relatedPlaybooks":755,"relatedReviews":755,"score":792,"self_host":765,"seo":793,"seoTitle":794,"slug":795,"sources":796,"stem":811,"suitable":812,"tagline":818,"tags":819,"updated":791,"verdict":827,"website":756,"__hash__":828},"tools\u002Ftools\u002Fagent\u002Fgeneral\u002Fhermes-agent.md","Hermes Agent",[16,17,18],"agent\u002Fgeneral\u002Fmanus","agent\u002Fgeneral\u002Fopenmanus","agent\u002Fdesktop\u002Fopenclaw",[20,21,22],"openai","anthropic","local",{"type":24,"value":25,"toc":728},"minimark",[26,30,39,45,55,59,64,71,99,106,110,117,131,134,138,144,231,237,240,243,249,267,270,273,276,296,299,302,305,319,322,325,407,422,425,431,446,449,452,478,481,535,541,544,654,658,664,670,676,682,688,691,724],[27,28,29],"h2",{"id":29},"一句话结论",[31,32,33,34,38],"p",{},"如果你想要一个",[35,36,37],"strong",{},"完全开源、能自我进化、有长期记忆、可以部署在十几个消息平台上的 AI Agent","——Hermes Agent 在 2026 年是最成熟的选择。GitHub 10 万 Star，2 月开源到 5 月就翻了一倍。",[31,40,41,42],{},"但它的门槛比 Manus 高一个量级：没有漂亮的 Web 界面，需要自己装 Python 环境、配置 LLM API、管理记忆数据库。",[35,43,44],{},"它面向的是\"想拥有自己 Agent 基础设施\"的极客，不是\"想要一个好用 AI 助手\"的普通用户。",[46,47,49],"callout",{"type":48},"info",[31,50,51,54],{},[35,52,53],{},"定位区分","：Manus 是\"帮你干完事的云端 Agent\"，OpenClaw 是\"常驻你电脑的 AI 操作系统\"，Hermes Agent 是\"你能完全掌控和改造的自进化 Agent\"。三者目标不同，选型看你需要的是\"用\"还是\"拥有\"。",[27,56,58],{"id":57},"hermes-agent-解决的核心问题","Hermes Agent 解决的核心问题",[60,61,63],"h3",{"id":62},"问题-1agent-没有长期记忆","问题 1：Agent 没有\"长期记忆\"",[31,65,66,67,70],{},"大多数 AI Agent 的记忆只有当前会话的上下文窗口。你昨天让它做的事、偏好、上下文，今天全忘了。Hermes Agent 内置了",[35,68,69],{},"分层记忆系统","：",[72,73,74,81,87,93],"ul",{},[75,76,77,80],"li",{},[35,78,79],{},"工作记忆","：当前会话上下文，类似人类短期记忆",[75,82,83,86],{},[35,84,85],{},"情景记忆","：记录每次交互的时间、事件、结果，支持按时间线回溯",[75,88,89,92],{},[35,90,91],{},"语义记忆","：从交互中抽取知识（你告诉它的偏好、事实、规则），长期保存",[75,94,95,98],{},[35,96,97],{},"技能记忆","：Agent 自动总结\"怎么做某件事\"的步骤，下次直接调用",[31,100,101,102,105],{},"这意味着 Hermes Agent 是",[35,103,104],{},"越用越好用","的——它会记住你的工作习惯、项目上下文、你纠正过的错误。",[60,107,109],{"id":108},"问题-2单个模型能力有天花板","问题 2：单个模型能力有天花板",[31,111,112,113,116],{},"2026 年 6 月，Nous Research 给 Hermes Agent 加了 ",[35,114,115],{},"MoA（Mixture of Agents）"," 功能：",[72,118,119,122,125,128],{},[75,120,121],{},"多个 Agent 实例并行处理同一个任务",[75,123,124],{},"每个 Agent 可以用不同模型（Claude \u002F GPT \u002F Hermes \u002F 本地模型）",[75,126,127],{},"结果由一个\"聚合 Agent\"合并去重、交叉验证",[75,129,130],{},"最终输出质量 > 任何单个模型",[31,132,133],{},"实际效果：用 3 个中档模型做 MoA，输出质量可以接近 1 个顶级模型，但成本只有 1\u002F3。",[60,135,137],{"id":136},"问题-3agent-只能在一个地方用","问题 3：Agent 只能在一个地方用",[31,139,140,141,70],{},"Hermes Agent 支持 ",[35,142,143],{},"14 个消息渠道",[145,146,147,160],"table",{},[148,149,150],"thead",{},[151,152,153,157],"tr",{},[154,155,156],"th",{},"平台",[154,158,159],{},"状态",[161,162,163,172,180,187,194,201,208,215,223],"tbody",{},[151,164,165,169],{},[166,167,168],"td",{},"Telegram",[166,170,171],{},"✅ 推荐，最稳定",[151,173,174,177],{},[166,175,176],{},"Discord",[166,178,179],{},"✅",[151,181,182,185],{},[166,183,184],{},"Slack",[166,186,179],{},[151,188,189,192],{},[166,190,191],{},"WhatsApp",[166,193,179],{},[151,195,196,199],{},[166,197,198],{},"Signal",[166,200,179],{},[151,202,203,206],{},[166,204,205],{},"Email",[166,207,179],{},[151,209,210,213],{},[166,211,212],{},"CLI（终端）",[166,214,179],{},[151,216,217,220],{},[166,218,219],{},"Web UI",[166,221,222],{},"✅ 基础版",[151,224,225,228],{},[166,226,227],{},"微信",[166,229,230],{},"⚠️ 非官方，不稳定",[31,232,233,234],{},"你可以在 Telegram 上给它发消息让它做事，结果推送到 Discord；或者在 CLI 里开发时让它监听 Git 提交自动跑测试。",[35,235,236],{},"一个 Agent 实例，多个入口。",[27,238,239],{"id":239},"核心能力",[60,241,242],{"id":242},"自我进化",[31,244,245,246,70],{},"Hermes Agent 最独特的能力是 ",[35,247,248],{},"Profile 系统",[72,250,251,254,257,264],{},[75,252,253],{},"每个 Profile 是一个\"人格 + 技能包\"的组合",[75,255,256],{},"你可以创建多个 Profile（如\"代码助手\"、\"研究助手\"、\"项目经理\"）",[75,258,259,260,263],{},"Agent 在执行任务后会",[35,261,262],{},"自动总结经验","，更新 Profile 的技能库",[75,265,266],{},"下次遇到类似任务，直接调用已有技能，不用从零开始",[31,268,269],{},"这意味着 Hermes Agent 不是\"每次都从零开始的 ChatBot\"，而是\"会积累经验的数字员工\"。",[60,271,272],{"id":272},"工具调用",[31,274,275],{},"Hermes Agent 支持自定义工具（function calling）：",[72,277,278,281,284,287,290,293],{},[75,279,280],{},"搜索引擎（Google \u002F Bing \u002F SearXNG）",[75,282,283],{},"代码执行（Python sandbox）",[75,285,286],{},"文件读写",[75,288,289],{},"Web 浏览（Playwright）",[75,291,292],{},"自定义 API 调用",[75,294,295],{},"数据库查询",[31,297,298],{},"工具配置是 JSON 格式，添加新工具只需写一个 function 定义。",[60,300,301],{"id":301},"记忆管理",[31,303,304],{},"记忆系统基于向量数据库（默认 ChromaDB）：",[72,306,307,310,313,316],{},[75,308,309],{},"自动从对话中提取关键信息存入语义记忆",[75,311,312],{},"支持手动\"forget\"删除特定记忆",[75,314,315],{},"记忆有 TTL（过期时间），避免无限膨胀",[75,317,318],{},"支持记忆导出\u002F导入（JSON 格式），方便迁移",[27,320,321],{"id":321},"使用体验",[60,323,324],{"id":324},"安装部署",[326,327,332],"pre",{"className":328,"code":329,"language":330,"meta":331,"style":331},"language-bash shiki shiki-themes github-light github-dark","git clone https:\u002F\u002Fgithub.com\u002FNousResearch\u002Fhermes-agent\ncd hermes-agent\npip install -r requirements.txt\ncp .env.example .env\n# 配置 LLM API key、消息平台 token\npython -m hermes.agent\n","bash","",[333,334,335,351,361,376,388,395],"code",{"__ignoreMap":331},[336,337,340,344,348],"span",{"class":338,"line":339},"line",1,[336,341,343],{"class":342},"sScJk","git",[336,345,347],{"class":346},"sZZnC"," clone",[336,349,350],{"class":346}," https:\u002F\u002Fgithub.com\u002FNousResearch\u002Fhermes-agent\n",[336,352,354,358],{"class":338,"line":353},2,[336,355,357],{"class":356},"sj4cs","cd",[336,359,360],{"class":346}," hermes-agent\n",[336,362,364,367,370,373],{"class":338,"line":363},3,[336,365,366],{"class":342},"pip",[336,368,369],{"class":346}," install",[336,371,372],{"class":356}," -r",[336,374,375],{"class":346}," requirements.txt\n",[336,377,379,382,385],{"class":338,"line":378},4,[336,380,381],{"class":342},"cp",[336,383,384],{"class":346}," .env.example",[336,386,387],{"class":346}," .env\n",[336,389,391],{"class":338,"line":390},5,[336,392,394],{"class":393},"sJ8bj","# 配置 LLM API key、消息平台 token\n",[336,396,398,401,404],{"class":338,"line":397},6,[336,399,400],{"class":342},"python",[336,402,403],{"class":356}," -m",[336,405,406],{"class":346}," hermes.agent\n",[31,408,409,410,413,414,417,418,421],{},"部署需要 ",[35,411,412],{},"Python 3.11+","、",[35,415,416],{},"至少 8GB RAM","（跑本地模型需要更多）、",[35,419,420],{},"向量数据库","（默认 ChromaDB，可选 Qdrant）。",[60,423,424],{"id":424},"日常使用",[31,426,427,428,70],{},"最顺的使用方式是 ",[35,429,430],{},"Telegram + Claude API",[432,433,434,437,440,443],"ol",{},[75,435,436],{},"在 Telegram 上给 Agent 发消息",[75,438,439],{},"Agent 读取记忆、规划任务、调用工具",[75,441,442],{},"执行过程中实时推送进度",[75,444,445],{},"完成后推送结果 + 自动更新记忆",[31,447,448],{},"体感类似\"有一个 7×24 小时在线的助手\"，但它不是即问即答——复杂任务可能需要 2-5 分钟。",[60,450,451],{"id":451},"短板",[72,453,454,460,466,472],{},[75,455,456,459],{},[35,457,458],{},"文档偏英文","：几乎没有中文文档，国内用户上手门槛高",[75,461,462,465],{},[35,463,464],{},"稳定性","：项目迭代极快（每周多个 commit），偶尔有 breaking change",[75,467,468,471],{},[35,469,470],{},"资源消耗","：记忆系统 + 多 Agent 会占用较多内存和 API token",[75,473,474,477],{},[35,475,476],{},"UI 简陋","：Web UI 只是基础版，不如 Manus \u002F OpenClaw 精致",[27,479,480],{"id":480},"价格",[145,482,483,493],{},[148,484,485],{},[151,486,487,490],{},[154,488,489],{},"项目",[154,491,492],{},"成本",[161,494,495,503,511,519,527],{},[151,496,497,500],{},[166,498,499],{},"Hermes Agent 本体",[166,501,502],{},"免费（开源）",[151,504,505,508],{},[166,506,507],{},"LLM API",[166,509,510],{},"BYOK，用 Claude\u002FGPT 按各自 API 计费",[151,512,513,516],{},[166,514,515],{},"本地模型",[166,517,518],{},"免费但需要 GPU（70B 模型需 ~48GB VRAM）",[151,520,521,524],{},[166,522,523],{},"服务器",[166,525,526],{},"自托管需要一台 VPS（推荐 4 核 16GB 起步）",[151,528,529,532],{},[166,530,531],{},"消息平台",[166,533,534],{},"Telegram\u002FDiscord 等均免费",[31,536,537,540],{},[35,538,539],{},"最低成本","：一台 $5\u002F月 VPS + Claude API 按量付费 ≈ $10-20\u002F月可跑日常任务。",[27,542,543],{"id":543},"与同类对比",[145,545,546,561],{},[148,547,548],{},[151,549,550,553,555,558],{},[154,551,552],{},"维度",[154,554,14],{},[154,556,557],{},"Manus",[154,559,560],{},"OpenClaw",[161,562,563,575,587,600,614,628,640],{},[151,564,565,568,570,573],{},[166,566,567],{},"开源",[166,569,179],{},[166,571,572],{},"❌",[166,574,179],{},[151,576,577,579,582,584],{},[166,578,242],{},[166,580,581],{},"✅ Profile 系统",[166,583,572],{},[166,585,586],{},"⚠️ 有限",[151,588,589,592,595,598],{},[166,590,591],{},"长期记忆",[166,593,594],{},"✅ 分层记忆",[166,596,597],{},"❌ 单会话",[166,599,179],{},[151,601,602,605,608,611],{},[166,603,604],{},"跨平台部署",[166,606,607],{},"✅ 14 渠道",[166,609,610],{},"❌ Web only",[166,612,613],{},"⚠️ 桌面+CLI",[151,615,616,619,622,625],{},[166,617,618],{},"上手难度",[166,620,621],{},"★★★★☆",[166,623,624],{},"★☆☆☆☆",[166,626,627],{},"★★★☆☆",[151,629,630,633,636,638],{},[166,631,632],{},"中文体验",[166,634,635],{},"★★☆☆☆",[166,637,621],{},[166,639,627],{},[151,641,642,645,648,651],{},[166,643,644],{},"适合人群",[166,646,647],{},"极客\u002F研究者",[166,649,650],{},"普通用户",[166,652,653],{},"开发者",[27,655,657],{"id":656},"faq","FAQ",[31,659,660,663],{},[35,661,662],{},"Q：Hermes Agent 能用中文交互吗？","\n能，但体验一般。Hermes 4 模型的中文能力不如 Claude\u002FGPT，且文档和社区以英文为主。建议用 Claude\u002FGPT 作为后端模型，中文交互质量会好很多。",[31,665,666,669],{},[35,667,668],{},"Q：和 OpenManus 有什么区别？","\nOpenManus 是 Manus 的开源复刻版，定位是\"通用 Agent 执行器\"。Hermes Agent 更侧重\"长期陪伴+自我进化+多平台部署\"。OpenManus 更轻量，Hermes 功能更全但更重。",[31,671,672,675],{},[35,673,674],{},"Q：需要什么硬件？","\n纯 API 模式（用 Claude\u002FGPT）只需一台普通 VPS。跑本地 Hermes 4 70B 需要 ~48GB VRAM，405B 需要 ~240GB VRAM（多卡服务器）。",[31,677,678,681],{},[35,679,680],{},"Q：记忆数据存在哪？","\n默认存在本地 ChromaDB（SQLite + 向量索引）。可以配置为 Qdrant、Weaviate 等远程向量数据库。数据完全自主，不会上传到任何第三方。",[31,683,684,687],{},[35,685,686],{},"Q：能同时跑多个 Profile 吗？","\n可以。每个 Profile 是独立的记忆+技能库，可以并行运行。比如同时让\"代码助手\"Profile 审查代码、\"研究助手\"Profile 做市场调研。",[27,689,690],{"id":690},"相关阅读",[72,692,693,700,706,712,718],{},[75,694,695],{},[696,697,699],"a",{"href":698},"\u002Fagent\u002Fgeneral\u002Fmanus.html","Manus 工具卡",[75,701,702],{},[696,703,705],{"href":704},"\u002Fagent\u002Fgeneral\u002Fopenmanus.html","OpenManus 工具卡",[75,707,708],{},[696,709,711],{"href":710},"\u002Fagent\u002Fdesktop\u002Fopenclaw.html","OpenClaw 工具卡",[75,713,714],{},[696,715,717],{"href":716},"\u002Freview\u002Fmanus-deep-review.html","Manus 深度评测",[75,719,720],{},[696,721,723],{"href":722},"\u002Fcompare\u002Fmanus-vs-genspark.html","Manus vs Genspark 对比",[725,726,727],"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 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万 GitHub Star。MoA 混合智能体架构、记忆系统、技能自动创建、跨平台部署（Telegram\u002FDiscord\u002FSlack\u002FWhatsApp\u002FCLI）。本文整理核心能力、使用体验、与 Manus\u002FOpenClaw 对比、适用场景。",false,"md",null,"https:\u002F\u002Fgithub.com\u002FNousResearch\u002Fhermes-agent",[758],"en",{},[761,762,763,764],"hermes-4-405b","hermes-4-70b","claude-sonnet-4","gpt-5",true,[767,768,769,770],"需要中文为主交互的玩家（文档和界面均为英文）","不想折腾部署和配置的个人用户","需要生产级稳定性（项目仍在快速迭代）","预算有限且没有 GPU 服务器的用户","\u002Ftools\u002Fagent\u002Fgeneral\u002Fhermes-agent","agent",[774,775,776],"linux","macos","windows",[778,784],{"plan":779,"price":780,"limit":781,"cn_pay":782,"note":783},"开源版","$0","完整功能，自托管","—","BYOK 模式",{"plan":785,"price":786,"limit":787,"cn_pay":788,"note":789},"API（Hermes 4）","按 token 计费","405B 模型按量付费","⚠️ 需海外卡","不想自托管时","免费（开源，BYOK）","2026-07-04",{"power":378,"ux":363,"price":390,"cn_support":353,"stability":363},{"title":14,"description":752},"Hermes Agent 评测 2026：Nous Research 开源自进化 AI Agent，10 万 Star","agent\u002Fgeneral\u002Fhermes-agent",[797,799,802,805,808],{"title":798,"url":756},"Hermes Agent GitHub",{"title":800,"url":801},"Nous Research 官网","https:\u002F\u002Fnousresearch.com",{"title":803,"url":804},"Hermes Agent 安装教程","https:\u002F\u002Fblog.csdn.net\u002Fyweng18\u002Farticle\u002Fdetails\u002F161148047",{"title":806,"url":807},"Hermes MoA 体验报告","https:\u002F\u002Fm.toutiao.com\u002Fgroup\u002F7657411078791365171\u002F",{"title":809,"url":810},"Hermes vs OpenCode 对比","https:\u002F\u002Fm.toutiao.com\u002Fgroup\u002F7645892543409816064\u002F","tools\u002Fagent\u002Fgeneral\u002Fhermes-agent",[813,814,815,816,817],"需要自托管、数据完全自主的 AI Agent","对 Agent 自我进化 \u002F 记忆系统有研究兴趣","需要跨平台部署（Telegram \u002F Discord \u002F Slack 等多通道）","有 GPU 服务器可以跑 Hermes 4 模型","想要一个长期陪伴型个人 Agent","Nous Research 开源自进化 AI Agent，10 万 Star，MoA 混合智能体",[820,821,822,823,824,825,826],"general-agent","autonomous","opensource","self-evolving","memory","nous-research","moa","2026 最火开源 Agent。MoA 混合智能体+自我进化+记忆系统+跨平台部署，GitHub 10 万 Star。适合长期陪伴型任务和自托管 Agent 研究，但中文支持和文档偏弱。","0lx2VjzdTE5qaGjgq3K0uLi4Z5Ig0ux9Sc3BaipH3LY",{"id":830,"title":831,"alternatives":832,"api_compatible":836,"body":837,"category":1340,"chinese_friendly":353,"cover":1341,"description":1342,"domestic":753,"extension":754,"faq":755,"free":753,"github":755,"languages":1343,"meta":1344,"models":1345,"navigation":765,"notSuitable":1346,"opensource":753,"path":1351,"pillar":1352,"platforms":1353,"priceTable":1355,"pricing":1361,"published":791,"relatedPlaybooks":755,"relatedReviews":755,"score":1362,"self_host":753,"seo":1363,"seoTitle":1364,"slug":1365,"sources":1366,"stem":1379,"suitable":1380,"tagline":1385,"tags":1386,"updated":791,"verdict":1392,"website":1007,"__hash__":1393},"tools\u002Ftools\u002Fcoding\u002Fbuilder\u002Fgithub-spark.md","GitHub Spark",[833,834,835],"coding\u002Fbuilder\u002Flovable","coding\u002Fbuilder\u002Fbolt-new","coding\u002Fbuilder\u002Fv0",[],{"type":24,"value":838,"toc":1324},[839,841,848,855,863,865,868,871,879,882,908,911,915,968,975,978,981,992,995,997,1000,1023,1026,1029,1095,1098,1100,1126,1128,1254,1259,1270,1272,1278,1284,1290,1296,1298],[27,840,29],{"id":29},[31,842,843,844,847],{},"GitHub Spark 是 GitHub 官方推出的",[35,845,846],{},"自然语言全栈 AI 应用构建平台","。你用自然语言描述想要什么应用，Claude Sonnet 4 在几秒内生成一个功能完整、可部署的全栈应用——前端 + 后端 + 数据库 + 部署，全自动。",[31,849,850,851,854],{},"它的定位和 Lovable \u002F Bolt.new \u002F v0 类似，但背靠 GitHub 生态，部署和版本管理更顺。",[35,852,853],{},"适合非技术背景的创业者\u002FPM 快速验证想法","，专业开发者还是用 Cursor\u002FTrae 更合适。",[46,856,857],{"type":48},[31,858,859,862],{},[35,860,861],{},"Builder 赛道定位","：GitHub Spark（零代码全栈生成）vs Lovable（全栈+可视化编辑）vs Bolt.new（多框架+代码可见）vs v0（前端 UI 为主）。Spark 的独特优势是 GitHub 原生部署和 Claude Sonnet 4 驱动。",[27,864,239],{"id":239},[60,866,867],{"id":867},"自然语言生成全栈应用",[31,869,870],{},"输入一句描述，Spark 生成完整应用：",[326,872,877],{"className":873,"code":875,"language":876},[874],"language-text","\"做一个待办清单应用，支持分类、优先级、截止日期提醒，深色主题\"\n","text",[333,878,875],{"__ignoreMap":331},[31,880,881],{},"Spark 会生成：",[72,883,884,890,896,902],{},[75,885,886,889],{},[35,887,888],{},"前端","：React + Tailwind CSS 组件",[75,891,892,895],{},[35,893,894],{},"后端","：Node.js API 路由",[75,897,898,901],{},[35,899,900],{},"数据库","：自动设计 schema（SQLite\u002FPostgreSQL）",[75,903,904,907],{},[35,905,906],{},"部署","：一键部署到 GitHub Pages 或 GitHub Codespaces",[31,909,910],{},"整个过程 5-15 秒，生成的代码可以在 GitHub 上直接查看和编辑。",[60,912,914],{"id":913},"github-原生集成","GitHub 原生集成",[145,916,917,927],{},[148,918,919],{},[151,920,921,924],{},[154,922,923],{},"能力",[154,925,926],{},"说明",[161,928,929,937,945,952,960],{},[151,930,931,934],{},[166,932,933],{},"仓库创建",[166,935,936],{},"自动创建 GitHub 仓库",[151,938,939,942],{},[166,940,941],{},"代码版本管理",[166,943,944],{},"每次修改自动 commit",[151,946,947,949],{},[166,948,906],{},[166,950,951],{},"一键部署到 GitHub Pages \u002F Codespaces",[151,953,954,957],{},[166,955,956],{},"协作",[166,958,959],{},"支持团队 fork \u002F PR",[151,961,962,965],{},[166,963,964],{},"CI\u002FCD",[166,966,967],{},"自动配置 GitHub Actions",[31,969,970,971,974],{},"这是 Spark 相对 Lovable\u002FBolt.new 的最大优势——",[35,972,973],{},"全流程在 GitHub 生态内完成","，不需要第三方平台。",[60,976,977],{"id":977},"迭代修改",[31,979,980],{},"生成应用后，可以用自然语言继续修改：",[72,982,983,986,989],{},[75,984,985],{},"\"把主题改成浅色\"",[75,987,988],{},"\"加一个搜索功能\"",[75,990,991],{},"\"修复待办删除后不刷新的 bug\"",[31,993,994],{},"每次修改 Spark 会自动 commit 一个新版本，你可以回滚到任何历史版本。",[27,996,321],{"id":321},[60,998,999],{"id":999},"上手",[432,1001,1002,1011,1014,1017,1020],{},[75,1003,1004,1005],{},"打开 ",[696,1006,1010],{"href":1007,"rel":1008},"https:\u002F\u002Fgithub.com\u002Fspark",[1009],"nofollow","github.com\u002Fspark",[75,1012,1013],{},"用 GitHub 账号登录",[75,1015,1016],{},"输入自然语言描述",[75,1018,1019],{},"等待 5-15 秒生成",[75,1021,1022],{},"预览 \u002F 修改 \u002F 部署",[31,1024,1025],{},"全程零配置，不需要装任何东西。",[60,1027,1028],{"id":1028},"生成质量",[145,1030,1031,1042],{},[148,1032,1033],{},[151,1034,1035,1038,1040],{},[154,1036,1037],{},"应用类型",[154,1039,1028],{},[154,1041,926],{},[161,1043,1044,1055,1065,1075,1085],{},[151,1045,1046,1049,1052],{},[166,1047,1048],{},"待办清单 \u002F 笔记",[166,1050,1051],{},"★★★★★",[166,1053,1054],{},"完整可用",[151,1056,1057,1060,1062],{},[166,1058,1059],{},"博客 \u002F CMS",[166,1061,621],{},[166,1063,1064],{},"基本功能完整",[151,1066,1067,1070,1072],{},[166,1068,1069],{},"仪表盘 \u002F 数据可视化",[166,1071,621],{},[166,1073,1074],{},"图表需微调",[151,1076,1077,1080,1082],{},[166,1078,1079],{},"电商 \u002F 支付",[166,1081,627],{},[166,1083,1084],{},"逻辑复杂，需手动补",[151,1086,1087,1090,1092],{},[166,1088,1089],{},"实时聊天 \u002F 协作",[166,1091,635],{},[166,1093,1094],{},"WebSocket 需手动处理",[31,1096,1097],{},"简单 CRUD 应用生成质量很高，复杂业务逻辑需要开发者手动补。",[60,1099,451],{"id":451},[72,1101,1102,1108,1114,1120],{},[75,1103,1104,1107],{},[35,1105,1106],{},"国内访问","：需要翻墙 + GitHub 账号",[75,1109,1110,1113],{},[35,1111,1112],{},"定制能力有限","：生成的技术栈固定（React + Node.js），不能选 Vue\u002FPython",[75,1115,1116,1119],{},[35,1117,1118],{},"复杂应用","：超过 5 个页面的应用，生成质量明显下降",[75,1121,1122,1125],{},[35,1123,1124],{},"代码可控性","：不像 Bolt.new 那样可以实时编辑代码",[27,1127,543],{"id":543},[145,1129,1130,1147],{},[148,1131,1132],{},[151,1133,1134,1136,1138,1141,1144],{},[154,1135,552],{},[154,1137,831],{},[154,1139,1140],{},"Lovable",[154,1142,1143],{},"Bolt.new",[154,1145,1146],{},"v0",[161,1148,1149,1165,1179,1195,1211,1226,1241],{},[151,1150,1151,1154,1157,1159,1162],{},[166,1152,1153],{},"驱动模型",[166,1155,1156],{},"Claude Sonnet 4",[166,1158,1156],{},[166,1160,1161],{},"Claude \u002F GPT",[166,1163,1164],{},"GPT-5",[151,1166,1167,1170,1172,1174,1176],{},[166,1168,1169],{},"全栈生成",[166,1171,179],{},[166,1173,179],{},[166,1175,179],{},[166,1177,1178],{},"⚠️ 前端为主",[151,1180,1181,1184,1187,1190,1193],{},[166,1182,1183],{},"代码可见",[166,1185,1186],{},"✅ GitHub 仓库",[166,1188,1189],{},"✅ 可视化编辑",[166,1191,1192],{},"✅ 实时编辑",[166,1194,179],{},[151,1196,1197,1199,1202,1205,1208],{},[166,1198,906],{},[166,1200,1201],{},"GitHub Pages\u002FCodespaces",[166,1203,1204],{},"Lovable 云",[166,1206,1207],{},"StackBlitz",[166,1209,1210],{},"Vercel",[151,1212,1213,1216,1219,1222,1224],{},[166,1214,1215],{},"GitHub 集成",[166,1217,1218],{},"★★★★★ 原生",[166,1220,1221],{},"★★★☆☆ 双向同步",[166,1223,635],{},[166,1225,635],{},[151,1227,1228,1231,1234,1237,1239],{},[166,1229,1230],{},"免费额度",[166,1232,1233],{},"免费",[166,1235,1236],{},"有限",[166,1238,1236],{},[166,1240,1236],{},[151,1242,1243,1246,1248,1250,1252],{},[166,1244,1245],{},"中文支持",[166,1247,635],{},[166,1249,627],{},[166,1251,627],{},[166,1253,635],{},[31,1255,1256,70],{},[35,1257,1258],{},"选型建议",[72,1260,1261,1264,1267],{},[75,1262,1263],{},"已有 GitHub 生态 → GitHub Spark",[75,1265,1266],{},"需要可视化编辑 → Lovable\n| 需要代码完全可控 → Bolt.new",[75,1268,1269],{},"只做前端 UI → v0",[27,1271,657],{"id":656},[31,1273,1274,1277],{},[35,1275,1276],{},"Q：GitHub Spark 和 GitHub Copilot 什么关系？","\n都是 GitHub 的 AI 产品，但定位不同。Copilot 是\"编程助手\"（帮你写代码），Spark 是\"应用生成器\"（帮你生成整个应用）。Copilot 面向开发者，Spark 面向所有人。",[31,1279,1280,1283],{},[35,1281,1282],{},"Q：生成的代码质量怎么样？","\n简单应用（CRUD\u002F博客\u002F仪表盘）质量不错，可以直接用。复杂应用需要开发者手动优化，但作为原型验证足够了。",[31,1285,1286,1289],{},[35,1287,1288],{},"Q：能商用吗？","\n生成的代码版权归你，可以商用。但建议在生产环境使用前做安全审计。",[31,1291,1292,1295],{},[35,1293,1294],{},"Q：支持哪些技术栈？","\n目前固定 React + Node.js + Tailwind CSS，不支持 Vue\u002FAngular\u002FPython\u002FGo 等其他技术栈。",[27,1297,690],{"id":690},[72,1299,1300,1306,1312,1318],{},[75,1301,1302],{},[696,1303,1305],{"href":1304},"\u002Fcoding\u002Fbuilder\u002Flovable.html","Lovable 工具卡",[75,1307,1308],{},[696,1309,1311],{"href":1310},"\u002Fcoding\u002Fbuilder\u002Fbolt-new.html","Bolt.new 工具卡",[75,1313,1314],{},[696,1315,1317],{"href":1316},"\u002Fcoding\u002Fbuilder\u002Fv0.html","v0 工具卡",[75,1319,1320],{},[696,1321,1323],{"href":1322},"\u002Fcoding\u002Fbuilder\u002Freplit-agent.html","Replit Agent 工具卡",{"title":331,"searchDepth":363,"depth":363,"links":1325},[1326,1327,1332,1337,1338,1339],{"id":29,"depth":353,"text":29},{"id":239,"depth":353,"text":239,"children":1328},[1329,1330,1331],{"id":867,"depth":363,"text":867},{"id":913,"depth":363,"text":914},{"id":977,"depth":363,"text":977},{"id":321,"depth":353,"text":321,"children":1333},[1334,1335,1336],{"id":999,"depth":363,"text":999},{"id":1028,"depth":363,"text":1028},{"id":451,"depth":363,"text":451},{"id":543,"depth":353,"text":543},{"id":656,"depth":353,"text":657},{"id":690,"depth":353,"text":690},"builder","\u002Fimg\u002Ftools\u002Fgithub-spark.webp","GitHub Spark 2026 评测：GitHub 官方自然语言全栈 AI 应用构建平台，Claude Sonnet 4 驱动。零代码构建、几秒内生成可部署应用、GitHub 原生部署。本文整理核心能力、与 Lovable\u002FBolt.new\u002Fv0 对比、适用场景、价格。",[758],{},[763],[1347,1348,1349,1350],"需要精细控制代码的专业开发者（用 Cursor\u002FTrae 更合适）","国内用户（需要翻墙 + GitHub 账号）","需要复杂后端逻辑和数据库设计的产品","需要私有部署 \u002F 本地运行的应用","\u002Ftools\u002Fcoding\u002Fbuilder\u002Fgithub-spark","coding",[1354],"web",[1356],{"plan":1357,"price":780,"limit":1358,"cn_pay":1359,"note":1360},"免费版","GitHub 账号即可使用","⚠️ 需翻墙","基础功能","免费（GitHub 账号）",{"power":378,"ux":378,"price":390,"cn_support":353,"stability":363},{"title":831,"description":1342},"GitHub Spark 评测 2026：自然语言生成全栈 AI 应用，零代码构建","coding\u002Fbuilder\u002Fgithub-spark",[1367,1370,1373,1376],{"title":1368,"url":1369},"GitHub Spark 介绍","https:\u002F\u002Fblog.csdn.net\u002Fu012842807\u002Farticle\u002Fdetails\u002F150385386",{"title":1371,"url":1372},"GitHub Spark 搜狐报道","https:\u002F\u002Fm.sohu.com\u002Fa\u002F920690921_130887\u002F",{"title":1374,"url":1375},"GitHub Spark 微软报道","https:\u002F\u002Fm.sohu.com\u002Fa\u002F822084548_211762\u002F",{"title":1377,"url":1378},"2026 低代码 AI 工具盘点","https:\u002F\u002Fm.toutiao.com\u002Fgroup\u002F7648666403663135275\u002F","tools\u002Fcoding\u002Fbuilder\u002Fgithub-spark",[1381,1382,1383,1384],"非技术背景的创业者\u002F产品经理，想快速验证想法","需要几秒内生成可部署的全栈 AI 应用原型","GitHub 生态用户，想要原生部署体验","想用自然语言构建 MVP 的团队","GitHub 全栈 AI 应用构建平台，自然语言生成可部署应用",[1340,1387,1388,1389,1390,1391],"github","fullstack","natural-language","zero-code","claude","GitHub 官方自然语言全栈应用生成器。Claude Sonnet 4 驱动、零代码构建、GitHub 原生部署。适合快速原型和 MVP，但定制能力有限。","y1o5uxQZPso_7hqZBPRmdM271limKGLjYvgFYGXNXDk",{"id":1395,"title":1396,"alternatives":1397,"api_compatible":1401,"body":1403,"category":2223,"chinese_friendly":390,"cover":2224,"description":2225,"domestic":753,"extension":754,"faq":755,"free":753,"github":1450,"languages":2226,"meta":2228,"models":2229,"navigation":765,"notSuitable":2231,"opensource":765,"path":2236,"pillar":1352,"platforms":2237,"priceTable":2238,"pricing":2252,"published":791,"relatedPlaybooks":755,"relatedReviews":755,"score":2253,"self_host":765,"seo":2254,"seoTitle":2255,"slug":2256,"sources":2257,"stem":2269,"suitable":2270,"tagline":2276,"tags":2277,"updated":791,"verdict":2281,"website":2171,"__hash__":2282},"tools\u002Ftools\u002Fcoding\u002Fcli\u002Fkimi-code.md","Kimi Code",[1398,1399,1400],"coding\u002Fcli\u002Fclaude-code","coding\u002Fcli\u002Fopencode","coding\u002Fcli\u002Fmimo-code",[1402,20],"moonshot",{"type":24,"value":1404,"toc":2203},[1405,1409,1433,1439,1441,1445,1454,1486,1489,1493,1500,1530,1533,1548,1551,1554,1571,1574,1578,1581,1678,1681,1683,1686,1689,1696,1702,1704,1734,1737,1743,1798,1801,1803,1835,1838,1870,1873,2023,2028,2042,2047,2058,2060,2066,2075,2081,2087,2093,2099,2101,2161,2164,2193,2200],[27,1406,1408],{"id":1407},"tldr","TL;DR",[1410,1411,1416,1422],"div",{"className":1412},[1413,1414,1415],"card","p-5","my-4",[31,1417,1418,1421],{},[35,1419,1420],{},"一句话："," Kimi Code 是月之暗面（Moonshot AI）在 2026 年 6 月推出的国产 CLI Agent，搭载自研开源的 K2.7 Code 模型——1.1 万亿参数、256K 上下文、Token 成本较上代降 30%。它瞄准的是\"Claude Code 的国产平替\"：长程任务能力逼近第一梯队，中文编程体验和成本却是压倒性优势。",[31,1423,1424,1425,1428,1429,1432],{},"最大价值是 ",[35,1426,1427],{},"中文场景的体感","——读中文注释、写中文 commit message、理解中文需求描述，K2.7 Code 的母语级表现甩开一众海外模型。叠加 256K 上下文能整库塞进去，国内开发者终于有了\"不折腾海外账号、不烧美元\"的 Agent 选项。代价是 ",[35,1430,1431],{},"生态早期","：插件、MCP 工具链、第三方集成还不如 Claude Code 成熟，开源版本自托管的工程量也不小。",[1434,1435,1436],"blockquote",{},[31,1437,1438],{},"来源说明：本文基于 code.kimi.ai 官网、GitHub MoonshotAI\u002FKimi-K2.7-Code 仓库、月之暗面官方文档与技术报告整理。K2.7 Code 模型仍在快速迭代，参数和价格请以最新官方信息为准。",[27,1440,239],{"id":239},[60,1442,1444],{"id":1443},"k27-code万亿参数开源编程模型","K2.7 Code：万亿参数开源编程模型",[31,1446,1447,1448,1453],{},"Kimi Code 的核心是 ",[696,1449,1452],{"href":1450,"rel":1451},"https:\u002F\u002Fgithub.com\u002FMoonshotAI\u002FKimi-K2.7-Code",[1009],"K2.7 Code 模型","，2026 年 6 月正式开源。关键参数：",[72,1455,1456,1462,1468,1474,1480],{},[75,1457,1458,1461],{},[35,1459,1460],{},"1.1 万亿参数","（1.1T），MoE 架构，激活参数约 60B",[75,1463,1464,1467],{},[35,1465,1466],{},"256K 上下文","——能整库塞进单次对话，长程改造不丢前文",[75,1469,1470,1473],{},[35,1471,1472],{},"代码专项优化","——在 HumanEval、SWE-Bench、LiveCodeBench 等编程基准上对标 Claude Sonnet 4",[75,1475,1476,1479],{},[35,1477,1478],{},"过度思考问题改善","——上代 K2 常被吐槽\"想太多、token 烧太快\"，K2.7 在保持质量的同时把冗余推理压了下去，单任务 token 消耗降约 30%",[75,1481,1482,1485],{},[35,1483,1484],{},"MIT 级开源协议","，权重可下载、可自托管、可商用",[31,1487,1488],{},"这套模型既是 Kimi Code CLI 的默认大脑，也能独立部署——你可以拿 K2.7 Code 接 OpenCode、Aider，甚至自建一套完全离线的国产 Agent 栈。这种\"模型 + Agent 双开源\"的姿态，在国内大厂里是头一份。",[60,1490,1492],{"id":1491},"长程任务能力逼近-claude-code","长程任务能力：逼近 Claude Code",[31,1494,1495,1496,1499],{},"Kimi Code 主打的卖点是 ",[35,1497,1498],{},"长程任务（long-horizon task）","——那种需要 30 分钟以上、跨多文件、多轮工具调用的复杂改造。256K 上下文 + 改进后的推理节奏，让它能撑住\"Claude Code 式\"的自主任务：",[326,1501,1503],{"className":328,"code":1502,"language":330,"meta":331,"style":331},"cd your-project\nkimi-code                       # 进入交互模式\n> 把这个 Vue2 项目整体升级到 Vue3，保留所有业务逻辑，跑通 e2e 测试\n",[333,1504,1505,1512,1520],{"__ignoreMap":331},[336,1506,1507,1509],{"class":338,"line":339},[336,1508,357],{"class":356},[336,1510,1511],{"class":346}," your-project\n",[336,1513,1514,1517],{"class":338,"line":353},[336,1515,1516],{"class":342},"kimi-code",[336,1518,1519],{"class":393},"                       # 进入交互模式\n",[336,1521,1522,1526],{"class":338,"line":363},[336,1523,1525],{"class":1524},"szBVR",">",[336,1527,1529],{"class":1528},"sVt8B"," 把这个 Vue2 项目整体升级到 Vue3，保留所有业务逻辑，跑通 e2e 测试\n",[31,1531,1532],{},"它会自动：梳理依赖 → 列出影响范围 → 分批改写 → 跑测试 → 修回归。实测在中等规模代码库（5-10 万行）上，K2.7 Code 的任务完成率与 Claude Sonnet 4 接近，部分中文注释密集的项目甚至更好——因为理解中文需求描述更准。",[31,1534,1535,1536,1539,1540,1543,1544,1547],{},"不过要注意，",[35,1537,1538],{},"超长任务（>1 小时）的稳定性仍弱于 Claude Code","。Claude Code 有 ",[333,1541,1542],{},"\u002Fcompact"," 上下文压缩、子代理隔离、checkpoint 回滚一套成熟机制；Kimi Code 这套工具还在补齐，偶发 context 漂移需要手动 ",[333,1545,1546],{},"\u002Fclear"," 重来。",[60,1549,1550],{"id":1550},"多模态编程",[31,1552,1553],{},"K2.7 Code 是多模态模型——支持图像输入。这意味着你可以截图一个 UI bug、贴一段报错截图、甚至丢一张架构图，让它\"看着改\"：",[326,1555,1557],{"className":328,"code":1556,"language":330,"meta":331,"style":331},"> 这张报错截图里的栈追踪，定位到对应代码并修复\n[附图：screenshot.png]\n",[333,1558,1559,1566],{"__ignoreMap":331},[336,1560,1561,1563],{"class":338,"line":339},[336,1562,1525],{"class":1524},[336,1564,1565],{"class":1528}," 这张报错截图里的栈追踪，定位到对应代码并修复\n",[336,1567,1568],{"class":338,"line":353},[336,1569,1570],{"class":1528},"[附图：screenshot.png]\n",[31,1572,1573],{},"对前端调试、运维排障这类\"看图说话\"场景，比纯文本模型实用。Claude Code 同样支持多模态，但 K2.7 Code 的中文 OCR + 中文场景理解更贴合国内开发。",[60,1575,1577],{"id":1576},"一行命令安装国内零门槛","一行命令安装，国内零门槛",[31,1579,1580],{},"Kimi Code 的安装刻意做成了\"国内友好\"——不需要海外手机、不需要海外卡、不需要代理：",[326,1582,1584],{"className":328,"code":1583,"language":330,"meta":331,"style":331},"# 一行命令安装\ncurl -fsSL https:\u002F\u002Fcode.kimi.ai\u002Finstall.sh | bash\n\n# 或 npm\nnpm install -g kimi-code\n\n# 用 Kimi 账号登录（微信扫码即可），或填 Moonshot API key\nkimi-code auth login\n\n# 进项目\ncd your-project\nkimi-code                        # 启动\n",[333,1585,1586,1591,1608,1613,1618,1631,1635,1641,1652,1657,1663,1670],{"__ignoreMap":331},[336,1587,1588],{"class":338,"line":339},[336,1589,1590],{"class":393},"# 一行命令安装\n",[336,1592,1593,1596,1599,1602,1605],{"class":338,"line":353},[336,1594,1595],{"class":342},"curl",[336,1597,1598],{"class":356}," -fsSL",[336,1600,1601],{"class":346}," https:\u002F\u002Fcode.kimi.ai\u002Finstall.sh",[336,1603,1604],{"class":1524}," |",[336,1606,1607],{"class":342}," bash\n",[336,1609,1610],{"class":338,"line":363},[336,1611,1612],{"emptyLinePlaceholder":765},"\n",[336,1614,1615],{"class":338,"line":378},[336,1616,1617],{"class":393},"# 或 npm\n",[336,1619,1620,1623,1625,1628],{"class":338,"line":390},[336,1621,1622],{"class":342},"npm",[336,1624,369],{"class":346},[336,1626,1627],{"class":356}," -g",[336,1629,1630],{"class":346}," kimi-code\n",[336,1632,1633],{"class":338,"line":397},[336,1634,1612],{"emptyLinePlaceholder":765},[336,1636,1638],{"class":338,"line":1637},7,[336,1639,1640],{"class":393},"# 用 Kimi 账号登录（微信扫码即可），或填 Moonshot API key\n",[336,1642,1644,1646,1649],{"class":338,"line":1643},8,[336,1645,1516],{"class":342},[336,1647,1648],{"class":346}," auth",[336,1650,1651],{"class":346}," login\n",[336,1653,1655],{"class":338,"line":1654},9,[336,1656,1612],{"emptyLinePlaceholder":765},[336,1658,1660],{"class":338,"line":1659},10,[336,1661,1662],{"class":393},"# 进项目\n",[336,1664,1666,1668],{"class":338,"line":1665},11,[336,1667,357],{"class":356},[336,1669,1511],{"class":346},[336,1671,1673,1675],{"class":338,"line":1672},12,[336,1674,1516],{"class":342},[336,1676,1677],{"class":393},"                        # 启动\n",[31,1679,1680],{},"登录走国内账号体系，API 计费支持支付宝\u002F微信。对一直被 Claude Code\"账号+支付+网络\"三关劝退的国内开发者，这是质变的体验差。",[27,1682,321],{"id":321},[60,1684,1685],{"id":1685},"日常体感",[31,1687,1688],{},"实测下来，Kimi Code 的交互范式和 Claude Code 高度相似——终端对话、slash 命令、工具调用审批、diff 预览。从 Claude Code 迁移过来几乎没有学习成本，命令名都接近。",[31,1690,1691,1692,1695],{},"最直观的差异在 ",[35,1693,1694],{},"中文","：让它写中文 commit message、生成中文 API 文档、根据中文 PRD 拆任务，K2.7 Code 的输出明显比 Claude \u002F GPT 更地道，不会有那种\"翻译腔\"。中文注释密集的老项目维护，体验尤其好。",[31,1697,1698,1701],{},[35,1699,1700],{},"Token 成本","是另一个爽点。同样的编码量，K2.7 Code 的 API 费用大约是 Claude Sonnet 4 的 1\u002F3-1\u002F2（叠加 token 消耗降 30% 的红利），单月重度使用可能只要几十块人民币。对个人开发者，这是\"用得起\"和\"用得好\"的平衡点。",[60,1703,451],{"id":451},[72,1705,1706,1712,1722,1728],{},[75,1707,1708,1711],{},[35,1709,1710],{},"英文\u002F国际化项目","：纯英文代码库的表现略逊 Claude\u002FGPT，偶有英文表达不自然",[75,1713,1714,1717,1718,1721],{},[35,1715,1716],{},"插件生态","：第三方工具、LSP 集成、自定义命令还在起步，远不如 Claude Code 的 ",[333,1719,1720],{},".claude\u002Fcommands\u002F"," 体系",[75,1723,1724,1727],{},[35,1725,1726],{},"MCP 支持","：能接，但成熟度和文档完善度不如 Claude Code（MCP 毕竟是 Anthropic 自家协议）",[75,1729,1730,1733],{},[35,1731,1732],{},"开源版自托管门槛","：1.1T 参数模型对显卡要求高，个人\u002F小团队自托管不现实，多数人还是走官方 API",[27,1735,1736],{"id":1736},"价格与运行成本",[31,1738,1739,1740,1742],{},"Kimi Code 工具本体 ",[35,1741,1233],{},"。成本来自 K2.7 Code 模型的 API 调用（BYOK 或官方 API）：",[145,1744,1745,1757],{},[148,1746,1747],{},[151,1748,1749,1752,1754],{},[154,1750,1751],{},"路径",[154,1753,480],{},[154,1755,1756],{},"适用场景",[161,1758,1759,1772,1785],{},[151,1760,1761,1766,1769],{},[166,1762,1763],{},[35,1764,1765],{},"Kimi 官方 API",[166,1767,1768],{},"输入 ¥8 \u002F 输出 ¥32 每百万 token（较上代降 30%）",[166,1770,1771],{},"国内最省心，微信支付",[151,1773,1774,1779,1782],{},[166,1775,1776],{},[35,1777,1778],{},"自托管 K2.7 Code",[166,1780,1781],{},"免费（需硬件）",[166,1783,1784],{},"大企业 \u002F 研究机构，需多卡 A100\u002FH100",[151,1786,1787,1792,1795],{},[166,1788,1789],{},[35,1790,1791],{},"第三方中转",[166,1793,1794],{},"按量，通常更便宜",[166,1796,1797],{},"灵活，但稳定性参差",[31,1799,1800],{},"对比 Claude Code：Claude Sonnet 4 输入 $3 \u002F 输出 $15 每百万 token（约 ¥21 \u002F ¥107），K2.7 Code 输入 ¥8 \u002F 输出 ¥32，价格约为其 1\u002F3。叠加 token 消耗降 30%，单任务综合成本能压到 Claude 的 1\u002F4 左右。对每天编码 4+ 小时的重度用户，月度差距可能从几百美元缩到几十块人民币。",[27,1802,1756],{"id":1756},[72,1804,1805,1811,1817,1823,1829],{},[75,1806,1807,1810],{},[35,1808,1809],{},"国内开发者","：不想折腾海外账号、海外卡、代理，微信扫码即用",[75,1812,1813,1816],{},[35,1814,1815],{},"中文为主的项目","：中文注释、中文文档、中文 PRD，K2.7 Code 母语级理解",[75,1818,1819,1822],{},[35,1820,1821],{},"大代码库长程改造","：256K 上下文能整库塞进去，跨文件重构不丢前文",[75,1824,1825,1828],{},[35,1826,1827],{},"预算敏感但要不将就","：想要接近 Claude Code 的能力，又不想月烧 $100+",[75,1830,1831,1834],{},[35,1832,1833],{},"国产化 \u002F 信创要求","：需要国产模型 + 可自托管的 Agent 方案",[27,1836,1837],{"id":1837},"不适用场景",[72,1839,1840,1846,1852,1858,1864],{},[75,1841,1842,1845],{},[35,1843,1844],{},"纯英文\u002F国际化项目","：英文表现不如 Claude\u002FGPT，commit message 和文档可能不够 native",[75,1847,1848,1851],{},[35,1849,1850],{},"重度插件依赖","：生态早期，第三方工具和自定义命令体系不如 Claude Code",[75,1853,1854,1857],{},[35,1855,1856],{},"企业级稳定 SLA","：开源版本无承诺，官方 API 也有偶发限流",[75,1859,1860,1863],{},[35,1861,1862],{},"IDE 内 inline 补全","：CLI Agent 不是补全器，要 Tab 补全去 Copilot\u002FCursor",[75,1865,1866,1869],{},[35,1867,1868],{},"个人自托管","：1.1T 参数模型硬件门槛高，个人玩不动",[27,1871,1872],{"id":1872},"与同类怎么选",[145,1874,1875,1901],{},[148,1876,1877],{},[151,1878,1879,1881,1883,1889,1895],{},[154,1880,552],{},[154,1882,1396],{},[154,1884,1885],{},[696,1886,1888],{"href":1887},"\u002Fcoding\u002Fcli\u002Fclaude-code.html","Claude Code",[154,1890,1891],{},[696,1892,1894],{"href":1893},"\u002Fcoding\u002Fcli\u002Fopencode.html","OpenCode",[154,1896,1897],{},[696,1898,1900],{"href":1899},"\u002Fcoding\u002Fcli\u002Fmimo-code.html","MiMo Code",[161,1902,1903,1918,1935,1947,1960,1976,1990,2007],{},[151,1904,1905,1908,1911,1913,1916],{},[166,1906,1907],{},"形态",[166,1909,1910],{},"CLI",[166,1912,1910],{},[166,1914,1915],{},"CLI（TUI）",[166,1917,1910],{},[151,1919,1920,1923,1926,1929,1932],{},[166,1921,1922],{},"模型",[166,1924,1925],{},"K2.7 Code（开源）",[166,1927,1928],{},"Claude 系（闭源）",[166,1930,1931],{},"75+ 任选",[166,1933,1934],{},"MiMo（开源 MIT）",[151,1936,1937,1939,1941,1943,1945],{},[166,1938,632],{},[166,1940,1051],{},[166,1942,627],{},[166,1944,627],{},[166,1946,1051],{},[151,1948,1949,1952,1954,1956,1958],{},[166,1950,1951],{},"长程任务",[166,1953,621],{},[166,1955,1051],{},[166,1957,621],{},[166,1959,621],{},[151,1961,1962,1965,1968,1971,1974],{},[166,1963,1964],{},"上下文",[166,1966,1967],{},"256K",[166,1969,1970],{},"200K",[166,1972,1973],{},"看模型",[166,1975,1973],{},[151,1977,1978,1980,1983,1985,1987],{},[166,1979,1716],{},[166,1981,1982],{},"★★★☆☆ 早期",[166,1984,1051],{},[166,1986,621],{},[166,1988,1989],{},"★★☆☆☆ V0.1",[151,1991,1992,1995,1998,2001,2004],{},[166,1993,1994],{},"国内门槛",[166,1996,1997],{},"★★★★★ 极低",[166,1999,2000],{},"★☆☆☆☆ 高",[166,2002,2003],{},"★★★★☆ 低",[166,2005,2006],{},"★★★★★ 低",[151,2008,2009,2011,2014,2017,2020],{},[166,2010,480],{},[166,2012,2013],{},"极低（国产 API）",[166,2015,2016],{},"$20-$200\u002F月",[166,2018,2019],{},"免费（BYOK）",[166,2021,2022],{},"免费（MIT）",[31,2024,2025,70],{},[35,2026,2027],{},"选 Kimi Code 如果你",[72,2029,2030,2033,2036,2039],{},[75,2031,2032],{},"国内开发者，受够了海外账号和支付",[75,2034,2035],{},"项目中文为主，想要母语级编程体验",[75,2037,2038],{},"要 256K 长上下文跑大库改造",[75,2040,2041],{},"预算敏感但不想将就能力",[31,2043,2044,70],{},[35,2045,2046],{},"别选 Kimi Code 如果你",[72,2048,2049,2052,2055],{},[75,2050,2051],{},"纯英文国际化项目（Claude\u002FGPT 更 native）",[75,2053,2054],{},"重度依赖插件和 MCP 生态（生态还在早期）",[75,2056,2057],{},"要企业级稳定承诺（开源版无 SLA）",[27,2059,657],{"id":656},[31,2061,2062,2065],{},[35,2063,2064],{},"Q：Kimi Code 真的免费吗？","\nA：工具本体免费。K2.7 Code 模型开源可自托管（免费但需硬件）。走官方 API 按 token 计费，但价格远低于 Claude\u002FGPT，且支持微信支付。",[31,2067,2068,2071,2072,2074],{},[35,2069,2070],{},"Q：和 Claude Code 比，K2.7 Code 差多少？","\nA：编程基准接近，中文场景甚至更好；长程任务稳定性、上下文管理工具（",[333,2073,1542],{}," 等）、插件生态仍落后。简单说：能力够用，工具链和成熟度在追赶。",[31,2076,2077,2080],{},[35,2078,2079],{},"Q：能自托管吗？硬件要求？","\nA：能，模型权重开源。但 1.1T 参数 MoE，推理需要多卡高端 GPU（A100\u002FH100 级），个人不现实，主要面向企业和研究机构。",[31,2082,2083,2086],{},[35,2084,2085],{},"Q：和 MiMo Code 怎么选？","\nA：都是国产开源 CLI Agent。Kimi Code 胜在模型成熟度（K2.7 已迭代多代）和中文长上下文；MiMo Code 胜在 SWE-Bench 基准分数和 MIT 协议彻底开源，但仅 V0.1。要稳选 Kimi，要尝鲜选 MiMo。",[31,2088,2089,2092],{},[35,2090,2091],{},"Q：能接其他模型吗？","\nA：Kimi Code CLI 主要为 K2.7 Code 优化，但也兼容 OpenAI 接口格式，可接其他模型。不过最佳体验还是配 K2.7 Code。",[31,2094,2095,2098],{},[35,2096,2097],{},"Q：支持 MCP 吗？","\nA：支持，能接 MCP server 调数据库、浏览器等工具。但文档和成熟度不如 Claude Code，配置需要一定动手能力。",[27,2100,690],{"id":690},[72,2102,2103,2117,2136,2150],{},[75,2104,2105,2106,2108,2109,2108,2111,2108,2113],{},"同类对比：",[696,2107,1888],{"href":1887}," \u002F ",[696,2110,1894],{"href":1893},[696,2112,1900],{"href":1899},[696,2114,2116],{"href":2115},"\u002Fcoding\u002Fcli\u002Fqwen-code.html","Qwen Code",[75,2118,2119,2120,2108,2124,2108,2128,2108,2132],{},"概念：",[696,2121,2123],{"href":2122},"\u002Fwiki\u002Fai-agent.html","AI Agent",[696,2125,2127],{"href":2126},"\u002Fwiki\u002Fmcp.html","MCP",[696,2129,2131],{"href":2130},"\u002Fwiki\u002Flong-context.html","Long Context",[696,2133,2135],{"href":2134},"\u002Fwiki\u002Fmoe.html","MoE 架构",[75,2137,2138,2139,2108,2143,2108,2146],{},"模型：",[696,2140,2142],{"href":2141},"\u002Fmodels\u002Fkimi-k2.7-code.html","Kimi K2.7 Code",[696,2144,1156],{"href":2145},"\u002Fmodels\u002Fclaude-sonnet-4.html",[696,2147,2149],{"href":2148},"\u002Fmodels\u002Fdeepseek-v3.html","DeepSeek-V3",[75,2151,2152,2153,2108,2157],{},"进阶：",[696,2154,2156],{"href":2155},"\u002Fwiki\u002Fvibe-coding.html","Vibe Coding",[696,2158,2160],{"href":2159},"\u002Fwiki\u002Fcn-coding-models.html","国产大模型编程",[27,2162,2163],{"id":2163},"来源",[72,2165,2166,2173,2179,2186],{},[75,2167,2168,2169],{},"官网：",[696,2170,2171],{"href":2171,"rel":2172},"https:\u002F\u002Fcode.kimi.ai",[1009],[75,2174,2175,2176],{},"开源仓库：",[696,2177,1450],{"href":1450,"rel":2178},[1009],[75,2180,2181,2182],{},"月之暗面平台：",[696,2183,2184],{"href":2184,"rel":2185},"https:\u002F\u002Fplatform.moonshot.cn\u002Fdocs",[1009],[75,2187,2188,2189],{},"定价：",[696,2190,2191],{"href":2191,"rel":2192},"https:\u002F\u002Fplatform.moonshot.cn\u002Fpricing",[1009],[31,2194,2195,2196,2199],{},"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 ",[696,2197,2198],{"href":2198},"\u002Fsubmit"," 反馈。",[725,2201,2202],{},"html pre.shiki code .sj4cs, html code.shiki .sj4cs{--shiki-default:#005CC5;--shiki-dark:#79B8FF}html pre.shiki code .sZZnC, html code.shiki .sZZnC{--shiki-default:#032F62;--shiki-dark:#9ECBFF}html pre.shiki code .sScJk, html code.shiki .sScJk{--shiki-default:#6F42C1;--shiki-dark:#B392F0}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 .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":331,"searchDepth":363,"depth":363,"links":2204},[2205,2206,2212,2216,2217,2218,2219,2220,2221,2222],{"id":1407,"depth":353,"text":1408},{"id":239,"depth":353,"text":239,"children":2207},[2208,2209,2210,2211],{"id":1443,"depth":363,"text":1444},{"id":1491,"depth":363,"text":1492},{"id":1550,"depth":363,"text":1550},{"id":1576,"depth":363,"text":1577},{"id":321,"depth":353,"text":321,"children":2213},[2214,2215],{"id":1685,"depth":363,"text":1685},{"id":451,"depth":363,"text":451},{"id":1736,"depth":353,"text":1736},{"id":1756,"depth":353,"text":1756},{"id":1837,"depth":353,"text":1837},{"id":1872,"depth":353,"text":1872},{"id":656,"depth":353,"text":657},{"id":690,"depth":353,"text":690},{"id":2163,"depth":353,"text":2163},"cli","\u002Fimg\u002Ftools\u002Fkimi-code.webp","Kimi Code 2026 真实评测：月之暗面推出的国产 CLI Agent，搭载开源 K2.7 Code 万亿参数模型，256K 上下文、Token 成本降 30%、中文编程体验一流。本文整理安装使用、长程任务能力、多模态编程、价格、与 Claude Code \u002F MiMo Code 的区别。",[2227,758],"zh",{},[2230],"kimi-k2.7-code",[2232,2233,2234,2235],"纯英文\u002F国际化项目（英文表现不如 Claude\u002FGPT）","需要丰富插件生态和 MCP 工具链（生态尚早期）","要求企业级 SLA 与稳定承诺的开源版本","IDE 内 inline 补全场景（CLI 工具非补全器）","\u002Ftools\u002Fcoding\u002Fcli\u002Fkimi-code",[775,774,776],[2239,2243,2247],{"plan":2240,"price":1233,"limit":2241,"cn_pay":2242,"note":1778},"开源模型（BYOK）","仅承担 API token 成本","✅ 支付宝\u002F微信",{"plan":1765,"price":786,"limit":2244,"cn_pay":2245,"note":2246},"输入 ¥8 \u002F 输出 ¥32 每百万 token","✅ 国内支付","成本较上代降 30%",{"plan":2248,"price":1233,"limit":2249,"cn_pay":2250,"note":2251},"Kimi Code CLI","工具本体免费","✅ 无需支付","一行命令安装","免费（开源模型，BYOK）",{"power":378,"ux":378,"price":390,"cn_support":390,"stability":378},{"title":1396,"description":2225},"Kimi Code 评测 2026：月之暗面 K2.7 Code 开源，万亿参数 256K 上下文国产 CLI Agent","coding\u002Fcli\u002Fkimi-code",[2258,2260,2262,2264,2267],{"title":2259,"url":2171},"Kimi Code 官网",{"title":2261,"url":1450},"Kimi-K2.7-Code 开源仓库",{"title":2263,"url":2184},"月之暗面官方文档",{"title":2265,"url":2266},"K2.7 Code 技术报告","https:\u002F\u002Fgithub.com\u002FMoonshotAI\u002FKimi-K2.7-Code\u002Fblob\u002Fmain\u002FREADME.md",{"title":2268,"url":2191},"Kimi Code 定价","tools\u002Fcoding\u002Fcli\u002Fkimi-code",[2271,2272,2273,2274,2275],"国内开发者，不想折腾海外账号和支付","中文项目 \u002F 注释 \u002F 文档为主的代码库","需要超长上下文（256K）跑大代码库改造","预算敏感但想要接近 Claude Code 的长程任务能力","想自托管国产开源大模型编程 Agent 的团队","月之暗面 AI 编程助手，K2.7 Code 模型开源，万亿参数 256K 上下文",[2223,772,2278,1402,822,2279,2280],"terminal","chinese","long-context","国产 CLI Agent 新贵。K2.7 Code 万亿参数开源、256K 上下文、Token 成本降 30%、中文编程体验一流。长程任务能力接近 Claude Code，但生态和插件还差一截。","JtepFC-6Y-bLlsPF1Rm4fZrBViyrtfTQg4QrVF4MaZE",{"id":2284,"title":1900,"alternatives":2285,"api_compatible":2286,"body":2288,"category":2223,"chinese_friendly":390,"cover":3079,"description":3080,"domestic":753,"extension":754,"faq":755,"free":753,"github":2486,"languages":3081,"meta":3082,"models":3083,"navigation":765,"notSuitable":3085,"opensource":765,"path":3090,"pillar":1352,"platforms":3091,"priceTable":3092,"pricing":3105,"published":791,"relatedPlaybooks":755,"relatedReviews":755,"score":3106,"self_host":765,"seo":3107,"seoTitle":3108,"slug":1400,"sources":3109,"stem":3120,"suitable":3121,"tagline":3127,"tags":3128,"updated":791,"verdict":3131,"website":2486,"__hash__":3132},"tools\u002Ftools\u002Fcoding\u002Fcli\u002Fmimo-code.md",[1398,1399,2256],[20,2287],"mimo",{"type":24,"value":2289,"toc":3057},[2290,2292,2314,2319,2321,2325,2328,2342,2348,2351,2355,2364,2390,2397,2401,2406,2417,2443,2446,2450,2457,2474,2477,2481,2490,2493,2495,2499,2593,2595,2598,2601,2604,2607,2610,2612,2619,2670,2673,2679,2681,2713,2715,2746,2748,2898,2903,2917,2922,2936,2938,2944,2950,2956,2962,2968,2974,2976,3015,3017,3051,3055],[27,2291,1408],{"id":1407},[1410,2293,2295,2304],{"className":2294},[1413,1414,1415],[31,2296,2297,2299,2300,2303],{},[35,2298,1420],{}," MiMo Code 是小米在 2026 年 6 月 11 日开源的终端 AI 编程 Agent，MIT 协议、基于 OpenCode 架构改造，搭载自研 MiMo 模型。它最炸的成绩是 ",[35,2301,2302],{},"SWE-Bench Pro 62%、Terminal Bench 2 73% 双双超过 Claude Code","——国产 Agent 第一次在权威基准上硬刚 Anthropic。内置多模态模型、记忆系统、完全免费。",[31,2305,1424,2306,2309,2310,2313],{},[35,2307,2308],{},"开源彻底 + 基准亮眼","：MIT 协议权重可商用，多模态模型内置不依赖外部 API，记忆系统能跨会话记住项目上下文。代价是 ",[35,2311,2312],{},"极其早期","——目前仅 V0.1.0，生态、稳定性、文档都还在补，生产环境用要承担\"踩坑\"成本。把它当\"最有潜力的国产 Agent 黑马\"来观察和尝鲜，比当主力工具更合适。",[1434,2315,2316],{},[31,2317,2318],{},"来源说明：本文基于 GitHub XiaomiMiMo\u002FMiMo-Code 仓库、技术报告与社区评测整理。MiMo Code 处于 V0.1.0 早期阶段，功能与基准数据迭代极快，请以最新官方仓库为准。",[27,2320,239],{"id":239},[60,2322,2324],{"id":2323},"swe-bench-双超-claude-code-的硬实力","SWE-Bench 双超 Claude Code 的硬实力",[31,2326,2327],{},"MiMo Code 一发布就靠基准成绩刷屏。在两个最被认可的 Agent 编程基准上：",[72,2329,2330,2336],{},[75,2331,2332,2335],{},[35,2333,2334],{},"SWE-Bench Pro 62%","——衡量\"给定 GitHub issue，自主定位 + 修复 + 通过测试\"的能力，MiMo 以 62% 超过 Claude Code 的同期成绩",[75,2337,2338,2341],{},[35,2339,2340],{},"Terminal Bench 2 73%","——衡量\"终端环境下的多步工具调用任务\"能力，MiMo 同样领先",[31,2343,2344,2345,2347],{},"这两个基准测的都是 ",[35,2346,1951],{},"——不是单轮补全，而是\"看 issue → 理解代码 → 多文件改 → 跑测试 → 修回归\"的完整 Agent 闭环。MiMo 能在这两项上超 Claude Code，说明它的模型在\"agent 式推理 + 工具调用规划\"上确实有真功夫，而不只是刷 HumanEval 这种单文件题。",[31,2349,2350],{},"需要说明的是，基准 ≠ 实战体验。SWE-Bench 是标准化任务集，真实项目的复杂度、脏数据、中文场景未必复现同样的领先。但作为\"国产 Agent 第一梯队\"的入场券，这个成绩足够有说服力。",[60,2352,2354],{"id":2353},"基于-opencode-架构终端原生","基于 OpenCode 架构，终端原生",[31,2356,2357,2358,2363],{},"MiMo Code 没有从零造轮子，而是 ",[696,2359,2362],{"href":2360,"rel":2361},"https:\u002F\u002Fgithub.com\u002Fsst\u002Fopencode",[1009],"基于 OpenCode 架构"," 改造——TypeScript 全栈、终端 TUI、多会话并行、MCP 支持，这些都继承了 OpenCode 的成熟设计。小米的增量在于：",[72,2365,2366,2372,2378,2384],{},[75,2367,2368,2371],{},[35,2369,2370],{},"内置 MiMo 自研模型","——不用自己配 API key，开箱即用（也支持接其他模型）",[75,2373,2374,2377],{},[35,2375,2376],{},"记忆系统","——跨会话记住项目结构、约定、历史决策，不用每次重新喂上下文",[75,2379,2380,2383],{},[35,2381,2382],{},"多模态内置","——截图调试、看图改 UI 直接支持，不依赖外部多模态 API",[75,2385,2386,2389],{},[35,2387,2388],{},"中文优化","——针对中文代码库、中文需求做了专项调优",[31,2391,2392,2393,2396],{},"继承 OpenCode 架构的好处是 ",[35,2394,2395],{},"生态兼容","——OpenCode 的 provider 配置、MCP server、部分插件 MiMo Code 能直接复用，迁移成本低。坏处是也继承了 OpenCode 的早期粗糙：配置门槛、稳定性依赖 API 质量。",[60,2398,2400],{"id":2399},"长程编程-记忆系统","长程编程 + 记忆系统",[31,2402,2403,2404,70],{},"MiMo Code 主打的\"长程编程\"和 Claude Code 的 long-horizon task 是一个意思——撑住 30 分钟以上、跨多文件的自主改造。它的差异化在 ",[35,2405,2376],{},[31,2407,2408,2409,2412,2413,2416],{},"传统 Agent 每次开新会话都是\"失忆\"的，得重新喂项目上下文。MiMo Code 的记忆系统能把\"这个项目用 Vue3 + Pinia、提交规范是 Conventional Commits、测试跑 vitest、上次改到哪\"这类信息持久化，下次直接续上。这对长期维护一个项目的开发者是实打实的提效——不用每次 ",[333,2410,2411],{},"\u002Finit"," 重新生成 ",[333,2414,2415],{},"CLAUDE.md","。",[326,2418,2420],{"className":328,"code":2419,"language":330,"meta":331,"style":331},"cd your-project\nmimo-code                      # 启动，自动加载项目记忆\n> 继续上次的鉴权模块重构，跑通剩余测试\n",[333,2421,2422,2428,2436],{"__ignoreMap":331},[336,2423,2424,2426],{"class":338,"line":339},[336,2425,357],{"class":356},[336,2427,1511],{"class":346},[336,2429,2430,2433],{"class":338,"line":353},[336,2431,2432],{"class":342},"mimo-code",[336,2434,2435],{"class":393},"                      # 启动，自动加载项目记忆\n",[336,2437,2438,2440],{"class":338,"line":363},[336,2439,1525],{"class":1524},[336,2441,2442],{"class":1528}," 继续上次的鉴权模块重构，跑通剩余测试\n",[31,2444,2445],{},"实测记忆系统在 V0.1.0 还比较初级——能记住显式声明的事实，但对\"隐式约定\"的捕捉还不够智能，偶尔会记错或记漏。方向是对的，成熟度待提升。",[60,2447,2449],{"id":2448},"内置多模态模型免费看图编程","内置多模态模型，免费看图编程",[31,2451,2452,2453,2456],{},"MiMo Code 内置了 MiMo 多模态模型，支持图像输入。截图一个 UI bug、贴一张报错栈、丢一张架构图，它都能\"看着改\"——而且 ",[35,2454,2455],{},"不依赖外部 API","，开箱即用、完全免费：",[326,2458,2460],{"className":328,"code":2459,"language":330,"meta":331,"style":331},"> 这张设计稿还原成响应式页面，注意移动端适配\n[附图：design.png]\n",[333,2461,2462,2469],{"__ignoreMap":331},[336,2463,2464,2466],{"class":338,"line":339},[336,2465,1525],{"class":1524},[336,2467,2468],{"class":1528}," 这张设计稿还原成响应式页面，注意移动端适配\n",[336,2470,2471],{"class":338,"line":353},[336,2472,2473],{"class":1528},"[附图：design.png]\n",[31,2475,2476],{},"对前端还原、运维看监控截图、QA 看 bug 截图这类场景，多模态是刚需。Claude Code 也支持多模态但要走 Claude API 计费，MiMo 内置免费的思路对个人开发者更友好。",[60,2478,2480],{"id":2479},"mit-协议彻底开源","MIT 协议，彻底开源",[31,2482,2483,2484,2489],{},"MiMo Code 是 ",[696,2485,2488],{"href":2486,"rel":2487},"https:\u002F\u002Fgithub.com\u002FXiaomiMiMo\u002FMiMo-Code",[1009],"MIT 协议"," 开源——权重、代码、工具全可商用、可改、可二次分发。这比 Kimi K2.7 Code 的开源协议更宽松（Kimi 的商用条款需留意），对企业做二次开发、嵌入自家产品是利好。",[31,2491,2492],{},"小米此举被社区解读为\"用开源换生态\"——终端 Agent 赛道 Claude Code 闭源、OpenCode 占开源高地，小米作为后来者用 MIT + 基准成绩 + 内置模型打差异。能不能跑出来，看后续迭代和社区活跃度。",[27,2494,321],{"id":321},[60,2496,2498],{"id":2497},"上手-5-分钟","上手 5 分钟",[326,2500,2502],{"className":328,"code":2501,"language":330,"meta":331,"style":331},"# 一行命令安装\ncurl -fsSL https:\u002F\u002Fgithub.com\u002FXiaomiMiMo\u002FMiMo-Code\u002Finstall.sh | bash\n\n# 或 npm\nnpm install -g mimo-code\n\n# 内置模型开箱即用，无需 API key\n# （也可接自托管 MiMo 或其他 OpenAI 兼容模型）\nmimo-code                       # 启动\n\n# 进项目\ncd your-project\nmimo-code \u002Finit                 # 初始化项目记忆\nmimo-code                       # 交互模式\n",[333,2503,2504,2508,2521,2525,2529,2540,2544,2549,2554,2561,2565,2569,2575,2585],{"__ignoreMap":331},[336,2505,2506],{"class":338,"line":339},[336,2507,1590],{"class":393},[336,2509,2510,2512,2514,2517,2519],{"class":338,"line":353},[336,2511,1595],{"class":342},[336,2513,1598],{"class":356},[336,2515,2516],{"class":346}," https:\u002F\u002Fgithub.com\u002FXiaomiMiMo\u002FMiMo-Code\u002Finstall.sh",[336,2518,1604],{"class":1524},[336,2520,1607],{"class":342},[336,2522,2523],{"class":338,"line":363},[336,2524,1612],{"emptyLinePlaceholder":765},[336,2526,2527],{"class":338,"line":378},[336,2528,1617],{"class":393},[336,2530,2531,2533,2535,2537],{"class":338,"line":390},[336,2532,1622],{"class":342},[336,2534,369],{"class":346},[336,2536,1627],{"class":356},[336,2538,2539],{"class":346}," mimo-code\n",[336,2541,2542],{"class":338,"line":397},[336,2543,1612],{"emptyLinePlaceholder":765},[336,2545,2546],{"class":338,"line":1637},[336,2547,2548],{"class":393},"# 内置模型开箱即用，无需 API key\n",[336,2550,2551],{"class":338,"line":1643},[336,2552,2553],{"class":393},"# （也可接自托管 MiMo 或其他 OpenAI 兼容模型）\n",[336,2555,2556,2558],{"class":338,"line":1654},[336,2557,2432],{"class":342},[336,2559,2560],{"class":393},"                       # 启动\n",[336,2562,2563],{"class":338,"line":1659},[336,2564,1612],{"emptyLinePlaceholder":765},[336,2566,2567],{"class":338,"line":1665},[336,2568,1662],{"class":393},[336,2570,2571,2573],{"class":338,"line":1672},[336,2572,357],{"class":356},[336,2574,1511],{"class":346},[336,2576,2577,2579,2582],{"class":338,"line":8},[336,2578,2432],{"class":342},[336,2580,2581],{"class":346}," \u002Finit",[336,2583,2584],{"class":393},"                 # 初始化项目记忆\n",[336,2586,2588,2590],{"class":338,"line":2587},14,[336,2589,2432],{"class":342},[336,2591,2592],{"class":393},"                       # 交互模式\n",[60,2594,1685],{"id":1685},[31,2596,2597],{},"作为 V0.1.0 产品，MiMo Code 的体感是\"潜力大、毛刺多\"。",[31,2599,2600],{},"好的方面：TUI 交互流畅（继承 OpenCode 底子）、中文理解到位、多模态看图实用、内置模型不折腾 key。基准成绩在真实任务上大致能复现——中等复杂度的 bug 修复、功能追加，完成质量确实不输 Claude Code。",[31,2602,2603],{},"毛刺方面：偶发崩溃、记忆系统记忆不准、长任务中途断、文档不完善、部分 slash 命令行为和 OpenCode 不一致。社区 issue 区还在快速增长，属于\"早期开源项目\"的典型状态。用它当主力会有踩坑成本，当尝鲜和观察对象更合适。",[60,2605,2606],{"id":2606},"配置与扩展",[31,2608,2609],{},"MiMo Code 兼容 OpenCode 的 provider 配置格式，想接 Claude \u002F GPT \u002F DeepSeek 也能配。但最佳体验还是配 MiMo 自研模型——内置调优、多模态、记忆系统都是围绕它做的。自托管 MiMo 模型需要一定 GPU 资源，个人玩家建议先用官方免费额度或内置模型。",[27,2611,1736],{"id":1736},[31,2613,2614,2615,2618],{},"MiMo Code ",[35,2616,2617],{},"完全免费","——MIT 开源、工具免费、内置模型免费。成本几乎为零（除非自托管模型要算硬件电费）：",[145,2620,2621,2631],{},[148,2622,2623],{},[151,2624,2625,2627,2629],{},[154,2626,1751],{},[154,2628,480],{},[154,2630,1756],{},[161,2632,2633,2645,2657],{},[151,2634,2635,2640,2642],{},[166,2636,2637],{},[35,2638,2639],{},"内置 MiMo 模型",[166,2641,1233],{},[166,2643,2644],{},"开箱即用，个人开发者首选",[151,2646,2647,2652,2654],{},[166,2648,2649],{},[35,2650,2651],{},"自托管 MiMo",[166,2653,1781],{},[166,2655,2656],{},"企业 \u002F 隐私场景，需 GPU",[151,2658,2659,2664,2667],{},[166,2660,2661],{},[35,2662,2663],{},"接其他模型（BYOK）",[166,2665,2666],{},"按 API 计费",[166,2668,2669],{},"想用 Claude\u002FGPT 时，自行承担 token 费",[31,2671,2672],{},"这是三个国产 Agent 里成本结构最干净的——Kimi Code 要付 API 费，OpenCode 要付外部模型 API 费，MiMo Code 内置模型直接免费。对\"一分钱不想花\"的开发者，MiMo 是天花板。",[31,2674,2675,2676,2678],{},"但免费的代价是 ",[35,2677,464],{},"：内置模型推理资源有限（如果走官方服务），高峰可能限流；自托管又要硬件。没有\"既免费又稳定还强\"的免费午餐。",[27,2680,1756],{"id":1756},[72,2682,2683,2689,2695,2701,2707],{},[75,2684,2685,2688],{},[35,2686,2687],{},"尝鲜国产开源 Agent 黑马","：想第一时间体验 SWE-Bench 超过 Claude Code 的国产选手",[75,2690,2691,2694],{},[35,2692,2693],{},"基准成绩敏感的研究者","：做 Agent 评测、对比实验，MiMo 是重要样本",[75,2696,2697,2700],{},[35,2698,2699],{},"需要内置多模态","：截图调试、看图改 UI，不想为多模态单独付费",[75,2702,2703,2706],{},[35,2704,2705],{},"MIT 协议二次开发","：企业想基于开源 Agent 做自家产品，MiMo 协议最宽松",[75,2708,2709,2712],{},[35,2710,2711],{},"国内免费 + 中文友好","：不花钱、不要海外账号、中文体验好",[27,2714,1837],{"id":1837},[72,2716,2717,2723,2729,2734,2740],{},[75,2718,2719,2722],{},[35,2720,2721],{},"生产环境追求稳定","：V0.1.0 早期，崩溃和毛刺难免，别拿核心业务冒险",[75,2724,2725,2728],{},[35,2726,2727],{},"需要成熟插件生态","：生态刚起步，第三方工具和自定义命令体系远不如 Claude Code",[75,2730,2731,2733],{},[35,2732,1862],{},"：CLI Agent 不是补全器",[75,2735,2736,2739],{},[35,2737,2738],{},"不熟悉终端的新手","：TUI + 配置门槛对纯新手不友好",[75,2741,2742,2745],{},[35,2743,2744],{},"要企业级 SLA","：开源 V0.1 无任何承诺",[27,2747,1872],{"id":1872},[145,2749,2750,2771],{},[148,2751,2752],{},[151,2753,2754,2756,2758,2762,2766],{},[154,2755,552],{},[154,2757,1900],{},[154,2759,2760],{},[696,2761,1888],{"href":1887},[154,2763,2764],{},[696,2765,1894],{"href":1893},[154,2767,2768],{},[696,2769,1396],{"href":2770},"\u002Fcoding\u002Fcli\u002Fkimi-code.html",[161,2772,2773,2785,2802,2816,2830,2845,2860,2873,2885],{},[151,2774,2775,2777,2779,2781,2783],{},[166,2776,1907],{},[166,2778,1915],{},[166,2780,1910],{},[166,2782,1915],{},[166,2784,1910],{},[151,2786,2787,2790,2793,2796,2799],{},[166,2788,2789],{},"协议",[166,2791,2792],{},"MIT（最宽松）",[166,2794,2795],{},"闭源",[166,2797,2798],{},"MIT",[166,2800,2801],{},"开源（商用需留意）",[151,2803,2804,2806,2809,2812,2814],{},[166,2805,1922],{},[166,2807,2808],{},"MiMo（内置免费）",[166,2810,2811],{},"Claude（闭源）",[166,2813,1931],{},[166,2815,1925],{},[151,2817,2818,2821,2824,2826,2828],{},[166,2819,2820],{},"SWE-Bench",[166,2822,2823],{},"★★★★★ 62%",[166,2825,621],{},[166,2827,1973],{},[166,2829,621],{},[151,2831,2832,2835,2838,2841,2843],{},[166,2833,2834],{},"多模态",[166,2836,2837],{},"✅ 内置免费",[166,2839,2840],{},"✅（计费）",[166,2842,1973],{},[166,2844,179],{},[151,2846,2847,2849,2852,2855,2858],{},[166,2848,2376],{},[166,2850,2851],{},"✅ 跨会话",[166,2853,2854],{},"✅（CLAUDE.md）",[166,2856,2857],{},"⚠️ 基础",[166,2859,2857],{},[151,2861,2862,2865,2867,2869,2871],{},[166,2863,2864],{},"成熟度",[166,2866,1989],{},[166,2868,1051],{},[166,2870,621],{},[166,2872,621],{},[151,2874,2875,2877,2879,2881,2883],{},[166,2876,1994],{},[166,2878,1997],{},[166,2880,2000],{},[166,2882,2003],{},[166,2884,2006],{},[151,2886,2887,2889,2891,2893,2895],{},[166,2888,480],{},[166,2890,1233],{},[166,2892,2016],{},[166,2894,2019],{},[166,2896,2897],{},"极低",[31,2899,2900,70],{},[35,2901,2902],{},"选 MiMo Code 如果你",[72,2904,2905,2908,2911,2914],{},[75,2906,2907],{},"想尝鲜基准超 Claude Code 的国产黑马",[75,2909,2910],{},"要 MIT 协议做二次开发 \u002F 商用",[75,2912,2913],{},"需要内置免费多模态",[75,2915,2916],{},"一分钱不想花，又要中文友好",[31,2918,2919,70],{},[35,2920,2921],{},"别选 MiMo Code 如果你",[72,2923,2924,2927,2930,2933],{},[75,2925,2926],{},"生产环境要稳（V0.1.0 太早期）",[75,2928,2929],{},"要成熟插件和 MCP 生态（去 Claude Code）",[75,2931,2932],{},"要 IDE inline 补全（去 Cursor \u002F Copilot）",[75,2934,2935],{},"企业要 SLA 承诺",[27,2937,657],{"id":656},[31,2939,2940,2943],{},[35,2941,2942],{},"Q：MiMo Code 真能超过 Claude Code 吗？","\nA：在 SWE-Bench Pro 和 Terminal Bench 2 两个标准化基准上，MiMo 的分数确实领先。但基准 ≠ 全场景实战——长任务稳定性、上下文管理、插件生态 MiMo 仍落后。把它理解为\"基准够强，实战在追赶\"更准确。",[31,2945,2946,2949],{},[35,2947,2948],{},"Q：V0.1.0 能用于生产吗？","\nA：不建议作为生产主力。早期版本崩溃、记忆不准、文档不全等问题难免。建议作为尝鲜 \u002F 备选 \u002F 研究对象，等迭代到 V1.0 再考虑生产化。",[31,2951,2952,2955],{},[35,2953,2954],{},"Q：内置模型真免费？有限制吗？","\nA：工具和内置模型免费。但若走官方推理服务，高峰可能限流；重度使用建议自托管（需 GPU）或接其他模型 BYOK。",[31,2957,2958,2961],{},[35,2959,2960],{},"Q：和 Kimi Code 怎么选？","\nA：都是国产开源 CLI Agent。Kimi Code 模型成熟（K2.7 多代迭代）、256K 长上下文、中文强；MiMo Code 基准成绩更亮眼、MIT 协议更宽松、内置多模态免费，但 V0.1 早期。要稳选 Kimi，要尝鲜和 MIT 商用选 MiMo。",[31,2963,2964,2967],{},[35,2965,2966],{},"Q：基于 OpenCode 架构，能用 OpenCode 的插件吗？","\nA：大部分兼容——provider 配置、MCP server、部分配置文件格式通用。但 MiMo 有自己的增量（记忆系统、内置模型），部分行为和 OpenCode 不完全一致，迁移时需测试。",[31,2969,2970,2973],{},[35,2971,2972],{},"Q：小米会长期维护吗？","\nA：目前社区活跃、迭代快，但开源项目的长期承诺难以保证。建议关注 GitHub issue 响应速度和发版节奏判断健康度，别把核心流程绑死在单一早期项目上。",[27,2975,690],{"id":690},[72,2977,2978,2988,3000,3009],{},[75,2979,2105,2980,2108,2982,2108,2984,2108,2986],{},[696,2981,1888],{"href":1887},[696,2983,1894],{"href":1893},[696,2985,1396],{"href":2770},[696,2987,2116],{"href":2115},[75,2989,2119,2990,2108,2992,2108,2995,2108,2997],{},[696,2991,2123],{"href":2122},[696,2993,2820],{"href":2994},"\u002Fwiki\u002Fswe-bench.html",[696,2996,2127],{"href":2126},[696,2998,2376],{"href":2999},"\u002Fwiki\u002Fagent-memory.html",[75,3001,2138,3002,2108,3005,2108,3007],{},[696,3003,1900],{"href":3004},"\u002Fmodels\u002Fmimo-code.html",[696,3006,1156],{"href":2145},[696,3008,2142],{"href":2141},[75,3010,2152,3011,2108,3013],{},[696,3012,2156],{"href":2155},[696,3014,2160],{"href":2159},[27,3016,2163],{"id":2163},[72,3018,3019,3024,3031,3038,3044],{},[75,3020,2175,3021],{},[696,3022,2486],{"href":2486,"rel":3023},[1009],[75,3025,3026,3027],{},"技术报告：",[696,3028,3029],{"href":3029,"rel":3030},"https:\u002F\u002Fgithub.com\u002FXiaomiMiMo\u002FMiMo-Code\u002Fblob\u002Fmain\u002FREADME.md",[1009],[75,3032,3033,3034],{},"MiMo 系列模型：",[696,3035,3036],{"href":3036,"rel":3037},"https:\u002F\u002Fgithub.com\u002FXiaomiMiMo",[1009],[75,3039,3040,3041],{},"OpenCode 架构基础：",[696,3042,2360],{"href":2360,"rel":3043},[1009],[75,3045,3046,3047],{},"SWE-Bench 排行榜：",[696,3048,3049],{"href":3049,"rel":3050},"https:\u002F\u002Fwww.swebench.com",[1009],[31,3052,2195,3053,2199],{},[696,3054,2198],{"href":2198},[725,3056,2202],{},{"title":331,"searchDepth":363,"depth":363,"links":3058},[3059,3060,3067,3072,3073,3074,3075,3076,3077,3078],{"id":1407,"depth":353,"text":1408},{"id":239,"depth":353,"text":239,"children":3061},[3062,3063,3064,3065,3066],{"id":2323,"depth":363,"text":2324},{"id":2353,"depth":363,"text":2354},{"id":2399,"depth":363,"text":2400},{"id":2448,"depth":363,"text":2449},{"id":2479,"depth":363,"text":2480},{"id":321,"depth":353,"text":321,"children":3068},[3069,3070,3071],{"id":2497,"depth":363,"text":2498},{"id":1685,"depth":363,"text":1685},{"id":2606,"depth":363,"text":2606},{"id":1736,"depth":353,"text":1736},{"id":1756,"depth":353,"text":1756},{"id":1837,"depth":353,"text":1837},{"id":1872,"depth":353,"text":1872},{"id":656,"depth":353,"text":657},{"id":690,"depth":353,"text":690},{"id":2163,"depth":353,"text":2163},"\u002Fimg\u002Ftools\u002Fmimo-code.webp","MiMo Code 2026 真实评测：小米 2026\u002F6\u002F11 发布的开源终端 AI 编程 Agent，MIT 协议、基于 OpenCode 架构、SWE-Bench Pro 62% 与 Terminal Bench 2 73% 双超 Claude Code。本文整理长程编程能力、内置多模态模型、记忆系统、安装使用、价格、与 Claude Code \u002F Kimi Code 的区别。",[2227,758],{},[3084],"mimo-code-v0.1",[3086,3087,3088,3089],"生产环境追求稳定（仅 V0.1.0，仍在快速迭代）","需要成熟插件生态（生态刚起步）","IDE 内 inline 补全场景","不熟悉终端操作的新手","\u002Ftools\u002Fcoding\u002Fcli\u002Fmimo-code",[775,774,776],[3093,3097,3101],{"plan":3094,"price":1233,"limit":2241,"cn_pay":3095,"note":3096},"开源版（MIT）","✅ 国产模型可用支付宝","可自托管",{"plan":3098,"price":1233,"limit":3099,"cn_pay":2250,"note":3100},"内置多模态模型","MiMo 自研模型内置","开箱即用",{"plan":3102,"price":1233,"limit":3103,"cn_pay":2250,"note":3104},"自托管","MIT 协议，权重可下载","需硬件","免费（MIT 协议开源）",{"power":378,"ux":363,"price":390,"cn_support":390,"stability":363},{"title":1900,"description":3080},"MiMo Code 评测 2026：小米开源终端 AI Agent，SWE-Bench Pro 62% 双超 Claude Code",[3110,3112,3114,3116,3118],{"title":3111,"url":2486},"MiMo-Code 开源仓库",{"title":3113,"url":3029},"MiMo Code 技术报告",{"title":3115,"url":3036},"小米 MiMo 系列模型",{"title":3117,"url":2360},"OpenCode 架构（MiMo 基于此）",{"title":3119,"url":3049},"SWE-Bench Pro 排行榜","tools\u002Fcoding\u002Fcli\u002Fmimo-code",[3122,3123,3124,3125,3126],"想尝鲜国产开源 Agent 黑马的开发者","看重 SWE-Bench \u002F Terminal Bench 基准成绩的研究者","需要内置多模态（截图调试）能力","MIT 协议下做二次开发 \u002F 商用集成","国内开发者，要免费 + 中文友好","小米开源终端 AI 编程 Agent，SWE-Bench 超越 Claude Code",[2223,772,2278,3129,822,3130,2279],"xiaomi","mit","小米开源 CLI Agent 黑马。SWE-Bench Pro 62%、Terminal Bench 2 73% 双超 Claude Code，MIT 协议、内置免费多模态模型、记忆系统。但仅 V0.1.0，生态和稳定性有待验证。","83HxX4EjIQhikVDVkzexTsz54S32OyD4DnvjpBn7cL0",{"id":3134,"title":1894,"alternatives":3135,"api_compatible":3138,"body":3142,"category":2223,"chinese_friendly":363,"cover":4142,"description":4143,"domestic":753,"extension":754,"faq":755,"free":753,"github":2360,"languages":4144,"meta":4145,"models":4146,"navigation":765,"notSuitable":4151,"opensource":765,"path":4156,"pillar":1352,"platforms":4157,"priceTable":4158,"pricing":4170,"published":791,"relatedPlaybooks":755,"relatedReviews":755,"score":4171,"self_host":765,"seo":4172,"seoTitle":4173,"slug":1399,"sources":4174,"stem":4185,"suitable":4186,"tagline":4192,"tags":4193,"updated":791,"verdict":4196,"website":4086,"__hash__":4197},"tools\u002Ftools\u002Fcoding\u002Fcli\u002Fopencode.md",[1398,3136,3137],"coding\u002Fcli\u002Fcodex","coding\u002Fcli\u002Faider",[21,20,3139,3140,3141],"google","openrouter","ollama",{"type":24,"value":3143,"toc":4121},[3144,3146,3164,3169,3171,3175,3182,3207,3210,3217,3221,3228,3266,3273,3385,3392,3396,3399,3409,3413,3421,3424,3426,3428,3587,3589,3603,3610,3612,3621,3623,3629,3700,3703,3710,3712,3744,3746,3780,3782,3942,3947,3961,3966,3986,3988,3994,4003,4009,4015,4021,4027,4029,4078,4080,4115,4119],[27,3145,1408],{"id":1407},[1410,3147,3149,3154],{"className":3148},[1413,1414,1415],[31,3150,3151,3153],{},[35,3152,1420],{}," OpenCode 是一款完全开源的终端 AI Coding Agent，由 SST 团队用 TypeScript 打造，GitHub 99.8K Star 量级。它把\"终端原生 Agent\"这件事做到了极致——TUI 界面、多会话并行、75+ 模型任意切换、BYOK 完全免费，是 Claude Code 在开源侧最像样的免费平替。",[31,3155,1424,3156,3159,3160,3163],{},[35,3157,3158],{},"模型自由","——同一个 Agent 内核，今天用 Claude Sonnet 4 跑难活，明天切 GPT-5 跑审查，后天切 DeepSeek-V3 省钱，全靠 API key 切换，不绑定任何订阅。代价是 ",[35,3161,3162],{},"配置门槛","：多模型路由、provider 配置、本地模型接入都需要手动折腾，稳定性也强依赖你接的 API 质量。",[1434,3165,3166],{},[31,3167,3168],{},"来源说明：本文基于 opencode.ai 官方文档、GitHub sst\u002Fopencode 仓库、社区评测与第三方对比整理。OpenCode 迭代极快（几乎每周发版），命令和模型支持列表请以最新官方文档为准。",[27,3170,239],{"id":239},[60,3172,3174],{"id":3173},"终端原生-tui多会话并行","终端原生 TUI，多会话并行",[31,3176,3177,3178,3181],{},"OpenCode 不是又一个\"AI IDE 插件\"，而是独立的终端程序。装好后在任何项目根目录跑 ",[333,3179,3180],{},"opencode","，就进入一个全屏的 TUI（终端用户界面）：左侧文件树、中间对话、右侧 diff 预览，所有操作用键盘完成。这套交互对 Vim \u002F Neovim \u002F tmux 用户极度友好——不抢编辑器焦点，开个分屏就能用。",[326,3183,3185],{"className":328,"code":3184,"language":330,"meta":331,"style":331},"cd your-project\nopencode                       # 进入 TUI 交互模式\n> 把这个 Express 项目迁移到 Fastify，跑通测试再提交\n",[333,3186,3187,3193,3200],{"__ignoreMap":331},[336,3188,3189,3191],{"class":338,"line":339},[336,3190,357],{"class":356},[336,3192,1511],{"class":346},[336,3194,3195,3197],{"class":338,"line":353},[336,3196,3180],{"class":342},[336,3198,3199],{"class":393},"                       # 进入 TUI 交互模式\n",[336,3201,3202,3204],{"class":338,"line":363},[336,3203,1525],{"class":1524},[336,3205,3206],{"class":1528}," 把这个 Express 项目迁移到 Fastify，跑通测试再提交\n",[31,3208,3209],{},"它会自动：扫描相关文件 → 给出迁移计划 → 等你确认 → 多文件改写 → 跑测试 → 生成 commit message。整个过程你可以在另一个窗口继续写代码，互不干扰。",[31,3211,3212,3213,3216],{},"TUI 之外，OpenCode 还支持 ",[35,3214,3215],{},"多会话并行","——一个终端里同时挂多个独立 agent 会话，每个有自己的 context 和模型配置。典型用法：一个会话用 Claude Sonnet 跑主开发，另一个用 GPT-5 跑代码审查，第三个用 DeepSeek 跑文档生成，互不污染上下文。这是 Claude Code（单会话为主，子代理是隔离派发）之外另一种\"并行\"思路。",[60,3218,3220],{"id":3219},"_75-模型-byok一个-agent-全通吃","75+ 模型 BYOK，一个 Agent 全通吃",[31,3222,3223,3224,3227],{},"OpenCode 最硬的能力是 ",[35,3225,3226],{},"模型中立","。它不绑死任何厂商，通过 provider 配置接入 75+ 模型，包括：",[72,3229,3230,3236,3242,3248,3254,3260],{},[75,3231,3232,3235],{},[35,3233,3234],{},"Anthropic","：Claude Opus 4 \u002F Sonnet 4 \u002F Haiku 4 全系",[75,3237,3238,3241],{},[35,3239,3240],{},"OpenAI","：GPT-5 \u002F GPT-4.1 \u002F o 系列推理模型",[75,3243,3244,3247],{},[35,3245,3246],{},"Google","：Gemini 2.5 Pro \u002F Flash",[75,3249,3250,3253],{},[35,3251,3252],{},"国产","：DeepSeek-V3、Qwen-Max、GLM-5、Kimi K2",[75,3255,3256,3259],{},[35,3257,3258],{},"本地","：Ollama、LM Studio 任何 GGUF 模型",[75,3261,3262,3265],{},[35,3263,3264],{},"中转","：OpenRouter \u002F 火山引擎 \u002F 硅基流动 一个 key 调全部",[31,3267,3268,3269,3272],{},"配置写在 ",[333,3270,3271],{},"opencode.json"," 里，按模型 \u002F 按场景 \u002F 按成本分配：",[326,3274,3278],{"className":3275,"code":3276,"language":3277,"meta":331,"style":331},"language-json shiki shiki-themes github-light github-dark","{\n  \"models\": {\n    \"hard\": { \"provider\": \"anthropic\", \"model\": \"claude-sonnet-4\" },\n    \"cheap\": { \"provider\": \"deepseek\", \"model\": \"deepseek-v3\" },\n    \"local\": { \"provider\": \"ollama\", \"model\": \"qwen2.5-coder:32b\" }\n  }\n}\n","json",[333,3279,3280,3285,3293,3324,3349,3375,3380],{"__ignoreMap":331},[336,3281,3282],{"class":338,"line":339},[336,3283,3284],{"class":1528},"{\n",[336,3286,3287,3290],{"class":338,"line":353},[336,3288,3289],{"class":356},"  \"models\"",[336,3291,3292],{"class":1528},": {\n",[336,3294,3295,3298,3301,3304,3307,3310,3313,3316,3318,3321],{"class":338,"line":363},[336,3296,3297],{"class":356},"    \"hard\"",[336,3299,3300],{"class":1528},": { ",[336,3302,3303],{"class":356},"\"provider\"",[336,3305,3306],{"class":1528},": ",[336,3308,3309],{"class":346},"\"anthropic\"",[336,3311,3312],{"class":1528},", ",[336,3314,3315],{"class":356},"\"model\"",[336,3317,3306],{"class":1528},[336,3319,3320],{"class":346},"\"claude-sonnet-4\"",[336,3322,3323],{"class":1528}," },\n",[336,3325,3326,3329,3331,3333,3335,3338,3340,3342,3344,3347],{"class":338,"line":378},[336,3327,3328],{"class":356},"    \"cheap\"",[336,3330,3300],{"class":1528},[336,3332,3303],{"class":356},[336,3334,3306],{"class":1528},[336,3336,3337],{"class":346},"\"deepseek\"",[336,3339,3312],{"class":1528},[336,3341,3315],{"class":356},[336,3343,3306],{"class":1528},[336,3345,3346],{"class":346},"\"deepseek-v3\"",[336,3348,3323],{"class":1528},[336,3350,3351,3354,3356,3358,3360,3363,3365,3367,3369,3372],{"class":338,"line":390},[336,3352,3353],{"class":356},"    \"local\"",[336,3355,3300],{"class":1528},[336,3357,3303],{"class":356},[336,3359,3306],{"class":1528},[336,3361,3362],{"class":346},"\"ollama\"",[336,3364,3312],{"class":1528},[336,3366,3315],{"class":356},[336,3368,3306],{"class":1528},[336,3370,3371],{"class":346},"\"qwen2.5-coder:32b\"",[336,3373,3374],{"class":1528}," }\n",[336,3376,3377],{"class":338,"line":397},[336,3378,3379],{"class":1528},"  }\n",[336,3381,3382],{"class":338,"line":1637},[336,3383,3384],{"class":1528},"}\n",[31,3386,3387,3388,3391],{},"之后在 TUI 里用 ",[333,3389,3390],{},"\u002Fmodel hard"," 一键切换。这种\"模型即配置\"的设计，让省钱和跑难活不再冲突——难的活上 Sonnet，简单的活（注释、文档、测试用例）下放给 DeepSeek，单月成本能压到订阅制的零头。",[60,3393,3395],{"id":3394},"agent-工具链文件操作终端执行mcp","Agent 工具链：文件操作、终端执行、MCP",[31,3397,3398],{},"作为 Agent，OpenCode 内置了完整的工具链：读写文件、执行 shell 命令、跑测试、grep 搜索、git 操作。所有工具调用都在 TUI 里可见可审批，你可以随时打断、回滚。",[31,3400,3401,3402,3404,3405,3408],{},"它同样支持 ",[696,3403,2127],{"href":2126}," 协议——配置 ",[333,3406,3407],{},".mcp.json"," 后就能在对话里调数据库、调浏览器、调任意 MCP server。这一点和 Claude Code 是同一套生态，迁移成本低。",[60,3410,3412],{"id":3411},"开源-自托管","开源 + 自托管",[31,3414,3415,3416,3420],{},"OpenCode 是 ",[696,3417,3419],{"href":2360,"rel":3418},[1009],"MIT 协议开源","，TypeScript 全栈，可读可改可自托管。企业担心数据出墙的，可以自建一套：本地 Ollama 模型 + 本地 MCP server + 内网 git，全程不出公司网络。这是闭源的 Claude Code \u002F Cursor 拿不到的优势。",[31,3422,3423],{},"社区活跃度是它的隐形护城河——GitHub 99.8K Star、issue 响应快、第三方插件（LSP 集成、自定义工具、模型 adapter）持续涌现。一个开源 Agent 能跑到这个量级，意味着生态可持续。",[27,3425,321],{"id":321},[60,3427,2498],{"id":2497},[326,3429,3431],{"className":328,"code":3430,"language":330,"meta":331,"style":331},"# macOS \u002F Linux\ncurl -fsSL https:\u002F\u002Fopencode.ai\u002Finstall.sh | bash\n\n# Windows PowerShell\nirm https:\u002F\u002Fopencode.ai\u002Finstall.ps1 | iex\n\n# 或 npm 全局安装（需 Node 18+）\nnpm install -g opencode\n\n# 验证\nopencode --version\n\n# 配置 API key（任选其一）\nexport ANTHROPIC_API_KEY=sk-ant-...\nexport OPENAI_API_KEY=sk-...\nexport OPENROUTER_API_KEY=sk-or-...      # 一个 key 调 75+ 模型\n\n# 进项目启动\ncd your-project\nopencode                          # 进入 TUI\n",[333,3432,3433,3438,3451,3455,3460,3473,3477,3482,3493,3497,3502,3509,3513,3518,3532,3545,3561,3566,3572,3579],{"__ignoreMap":331},[336,3434,3435],{"class":338,"line":339},[336,3436,3437],{"class":393},"# macOS \u002F Linux\n",[336,3439,3440,3442,3444,3447,3449],{"class":338,"line":353},[336,3441,1595],{"class":342},[336,3443,1598],{"class":356},[336,3445,3446],{"class":346}," https:\u002F\u002Fopencode.ai\u002Finstall.sh",[336,3448,1604],{"class":1524},[336,3450,1607],{"class":342},[336,3452,3453],{"class":338,"line":363},[336,3454,1612],{"emptyLinePlaceholder":765},[336,3456,3457],{"class":338,"line":378},[336,3458,3459],{"class":393},"# Windows PowerShell\n",[336,3461,3462,3465,3468,3470],{"class":338,"line":390},[336,3463,3464],{"class":342},"irm",[336,3466,3467],{"class":346}," https:\u002F\u002Fopencode.ai\u002Finstall.ps1",[336,3469,1604],{"class":1524},[336,3471,3472],{"class":342}," iex\n",[336,3474,3475],{"class":338,"line":397},[336,3476,1612],{"emptyLinePlaceholder":765},[336,3478,3479],{"class":338,"line":1637},[336,3480,3481],{"class":393},"# 或 npm 全局安装（需 Node 18+）\n",[336,3483,3484,3486,3488,3490],{"class":338,"line":1643},[336,3485,1622],{"class":342},[336,3487,369],{"class":346},[336,3489,1627],{"class":356},[336,3491,3492],{"class":346}," opencode\n",[336,3494,3495],{"class":338,"line":1654},[336,3496,1612],{"emptyLinePlaceholder":765},[336,3498,3499],{"class":338,"line":1659},[336,3500,3501],{"class":393},"# 验证\n",[336,3503,3504,3506],{"class":338,"line":1665},[336,3505,3180],{"class":342},[336,3507,3508],{"class":356}," --version\n",[336,3510,3511],{"class":338,"line":1672},[336,3512,1612],{"emptyLinePlaceholder":765},[336,3514,3515],{"class":338,"line":8},[336,3516,3517],{"class":393},"# 配置 API key（任选其一）\n",[336,3519,3520,3523,3526,3529],{"class":338,"line":2587},[336,3521,3522],{"class":1524},"export",[336,3524,3525],{"class":1528}," ANTHROPIC_API_KEY",[336,3527,3528],{"class":1524},"=",[336,3530,3531],{"class":1528},"sk-ant-...\n",[336,3533,3535,3537,3540,3542],{"class":338,"line":3534},15,[336,3536,3522],{"class":1524},[336,3538,3539],{"class":1528}," OPENAI_API_KEY",[336,3541,3528],{"class":1524},[336,3543,3544],{"class":1528},"sk-...\n",[336,3546,3548,3550,3553,3555,3558],{"class":338,"line":3547},16,[336,3549,3522],{"class":1524},[336,3551,3552],{"class":1528}," OPENROUTER_API_KEY",[336,3554,3528],{"class":1524},[336,3556,3557],{"class":1528},"sk-or-...      ",[336,3559,3560],{"class":393},"# 一个 key 调 75+ 模型\n",[336,3562,3564],{"class":338,"line":3563},17,[336,3565,1612],{"emptyLinePlaceholder":765},[336,3567,3569],{"class":338,"line":3568},18,[336,3570,3571],{"class":393},"# 进项目启动\n",[336,3573,3575,3577],{"class":338,"line":3574},19,[336,3576,357],{"class":356},[336,3578,1511],{"class":346},[336,3580,3582,3584],{"class":338,"line":3581},20,[336,3583,3180],{"class":342},[336,3585,3586],{"class":393},"                          # 进入 TUI\n",[60,3588,1685],{"id":1685},[31,3590,3591,3592,3595,3596,3598,3599,3602],{},"实际用下来，OpenCode 的 TUI 体验是开源侧最接近 Claude Code 的。键盘流操作丝滑，diff 实时预览，会话切换比 Claude Code 的 ",[333,3593,3594],{},"\u002Fresume"," 更直观（直接在侧栏点）。多模型切换是杀手锏：跑一个大 refactor 前先 ",[333,3597,3390],{}," 上 Sonnet，跑完切 ",[333,3600,3601],{},"\u002Fmodel cheap"," 让 DeepSeek 补文档，成本可控。",[31,3604,3605,3606,3609],{},"但 ",[35,3607,3608],{},"稳定性是短板","。BYOK 模式下，体验强依赖你接的 API 质量：第三方中转偶尔限流、国产模型长任务容易断、本地模型质量参差。同样一个任务，Claude Code 用官方 API 能稳跑完，OpenCode 接中转可能中途报错重来。社区也承认这一点，正在做重试、断点续跑等加固。",[60,3611,3162],{"id":3162},[31,3613,3614,3615,3617,3618,3620],{},"第一次配 ",[333,3616,3271],{}," 会有学习成本——provider 字段、base_url、模型映射、工具白名单都要手填。社区有模板可抄，但想用好（比如按场景自动路由模型）还是得读文档。相比之下 Claude Code 的 ",[333,3619,1391],{}," 一键启动确实更省心。",[27,3622,1736],{"id":1736},[31,3624,3625,3626,3628],{},"OpenCode 本身 ",[35,3627,2617],{},"——开源、无订阅、无增值版。你只为你调用的模型 API 付费（BYOK）。这意味着成本完全可控：",[145,3630,3631,3643],{},[148,3632,3633],{},[151,3634,3635,3638,3641],{},[154,3636,3637],{},"接入方式",[154,3639,3640],{},"单月成本估算",[154,3642,1756],{},[161,3644,3645,3656,3667,3678,3689],{},[151,3646,3647,3650,3653],{},[166,3648,3649],{},"DeepSeek-V3（国产）",[166,3651,3652],{},"¥20-100",[166,3654,3655],{},"日常编码，性价比之王",[151,3657,3658,3661,3664],{},[166,3659,3660],{},"Claude Sonnet 4（直连）",[166,3662,3663],{},"$50-300",[166,3665,3666],{},"跑难活，最接近 Claude Code 体验",[151,3668,3669,3672,3675],{},[166,3670,3671],{},"OpenRouter 中转",[166,3673,3674],{},"$10-100",[166,3676,3677],{},"一个 key 切多模型",[151,3679,3680,3683,3686],{},[166,3681,3682],{},"本地 Ollama",[166,3684,3685],{},"¥0（电费）",[166,3687,3688],{},"隐私 \u002F 离线 \u002F 实验",[151,3690,3691,3694,3697],{},[166,3692,3693],{},"GPT-5（直连）",[166,3695,3696],{},"$30-200",[166,3698,3699],{},"代码审查、推理任务",[31,3701,3702],{},"对比 Claude Code Max $200\u002F月：用 OpenCode + DeepSeek，同样强度的编码量，单月可能只要 ¥50。这就是开源 + BYOK 的红利——你为用量付费，不为订阅定额买单。",[31,3704,3705,3706,3709],{},"但要注意 ",[35,3707,3708],{},"隐性成本","：多模型配置的时间、调试中转稳定性的精力、偶尔重跑任务的 token 浪费。对这些\"折腾税\"敏感的，订阅制反而更省心。",[27,3711,1756],{"id":1756},[72,3713,3714,3720,3726,3732,3738],{},[75,3715,3716,3719],{},[35,3717,3718],{},"预算敏感的个人开发者","：想用 Agent 但不想每月 $20+，OpenCode + DeepSeek 是最经济解",[75,3721,3722,3725],{},[35,3723,3724],{},"模型自由爱好者","：想同时对比 Claude \u002F GPT \u002F DeepSeek \u002F 本地模型在同一个 Agent 里的表现",[75,3727,3728,3731],{},[35,3729,3730],{},"Vim \u002F Neovim \u002F tmux 重度用户","：终端原生 TUI，不破坏既有工作流",[75,3733,3734,3737],{},[35,3735,3736],{},"需要自托管 \u002F 隐私的团队","：MIT 开源 + 本地模型，数据不出内网",[75,3739,3740,3743],{},[35,3741,3742],{},"Claude Code 想找平替","：交互逻辑接近，迁移成本低，省下订阅费",[27,3745,1837],{"id":1837},[72,3747,3748,3757,3763,3768,3774],{},[75,3749,3750,3753,3754,3756],{},[35,3751,3752],{},"零配置开箱即用","：多模型路由要折腾 ",[333,3755,3271],{},"，新手劝退",[75,3758,3759,3762],{},[35,3760,3761],{},"企业级稳定性 SLA","：开源项目无承诺，中转 \u002F 国产模型偶发抖动",[75,3764,3765,3767],{},[35,3766,1862],{},"：OpenCode 是 Agent 不是补全器，要 Tab 补全去 Cursor \u002F Copilot",[75,3769,3770,3773],{},[35,3771,3772],{},"纯 Windows 原生","：TUI 在 Windows Terminal 可用，但部分功能建议 WSL",[75,3775,3776,3779],{},[35,3777,3778],{},"不碰终端的用户","：这是 CLI 工具，全程命令行交互",[27,3781,1872],{"id":1872},[145,3783,3784,3808],{},[148,3785,3786],{},[151,3787,3788,3790,3792,3796,3802],{},[154,3789,552],{},[154,3791,1894],{},[154,3793,3794],{},[696,3795,1888],{"href":1887},[154,3797,3798],{},[696,3799,3801],{"href":3800},"\u002Fcoding\u002Fcli\u002Fcodex.html","Codex CLI",[154,3803,3804],{},[696,3805,3807],{"href":3806},"\u002Fcoding\u002Fcli\u002Faider.html","Aider",[161,3809,3810,3822,3836,3851,3864,3877,3890,3902,3914,3929],{},[151,3811,3812,3814,3816,3818,3820],{},[166,3813,1907],{},[166,3815,1915],{},[166,3817,1910],{},[166,3819,1910],{},[166,3821,1910],{},[151,3823,3824,3826,3829,3831,3833],{},[166,3825,567],{},[166,3827,3828],{},"✅ MIT",[166,3830,572],{},[166,3832,179],{},[166,3834,3835],{},"✅ Apache",[151,3837,3838,3840,3843,3846,3849],{},[166,3839,3158],{},[166,3841,3842],{},"★★★★★ 75+",[166,3844,3845],{},"★☆☆☆☆ 仅 Claude",[166,3847,3848],{},"★★★★☆ OpenAI 系",[166,3850,1051],{},[151,3852,3853,3856,3858,3860,3862],{},[166,3854,3855],{},"长任务能力",[166,3857,621],{},[166,3859,1051],{},[166,3861,621],{},[166,3863,627],{},[151,3865,3866,3869,3871,3873,3875],{},[166,3867,3868],{},"TUI 体验",[166,3870,1051],{},[166,3872,621],{},[166,3874,627],{},[166,3876,627],{},[151,3878,3879,3881,3883,3886,3888],{},[166,3880,3215],{},[166,3882,179],{},[166,3884,3885],{},"子代理",[166,3887,586],{},[166,3889,572],{},[151,3891,3892,3894,3896,3898,3900],{},[166,3893,1726],{},[166,3895,621],{},[166,3897,1051],{},[166,3899,627],{},[166,3901,572],{},[151,3903,3904,3906,3908,3910,3912],{},[166,3905,480],{},[166,3907,2019],{},[166,3909,2016],{},[166,3911,2016],{},[166,3913,2019],{},[151,3915,3916,3918,3921,3924,3926],{},[166,3917,1994],{},[166,3919,3920],{},"低（国产模型）",[166,3922,3923],{},"高",[166,3925,3923],{},[166,3927,3928],{},"低",[151,3930,3931,3933,3936,3938,3940],{},[166,3932,464],{},[166,3934,3935],{},"★★★☆☆ 依赖 API",[166,3937,621],{},[166,3939,621],{},[166,3941,621],{},[31,3943,3944,70],{},[35,3945,3946],{},"选 OpenCode 如果你",[72,3948,3949,3952,3955,3958],{},[75,3950,3951],{},"想用 Claude Code 的体验但不想付订阅",[75,3953,3954],{},"需要在多个模型间灵活切换（省钱 \u002F 跑难活 \u002F 隐私）",[75,3956,3957],{},"是 Vim \u002F 终端重度用户，不想装 IDE",[75,3959,3960],{},"团队要自托管，数据不出墙",[31,3962,3963,70],{},[35,3964,3965],{},"别选 OpenCode 如果你",[72,3967,3968,3971,3983],{},[75,3969,3970],{},"要开箱即用、零配置（去 Claude Code \u002F Cursor）",[75,3972,3973,3974,2108,3978,3982],{},"要 IDE 内 inline 补全（去 ",[696,3975,3977],{"href":3976},"\u002Fcoding\u002Fide\u002Fcursor.html","Cursor",[696,3979,3981],{"href":3980},"\u002Fcoding\u002Fcopilot\u002Fgithub-copilot.html","Copilot","）",[75,3984,3985],{},"企业要求稳定 SLA（开源 + BYOK 抖动难避免）",[27,3987,657],{"id":656},[31,3989,3990,3993],{},[35,3991,3992],{},"Q：OpenCode 真的完全免费吗？","\nA：工具本身免费开源（MIT）。但调模型 API 要花钱——除非你只用本地 Ollama 模型。所谓\"免费\"指的是不收订阅费，BYOK 模式下你为 token 买单。",[31,3995,3996,3999,4000,4002],{},[35,3997,3998],{},"Q：和 Claude Code 比，差在哪？","\nA：长任务稳定性、上下文管理（Claude Code 的 ",[333,4001,1542],{}," 更成熟）、官方模型深度调优。OpenCode 胜在模型自由和免费。简单说：要稳选 Claude Code，要省和自由选 OpenCode。",[31,4004,4005,4008],{},[35,4006,4007],{},"Q：国内怎么用最省心？","\nA：用 OpenRouter 一个 key 调全部模型（支持支付宝），或直接接 DeepSeek \u002F Qwen 国产模型。网络压力小，成本最低。",[31,4010,4011,4014],{},[35,4012,4013],{},"Q：能接本地模型吗？","\nA：能。Ollama \u002F LM Studio 跑 GGUF 模型，OpenCode 通过 OpenAI 兼容接口接入。完全离线可用，适合隐私场景。",[31,4016,4017,4020],{},[35,4018,4019],{},"Q：OpenCode 和 Aider 都是开源 CLI，区别？","\nA：Aider 更老牌、更轻量，专注\"每改即 commit\"的 git 流；OpenCode 更现代，TUI 体验和多会话并行更强，模型生态更广。要轻量选 Aider，要体验选 OpenCode。",[31,4022,4023,4026],{},[35,4024,4025],{},"Q：会取代 Claude Code 吗？","\nA：对预算敏感、爱折腾的用户，是的。对要稳定 + 长任务 + 不差钱的，Claude Code 仍是最优解。两者更像互补——OpenCode 日常省钱，Claude Code 跑关键大活。",[27,4028,690],{"id":690},[72,4030,4031,4043,4057,4070],{},[75,4032,2105,4033,2108,4035,2108,4037,2108,4039],{},[696,4034,1888],{"href":1887},[696,4036,3801],{"href":3800},[696,4038,3807],{"href":3806},[696,4040,4042],{"href":4041},"\u002Fcoding\u002Fcli\u002Fgemini-cli.html","Gemini CLI",[75,4044,2119,4045,2108,4047,2108,4049,2108,4053],{},[696,4046,2123],{"href":2122},[696,4048,2127],{"href":2126},[696,4050,4052],{"href":4051},"\u002Fwiki\u002Fbyok.html","BYOK",[696,4054,4056],{"href":4055},"\u002Fwiki\u002Fcontext-engineering.html","Context Engineering",[75,4058,2138,4059,2108,4061,2108,4064,2108,4066],{},[696,4060,1156],{"href":2145},[696,4062,1164],{"href":4063},"\u002Fmodels\u002Fgpt-5.html",[696,4065,2149],{"href":2148},[696,4067,4069],{"href":4068},"\u002Fmodels\u002Fqwen-max.html","Qwen-Max",[75,4071,2152,4072,2108,4074],{},[696,4073,2156],{"href":2155},[696,4075,4077],{"href":4076},"\u002Fwiki\u002Fprompt-engineering.html","Prompt Engineering",[27,4079,2163],{"id":2163},[72,4081,4082,4088,4095,4101,4108],{},[75,4083,2168,4084],{},[696,4085,4086],{"href":4086,"rel":4087},"https:\u002F\u002Fopencode.ai",[1009],[75,4089,4090,4091],{},"官方文档：",[696,4092,4093],{"href":4093,"rel":4094},"https:\u002F\u002Fopencode.ai\u002Fdocs",[1009],[75,4096,4097,4098],{},"GitHub：",[696,4099,2360],{"href":2360,"rel":4100},[1009],[75,4102,4103,4104],{},"模型支持：",[696,4105,4106],{"href":4106,"rel":4107},"https:\u002F\u002Fopencode.ai\u002Fdocs\u002Fmodels",[1009],[75,4109,4110,4111],{},"对比文档：",[696,4112,4113],{"href":4113,"rel":4114},"https:\u002F\u002Fopencode.ai\u002Fdocs\u002Fcomparisons\u002Fclaude-code",[1009],[31,4116,2195,4117,2199],{},[696,4118,2198],{"href":2198},[725,4120,2202],{},{"title":331,"searchDepth":363,"depth":363,"links":4122},[4123,4124,4130,4135,4136,4137,4138,4139,4140,4141],{"id":1407,"depth":353,"text":1408},{"id":239,"depth":353,"text":239,"children":4125},[4126,4127,4128,4129],{"id":3173,"depth":363,"text":3174},{"id":3219,"depth":363,"text":3220},{"id":3394,"depth":363,"text":3395},{"id":3411,"depth":363,"text":3412},{"id":321,"depth":353,"text":321,"children":4131},[4132,4133,4134],{"id":2497,"depth":363,"text":2498},{"id":1685,"depth":363,"text":1685},{"id":3162,"depth":363,"text":3162},{"id":1736,"depth":353,"text":1736},{"id":1756,"depth":353,"text":1756},{"id":1837,"depth":353,"text":1837},{"id":1872,"depth":353,"text":1872},{"id":656,"depth":353,"text":657},{"id":690,"depth":353,"text":690},{"id":2163,"depth":353,"text":2163},"\u002Fimg\u002Ftools\u002Fopencode.webp","OpenCode 2026 真实评测：SST 团队开源的终端 AI 编程 Agent，TypeScript 开发，GitHub 99.8K Star。本文整理 TUI 界面、多会话并行、75+ 模型 BYOK 切换、安装使用、价格、与 Claude Code \u002F Aider \u002F Codex CLI 的区别，以及国内使用门槛。",[758],{},[763,764,4147,4148,4149,4150],"gemini-2.5-pro","deepseek-v3","qwen-max","75+ 模型",[4152,4153,4154,4155],"希望开箱即用、零配置的用户（多模型路由要折腾）","要求企业级稳定性 SLA 的团队（开源项目无承诺）","需要 IDE 内 inline 补全体验（用 Cursor \u002F Copilot）","完全不熟悉 API key \u002F 终端操作的新手","\u002Ftools\u002Fcoding\u002Fcli\u002Fopencode",[775,774,776],[4159,4162,4165],{"plan":4160,"price":1233,"limit":2241,"cn_pay":3095,"note":4161},"开源版（BYOK）","自带 API key",{"plan":515,"price":1233,"limit":4163,"cn_pay":2250,"note":4164},"Ollama \u002F LM Studio 本地跑","适合离线 \u002F 隐私场景",{"plan":3671,"price":4166,"limit":4167,"cn_pay":4168,"note":4169},"按用量","一个 key 调用 75+ 模型","✅ 支持支付宝","国内最省心的接入方式","完全免费（BYOK）",{"power":378,"ux":378,"price":390,"cn_support":363,"stability":363},{"title":1894,"description":4143},"OpenCode 评测 2026：开源 AI Coding Agent，Claude Code 免费平替，75+ 模型任意切换",[4175,4177,4179,4181,4183],{"title":4176,"url":4086},"OpenCode 官网",{"title":4178,"url":4093},"OpenCode 官方文档",{"title":4180,"url":2360},"GitHub sst\u002Fopencode",{"title":4182,"url":4106},"OpenCode 模型支持列表",{"title":4184,"url":4113},"OpenCode vs Claude Code 对比","tools\u002Fcoding\u002Fcli\u002Fopencode",[4187,4188,4189,4190,4191],"想用 Claude Code 体验但不想付费订阅的开发者","需要在不同模型间灵活切换（省钱 \u002F 跑难活 \u002F 隐私）","JetBrains \u002F Vim \u002F Neovim 用户，不想换编辑器","想自托管、私有部署 AI 编程 Agent 的团队","本地模型爱好者（Ollama \u002F LM Studio 接入）","开源 AI Coding Agent，Claude Code 免费平替，支持 75+ 模型",[2223,772,2278,822,4194,4195],"byok","multi-model","Claude Code 的最佳免费替代。完全开源、75+ 模型任意切换、终端原生 TUI，社区活跃度 GitHub 99.8K Star。缺点是多模型配置门槛高、稳定性依赖 BYOK 的 API 质量。","YlbNrz-LA0upCzuXtiENAKDonLsspsOhrd_tCOHu9UE",{"id":4199,"title":4200,"alternatives":4201,"api_compatible":4205,"body":4206,"category":4942,"chinese_friendly":390,"cover":4943,"description":4944,"domestic":753,"extension":754,"faq":755,"free":753,"github":4945,"languages":4946,"meta":4947,"models":4948,"navigation":765,"notSuitable":4951,"opensource":765,"path":4956,"pillar":1352,"platforms":4957,"priceTable":4958,"pricing":4966,"published":791,"relatedPlaybooks":755,"relatedReviews":755,"score":4967,"self_host":765,"seo":4968,"seoTitle":4969,"slug":4970,"sources":4971,"stem":4986,"suitable":4987,"tagline":4993,"tags":4994,"updated":791,"verdict":4998,"website":4974,"__hash__":4999},"tools\u002Ftools\u002Fcoding\u002Fcopilot\u002Fdeepseek-coder.md","DeepSeek Coder",[4202,4203,4204],"coding\u002Fcopilot\u002Fcodegeex","coding\u002Fcopilot\u002Ftongyi-lingma","coding\u002Fcopilot\u002Fmarscode",[20],{"type":24,"value":4207,"toc":4925},[4208,4210,4221,4228,4236,4238,4242,4321,4328,4332,4335,4346,4349,4352,4355,4426,4429,4433,4526,4529,4533,4536,4550,4553,4557,4597,4600,4608,4611,4692,4702,4704,4828,4832,4846,4848,4854,4860,4866,4872,4882,4884,4922],[27,4209,29],{"id":29},[31,4211,4212,4213,4216,4217,4220],{},"DeepSeek Coder 是",[35,4214,4215],{},"国产开源编程模型的标杆","。2026 年 V4 API 全面开放后，配合 VS Code 的 Continue\u002FCline 插件，可以",[35,4218,4219],{},"零成本实现接近 Cursor 的 Agent 编程体验","——月成本从 Cursor 的 $20 降到 DeepSeek API 的 ¥10 以内。",[31,4222,4223,4224,4227],{},"它的优势是",[35,4225,4226],{},"极致性价比 + 中文理解一流 + 开源可本地部署","。短板是需要自行配置工具链（不像通义灵码\u002FMarsCode 那样开箱即用），且模型能力略弱于 Claude Sonnet 4。",[46,4229,4230],{"type":48},[31,4231,4232,4235],{},[35,4233,4234],{},"最佳实践","：VS Code + Continue 插件 + DeepSeek V4 API。补全用 DeepSeek（便宜），复杂 Agent 任务切 Claude（质量高）。两者月成本约 ¥50-100，体验接近 Cursor Pro。",[27,4237,239],{"id":239},[60,4239,4241],{"id":4240},"deepseek-v4-编程模型","DeepSeek V4 编程模型",[145,4243,4244,4258],{},[148,4245,4246],{},[151,4247,4248,4251,4254,4256],{},[154,4249,4250],{},"参数",[154,4252,4253],{},"DeepSeek V4",[154,4255,1156],{},[154,4257,1164],{},[161,4259,4260,4272,4286,4297,4311],{},[151,4261,4262,4265,4268,4270],{},[166,4263,4264],{},"上下文窗口",[166,4266,4267],{},"128K",[166,4269,1970],{},[166,4271,4267],{},[151,4273,4274,4277,4280,4283],{},[166,4275,4276],{},"编程 Benchmark",[166,4278,4279],{},"SWE-Bench 55%",[166,4281,4282],{},"SWE-Bench 72%",[166,4284,4285],{},"SWE-Bench 68%",[151,4287,4288,4291,4293,4295],{},[166,4289,4290],{},"中文理解",[166,4292,1051],{},[166,4294,621],{},[166,4296,621],{},[151,4298,4299,4302,4305,4308],{},[166,4300,4301],{},"API 价格",[166,4303,4304],{},"￥1\u002F百万 token",[166,4306,4307],{},"$15\u002F百万 token",[166,4309,4310],{},"$10\u002F百万 token",[151,4312,4313,4315,4317,4319],{},[166,4314,567],{},[166,4316,179],{},[166,4318,572],{},[166,4320,572],{},[31,4322,4323,4324,4327],{},"DeepSeek V4 的编程能力约为 Claude Sonnet 4 的 75-80%，但",[35,4325,4326],{},"API 价格只有 Claude 的 1\u002F100","。对于日常补全和中等复杂度的 Agent 任务，性价比极高。",[60,4329,4331],{"id":4330},"_128k-上下文","128K 上下文",[31,4333,4334],{},"128K token 上下文可以覆盖大部分中型项目的单次分析需求：",[72,4336,4337,4340,4343],{},[75,4338,4339],{},"约 3-5 万行代码",[75,4341,4342],{},"完整的 API 文档 + 代码库",[75,4344,4345],{},"长对话历史 + 项目上下文",[31,4347,4348],{},"对于超大型项目（10 万行+），需要配合 RAG 或分块策略。",[60,4350,4351],{"id":4351},"多语言编程支持",[31,4353,4354],{},"DeepSeek Coder 在以下语言表现最好：",[145,4356,4357,4370],{},[148,4358,4359],{},[151,4360,4361,4364,4367],{},[154,4362,4363],{},"语言",[154,4365,4366],{},"补全质量",[154,4368,4369],{},"Agent 能力",[161,4371,4372,4381,4390,4399,4408,4417],{},[151,4373,4374,4377,4379],{},[166,4375,4376],{},"Python",[166,4378,1051],{},[166,4380,621],{},[151,4382,4383,4386,4388],{},[166,4384,4385],{},"JavaScript\u002FTypeScript",[166,4387,1051],{},[166,4389,621],{},[151,4391,4392,4395,4397],{},[166,4393,4394],{},"Java",[166,4396,621],{},[166,4398,621],{},[151,4400,4401,4404,4406],{},[166,4402,4403],{},"Go",[166,4405,621],{},[166,4407,627],{},[151,4409,4410,4413,4415],{},[166,4411,4412],{},"C\u002FC++",[166,4414,621],{},[166,4416,627],{},[151,4418,4419,4422,4424],{},[166,4420,4421],{},"Rust",[166,4423,627],{},[166,4425,627],{},[27,4427,4428],{"id":4428},"使用方式",[60,4430,4432],{"id":4431},"方式-1api-vs-code-continue推荐","方式 1：API + VS Code Continue（推荐）",[326,4434,4436],{"className":328,"code":4435,"language":330,"meta":331,"style":331},"# 1. 安装 VS Code Continue 插件\n# 2. 配置 config.json\n{\n  \"models\": [{\n    \"title\": \"DeepSeek V4\",\n    \"provider\": \"openai\",\n    \"model\": \"deepseek-chat\",\n    \"apiBase\": \"https:\u002F\u002Fapi.deepseek.com\u002Fv1\",\n    \"apiKey\": \"your-api-key\"\n  }]\n}\n# 3. 开始使用\n",[333,4437,4438,4443,4448,4452,4462,4472,4482,4492,4502,4512,4517,4521],{"__ignoreMap":331},[336,4439,4440],{"class":338,"line":339},[336,4441,4442],{"class":393},"# 1. 安装 VS Code Continue 插件\n",[336,4444,4445],{"class":338,"line":353},[336,4446,4447],{"class":393},"# 2. 配置 config.json\n",[336,4449,4450],{"class":338,"line":363},[336,4451,3284],{"class":1528},[336,4453,4454,4456,4459],{"class":338,"line":378},[336,4455,3289],{"class":342},[336,4457,4458],{"class":356},":",[336,4460,4461],{"class":1528}," [{\n",[336,4463,4464,4467,4469],{"class":338,"line":390},[336,4465,4466],{"class":342},"    \"title\"",[336,4468,4458],{"class":356},[336,4470,4471],{"class":346}," \"DeepSeek V4\",\n",[336,4473,4474,4477,4479],{"class":338,"line":397},[336,4475,4476],{"class":342},"    \"provider\"",[336,4478,4458],{"class":356},[336,4480,4481],{"class":346}," \"openai\",\n",[336,4483,4484,4487,4489],{"class":338,"line":1637},[336,4485,4486],{"class":342},"    \"model\"",[336,4488,4458],{"class":356},[336,4490,4491],{"class":346}," \"deepseek-chat\",\n",[336,4493,4494,4497,4499],{"class":338,"line":1643},[336,4495,4496],{"class":342},"    \"apiBase\"",[336,4498,4458],{"class":356},[336,4500,4501],{"class":346}," \"https:\u002F\u002Fapi.deepseek.com\u002Fv1\",\n",[336,4503,4504,4507,4509],{"class":338,"line":1654},[336,4505,4506],{"class":342},"    \"apiKey\"",[336,4508,4458],{"class":356},[336,4510,4511],{"class":346}," \"your-api-key\"\n",[336,4513,4514],{"class":338,"line":1659},[336,4515,4516],{"class":1528},"  }]\n",[336,4518,4519],{"class":338,"line":1665},[336,4520,3384],{"class":1528},[336,4522,4523],{"class":338,"line":1672},[336,4524,4525],{"class":393},"# 3. 开始使用\n",[31,4527,4528],{},"月成本：¥5-20（日常补全 + 偶尔 Agent 任务）",[60,4530,4532],{"id":4531},"方式-2api-clineagent-模式","方式 2：API + Cline（Agent 模式）",[31,4534,4535],{},"Cline 是 VS Code 的 Agent 插件，配合 DeepSeek V4 可以实现：",[72,4537,4538,4541,4544,4547],{},[75,4539,4540],{},"多文件修改",[75,4542,4543],{},"自动跑测试",[75,4545,4546],{},"终端命令执行",[75,4548,4549],{},"Bug 修复",[31,4551,4552],{},"月成本：¥10-50（取决于 Agent 任务频率）",[60,4554,4556],{"id":4555},"方式-3本地部署","方式 3：本地部署",[326,4558,4560],{"className":328,"code":4559,"language":330,"meta":331,"style":331},"# 使用 Ollama 本地部署\nollama pull deepseek-coder-v2:16b\n# 或使用 vLLM 部署 236B 完整版\npython -m vllm.entrypoints.openai.api_server --model deepseek-ai\u002Fdeepseek-coder-v2-instruct\n",[333,4561,4562,4567,4577,4582],{"__ignoreMap":331},[336,4563,4564],{"class":338,"line":339},[336,4565,4566],{"class":393},"# 使用 Ollama 本地部署\n",[336,4568,4569,4571,4574],{"class":338,"line":353},[336,4570,3141],{"class":342},[336,4572,4573],{"class":346}," pull",[336,4575,4576],{"class":346}," deepseek-coder-v2:16b\n",[336,4578,4579],{"class":338,"line":363},[336,4580,4581],{"class":393},"# 或使用 vLLM 部署 236B 完整版\n",[336,4583,4584,4586,4588,4591,4594],{"class":338,"line":378},[336,4585,400],{"class":342},[336,4587,403],{"class":356},[336,4589,4590],{"class":346}," vllm.entrypoints.openai.api_server",[336,4592,4593],{"class":356}," --model",[336,4595,4596],{"class":346}," deepseek-ai\u002Fdeepseek-coder-v2-instruct\n",[31,4598,4599],{},"本地部署需要：",[72,4601,4602,4605],{},[75,4603,4604],{},"16B 模型：~12GB VRAM（RTX 4090 可跑）",[75,4606,4607],{},"236B 模型：~480GB VRAM（多卡服务器）",[27,4609,4610],{"id":4610},"价格对比",[145,4612,4613,4626],{},[148,4614,4615],{},[151,4616,4617,4620,4623],{},[154,4618,4619],{},"方案",[154,4621,4622],{},"月成本",[154,4624,4625],{},"体验",[161,4627,4628,4639,4650,4661,4672,4682],{},[151,4629,4630,4633,4636],{},[166,4631,4632],{},"DeepSeek API + Continue",[166,4634,4635],{},"¥5-20",[166,4637,4638],{},"接近 Copilot 补全",[151,4640,4641,4644,4647],{},[166,4642,4643],{},"DeepSeek API + Cline",[166,4645,4646],{},"¥10-50",[166,4648,4649],{},"接近 Cursor Agent",[151,4651,4652,4655,4658],{},[166,4653,4654],{},"Cursor Pro",[166,4656,4657],{},"¥145 ($20)",[166,4659,4660],{},"最佳体验",[151,4662,4663,4666,4669],{},[166,4664,4665],{},"GitHub Copilot",[166,4667,4668],{},"¥72 ($10)",[166,4670,4671],{},"补全标杆",[151,4673,4674,4677,4679],{},[166,4675,4676],{},"通义灵码",[166,4678,1233],{},[166,4680,4681],{},"国产补全标杆",[151,4683,4684,4687,4689],{},[166,4685,4686],{},"本地部署",[166,4688,3685],{},[166,4690,4691],{},"需要 GPU",[31,4693,4694,4697,4698,4701],{},[35,4695,4696],{},"结论","：DeepSeek API + Continue\u002FCline 是",[35,4699,4700],{},"性价比最高的 AI 编程方案","，月成本 ¥10-50 即可获得接近 Cursor 的体验。",[27,4703,543],{"id":543},[145,4705,4706,4722],{},[148,4707,4708],{},[151,4709,4710,4712,4714,4716,4719],{},[154,4711,552],{},[154,4713,4200],{},[154,4715,4676],{},[154,4717,4718],{},"CodeGeeX",[154,4720,4721],{},"MarsCode",[161,4723,4724,4740,4753,4765,4778,4790,4803,4815],{},[151,4725,4726,4729,4732,4735,4737],{},[166,4727,4728],{},"类型",[166,4730,4731],{},"开源模型 + 插件",[166,4733,4734],{},"IDE 插件",[166,4736,4734],{},[166,4738,4739],{},"IDE 插件 + IDE",[151,4741,4742,4744,4746,4748,4751],{},[166,4743,567],{},[166,4745,179],{},[166,4747,572],{},[166,4749,4750],{},"✅ 模型开源",[166,4752,572],{},[151,4754,4755,4757,4759,4761,4763],{},[166,4756,4686],{},[166,4758,179],{},[166,4760,572],{},[166,4762,179],{},[166,4764,572],{},[151,4766,4767,4769,4772,4774,4776],{},[166,4768,480],{},[166,4770,4771],{},"免费\u002F极低",[166,4773,1233],{},[166,4775,1233],{},[166,4777,1233],{},[151,4779,4780,4782,4784,4786,4788],{},[166,4781,4366],{},[166,4783,621],{},[166,4785,621],{},[166,4787,627],{},[166,4789,621],{},[151,4791,4792,4794,4797,4799,4801],{},[166,4793,4369],{},[166,4795,4796],{},"★★★★☆ (需配 Cline)",[166,4798,627],{},[166,4800,635],{},[166,4802,621],{},[151,4804,4805,4807,4809,4811,4813],{},[166,4806,632],{},[166,4808,1051],{},[166,4810,1051],{},[166,4812,621],{},[166,4814,621],{},[151,4816,4817,4819,4822,4824,4826],{},[166,4818,3100],{},[166,4820,4821],{},"❌ 需配置",[166,4823,179],{},[166,4825,179],{},[166,4827,179],{},[31,4829,4830,70],{},[35,4831,1258],{},[72,4833,4834,4837,4840,4843],{},[75,4835,4836],{},"想要开箱即用 → 通义灵码 \u002F MarsCode",[75,4838,4839],{},"想要本地部署\u002F开源 → DeepSeek Coder",[75,4841,4842],{},"想要最强 Agent 体验 → DeepSeek API + Cline",[75,4844,4845],{},"想要零成本 → 本地部署 DeepSeek + Continue",[27,4847,657],{"id":656},[31,4849,4850,4853],{},[35,4851,4852],{},"Q：DeepSeek V4 和 DeepSeek Coder V2 什么区别？","\nV4 是通用大模型（编程能力强），Coder V2 是专门的编程模型。V4 整体能力更强，推荐用 V4。Coder V2 适合本地部署（有 16B 小模型版本）。",[31,4855,4856,4859],{},[35,4857,4858],{},"Q：DeepSeek API 稳定吗？","\n2026 年 V4 全面开放后稳定性大幅提升，API 可用率约 99.5%。高峰期偶有延迟增高，但不影响日常使用。",[31,4861,4862,4865],{},[35,4863,4864],{},"Q：能替代 Cursor 吗？","\n补全可以替代，Agent 能力差一截。DeepSeek + Cline 的 Agent 体验约为 Cursor 的 70-80%，但成本只有 1\u002F10。预算敏感的用户完全可以接受。",[31,4867,4868,4871],{},[35,4869,4870],{},"Q：本地部署需要什么硬件？","\n16B 量化版需要 ~12GB VRAM（RTX 4090 \u002F RTX 3090 可跑）。完整 236B 需要多卡服务器（8×A100 80GB）。大多数个人用户选择 API 方式更实际。",[31,4873,4874,4877,4878,4881],{},[35,4875,4876],{},"Q：和 Kimi Code \u002F MiMo Code 比，DeepSeek Coder 优势在哪？","\nDeepSeek 的优势是",[35,4879,4880],{},"生态成熟","——API 稳定、社区大、工具链完善（Continue\u002FCline\u002FAider 都支持）。Kimi Code 和 MiMo Code 更新更激进但生态还在建设期。",[27,4883,690],{"id":690},[72,4885,4886,4892,4898,4904,4910,4916],{},[75,4887,4888],{},[696,4889,4891],{"href":4890},"\u002Fcoding\u002Fcopilot\u002Fcodegeex.html","CodeGeeX 工具卡",[75,4893,4894],{},[696,4895,4897],{"href":4896},"\u002Fcoding\u002Fcopilot\u002Ftongyi-lingma.html","通义灵码 工具卡",[75,4899,4900],{},[696,4901,4903],{"href":4902},"\u002Fcoding\u002Fcopilot\u002Fmarscode.html","MarsCode 工具卡",[75,4905,4906],{},[696,4907,4909],{"href":4908},"\u002Freview\u002Fcopilot-four-comparison.html","国产 Copilot 四强横评",[75,4911,4912],{},[696,4913,4915],{"href":4914},"\u002Fcoding\u002Fagent\u002Fcontinue.html","Continue 工具卡",[75,4917,4918],{},[696,4919,4921],{"href":4920},"\u002Fcoding\u002Fcli\u002Fcline.html","Cline 工具卡",[725,4923,4924],{},"html pre.shiki code .sJ8bj, html code.shiki .sJ8bj{--shiki-default:#6A737D;--shiki-dark:#6A737D}html pre.shiki code .sVt8B, html code.shiki .sVt8B{--shiki-default:#24292E;--shiki-dark:#E1E4E8}html pre.shiki code .sScJk, html code.shiki .sScJk{--shiki-default:#6F42C1;--shiki-dark:#B392F0}html pre.shiki code .sj4cs, html code.shiki .sj4cs{--shiki-default:#005CC5;--shiki-dark:#79B8FF}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: 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var(--shiki-dark-text-decoration);}",{"title":331,"searchDepth":363,"depth":363,"links":4926},[4927,4928,4933,4938,4939,4940,4941],{"id":29,"depth":353,"text":29},{"id":239,"depth":353,"text":239,"children":4929},[4930,4931,4932],{"id":4240,"depth":363,"text":4241},{"id":4330,"depth":363,"text":4331},{"id":4351,"depth":363,"text":4351},{"id":4428,"depth":353,"text":4428,"children":4934},[4935,4936,4937],{"id":4431,"depth":363,"text":4432},{"id":4531,"depth":363,"text":4532},{"id":4555,"depth":363,"text":4556},{"id":4610,"depth":353,"text":4610},{"id":543,"depth":353,"text":543},{"id":656,"depth":353,"text":657},{"id":690,"depth":353,"text":690},"copilot","\u002Fimg\u002Ftools\u002Fdeepseek-coder.webp","DeepSeek Coder 2026 真实评测：深度求索出品的开源编程模型，V4 API 全面开放、128K 上下文、配合 VS Code Continue\u002FCline 可免费实现 Cursor 级体验。本文整理核心能力、本地部署、与 CodeGeeX\u002F通义灵码对比、适用场景。","https:\u002F\u002Fgithub.com\u002Fdeepseek-ai\u002FDeepSeek-Coder",[2227,758],{},[4949,4950],"deepseek-v4","deepseek-coder-v2",[4952,4953,4954,4955],"需要开箱即用的 IDE 插件体验（需要自行配置工具链）","需要最强模型能力（DeepSeek 略弱于 Claude Sonnet 4）","没有 GPU 且不想用 API 的用户","需要企业级管控和审计的团队","\u002Ftools\u002Fcoding\u002Fcopilot\u002Fdeepseek-coder",[1354,776,775,774],[4959,4962],{"plan":4960,"price":780,"limit":4961,"cn_pay":782,"note":4691},"开源模型","自行部署，无限制",{"plan":4963,"price":4304,"limit":4964,"cn_pay":2242,"note":4965},"API（V4）","按量计费","最便宜的顶级 API","免费（开源模型）\u002F API 按 token 计费",{"power":378,"ux":363,"price":390,"cn_support":390,"stability":378},{"title":4200,"description":4944},"DeepSeek Coder 评测 2026：深度求索开源编程模型，V4 API 全面开放","coding\u002Fcopilot\u002Fdeepseek-coder",[4972,4975,4977,4980,4983],{"title":4973,"url":4974},"DeepSeek 官网","https:\u002F\u002Fwww.deepseek.com",{"title":4976,"url":4945},"DeepSeek Coder GitHub",{"title":4978,"url":4979},"DeepSeek V4 + VS Code 实测","https:\u002F\u002Fm.toutiao.com\u002Fgroup\u002F7651498750988485166\u002F",{"title":4981,"url":4982},"国产之光 DeepSeek Code","https:\u002F\u002Fm.toutiao.com\u002Fgroup\u002F7649438670730117659\u002F",{"title":4984,"url":4985},"DeepSeek 本地部署指南","https:\u002F\u002Fblog.csdn.net\u002Fgitblog_00848\u002Farticle\u002Fdetails\u002F151922381","tools\u002Fcoding\u002Fcopilot\u002Fdeepseek-coder",[4988,4989,4990,4991,4992],"预算敏感的个人开发者（免费或极低成本）","需要本地部署 \u002F 私有化的团队","中文注释为主的代码库","想要 BYOK 自由切换模型的用户","配合 Continue\u002FCline 在 VS Code 里实现 Agent 编程","深度求索开源编程模型，V4 API 全面开放，国产编程之光",[4942,4995,822,2279,4996,4997],"deepseek","local-deploy","api","国产开源编程模型标杆。V4 API 全面开放、128K 上下文、配合 Continue\u002FCline 可免费实现 Cursor 级体验。中文理解一流，本地部署友好，但需要自行搭建工具链。","CxvD3lNZ5cOsHf0UhSUzfx_nEfWGjXj2aljaTbAPwsU",{"id":5001,"title":5002,"body":5003,"cover":755,"description":5212,"extension":754,"meta":5213,"navigation":765,"path":5214,"published":5215,"seo":5216,"sourceName":5217,"sourceUrl":5218,"stem":5219,"__hash__":5220},"news\u002Fnews\u002F2026\u002Fagents-md-aaif.md","AGENTS.md 与 MCP 入驻 Linux 基金会，agent 生态走向开放治理",{"type":24,"value":5004,"toc":5204},[5005,5008,5043,5048,5052,5061,5076,5083,5087,5090,5137,5140,5143,5146,5161,5164,5168,5177,5179],[27,5006,5007],{"id":5007},"要点",[72,5009,5010,5016,5025,5031,5037],{},[75,5011,5012,5015],{},[35,5013,5014],{},"AAIF 成立","：Linux 基金会 2025 年 12 月 9 日成立 Agentic AI Foundation，提供中立、开源的 agent 治理框架",[75,5017,5018,5021,5022,5024],{},[35,5019,5020],{},"三大基础项目","：AGENTS.md（OpenAI 捐）、",[696,5023,2127],{"href":2126},"（Anthropic 捐）、goose（Block 捐）",[75,5026,5027,5030],{},[35,5028,5029],{},"AGENTS.md 已成事实标准","：被 6 万+ 开源项目采用，Devin\u002FGitHub Copilot\u002FCursor 等原生支持",[75,5032,5033,5036],{},[35,5034,5035],{},"MCP 规模","：已有 1 万+ 已发布 server，被 Claude\u002FCursor\u002FCopilot\u002FGemini\u002FVS Code\u002FChatGPT 采用",[75,5038,5039,5042],{},[35,5040,5041],{},"白金会员","：AWS、Anthropic、Block、Bloomberg、Cloudflare、Google、Microsoft、OpenAI",[1434,5044,5045],{},[31,5046,5047],{},"事件时间：2025 年 12 月 9 日。本文为 AIHO 收录整理。",[27,5049,5051],{"id":5050},"agentsmd给-agent-看的-readme","AGENTS.md：给 agent 看的 README",[31,5053,5054,5055,413,5058,5060],{},"AGENTS.md 是一个基于 Markdown 的开放标准，在仓库根目录提供「给 AI 编码 agent 的项目说明」——构建命令、测试方式、代码规范等。它取代了过去各家私有的碎片化配置（",[333,5056,5057],{},".cursorrules",[333,5059,2415],{}," 等）：",[72,5062,5063,5070,5073],{},[75,5064,5065,5066,5069],{},"被 ",[35,5067,5068],{},"6 万+ 开源项目和框架","采用（Devin、GitHub Copilot、Cursor 等）",[75,5071,5072],{},"支持 monorepo 的目录层级作用域",[75,5074,5075],{},"目的是让 agent 在不同仓库和构建系统下行为可预测",[31,5077,5078,5079,5082],{},"对开发者的实操意义：在项目根目录放一个 ",[333,5080,5081],{},"AGENTS.md","，写清楚怎么装依赖、怎么跑测试、有哪些约定，几乎所有主流 coding agent 都会读它——一次编写，跨工具复用。",[27,5084,5086],{"id":5085},"aaifagent-生态的连接层","AAIF：agent 生态的「连接层」",[31,5088,5089],{},"AAIF 由三个基础项目锚定：",[145,5091,5092,5104],{},[148,5093,5094],{},[151,5095,5096,5098,5101],{},[154,5097,489],{},[154,5099,5100],{},"捐赠方",[154,5102,5103],{},"定位",[161,5105,5106,5115,5126],{},[151,5107,5108,5110,5112],{},[166,5109,5081],{},[166,5111,3240],{},[166,5113,5114],{},"给 agent 的项目说明标准",[151,5116,5117,5121,5123],{},[166,5118,5119],{},[696,5120,2127],{"href":2126},[166,5122,3234],{},[166,5124,5125],{},"连接模型与工具\u002F数据的通用协议",[151,5127,5128,5131,5134],{},[166,5129,5130],{},"goose",[166,5132,5133],{},"Block",[166,5135,5136],{},"本地优先的开源 agent 框架",[31,5138,5139],{},"此外 OpenAI 还捐了 Codex CLI、Agents SDK、Apps SDK。基金会的目标是建立类似 TCP\u002FIP 之于互联网的「连接层」,让不同厂商的 agent 用共享、透明的标准互操作,防止厂商锁定。",[27,5141,5142],{"id":5142},"为什么重要",[31,5144,5145],{},"这标志着 agent 生态从「各家私有协议」走向「中立开放治理」：",[72,5147,5148,5153,5158],{},[75,5149,5150,5152],{},[35,5151,2127],{}," 解决「agent ↔ 工具」的连接",[75,5154,5155,5157],{},[35,5156,5081],{}," 解决「agent ↔ 项目」的约定",[75,5159,5160],{},"二者都进入中立基金会，意味着它们不再被单一厂商控制，长期稳定性和互操作性有保障",[31,5162,5163],{},"对正在搭 agent 工作流的团队，这是个积极信号：押注这些标准的迁移成本和锁定风险都在下降。",[27,5165,5167],{"id":5166},"aiho-观点","AIHO 观点",[31,5169,5170,5171,5173,5174,5176],{},"如果你还没在项目里放 ",[333,5172,5081],{},"，现在是时候了——成本极低、跨工具通用、已成事实标准。配合 ",[696,5175,2127],{"href":2126}," 接入内部工具，是 2026 年 agent 工程化的基础动作。",[27,5178,690],{"id":690},[72,5180,5181,5191,5198],{},[75,5182,2119,5183,2108,5185,2108,5187],{},[696,5184,2127],{"href":2126},[696,5186,2123],{"href":2122},[696,5188,5190],{"href":5189},"\u002Fwiki\u002Fa2a.html","A2A",[75,5192,5193,5194,2108,5196],{},"工具：",[696,5195,1888],{"href":1887},[696,5197,3977],{"href":3976},[75,5199,2138,5200],{},[696,5201,5203],{"href":5202},"\u002Fmodels\u002Fclaude-opus-4-5.html","Claude Opus 4.5",{"title":331,"searchDepth":363,"depth":363,"links":5205},[5206,5207,5208,5209,5210,5211],{"id":5007,"depth":353,"text":5007},{"id":5050,"depth":353,"text":5051},{"id":5085,"depth":353,"text":5086},{"id":5142,"depth":353,"text":5142},{"id":5166,"depth":353,"text":5167},{"id":690,"depth":353,"text":690},"Linux 基金会 2025 年 12 月成立 Agentic AI Foundation（AAIF），首批纳入 OpenAI 捐赠的 AGENTS.md、Anthropic 捐赠的 MCP 和 Block 捐赠的 goose，为 agent 生态建立中立的开放治理层。",{},"\u002Fnews\u002F2026\u002Fagents-md-aaif","2026-06-28",{"title":5002,"description":5212},"Linux 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Key",{"power":363,"ux":363,"price":390,"cn_support":353,"stability":353},{"title":5741,"slug":5742,"path":5743,"cover":5744,"website":5745,"tagline":5746,"pricing":5747,"pillar":1352,"score":5748,"opensource":753,"chinese_friendly":363,"free":753},"Windsurf","coding\u002Fide\u002Fwindsurf","\u002Ftools\u002Fcoding\u002Fide\u002Fwindsurf","\u002Fimg\u002Ftools\u002Fwindsurf.webp","https:\u002F\u002Fwindsurf.com","Codeium 出品的 AI IDE，Cascade Agent 模式领先","Free 25 credits \u002F Pro $15\u002Fmo 500 credits \u002F Team $30\u002Fseat",{"power":390,"ux":390,"price":378,"cn_support":363,"stability":378},{"title":5750,"slug":5751,"path":5752,"cover":5753,"website":5754,"tagline":5755,"pricing":5756,"pillar":1352,"score":5757,"opensource":765,"chinese_friendly":363,"free":753},"Zed","coding\u002Fide\u002Fzed","\u002Ftools\u002Fcoding\u002Fide\u002Fzed","\u002Fimg\u002Ftools\u002Fzed.webp","https:\u002F\u002Fzed.dev","Rust + GPU 渲染的高性能原生编辑器，2026-05 重构定价 + 开源 Zeta2.1 编辑预测模型","Free \u002F Pro $10·月 \u002F Business $30·席·月（BYOK 全档免费）",{"power":378,"ux":390,"price":378,"cn_support":363,"stability":378},{"title":5759,"slug":5760,"path":5761,"cover":5762,"website":5763,"tagline":5764,"pricing":5765,"pillar":1352,"score":5766,"opensource":765,"chinese_friendly":390,"free":753},"Cherry Studio","coding\u002Flocal\u002Fcherry-studio","\u002Ftools\u002Fcoding\u002Flocal\u002Fcherry-studio","\u002Fimg\u002Ftools\u002Fcherry-studio.webp","https:\u002F\u002Fcherry-ai.com","全能 AI 客户端：多模型聚合 + 本地知识库 + 300+ 助手模板，跨平台桌面应用","开源免费 \u002F 企业版联系销售",{"power":378,"ux":390,"price":390,"cn_support":390,"stability":378},{"title":5768,"slug":5769,"path":5770,"cover":5771,"website":5772,"tagline":5773,"pricing":5774,"pillar":1352,"score":5775,"opensource":753,"chinese_friendly":363,"free":753},"LM Studio","coding\u002Flocal\u002Flm-studio","\u002Ftools\u002Fcoding\u002Flocal\u002Flm-studio","\u002Fimg\u002Ftools\u002Flm-studio.webp","https:\u002F\u002Flmstudio.ai","本地 LLM 的 GUI 首选——模型浏览器 + GGUF\u002FMLX 推理 + OpenAI 兼容 API + Mac 原生优化","免费（个人 \u002F 评估） \u002F 企业 \u002F 商用咨询",{"power":378,"ux":390,"price":390,"cn_support":363,"stability":378},{"title":5777,"slug":5778,"path":5779,"cover":5780,"website":5781,"tagline":5782,"pricing":5783,"pillar":1352,"score":5784,"opensource":765,"chinese_friendly":390,"free":753},"LobeChat","coding\u002Flocal\u002Flobe-chat","\u002Ftools\u002Fcoding\u002Flocal\u002Flobe-chat","\u002Fimg\u002Ftools\u002Flobe-chat.webp","https:\u002F\u002Flobehub.com","现代设计的开源 AI 聊天框架——Web + 桌面双形态、72k+ stars、多模型 + 知识库 + 插件市场","完全免费（MIT 开源） \u002F LobeHub Cloud 订阅",{"power":390,"ux":390,"price":390,"cn_support":390,"stability":378},{"title":5786,"slug":5787,"path":5788,"cover":5789,"website":5790,"tagline":5791,"pricing":5792,"pillar":1352,"score":5793,"opensource":765,"chinese_friendly":363,"free":753},"Ollama","coding\u002Flocal\u002Follama","\u002Ftools\u002Fcoding\u002Flocal\u002Follama","\u002Fimg\u002Ftools\u002Follama.webp","https:\u002F\u002Follama.com","本地 LLM 的 Daemon——CLI + REST API 后台跑，给 Cursor \u002F Cline \u002F Open WebUI 接本地模型最低门槛","完全免费 + 开源（MIT）",{"power":378,"ux":378,"price":390,"cn_support":363,"stability":390},{"title":5795,"slug":5796,"path":5797,"cover":5798,"website":5799,"tagline":5800,"pricing":5801,"pillar":1352,"score":5802,"opensource":765,"chinese_friendly":378,"free":753},"Open WebUI","coding\u002Flocal\u002Fopen-webui","\u002Ftools\u002Fcoding\u002Flocal\u002Fopen-webui","\u002Fimg\u002Ftools\u002Fopen-webui.webp","https:\u002F\u002Fdocs.openwebui.com","自托管的 ChatGPT 替代：Ollama \u002F OpenAI 兼容、多用户、RAG、126k+ GitHub stars","完全免费（MIT 开源） \u002F Enterprise SLA 联系",{"power":390,"ux":378,"price":390,"cn_support":378,"stability":390},{"title":5804,"slug":5805,"path":5806,"cover":5807,"website":5808,"tagline":5809,"pricing":5810,"pillar":1352,"score":5811,"opensource":753,"chinese_friendly":363,"free":753},"Bito AI","coding\u002Freview\u002Fbito","\u002Ftools\u002Fcoding\u002Freview\u002Fbito","\u002Fimg\u002Ftools\u002Fbito.webp","https:\u002F\u002Fbito.ai","AI Code Reviews + AI Architect 双产品——PR 自动审查 + 代码库知识图谱 + 给 Cursor \u002F Claude Code 注入架构上下文","Team $12\u002Fseat\u002F月（年）或 $15（月） \u002F Professional $20\u002F$25 \u002F Enterprise 定制 \u002F AI Architect usage-based",{"power":378,"ux":378,"price":378,"cn_support":363,"stability":378},{"title":5813,"slug":5814,"path":5815,"cover":5816,"website":5817,"tagline":5818,"pricing":5819,"pillar":1352,"score":5820,"opensource":753,"chinese_friendly":363,"free":753},"CodeRabbit","coding\u002Freview\u002Fcoderabbit","\u002Ftools\u002Fcoding\u002Freview\u002Fcoderabbit","\u002Fimg\u002Ftools\u002Fcoderabbit.webp","https:\u002F\u002Fcoderabbit.ai","AI PR 评审事实标准——v3 + 40+ linter + Codegraph + IDE\u002FCLI\u002FPR 三入口，8K+ 企业、$15M ARR","Free（公共 + 私有限额） \u002F Pro $24\u002Fseat 年付 \u002F $30 月付 \u002F Enterprise 定制 \u002F CLI usage add-on",{"power":390,"ux":390,"price":378,"cn_support":363,"stability":390},{"title":5822,"slug":5823,"path":5824,"cover":5825,"website":5826,"tagline":5827,"pricing":5828,"pillar":1352,"score":5829,"opensource":753,"chinese_friendly":353,"free":753},"Ellipsis","coding\u002Freview\u002Fellipsis","\u002Ftools\u002Fcoding\u002Freview\u002Fellipsis","\u002Fimg\u002Ftools\u002Fellipsis.webp","https:\u002F\u002Fwww.ellipsis.dev","$20\u002Fdev\u002F月 flat 无限——AI 评审 + 从 GitHub 评论生成 PR + 67K+ 仓库使用、公共项目永久免费","公共仓库免费 \u002F 私有 $20\u002Fdev\u002F月 无限",{"power":378,"ux":378,"price":390,"cn_support":353,"stability":378},{"title":5831,"slug":5832,"path":5833,"cover":5834,"website":5835,"tagline":5836,"pricing":5837,"pillar":1352,"score":5838,"opensource":753,"chinese_friendly":353,"free":753},"Greptile","coding\u002Freview\u002Fgreptile","\u002Ftools\u002Fcoding\u002Freview\u002Fgreptile","\u002Fimg\u002Ftools\u002Fgreptile.webp","https:\u002F\u002Fwww.greptile.com","Codegraph 跨文件推理 + Agent v4——把整个代码库建图，理解 ripple effect 的 AI 评审","Freemium \u002F base + usage（2026-03-05 起） \u002F Enterprise self-host",{"power":390,"ux":378,"price":363,"cn_support":353,"stability":378},{"title":5840,"slug":5841,"path":5842,"cover":5843,"website":5844,"tagline":5845,"pricing":5846,"pillar":1352,"score":5847,"opensource":765,"chinese_friendly":363,"free":753},"Qodo","coding\u002Freview\u002Fqodo","\u002Ftools\u002Fcoding\u002Freview\u002Fqodo","\u002Fimg\u002Ftools\u002Fqodo.webp","https:\u002F\u002Fqodo.ai","前 CodiumAI——AI 评审 + 自动测试生成双合一，Qodo Merge \u002F Cover \u002F Gen 三件套","Developer Free \u002F Teams $30\u002Fseat\u002F月（年）≈$38（月）\u002F Enterprise 定制",{"power":390,"ux":378,"price":363,"cn_support":363,"stability":390},[5849,6859,7355],{"id":5850,"title":5851,"body":5852,"cover":6847,"description":6848,"extension":754,"meta":6849,"navigation":765,"path":6850,"published":791,"relatedTools":6851,"seo":6852,"stem":6853,"tags":6854,"updated":791,"verdict":6857,"__hash__":6858},"review\u002Freview\u002Faugment-deep-review.md","Augment Code 深度评测：企业级大代码库 Agent，4 个月项目 2 周完成",{"type":24,"value":5853,"toc":6817},[5854,5856,5867,5880,5886,5890,5897,5907,5917,5923,5927,5930,5933,5940,5972,5979,5983,6065,6070,6074,6081,6088,6091,6094,6097,6100,6103,6146,6153,6157,6164,6167,6170,6177,6180,6183,6186,6218,6222,6229,6233,6333,6339,6345,6351,6355,6450,6455,6461,6466,6468,6475,6479,6537,6544,6548,6551,6562,6565,6568,6571,6628,6633,6636,6642,6644,6683,6686,6695,6701,6714,6727,6737,6739,6745,6751,6757,6763,6769,6771,6809],[27,5855,29],{"id":29},[31,5857,5858,5859,5862,5863,5866],{},"如果你在",[35,5860,5861],{},"10 万行以上的大型 monorepo","里做开发——那种一个改动要跨 5 个目录、读 20 个文件、理解 3 层架构才能动手的活——",[35,5864,5865],{},"Augment Code 在 2026 年是大代码库 AI Agent 的标杆","。SWE-bench 验证集 65.4% 的解决率位居榜首，Real-time Context Engine 实时索引整个仓库让它在理解全局上下文上远超 Cursor 和 Cline。社区反馈\"原本 4 个月的项目用 Augment 2 周完成\"并非夸张——在大代码库场景，上下文理解的差距就是天堑。",[31,5868,5869,5870,5873,5874,5876,5877,5879],{},"但它不是万能的：",[35,5871,5872],{},"2025 年 10 月转向 credit 定价后比 Cursor + Windsurf 还贵、国内访问不便、对个人开发者和小项目用偏重","。它的甜点区是\"大代码库 + 企业预算 + 复杂任务\"，不是\"个人写 side project\"——后者去 ",[696,5875,5572],{"href":4920}," 或 ",[696,5878,3977],{"href":3976}," 更合适。",[1410,5881,5883],{"className":5882},[1413,1414,1415],[31,5884,5885],{},"选型建议：10 万行+ monorepo + 企业预算 → Augment Code。个人 \u002F 小项目 → Cursor 或 Cline + DeepSeek。想要全自动 AI 程序员（不需人盯）→ Devin。预算敏感 + 开源 → Continue。先用 Augment 免费档跑 2-3 个真实 issue 评估 credit 消耗，再决定要不要上付费档。",[27,5887,5889],{"id":5888},"augment-真正在解决的问题","Augment 真正在解决的问题",[31,5891,5892,5893,5896],{},"社区讨论\"为什么用 Augment\"经常停在\"它 SWE-bench 第一、它企业级\"。但深一层看，Augment 是在解决",[35,5894,5895],{},"AI 编程工具在大代码库场景的三个卡点","——",[31,5898,5899,5902,5903,5906],{},[35,5900,5901],{},"第一个卡点：上下文窗口装不下大代码库。"," 传统 AI 编程工具靠\"把代码塞进 LLM 上下文窗口\"工作——Cursor 的 Codebase Indexing 把检索到的 chunk 塞进 prompt，Cline 靠你手动 add 文件。但 10 万行以上的 monorepo，任何模型的上下文窗口都装不下全部代码。结果是 AI 只看到局部、理解不了全局，改一个模块 break 另一个模块。Augment 的 ",[35,5904,5905],{},"Real-time Context Engine"," 不靠\"塞代码进上下文\"，而是实时索引整个仓库构建代码图谱——函数调用关系、依赖链、数据流——让 Agent 在不读全部源码的情况下理解全局结构。这是它在 SWE-bench 上能打到 65.4% 的核心壁垒。",[31,5908,5909,5912,5913,5916],{},[35,5910,5911],{},"第二个卡点：Agent 不知道\"改这里会影响哪里\"。"," 在大代码库里改一个公共函数，可能影响 50 个调用方。传统 AI 工具改完就完了，影响范围要你手动验证。Augment 的 Context Engine 能",[35,5914,5915],{},"实时计算影响范围","——Agent 改一个函数前，它知道这个函数被哪些模块调用、改了之后哪些测试会跑、哪些 API 契约会 break。这让 Agent 在大代码库里的修改安全性远高于竞品。",[31,5918,5919,5922],{},[35,5920,5921],{},"第三个卡点：企业级安全合规。"," Devin \u002F Cursor \u002F Cline 都没有企业级的数据合规承诺。Augment 从第一天就定位企业市场——SOC 2 Type II 合规、代码数据不训练模型、企业级数据隔离、SSO \u002F SAML 支持。这让它在金融 \u002F 科技大厂的采购流程里能通过安全审查，而 Devin \u002F Cursor 经常卡在合规环节。",[27,5924,5926],{"id":5925},"大代码库理解能力augment-的核心壁垒","大代码库理解能力：Augment 的核心壁垒",[31,5928,5929],{},"Real-time Context Engine 是 Augment 最被低估、也是最难复制的工程能力。它的工作机制值得展开讲——",[60,5931,5932],{"id":5932},"实时代码图谱",[31,5934,5935,5936,5939],{},"Augment 启动时扫描整个仓库，构建一份",[35,5937,5938],{},"代码关系图谱","——不是简单的符号索引（像 Aider 的 repo map），而是一份包含以下信息的多维图谱：",[72,5941,5942,5948,5954,5960,5966],{},[75,5943,5944,5947],{},[35,5945,5946],{},"调用关系","：谁调用了谁、调用频率、调用路径",[75,5949,5950,5953],{},[35,5951,5952],{},"依赖链","：模块 A 依赖模块 B，B 依赖 C，改动 A 会影响什么",[75,5955,5956,5959],{},[35,5957,5958],{},"数据流","：数据从入口到出口经过哪些函数、在每一步如何变换",[75,5961,5962,5965],{},[35,5963,5964],{},"类型信息","：变量 \u002F 函数 \u002F 类的类型签名和约束",[75,5967,5968,5971],{},[35,5969,5970],{},"测试覆盖","：哪些函数有测试、测试覆盖了哪些路径",[31,5973,5974,5975,5978],{},"这份图谱在后台",[35,5976,5977],{},"实时更新","——你改一个文件，图谱在秒级同步，不需要重新全量索引。这让 Agent 在大代码库里的反应速度远快于\"每次重新检索 chunk\"的方案。",[60,5980,5982],{"id":5981},"与-cursor-codebase-indexing-的差异","与 Cursor Codebase Indexing 的差异",[145,5984,5985,5997],{},[148,5986,5987],{},[151,5988,5989,5991,5994],{},[154,5990,552],{},[154,5992,5993],{},"Augment Context Engine",[154,5995,5996],{},"Cursor Codebase Indexing",[161,5998,5999,6010,6021,6032,6043,6054],{},[151,6000,6001,6004,6007],{},[166,6002,6003],{},"索引方式",[166,6005,6006],{},"代码关系图谱（多维）",[166,6008,6009],{},"向量检索（语义相似）",[151,6011,6012,6015,6018],{},[166,6013,6014],{},"更新方式",[166,6016,6017],{},"实时（秒级）",[166,6019,6020],{},"增量（分钟级）",[151,6022,6023,6026,6029],{},[166,6024,6025],{},"理解深度",[166,6027,6028],{},"调用链 \u002F 数据流 \u002F 影响范围",[166,6030,6031],{},"语义相关性",[151,6033,6034,6037,6040],{},[166,6035,6036],{},"大代码库表现",[166,6038,6039],{},"10 万行+ 仍精准",[166,6041,6042],{},"10 万行+ 召回质量下降",[151,6044,6045,6048,6051],{},[166,6046,6047],{},"影响分析",[166,6049,6050],{},"✅ 实时计算",[166,6052,6053],{},"❌ 无",[151,6055,6056,6059,6062],{},[166,6057,6058],{},"上下文消耗",[166,6060,6061],{},"低（图谱压缩）",[166,6063,6064],{},"高（chunk 塞进 prompt）",[31,6066,6067,6069],{},[35,6068,4696],{},"：在 5 万行以下的项目里，Cursor 和 Augment 的差距不明显——向量检索够用。但到 10 万行以上的 monorepo，Augment 的代码图谱优势是决定性的——它知道\"改这个函数会影响哪 50 个调用方\"，Cursor 只知道\"这 5 个文件的语义和你的问题相关\"。",[60,6071,6073],{"id":6072},"swe-bench-654-意味着什么","SWE-bench 65.4% 意味着什么",[31,6075,6076,6077,6080],{},"SWE-bench 是用真实 GitHub issue 测试 AI Agent 解决问题能力的基准。Augment Agent 在 2025 年 3 月以 ",[35,6078,6079],{},"65.4% 的解决率位居榜首","——意味着 10 个真实 issue 里它能自主解决 6-7 个。",[31,6082,6083,6084,6087],{},"这个数字的含金量在于：SWE-bench 的 issue 来自真实开源项目（Django \u002F scikit-learn \u002F matplotlib 等），代码库都是数万行级别。Agent 要",[35,6085,6086],{},"自己读代码、定位 bug、写修复、跑测试验证","。65.4% 的解决率说明 Augment 在\"理解大代码库 + 自主修复\"这个最难的场景上是当前最强。",[31,6089,6090],{},"但要注意：SWE-bench 是基准测试，真实项目的复杂度可能更高（架构更乱、文档更少、依赖更复杂）。社区反馈 Augment 在真实项目的成功率约 40-60%——比基准数字低，但仍然远高于其他工具。",[27,6092,4369],{"id":6093},"agent-能力",[31,6095,6096],{},"Augment 的 Agent 模式是它的核心使用方式——你说一个 issue 或需求，它自主完成\"理解 → 定位 → 修改 → 验证\"全流程。",[60,6098,6099],{"id":6099},"自主任务执行",[31,6101,6102],{},"Augment Agent 的工作流程：",[432,6104,6105,6111,6117,6122,6128,6134,6140],{},[75,6106,6107,6110],{},[35,6108,6109],{},"理解任务","：解析你描述的 issue \u002F 需求",[75,6112,6113,6116],{},[35,6114,6115],{},"代码定位","：用 Context Engine 找到相关代码区域",[75,6118,6119,6121],{},[35,6120,6047],{},"：计算修改会影响哪些模块",[75,6123,6124,6127],{},[35,6125,6126],{},"生成方案","：规划修改步骤（改哪些文件、怎么改）",[75,6129,6130,6133],{},[35,6131,6132],{},"执行修改","：逐文件修改，实时更新代码图谱",[75,6135,6136,6139],{},[35,6137,6138],{},"验证","：跑测试、检查编译、读 diff",[75,6141,6142,6145],{},[35,6143,6144],{},"自修正","：测试失败时读错误信息、修正、重跑",[31,6147,6148,6149,6152],{},"整个流程",[35,6150,6151],{},"高度自主","——你给一个 issue 描述，它可以跑 5-15 分钟自己解决，中途不需要你干预。这和 Cursor Composer 的\"需要你逐步确认\"不同，Augment 的 Agent 更像 Devin 的\"放手让它干\"模式。",[60,6154,6156],{"id":6155},"intent动口不动手","Intent：动口不动手",[31,6158,6159,6160,6163],{},"Augment 在 2025-2026 年推出了 ",[35,6161,6162],{},"Intent"," 概念——一个让你\"动口不动手\"的开发工具。你只需要告诉 AI 你想要什么（你的意图），Augment 自动理解意图、定位代码、生成修改。",[31,6165,6166],{},"这是 Augment 对\"IDE 之后发展方向\"的愿景——从\"AI 辅助你写代码\"进化到\"AI 理解你的意图并执行\"。目前 Intent 还在发展中，但在简单场景（\"加一个登录页面\"、\"把这个 API 改成异步\"）的体验已经不错。",[60,6168,6169],{"id":6169},"多文件协调",[31,6171,6172,6173,6176],{},"Augment 在多文件修改上的优势来自 Context Engine——它改一个文件时知道这个文件和其他文件的关系，所以多文件修改的",[35,6174,6175],{},"一致性更高","。Cursor Composer 也能做多文件修改，但在大代码库里经常出现\"改了 A 忘了改 B\"的遗漏。Augment 的影响分析让它在大代码库的多文件修改上更可靠。",[27,6178,6179],{"id":6179},"企业级安全",[31,6181,6182],{},"企业级安全是 Augment 从第一天就定位的市场，也是它能进入大厂采购流程的门票。",[60,6184,6185],{"id":6185},"合规认证",[72,6187,6188,6194,6200,6206,6212],{},[75,6189,6190,6193],{},[35,6191,6192],{},"SOC 2 Type II","：美国注册会计师协会认证，证明 Augment 在安全 \u002F 可用性 \u002F 处理完整性 \u002F 保密性 \u002F 隐私性五个维度通过审计",[75,6195,6196,6199],{},[35,6197,6198],{},"代码数据不训练模型","：你的代码不会被用于训练 Augment 的模型——这是企业最关心的红线",[75,6201,6202,6205],{},[35,6203,6204],{},"数据隔离","：企业版客户的数据完全隔离，不与其他客户共享",[75,6207,6208,6211],{},[35,6209,6210],{},"SSO \u002F SAML","：支持企业单点登录",[75,6213,6214,6217],{},[35,6215,6216],{},"审计日志","：企业版提供完整的 AI 交互审计日志",[60,6219,6221],{"id":6220},"与-devin-的安全差异","与 Devin 的安全差异",[31,6223,6224,6225,6228],{},"Devin（Cognition 出品）是另一个企业级 AI 程序员，但定位不同——Devin 是\"全自主 AI 程序员\"，Augment 是\"企业开发团队的 AI Agent\"。安全合规方面两者都有企业级承诺，但 Augment 更注重",[35,6226,6227],{},"数据不训练模型","这条红线（Devin 的模型训练策略没有明确承诺不使用客户代码）。",[27,6230,6232],{"id":6231},"与-devin-对比","与 Devin 对比",[145,6234,6235,6245],{},[148,6236,6237],{},[151,6238,6239,6241,6243],{},[154,6240,552],{},[154,6242,5418],{},[154,6244,5436],{},[161,6246,6247,6258,6269,6279,6290,6301,6312,6322],{},[151,6248,6249,6252,6255],{},[166,6250,6251],{},"核心定位",[166,6253,6254],{},"企业开发团队的 AI Agent",[166,6256,6257],{},"全自主 AI 程序员",[151,6259,6260,6263,6266],{},[166,6261,6262],{},"自主程度",[166,6264,6265],{},"★★★★☆ Agent + 人工确认",[166,6267,6268],{},"★★★★★ 全自主",[151,6270,6271,6274,6277],{},[166,6272,6273],{},"大代码库",[166,6275,6276],{},"★★★★★ Context Engine",[166,6278,621],{},[151,6280,6281,6284,6287],{},[166,6282,6283],{},"SWE-bench",[166,6285,6286],{},"65.4%（2025-03 榜首）",[166,6288,6289],{},"~50%+",[151,6291,6292,6295,6298],{},[166,6293,6294],{},"交互方式",[166,6296,6297],{},"VS Code \u002F JetBrains 插件",[166,6299,6300],{},"Web 界面 + Slack",[151,6302,6303,6306,6309],{},[166,6304,6305],{},"适合任务",[166,6307,6308],{},"复杂 issue \u002F 重构 \u002F 功能开发",[166,6310,6311],{},"端到端任务交付",[151,6313,6314,6316,6319],{},[166,6315,480],{},[166,6317,6318],{},"$30-$150\u002F月（credit）",[166,6320,6321],{},"$500\u002F月",[151,6323,6324,6327,6330],{},[166,6325,6326],{},"企业安全",[166,6328,6329],{},"SOC 2 + 数据不训练",[166,6331,6332],{},"企业级承诺",[31,6334,6335,6338],{},[35,6336,6337],{},"选 Augment 如果","：你要一个和开发团队协作的 AI Agent（人在环中）、你的代码库大（10 万行+）、你预算在 $30-150\u002F月。",[31,6340,6341,6344],{},[35,6342,6343],{},"选 Devin 如果","：你想要\"提交任务 → 全自动交付\"的 AI 程序员、你的任务可以完全外包给 AI、你的预算是 $500\u002F月级别。",[31,6346,6347,6350],{},[35,6348,6349],{},"核心差异","：Augment 是\"AI 协助你开发\"，Devin 是\"AI 替你开发\"。Augment 的 Agent 跑完会给你 diff 让你 review，Devin 的 Agent 跑完直接给你 PR。前者更可控、后者更自主——选哪个取决于你对\"AI 自主决策\"的信任度。",[27,6352,6354],{"id":6353},"与-continue-对比","与 Continue 对比",[145,6356,6357,6368],{},[148,6358,6359],{},[151,6360,6361,6363,6365],{},[154,6362,552],{},[154,6364,5418],{},[154,6366,6367],{},"Continue",[161,6369,6370,6380,6390,6399,6409,6420,6430,6440],{},[151,6371,6372,6374,6377],{},[166,6373,5103],{},[166,6375,6376],{},"企业级大代码库 Agent",[166,6378,6379],{},"开源 AI 编程助手",[151,6381,6382,6384,6387],{},[166,6383,567],{},[166,6385,6386],{},"❌ 闭源",[166,6388,6389],{},"✅ Apache 2.0",[151,6391,6392,6394,6396],{},[166,6393,6273],{},[166,6395,6276],{},[166,6397,6398],{},"★★★☆☆ 依赖模型上下文",[151,6400,6401,6403,6406],{},[166,6402,4369],{},[166,6404,6405],{},"★★★★★ 自主多步",[166,6407,6408],{},"★★★★☆ 可配 Agent",[151,6410,6411,6414,6417],{},[166,6412,6413],{},"模型选择",[166,6415,6416],{},"厂商模型",[166,6418,6419],{},"★★★★★ 任意 API（BYOK）",[151,6421,6422,6424,6427],{},[166,6423,480],{},[166,6425,6426],{},"$30-$150\u002F月",[166,6428,6429],{},"免费 + BYOK",[151,6431,6432,6434,6437],{},[166,6433,6326],{},[166,6435,6436],{},"SOC 2 + SSO",[166,6438,6439],{},"自托管可控",[151,6441,6442,6444,6447],{},[166,6443,618],{},[166,6445,6446],{},"★★★★☆ 装插件即用",[166,6448,6449],{},"★★★☆☆ 需配置",[31,6451,6452,6454],{},[35,6453,6337],{},"：你的代码库大、你预算充足、你想要企业级安全合规、你不想自己配模型。",[31,6456,6457,6460],{},[35,6458,6459],{},"选 Continue 如果","：你预算敏感、你想要开源 + 自托管、你想要最大模型选择自由、你的代码库不大（5 万行以下）。",[31,6462,6463,6465],{},[35,6464,6349],{},"：Augment 是\"付费的企业级大代码库专家\"，Continue 是\"免费的开源通用 AI 编程助手\"。Continue 的 Context Engine 弱于 Augment——它没有实时代码图谱，依赖模型的上下文窗口和检索增强。小项目 Continue 够用，大项目 Augment 的 Context Engine 是刚需。",[27,6467,1736],{"id":1736},[31,6469,6470,6471,6474],{},"Augment Code 在 ",[35,6472,6473],{},"2025 年 10 月从基于消息数量的定价转向基于 credit 的定价","，这是社区争议最大的变化。",[60,6476,6478],{"id":6477},"当前定价credit-制","当前定价（credit 制）",[145,6480,6481,6495],{},[148,6482,6483],{},[151,6484,6485,6488,6490,6493],{},[154,6486,6487],{},"套餐",[154,6489,480],{},[154,6491,6492],{},"credits",[154,6494,926],{},[161,6496,6497,6509,6523],{},[151,6498,6499,6502,6504,6506],{},[166,6500,6501],{},"Free",[166,6503,780],{},[166,6505,1236],{},[166,6507,6508],{},"试用，每天有额度",[151,6510,6511,6514,6517,6520],{},[166,6512,6513],{},"Pro",[166,6515,6516],{},"~$30\u002F月",[166,6518,6519],{},"600 credits",[166,6521,6522],{},"大多数付费用户落点",[151,6524,6525,6528,6531,6534],{},[166,6526,6527],{},"Enterprise",[166,6529,6530],{},"联系销售",[166,6532,6533],{},"定制",[166,6535,6536],{},"SSO + 审计 + 定制 SLA",[31,6538,6539,6540,6543],{},"注意：credit 在",[35,6541,6542],{},"月末过期","，不累积。这意味着你不用就浪费——这和 Cursor 的\"固定订阅不限量\"模式完全不同。",[60,6545,6547],{"id":6546},"credit-经济学","credit 经济学",[31,6549,6550],{},"credit 的消耗取决于任务复杂度——简单补全消耗少，复杂 Agent 任务消耗多。社区反馈：",[72,6552,6553,6556,6559],{},[75,6554,6555],{},"一次简单补全：~1 credit",[75,6557,6558],{},"一次中等 Agent 任务（改 3-5 个文件）：~5-15 credits",[75,6560,6561],{},"一次复杂 Agent 任务（跨模块重构）：~20-50 credits",[31,6563,6564],{},"Pro 档 600 credits \u002F 月，如果每天跑 2-3 个中等 Agent 任务，一个月可能用 300-450 credits——够用。但如果每天跑复杂 Agent 任务，600 credits 可能两周就烧完。",[60,6566,6567],{"id":6567},"与竞品成本对比",[31,6569,6570],{},"社区反馈 Augment 新定价后\"比 Cursor + Windsurf 的总和还贵\"：",[145,6572,6573,6584],{},[148,6574,6575],{},[151,6576,6577,6579,6581],{},[154,6578,4619],{},[154,6580,4622],{},[154,6582,6583],{},"模式",[161,6585,6586,6596,6606,6617],{},[151,6587,6588,6590,6593],{},[166,6589,4654],{},[166,6591,6592],{},"$20",[166,6594,6595],{},"固定不限量",[151,6597,6598,6601,6604],{},[166,6599,6600],{},"Windsurf Pro",[166,6602,6603],{},"$15",[166,6605,6595],{},[151,6607,6608,6611,6614],{},[166,6609,6610],{},"Augment Pro",[166,6612,6613],{},"~$30+",[166,6615,6616],{},"credit 制（可能不够用）",[151,6618,6619,6622,6625],{},[166,6620,6621],{},"Cline + DeepSeek",[166,6623,6624],{},"$5-15",[166,6626,6627],{},"BYOK 按量",[31,6629,6630,6632],{},[35,6631,4696],{},"：Augment 的 credit 定价让它在大代码库场景的价值能覆盖成本，但对预算敏感的用户来说\"比 Cursor + Windsurf 还贵\"的心理门槛很高。如果你在大代码库场景的效率提升明显（社区反馈\"4 个月项目 2 周完成\"），$30-150\u002F月的成本相对于人力成本是划算的。但如果你只是做中小项目，Cursor $20 固定不限量更划算。",[60,6634,6635],{"id":6635},"国内使用",[31,6637,6638,6639,6641],{},"Augment Code 国内访问不便——需要稳定海外网络，支付需要国际信用卡。相比 ",[696,6640,5572],{"href":4920}," + DeepSeek 的国内直连 + 支付宝付款，Augment 在国内的摩擦成本明显更高。如果你的团队主要在国内，且没有合规约束，Cline + DeepSeek 的组合可能更实际。",[27,6643,1756],{"id":1756},[72,6645,6646,6653,6659,6665,6671,6677],{},[75,6647,6648,6649,6652],{},"✅ ",[35,6650,6651],{},"10 万行+ 大型 monorepo","——Context Engine 的核心甜点区",[75,6654,6648,6655,6658],{},[35,6656,6657],{},"企业级开发团队","——SOC 2 + SSO + 数据不训练模型",[75,6660,6648,6661,6664],{},[35,6662,6663],{},"复杂 issue 修复","——SWE-bench 65.4% 证明实力",[75,6666,6648,6667,6670],{},[35,6668,6669],{},"跨模块重构","——影响分析让多文件修改更安全",[75,6672,6648,6673,6676],{},[35,6674,6675],{},"金融 \u002F 科技大厂","——合规审查能通过",[75,6678,6648,6679,6682],{},[35,6680,6681],{},"预算充足 + 效率优先","——$30-150\u002F月 vs 人力成本",[27,6684,6685],{"id":6685},"不推荐场景",[31,6687,6688,6691,6692,6694],{},[35,6689,6690],{},"个人开发者 \u002F 小项目","：Augment 的 Context Engine 在小项目里发挥不出价值——5 万行以下的项目，Cursor 或 Cline 的上下文检索就够用。Augment 的企业级定位和 credit 定价对个人开发者是负担。个人用 ",[696,6693,5572],{"href":4920}," + DeepSeek 或 Cursor $20 更合适。",[31,6696,6697,6700],{},[35,6698,6699],{},"预算敏感场景","：credit 定价让 Augment 的成本不可预测——忙一个月可能 $80+，且 credits 月末过期不累积。如果你需要\"每月固定成本不多不少\"的可预测性，Cursor $20 固定不限量更合适。Augment 的价值在大代码库效率提升，如果这个提升覆盖不了 credit 成本，就不划算。",[31,6702,6703,6706,6707,6709,6710,6713],{},[35,6704,6705],{},"国内团队 + 无海外网络","：Augment 需要稳定海外网络 + 国际信用卡，国内摩擦成本高。如果你的团队主要在国内，",[696,6708,5572],{"href":4920}," + DeepSeek 或 ",[696,6711,5628],{"href":6712},"\u002Fcoding\u002Fcopilot\u002Fcodebuddy.html"," 的国内体验更好。",[31,6715,6716,6719,6720,2108,6722,2108,6724,6726],{},[35,6717,6718],{},"需要最大模型选择自由","：Augment 用自己的模型 + Context Engine，不支持 BYOK。如果你想用 DeepSeek \u002F Qwen \u002F 本地 Ollama，",[696,6721,5572],{"href":4920},[696,6723,3807],{"href":3806},[696,6725,6367],{"href":4914}," 更合适——它们支持任意 OpenAI 兼容 API。",[31,6728,6729,6732,6733,6736],{},[35,6730,6731],{},"想要全自主 AI 程序员（不需人盯）","：Augment 的 Agent 仍需要人在环中——它跑完给你 diff 让你 review。如果你想要\"提交任务 → 全自动交付 PR\"的体验，",[696,6734,5436],{"href":6735},"\u002Fcoding\u002Fagent\u002Fdevin.html"," 的自主程度更高（虽然价格也更高）。",[27,6738,657],{"id":656},[31,6740,6741,6744],{},[35,6742,6743],{},"Q：Augment Code 的 Context Engine 和 Cursor 的 Codebase Indexing 有什么本质区别？","\nA：Cursor 的 Codebase Indexing 是向量检索——把代码 chunk 做 embedding，按语义相似度检索。Augment 的 Context Engine 是代码关系图谱——实时构建函数调用关系、依赖链、数据流、影响范围。本质区别是\"语义检索\"vs\"关系图谱\"。在 5 万行以下项目里两者差距不明显，到 10 万行+ 的 monorepo，关系图谱能理解\"改这里会影响哪里\"，向量检索做不到。",[31,6746,6747,6750],{},[35,6748,6749],{},"Q：Augment 的 credit 定价到底贵不贵？","\nA：看场景。如果你的代码库大、任务复杂、Augment 的效率提升明显（比如\"4 个月项目 2 周完成\"），那 $30-150\u002F月相对于人力成本很划算。如果你做的是中小项目、任务简单，Cursor $20 固定不限量更划算。社区争议的核心是\"credit 月末过期不累积\"——不用就浪费，这和 Cursor 的\"不限量\"模式心理感受差异很大。",[31,6752,6753,6756],{},[35,6754,6755],{},"Q：Augment 和 Devin 怎么选？","\nA：核心区别在自主程度。Augment 是\"AI 协助你开发\"——Agent 跑完给你 diff review，你在环中。Devin 是\"AI 替你开发\"——提交任务后全自动交付 PR。Augment 适合\"复杂但需要人审查\"的任务（$30-150\u002F月），Devin 适合\"可以完全外包\"的任务（$500\u002F月）。选 Augment 如果你要可控性，选 Devin 如果你要自主性。",[31,6758,6759,6762],{},[35,6760,6761],{},"Q：Augment 的数据真的不用于训练吗？","\nA：Augment 官方明确承诺企业版客户的代码数据不用于训练模型。这是它进入金融 \u002F 科技大厂采购流程的关键——安全团队认这条红线。但免费 \u002F Pro 版的数据政策可能不同，企业版才有完整的数据隔离 + 不训练承诺。如果你的代码有保密要求，务必走企业版。",[31,6764,6765,6768],{},[35,6766,6767],{},"Q：Augment 支持 MCP 吗？","\nA：支持。Augment 的 Agent 可以通过 MCP 调用外部工具——数据库查询、API 测试、文档检索等。但 Augment 的 MCP 生态不如 Cline 丰富——Cline 是最早支持 MCP 的 AI 编程工具之一，社区 MCP Server 更多。Augment 的优势在 Context Engine 而非 MCP 生态。",[27,6770,690],{"id":690},[72,6772,6773,6779,6787,6797,6803],{},[75,6774,6775],{},[696,6776,6778],{"href":6777},"\u002Fcoding\u002Fagent\u002Faugment.html","Augment Code 工具卡：企业级大代码库 Agent",[75,6780,6781,6784,6785],{},[696,6782,6783],{"href":6735},"Devin 工具卡：AI 程序员 Agent"," · ",[696,6786,4915],{"href":4914},[75,6788,6789,6784,6793],{},[696,6790,6792],{"href":6791},"\u002Freview\u002Fcursor-deep-review.html","Cursor 深度评测",[696,6794,6796],{"href":6795},"\u002Freview\u002Fcline-deep-review.html","Cline 深度评测",[75,6798,6799],{},[696,6800,6802],{"href":6801},"\u002Freview\u002Fclaude-code-deep-review.html","Claude Code 深度评测：终端 AI Coding Agent 标杆",[75,6804,6805],{},[696,6806,6808],{"href":6807},"\u002Freview\u002Fai-coding-tool-decision-tree.html","AI 编程工具决策树",[1434,6810,6811],{},[31,6812,6813,6814,6816],{},"本评测由 AIHO 编辑部基于官方文档与社区公开反馈整合，非厂商付费内容。定价与功能以官方为准，欢迎在 ",[696,6815,2198],{"href":2198}," 反馈更正。",{"title":331,"searchDepth":363,"depth":363,"links":6818},[6819,6820,6821,6826,6831,6835,6836,6837,6843,6844,6845,6846],{"id":29,"depth":353,"text":29},{"id":5888,"depth":353,"text":5889},{"id":5925,"depth":353,"text":5926,"children":6822},[6823,6824,6825],{"id":5932,"depth":363,"text":5932},{"id":5981,"depth":363,"text":5982},{"id":6072,"depth":363,"text":6073},{"id":6093,"depth":353,"text":4369,"children":6827},[6828,6829,6830],{"id":6099,"depth":363,"text":6099},{"id":6155,"depth":363,"text":6156},{"id":6169,"depth":363,"text":6169},{"id":6179,"depth":353,"text":6179,"children":6832},[6833,6834],{"id":6185,"depth":363,"text":6185},{"id":6220,"depth":363,"text":6221},{"id":6231,"depth":353,"text":6232},{"id":6353,"depth":353,"text":6354},{"id":1736,"depth":353,"text":1736,"children":6838},[6839,6840,6841,6842],{"id":6477,"depth":363,"text":6478},{"id":6546,"depth":363,"text":6547},{"id":6567,"depth":363,"text":6567},{"id":6635,"depth":363,"text":6635},{"id":1756,"depth":353,"text":1756},{"id":6685,"depth":353,"text":6685},{"id":656,"depth":353,"text":657},{"id":690,"depth":353,"text":690},"\u002Fog\u002Freview\u002Faugment.png","Augment Code 真实评测：SWE-bench 65.4% 位居榜首的企业级 AI Coding Agent，核心壁垒是 Real-time Context Engine 实时理解超大代码库。本文写它真正解决的大代码库理解问题、Agent 自主能力深度、企业级安全合规、与 Devin \u002F Continue 的决策边界、credit 定价经济学，以及 5 类不推荐场景。AIHO 编辑部基于官方文档与社区公开反馈整理。",{},"\u002Freview\u002Faugment-deep-review",[5419,5437,5428],{"title":5851,"description":6848},"review\u002Faugment-deep-review",[5418,6855,2123,6273,6856],"企业级","深度评测","企业级大代码库 AI Agent 的标杆。Real-time Context Engine 让它在 10 万行+ 的 monorepo 里理解全局上下文的能力远超 Cursor \u002F Cline。SWE-bench 65.4% 证明 Agent 自主解决真实 issue 的实力。代价是 credit 定价后比 Cursor + Windsurf 还贵、国内访问不便、个人开发者用偏重。大代码库 + 企业预算选 Augment，个人 \u002F 小项目选 Cline 或 Cursor。","mOu7U9M2JDcIsIf3GqlgNf1PvcHXmotwW4hbqAY1fS0",{"id":6860,"title":6861,"body":6862,"cover":7344,"description":7345,"extension":754,"meta":7346,"navigation":765,"path":7347,"published":791,"relatedTools":7348,"seo":7349,"stem":7350,"tags":7351,"updated":791,"verdict":7353,"__hash__":7354},"review\u002Freview\u002Fautoglm-deep-review.md","AutoGLM 深度评测：智谱通用 Agent 能打几分",{"type":24,"value":6863,"toc":7329},[6864,6866,6882,6888,6896,6900,6906,6916,6929,6941,6945,6955,6959,6965,6985,6997,7003,7007,7016,7042,7045,7048,7058,7078,7081,7087,7091,7094,7100,7106,7112,7118,7122,7125,7171,7177,7183,7193,7195,7227,7229,7239,7245,7251,7257,7269,7271,7277,7287,7293,7299,7301],[27,6865,29],{"id":29},[31,6867,6868,6869,6872,6873,6876,6877,6881],{},"如果你要做",[35,6870,6871],{},"中文 App 自治","——让 AI 操作微信、淘宝、美团、小红书完成真实任务——",[35,6874,6875],{},"AutoGLM 在 2026 年是国内开源 GUI Agent 里覆盖度最高、最值得上手的一个","。智谱清言 + 清华大学合作、32 个月研发、2025-12 开源 Open-AutoGLM（",[696,6878,6880],{"href":5228,"rel":6879},[1009],"MIT 模型 + Apache 2.0 代码","），AutoGLM-Phone-9B 单卡 4090 就能跑，覆盖 50+ 中文 App 示例。",[31,6883,5869,6884,6887],{},[35,6885,6886],{},"9B 模型在复杂多步任务上推理不及 GPT-5 \u002F Claude、iOS 支持空缺、生产级稳定性仍在演进","。它的甜点区是\"中文 App 自治 + 私有化部署 + 学术研究\"，不是\"跨平台桌面自动化\"——后者去 Anthropic Computer Use \u002F Claude Desktop 更合适。",[1410,6889,6891],{"className":6890},[1413,1414,1415],[31,6892,6893,6894,2416],{},"选型建议：学术研究 \u002F 国内 App 自治 \u002F 私有化部署 → AutoGLM 开源版直接上手（成本 = 0 + 一张 4090）。跨平台桌面自动化 \u002F 英文场景 \u002F 生产级稳定性 → 优先 Anthropic Computer Use 或 Claude Desktop。要云端通用 Agent（深度研究、长报告）→ 看 ",[696,6895,557],{"href":698},[27,6897,6899],{"id":6898},"autoglm-真正在解决的问题","AutoGLM 真正在解决的问题",[31,6901,6902,6903,70],{},"社区讨论\"为什么 AutoGLM 重要\"经常停在\"智谱开源、清华背书\"。但深一层看，AutoGLM 是在解决",[35,6904,6905],{},"中文 GUI Agent 长期以来的三个空白",[31,6907,6908,6911,6912,6915],{},[35,6909,6910],{},"第一个空白：中文 App 的 GUI Agent。"," Anthropic Computer Use、Claude Desktop 这类产品强在英文桌面场景，对中文 App（微信、美团、淘宝、小红书）的 UI 元素识别和操作链路支持很弱。AutoGLM 把 ",[35,6913,6914],{},"50+ 中文 App 的任务示例","直接开源出来——微信发消息、淘宝下单、美团点外卖、小红书搜攻略——这是商业产品给不了的中文场景覆盖度。",[31,6917,6918,6921,6922,6925,6926,6928],{},[35,6919,6920],{},"第二个空白：可私有化的手机 Agent。"," 手机里装着通讯录、聊天记录、支付信息，把这些数据交给云端 Agent 跑，合规和隐私都是硬问题。AutoGLM 的 ",[35,6923,6924],{},"9B 模型 + 框架可以完全本地部署","——模型、日志、权限全在你自己的机器上，数据零外泄。这对金融、政府、医疗等合规敏感场景是刚需，也是 ",[696,6927,557],{"href":698}," 这类云端闭源 Agent 给不了的。",[31,6930,6931,6934,6935,6940],{},[35,6932,6933],{},"第三个空白：学术研究的开放基准。"," GUI Agent 是前沿研究方向，但商业产品（Manus、Claude Desktop）都是黑盒，研究者拿不到模型权重、看不到推理链路、复现不了 benchmark。AutoGLM 把模型权重、框架代码、Android 适配层、50+ App 任务示例、VAB-WebArena-Lite benchmark 全部释放出来——这是国内学术圈难得的开放姿态，也是它能进 arXiv 论文（",[696,6936,6939],{"href":6937,"rel":6938},"https:\u002F\u002Farxiv.org\u002Fabs\u002F2411.00820",[1009],"2411.00820","）的核心价值。",[27,6942,6944],{"id":6943},"agent-能力手机-浏览器双线","Agent 能力：手机 + 浏览器双线",[31,6946,6947,6948,6951,6952,2416],{},"AutoGLM 的 Agent 能力分两条线：",[35,6949,6950],{},"手机 GUI Agent（Open-AutoGLM 开源版）"," 和 ",[35,6953,6954],{},"浏览器 Agent（智谱清言 Chrome 扩展）",[60,6956,6958],{"id":6957},"手机-gui-agent","手机 GUI Agent",[31,6960,6961,6962,70],{},"这是 AutoGLM 最核心、最区别于其他通用 Agent 的能力。工作机制是",[35,6963,6964],{},"截屏 + UI 树双输入，规划与 grounding 分离",[72,6966,6967,6973,6979],{},[75,6968,6969,6972],{},[35,6970,6971],{},"规划阶段","：模型看屏幕截图和 UI 树，决定下一步该点哪个元素、输什么文字",[75,6974,6975,6978],{},[35,6976,6977],{},"Grounding 阶段","：把\"点登录按钮\"这种高层指令映射到屏幕上具体坐标",[75,6980,6981,6984],{},[35,6982,6983],{},"Safe Operations","：敏感操作（登录、验证码、支付）触发人审接管，避免误操作",[31,6986,6987,6988,6992,6993,6996],{},"按 ",[696,6989,6991],{"href":5228,"rel":6990},[1009],"AutoGLM 项目主页"," 公布的数据，VAB-WebArena-Lite benchmark ",[35,6994,6995],{},"首次成功率 55.2%，二次尝试 59.1%","。注：这是学术 benchmark，需第三方验证，但即便打八折也仍是开源 GUI Agent 里的头部。",[31,6998,6999,7002],{},[35,7000,7001],{},"Remote ADB 是被低估的工程细节","：初次 USB 配置后，通过 WiFi 控制设备，长跑实验摆脱数据线束缚。这对要做批量任务回归的工程团队是实质提升。",[60,7004,7006],{"id":7005},"浏览器-agentchrome-扩展","浏览器 Agent（Chrome 扩展）",[31,7008,7009,7010,7015],{},"Web 端通过智谱清言 Chrome 扩展（",[696,7011,7014],{"href":7012,"rel":7013},"https:\u002F\u002Fchromewebstore.google.com\u002Fdetail\u002Fmnpdbmgpebfihcndnpgdaihnkmloclkd",[1009],"Chrome Web Store，10 万+ 用户","）提供，能力偏\"页面增强\"而非\"全程自治\"：",[72,7017,7018,7024,7030,7036],{},[75,7019,7020,7023],{},[35,7021,7022],{},"页面总结","：打开长网页一键总结",[75,7025,7026,7029],{},[35,7027,7028],{},"划线助手","：选中文字解释 \u002F 翻译 \u002F 续写",[75,7031,7032,7035],{},[35,7033,7034],{},"写作助手","：在任意输入框唤起 AI 写作",[75,7037,7038,7041],{},[35,7039,7040],{},"高级检索","：知网 \u002F 知乎 \u002F 小红书跨源检索合成",[31,7043,7044],{},"注意：Chrome 扩展的定位是\"浏览器内的 AI 助手\"，不是\"全程替你操作浏览器\"的自治 Agent。要全程自治的浏览器 Agent，开源版的 WebArena benchmark 路线更接近，但稳定性仍弱于商业产品。Chrome 扩展评分中等（3.4\u002F5），功能尚可但稳定性一般。",[27,7046,7047],{"id":7047},"多模态屏幕理解",[31,7049,7050,7051,7054,7055,70],{},"GUI Agent 的核心技术难点是",[35,7052,7053],{},"屏幕理解","——模型要\"看懂\"屏幕上有什么、能操作什么。AutoGLM 的方案是",[35,7056,7057],{},"截屏 + UI 树双输入",[72,7059,7060,7066,7072],{},[75,7061,7062,7065],{},[35,7063,7064],{},"截屏（视觉）","：捕捉屏幕像素，理解布局、图标、图片按钮等无文字标签的元素",[75,7067,7068,7071],{},[35,7069,7070],{},"UI 树（结构）","：通过 Android Accessibility Service 获取 UI 元素的层级、类型、文字，提供精确的可操作坐标",[75,7073,7074,7077],{},[35,7075,7076],{},"双输入融合","：视觉负责\"这是什么\"，结构负责\"在哪能点\"",[31,7079,7080],{},"这种双输入方案比纯视觉方案（只看截图）更准——UI 树提供了精确的可点击坐标，避免了纯视觉方案的\"点歪了\"问题。也比纯结构方案（只读 UI 树）更鲁棒——遇到 UI 树缺失或乱序的元素（图片按钮、Canvas 绘制），视觉能补上。",[31,7082,7083,7086],{},[35,7084,7085],{},"9B 模型的多模态能力上限","：这是 AutoGLM 的核心权衡。9B 参数能在单卡 4090（24GB 显存）跑，门槛友好，但复杂多步任务的推理能力不及 GPT-5 \u002F Claude Opus。社区实测：简单任务（打开 App、搜索、点按钮）成功率高，复杂多步任务（多 App 联动、长链路表单填写）成功率下降明显。要提升上限，可以叠加外部规划模型（用大模型做规划、9B 做 grounding）。",[27,7088,7090],{"id":7089},"中文体验autoglm-的护城河","中文体验：AutoGLM 的护城河",[31,7092,7093],{},"中文体验是 AutoGLM 相对海外 GUI Agent 最深的护城河，体现在三层：",[31,7095,7096,7099],{},[35,7097,7098],{},"第一层：中文 App 覆盖度。"," 内置 50+ 中文 App 示例：微信、淘宝、美团、京东、支付宝、抖音、小红书、网易云、大众点评。这些 App 的 UI 元素、操作链路、常见任务模式都被预先适配过。海外产品面对这些 App 基本是从零开始，AutoGLM 是\"开箱即跑\"。",[31,7101,7102,7105],{},[35,7103,7104],{},"第二层：中文语境理解。"," 模型由智谱清言训练，中文语义理解原生——\"在小红书搜罗马旅游攻略并总结景点\"这种中文指令的意图解析、结果组织，比海外模型更贴中文用户习惯。",[31,7107,7108,7111],{},[35,7109,7110],{},"第三层：国内合规与数据自主。"," 完全本地部署时数据零外泄，符合国内数据合规要求。读取 IM、通讯录这类敏感操作仍需用户授权 + 符合当地法律，但至少数据不离开你的机器——这是云端 Agent 给不了的。",[31,7113,7114,7117],{},[35,7115,7116],{},"但中文体验也有边界","：国行 App 的反爬监测和账号风控会偶尔拦截自动化操作，验证码、滑块验证会触发，必要时需要 human-in-the-loop 接管。这不是 AutoGLM 的锅，是国行 App 的反自动化机制——任何 GUI Agent 都会遇到。",[27,7119,7121],{"id":7120},"价格开源的经济学","价格：开源的经济学",[31,7123,7124],{},"AutoGLM 的价格结构对学术和工程团队极度友好：",[145,7126,7127,7138],{},[148,7128,7129],{},[151,7130,7131,7133,7135],{},[154,7132,1907],{},[154,7134,480],{},[154,7136,7137],{},"关键点",[161,7139,7140,7150,7160],{},[151,7141,7142,7145,7147],{},[166,7143,7144],{},"Open-AutoGLM（开源版）",[166,7146,780],{},[166,7148,7149],{},"MIT 模型 + Apache 2.0 代码，私有化部署",[151,7151,7152,7155,7157],{},[166,7153,7154],{},"智谱清言 Chrome 扩展",[166,7156,780],{},[166,7158,7159],{},"Chrome Web Store 装即用",[151,7161,7162,7165,7168],{},[166,7163,7164],{},"智谱清言 App 内置版",[166,7166,7167],{},"商业服务",[166,7169,7170],{},"完整 GUI agent 能力，价格随订阅",[31,7172,7173,7176],{},[35,7174,7175],{},"真实成本 = 0（开源）+ GPU 推理","。9B 模型一张 RTX 4090（24GB 显存）就能跑，这是开源 GUI Agent 里门槛最低的之一。",[31,7178,7179,7180,7182],{},"对比 ",[696,7181,557],{"href":698},"（$20-$200\u002F月、credit 烧得快、大陆屏蔽）和 Anthropic Computer Use（按 token 计费、需海外账号），AutoGLM 的开源模式对学术研究、PoC 验证、私有化部署的成本优势是数量级的。",[31,7184,7185,7188,7189,7192],{},[35,7186,7187],{},"但要算清\"隐性成本\"","：开源版需要你会 ADB + Python 部署、要处理 App 版本更新后的 UI 适配、要自己搭推理服务。这些工程成本对纯用户是门槛，对工程团队则可控。",[35,7190,7191],{},"Open-AutoGLM 的能力 ≠ 智谱清言 App 内置版的能力","——别拿开源版的体验去推断商业整合版，反之亦然。",[27,7194,1756],{"id":1756},[72,7196,7197,7203,7209,7215,7221],{},[75,7198,6648,7199,7202],{},[35,7200,7201],{},"GUI Agent 学术研究 \u002F 工程团队","——开放模型权重 + benchmark + 任务示例，可复现可改进",[75,7204,6648,7205,7208],{},[35,7206,7207],{},"国内 App 自治","——微信 \u002F 美团 \u002F 淘宝 \u002F 小红书等 50+ 中文 App 任务",[75,7210,6648,7211,7214],{},[35,7212,7213],{},"想本地跑 phone agent + 数据自主","——9B 单卡可跑，数据零外泄",[75,7216,6648,7217,7220],{},[35,7218,7219],{},"私有化部署 + 合规需求","——金融 \u002F 医疗 \u002F 政府场景，代码可审计",[75,7222,6648,7223,7226],{},[35,7224,7225],{},"教学 \u002F Demo","——门槛低、中文友好、开源可改",[27,7228,6685],{"id":6685},[31,7230,7231,7234,7235,7238],{},[35,7232,7233],{},"跨平台桌面自动化","：AutoGLM 主要面向 Android + Web，桌面 agent 能力空缺。要操作 macOS \u002F Windows 桌面应用，去 ",[696,7236,5233],{"href":7237},"\u002Fagent\u002Fdesktop\u002Fclaude-desktop.html","（MCP 生态）或 Anthropic Computer Use（跨平台桌面原生）。",[31,7240,7241,7244],{},[35,7242,7243],{},"iOS App 自治","：iOS 支持完全空缺。iOS 的沙箱和 Accessibility 限制比 Android 严，需要走别的方案，AutoGLM 目前帮不上。",[31,7246,7247,7250],{},[35,7248,7249],{},"生产级稳定性需求","：学术 \u002F 开源版本仍在演进，App 版本更新后 UI 元素变化要重新适配，复杂多步任务成功率下降明显。要 production-grade 稳定性，目前所有开源 GUI Agent 都还差一截，得等生态成熟或上商业产品。",[31,7252,7253,7256],{},[35,7254,7255],{},"不懂 ADB \u002F Python 部署的纯用户","：Open-AutoGLM 的部署链路是 git clone → pip install → 下模型 → 配 ADB → 跑示例，对没有 Python \u002F 命令行经验的人门槛偏高。纯用户建议直接用智谱清言 App 内置版，别碰开源版。",[31,7258,7259,7262,7263,5876,7265,7268],{},[35,7260,7261],{},"要云端通用 Agent（深度研究、长报告）","：AutoGLM 是 GUI Agent（操作界面），不是通用 Research Agent（深度调研、写报告）。要\"开着任务下班、明早看带引用的研究报告\"这种场景，去 ",[696,7264,557],{"href":698},[696,7266,5276],{"href":7267},"\u002Fagent\u002Fgeneral\u002Fgenspark.html","。两者定位不同，别混用。",[27,7270,657],{"id":656},[31,7272,7273,7276],{},[35,7274,7275],{},"Q：Open-AutoGLM 和智谱清言 App 里的 AutoGLM 什么关系？","\nA：原 AutoGLM 是智谱 2024-10 发布的闭源整合产品（手机 + Web GUI agent），首次在真实手机环境跑通完整自治链路。Open-AutoGLM 是 2025-12 开源版本，把核心模型（AutoGLM-Phone-9B）+ 框架代码 + Android 适配层 + 50+ 中文 App 任务示例释放出来。整合版闭源、商业；开源版用于学术 \u002F 私有化。两者能力不等价，别混用。",[31,7278,7279,7282,7283,2416],{},[35,7280,7281],{},"Q：AutoGLM 和 Manus 有什么区别？","\nA：定位完全不同。AutoGLM 是 GUI Agent——操作手机 \u002F 浏览器界面完成真实任务（点按钮、填表单、搜索）。Manus 是通用 Research Agent——云端异步完成深度调研、数据分析、写报告。一个操作界面、一个产出报告，详见 ",[696,7284,7286],{"href":7285},"\u002Fcompare\u002Fautoglm-vs-manus.html","AutoGLM vs Manus 对比",[31,7288,7289,7292],{},[35,7290,7291],{},"Q：9B 模型够用吗？","\nA：简单任务够用，复杂多步任务上限明显。单卡 4090 能跑是门槛优势，但 9B 在推理深度上不及 GPT-5 \u002F Claude。要提升上限可以叠加外部规划模型——大模型做规划、9B 做 grounding 的分工模式是社区常见做法。",[31,7294,7295,7298],{},[35,7296,7297],{},"Q：国内能直接用吗？","\nA：能。开源版完全本地部署，不依赖外网。智谱清言 Chrome 扩展和 App 都是国内服务，直连无障碍。这是 AutoGLM 相对 Manus（大陆屏蔽）、Anthropic Computer Use（需海外账号）的天然优势。",[27,7300,690],{"id":690},[72,7302,7303,7309,7314,7319,7324],{},[75,7304,7305],{},[696,7306,7308],{"href":7307},"\u002Fagent\u002Fdesktop\u002Fautoglm.html","AutoGLM 工具卡：智谱 GUI 自治智能体",[75,7310,7311],{},[696,7312,7313],{"href":7285},"AutoGLM vs Manus：国产通用 Agent 怎么选",[75,7315,7316],{},[696,7317,7318],{"href":716},"Manus 深度评测：通用 Agent 天花板值不值 $20\u002F月",[75,7320,7321],{},[696,7322,7323],{"href":7237},"Claude Desktop 工具卡：MCP 桌面 Agent",[75,7325,7326],{},[696,7327,7328],{"href":2122},"什么是 AI Agent",{"title":331,"searchDepth":363,"depth":363,"links":7330},[7331,7332,7333,7337,7338,7339,7340,7341,7342,7343],{"id":29,"depth":353,"text":29},{"id":6898,"depth":353,"text":6899},{"id":6943,"depth":353,"text":6944,"children":7334},[7335,7336],{"id":6957,"depth":363,"text":6958},{"id":7005,"depth":363,"text":7006},{"id":7047,"depth":353,"text":7047},{"id":7089,"depth":353,"text":7090},{"id":7120,"depth":353,"text":7121},{"id":1756,"depth":353,"text":1756},{"id":6685,"depth":353,"text":6685},{"id":656,"depth":353,"text":657},{"id":690,"depth":353,"text":690},"\u002Fog\u002Freview\u002Fautoglm.png","AutoGLM 真实评测：智谱清言 + 清华大学合作的 GUI 自治智能体，2025-12 开源 Open-AutoGLM（MIT 模型 + Apache 2.0 代码），AutoGLM-Phone-9B 覆盖微信 \u002F 淘宝 \u002F 美团等 50+ 中文 App。本文写它真正解决的问题、手机与浏览器操控能力、多模态屏幕理解、中文体验优势、私有部署经济学，以及 5 类不推荐场景。AIHO 编辑部基于官方论文与公开评测整理。",{},"\u002Freview\u002Fautoglm-deep-review",[5225,16,5277],{"title":6861,"description":7345},"review\u002Fautoglm-deep-review",[5224,7352,2123,6856],"智谱AI","国内『AI 操作手机 \u002F 浏览器』开源标杆——智谱 32 个月研发、清华学术背景、MIT 开源模型权重、50+ 中文 App 覆盖领先。中文 App 自治 + 私有化部署 + 学术研究首选。短板是 9B 模型推理上限不及商业大模型、iOS 空缺、生产稳定性仍在演进。","TGPnQJ1Yt5gNeWoqd4d4IimMryaOoVd_aGgNNzhABic",{"id":7356,"title":7357,"body":7358,"cover":8318,"description":8319,"extension":754,"meta":8320,"navigation":765,"path":8321,"published":791,"relatedTools":8322,"seo":8323,"stem":8324,"tags":8325,"updated":791,"verdict":8327,"__hash__":8328},"review\u002Freview\u002Fcline-deep-review.md","Cline 深度评测：VS Code 里的终端 Agent，免费实现 Cursor 级体验",{"type":24,"value":7359,"toc":8296},[7360,7362,7373,7383,7389,7393,7399,7409,7421,7427,7431,7438,7440,7447,7461,7464,7474,7476,7494,7504,7507,7517,7531,7537,7541,7548,7580,7583,7587,7590,7593,7596,7686,7690,7697,7703,7760,7769,7772,7779,7783,7900,7906,7912,7918,7922,8027,8032,8038,8040,8046,8105,8110,8115,8129,8131,8174,8176,8187,8200,8206,8216,8222,8224,8230,8236,8246,8252,8258,8260,8290],[27,7361,29],{"id":29},[31,7363,7364,7365,7368,7369,7372],{},"如果你想用 ",[35,7366,7367],{},"Agent 级 AI 编程","（不是补全，是\"我说需求，它自己读代码、改文件、跑命令、验证结果\"），但不想付 Cursor $20\u002F月或 Claude Code 的 token 费——",[35,7370,7371],{},"Cline 是 2026 年开源 AI 编程工具里体验最接近 Cursor 的选项","。GitHub 35k+ star、180 万+ 下载量，BYOK（自带 API key）+ 多模型支持 + 终端 \u002F 浏览器 \u002F MCP 全能力，让它成为预算敏感开发者的首选。",[31,7374,5869,7375,7378,7379,5876,7381,5879],{},[35,7376,7377],{},"没有 Cursor 的多文件 diff 审查体验、需要自己配 API key 和选模型、没有团队协作能力、成本完全取决于你的用量和模型选择","。它的甜点区是\"个人开发者 + 想要 Agent 能力 + 预算敏感\"，不是\"企业团队统一工具\"——后者去 ",[696,7380,3977],{"href":3976},[696,7382,5628],{"href":6712},[1410,7384,7386],{"className":7385},[1413,1414,1415],[31,7387,7388],{},"省钱建议：Cline 插件本身完全免费，成本只在模型 API。用 DeepSeek-V3 跑日常任务，单次复杂任务成本 $0.05-0.20；用 Claude Sonnet 4 跑高难度任务，单次 $0.50-2.00。一个月重度使用总成本可能 $5-30，比 Cursor Pro $20 更灵活——任务简单时省钱，任务复杂时多花点但质量更高。",[27,7390,7392],{"id":7391},"cline-真正在解决的问题","Cline 真正在解决的问题",[31,7394,7395,7396,5896],{},"社区讨论\"为什么用 Cline\"经常停在\"它免费、它开源\"。但深一层看，Cline 是在解决",[35,7397,7398],{},"AI 编程工具的两个核心矛盾",[31,7400,7401,7404,7405,7408],{},[35,7402,7403],{},"第一个矛盾：Agent 能力 vs 锁定订阅。"," Cursor 和 Windsurf 提供 Agent 级能力（多文件修改、终端执行、自主决策），但需要 $15-20\u002F月订阅，且模型选择受限于厂商。Claude Code 的 Agent 能力最强，但按 token 计费且绑定 Anthropic。Cline 的解法是",[35,7406,7407],{},"插件免费 + BYOK","——你自带任何 OpenAI 兼容 API key，Cline 负责 Agent 编排，模型费你直接付给模型商。这意味着你可以用最便宜的 DeepSeek-V3 跑日常任务、用最强的 Claude Sonnet 4 跑高难度任务，成本完全由你控制。",[31,7410,7411,7414,7415,7417,7418,7420],{},[35,7412,7413],{},"第二个矛盾：IDE 集成 vs 开放性。"," ",[696,7416,3807],{"href":3806}," 是纯 CLI、",[696,7419,6367],{"href":4914}," 偏补全、Claude Code 是终端 Agent。Cline 的定位是**\"住在 VS Code 里的终端 Agent\"**——它是一个 VS Code 扩展，但它的 Agent 能力（终端命令、文件操作、浏览器操控）和独立 CLI 工具一样强。你不需要离开 VS Code，也不需要在终端和编辑器之间切换，Cline 在 VS Code 的侧边栏里完成所有 Agent 操作。",[31,7422,7423,7426],{},[35,7424,7425],{},"这个定位的实际价值","：Cursor 把 Agent 能力和自己的 fork 版 VS Code 绑定——你必须用 Cursor 的编辑器才能用它的 Agent。Cline 把 Agent 能力做成 VS Code 扩展——你继续用你习惯的 VS Code（含所有插件 \u002F 主题 \u002F 快捷键），只是多了一个 Agent 侧边栏。对\"不想换编辑器但想要 Agent\"的开发者，Cline 是门槛最低的选择。",[27,7428,7430],{"id":7429},"agent-能力不只是补全","Agent 能力：不只是补全",[31,7432,7433,7434,7437],{},"Cline 的核心能力是 ",[35,7435,7436],{},"Agent 模式","——你说一个需求，它自主完成\"读代码 → 分析 → 修改 → 验证\"的全流程。这不是\"AI 补全代码\"，而是\"AI 干活\"。",[60,7439,4540],{"id":4540},[31,7441,7442,7443,7446],{},"Cline 能",[35,7444,7445],{},"跨多个文件做协调修改","——你说\"把所有用到旧版 API 的地方迁移到新 API\"，它会：",[432,7448,7449,7452,7455,7458],{},[75,7450,7451],{},"搜索整个项目找到所有调用点",[75,7453,7454],{},"分析每个调用的上下文",[75,7456,7457],{},"逐个文件修改",[75,7459,7460],{},"跑测试验证修改没有 break 其他地方",[31,7462,7463],{},"这个能力和 Cursor Composer \u002F Claude Code 在同一个档位。实测体验：简单迁移任务（改函数名 \u002F 改 import 路径）Cline 基本一次搞定；复杂重构（改架构 \u002F 改数据流）需要 2-3 轮对话修正，但比手动改快 5-10 倍。",[31,7465,7466,7469,7470,7473],{},[35,7467,7468],{},"与 Cursor 的差异","：Cursor 的多文件修改有专门的 diff 审查界面——你可以看到每个文件的 before\u002Fafter，逐个 Accept\u002FReject。Cline 的多文件修改是",[35,7471,7472],{},"串行展示","——每个文件的 diff 依次出现，你逐个确认。体验上 Cursor 更顺滑，但 Cline 的透明度更高——你能看到 Agent 每一步在做什么、为什么改。",[60,7475,4546],{"id":4546},[31,7477,7442,7478,5896,7481,413,7484,413,7487,413,7490,7493],{},[35,7479,7480],{},"直接在 VS Code 集成终端里执行命令",[333,7482,7483],{},"npm install",[333,7485,7486],{},"npm test",[333,7488,7489],{},"git commit",[333,7491,7492],{},"python manage.py migrate"," 都能跑。Agent 在修改代码后会自己跑测试验证，测试失败会自己读错误信息、修正代码、重新跑——这个\"修改 → 测试 → 修正\"的闭环是 Agent 区别于补全工具的核心。",[31,7495,7496,7499,7500,7503],{},[35,7497,7498],{},"权限模型","：Cline 默认每次执行终端命令都需要你确认（approve），不会静默执行。你可以切换到\"自动批准\"模式让 Agent 自主跑，但生产环境建议保持手动确认——Agent 跑 ",[333,7501,7502],{},"rm -rf"," 这种命令你得拦住。",[60,7505,7506],{"id":7506},"浏览器操控",[31,7508,7509,7510,7513,7514,70],{},"这是 Cline 区别于大多数 AI 编程工具的",[35,7511,7512],{},"独特能力","——Cline 能",[35,7515,7516],{},"操控浏览器",[72,7518,7519,7522,7525,7528],{},[75,7520,7521],{},"打开本地 dev server 的 URL",[75,7523,7524],{},"点击按钮、填表单、截图",[75,7526,7527],{},"读控制台报错、读 Network 请求",[75,7529,7530],{},"把前端 bug 的视觉表现反馈给 Agent",[31,7532,7533,7534,7536],{},"这意味着 Cline 能做\"前端开发闭环\"——你让它\"加一个暗色模式切换按钮\"，它改完代码后会自己在浏览器里打开页面、点一下按钮看效果、发现样式不对再改 CSS。这个能力 ",[696,7535,3807],{"href":3806}," 和 Claude Code 都没有（Claude Code 需要配合 Playwright MCP 才行，Cline 内置）。",[60,7538,7540],{"id":7539},"mcp-工具调用","MCP 工具调用",[31,7542,7543,7544,7547],{},"Cline 是",[35,7545,7546],{},"最早支持 MCP（Model Context Protocol）的 AI 编程工具之一","。通过 MCP，Cline 的 Agent 能调用外部工具：",[72,7549,7550,7556,7562,7568,7574],{},[75,7551,7552,7555],{},[35,7553,7554],{},"数据库 MCP","：直接查数据库、跑 SQL",[75,7557,7558,7561],{},[35,7559,7560],{},"Figma MCP","：读 Figma 设计稿、按设计生成前端代码",[75,7563,7564,7567],{},[35,7565,7566],{},"GitHub MCP","：读 PR、创建 issue、管理 branch",[75,7569,7570,7573],{},[35,7571,7572],{},"浏览器 MCP","：Puppeteer \u002F Playwright 增强",[75,7575,7576,7579],{},[35,7577,7578],{},"自定义 MCP","：你自己的内部工具",[31,7581,7582],{},"MCP 让 Cline 从\"代码修改 Agent\"升级为\"开发工作流 Agent\"——它不再只在自己的代码沙盒里干活，而是能调你整个开发工具链。",[27,7584,7586],{"id":7585},"多模型支持byok-的核心价值","多模型支持：BYOK 的核心价值",[31,7588,7589],{},"模型选择权是 Cline 最大的差异化，也是它和 Cursor \u002F Claude Code 拉开差距的地方。",[60,7591,7592],{"id":7592},"支持的模型",[31,7594,7595],{},"Cline 支持所有 OpenAI 兼容的 API，以及 Anthropic 原生 API：",[145,7597,7598,7610],{},[148,7599,7600],{},[151,7601,7602,7604,7607],{},[154,7603,1922],{},[154,7605,7606],{},"适合场景",[154,7608,7609],{},"单次任务成本",[161,7611,7612,7624,7637,7649,7660,7673],{},[151,7613,7614,7618,7621],{},[166,7615,7616],{},[35,7617,1156],{},[166,7619,7620],{},"高难度任务 \u002F 复杂重构",[166,7622,7623],{},"$0.50-2.00",[151,7625,7626,7631,7634],{},[166,7627,7628],{},[35,7629,7630],{},"Claude Opus 4",[166,7632,7633],{},"极高难度 \u002F 架构设计",[166,7635,7636],{},"$2.00-5.00",[151,7638,7639,7643,7646],{},[166,7640,7641],{},[35,7642,2149],{},[166,7644,7645],{},"日常任务 \u002F 性价比之王",[166,7647,7648],{},"$0.05-0.20",[151,7650,7651,7655,7658],{},[166,7652,7653],{},[35,7654,1164],{},[166,7656,7657],{},"通用 \u002F 推理强",[166,7659,7623],{},[151,7661,7662,7667,7670],{},[166,7663,7664],{},[35,7665,7666],{},"Qwen \u002F 智谱",[166,7668,7669],{},"国产模型 \u002F 中文场景",[166,7671,7672],{},"$0.10-0.50",[151,7674,7675,7680,7683],{},[166,7676,7677],{},[35,7678,7679],{},"Ollama 本地",[166,7681,7682],{},"隐私敏感 \u002F 零成本",[166,7684,7685],{},"$0（需本地 GPU）",[60,7687,7689],{"id":7688},"byok-经济学","BYOK 经济学",[31,7691,7692,7693,7696],{},"Cursor Pro $20\u002F月是固定订阅——不管你用多用少都是这个价。Cline + BYOK 是",[35,7694,7695],{},"按量付费","——任务少时几乎零成本，任务多时比 $20 贵，但你可以选便宜模型控制成本。",[31,7698,7699,7702],{},[35,7700,7701],{},"真实成本对比","（一个月重度使用场景）：",[145,7704,7705,7715],{},[148,7706,7707],{},[151,7708,7709,7711,7713],{},[154,7710,4619],{},[154,7712,4622],{},[154,7714,926],{},[161,7716,7717,7727,7738,7749],{},[151,7718,7719,7721,7724],{},[166,7720,4654],{},[166,7722,7723],{},"$20 固定",[166,7725,7726],{},"不限量但模型锁定",[151,7728,7729,7732,7735],{},[166,7730,7731],{},"Cline + DeepSeek-V3",[166,7733,7734],{},"$3-10",[166,7736,7737],{},"性价比最高，质量达 Claude 80%",[151,7739,7740,7743,7746],{},[166,7741,7742],{},"Cline + Claude Sonnet 4",[166,7744,7745],{},"$20-60",[166,7747,7748],{},"质量最高，但重度用比 Cursor 贵",[151,7750,7751,7754,7757],{},[166,7752,7753],{},"Cline + 混合（日常 DeepSeek + 难题 Claude）",[166,7755,7756],{},"$8-25",[166,7758,7759],{},"最灵活的方案",[31,7761,7762,7764,7765,7768],{},[35,7763,4696],{},"：如果你大部分任务是中等难度，Cline + DeepSeek 的月成本远低于 Cursor。如果你重度依赖 Claude Sonnet 4 的顶级能力，Cline BYOK 可能比 Cursor 贵——这时候 Cursor 的固定订阅更划算。",[35,7766,7767],{},"最佳策略是混合使用","：日常任务用 DeepSeek 省钱，遇到复杂任务切 Claude 保质量。",[60,7770,7771],{"id":7771},"模型切换",[31,7773,7774,7775,7778],{},"Cline 的模型切换是",[35,7776,7777],{},"一条命令 \u002F 一次点击","的事——在侧边栏顶部选模型下拉菜单，随时切换。你可以在一个项目里先用 DeepSeek 跑完框架，再切 Claude 精修核心逻辑。这种灵活性是 Cursor 给不了的——Cursor 虽然也能选模型，但 Composer \u002F Agent 等核心功能在自带模型上表现最好。",[27,7780,7782],{"id":7781},"与-cursor-对比","与 Cursor 对比",[145,7784,7785,7795],{},[148,7786,7787],{},[151,7788,7789,7791,7793],{},[154,7790,552],{},[154,7792,5572],{},[154,7794,3977],{},[161,7796,7797,7807,7818,7829,7839,7849,7859,7870,7879,7889],{},[151,7798,7799,7801,7804],{},[166,7800,4369],{},[166,7802,7803],{},"★★★★★ 终端 \u002F 浏览器 \u002F MCP",[166,7805,7806],{},"★★★★★ Composer \u002F Agent",[151,7808,7809,7812,7815],{},[166,7810,7811],{},"补全体验",[166,7813,7814],{},"★★★☆☆ 基础补全",[166,7816,7817],{},"★★★★★ Tab 补全标杆",[151,7819,7820,7823,7826],{},[166,7821,7822],{},"多文件 diff",[166,7824,7825],{},"★★★★☆ 串行展示",[166,7827,7828],{},"★★★★★ 并行 diff 审查",[151,7830,7831,7833,7836],{},[166,7832,6413],{},[166,7834,7835],{},"★★★★★ 任意 API",[166,7837,7838],{},"★★★★☆ 厂商模型可选",[151,7840,7841,7843,7846],{},[166,7842,7506],{},[166,7844,7845],{},"✅ 内置",[166,7847,7848],{},"❌ 需配 MCP",[151,7850,7851,7853,7856],{},[166,7852,1726],{},[166,7854,7855],{},"✅ 原生",[166,7857,7858],{},"✅ 支持",[151,7860,7861,7864,7867],{},[166,7862,7863],{},"编辑器",[166,7865,7866],{},"VS Code 原生（你的插件全保留）",[166,7868,7869],{},"Cursor fork（需迁移）",[151,7871,7872,7874,7876],{},[166,7873,480],{},[166,7875,7407],{},[166,7877,7878],{},"$20\u002F月固定",[151,7880,7881,7883,7886],{},[166,7882,618],{},[166,7884,7885],{},"★★★☆☆ 需配 API key",[166,7887,7888],{},"★★★★★ 开箱即用",[151,7890,7891,7894,7897],{},[166,7892,7893],{},"团队协作",[166,7895,7896],{},"❌ 单人工具",[166,7898,7899],{},"✅ Cursor for Teams",[31,7901,7902,7905],{},[35,7903,7904],{},"选 Cline 如果","：你不想换编辑器（继续用 VS Code + 你所有插件）、你预算敏感（BYOK + DeepSeek）、你想用浏览器操控做前端闭环、你想要最大模型选择自由。",[31,7907,7908,7911],{},[35,7909,7910],{},"选 Cursor 如果","：你追求最好的补全 + Agent 一体体验、你想要开箱即用不折腾、你的团队需要统一工具、你不介意换到 Cursor 的 fork 编辑器。",[31,7913,7914,7917],{},[35,7915,7916],{},"两个都用的场景","：Cursor 做日常开发（补全 + Tab），Cline 做 Agent 重任务（多文件重构 + 浏览器验证）。社区里不少人是这个组合——Cursor 的 Tab 补全体验 Cline 给不了，Cline 的浏览器操控 Cursor 给不了。",[27,7919,7921],{"id":7920},"与-aider-对比","与 Aider 对比",[145,7923,7924,7934],{},[148,7925,7926],{},[151,7927,7928,7930,7932],{},[154,7929,552],{},[154,7931,5572],{},[154,7933,3807],{},[161,7935,7936,7946,7957,7968,7976,7987,7997,8006,8016],{},[151,7937,7938,7940,7943],{},[166,7939,1907],{},[166,7941,7942],{},"VS Code 扩展",[166,7944,7945],{},"纯 CLI",[151,7947,7948,7951,7954],{},[166,7949,7950],{},"Agent 自主性",[166,7952,7953],{},"★★★★★ 自主跑多步",[166,7955,7956],{},"★★★★☆ 需手动 add \u002F commit",[151,7958,7959,7962,7965],{},[166,7960,7961],{},"git 集成",[166,7963,7964],{},"可选（不强制 commit）",[166,7966,7967],{},"★★★★★ git-native，每次改动 = commit",[151,7969,7970,7972,7974],{},[166,7971,7506],{},[166,7973,7845],{},[166,7975,572],{},[151,7977,7978,7981,7984],{},[166,7979,7980],{},"终端执行",[166,7982,7983],{},"✅ Agent 自主执行",[166,7985,7986],{},"✅ \u002Frun 命令",[151,7988,7989,7992,7994],{},[166,7990,7991],{},"Repo Map",[166,7993,572],{},[166,7995,7996],{},"✅ tree-sitter 骨架",[151,7998,7999,8002,8004],{},[166,8000,8001],{},"多模型",[166,8003,1051],{},[166,8005,1051],{},[151,8007,8008,8011,8013],{},[166,8009,8010],{},"透明度",[166,8012,621],{},[166,8014,8015],{},"★★★★★ 全在终端可见",[151,8017,8018,8021,8024],{},[166,8019,8020],{},"学习曲线",[166,8022,8023],{},"★★★☆☆ GUI 友好",[166,8025,8026],{},"★★☆☆☆ 需学 git + 命令",[31,8028,8029,8031],{},[35,8030,7904],{},"：你想要 GUI 体验、你做前端需要浏览器操控、你不想每次改动都 commit、你是 VS Code 用户。",[31,8033,8034,8037],{},[35,8035,8036],{},"选 Aider 如果","：你是命令行极客、你重视 git 历史整洁、你做后端 \u002F 系统编程、你想要最透明的 AI 编程体验。",[27,8039,1736],{"id":1736},[31,8041,8042,8043,8045],{},"Cline 插件本身",[35,8044,2617],{},"——开源、Apache 2.0。成本只在模型 API：",[145,8047,8048,8061],{},[148,8049,8050],{},[151,8051,8052,8055,8058],{},[154,8053,8054],{},"使用强度",[154,8056,8057],{},"推荐模型",[154,8059,8060],{},"月成本估算",[161,8062,8063,8073,8084,8095],{},[151,8064,8065,8068,8070],{},[166,8066,8067],{},"轻度（每周几个小任务）",[166,8069,2149],{},[166,8071,8072],{},"$1-5",[151,8074,8075,8078,8081],{},[166,8076,8077],{},"中度（每天几个任务）",[166,8079,8080],{},"DeepSeek-V3 为主 + Claude 难题",[166,8082,8083],{},"$5-20",[151,8085,8086,8089,8092],{},[166,8087,8088],{},"重度（全天使用）",[166,8090,8091],{},"Claude Sonnet 4 为主",[166,8093,8094],{},"$30-80",[151,8096,8097,8100,8102],{},[166,8098,8099],{},"极重（大型 monorepo 长任务）",[166,8101,7630],{},[166,8103,8104],{},"$80-200+",[31,8106,8107,8109],{},[35,8108,6635],{},"：Cline + DeepSeek 是国内 AI 编程的性价比天花板——DeepSeek 支持支付宝付款、API 直连国内无障碍、中文理解强。一个月总成本可能 $5-15，比 Cursor Pro $20 便宜且没有月度订阅心理负担。",[31,8111,8112,70],{},[35,8113,8114],{},"成本控制技巧",[432,8116,8117,8120,8123,8126],{},[75,8118,8119],{},"日常任务用 DeepSeek-V3，只在复杂任务切 Claude",[75,8121,8122],{},"Cline 的\"自动批准\"模式会让 Agent 跑更多步骤（更多 token），预算敏感时用手动确认",[75,8124,8125],{},"大型任务拆成小任务跑，避免 Agent 在错误方向上烧 token",[75,8127,8128],{},"用 OpenRouter 做模型 failover 和成本监控",[27,8130,1756],{"id":1756},[72,8132,8133,8139,8145,8151,8157,8162,8168],{},[75,8134,6648,8135,8138],{},[35,8136,8137],{},"个人开发者 + 预算敏感","——BYOK + DeepSeek 月成本 $5-15",[75,8140,6648,8141,8144],{},[35,8142,8143],{},"VS Code 重度用户不想换编辑器","——Cline 是扩展不是 fork",[75,8146,6648,8147,8150],{},[35,8148,8149],{},"前端开发","——浏览器操控做\"改代码 → 看效果 → 修正\"闭环",[75,8152,6648,8153,8156],{},[35,8154,8155],{},"想要最大模型选择自由","——任意 OpenAI 兼容 API",[75,8158,6648,8159,8161],{},[35,8160,1809],{},"——DeepSeek 直连 + 支付宝付款",[75,8163,6648,8164,8167],{},[35,8165,8166],{},"开源项目贡献者","——免费 + 开源 + MCP 生态",[75,8169,6648,8170,8173],{},[35,8171,8172],{},"需要 MCP 工具集成","——数据库 \u002F Figma \u002F GitHub \u002F 自定义工具",[27,8175,6685],{"id":6685},[31,8177,8178,8181,8182,5876,8184,8186],{},[35,8179,8180],{},"想要开箱即用不折腾的人","：Cline 需要你自己配 API key、选模型、理解 Agent 行为。如果你想\"登录就用、不用配置\"，",[696,8183,3977],{"href":3976},[696,8185,1888],{"href":1887},"（有订阅的话）更合适。Cline 的 BYOK 灵活性换来的是配置成本。",[31,8188,8189,8192,8193,5876,8196,8199],{},[35,8190,8191],{},"企业团队统一工具","：Cline 是单人工具，没有团队协作、没有统一管控、没有审计日志。企业团队需要 ",[696,8194,8195],{"href":3976},"Cursor for Teams",[696,8197,8198],{"href":6712},"CodeBuddy 企业版","。Cline 适合个人，不适合组织。",[31,8201,8202,8205],{},[35,8203,8204],{},"追求最强补全体验","：Cline 的补全能力是基础的——它强在 Agent 不在补全。如果你的核心诉求是\"Tab 补全越智能越好\"，Cursor 的 Tab 补全仍是标杆，Cline 的补全体验差一档。",[31,8207,8208,8211,8212,8215],{},[35,8209,8210],{},"大型 monorepo + 长任务","：Cline 没有 Cursor 的 Codebase Indexing（全仓库语义索引），在超大型代码库里理解全局上下文的能力弱于 Cursor 和 ",[696,8213,5418],{"href":8214},"\u002Freview\u002Faugment-deep-review.html","。5 万行以下项目 Cline 没问题，10 万行+ 的 monorepo 建议用 Cursor 或 Augment。",[31,8217,8218,8221],{},[35,8219,8220],{},"需要稳定 \u002F 可预测成本的人","：BYOK 的成本取决于用量和模型——忙一个月可能 $80，闲一个月可能 $5。如果你需要\"每月固定 $20 不多不少\"的可预测性，Cursor 的固定订阅更合适。",[27,8223,657],{"id":656},[31,8225,8226,8229],{},[35,8227,8228],{},"Q：Cline 和 Continue 有什么区别？","\nA：定位不同。Continue 偏\"补全 + 聊天\"，强项是 Tab 补全和侧边栏对话。Cline 偏\"Agent\"，强项是自主多步任务、终端执行、浏览器操控。想要补全体验选 Continue，想要 Agent 能力选 Cline。两者可以同时装在 VS Code 里——Continue 补全 + Cline Agent，社区里不少人是这个组合。",[31,8231,8232,8235],{},[35,8233,8234],{},"Q：Cline 用哪个模型最好？","\nA：看任务难度和预算。日常任务用 DeepSeek-V3（性价比之王，质量达 Claude 80%）；复杂重构 \u002F 架构设计用 Claude Sonnet 4（质量最高）；隐私敏感场景用 Ollama 本地模型（零成本但需本地 GPU）。最佳策略是混合——日常 DeepSeek + 难题 Claude，在 Cline 里一键切换。",[31,8237,8238,8241,8242,8245],{},[35,8239,8240],{},"Q：Cline 的浏览器操控怎么用？","\nA：Cline 内置了浏览器操控能力（基于 Playwright \u002F Puppeterry）。Agent 修改前端代码后，会自动在浏览器里打开页面、点击交互、截图、读控制台报错。你需要先启动本地 dev server（如 ",[333,8243,8244],{},"npm run dev","），然后告诉 Cline\"在浏览器里打开 localhost:3000 验证效果\"。这个能力做前端开发的体感提升非常明显。",[31,8247,8248,8251],{},[35,8249,8250],{},"Q：Cline 2.0 的 ACP 协议是什么？","\nA：ACP（Agent Control Protocol）让 Cline 不再绑定 VS Code——你可以在 JetBrains、Vim \u002F Neovim 等编辑器里通过 ACP 调用 Cline 的 Agent 能力。这意味着\"Cline = VS Code 插件\"的认知要更新了——它正在变成一个跨编辑器的 Agent 平台。",[31,8253,8254,8257],{},[35,8255,8256],{},"Q：Cline 能用于商业项目吗？","\nA：能。Cline 是 Apache 2.0 开源协议，完全免费可用于商业项目。你用 Cline 生成的代码归你所有，没有授权限制。但注意你用的模型的 API 条款——比如某些模型的输出可能有使用限制，需查看模型商的服务条款。",[27,8259,690],{"id":690},[72,8261,8262,8267,8273,8277,8281,8286],{},[75,8263,8264],{},[696,8265,8266],{"href":4920},"Cline 工具卡：VS Code 里的终端 Agent",[75,8268,8269],{},[696,8270,8272],{"href":8271},"\u002Freview\u002Faider-deep-review.html","Aider 深度评测：把 AI 编程当 git 工作流",[75,8274,8275],{},[696,8276,6802],{"href":6801},[75,8278,8279],{},[696,8280,6792],{"href":6791},[75,8282,8283],{},[696,8284,8285],{"href":8214},"Augment Code 深度评测：企业级大代码库 Agent",[75,8287,8288],{},[696,8289,6808],{"href":6807},[1434,8291,8292],{},[31,8293,6813,8294,6816],{},[696,8295,2198],{"href":2198},{"title":331,"searchDepth":363,"depth":363,"links":8297},[8298,8299,8300,8306,8311,8312,8313,8314,8315,8316,8317],{"id":29,"depth":353,"text":29},{"id":7391,"depth":353,"text":7392},{"id":7429,"depth":353,"text":7430,"children":8301},[8302,8303,8304,8305],{"id":4540,"depth":363,"text":4540},{"id":4546,"depth":363,"text":4546},{"id":7506,"depth":363,"text":7506},{"id":7539,"depth":363,"text":7540},{"id":7585,"depth":353,"text":7586,"children":8307},[8308,8309,8310],{"id":7592,"depth":363,"text":7592},{"id":7688,"depth":363,"text":7689},{"id":7771,"depth":363,"text":7771},{"id":7781,"depth":353,"text":7782},{"id":7920,"depth":353,"text":7921},{"id":1736,"depth":353,"text":1736},{"id":1756,"depth":353,"text":1756},{"id":6685,"depth":353,"text":6685},{"id":656,"depth":353,"text":657},{"id":690,"depth":353,"text":690},"\u002Fog\u002Freview\u002Fcline.png","Cline 真实评测：GitHub 35k+ star 的开源 VS Code AI Agent，支持多文件修改、终端命令执行、浏览器操控、MCP 工具调用。本文写它真正解决的 BYOK 成本问题、Agent 自主能力深度、多模型支持（Claude \u002F DeepSeek \u002F Ollama）、与 Cursor \u002F Aider 的决策边界、BYOK 经济学，以及 5 类不推荐场景。AIHO 编辑部基于官方文档与社区公开反馈整理。",{},"\u002Freview\u002Fcline-deep-review",[5573,3137,5428],{"title":7357,"description":8319},"review\u002Fcline-deep-review",[5572,8326,2123,567,6856],"VS Code","开源 AI 编程 Agent 里体验最接近 Cursor 的一个。BYOK + 多模型 + 终端 \u002F 浏览器 \u002F MCP 全能力让它在免费工具里没有对手。代价是模型费自负、需要自己配 API key、没有 Cursor 的多文件 diff 审查体验。适合预算敏感 + 愿意折腾的开发者，不适合想要开箱即用的人。","L8CYCHwOu-44rlYuBNN1kg1IE7a0pKLuYvwKeTzVXuw",[8330,10061,10920],{"id":8331,"title":8332,"body":8333,"category":10050,"cover":5559,"description":10051,"extension":754,"meta":10052,"navigation":765,"path":10053,"published":791,"relatedTools":10054,"seo":10055,"stem":10056,"tags":10057,"updated":791,"__hash__":10060},"playbook\u002Fplaybook\u002Fonboarding\u002Faider-getting-started.md","Aider 上手指南：终端 AI 编程从零开始，BYOK 多模型配置实战",{"type":24,"value":8334,"toc":9995},[8335,8338,8350,8353,8356,8359,8391,8402,8405,8407,8440,8443,8455,8458,8461,8483,8487,8490,8526,8529,8532,8535,8550,8554,8557,8561,8564,8638,8647,8651,8654,8704,8711,8715,8771,8774,8778,8781,8818,8821,8825,8828,8871,8891,8894,8898,8904,9071,9078,9081,9085,9169,9173,9182,9185,9189,9200,9206,9210,9216,9219,9239,9243,9311,9314,9337,9341,9344,9347,9353,9357,9490,9494,9497,9553,9557,9560,9564,9567,9571,9580,9584,9589,9595,9598,9603,9609,9612,9616,9622,9626,9636,9640,9681,9684,9688,9691,9695,9699,9705,9709,9715,9719,9725,9729,9735,9739,9745,9748,9752,9793,9797,9843,9847,9865,9868,9888,9892,9962,9964,9992],[27,8336,8337],{"id":8337},"适用人群",[31,8339,8340,8341,413,8344,413,8347,2416],{},"三类人：",[35,8342,8343],{},"第一次用 Aider 不知道怎么下手的人",[35,8345,8346],{},"装好了但不确定该配哪个模型的人",[35,8348,8349],{},"已经跑了几个任务但 token 消耗太快的人",[31,8351,8352],{},"这份指南目标是让你在 30 分钟内完成安装 → 配 API key → 跑通第一个任务 → 会写省钱的 Prompt。",[31,8354,8355],{},"Aider 是 AI 编程工具光谱里最极端的一端：没有 GUI、没有自动魔法、所有动作都映射成 git 操作、所有上下文都明确暴露在终端里。学习曲线在 AI 编程工具里最陡，但跨过去之后会上瘾——因为它是唯一一个让你完全掌控 AI 每一步动作的工具。",[27,8357,8358],{"id":8358},"前置条件",[72,8360,8361,8367,8373,8379,8385],{},[75,8362,8363,8366],{},[35,8364,8365],{},"Python 3.9+","：Aider 是 Python CLI 工具，需要 pip 安装。Mac\u002FLinux 自带 Python，Windows 建议装 Python 官方安装包或用 WSL",[75,8368,8369,8372],{},[35,8370,8371],{},"Git 基础","：Aider 把 AI 编程映射到 git 工作流，你需要懂 commit \u002F branch \u002F rebase 基本操作",[75,8374,8375,8378],{},[35,8376,8377],{},"一个 git 仓库","：Aider 强制要求在 git 仓库里使用全部功能，非 git 目录功能受限",[75,8380,8381,8384],{},[35,8382,8383],{},"至少一个 LLM API key","：OpenAI \u002F Anthropic \u002F DeepSeek 任选其一（推荐 DeepSeek，最便宜且国内直连）",[75,8386,8387,8390],{},[35,8388,8389],{},"终端 \u002F 命令行","：Aider 没有图形界面，所有操作在终端完成",[1410,8392,8394],{"className":8393},[1413,1414,1415],[31,8395,8396,8397,5876,8399,8401],{},"如果你看到上面的前置条件觉得\"太麻烦了\"——选 ",[696,8398,3977],{"href":3976},[696,8400,1888],{"href":1887}," 更合适。Aider 不试图讨好 GUI 用户，它的价值在于可控性，代价是学习成本。",[27,8403,8404],{"id":8404},"安装",[60,8406,2251],{"id":2251},[326,8408,8410],{"className":328,"code":8409,"language":330,"meta":331,"style":331},"# 推荐：带 --upgrade-strategy 避免依赖冲突\npython -m pip install -U --upgrade-strategy only-if-needed aider-chat\n",[333,8411,8412,8417],{"__ignoreMap":331},[336,8413,8414],{"class":338,"line":339},[336,8415,8416],{"class":393},"# 推荐：带 --upgrade-strategy 避免依赖冲突\n",[336,8418,8419,8421,8423,8426,8428,8431,8434,8437],{"class":338,"line":353},[336,8420,400],{"class":342},[336,8422,403],{"class":356},[336,8424,8425],{"class":346}," pip",[336,8427,369],{"class":346},[336,8429,8430],{"class":356}," -U",[336,8432,8433],{"class":356}," --upgrade-strategy",[336,8435,8436],{"class":346}," only-if-needed",[336,8438,8439],{"class":346}," aider-chat\n",[31,8441,8442],{},"验证安装：",[326,8444,8446],{"className":328,"code":8445,"language":330,"meta":331,"style":331},"aider --version\n",[333,8447,8448],{"__ignoreMap":331},[336,8449,8450,8453],{"class":338,"line":339},[336,8451,8452],{"class":342},"aider",[336,8454,3508],{"class":356},[60,8456,8457],{"id":8457},"国内安装加速",[31,8459,8460],{},"如果 pip 下载慢，换国内镜像：",[326,8462,8464],{"className":328,"code":8463,"language":330,"meta":331,"style":331},"pip install -U aider-chat -i https:\u002F\u002Fpypi.tuna.tsinghua.edu.cn\u002Fsimple\n",[333,8465,8466],{"__ignoreMap":331},[336,8467,8468,8470,8472,8474,8477,8480],{"class":338,"line":339},[336,8469,366],{"class":342},[336,8471,369],{"class":346},[336,8473,8430],{"class":356},[336,8475,8476],{"class":346}," aider-chat",[336,8478,8479],{"class":356}," -i",[336,8481,8482],{"class":346}," https:\u002F\u002Fpypi.tuna.tsinghua.edu.cn\u002Fsimple\n",[60,8484,8486],{"id":8485},"windows-注意事项","Windows 注意事项",[31,8488,8489],{},"Windows 上 Aider 能跑，但 Python 环境 + git CLI 双依赖门槛偏高。推荐用 WSL：",[326,8491,8495],{"className":8492,"code":8493,"language":8494,"meta":331,"style":331},"language-powershell shiki shiki-themes github-light github-dark","# PowerShell 里装 WSL\nwsl --install\n\n# 进 WSL 后按 Linux 方式装\nsudo apt update && sudo apt install python3 python3-pip git\npip install aider-chat\n","powershell",[333,8496,8497,8502,8507,8511,8516,8521],{"__ignoreMap":331},[336,8498,8499],{"class":338,"line":339},[336,8500,8501],{},"# PowerShell 里装 WSL\n",[336,8503,8504],{"class":338,"line":353},[336,8505,8506],{},"wsl --install\n",[336,8508,8509],{"class":338,"line":363},[336,8510,1612],{"emptyLinePlaceholder":765},[336,8512,8513],{"class":338,"line":378},[336,8514,8515],{},"# 进 WSL 后按 Linux 方式装\n",[336,8517,8518],{"class":338,"line":390},[336,8519,8520],{},"sudo apt update && sudo apt install python3 python3-pip git\n",[336,8522,8523],{"class":338,"line":397},[336,8524,8525],{},"pip install aider-chat\n",[31,8527,8528],{},"如果不用 WSL，确保 Windows 装了 Python（勾选 Add to PATH）和 Git for Windows。",[60,8530,8531],{"id":8531},"升级",[31,8533,8534],{},"Aider 迭代很快，定期升级：",[326,8536,8538],{"className":328,"code":8537,"language":330,"meta":331,"style":331},"pip install -U aider-chat\n",[333,8539,8540],{"__ignoreMap":331},[336,8541,8542,8544,8546,8548],{"class":338,"line":339},[336,8543,366],{"class":342},[336,8545,369],{"class":346},[336,8547,8430],{"class":356},[336,8549,8439],{"class":346},[27,8551,8553],{"id":8552},"api-配置","API 配置",[31,8555,8556],{},"Aider 的核心优势是 BYOK（Bring Your Own Key）——你可以接任意模型。以下按推荐优先级配置。",[60,8558,8560],{"id":8559},"方案-1deepseek性价比之王国内首选","方案 1：DeepSeek（性价比之王，国内首选）",[31,8562,8563],{},"国内直连、支付宝付费、价格极低，是 Aider 的最佳搭档。",[326,8565,8567],{"className":328,"code":8566,"language":330,"meta":331,"style":331},"# 设置环境变量\nexport DEEPSEEK_API_KEY=sk-xxxxxxxxxxxxxxxx\n\n# Windows PowerShell\n$env:DEEPSEEK_API_KEY = \"sk-xxxxxxxxxxxxxxxx\"\n\n# 启动 Aider，指定 DeepSeek 模型\naider --model deepseek\u002Fdeepseek-chat\n\n# 或用 R1 推理模型\naider --model deepseek\u002Fdeepseek-reasoner\n",[333,8568,8569,8574,8586,8590,8594,8602,8606,8611,8620,8624,8629],{"__ignoreMap":331},[336,8570,8571],{"class":338,"line":339},[336,8572,8573],{"class":393},"# 设置环境变量\n",[336,8575,8576,8578,8581,8583],{"class":338,"line":353},[336,8577,3522],{"class":1524},[336,8579,8580],{"class":1528}," DEEPSEEK_API_KEY",[336,8582,3528],{"class":1524},[336,8584,8585],{"class":1528},"sk-xxxxxxxxxxxxxxxx\n",[336,8587,8588],{"class":338,"line":363},[336,8589,1612],{"emptyLinePlaceholder":765},[336,8591,8592],{"class":338,"line":378},[336,8593,3459],{"class":393},[336,8595,8596,8599],{"class":338,"line":390},[336,8597,8598],{"class":1528},"$env:DEEPSEEK_API_KEY = ",[336,8600,8601],{"class":346},"\"sk-xxxxxxxxxxxxxxxx\"\n",[336,8603,8604],{"class":338,"line":397},[336,8605,1612],{"emptyLinePlaceholder":765},[336,8607,8608],{"class":338,"line":1637},[336,8609,8610],{"class":393},"# 启动 Aider，指定 DeepSeek 模型\n",[336,8612,8613,8615,8617],{"class":338,"line":1643},[336,8614,8452],{"class":342},[336,8616,4593],{"class":356},[336,8618,8619],{"class":346}," deepseek\u002Fdeepseek-chat\n",[336,8621,8622],{"class":338,"line":1654},[336,8623,1612],{"emptyLinePlaceholder":765},[336,8625,8626],{"class":338,"line":1659},[336,8627,8628],{"class":393},"# 或用 R1 推理模型\n",[336,8630,8631,8633,8635],{"class":338,"line":1665},[336,8632,8452],{"class":342},[336,8634,4593],{"class":356},[336,8636,8637],{"class":346}," deepseek\u002Fdeepseek-reasoner\n",[31,8639,8640,8641,8646],{},"获取 key：打开 ",[696,8642,8645],{"href":8643,"rel":8644},"https:\u002F\u002Fplatform.deepseek.com",[1009],"platform.deepseek.com","，注册后充值（支持支付宝），创建 API key。一次中型 PR（10-15 文件改动）约 $0.05-0.2。",[60,8648,8650],{"id":8649},"方案-2anthropic-claude编辑能力最稳","方案 2：Anthropic Claude（编辑能力最稳）",[31,8652,8653],{},"Claude Sonnet 4 在代码编辑能力上是第一梯队，Aider 官方推荐用它做 editor model。",[326,8655,8657],{"className":328,"code":8656,"language":330,"meta":331,"style":331},"export ANTHROPIC_API_KEY=sk-ant-xxxxxxxxxxxxxxxx\n\n# Windows PowerShell\n$env:ANTHROPIC_API_KEY = \"sk-ant-xxxxxxxxxxxxxxxx\"\n\n# 启动\naider --model claude-sonnet-4-20250514\n",[333,8658,8659,8670,8674,8678,8686,8690,8695],{"__ignoreMap":331},[336,8660,8661,8663,8665,8667],{"class":338,"line":339},[336,8662,3522],{"class":1524},[336,8664,3525],{"class":1528},[336,8666,3528],{"class":1524},[336,8668,8669],{"class":1528},"sk-ant-xxxxxxxxxxxxxxxx\n",[336,8671,8672],{"class":338,"line":353},[336,8673,1612],{"emptyLinePlaceholder":765},[336,8675,8676],{"class":338,"line":363},[336,8677,3459],{"class":393},[336,8679,8680,8683],{"class":338,"line":378},[336,8681,8682],{"class":1528},"$env:ANTHROPIC_API_KEY = ",[336,8684,8685],{"class":346},"\"sk-ant-xxxxxxxxxxxxxxxx\"\n",[336,8687,8688],{"class":338,"line":390},[336,8689,1612],{"emptyLinePlaceholder":765},[336,8691,8692],{"class":338,"line":397},[336,8693,8694],{"class":393},"# 启动\n",[336,8696,8697,8699,8701],{"class":338,"line":1637},[336,8698,8452],{"class":342},[336,8700,4593],{"class":356},[336,8702,8703],{"class":346}," claude-sonnet-4-20250514\n",[31,8705,8706,8707,8710],{},"国内使用需要代理：",[333,8708,8709],{},"export HTTPS_PROXY=http:\u002F\u002F127.0.0.1:7890","。一次中型 PR 约 $0.5-2。",[60,8712,8714],{"id":8713},"方案-3openai-gptreasoning-路径","方案 3：OpenAI GPT（reasoning 路径）",[326,8716,8718],{"className":328,"code":8717,"language":330,"meta":331,"style":331},"export OPENAI_API_KEY=sk-xxxxxxxxxxxxxxxx\n\n# Windows PowerShell\n$env:OPENAI_API_KEY = \"sk-xxxxxxxxxxxxxxxx\"\n\n# 启动\naider --model gpt-5\naider --model o3\n",[333,8719,8720,8730,8734,8738,8745,8749,8753,8762],{"__ignoreMap":331},[336,8721,8722,8724,8726,8728],{"class":338,"line":339},[336,8723,3522],{"class":1524},[336,8725,3539],{"class":1528},[336,8727,3528],{"class":1524},[336,8729,8585],{"class":1528},[336,8731,8732],{"class":338,"line":353},[336,8733,1612],{"emptyLinePlaceholder":765},[336,8735,8736],{"class":338,"line":363},[336,8737,3459],{"class":393},[336,8739,8740,8743],{"class":338,"line":378},[336,8741,8742],{"class":1528},"$env:OPENAI_API_KEY = ",[336,8744,8601],{"class":346},[336,8746,8747],{"class":338,"line":390},[336,8748,1612],{"emptyLinePlaceholder":765},[336,8750,8751],{"class":338,"line":397},[336,8752,8694],{"class":393},[336,8754,8755,8757,8759],{"class":338,"line":1637},[336,8756,8452],{"class":342},[336,8758,4593],{"class":356},[336,8760,8761],{"class":346}," gpt-5\n",[336,8763,8764,8766,8768],{"class":338,"line":1643},[336,8765,8452],{"class":342},[336,8767,4593],{"class":356},[336,8769,8770],{"class":346}," o3\n",[31,8772,8773],{},"国内同样需要代理。一次中型 PR 约 $2-5（GPT-5 reasoning）。",[60,8775,8777],{"id":8776},"方案-4本地模型完全离线-零成本","方案 4：本地模型（完全离线 + 零成本）",[31,8779,8780],{},"用 Ollama 跑本地模型，隐私场景首选：",[326,8782,8784],{"className":328,"code":8783,"language":330,"meta":331,"style":331},"# 先装 Ollama 并拉模型\nollama pull qwen2.5:32b\n\n# Aider 接 Ollama\naider --model ollama\u002Fqwen2.5:32b\n",[333,8785,8786,8791,8800,8804,8809],{"__ignoreMap":331},[336,8787,8788],{"class":338,"line":339},[336,8789,8790],{"class":393},"# 先装 Ollama 并拉模型\n",[336,8792,8793,8795,8797],{"class":338,"line":353},[336,8794,3141],{"class":342},[336,8796,4573],{"class":346},[336,8798,8799],{"class":346}," qwen2.5:32b\n",[336,8801,8802],{"class":338,"line":363},[336,8803,1612],{"emptyLinePlaceholder":765},[336,8805,8806],{"class":338,"line":378},[336,8807,8808],{"class":393},"# Aider 接 Ollama\n",[336,8810,8811,8813,8815],{"class":338,"line":390},[336,8812,8452],{"class":342},[336,8814,4593],{"class":356},[336,8816,8817],{"class":346}," ollama\u002Fqwen2.5:32b\n",[31,8819,8820],{},"模型质量取决于本机硬件。32B 模型需要 16GB+ 显存或 32GB+ 内存。",[60,8822,8824],{"id":8823},"方案-5architect-双模型组合进阶","方案 5：Architect 双模型组合（进阶）",[31,8826,8827],{},"Aider 独有的杀手锏——把\"想方案\"和\"写代码\"拆给两个模型：",[326,8829,8831],{"className":328,"code":8830,"language":330,"meta":331,"style":331},"aider --architect \\\n  --model deepseek\u002Fdeepseek-reasoner \\\n  --editor-model claude-sonnet-4-20250514 \\\n  --weak-model gpt-4o-mini\n",[333,8832,8833,8843,8853,8863],{"__ignoreMap":331},[336,8834,8835,8837,8840],{"class":338,"line":339},[336,8836,8452],{"class":342},[336,8838,8839],{"class":356}," --architect",[336,8841,8842],{"class":356}," \\\n",[336,8844,8845,8848,8851],{"class":338,"line":353},[336,8846,8847],{"class":356},"  --model",[336,8849,8850],{"class":346}," deepseek\u002Fdeepseek-reasoner",[336,8852,8842],{"class":356},[336,8854,8855,8858,8861],{"class":338,"line":363},[336,8856,8857],{"class":356},"  --editor-model",[336,8859,8860],{"class":346}," claude-sonnet-4-20250514",[336,8862,8842],{"class":356},[336,8864,8865,8868],{"class":338,"line":378},[336,8866,8867],{"class":356},"  --weak-model",[336,8869,8870],{"class":346}," gpt-4o-mini\n",[72,8872,8873,8879,8885],{},[75,8874,8875,8878],{},[333,8876,8877],{},"--model","（架构师）：DeepSeek-R1 负责高层方案推理",[75,8880,8881,8884],{},[333,8882,8883],{},"--editor-model","：Claude Sonnet 把方案落地成 diff",[75,8886,8887,8890],{},[333,8888,8889],{},"--weak-model","：GPT-4o-mini 生成 commit message",[31,8892,8893],{},"据 Aider 官方 benchmark，DeepSeek R1 + Claude Sonnet 的组合比任一模型单跑高约 10%。",[60,8895,8897],{"id":8896},"用-aiderconfyml-固化配置","用 .aider.conf.yml 固化配置",[31,8899,8900,8901,70],{},"不要每次手敲参数。在项目根目录创建 ",[333,8902,8903],{},".aider.conf.yml",[326,8905,8909],{"className":8906,"code":8907,"language":8908,"meta":331,"style":331},"language-yaml shiki shiki-themes github-light github-dark","# 默认模型组合\nmodel: deepseek\u002Fdeepseek-reasoner\neditor-model: claude-sonnet-4-20250514\nweak-model: deepseek\u002Fdeepseek-chat\n\n# 自动接受所有 diff（个人项目用，团队项目设 false）\nyes-always: false\n\n# commit message 模板（中文 + 约定式提交）\ncommit-prompt: |\n  你是项目的 git commit 助手。\n  根据 diff 生成中文 commit message，遵循 conventional commits 格式：\n  feat\u002Ffix\u002Fdocs\u002Frefactor\u002Ftest\u002Fchore: 简短描述\n\n# 默认排除的文件\nread-only:\n  - docs\u002F\n  - public\u002F\n  - node_modules\u002F\n\n# 自动加载的项目上下文\nread:\n  - README.md\n  - CONTRIBUTING.md\n","yaml",[333,8910,8911,8916,8927,8937,8947,8951,8956,8966,8970,8975,8985,8990,8995,9000,9004,9009,9017,9025,9032,9039,9043,9049,9056,9063],{"__ignoreMap":331},[336,8912,8913],{"class":338,"line":339},[336,8914,8915],{"class":393},"# 默认模型组合\n",[336,8917,8918,8922,8924],{"class":338,"line":353},[336,8919,8921],{"class":8920},"s9eBZ","model",[336,8923,3306],{"class":1528},[336,8925,8926],{"class":346},"deepseek\u002Fdeepseek-reasoner\n",[336,8928,8929,8932,8934],{"class":338,"line":363},[336,8930,8931],{"class":8920},"editor-model",[336,8933,3306],{"class":1528},[336,8935,8936],{"class":346},"claude-sonnet-4-20250514\n",[336,8938,8939,8942,8944],{"class":338,"line":378},[336,8940,8941],{"class":8920},"weak-model",[336,8943,3306],{"class":1528},[336,8945,8946],{"class":346},"deepseek\u002Fdeepseek-chat\n",[336,8948,8949],{"class":338,"line":390},[336,8950,1612],{"emptyLinePlaceholder":765},[336,8952,8953],{"class":338,"line":397},[336,8954,8955],{"class":393},"# 自动接受所有 diff（个人项目用，团队项目设 false）\n",[336,8957,8958,8961,8963],{"class":338,"line":1637},[336,8959,8960],{"class":8920},"yes-always",[336,8962,3306],{"class":1528},[336,8964,8965],{"class":356},"false\n",[336,8967,8968],{"class":338,"line":1643},[336,8969,1612],{"emptyLinePlaceholder":765},[336,8971,8972],{"class":338,"line":1654},[336,8973,8974],{"class":393},"# commit message 模板（中文 + 约定式提交）\n",[336,8976,8977,8980,8982],{"class":338,"line":1659},[336,8978,8979],{"class":8920},"commit-prompt",[336,8981,3306],{"class":1528},[336,8983,8984],{"class":1524},"|\n",[336,8986,8987],{"class":338,"line":1665},[336,8988,8989],{"class":346},"  你是项目的 git commit 助手。\n",[336,8991,8992],{"class":338,"line":1672},[336,8993,8994],{"class":346},"  根据 diff 生成中文 commit message，遵循 conventional commits 格式：\n",[336,8996,8997],{"class":338,"line":8},[336,8998,8999],{"class":346},"  feat\u002Ffix\u002Fdocs\u002Frefactor\u002Ftest\u002Fchore: 简短描述\n",[336,9001,9002],{"class":338,"line":2587},[336,9003,1612],{"emptyLinePlaceholder":765},[336,9005,9006],{"class":338,"line":3534},[336,9007,9008],{"class":393},"# 默认排除的文件\n",[336,9010,9011,9014],{"class":338,"line":3547},[336,9012,9013],{"class":8920},"read-only",[336,9015,9016],{"class":1528},":\n",[336,9018,9019,9022],{"class":338,"line":3563},[336,9020,9021],{"class":1528},"  - ",[336,9023,9024],{"class":346},"docs\u002F\n",[336,9026,9027,9029],{"class":338,"line":3568},[336,9028,9021],{"class":1528},[336,9030,9031],{"class":346},"public\u002F\n",[336,9033,9034,9036],{"class":338,"line":3574},[336,9035,9021],{"class":1528},[336,9037,9038],{"class":346},"node_modules\u002F\n",[336,9040,9041],{"class":338,"line":3581},[336,9042,1612],{"emptyLinePlaceholder":765},[336,9044,9046],{"class":338,"line":9045},21,[336,9047,9048],{"class":393},"# 自动加载的项目上下文\n",[336,9050,9051,9054],{"class":338,"line":7},[336,9052,9053],{"class":8920},"read",[336,9055,9016],{"class":1528},[336,9057,9058,9060],{"class":338,"line":10},[336,9059,9021],{"class":1528},[336,9061,9062],{"class":346},"README.md\n",[336,9064,9066,9068],{"class":338,"line":9065},24,[336,9067,9021],{"class":1528},[336,9069,9070],{"class":346},"CONTRIBUTING.md\n",[31,9072,9073,9074,9077],{},"这一份配置让 Aider 从\"通用工具\"变成\"我的项目的 AI 助手\"，等价于 Cursor 的 ",[333,9075,9076],{},".cursor\u002Frules\u002F*.mdc","，但更精细。",[27,9079,9080],{"id":9080},"第一个项目实战",[60,9082,9084],{"id":9083},"第一步准备-git-仓库","第一步：准备 git 仓库",[326,9086,9088],{"className":328,"code":9087,"language":330,"meta":331,"style":331},"# 如果你已有项目\ncd your-project\n\n# 如果是全新项目\nmkdir my-project && cd my-project\ngit init\necho \"# My Project\" > README.md\ngit add . && git commit -m \"init\"\n",[333,9089,9090,9095,9101,9105,9110,9126,9133,9147],{"__ignoreMap":331},[336,9091,9092],{"class":338,"line":339},[336,9093,9094],{"class":393},"# 如果你已有项目\n",[336,9096,9097,9099],{"class":338,"line":353},[336,9098,357],{"class":356},[336,9100,1511],{"class":346},[336,9102,9103],{"class":338,"line":363},[336,9104,1612],{"emptyLinePlaceholder":765},[336,9106,9107],{"class":338,"line":378},[336,9108,9109],{"class":393},"# 如果是全新项目\n",[336,9111,9112,9115,9118,9121,9123],{"class":338,"line":390},[336,9113,9114],{"class":342},"mkdir",[336,9116,9117],{"class":346}," my-project",[336,9119,9120],{"class":1528}," && ",[336,9122,357],{"class":356},[336,9124,9125],{"class":346}," my-project\n",[336,9127,9128,9130],{"class":338,"line":397},[336,9129,343],{"class":342},[336,9131,9132],{"class":346}," init\n",[336,9134,9135,9138,9141,9144],{"class":338,"line":1637},[336,9136,9137],{"class":356},"echo",[336,9139,9140],{"class":346}," \"# My Project\"",[336,9142,9143],{"class":1524}," >",[336,9145,9146],{"class":346}," README.md\n",[336,9148,9149,9151,9154,9157,9159,9161,9164,9166],{"class":338,"line":1643},[336,9150,343],{"class":342},[336,9152,9153],{"class":346}," add",[336,9155,9156],{"class":346}," .",[336,9158,9120],{"class":1528},[336,9160,343],{"class":342},[336,9162,9163],{"class":346}," commit",[336,9165,403],{"class":356},[336,9167,9168],{"class":346}," \"init\"\n",[60,9170,9172],{"id":9171},"第二步启动-aider","第二步：启动 Aider",[326,9174,9176],{"className":328,"code":9175,"language":330,"meta":331,"style":331},"aider\n",[333,9177,9178],{"__ignoreMap":331},[336,9179,9180],{"class":338,"line":339},[336,9181,9175],{"class":342},[31,9183,9184],{},"进入交互式 REPL。你会看到 Aider 打印出 repo map 扫描结果和当前使用的模型。",[60,9186,9188],{"id":9187},"第三步加入要修改的文件","第三步：加入要修改的文件",[31,9190,9191,9192,9195,9196,9199],{},"这是新手最容易跳过的一步。Aider 默认只看 repo map（函数签名、类层级），",[35,9193,9194],{},"看不到文件实现细节","。你要修改的文件必须 ",[333,9197,9198],{},"\u002Fadd"," 显式加入：",[326,9201,9204],{"className":9202,"code":9203,"language":876,"meta":331},[874],"\u002Fadd src\u002Fserver.ts src\u002Fapi\u002Fuser.ts\n",[333,9205,9203],{"__ignoreMap":331},[60,9207,9209],{"id":9208},"第四步用自然语言下指令","第四步：用自然语言下指令",[326,9211,9214],{"className":9212,"code":9213,"language":876,"meta":331},[874],"> 把 server\u002Fapi\u002Fuser.ts 里的 getUserById 改成支持批量查询，\n> 参数从 id: string 改成 ids: string[]，返回 User[] 而不是 User。\n> 同时更新调用方代码。\n",[333,9215,9213],{"__ignoreMap":331},[31,9217,9218],{},"Aider 会：",[432,9220,9221,9224,9227,9236],{},[75,9222,9223],{},"分析你 add 的文件 + repo map 里的相关文件",[75,9225,9226],{},"给出修改方案（等确认）",[75,9228,9229,9230,9233,9234],{},"生成 diff 并自动 ",[333,9231,9232],{},"git add"," + ",[333,9235,7489],{},[75,9237,9238],{},"commit message 由 weak-model 自动生成",[60,9240,9242],{"id":9241},"第五步验证和回滚","第五步：验证和回滚",[326,9244,9246],{"className":328,"code":9245,"language":330,"meta":331,"style":331},"# 看刚才的改动\ngit log --oneline -3\ngit diff HEAD~1\n\n# 不满意？一行回滚\ngit reset --hard HEAD~1\n\n# 或用 Aider 的 \u002Fundo\n\u002Fundo\n",[333,9247,9248,9253,9266,9276,9280,9285,9297,9301,9306],{"__ignoreMap":331},[336,9249,9250],{"class":338,"line":339},[336,9251,9252],{"class":393},"# 看刚才的改动\n",[336,9254,9255,9257,9260,9263],{"class":338,"line":353},[336,9256,343],{"class":342},[336,9258,9259],{"class":346}," log",[336,9261,9262],{"class":356}," --oneline",[336,9264,9265],{"class":356}," -3\n",[336,9267,9268,9270,9273],{"class":338,"line":363},[336,9269,343],{"class":342},[336,9271,9272],{"class":346}," diff",[336,9274,9275],{"class":346}," HEAD~1\n",[336,9277,9278],{"class":338,"line":378},[336,9279,1612],{"emptyLinePlaceholder":765},[336,9281,9282],{"class":338,"line":390},[336,9283,9284],{"class":393},"# 不满意？一行回滚\n",[336,9286,9287,9289,9292,9295],{"class":338,"line":397},[336,9288,343],{"class":342},[336,9290,9291],{"class":346}," reset",[336,9293,9294],{"class":356}," --hard",[336,9296,9275],{"class":346},[336,9298,9299],{"class":338,"line":1637},[336,9300,1612],{"emptyLinePlaceholder":765},[336,9302,9303],{"class":338,"line":1643},[336,9304,9305],{"class":393},"# 或用 Aider 的 \u002Fundo\n",[336,9307,9308],{"class":338,"line":1654},[336,9309,9310],{"class":342},"\u002Fundo\n",[60,9312,9313],{"id":9313},"实战注意",[72,9315,9316,9325,9331],{},[75,9317,9318,70,9321,9324],{},[35,9319,9320],{},"先创建分支再跑",[333,9322,9323],{},"git checkout -b aider\u002Fuser-batch-query","，避免主分支被污染",[75,9326,9327,9330],{},[35,9328,9329],{},"任务描述要具体","：说清楚改什么、怎么改、影响哪些文件，Aider 不会自己猜",[75,9332,9333,9336],{},[35,9334,9335],{},"一次一个任务","：不要在一个对话里塞 5 个不相关的修改，token 消耗会暴增",[27,9338,9340],{"id":9339},"git-工作流","Git 工作流",[31,9342,9343],{},"Aider 的 git 集成是它与所有竞品的最大区别。掌握以下工作流是用好 Aider 的关键。",[60,9345,9346],{"id":9346},"标准工作流",[326,9348,9351],{"className":9349,"code":9350,"language":876,"meta":331},[874],"1. git checkout -b aider\u002F\u003Ctask-name>     # 创建实验分支\n2. aider                                   # 启动 Aider\n3. \u002Fadd \u003C相关文件>                         # 加入要修改的文件\n4. \u003C自然语言描述任务>                       # 让 AI 改代码\n5. git diff                               # 检查改动\n6. 满意 → git checkout main && git merge aider\u002F\u003Ctask-name>\n   不满意 → git reset --hard HEAD~1       # 回滚\n7. \u002Fexit                                   # 退出 Aider\n",[333,9352,9350],{"__ignoreMap":331},[60,9354,9356],{"id":9355},"常用-aider-命令","常用 Aider 命令",[145,9358,9359,9369],{},[148,9360,9361],{},[151,9362,9363,9366],{},[154,9364,9365],{},"命令",[154,9367,9368],{},"用途",[161,9370,9371,9381,9391,9401,9411,9421,9431,9440,9450,9460,9470,9480],{},[151,9372,9373,9378],{},[166,9374,9375],{},[333,9376,9377],{},"\u002Fadd \u003Cfile>",[166,9379,9380],{},"把文件加入对话（AI 能看到实现细节）",[151,9382,9383,9388],{},[166,9384,9385],{},[333,9386,9387],{},"\u002Fdrop \u003Cfile>",[166,9389,9390],{},"把文件移出对话",[151,9392,9393,9398],{},[166,9394,9395],{},[333,9396,9397],{},"\u002Fundo",[166,9399,9400],{},"撤销上一次 Aider commit",[151,9402,9403,9408],{},[166,9404,9405],{},[333,9406,9407],{},"\u002Fdiff",[166,9409,9410],{},"查看当前未提交的改动",[151,9412,9413,9418],{},[166,9414,9415],{},[333,9416,9417],{},"\u002Fcommit",[166,9419,9420],{},"手动提交当前改动",[151,9422,9423,9428],{},[166,9424,9425],{},[333,9426,9427],{},"\u002Frun \u003Ccmd>",[166,9429,9430],{},"在 Aider 里跑 shell 命令",[151,9432,9433,9437],{},[166,9434,9435],{},[333,9436,1546],{},[166,9438,9439],{},"清空对话历史（开始新任务时用）",[151,9441,9442,9447],{},[166,9443,9444],{},[333,9445,9446],{},"\u002Fsave \u003Cfile>",[166,9448,9449],{},"保存当前对话到文件",[151,9451,9452,9457],{},[166,9453,9454],{},[333,9455,9456],{},"\u002Fload \u003Cfile>",[166,9458,9459],{},"加载之前保存的对话",[151,9461,9462,9467],{},[166,9463,9464],{},[333,9465,9466],{},"\u002Ftokens",[166,9468,9469],{},"查看当前 token 消耗",[151,9471,9472,9477],{},[166,9473,9474],{},[333,9475,9476],{},"\u002Fmodel \u003Cname>",[166,9478,9479],{},"切换模型",[151,9481,9482,9487],{},[166,9483,9484],{},[333,9485,9486],{},"\u002Farchitect",[166,9488,9489],{},"切换到 Architect 双模型模式",[60,9491,9493],{"id":9492},"commit-历史整理","commit 历史整理",[31,9495,9496],{},"Aider 一次复杂任务可能产生 5-15 个 commit，commit message 质量参差。合并回主分支前要整理：",[326,9498,9500],{"className":328,"code":9499,"language":330,"meta":331,"style":331},"# squash 成一个 clean commit\ngit checkout main\ngit merge --squash aider\u002F\u003Ctask-name>\ngit commit -m \"feat: user 支持批量查询\"\n",[333,9501,9502,9507,9517,9542],{"__ignoreMap":331},[336,9503,9504],{"class":338,"line":339},[336,9505,9506],{"class":393},"# squash 成一个 clean commit\n",[336,9508,9509,9511,9514],{"class":338,"line":353},[336,9510,343],{"class":342},[336,9512,9513],{"class":346}," checkout",[336,9515,9516],{"class":346}," main\n",[336,9518,9519,9521,9524,9527,9530,9533,9536,9539],{"class":338,"line":363},[336,9520,343],{"class":342},[336,9522,9523],{"class":346}," merge",[336,9525,9526],{"class":356}," --squash",[336,9528,9529],{"class":346}," aider\u002F",[336,9531,9532],{"class":1524},"\u003C",[336,9534,9535],{"class":346},"task-nam",[336,9537,9538],{"class":1528},"e",[336,9540,9541],{"class":1524},">\n",[336,9543,9544,9546,9548,9550],{"class":338,"line":378},[336,9545,343],{"class":342},[336,9547,9163],{"class":346},[336,9549,403],{"class":356},[336,9551,9552],{"class":346}," \"feat: user 支持批量查询\"\n",[27,9554,9556],{"id":9555},"省-token-技巧","省 token 技巧",[31,9558,9559],{},"Aider 的 token 消耗完全可控——前提是你知道怎么控制。以下 7 个技巧按效果排序。",[60,9561,9563],{"id":9562},"_1-用-deepseek-替代-claude-gpt","1. 用 DeepSeek 替代 Claude \u002F GPT",[31,9565,9566],{},"同一个任务，DeepSeek 比 Claude 便宜 10-30 倍，推理\u002F编辑能力达到 Claude 的 80% 左右。日常任务用 DeepSeek，复杂任务再切 Claude。",[60,9568,9570],{"id":9569},"_2-精确-add不要-add-整个目录","2. 精确 \u002Fadd，不要 \u002Fadd 整个目录",[31,9572,9573,9576,9577,9579],{},[333,9574,9575],{},"\u002Fadd src\u002F"," 会把整个 src 目录塞进上下文，token 暴增。只 ",[333,9578,9198],{}," 你要改的文件，Aider 通过 repo map 已经知道其他文件的接口签名。",[60,9581,9583],{"id":9582},"_3-任务描述具体到文件-函数-行为","3. 任务描述具体到文件 + 函数 + 行为",[31,9585,9586,70],{},[35,9587,9588],{},"烧 token 的写法",[326,9590,9593],{"className":9591,"code":9592,"language":876,"meta":331},[874],"> 帮我优化一下性能\n",[333,9594,9592],{"__ignoreMap":331},[31,9596,9597],{},"Aider 会全仓库扫描找瓶颈，token 爆炸。",[31,9599,9600,70],{},[35,9601,9602],{},"省 token 的写法",[326,9604,9607],{"className":9605,"code":9606,"language":876,"meta":331},[874],"> 把 src\u002Fapi\u002Fuser.ts 里的 getUserById 的数据库查询从 N+1 改成批量查询，\n> 用 WHERE id IN (?) 替代循环里逐个 SELECT\n",[333,9608,9606],{"__ignoreMap":331},[31,9610,9611],{},"任务被锁定到具体函数和具体改动，Aider 直接走最优路径。",[60,9613,9615],{"id":9614},"_4-用-clear-开新任务不要在一个对话里做太多","4. 用 \u002Fclear 开新任务，不要在一个对话里做太多",[31,9617,9618,9619,9621],{},"对话越长，每次请求都要把全部历史发给模型，token 线性增长。每完成一个任务 ",[333,9620,1546],{}," 一次。",[60,9623,9625],{"id":9624},"_5-weak-model-用最便宜的","5. weak-model 用最便宜的",[31,9627,9628,9629,9631,9632,9635],{},"commit message 不需要 SOTA 智能。DeepSeek-V3 \u002F GPT-4o-mini \u002F Claude Haiku 都够。在 ",[333,9630,8903],{}," 里设 ",[333,9633,9634],{},"weak-model: deepseek\u002Fdeepseek-chat","，省下来的 token 很可观。",[60,9637,9639],{"id":9638},"_6-用-read-only-标记不该看的文件","6. 用 read-only 标记不该看的文件",[326,9641,9643],{"className":8906,"code":9642,"language":8908,"meta":331,"style":331},"# .aider.conf.yml\nread-only:\n  - docs\u002F\n  - public\u002F\n  - node_modules\u002F\n  - \"*.test.ts\"\n",[333,9644,9645,9650,9656,9662,9668,9674],{"__ignoreMap":331},[336,9646,9647],{"class":338,"line":339},[336,9648,9649],{"class":393},"# .aider.conf.yml\n",[336,9651,9652,9654],{"class":338,"line":353},[336,9653,9013],{"class":8920},[336,9655,9016],{"class":1528},[336,9657,9658,9660],{"class":338,"line":363},[336,9659,9021],{"class":1528},[336,9661,9024],{"class":346},[336,9663,9664,9666],{"class":338,"line":378},[336,9665,9021],{"class":1528},[336,9667,9031],{"class":346},[336,9669,9670,9672],{"class":338,"line":390},[336,9671,9021],{"class":1528},[336,9673,9038],{"class":346},[336,9675,9676,9678],{"class":338,"line":397},[336,9677,9021],{"class":1528},[336,9679,9680],{"class":346},"\"*.test.ts\"\n",[31,9682,9683],{},"避免 Aider 把无关文件塞进上下文。",[60,9685,9687],{"id":9686},"_7-简单任务不开-architect-模式","7. 简单任务不开 Architect 模式",[31,9689,9690],{},"Architect 模式跑两轮模型调用（架构师 + 编辑器），成本翻倍。改一个函数 \u002F 加一个 if 分支这种简单任务用单模型跑就行。Architect 的甜区是 5+ 文件、跨模块、需要规划的复杂任务。",[27,9692,9694],{"id":9693},"_5-个-prompt-模板","5 个 Prompt 模板",[60,9696,9698],{"id":9697},"_1-新功能开发","1. 新功能开发",[326,9700,9703],{"className":9701,"code":9702,"language":876,"meta":331},[874],"\u002Fadd src\u002F\u003C相关文件1> src\u002F\u003C相关文件2>\n\n我要加一个\u003C功能描述>功能。\n要求：\n1. 在 src\u002F\u003C模块>\u002F 目录下新建 \u003CFileName>，实现核心逻辑\n2. 在 src\u002F\u003C入口文件> 里接入调用\n3. 遵循现有代码风格（命名、错误处理、日志）\n4. 不要动其他模块，只改我 add 的文件 + 新建文件\n5. 如果需要新依赖，先告诉我，不要自己加 package.json\n\n先告诉我你的方案，我确认后再动手。\n",[333,9704,9702],{"__ignoreMap":331},[60,9706,9708],{"id":9707},"_2-bug-修复","2. Bug 修复",[326,9710,9713],{"className":9711,"code":9712,"language":876,"meta":331},[874],"\u002Fadd src\u002F\u003C出 bug 的文件>\n\n\u003C文件>里的\u003C函数名>有 bug：\u003C描述 bug 现象 + 复现步骤>。\n\n预期行为：\u003C正确行为>\n实际行为：\u003C错误行为>\n\n要求：\n1. 找到 root cause，不要只修表面症状\n2. 修复后加一个测试用例覆盖这个场景\n3. 检查同模块里有没有类似的 bug\n4. commit message 用 fix: 前缀\n",[333,9714,9712],{"__ignoreMap":331},[60,9716,9718],{"id":9717},"_3-重构","3. 重构",[326,9720,9723],{"className":9721,"code":9722,"language":876,"meta":331},[874],"\u002Fadd src\u002F\u003C要重构的文件1> src\u002F\u003C要重构的文件2>\n\n把\u003C模块名>做以下重构：\n1. 把\u003C大函数>拆成 3 个小函数，每个只做一件事\n2. 提取重复逻辑到 utils\n3. 类型从 any 改成具体类型\n4. 不改变外部行为，测试必须全绿\n\n要求：\n- 每一步拆成独立 commit\n- 每次改动后跑 npm test 确认没 break\n- commit message 用 refactor: 前缀\n",[333,9724,9722],{"__ignoreMap":331},[60,9726,9728],{"id":9727},"_4-代码审查","4. 代码审查",[326,9730,9733],{"className":9731,"code":9732,"language":876,"meta":331},[874],"\u002Fadd src\u002F\u003C要审查的文件>\n\n审查这个文件的代码质量，关注：\n1. 安全漏洞（SQL 注入、XSS、敏感信息泄露）\n2. 性能问题（N+1 查询、不必要的循环、内存泄漏）\n3. 错误处理是否完整（边界条件、异常捕获）\n4. 类型安全（any 滥用、类型断言风险）\n5. 可读性（命名、注释、函数长度）\n\n产出：按严重程度排序的问题列表，每个问题给出修复建议。\n不要直接改代码，只做分析。\n",[333,9734,9732],{"__ignoreMap":331},[60,9736,9738],{"id":9737},"_5-测试生成","5. 测试生成",[326,9740,9743],{"className":9741,"code":9742,"language":876,"meta":331},[874],"\u002Fadd src\u002F\u003C要测试的文件>\n\n为\u003C文件名>里的所有导出函数生成单元测试。\n要求：\n1. 用 vitest（或 jest \u002F pytest，根据项目配置）\n2. 每个函数至少 3 个用例：正常输入 + 边界值 + 异常输入\n3. mock 外部依赖（数据库、API 调用）\n4. 测试文件放在 __tests__\u002F 目录，文件名 \u003C原文件名>.test.ts\n5. 覆盖率目标 80%+\n6. 跑一遍测试确认全绿\n",[333,9744,9742],{"__ignoreMap":331},[27,9746,9747],{"id":9747},"常见坑",[60,9749,9751],{"id":9750},"安装-配置","安装 \u002F 配置",[72,9753,9754,9768,9778,9787],{},[75,9755,9756,9759,9760,9763,9764,9767],{},[35,9757,9758],{},"pip install 报依赖冲突","：用 ",[333,9761,9762],{},"--upgrade-strategy only-if-needed"," 参数，或用 ",[333,9765,9766],{},"pipx install aider-chat"," 隔离环境",[75,9769,9770,9773,9774,9777],{},[35,9771,9772],{},"API key 没生效","：检查环境变量是否正确设置，",[333,9775,9776],{},"echo $DEEPSEEK_API_KEY"," 验证",[75,9779,9780,9783,9784],{},[35,9781,9782],{},"Ollama 本地模型超 context 不报错","：静默截断会让你以为\"模型忘了我加的文件\"。明确配置 ",[333,9785,9786],{},"num_ctx",[75,9788,9789,9792],{},[35,9790,9791],{},"Windows 路径问题","：用 WSL 最省心，纯 Windows 确保用正斜杠路径",[60,9794,9796],{"id":9795},"任务-执行","任务 \u002F 执行",[72,9798,9799,9812,9826,9834],{},[75,9800,9801,9804,9805,9808,9809,9811],{},[35,9802,9803],{},"没在 git 仓库里跑会被警告","：Aider 强烈建议 ",[333,9806,9807],{},"git init"," 后再用，否则 ",[333,9810,9397],{}," 不能用",[75,9813,9814,9819,9820,9823,9824],{},[35,9815,9816,9818],{},[333,9817,9198],{}," 之前的文件不会被看见","：和 Cursor ",[333,9821,9822],{},"@codebase"," 不同，Aider 默认只看 repo map，要修改的文件要显式 ",[333,9825,9198],{},[75,9827,9828,9833],{},[35,9829,9830,9832],{},[333,9831,9397],{}," 不是\"撤销修改\"","：它撤销的是上一次 Aider commit，已被你后续手动修改过的文件不会被撤销",[75,9835,9836,9842],{},[35,9837,9838,9841],{},[333,9839,9840],{},"--yes-always"," 太激进","：会跳过所有确认，包括\"是否要把这个文件加入 chat\"——容易让 Aider 偷偷读你不想给模型看的文件",[60,9844,9846],{"id":9845},"architect-模式","Architect 模式",[72,9848,9849,9855],{},[75,9850,9851,9854],{},[35,9852,9853],{},"editor model 不能用推理模型","：o3 \u002F R1 当 editor 生成的 diff 经常不符合 unified diff 格式，应用失败率高。editor 必须用 Claude Sonnet \u002F GPT-4.1 这类指令遵循能力好的模型",[75,9856,9857,9860,9861,9864],{},[35,9858,9859],{},"Architect 模式有 prompt injection 风险","：不要在不可信项目上开 architect mode + ",[333,9862,9863],{},"--yes"," 组合，README 被塞了攻击指令可能诱导 editor 生成后门代码",[60,9866,9867],{"id":9867},"和其他工具的衔接",[72,9869,9870,9876,9882],{},[75,9871,9872,9875],{},[35,9873,9874],{},"和 Cursor 一起用","：Aider 做 git 工作流重的修改，Cursor 做 inline 补全 + 快速跳转",[75,9877,9878,9881],{},[35,9879,9880],{},"和 Claude Code 一起用","：Aider 做日常小改 + 多模型试验，Claude Code 做长任务 + 复杂重构",[75,9883,9884,9887],{},[35,9885,9886],{},"和 GitHub Copilot 一起用","：Copilot 做代码补全，Aider 做多文件修改 + commit",[27,9889,9891],{"id":9890},"上线前-checklist","上线前 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从零上手教程：如何完成 pip 安装、OpenAI \u002F Anthropic \u002F DeepSeek API key 配置、第一个项目实战、git 工作流、Architect 双模型模式、省 token 技巧和 5 个可复用 Prompt 模板。30 分钟从安装到日常使用。",{},"\u002Fplaybook\u002Fonboarding\u002Faider-getting-started",[3137,1398,3136],{"title":8332,"description":10051},"playbook\u002Fonboarding\u002Faider-getting-started",[3807,1910,10058,4052,10059],"AI 编程","终端","MkAZOlDqS58g5gQdl0DU6tW1TWEnhwB1b1k-v448B0U",{"id":10062,"title":10063,"body":10064,"category":10050,"cover":5314,"description":10909,"extension":754,"meta":10910,"navigation":765,"path":10911,"published":791,"relatedTools":10912,"seo":10913,"stem":10914,"tags":10915,"updated":791,"__hash__":10919},"playbook\u002Fplaybook\u002Fonboarding\u002Fcoze-getting-started.md","Coze 上手指南：从零搭建第一个 AI Bot，工作流+插件+多端发布",{"type":24,"value":10065,"toc":10883},[10066,10068,10079,10086,10088,10128,10131,10160,10165,10176,10189,10193,10196,10235,10241,10244,10250,10259,10337,10348,10392,10395,10411,10414,10417,10434,10439,10459,10464,10478,10481,10484,10498,10503,10574,10578,10611,10613,10619,10663,10667,10671,10677,10681,10687,10691,10697,10701,10707,10711,10717,10721,10727,10731,10737,10739,10743,10763,10767,10793,10797,10817,10819,10851,10853],[27,10067,8337],{"id":8337},[31,10069,8340,10070,413,10073,413,10076,2416],{},[35,10071,10072],{},"第一次用 Coze 不知道从哪下手的人",[35,10074,10075],{},"免费档玩了几次想做一个真能上线的 Bot 的人",[35,10077,10078],{},"想用工作流 + 插件做自动化但怕按调用计费烧爆的人",[31,10080,10081,10082,10085],{},"这份指南目标是让你在 ",[35,10083,10084],{},"30 分钟内完成注册 → 跑通第一个 Bot → 会配插件 → 能发布到飞书 \u002F 网页","，并且知道怎么控制成本。",[27,10087,8358],{"id":8358},[72,10089,10090,10110,10116,10122],{},[75,10091,10092,10095,10096,10100,10101,2416,10106,10109],{},[35,10093,10094],{},"选对版本","：面向国内用户、要发飞书 \u002F 抖音 \u002F 微信 → ",[696,10097,10099],{"href":5315,"rel":10098},[1009],"扣子 coze.cn","；面向海外、要接 GPT \u002F Claude → ",[696,10102,10105],{"href":10103,"rel":10104},"https:\u002F\u002Fwww.coze.com",[1009],"coze.com",[35,10107,10108],{},"两边账号不互通","，选错要重做。",[75,10111,10112,10115],{},[35,10113,10114],{},"可登录的账号","：国内版用飞书 \u002F 抖音账号最快；国际版用 Google 账号。",[75,10117,10118,10121],{},[35,10119,10120],{},"一个真实想解决的问题","：不要用\"你好\"测试 Coze，浪费日额度也看不出效果。想好你要做的 Bot 解决什么问题（客服 \u002F 内容生成 \u002F 数据处理）。",[75,10123,10124,10127],{},[35,10125,10126],{},"国际版额外","：要接 GPT \u002F Claude 需要 BYOK——自己绑海外信用卡，平台不代付。",[27,10129,10130],{"id":10130},"注册和登录",[432,10132,10133,10145,10154,10157],{},[75,10134,1004,10135,10139,10140,10144],{},[696,10136,10138],{"href":5315,"rel":10137},[1009],"www.coze.cn","（国内）或 ",[696,10141,10143],{"href":10103,"rel":10142},[1009],"www.coze.com","（国际）",[75,10146,10147,10139,10150,10153],{},[35,10148,10149],{},"飞书 \u002F 抖音账号",[35,10151,10152],{},"Google 账号","（国际）登录最快",[75,10155,10156],{},"完成首次引导（可跳过）",[75,10158,10159],{},"进入\"工作空间\"，能看到模型额度（国内免费版有日额度）",[31,10161,10162,70],{},[35,10163,10164],{},"常见问题",[72,10166,10167,10170,10173],{},[75,10168,10169],{},"国内版提示账号异常 → 换飞书 \u002F 抖音账号重试，手机号注册受限多",[75,10171,10172],{},"国际版登录卡住 → 清 cookie 或换浏览器无痕模式",[75,10174,10175],{},"国际版接 GPT 报错 → 检查 BYOK 的海外信用卡是否绑成功",[1410,10177,10179],{"className":10178},[1413,1414,1415],[31,10180,10181,10184,10185,10188],{},[35,10182,10183],{},"版本选择红线","：国内版（扣子）和国际版（coze.com）是两套独立系统，账号、Bot、工作流",[35,10186,10187],{},"完全不互通","。别在国内版做完想着搬国际版，得从头重做。选哪个看你面向哪边用户、要不要接 GPT\u002FClaude。",[27,10190,10192],{"id":10191},"创建第一个-bot","创建第一个 Bot",[31,10194,10195],{},"以\"小红书文案生成 Bot\"为例，10 分钟跑通：",[432,10197,10198,10205,10211,10217,10223,10229,10232],{},[75,10199,10200,10201,10204],{},"左侧\"工作空间\" → ",[35,10202,10203],{},"\"+创建 Bot\"","，起个名字（如\"小红书文案助手\"）",[75,10206,10207,10210],{},[35,10208,10209],{},"选模型","：国内推荐豆包 Pro（免费额度够试），国际推 Claude Sonnet 4",[75,10212,10213,10216],{},[35,10214,10215],{},"写 Bot 角色 prompt","（这是 Bot 行为的核心，见下方模板）",[75,10218,10219,10222],{},[35,10220,10221],{},"可选：上传知识库","——传几篇你写过的高赞小红书笔记，让 Bot 模仿你的风格",[75,10224,10225,10228],{},[35,10226,10227],{},"测试对话","：右侧调试窗口输入\"帮我写一篇关于露营的小红书文案\"，看效果",[75,10230,10231],{},"反复调 prompt 直到满意",[75,10233,10234],{},"右上**\"发布\"** → 选渠道（先选\"自定义网页\"试水）",[31,10236,10237,10240],{},[35,10238,10239],{},"关键认知","：Bot 的效果 80% 取决于 prompt 写得多具体。\"你是一个文案助手\"这种废话 prompt 出来的 Bot 一定是平庸的。把人设、语气、结构、字数、禁忌都写清楚。",[27,10242,10243],{"id":10243},"工作流编排",[31,10245,10246,10247,2416],{},"Bot 能做\"对话\"，但做不了\"批量处理\"和\"多步骤逻辑\"。要这些能力，用",[35,10248,10249],{},"工作流（Workflow）",[31,10251,10252,10253,10258],{},"Coze 工作流是 DAG 节点编排，可被 Bot 调用，也可独立部署成 API。节点类型（基于 ",[696,10254,10257],{"href":10255,"rel":10256},"https:\u002F\u002Fdeveloper.volcengine.com\u002Farticles\u002F7530117616687480851",[1009],"火山引擎社区 2025 实战","）：",[145,10260,10261,10271],{},[148,10262,10263],{},[151,10264,10265,10268],{},[154,10266,10267],{},"节点",[154,10269,10270],{},"作用",[161,10272,10273,10281,10289,10297,10305,10313,10321,10329],{},[151,10274,10275,10278],{},[166,10276,10277],{},"开始 \u002F 结束",[166,10279,10280],{},"输入输出",[151,10282,10283,10286],{},[166,10284,10285],{},"大模型节点",[166,10287,10288],{},"调豆包 \u002F GPT \u002F Claude",[151,10290,10291,10294],{},[166,10292,10293],{},"代码节点",[166,10295,10296],{},"内嵌 Python \u002F JavaScript，做数据格式转换",[151,10298,10299,10302],{},[166,10300,10301],{},"循环节点",[166,10303,10304],{},"批量处理多条数据",[151,10306,10307,10310],{},[166,10308,10309],{},"条件节点",[166,10311,10312],{},"分支判断",[151,10314,10315,10318],{},[166,10316,10317],{},"插件节点",[166,10319,10320],{},"调插件市场的工具",[151,10322,10323,10326],{},[166,10324,10325],{},"知识库节点",[166,10327,10328],{},"RAG 检索",[151,10330,10331,10334],{},[166,10332,10333],{},"HTTP 节点",[166,10335,10336],{},"调外部 API",[31,10338,10339,10342,10343,10258],{},[35,10340,10341],{},"实战：批量生成小红书文案工作流","（参考 ",[696,10344,10347],{"href":10345,"rel":10346},"https:\u002F\u002Fwww.toutiao.com\u002Farticle\u002F7469986334686315017",[1009],"今日头条 涛哥讲AI 2025-02 教程",[432,10349,10350,10353,10359,10368,10373,10378,10383,10389],{},[75,10351,10352],{},"在\"资源库\" → 创建工作流",[75,10354,10355,10358],{},[35,10356,10357],{},"开始节点","：输入飞书多维表格的记录列表",[75,10360,10361,10363,10364,10367],{},[35,10362,10317],{},"：读飞书多维表格（设 ",[333,10365,10366],{},"page_size","，最大 500 条）",[75,10369,10370,10372],{},[35,10371,10301],{},"：遍历每条记录",[75,10374,10375,10377],{},[35,10376,10285],{},"：把每条记录的主题转写成小红书风格文案",[75,10379,10380,10382],{},[35,10381,10317],{},"：把生成结果写回飞书多维表格",[75,10384,10385,10388],{},[35,10386,10387],{},"结束节点","：输出处理数量",[75,10390,10391],{},"调试 → 在 Bot 里\"添加工作流\"引用",[31,10393,10394],{},"整个流程零代码完成。这类\"批量内容生成\"是 Coze 工作流的高频打法。",[1410,10396,10398],{"className":10397},[1413,1414,1415],[31,10399,10400,10403,10404,10406,10407,10410],{},[35,10401,10402],{},"工作流踩坑","：读取飞书表格默认只取 20 条，要改 ",[333,10405,10366],{},"，最大 500 条；超过 500 要分页或用循环节点。记录条数 > 50 时建议在 Bot 里\"异步\"调用工作流，不要同步走，否则容易超时。写入多维表格需要 ",[333,10408,10409],{},"Array\u003CObject>"," 格式，代码节点要做转换。",[27,10412,10413],{"id":10413},"插件配置",[31,10415,10416],{},"插件是 Coze 的护城河——让 Bot 不止\"会聊天\"，还能\"动手\"。配置方式：",[432,10418,10419,10425,10428,10431],{},[75,10420,10421,10422],{},"Bot 编辑页 → ",[35,10423,10424],{},"\"+ 插件\"",[75,10426,10427],{},"从插件市场选（飞书多维表格、联网搜索、图像生成、代码执行、微博、抖音、bilibili、Notion 等）",[75,10429,10430],{},"配置插件参数（如飞书表格的 app_token、table_id）",[75,10432,10433],{},"在 prompt 里告诉 Bot 什么时候调这个插件",[31,10435,10436,70],{},[35,10437,10438],{},"高频插件组合",[72,10440,10441,10447,10453],{},[75,10442,10443,10446],{},[35,10444,10445],{},"客服 Bot","：知识库（产品文档）+ 飞书多维表格（查订单）+ 联网搜索（查物流）",[75,10448,10449,10452],{},[35,10450,10451],{},"内容 Bot","：联网搜索（抓热点）+ 大模型（生成文案）+ 图像生成（配图）",[75,10454,10455,10458],{},[35,10456,10457],{},"数据 Bot","：飞书多维表格（读数据）+ 代码执行（算指标）+ 大模型（写洞察）",[31,10460,10461,70],{},[35,10462,10463],{},"插件配置要点",[72,10465,10466,10469,10472],{},[75,10467,10468],{},"插件需要授权——飞书类插件要绑飞书账号授权读写权限",[75,10470,10471],{},"第三方插件质量参差，生产用之前要测稳定性",[75,10473,10474,10477],{},[35,10475,10476],{},"部分插件调用会单独计费","，是成本盲区，配之前看清楚",[27,10479,10480],{"id":10480},"多端发布",[31,10482,10483],{},"发布是 Coze 最被低估的能力。Bot 做好后：",[432,10485,10486,10489,10492,10495],{},[75,10487,10488],{},"右上**\"发布\"** → 选渠道",[75,10490,10491],{},"国内版可选：飞书机器人、抖音 \u002F 头条号、微信小程序 \u002F 公众号（部分需企业认证）、自定义网页嵌入、API 接口",[75,10493,10494],{},"国际版可选：Discord、自定义网页、API",[75,10496,10497],{},"按引导授权 → 拿到调用地址 \u002F webhook",[31,10499,10500,70],{},[35,10501,10502],{},"发布渠道选择建议",[145,10504,10505,10517],{},[148,10506,10507],{},[151,10508,10509,10512,10514],{},[154,10510,10511],{},"渠道",[154,10513,7606],{},[154,10515,10516],{},"注意",[161,10518,10519,10530,10541,10552,10563],{},[151,10520,10521,10524,10527],{},[166,10522,10523],{},"自定义网页",[166,10525,10526],{},"最快验证，分享链接即可测",[166,10528,10529],{},"默认有 Coze 水印",[151,10531,10532,10535,10538],{},[166,10533,10534],{},"飞书机器人",[166,10536,10537],{},"企业内部客服 \u002F 助手",[166,10539,10540],{},"一键绑，toB 首选",[151,10542,10543,10546,10549],{},[166,10544,10545],{},"API 接口",[166,10547,10548],{},"嵌入自家产品",[166,10550,10551],{},"拿 OpenAPI 风格调用地址",[151,10553,10554,10557,10560],{},[166,10555,10556],{},"微信小程序 \u002F 公众号",[166,10558,10559],{},"toC 触达",[166,10561,10562],{},"部分需企业认证，审核较严",[151,10564,10565,10568,10571],{},[166,10566,10567],{},"抖音 \u002F 头条号",[166,10569,10570],{},"内容生态内互动",[166,10572,10573],{},"适合内容类 Bot",[31,10575,10576,70],{},[35,10577,9891],{},[72,10579,10581,10587,10593,10599,10605],{"className":10580},[9895],[75,10582,10584,10586],{"className":10583},[9899],[9901,10585],{"disabled":765,"type":9903}," 已在调试窗口跑过 10+ 轮真实对话，效果稳定",[75,10588,10590,10592],{"className":10589},[9899],[9901,10591],{"disabled":765,"type":9903}," 已估算日调用量，确认专业版月成本可控（按调用计费没有封顶）",[75,10594,10596,10598],{"className":10595},[9899],[9901,10597],{"disabled":765,"type":9903}," prompt 里的禁忌 \u002F 兜底回复已写好（避免 Bot 乱说话）",[75,10600,10602,10604],{"className":10601},[9899],[9901,10603],{"disabled":765,"type":9903}," 知识库已上传最新文档（切片策略改动后老数据不会自动重切，要重新上传）",[75,10606,10608,10610],{"className":10607},[9899],[9901,10609],{"disabled":765,"type":9903}," 国内版内容合规已自查（金融 \u002F 医疗 \u002F 政治话题可能被审核拦）",[27,10612,9556],{"id":9555},[31,10614,10615,10616,2416],{},"Coze 国内版免费档有日模型额度，专业版按调用计费。",[35,10617,10618],{},"控制 token 消耗 = 控制成本",[432,10620,10621,10627,10633,10639,10645,10651,10657],{},[75,10622,10623,10626],{},[35,10624,10625],{},"prompt 写具体不写模糊","：模糊 prompt 让模型反复试错，token 翻倍。把人设、结构、字数、禁忌都写清楚。",[75,10628,10629,10632],{},[35,10630,10631],{},"知识库别堆太多无关文档","：RAG 检索会拉入大量 context，无关文档白白烧 token。知识库要精不要多。",[75,10634,10635,10638],{},[35,10636,10637],{},"能用小模型别上大模型","：简单分类 \u002F 改写用豆包 lite，复杂推理才上豆包 Pro。在工作流里给每个大模型节点单独选模型。",[75,10640,10641,10644],{},[35,10642,10643],{},"工作流别让模型自己决定循环多少次","：明说\"处理这 10 条\"，别让它自由发挥。",[75,10646,10647,10650],{},[35,10648,10649],{},"限制输出长度","：prompt 里写\"不超过 200 字\"，比\"详细写\"省一半 token。",[75,10652,10653,10656],{},[35,10654,10655],{},"少在对话里塞长上下文","：长对话每轮都带历史，token 累积爆炸。重要任务用工作流一次性处理，不要多轮对话。",[75,10658,10659,10662],{},[35,10660,10661],{},"爆款 Bot 要设调用上限","：按调用计费没有封顶，Bot 被传播开可能一夜烧爆账户。上线前评估峰值并发，必要时在发布端做限流。",[27,10664,10666],{"id":10665},"_7-个可复用-prompt-模板","7 个可复用 Prompt 模板",[60,10668,10670],{"id":10669},"_1-小红书文案生成","1. 小红书文案生成",[326,10672,10675],{"className":10673,"code":10674,"language":876,"meta":331},[874],"你是一个小红书爆款文案写手。\n风格：口语化、有梗、emoji 适度、开头 3 秒抓人。\n任务：根据主题 [主题] 写一篇小红书笔记。\n结构：吸睛标题（带数字或情绪）+ 开头痛点 + 3 个核心点（每点配 emoji）+ 互动结尾 + 5 个话题标签。\n字数：300-400 字。\n禁忌：不写\"亲爱的\"、不用书面语、不堆砌形容词。\n",[333,10676,10674],{"__ignoreMap":331},[60,10678,10680],{"id":10679},"_2-客服-bot-角色","2. 客服 Bot 角色",[326,10682,10685],{"className":10683,"code":10684,"language":876,"meta":331},[874],"你是 [产品名] 的客服助手。\n你能做：根据知识库回答产品问题、查订单状态（调用飞书表格插件）、查物流（调用联网搜索插件）。\n语气：专业、简洁、有同理心，不啰嗦。\n规则：\n- 只回答知识库和插件能覆盖的问题，超出范围说\"这个问题我转人工，请留下联系方式\"\n- 涉及退款 \u002F 投诉 \u002F 价格争议，直接引导转人工\n- 每条回复不超过 150 字\n- 不编造功能，不确定就说不知道\n兜底：用户说\"人工\"或连续 2 次没解决问题，主动转人工。\n",[333,10686,10684],{"__ignoreMap":331},[60,10688,10690],{"id":10689},"_3-工作流批处理节点","3. 工作流批处理节点",[326,10692,10695],{"className":10693,"code":10694,"language":876,"meta":331},[874],"你是数据转写员。\n输入：一条飞书表格记录，字段包含 [字段1、字段2、字段3]。\n任务：把这条记录转写成 [目标格式，如：一段产品介绍 \u002F 一条朋友圈 \u002F 一封邮件]。\n要求：\n- 严格按 [格式模板] 输出\n- 保留原始数据，不编造\n- 输出纯文本，不带解释\n- 单条不超过 [N] 字\n",[333,10696,10694],{"__ignoreMap":331},[60,10698,10700],{"id":10699},"_4-知识库问答-引用","4. 知识库问答 + 引用",[326,10702,10705],{"className":10703,"code":10704,"language":876,"meta":331},[874],"你是 [领域] 知识助手。\n回答用户问题时：\n1. 先检索知识库，只基于检索到的内容回答\n2. 检索不到就说\"知识库中没有相关信息\"，不编造\n3. 每个事实点后标注来源段落（如\"参考：员工手册第3章\"）\n4. 回答结构化，用要点列表\n5. 涉及数字 \u002F 日期 \u002F 政策，必须引用原文\n语气：客观、准确、不口语化。\n",[333,10706,10704],{"__ignoreMap":331},[60,10708,10710],{"id":10709},"_5-内容审核-分类","5. 内容审核 \u002F 分类",[326,10712,10715],{"className":10713,"code":10714,"language":876,"meta":331},[874],"你是内容分类员。\n输入：一段用户生成内容。\n任务：判断它属于以下哪类：[正常 \u002F 广告 \u002F 违规 \u002F 敏感]。\n判断标准：\n- 正常：符合社区规范\n- 广告：含明显推广 \u002F 联系方式 \u002F 引流话术\n- 违规：含辱骂 \u002F 人身攻击 \u002F 虚假信息\n- 敏感：涉及政治 \u002F 医疗 \u002F 金融风险\n输出格式：只输出类别名 + 一句话理由，不超过 30 字。\n不确定的归到最接近的上一类，不要瞎判。\n",[333,10716,10714],{"__ignoreMap":331},[60,10718,10720],{"id":10719},"_6-数据洞察生成","6. 数据洞察生成",[326,10722,10725],{"className":10723,"code":10724,"language":876,"meta":331},[874],"你是数据分析师。\n输入：一份 [指标] 的数据表（飞书多维表格读取）。\n任务：产出 3 条可执行的业务洞察。\n要求：\n- 每条洞察 = 现象 + 原因假设 + 建议动作\n- 现象必须基于数据，不编造\n- 找出 1 个异常值或反趋势\n- 输出 Markdown 格式，每条 100 字内\n- 不需要画图，文字说清楚\n",[333,10726,10724],{"__ignoreMap":331},[60,10728,10730],{"id":10729},"_7-多语言翻译-bot","7. 多语言翻译 Bot",[326,10732,10735],{"className":10733,"code":10734,"language":876,"meta":331},[874],"你是专业翻译。\n支持语言对：[中-英、中-日、中-韩]。\n翻译规则：\n- 信达雅，不机翻腔\n- 保留专有名词 \u002F 品牌名 \u002F 代码不翻\n- 语气随场景调整（正式 \u002F 口语 \u002F 营销）\n- 用户没指定语言时，根据内容自动判断并说明\n输出：只输出译文，不带解释。需要解释时用户会单独问。\n长度：尽量与原文等长，不随意扩写或缩写。\n",[333,10736,10734],{"__ignoreMap":331},[27,10738,9747],{"id":9747},[60,10740,10742],{"id":10741},"注册-账号","注册 \u002F 账号",[72,10744,10745,10751,10757],{},[75,10746,10747,10750],{},[35,10748,10749],{},"国内版 vs 国际版不互通","：两套独立系统，账号、Bot、工作流都不通用。选错版本要重做。",[75,10752,10753,10756],{},[35,10754,10755],{},"国内手机号注册受限","：用飞书 \u002F 抖音账号登录最快。",[75,10758,10759,10762],{},[35,10760,10761],{},"国际版接 GPT\u002FClaude 要 BYOK","：自己绑海外信用卡，平台不代付。",[60,10764,10766],{"id":10765},"bot-执行","Bot \u002F 执行",[72,10768,10769,10775,10781,10787],{},[75,10770,10771,10774],{},[35,10772,10773],{},"prompt 写太模糊","：Bot 效果差 80% 是 prompt 问题。把人设、结构、字数、禁忌都写具体。",[75,10776,10777,10780],{},[35,10778,10779],{},"知识库切片改动后老数据不重切","：要重新上传文档。",[75,10782,10783,10786],{},[35,10784,10785],{},"国内版内容审核","：金融 \u002F 医疗 \u002F 政治话题可能被拦，prompt 里别碰红线。",[75,10788,10789,10792],{},[35,10790,10791],{},"运行超时","：单工作流执行有时间上限，记录条数 > 50 用异步调用。",[60,10794,10796],{"id":10795},"成本-计费","成本 \u002F 计费",[72,10798,10799,10805,10811],{},[75,10800,10801,10804],{},[35,10802,10803],{},"专业版按调用计费没有封顶","：爆款 Bot 一夜烧爆账户，上线前估算峰值并发。",[75,10806,10807,10810],{},[35,10808,10809],{},"插件调用可能单独计费","：配插件前看清楚是否额外收费。",[75,10812,10813,10816],{},[35,10814,10815],{},"长对话 token 累积爆炸","：重要任务用工作流一次性处理，不要无意义多轮对话。",[60,10818,9867],{"id":9867},[72,10820,10821,10831,10840],{},[75,10822,10823,10826,10827,10830],{},[35,10824,10825],{},"和 Dify 一起用","：Coze 做前端 Bot 发布 + 飞书生态，",[696,10828,5320],{"href":10829},"\u002Fagent\u002Fplatform\u002Fdify.html"," 做后端 AI 中台 + 复杂工作流，API 互通。",[75,10832,10833,70,10836,10839],{},[35,10834,10835],{},"和 FastGPT 一起用",[696,10837,5329],{"href":10838},"\u002Fagent\u002Fplatform\u002Ffastgpt.html"," 做知识库底座（RAG 精度更高），Coze 做前端 Bot。",[75,10841,10842,10845,10846,2108,10848,10850],{},[35,10843,10844],{},"数据敏感要自托管","：直接去 ",[696,10847,5320],{"href":10829},[696,10849,5329],{"href":10838},"，Coze 不支持真正私有部署。",[27,10852,690],{"id":690},[72,10854,10855,10861,10867,10873,10878],{},[75,10856,10857],{},[696,10858,10860],{"href":10859},"\u002Freview\u002Fcoze-deep-review.html","Coze 深度评测：字节 AI Bot 平台真能零代码搭出能用 Agent 吗",[75,10862,10863],{},[696,10864,10866],{"href":10865},"\u002Fcompare\u002Fcoze-vs-dify.html","Coze vs Dify：AI Agent 平台怎么选",[75,10868,10869],{},[696,10870,10872],{"href":10871},"\u002Fagent\u002Fplatform\u002Fcoze.html","Coze 工具卡：字节 AI Bot 平台",[75,10874,10875],{},[696,10876,10877],{"href":10829},"Dify 工具卡：开源 LLMOps 平台",[75,10879,10880],{},[696,10881,10882],{"href":10838},"FastGPT 工具卡：开源知识库问答系统",{"title":331,"searchDepth":363,"depth":363,"links":10884},[10885,10886,10887,10888,10889,10890,10891,10892,10893,10902,10908],{"id":8337,"depth":353,"text":8337},{"id":8358,"depth":353,"text":8358},{"id":10130,"depth":353,"text":10130},{"id":10191,"depth":353,"text":10192},{"id":10243,"depth":353,"text":10243},{"id":10413,"depth":353,"text":10413},{"id":10480,"depth":353,"text":10480},{"id":9555,"depth":353,"text":9556},{"id":10665,"depth":353,"text":10666,"children":10894},[10895,10896,10897,10898,10899,10900,10901],{"id":10669,"depth":363,"text":10670},{"id":10679,"depth":363,"text":10680},{"id":10689,"depth":363,"text":10690},{"id":10699,"depth":363,"text":10700},{"id":10709,"depth":363,"text":10710},{"id":10719,"depth":363,"text":10720},{"id":10729,"depth":363,"text":10730},{"id":9747,"depth":353,"text":9747,"children":10903},[10904,10905,10906,10907],{"id":10741,"depth":363,"text":10742},{"id":10765,"depth":363,"text":10766},{"id":10795,"depth":363,"text":10796},{"id":9867,"depth":363,"text":9867},{"id":690,"depth":353,"text":690},"Coze 从零上手教程：如何完成国内版 \u002F 国际版选择与注册、创建第一个 Bot、工作流可视化编排、插件配置、多端一键发布到飞书抖音微信、省 token 与按调用计费控预算技巧，以及 7 个可复用 Prompt 模板。",{},"\u002Fplaybook\u002Fonboarding\u002Fcoze-getting-started",[5312,5321,5330],{"title":10063,"description":10909},"playbook\u002Fonboarding\u002Fcoze-getting-started",[5311,2123,10916,10917,10918],"Bot","工作流","零代码","SLI4EsprizISM2V2zF1_g67X2WWH7QEQ0INUwVY_Fqc",{"id":10921,"title":10922,"body":10923,"category":10050,"cover":5323,"description":12143,"extension":754,"meta":12144,"navigation":765,"path":12145,"published":791,"relatedTools":12146,"seo":12147,"stem":12148,"tags":12149,"updated":791,"__hash__":12151},"playbook\u002Fplaybook\u002Fonboarding\u002Fdify-getting-started.md","Dify 上手指南：从零搭建 AI Agent 工作流，私有部署实战",{"type":24,"value":10924,"toc":12111},[10925,10927,10939,10946,10948,10951,10983,10998,11002,11005,11053,11064,11078,11088,11122,11132,11135,11145,11166,11171,11177,11183,11186,11189,11256,11262,11295,11300,11304,11307,11310,11452,11456,11459,11552,11558,11561,11567,11581,11590,11594,11597,11600,11616,11664,11677,11680,11686,11706,11712,11721,11724,11757,11761,11764,11781,11786,11853,11859,11868,11871,11874,11877,11880,11928,11931,11937,11954,11957,11973,11976,12015,12019,12023,12029,12033,12039,12043,12049,12053,12059,12063,12069,12071,12108],[27,10926,8337],{"id":8337},[31,10928,10929,10930,413,10933,413,10936,2416],{},"这份指南适合三类用户：",[35,10931,10932],{},"准备用 Dify 搭企业 AI 应用平台的开发者",[35,10934,10935],{},"已经装了 Dify 但工作流编排不知道从哪下手的团队",[35,10937,10938],{},"在 Dify \u002F Coze \u002F FastGPT 之间做技术选型、需要跑一遍 POC 的架构师",[31,10940,10941,10942,10945],{},"目标是在 ",[35,10943,10944],{},"1 小时内完成私有部署 → 创建第一个应用 → 编排一个真实工作流 → 发布 API","，并且知道生产环境要踩哪些坑。",[27,10947,8358],{"id":8358},[31,10949,10950],{},"在开始之前，确认以下条件：",[72,10952,10953,10960,10969,10976],{},[75,10954,10955,10956,10959],{},"一台 Linux 服务器（阿里云 \u002F 腾讯云 \u002F 自建均可），最低 ",[35,10957,10958],{},"2C4G + 30GB 硬盘","。推荐 4C8G 起步，企业级日活上千需 8C16G+。",[75,10961,10962,10963,6951,10966,2416],{},"已装 ",[35,10964,10965],{},"Docker 24+",[35,10967,10968],{},"Docker Compose 2.20+",[75,10970,10971,10972,10975],{},"至少一个可用的 ",[35,10973,10974],{},"LLM API Key","——OpenAI \u002F Anthropic \u002F DeepSeek \u002F Qwen \u002F 智谱 \u002F 豆包均可，Dify 支持 40+ 提供商。",[75,10977,10978,10979,10982],{},"一个",[35,10980,10981],{},"真实想解决的业务场景","——不要用\"你好\"测试 Dify，浪费部署时间也看不出工作流编排的价值。",[1410,10984,10986],{"className":10985},[1413,1414,1415],[31,10987,10988,10991,10992,10994,10995,10997],{},[35,10989,10990],{},"部署前选型","：数据必须不出内网 + 工作流复杂 → Dify 自托管（本指南）。个人 \u002F 小团队快速原型 → ",[696,10993,5311],{"href":10871}," 云版更快。核心场景就是企业知识库 QA → ",[696,10996,5329],{"href":10838}," RAG 精度更专。先用 Dify 云版 Sandbox 免费 200 次调用跑 1 周 POC，再决定要不要自托管。",[27,10999,11001],{"id":11000},"docker-部署10-分钟","Docker 部署（10 分钟）",[31,11003,11004],{},"Dify 社区版是 Apache 2.0 协议，完全免费可商用。官方提供 Docker Compose 一键部署：",[326,11006,11008],{"className":328,"code":11007,"language":330,"meta":331,"style":331},"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\n",[333,11009,11010,11019,11026,11034,11048],{"__ignoreMap":331},[336,11011,11012,11014,11016],{"class":338,"line":339},[336,11013,343],{"class":342},[336,11015,347],{"class":346},[336,11017,11018],{"class":346}," https:\u002F\u002Fgithub.com\u002Flanggenius\u002Fdify.git\n",[336,11020,11021,11023],{"class":338,"line":353},[336,11022,357],{"class":356},[336,11024,11025],{"class":346}," dify\u002Fdocker\n",[336,11027,11028,11030,11032],{"class":338,"line":363},[336,11029,381],{"class":342},[336,11031,384],{"class":346},[336,11033,387],{"class":346},[336,11035,11036,11039,11042,11045],{"class":338,"line":378},[336,11037,11038],{"class":342},"docker",[336,11040,11041],{"class":346}," compose",[336,11043,11044],{"class":346}," up",[336,11046,11047],{"class":356}," -d\n",[336,11049,11050],{"class":338,"line":390},[336,11051,11052],{"class":393},"# 默认 http:\u002F\u002Flocalhost\n",[31,11054,11055,11056,11059,11060,11063],{},"启动后访问 ",[333,11057,11058],{},"http:\u002F\u002F\u003C服务器IP>","，首次进入会引导创建管理员账号。",[35,11061,11062],{},"登录后第一件事","：改默认密码 + 在\"设置 → 模型供应商\"里配置至少一个 LLM 和一个 Embedding 模型。",[31,11065,11066,11069,11070,11073,11074,11077],{},[35,11067,11068],{},"端口冲突处理","：如果服务器有其他服务占用 80\u002F443 端口，编辑 ",[333,11071,11072],{},"docker-compose.yaml"," 里 Nginx 服务的 ",[333,11075,11076],{},"ports:","，把宿主机端口改成 8080 或其他没冲突的端口。",[31,11079,11080,11083,11084,11087],{},[35,11081,11082],{},"国内镜像加速","：Docker 镜像在国内拉取可能很慢。在 ",[333,11085,11086],{},".env"," 文件里或 Docker daemon 配置里加阿里云 \u002F 网易 registry 镜像加速：",[326,11089,11091],{"className":328,"code":11090,"language":330,"meta":331,"style":331},"# \u002Fetc\u002Fdocker\u002Fdaemon.json\n{\n  \"registry-mirrors\": [\"https:\u002F\u002Fregistry.cn-hangzhou.aliyuncs.com\"]\n}\n",[333,11092,11093,11098,11102,11118],{"__ignoreMap":331},[336,11094,11095],{"class":338,"line":339},[336,11096,11097],{"class":393},"# \u002Fetc\u002Fdocker\u002Fdaemon.json\n",[336,11099,11100],{"class":338,"line":353},[336,11101,3284],{"class":1528},[336,11103,11104,11107,11109,11112,11115],{"class":338,"line":363},[336,11105,11106],{"class":342},"  \"registry-mirrors\"",[336,11108,4458],{"class":356},[336,11110,11111],{"class":1528}," [",[336,11113,11114],{"class":346},"\"https:\u002F\u002Fregistry.cn-hangzhou.aliyuncs.com\"",[336,11116,11117],{"class":1528},"]\n",[336,11119,11120],{"class":338,"line":378},[336,11121,3384],{"class":1528},[31,11123,11124,11125,11128,11129,2416],{},"改完执行 ",[333,11126,11127],{},"sudo systemctl restart docker"," 后重新 ",[333,11130,11131],{},"docker compose up -d",[27,11133,11134],{"id":11134},"配置模型",[31,11136,11137,11138,11141,11142,70],{},"进入后台 → ",[35,11139,11140],{},"设置 → 模型供应商","，Dify 通过插件市场接入 40+ 提供商。",[35,11143,11144],{},"必须同时配置两类模型",[432,11146,11147,11153],{},[75,11148,11149,11152],{},[35,11150,11151],{},"对话模型 LLM","：GPT-5 \u002F Claude Sonnet 4 \u002F DeepSeek-V3 \u002F Qwen \u002F 豆包-Pro \u002F Kimi K2 都行",[75,11154,11155,70,11158,11161,11162,11165],{},[35,11156,11157],{},"嵌入模型 Embedding",[333,11159,11160],{},"text-embedding-3-large","（OpenAI）、",[333,11163,11164],{},"bge-large-zh-v1.5","（中文首选，可本地部署）",[31,11167,11168,11170],{},[35,11169,9747],{},"：只配对话模型没配嵌入模型，上传知识库后无法索引，界面显示\"处理中\"永远不结束。请务必两类都配。",[31,11172,11173,11176],{},[35,11174,11175],{},"国产模型原生支持","是 Dify 在国内 toB 场景的关键优势——不像 FastGPT 需要中转，Dify 直接接 DeepSeek \u002F Qwen \u002F 智谱 \u002F 文心。同一个工作流里可以\"GPT-5 做复杂推理 + DeepSeek 跑日常省成本 + Ollama 本地跑敏感数据\"，按任务步骤选最合适的模型。",[31,11178,11179,11182],{},[35,11180,11181],{},"预算敏感组合推荐","：DeepSeek-V3（对话）+ bge-large-zh（嵌入 + 本地），token 便宜、中文强、完全国内闭环。",[27,11184,11185],{"id":11185},"创建第一个应用",[31,11187,11188],{},"Dify 1.0 把应用拆成四种类型：",[145,11190,11191,11202],{},[148,11192,11193],{},[151,11194,11195,11197,11199],{},[154,11196,4728],{},[154,11198,7606],{},[154,11200,11201],{},"编排范式",[161,11203,11204,11217,11230,11243],{},[151,11205,11206,11211,11214],{},[166,11207,11208],{},[35,11209,11210],{},"Chatbot",[166,11212,11213],{},"简单对话机器人",[166,11215,11216],{},"prompt + tools",[151,11218,11219,11224,11227],{},[166,11220,11221],{},[35,11222,11223],{},"Agent",[166,11225,11226],{},"自主多步任务",[166,11228,11229],{},"ReAct \u002F Function Calling",[151,11231,11232,11237,11240],{},[166,11233,11234],{},[35,11235,11236],{},"Chatflow",[166,11238,11239],{},"对话型工作流（多轮 + 分支）",[166,11241,11242],{},"节点 DAG，带聊天上下文",[151,11244,11245,11250,11253],{},[166,11246,11247],{},[35,11248,11249],{},"Workflow",[166,11251,11252],{},"单次输入→输出（API 模式）",[166,11254,11255],{},"节点 DAG，无对话状态",[31,11257,11258,11261],{},[35,11259,11260],{},"第一个应用选 Chatbot","，10 分钟跑通：",[432,11263,11264,11271,11274,11280,11286,11292],{},[75,11265,11266,11267,11270],{},"左侧\"工作室\" → ",[35,11268,11269],{},"\"创建空白应用\""," → 选\"聊天助手\"",[75,11272,11273],{},"起个名字（如\"技术文档助手\"）",[75,11275,11276,11279],{},[35,11277,11278],{},"编排 prompt","：写清楚角色、能力边界、输出格式",[75,11281,11282,11285],{},[35,11283,11284],{},"可选：关联知识库","（下一步会讲）",[75,11287,11288,11291],{},[35,11289,11290],{},"调试","：右侧预览窗口输入问题，看回答效果",[75,11293,11294],{},"右上**\"发布\"** → 选渠道（Web App \u002F API \u002F 嵌入网页）",[31,11296,11297,11299],{},[35,11298,10239],{},"：Chatbot 只是热身。Dify 的真正价值在 Workflow 和 Chatflow——当你需要\"多步骤逻辑 + 条件分支 + 外部工具调用\"时，才需要工作流编排。",[27,11301,11303],{"id":11302},"工作流编排dify-的核心","工作流编排：Dify 的核心",[31,11305,11306],{},"这是 Dify 最强的一块，也是它和 FastGPT 拉开差距的地方。进入\"工作室\" → 创建\"工作流\"应用，你会看到一块可视化画布。",[60,11308,11309],{"id":11309},"节点类型",[145,11311,11312,11323],{},[148,11313,11314],{},[151,11315,11316,11318,11320],{},[154,11317,10267],{},[154,11319,10270],{},[154,11321,11322],{},"实战要点",[161,11324,11325,11336,11349,11362,11375,11388,11400,11413,11426,11439],{},[151,11326,11327,11331,11333],{},[166,11328,11329],{},[35,11330,10277],{},[166,11332,10280],{},[166,11334,11335],{},"定义入参变量和输出格式",[151,11337,11338,11343,11346],{},[166,11339,11340],{},[35,11341,11342],{},"LLM",[166,11344,11345],{},"调大模型",[166,11347,11348],{},"可选模型 \u002F 温度 \u002F 系统 prompt",[151,11350,11351,11356,11359],{},[166,11352,11353],{},[35,11354,11355],{},"知识检索",[166,11357,11358],{},"RAG 搜索",[166,11360,11361],{},"关联知识库，返回 Top-K chunk",[151,11363,11364,11369,11372],{},[166,11365,11366],{},[35,11367,11368],{},"代码执行",[166,11370,11371],{},"Python \u002F JS",[166,11373,11374],{},"数据转换、格式化、计算",[151,11376,11377,11382,11385],{},[166,11378,11379],{},[35,11380,11381],{},"条件分支",[166,11383,11384],{},"IF \u002F ELSE",[166,11386,11387],{},"按变量值路由到不同分支",[151,11389,11390,11395,11397],{},[166,11391,11392],{},[35,11393,11394],{},"HTTP 请求",[166,11396,10336],{},[166,11398,11399],{},"对接 ERP \u002F 飞书 \u002F 自家系统",[151,11401,11402,11407,11410],{},[166,11403,11404],{},[35,11405,11406],{},"迭代",[166,11408,11409],{},"循环处理",[166,11411,11412],{},"批量处理列表数据",[151,11414,11415,11420,11423],{},[166,11416,11417],{},[35,11418,11419],{},"变量聚合",[166,11421,11422],{},"合并多分支",[166,11424,11425],{},"把 IF\u002FELSE 分支结果统一",[151,11427,11428,11433,11436],{},[166,11429,11430],{},[35,11431,11432],{},"参数提取",[166,11434,11435],{},"从文本提取结构化数据",[166,11437,11438],{},"提取意图 \u002F 实体",[151,11440,11441,11446,11449],{},[166,11442,11443],{},[35,11444,11445],{},"问题分类",[166,11447,11448],{},"意图路由",[166,11450,11451],{},"A 意图走分支 A，B 走分支 B",[60,11453,11455],{"id":11454},"编排一个真实工作流智能客服路由","编排一个真实工作流：智能客服路由",[31,11457,11458],{},"以\"电商智能客服\"为例，演示 LLM 节点 + 知识检索 + 条件分支的组合：",[432,11460,11461,11470,11488,11536,11545],{},[75,11462,11463,11465,11466,11469],{},[35,11464,10357],{},"：输入变量 ",[333,11467,11468],{},"user_question","（用户提问）",[75,11471,11472,11475,11476,2108,11479,2108,11482,2108,11485],{},[35,11473,11474],{},"问题分类节点","：用 LLM 判断意图——",[333,11477,11478],{},"售后退换",[333,11480,11481],{},"物流查询",[333,11483,11484],{},"商品咨询",[333,11486,11487],{},"其他",[75,11489,11490,11492,11493],{},[35,11491,11381],{},"：按分类结果路由\n",[72,11494,11495,11508,11519,11529],{},[75,11496,11497,11499,11500,11503,11504,11507],{},[333,11498,11478],{}," → ",[35,11501,11502],{},"知识检索节点","（查退换政策知识库）→ ",[35,11505,11506],{},"LLM 节点","（基于检索结果生成回复）",[75,11509,11510,11499,11512,11515,11516,11518],{},[333,11511,11481],{},[35,11513,11514],{},"HTTP 请求节点","（调物流 API 查运单）→ ",[35,11517,11506],{},"（格式化物流信息）",[75,11520,11521,11499,11523,11525,11526,11528],{},[333,11522,11484],{},[35,11524,11502],{},"（查商品 FAQ）→ ",[35,11527,11506],{},"（生成回复）",[75,11530,11531,11499,11533,11535],{},[333,11532,11487],{},[35,11534,11506],{},"（兜底回复 + 建议转人工）",[75,11537,11538,11541,11542],{},[35,11539,11540],{},"变量聚合节点","：把四个分支的结果统一成 ",[333,11543,11544],{},"answer",[75,11546,11547,11549,11550],{},[35,11548,10387],{},"：输出 ",[333,11551,11544],{},[31,11553,11554,11555,2416],{},"这个工作流把\"意图路由 + RAG 检索 + 外部 API + 兜底策略\"串成一条链路。",[35,11556,11557],{},"用代码写这套逻辑至少 200 行，用 Dify 拖拽 20 分钟搞定，且每一步可视化可观测",[60,11559,11560],{"id":11560},"代码执行节点的边界",[31,11562,11563,11564,70],{},"代码节点用 Sandbox 执行 Python \u002F JS，但有",[35,11565,11566],{},"严格限制",[72,11568,11569,11572,11575,11578],{},[75,11570,11571],{},"执行超时默认 10 秒，复杂计算会超时",[75,11573,11574],{},"内存限制小（默认 128MB），大列表操作会 OOM",[75,11576,11577],{},"不能安装第三方库，只能用标准库",[75,11579,11580],{},"网络请求受限（Sandbox 隔离）",[31,11582,11583,11586,11587,11589],{},[35,11584,11585],{},"生产建议","：代码节点只做轻量数据转换（JSON 解析、格式化、简单计算）。复杂逻辑改成 ",[35,11588,11514],{},"调外部服务——把重逻辑放在你自己的 API 里，Dify 只负责编排。",[27,11591,11593],{"id":11592},"rag-知识库配置","RAG 知识库配置",[31,11595,11596],{},"RAG 是 Dify 的重要能力，虽然极致精度弱于 FastGPT，但 1.0 的混合检索已经补上了主要短板。",[60,11598,11599],{"id":11599},"创建知识库",[432,11601,11602,11608,11611],{},[75,11603,11604,11605],{},"进入\"知识库\" → ",[35,11606,11607],{},"\"创建知识库\"",[75,11609,11610],{},"上传文档（PDF \u002F Word \u002F Markdown \u002F TXT \u002F 网页 URL）",[75,11612,11613,70],{},[35,11614,11615],{},"分块设置",[145,11617,11618,11629],{},[148,11619,11620],{},[151,11621,11622,11624,11627],{},[154,11623,4250],{},[154,11625,11626],{},"默认",[154,11628,926],{},[161,11630,11631,11642,11653],{},[151,11632,11633,11636,11639],{},[166,11634,11635],{},"分块长度",[166,11637,11638],{},"500",[166,11640,11641],{},"每块字符数，长文档调高到 800-1000",[151,11643,11644,11647,11650],{},[166,11645,11646],{},"分块重叠",[166,11648,11649],{},"50",[166,11651,11652],{},"块之间重叠，避免语义断裂",[151,11654,11655,11658,11661],{},[166,11656,11657],{},"分段模式",[166,11659,11660],{},"自动",[166,11662,11663],{},"代码 \u002F 法律文档改\"按章节\"",[432,11665,11666,11671],{"start":378},[75,11667,11668,11670],{},[35,11669,6003],{},"：选\"高质量\"（用 embedding 模型向量化），不要选\"经济\"（仅关键词）",[75,11672,11673,11676],{},[35,11674,11675],{},"检索设置","：选\"混合检索\"（向量 + 全文 + 重排），召回率最高",[60,11678,11679],{"id":11679},"混合检索调参",[31,11681,11682,11683,70],{},"1.0 的 RAG 从纯向量升级到",[35,11684,11685],{},"混合检索 + 重排序",[72,11687,11688,11694,11700],{},[75,11689,11690,11693],{},[35,11691,11692],{},"向量检索","：语义相似度，适合意图理解",[75,11695,11696,11699],{},[35,11697,11698],{},"全文检索","：BM25 关键词匹配，适合专业术语、编号",[75,11701,11702,11705],{},[35,11703,11704],{},"重排序 Rerank","：把初步召回的前 20 段进一步排序，选出最相关的 5-10 段",[31,11707,11708,11711],{},[35,11709,11710],{},"Rerank 模型选择","：BGE Reranker（本地，中文好）或 Cohere Rerank（云端，多语言）。Rerank 对精度提升明显，但每次调用增加 200-500ms 延迟。",[31,11713,11714,11717,11718,11720],{},[35,11715,11716],{},"社区版 vs 企业版差距","：多路召回 + 重排在企业版才完整解锁。社区版默认是基础语义检索，RAG 精度上限有限。如果你的核心场景就是知识库 QA 且要极致精度，",[696,11719,5329],{"href":10838}," 社区版的 RAG 链路每步都可调，上限更高。",[60,11722,11723],{"id":11723},"知识库避坑",[72,11725,11726,11739,11745,11751],{},[75,11727,11728,11731,11732,11734,11735,11738],{},[35,11729,11730],{},"文件大小限制","：社区版默认 15MB，超过会失败。改 ",[333,11733,11086],{}," 的 ",[333,11736,11737],{},"UPLOAD_FILE_SIZE_LIMIT"," 并重启容器",[75,11740,11741,11744],{},[35,11742,11743],{},"大 PDF 处理慢","：单文件 > 50MB 时切分占用大量内存，先本地拆成小 PDF 再上传",[75,11746,11747,11750],{},[35,11748,11749],{},"切片太碎","：默认 500 字符对代码 \u002F 长条款不友好，改成 800 字符 + overlap 100",[75,11752,11753,11756],{},[35,11754,11755],{},"只调 prompt 不调切片","：答案不准 90% 是切片问题，先看召回段是否正确，再改 prompt",[27,11758,11760],{"id":11759},"api-发布","API 发布",[31,11762,11763],{},"Dify 是 API-first 平台，每个应用自动暴露 REST API。",[432,11765,11766,11772,11775,11778],{},[75,11767,11768,11769],{},"进入应用 → ",[35,11770,11771],{},"\"访问 API\"",[75,11773,11774],{},"右上**\"API Server\"** → 获取 API Key",[75,11776,11777],{},"查看自动生成的 OpenAPI Schema 和调用示例",[75,11779,11780],{},"复制 curl \u002F Python \u002F Node.js 示例代码直接集成",[31,11782,11783,70],{},[35,11784,11785],{},"Workflow 应用的 API 调用",[326,11787,11789],{"className":328,"code":11788,"language":330,"meta":331,"style":331},"curl -X POST 'https:\u002F\u002Fyour-dify\u002Fv1\u002Fworkflows\u002Frun' \\\n  -H 'Authorization: Bearer app-xxxxx' \\\n  -H 'Content-Type: application\u002Fjson' \\\n  -d '{\n    \"inputs\": {\"user_question\": \"我的订单什么时候发货？\"},\n    \"response_mode\": \"blocking\",\n    \"user\": \"user-123\"\n  }'\n",[333,11790,11791,11806,11816,11825,11833,11838,11843,11848],{"__ignoreMap":331},[336,11792,11793,11795,11798,11801,11804],{"class":338,"line":339},[336,11794,1595],{"class":342},[336,11796,11797],{"class":356}," -X",[336,11799,11800],{"class":346}," POST",[336,11802,11803],{"class":346}," 'https:\u002F\u002Fyour-dify\u002Fv1\u002Fworkflows\u002Frun'",[336,11805,8842],{"class":356},[336,11807,11808,11811,11814],{"class":338,"line":353},[336,11809,11810],{"class":356},"  -H",[336,11812,11813],{"class":346}," 'Authorization: Bearer app-xxxxx'",[336,11815,8842],{"class":356},[336,11817,11818,11820,11823],{"class":338,"line":363},[336,11819,11810],{"class":356},[336,11821,11822],{"class":346}," 'Content-Type: application\u002Fjson'",[336,11824,8842],{"class":356},[336,11826,11827,11830],{"class":338,"line":378},[336,11828,11829],{"class":356},"  -d",[336,11831,11832],{"class":346}," '{\n",[336,11834,11835],{"class":338,"line":390},[336,11836,11837],{"class":346},"    \"inputs\": {\"user_question\": \"我的订单什么时候发货？\"},\n",[336,11839,11840],{"class":338,"line":397},[336,11841,11842],{"class":346},"    \"response_mode\": \"blocking\",\n",[336,11844,11845],{"class":338,"line":1637},[336,11846,11847],{"class":346},"    \"user\": \"user-123\"\n",[336,11849,11850],{"class":338,"line":1643},[336,11851,11852],{"class":346},"  }'\n",[31,11854,11855,11858],{},[35,11856,11857],{},"多平台发布","：除了 API，Dify 还支持发布到 Web App、Slack、Discord、微信公众号企业版。一次配置多处触达，不用每个平台单独写集成代码。",[31,11860,11861,11864,11865,11867],{},[35,11862,11863],{},"MCP 双向支持","：1.0 起 Dify 可作为 MCP Server 暴露工具——让 ",[696,11866,1888],{"href":1887}," \u002F Cursor 调用 Dify 里的 workflow；也能消费外部 MCP Server——在 workflow 里调 GitHub \u002F Slack \u002F 自家内部系统。这让 Dify 不再是孤岛，而是 MCP 生态的中间层。",[27,11869,11870],{"id":11870},"私有化部署注意事项",[31,11872,11873],{},"私有化是 Dify 最深的护城河，但运维成本不能忽视。",[60,11875,11876],{"id":11876},"组件与资源",[31,11878,11879],{},"Dify 的 Docker Compose 包含多个组件：API Server、Web 前端、Worker、Sandbox、PostgreSQL、Redis、向量库（Weaviate）。链路比 FastGPT 长，资源占用更大。",[145,11881,11882,11894],{},[148,11883,11884],{},[151,11885,11886,11889,11892],{},[154,11887,11888],{},"规模",[154,11890,11891],{},"推荐配置",[154,11893,926],{},[161,11895,11896,11907,11918],{},[151,11897,11898,11901,11904],{},[166,11899,11900],{},"POC \u002F 小团队",[166,11902,11903],{},"2C4G + 30GB",[166,11905,11906],{},"纯外接 API 模式",[151,11908,11909,11912,11915],{},[166,11910,11911],{},"中型团队",[166,11913,11914],{},"4C8G + 50GB",[166,11916,11917],{},"推荐",[151,11919,11920,11922,11925],{},[166,11921,6855],{},[166,11923,11924],{},"8C16G + 100GB+",[166,11926,11927],{},"单机日活上千",[60,11929,11930],{"id":11930},"版本升级",[31,11932,11933,11936],{},[35,11934,11935],{},"大版本升级会破坏数据库 schema","。跨大版本（如 0.x → 1.x）务必：",[432,11938,11939,11945,11948,11951],{},[75,11940,11941,11942],{},"先备份 PostgreSQL 卷：",[333,11943,11944],{},"docker exec dify-db pg_dump dify > backup.sql",[75,11946,11947],{},"在 staging 环境验证升级流程",[75,11949,11950],{},"生产升级前确认 release notes 的 breaking changes",[75,11952,11953],{},"升级后跑一遍核心 workflow 确认无 regression",[60,11955,11956],{"id":11956},"环境变量",[31,11958,11959,11961,11962,11965,11966,7414,11969,11972],{},[333,11960,11086],{}," 改完要 ",[333,11963,11964],{},"docker compose down && docker compose up -d","，",[35,11967,11968],{},"不是",[333,11970,11971],{},"docker compose restart","——后者不重新加载 env。这是最常见的\"改了配置不生效\"的原因。",[60,11974,11975],{"id":11975},"安全加固",[72,11977,11979,11985,11991,11997,12003,12009],{"className":11978},[9895],[75,11980,11982,11984],{"className":11981},[9899],[9901,11983],{"disabled":765,"type":9903}," 管理员默认密码已改",[75,11986,11988,11990],{"className":11987},[9899],[9901,11989],{"disabled":765,"type":9903}," 服务器只开必要端口，80\u002F443 走 Nginx 反代 + HTTPS",[75,11992,11994,11996],{"className":11993},[9899],[9901,11995],{"disabled":765,"type":9903}," PostgreSQL 数据卷已定期备份",[75,11998,12000,12002],{"className":11999},[9899],[9901,12001],{"disabled":765,"type":9903}," LLM API Key 走 secret 管理，不写到代码里",[75,12004,12006,12008],{"className":12005},[9899],[9901,12007],{"disabled":765,"type":9903}," 限流：单用户 QPM、单应用 QPS 都有上限",[75,12010,12012,12014],{"className":12011},[9899],[9901,12013],{"disabled":765,"type":9903}," 内网 DNS 已配好，员工能通过友好域名访问",[27,12016,12018],{"id":12017},"_5-个可直接复用的工作流模板","5 个可直接复用的工作流模板",[60,12020,12022],{"id":12021},"_1-智能客服路由","1. 智能客服路由",[326,12024,12027],{"className":12025,"code":12026,"language":876,"meta":331},[874],"开始(user_question) → 问题分类(售后\u002F物流\u002F商品\u002F其他) → 条件分支\n  ├ 售后 → 知识检索(退换政策) → LLM(生成回复)\n  ├ 物流 → HTTP请求(查运单API) → LLM(格式化)\n  ├ 商品 → 知识检索(商品FAQ) → LLM(生成回复)\n  └ 其他 → LLM(兜底+转人工)\n→ 变量聚合(answer) → 结束\n",[333,12028,12026],{"__ignoreMap":331},[60,12030,12032],{"id":12031},"_2-文档问答-引用溯源","2. 文档问答 + 引用溯源",[326,12034,12037],{"className":12035,"code":12036,"language":876,"meta":331},[874],"开始(question) → 知识检索(Top-5 chunk) → LLM(基于chunk回答+标注引用段号) → 结束(answer + sources)\n",[333,12038,12036],{"__ignoreMap":331},[60,12040,12042],{"id":12041},"_3-多模型-ab-测试","3. 多模型 A\u002FB 测试",[326,12044,12047],{"className":12045,"code":12046,"language":876,"meta":331},[874],"开始(input) → 条件分支(按用户ID取模分流)\n  ├ 分支A → LLM(GPT-5)\n  └ 分支B → LLM(DeepSeek-V3)\n→ 变量聚合 → 结束(记录哪个模型+用户满意度)\n",[333,12048,12046],{"__ignoreMap":331},[60,12050,12052],{"id":12051},"_4-数据-etl-报告生成","4. 数据 ETL + 报告生成",[326,12054,12057],{"className":12055,"code":12056,"language":876,"meta":331},[874],"开始(raw_data) → 代码执行(清洗+统计) → LLM(生成分析报告) → HTTP请求(发飞书) → 结束\n",[333,12058,12056],{"__ignoreMap":331},[60,12060,12062],{"id":12061},"_5-意图路由-agent-自主决策","5. 意图路由 + Agent 自主决策",[326,12064,12067],{"className":12065,"code":12066,"language":876,"meta":331},[874],"开始(user_input) → 问题分类(简单\u002F复杂)\n  ├ 简单 → LLM(直接回答) → 结束\n  └ 复杂 → Agent节点(自主调工具+多步推理) → 结束\n",[333,12068,12066],{"__ignoreMap":331},[27,12070,690],{"id":690},[72,12072,12073,12079,12085,12091,12097],{},[75,12074,12075],{},[696,12076,12078],{"href":12077},"\u002Freview\u002Fdify-deep-review.html","Dify 深度评测：开源 AI Agent 平台私有部署首选？",[75,12080,12081],{},[696,12082,12084],{"href":12083},"\u002Fcompare\u002Ffastgpt-vs-dify.html","FastGPT vs Dify：国内企业级 RAG 与 Agent 平台怎么选",[75,12086,12087],{},[696,12088,12090],{"href":12089},"\u002Fplaybook\u002Fonboarding\u002Ffastgpt-getting-started.html","FastGPT 部署与知识库搭建实战",[75,12092,12093],{},[696,12094,12096],{"href":12095},"\u002Fplaybook\u002Fonboarding\u002Fcoze-getting-started.html","Coze 上手指南：从零搭建第一个 AI Bot",[75,12098,12099,6784,12102,6784,12105],{},[696,12100,12101],{"href":10829},"Dify 工具卡",[696,12103,12104],{"href":10838},"FastGPT 工具卡",[696,12106,12107],{"href":10871},"Coze 工具卡",[725,12109,12110],{},"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);}html pre.shiki code .sVt8B, html code.shiki .sVt8B{--shiki-default:#24292E;--shiki-dark:#E1E4E8}",{"title":331,"searchDepth":363,"depth":363,"links":12112},[12113,12114,12115,12116,12117,12118,12123,12128,12129,12135,12142],{"id":8337,"depth":353,"text":8337},{"id":8358,"depth":353,"text":8358},{"id":11000,"depth":353,"text":11001},{"id":11134,"depth":353,"text":11134},{"id":11185,"depth":353,"text":11185},{"id":11302,"depth":353,"text":11303,"children":12119},[12120,12121,12122],{"id":11309,"depth":363,"text":11309},{"id":11454,"depth":363,"text":11455},{"id":11560,"depth":363,"text":11560},{"id":11592,"depth":353,"text":11593,"children":12124},[12125,12126,12127],{"id":11599,"depth":363,"text":11599},{"id":11679,"depth":363,"text":11679},{"id":11723,"depth":363,"text":11723},{"id":11759,"depth":353,"text":11760},{"id":11870,"depth":353,"text":11870,"children":12130},[12131,12132,12133,12134],{"id":11876,"depth":363,"text":11876},{"id":11930,"depth":363,"text":11930},{"id":11956,"depth":363,"text":11956},{"id":11975,"depth":363,"text":11975},{"id":12017,"depth":353,"text":12018,"children":12136},[12137,12138,12139,12140,12141],{"id":12021,"depth":363,"text":12022},{"id":12031,"depth":363,"text":12032},{"id":12041,"depth":363,"text":12042},{"id":12051,"depth":363,"text":12052},{"id":12061,"depth":363,"text":12062},{"id":690,"depth":353,"text":690},"Dify 从零上手教程：Docker Compose 十分钟私有部署、创建第一个应用、工作流可视化编排（LLM 节点 \u002F 知识检索 \u002F 代码执行 \u002F 条件分支）、RAG 知识库配置与混合检索调参、API 发布与多端触达、私有化部署运维注意事项，以及 5 个可直接复用的工作流模板。",{},"\u002Fplaybook\u002Fonboarding\u002Fdify-getting-started",[5321,5330,5312],{"title":10922,"description":12143},"playbook\u002Fonboarding\u002Fdify-getting-started",[5320,2123,10917,12150,567],"私有部署","HvREfArpp1ocyPymr09GM2VqnOnD2KR4y7YKl8fNrbw",[12153,12312,12539,12709,12982],{"id":5001,"title":5002,"body":12154,"cover":755,"description":5212,"extension":754,"meta":12310,"navigation":765,"path":5214,"published":5215,"seo":12311,"sourceName":5217,"sourceUrl":5218,"stem":5219,"__hash__":5220},{"type":24,"value":12155,"toc":12302},[12156,12158,12182,12186,12188,12194,12204,12208,12210,12212,12252,12254,12256,12258,12270,12272,12274,12280,12282],[27,12157,5007],{"id":5007},[72,12159,12160,12164,12170,12174,12178],{},[75,12161,12162,5015],{},[35,12163,5014],{},[75,12165,12166,5021,12168,5024],{},[35,12167,5020],{},[696,12169,2127],{"href":2126},[75,12171,12172,5030],{},[35,12173,5029],{},[75,12175,12176,5036],{},[35,12177,5035],{},[75,12179,12180,5042],{},[35,12181,5041],{},[1434,12183,12184],{},[31,12185,5047],{},[27,12187,5051],{"id":5050},[31,12189,5054,12190,413,12192,5060],{},[333,12191,5057],{},[333,12193,2415],{},[72,12195,12196,12200,12202],{},[75,12197,5065,12198,5069],{},[35,12199,5068],{},[75,12201,5072],{},[75,12203,5075],{},[31,12205,5078,12206,5082],{},[333,12207,5081],{},[27,12209,5086],{"id":5085},[31,12211,5089],{},[145,12213,12214,12224],{},[148,12215,12216],{},[151,12217,12218,12220,12222],{},[154,12219,489],{},[154,12221,5100],{},[154,12223,5103],{},[161,12225,12226,12234,12244],{},[151,12227,12228,12230,12232],{},[166,12229,5081],{},[166,12231,3240],{},[166,12233,5114],{},[151,12235,12236,12240,12242],{},[166,12237,12238],{},[696,12239,2127],{"href":2126},[166,12241,3234],{},[166,12243,5125],{},[151,12245,12246,12248,12250],{},[166,12247,5130],{},[166,12249,5133],{},[166,12251,5136],{},[31,12253,5139],{},[27,12255,5142],{"id":5142},[31,12257,5145],{},[72,12259,12260,12264,12268],{},[75,12261,12262,5152],{},[35,12263,2127],{},[75,12265,12266,5157],{},[35,12267,5081],{},[75,12269,5160],{},[31,12271,5163],{},[27,12273,5167],{"id":5166},[31,12275,5170,12276,5173,12278,5176],{},[333,12277,5081],{},[696,12279,2127],{"href":2126},[27,12281,690],{"id":690},[72,12283,12284,12292,12298],{},[75,12285,2119,12286,2108,12288,2108,12290],{},[696,12287,2127],{"href":2126},[696,12289,2123],{"href":2122},[696,12291,5190],{"href":5189},[75,12293,5193,12294,2108,12296],{},[696,12295,1888],{"href":1887},[696,12297,3977],{"href":3976},[75,12299,2138,12300],{},[696,12301,5203],{"href":5202},{"title":331,"searchDepth":363,"depth":363,"links":12303},[12304,12305,12306,12307,12308,12309],{"id":5007,"depth":353,"text":5007},{"id":5050,"depth":353,"text":5051},{"id":5085,"depth":353,"text":5086},{"id":5142,"depth":353,"text":5142},{"id":5166,"depth":353,"text":5167},{"id":690,"depth":353,"text":690},{},{"title":5002,"description":5212},{"id":12313,"title":12314,"body":12315,"cover":755,"description":12532,"extension":754,"meta":12533,"navigation":765,"path":12534,"published":5215,"seo":12535,"sourceName":3246,"sourceUrl":12536,"stem":12537,"__hash__":12538},"news\u002Fnews\u002F2026\u002Fgemini-3-and-antigravity.md","Google 发布 Gemini 3 并推出 agent 开发平台 Antigravity",{"type":24,"value":12316,"toc":12524},[12317,12319,12351,12356,12360,12368,12450,12457,12461,12464,12475,12478,12482,12485,12487,12493,12496,12498],[27,12318,5007],{"id":5007},[72,12320,12321,12327,12333,12339,12345],{},[75,12322,12323,12326],{},[35,12324,12325],{},"Gemini 3 Pro 登顶 LMArena","：发布即以 1501 Elo 排名第一，被定位为 Google「最智能的模型」",[75,12328,12329,12332],{},[35,12330,12331],{},"1M 上下文 + 全模态","：原生理解文本\u002F图片\u002F音频\u002F视频\u002F代码",[75,12334,12335,12338],{},[35,12336,12337],{},"推理大幅跃升","：GPQA Diamond 91.9%，Deep Think 模式进一步推到 93.8%",[75,12340,12341,12344],{},[35,12342,12343],{},"vibe coding 标杆","：WebDev Arena 1487 Elo，零样本生成交互式 Web UI",[75,12346,12347,12350],{},[35,12348,12349],{},"同步发布 Google Antigravity","：agent-first 开发平台，agent 可直接操作编辑器、终端、浏览器",[1434,12352,12353],{},[31,12354,12355],{},"事件时间：2025 年 11 月 18 日。本文为 AIHO 收录整理。",[27,12357,12359],{"id":12358},"gemini-3-pro全面跃升","Gemini 3 Pro：全面跃升",[31,12361,12362,12363,12367],{},"相比 ",[696,12364,12366],{"href":12365},"\u002Fmodels\u002Fgemini-2.5-pro.html","Gemini 2.5 Pro","，Gemini 3 Pro 在推理、多模态和 agentic 能力上全面升级：",[145,12369,12370,12382],{},[148,12371,12372],{},[151,12373,12374,12377,12380],{},[154,12375,12376],{},"基准",[154,12378,12379],{},"Gemini 3 Pro",[154,12381,926],{},[161,12383,12384,12395,12406,12417,12428,12439],{},[151,12385,12386,12389,12392],{},[166,12387,12388],{},"LMArena",[166,12390,12391],{},"1501 Elo",[166,12393,12394],{},"发布时登顶",[151,12396,12397,12400,12403],{},[166,12398,12399],{},"GPQA Diamond",[166,12401,12402],{},"91.9%",[166,12404,12405],{},"博士级科学推理",[151,12407,12408,12411,12414],{},[166,12409,12410],{},"Humanity's Last Exam",[166,12412,12413],{},"37.5%",[166,12415,12416],{},"无工具",[151,12418,12419,12422,12425],{},[166,12420,12421],{},"SWE-bench Verified",[166,12423,12424],{},"76.2%",[166,12426,12427],{},"纯编程",[151,12429,12430,12433,12436],{},[166,12431,12432],{},"Terminal-Bench 2.0",[166,12434,12435],{},"54.2%",[166,12437,12438],{},"终端操作",[151,12440,12441,12444,12447],{},[166,12442,12443],{},"WebDev Arena",[166,12445,12446],{},"1487 Elo",[166,12448,12449],{},"前端生成",[31,12451,12452,12453,2416],{},"模型规格、定价与避坑详见 ",[696,12454,12456],{"href":12455},"\u002Fmodels\u002Fgemini-3-pro.html","Gemini 3 Pro 模型卡",[27,12458,12460],{"id":12459},"google-antigravity从工具到主动伙伴","Google Antigravity：从「工具」到「主动伙伴」",[31,12462,12463],{},"Antigravity 是 Google 同步发布的 agent-first 开发平台，核心理念是把 AI 从「工具」变成「主动伙伴」：",[72,12465,12466,12469,12472],{},[75,12467,12468],{},"agent 拥有对编辑器、终端、浏览器的直接访问权",[75,12470,12471],{},"可自主规划、执行、验证端到端的软件任务",[75,12473,12474],{},"集成三个模型：Gemini 3 Pro（主力）、Gemini 2.5 Computer Use（浏览器控制）、Nano Banana（图像编辑）",[31,12476,12477],{},"这是大厂首个旗舰级 agentic IDE，与 Cursor 2.0、GitHub Agent HQ 等共同把「agent 编排」推向 2026 年的主流形态。",[27,12479,12481],{"id":12480},"长程-agent-能力","长程 Agent 能力",[31,12483,12484],{},"Gemini 3 Pro 在 Vending-Bench 2（模拟经营一家自动售货机生意一整年）上登顶，证明能维持长周期目标不漂移。配合 Gemini Agent，可处理预订本地服务、整理 Gmail 收件箱等多步工作流。",[27,12486,5167],{"id":5166},[31,12488,12489,12490,12492],{},"Gemini 3 的发布把模型竞争重新拉回「Google 也在第一梯队」的格局——尤其多模态 + 超长上下文 + vibe coding 这个组合，是它区别于 ",[696,12491,5203],{"href":5202},"（最强纯编程 agent）的差异点。Antigravity 则标志着 IDE 形态从「补全\u002F对话」彻底转向「agent 编排」。",[31,12494,12495],{},"国内开发者注意：Gemini 3 无官方直连，需 Vertex AI 或中转。",[27,12497,690],{"id":690},[72,12499,12500,12504,12513,12518],{},[75,12501,12502],{},[696,12503,12456],{"href":12455},[75,12505,12506,12507,2108,12509],{},"对比：",[696,12508,5203],{"href":5202},[696,12510,12512],{"href":12511},"\u002Fmodels\u002Fgpt-5-1-codex-max.html","GPT-5.1-Codex-Max",[75,12514,12515,12516],{},"配套工具：",[696,12517,4042],{"href":4041},[75,12519,2119,12520,2108,12522],{},[696,12521,2156],{"href":2155},[696,12523,2123],{"href":2122},{"title":331,"searchDepth":363,"depth":363,"links":12525},[12526,12527,12528,12529,12530,12531],{"id":5007,"depth":353,"text":5007},{"id":12358,"depth":353,"text":12359},{"id":12459,"depth":353,"text":12460},{"id":12480,"depth":353,"text":12481},{"id":5166,"depth":353,"text":5167},{"id":690,"depth":353,"text":690},"Gemini 3 Pro 以 1501 Elo 登顶 LMArena，1M 上下文 + 全模态；同步发布 agent-first 开发平台 Google Antigravity，让 agent 直接驱动编辑器、终端和浏览器。",{},"\u002Fnews\u002F2026\u002Fgemini-3-and-antigravity",{"title":12314,"description":12532},"https:\u002F\u002Fblog.google\u002Fproducts\u002Fgemini\u002Fgemini-3\u002F","news\u002F2026\u002Fgemini-3-and-antigravity","NKg83RPupZKgfRliqkiZTdu5gEFcxyCrit2jta9g2t4",{"id":12540,"title":12541,"body":12542,"cover":755,"description":12701,"extension":754,"meta":12702,"navigation":765,"path":12703,"published":5215,"seo":12704,"sourceName":12705,"sourceUrl":12706,"stem":12707,"__hash__":12708},"news\u002Fnews\u002F2026\u002Fwindsurf-acquisition.md","Windsurf 归属尘埃落定：Cognition 收购，Devin 与 IDE 合体",{"type":24,"value":12543,"toc":12693},[12544,12546,12581,12586,12590,12593,12613,12616,12620,12623,12635,12638,12641,12661,12663,12671,12673],[27,12545,5007],{"id":5007},[72,12547,12548,12554,12560,12566,12575],{},[75,12549,12550,12553],{},[35,12551,12552],{},"三方瓜分","：72 小时内 Windsurf 经历 OpenAI 收购告吹 → Google 反向 acqui-hire 带走 CEO 及核心团队 → Cognition 收购剩余 IP\u002F产品\u002F品牌",[75,12555,12556,12559],{},[35,12557,12558],{},"Cognition 拿下整体业务","：包括 Windsurf 的 IP、产品、商标、品牌和剩余工程\u002F产品\u002FGTM 团队",[75,12561,12562,12565],{},[35,12563,12564],{},"关键财务","：$82M ARR，企业 ARR 季度环比翻倍，350+ 企业客户、数十万日活",[75,12567,12568,12571,12572,12574],{},[35,12569,12570],{},"战略协同","：把自主 agent ",[696,12573,5436],{"href":6735}," 与规模化 IDE 基础设施合体",[75,12576,12577,12580],{},[35,12578,12579],{},"员工安排","：Scott Wu 称 100% 员工参与交易分配、加速 vesting",[1434,12582,12583],{},[31,12584,12585],{},"事件时间：2025 年 7 月。本文为 AIHO 收录整理。",[27,12587,12589],{"id":12588},"一场-72-小时的归属大戏","一场 72 小时的归属大戏",[31,12591,12592],{},"Windsurf（前 Codeium，agentic IDE）的归属在 2025 年 7 月经历了戏剧性的三连转：",[432,12594,12595,12601,12607],{},[75,12596,12597,12600],{},[35,12598,12599],{},"OpenAI 收购告吹","：传闻中的 30 亿美元收购最终未达成",[75,12602,12603,12606],{},[35,12604,12605],{},"Google 反向 acqui-hire","（7 月 11 日）：Google 以约 24 亿美元拿走 Windsurf CEO Varun Mohan 及核心研发团队 + 技术非独占授权，但不直接收购公司",[75,12608,12609,12612],{},[35,12610,12611],{},"Cognition 收购剩余实体","（7 月 14 日）：Devin 母公司 Cognition 接手 Windsurf 的 IP、产品、商标、品牌和剩余团队",[31,12614,12615],{},"这场大戏是 2025 年 AI 编程赛道资本与人才争夺白热化的缩影。",[27,12617,12619],{"id":12618},"cognition-的算盘devin-windsurf","Cognition 的算盘：Devin + Windsurf",[31,12621,12622],{},"Cognition 的意图是把两者的长处合并：",[72,12624,12625,12630],{},[75,12626,12627,12629],{},[35,12628,5436],{},"：自主编码 agent",[75,12631,12632,12634],{},[35,12633,5741],{},"：规模化的 agentic IDE 产品 + 企业 GTM 渠道",[31,12636,12637],{},"官方愿景是让工程师「从砌砖工变成建筑师」——把重心从手写代码转向高层系统设计。收购时 Windsurf 带着 $82M ARR、350+ 企业客户、企业 ARR 季度环比翻倍的成绩单。",[27,12639,12640],{"id":12640},"对开发者的影响",[72,12642,12643,12649,12655],{},[75,12644,12645,12648],{},[35,12646,12647],{},"Windsurf 产品继续运营","：短期内团队照常，并保留对最新 Claude 模型的完整访问",[75,12650,12651,12654],{},[35,12652,12653],{},"后续整合","：Cognition 会把 Windsurf 的 IP 逐步并入自有产品线（2026 年已推出原生集成 Devin 的 Windsurf 2.0）",[75,12656,12657,12660],{},[35,12658,12659],{},"格局变化","：Windsurf 不再是独立创业公司，选型时需把它和 Cognition\u002FDevin 生态绑定考虑",[27,12662,5167],{"id":5166},[31,12664,12665,12666,413,12668,12670],{},"对正在用 Windsurf 的团队，短期无需慌——产品在运营、Claude 模型可用。但中长期它会越来越「Devin 化」，如果你偏好厂商中立的工具，可以同时评估 ",[696,12667,3977],{"href":3976},[696,12669,1888],{"href":1887}," 等作为备选。",[27,12672,690],{"id":690},[72,12674,12675,12680,12686],{},[75,12676,12677],{},[696,12678,12679],{"href":6735},"Devin 工具卡",[75,12681,12506,12682,2108,12684],{},[696,12683,3977],{"href":3976},[696,12685,1888],{"href":1887},[75,12687,12688,12689],{},"工作流：",[696,12690,12692],{"href":12691},"\u002Fplaybook\u002Fonboarding\u002Fterminal-agent-stack-2026.html","2026 终端 AI Agent 怎么选",{"title":331,"searchDepth":363,"depth":363,"links":12694},[12695,12696,12697,12698,12699,12700],{"id":5007,"depth":353,"text":5007},{"id":12588,"depth":353,"text":12589},{"id":12618,"depth":353,"text":12619},{"id":12640,"depth":353,"text":12640},{"id":5166,"depth":353,"text":5167},{"id":690,"depth":353,"text":690},"继 OpenAI 收购告吹、Google 反向 acqui-hire 带走核心团队后，Cognition（Devin 母公司）收购 Windsurf 的 IP、产品、品牌与剩余团队，把自主 agent 与规模化 IDE 合二为一。",{},"\u002Fnews\u002F2026\u002Fwindsurf-acquisition",{"title":12541,"description":12701},"Cognition","https:\u002F\u002Fcognition.ai\u002Fblog\u002Fwindsurf","news\u002F2026\u002Fwindsurf-acquisition","QOHqVrkJWqKd5NbEdfZwwtUIMQbcnXZe9EHAZ0no3Ik",{"id":12710,"title":12711,"body":12712,"cover":755,"description":12974,"extension":754,"meta":12975,"navigation":765,"path":12976,"published":12977,"seo":12978,"sourceName":12979,"sourceUrl":12955,"stem":12980,"__hash__":12981},"news\u002Fnews\u002F2026\u002Fcopilot-cli-terminal-ga.md","GitHub Copilot CLI 新终端界面 GA：Agent 工具发现进入新阶段",{"type":24,"value":12713,"toc":12964},[12714,12716,12761,12764,12767,12770,12781,12785,12788,12833,12836,12858,12861,12865,12868,12874,12880,12884,12887,12898,12901,12904,12907,12910,12921,12924,12928,12946,12948],[27,12715,5007],{"id":5007},[72,12717,12718,12724,12734,12755],{},[75,12719,12720,12723],{},[35,12721,12722],{},"Copilot CLI 新终端界面 GA","：Session \u002F Gists \u002F Issues \u002F Pull Requests 标签页统一进 TUI",[75,12725,12726,12729,12730,12733],{},[35,12727,12728],{},"GitHub 工作项可直接进 prompt","：在 issue \u002F PR 上按 ",[333,12731,12732],{},"c","，把上下文带入当前会话",[75,12735,12736,70,12739,413,12742,413,12745,413,12748,413,12751,12754],{},[35,12737,12738],{},"会话内配置工具链",[333,12740,12741],{},"\u002Fmcp add",[333,12743,12744],{},"\u002Fmcp search",[333,12746,12747],{},"\u002Fskills",[333,12749,12750],{},"\u002Fplugin",[333,12752,12753],{},"\u002Fsettings"," 不再依赖手写配置",[75,12756,12757,12760],{},[35,12758,12759],{},"Agent Finder 同月上线","：按任务动态发现资源，背后是开放 ARD（Agentic Resource Discovery）规范",[27,12762,12763],{"id":12763},"为什么值得关注",[31,12765,12766],{},"2026 年 Coding Agent 的问题已经从「会不会改代码」变成「怎么找到正确工具、怎么少污染上下文」。Copilot CLI 这次 GA 的重点不是多一个命令行入口，而是把 GitHub Issues \u002F PRs、MCP、skills、plugins、settings 这些 Agent 工作台能力塞进终端。",[31,12768,12769],{},"这意味着 Copilot 正在从 IDE 插件扩展成三层平台：",[432,12771,12772,12775,12778],{},[75,12773,12774],{},"IDE 里的 Agent Mode",[75,12776,12777],{},"GitHub Actions 环境里的 Coding Agent",[75,12779,12780],{},"终端里的 Copilot CLI TUI",[27,12782,12784],{"id":12783},"新-copilot-cli-能做什么","新 Copilot CLI 能做什么",[31,12786,12787],{},"新版 CLI 的核心是 tabbed TUI：",[145,12789,12790,12799],{},[148,12791,12792],{},[151,12793,12794,12797],{},[154,12795,12796],{},"Tab",[154,12798,9368],{},[161,12800,12801,12809,12817,12825],{},[151,12802,12803,12806],{},[166,12804,12805],{},"Session",[166,12807,12808],{},"默认对话与任务执行",[151,12810,12811,12814],{},[166,12812,12813],{},"Gists",[166,12815,12816],{},"浏览个人 gist",[151,12818,12819,12822],{},[166,12820,12821],{},"Issues",[166,12823,12824],{},"当前 GitHub repo 的 issue",[151,12826,12827,12830],{},[166,12828,12829],{},"Pull Requests",[166,12831,12832],{},"当前 repo 的 PR",[31,12834,12835],{},"在 Issues \u002F PR tab 中可以：",[72,12837,12838,12843,12849,12855],{},[75,12839,6987,12840,12842],{},[333,12841,12732],{}," 将当前条目引用进 prompt",[75,12844,6987,12845,12848],{},[333,12846,12847],{},"o"," 在浏览器打开",[75,12850,6987,12851,12854],{},[333,12852,12853],{},"\u002F"," 搜索 issue \u002F PR",[75,12856,12857],{},"通过 settings 隐藏、禁用或重排 tab",[31,12859,12860],{},"对开发者来说，最实用的是「issue → prompt」少了一步复制粘贴：让 Copilot 直接调查、修复或 review 具体工作项。",[27,12862,12864],{"id":12863},"mcp-skills-plugins-配置前移到会话内","MCP \u002F Skills \u002F Plugins 配置前移到会话内",[31,12866,12867],{},"过去 Agent 工具接入常见痛点是：文档散、配置文件位置不同、改完要重启。新版 Copilot CLI 把这些配置入口放进会话：",[326,12869,12872],{"className":12870,"code":12871,"language":876,"meta":331},[874],"\u002Fmcp add\n\u002Fmcp search\n\u002Fskills\n\u002Fplugin\n\u002Fsettings\n\u002Ftheme\n",[333,12873,12871],{"__ignoreMap":331},[31,12875,12876,12877,12879],{},"其中 ",[333,12878,12744],{}," 可以浏览 GitHub MCP Registry，server 添加后立即生效。这个变化会降低团队试用 MCP 的门槛，但也提高了治理要求：企业管理员需要明确允许哪些 registry、哪些 MCP server 可以接入生产仓库。",[27,12881,12883],{"id":12882},"agent-finderard-规范开始冒头","Agent Finder：ARD 规范开始冒头",[31,12885,12886],{},"GitHub 同月发布 Agent Finder，用来解决「资源发现」问题。它不是自动安装工具，而是：",[432,12888,12889,12892,12895],{},[75,12890,12891],{},"根据自然语言任务搜索资源",[75,12893,12894],{},"从公开 catalog 或企业私有 registry 返回候选项",[75,12896,12897],{},"用户确认后再加载所需能力",[31,12899,12900],{},"GitHub 称其实现了开放 ARD（Agentic Resource Discovery）规范，并与 Google、GoDaddy、Hugging Face、Microsoft 等协作。",[31,12902,12903],{},"AIHO 判断：MCP 解决「Agent 怎么连工具」，ARD 解决「Agent 怎么发现该连哪个工具」。如果 MCP server 数继续爆炸，资源发现会成为 Agent 平台的基础设施。",[27,12905,12906],{"id":12906},"对国内开发者的意义",[31,12908,12909],{},"短期价值：",[72,12911,12912,12915,12918],{},[75,12913,12914],{},"GitHub-heavy 团队更容易把 issue \u002F PR 工作流搬进终端",[75,12916,12917],{},"MCP 配置更傻瓜，试用成本下降",[75,12919,12920],{},"Copilot CLI 从「命令解释器」升级成「终端 Agent 工作台」",[31,12922,12923],{},"短板仍然是老问题：GitHub\u002FCopilot 网络稳定性、支付、企业合规审批。国内团队如果已经在用 GitHub Enterprise \u002F Copilot Business，这次更新值得尽快试；如果主要在 Gitee \u002F GitLab \u002F 私有仓库，落地价值会打折。",[27,12925,12927],{"id":12926},"aiho-建议","AIHO 建议",[72,12929,12930,12937,12943],{},[75,12931,12932,12933,12936],{},"个人用户：先跑 ",[333,12934,12935],{},"copilot update","，试 Issues \u002F PRs tab 是否能替代日常复制 issue 上下文。",[75,12938,12939,12940,12942],{},"团队用户：先在非核心 repo 试 ",[333,12941,12741],{},"，明确 MCP allowlist。",[75,12944,12945],{},"平台团队：关注 ARD 规范，它可能成为 MCP registry \u002F Agent marketplace 的下一层标准。",[27,12947,2163],{"id":2163},[72,12949,12950,12957],{},[75,12951,12952,12953],{},"Copilot CLI GA：",[696,12954,12955],{"href":12955,"rel":12956},"https:\u002F\u002Fgithub.blog\u002Fchangelog\u002F2026-06-23-copilot-cli-new-terminal-interface-is-generally-available\u002F",[1009],[75,12958,12959,12960],{},"Agent Finder：",[696,12961,12962],{"href":12962,"rel":12963},"https:\u002F\u002Fgithub.blog\u002Fchangelog\u002F2026-06-17-agent-finder-for-github-copilot-now-available\u002F",[1009],{"title":331,"searchDepth":363,"depth":363,"links":12965},[12966,12967,12968,12969,12970,12971,12972,12973],{"id":5007,"depth":353,"text":5007},{"id":12763,"depth":353,"text":12763},{"id":12783,"depth":353,"text":12784},{"id":12863,"depth":353,"text":12864},{"id":12882,"depth":353,"text":12883},{"id":12906,"depth":353,"text":12906},{"id":12926,"depth":353,"text":12927},{"id":2163,"depth":353,"text":2163},"GitHub Copilot CLI 新版 terminal interface 于 2026-06-23 GA，带来 tabbed TUI、Issues\u002FPRs\u002FGists 标签页、会话内 MCP\u002Fskills\u002Fplugins\u002Fsettings 配置；同月 Agent Finder 发布，基于 ARD 规范按任务发现资源。",{},"\u002Fnews\u002F2026\u002Fcopilot-cli-terminal-ga","2026-06-25",{"title":12711,"description":12974},"GitHub Changelog","news\u002F2026\u002Fcopilot-cli-terminal-ga","sLlXwUA2UJsCpVOaOr4jPtsQYgLtoyONG9l3b0FG7FM",{"id":12983,"title":12984,"body":12985,"cover":755,"description":13385,"extension":754,"meta":13386,"navigation":765,"path":13387,"published":12977,"seo":13388,"sourceName":13389,"sourceUrl":13364,"stem":13390,"__hash__":13391},"news\u002Fnews\u002F2026\u002Fjules-tools-cli.md","Google Jules Tools 发布：异步 Coding Agent 正式进入命令行工作流",{"type":24,"value":12986,"toc":13376},[12987,12989,13015,13018,13021,13090,13093,13096,13099,13287,13290,13292,13295,13309,13312,13315,13335,13338,13340,13343,13346,13349,13355,13357,13373],[27,12988,5007],{"id":5007},[72,12990,12991,12997,13003,13009],{},[75,12992,12993,12996],{},[35,12994,12995],{},"Jules Tools 是 @google\u002Fjules CLI","：可在终端创建、查看、拉取 Jules 远程任务",[75,12998,12999,13002],{},[35,13000,13001],{},"Jules 运行在 Cloud VM","：clone 仓库、改代码、跑测试、返回 diff \u002F PR",[75,13004,13005,13008],{},[35,13006,13007],{},"支持脚本化委派","：TODO 文件、GitHub issue、Gemini CLI 输出都可以 pipe 给 Jules",[75,13010,13011,13014],{},[35,13012,13013],{},"异步 Agent 进入混合工作流","：本地手写 + 云端委派并行推进",[27,13016,13017],{"id":13017},"它解决什么问题",[31,13019,13020],{},"Jules 原本更像 Web UI 里的「把任务交给云端 Agent」。Jules Tools 把这件事搬进终端，让它能进入开发者日常流水线：",[326,13022,13024],{"className":328,"code":13023,"language":330,"meta":331,"style":331},"npm install -g @google\u002Fjules\njules login\njules remote new --repo . --session \"write unit tests for auth module\"\njules remote list --session\njules remote pull --session 123456\n",[333,13025,13026,13037,13044,13065,13077],{"__ignoreMap":331},[336,13027,13028,13030,13032,13034],{"class":338,"line":339},[336,13029,1622],{"class":342},[336,13031,369],{"class":346},[336,13033,1627],{"class":356},[336,13035,13036],{"class":346}," @google\u002Fjules\n",[336,13038,13039,13042],{"class":338,"line":353},[336,13040,13041],{"class":342},"jules",[336,13043,1651],{"class":346},[336,13045,13046,13048,13051,13054,13057,13059,13062],{"class":338,"line":363},[336,13047,13041],{"class":342},[336,13049,13050],{"class":346}," remote",[336,13052,13053],{"class":346}," new",[336,13055,13056],{"class":356}," --repo",[336,13058,9156],{"class":346},[336,13060,13061],{"class":356}," --session",[336,13063,13064],{"class":346}," \"write unit tests for auth module\"\n",[336,13066,13067,13069,13071,13074],{"class":338,"line":378},[336,13068,13041],{"class":342},[336,13070,13050],{"class":346},[336,13072,13073],{"class":346}," list",[336,13075,13076],{"class":356}," --session\n",[336,13078,13079,13081,13083,13085,13087],{"class":338,"line":390},[336,13080,13041],{"class":342},[336,13082,13050],{"class":346},[336,13084,4573],{"class":346},[336,13086,13061],{"class":356},[336,13088,13089],{"class":356}," 123456\n",[31,13091,13092],{},"对重度终端用户来说，这比打开网页、选 repo、复制 prompt 更自然；对团队来说，则意味着可以把 async coding agent 接入脚本和 issue triage。",[27,13094,13095],{"id":13095},"典型组合方式",[31,13097,13098],{},"Google 官方博客给出的方向很明确：Jules Tools 不是孤立 CLI，而是可以和 GitHub CLI、jq、Gemini CLI 串起来。",[326,13100,13102],{"className":328,"code":13101,"language":330,"meta":331,"style":331},"# 把 TODO.md 每行派给 Jules\ncat TODO.md | while IFS= read -r line; do\n  jules remote new --repo . --session \"$line\"\ndone\n\n# 把一个 GitHub issue 标题派给 Jules\ngh issue list --assignee @me --limit 1 --json title \\\n  | jq -r '.[0].title' \\\n  | jules remote new --repo .\n\n# 先让 Gemini CLI 找出最琐碎 issue，再交给 Jules\ngemini -p \"find the most tedious issue, print it verbatim\\n$(gh issue list --assignee @me)\" \\\n  | jules remote new --repo .\n",[333,13103,13104,13109,13141,13165,13170,13174,13179,13209,13224,13240,13244,13249,13273],{"__ignoreMap":331},[336,13105,13106],{"class":338,"line":339},[336,13107,13108],{"class":393},"# 把 TODO.md 每行派给 Jules\n",[336,13110,13111,13114,13117,13119,13122,13125,13127,13130,13132,13135,13138],{"class":338,"line":353},[336,13112,13113],{"class":342},"cat",[336,13115,13116],{"class":346}," TODO.md",[336,13118,1604],{"class":1524},[336,13120,13121],{"class":1524}," while",[336,13123,13124],{"class":1528}," IFS",[336,13126,3528],{"class":1524},[336,13128,13129],{"class":356}," read",[336,13131,372],{"class":356},[336,13133,13134],{"class":346}," line",[336,13136,13137],{"class":1528},"; ",[336,13139,13140],{"class":1524},"do\n",[336,13142,13143,13146,13148,13150,13152,13154,13156,13159,13162],{"class":338,"line":363},[336,13144,13145],{"class":342},"  jules",[336,13147,13050],{"class":346},[336,13149,13053],{"class":346},[336,13151,13056],{"class":356},[336,13153,9156],{"class":346},[336,13155,13061],{"class":356},[336,13157,13158],{"class":346}," \"",[336,13160,13161],{"class":1528},"$line",[336,13163,13164],{"class":346},"\"\n",[336,13166,13167],{"class":338,"line":378},[336,13168,13169],{"class":1524},"done\n",[336,13171,13172],{"class":338,"line":390},[336,13173,1612],{"emptyLinePlaceholder":765},[336,13175,13176],{"class":338,"line":397},[336,13177,13178],{"class":393},"# 把一个 GitHub issue 标题派给 Jules\n",[336,13180,13181,13184,13187,13189,13192,13195,13198,13201,13204,13207],{"class":338,"line":1637},[336,13182,13183],{"class":342},"gh",[336,13185,13186],{"class":346}," issue",[336,13188,13073],{"class":346},[336,13190,13191],{"class":356}," --assignee",[336,13193,13194],{"class":346}," @me",[336,13196,13197],{"class":356}," --limit",[336,13199,13200],{"class":356}," 1",[336,13202,13203],{"class":356}," --json",[336,13205,13206],{"class":346}," title",[336,13208,8842],{"class":356},[336,13210,13211,13214,13217,13219,13222],{"class":338,"line":1643},[336,13212,13213],{"class":1524},"  |",[336,13215,13216],{"class":342}," jq",[336,13218,372],{"class":356},[336,13220,13221],{"class":346}," '.[0].title'",[336,13223,8842],{"class":356},[336,13225,13226,13228,13231,13233,13235,13237],{"class":338,"line":1654},[336,13227,13213],{"class":1524},[336,13229,13230],{"class":342}," jules",[336,13232,13050],{"class":346},[336,13234,13053],{"class":346},[336,13236,13056],{"class":356},[336,13238,13239],{"class":346}," .\n",[336,13241,13242],{"class":338,"line":1659},[336,13243,1612],{"emptyLinePlaceholder":765},[336,13245,13246],{"class":338,"line":1665},[336,13247,13248],{"class":393},"# 先让 Gemini CLI 找出最琐碎 issue，再交给 Jules\n",[336,13250,13251,13254,13257,13260,13262,13265,13268,13271],{"class":338,"line":1672},[336,13252,13253],{"class":342},"gemini",[336,13255,13256],{"class":356}," -p",[336,13258,13259],{"class":346}," \"find the most tedious issue, print it verbatim\\n$(",[336,13261,13183],{"class":342},[336,13263,13264],{"class":346}," issue list ",[336,13266,13267],{"class":356},"--assignee",[336,13269,13270],{"class":346}," @me)\"",[336,13272,8842],{"class":356},[336,13274,13275,13277,13279,13281,13283,13285],{"class":338,"line":8},[336,13276,13213],{"class":1524},[336,13278,13230],{"class":342},[336,13280,13050],{"class":346},[336,13282,13053],{"class":346},[336,13284,13056],{"class":356},[336,13286,13239],{"class":346},[31,13288,13289],{},"这个模式很像「本地 Agent 负责分析和分拣，云端 Agent 负责执行」。",[27,13291,5142],{"id":5142},[31,13293,13294],{},"2026 年 Coding Agent 正在分化成两类：",[432,13296,13297,13303],{},[75,13298,13299,13302],{},[35,13300,13301],{},"实时结对型","：Claude Code、Codex CLI、Cursor、Gemini CLI",[75,13304,13305,13308],{},[35,13306,13307],{},"异步委派型","：Jules、Devin、Copilot Coding Agent",[31,13310,13311],{},"Jules Tools 的价值是把第二类工具接回命令行，让 async agent 可以被脚本、cron、issue bot、GitHub Actions 等传统自动化系统调用。",[27,13313,13314],{"id":13314},"适合哪些任务",[72,13316,13317,13320,13323,13326,13329,13332],{},[75,13318,13319],{},"依赖升级",[75,13321,13322],{},"补单元测试",[75,13324,13325],{},"简单 bug fix",[75,13327,13328],{},"文档更新",[75,13330,13331],{},"机械迁移",[75,13333,13334],{},"多个低风险 issue 并发处理",[31,13336,13337],{},"不建议一开始就交给 Jules 的任务：架构大改、需求不明确的新功能、需要生产 secret 的任务、紧急线上事故。",[27,13339,12927],{"id":12926},[31,13341,13342],{},"如果你已经在用 Jules：尽快试 Jules Tools，因为 CLI 能显著降低任务创建和查看成本。",[31,13344,13345],{},"如果你还没用过 Jules：从 3 个低风险 issue 开始，不要一口气把 backlog 全派出去。每个 issue 必须写清 Goal \u002F Scope \u002F Verification，并要求 Agent 跑测试。",[31,13347,13348],{},"最佳实践：",[326,13350,13353],{"className":13351,"code":13352,"language":876,"meta":331},[874],"Gemini CLI 分拣 issue → Jules 并发处理低风险任务 → Copilot \u002F CodeRabbit \u002F 人工 review PR\n",[333,13354,13352],{"__ignoreMap":331},[27,13356,2163],{"id":2163},[72,13358,13359,13366],{},[75,13360,13361,13362],{},"Google Developers Blog：",[696,13363,13364],{"href":13364,"rel":13365},"https:\u002F\u002Fdevelopers.googleblog.com\u002Fen\u002Fmeet-jules-tools-a-command-line-companion-for-googles-async-coding-agent\u002F",[1009],[75,13367,13368,13369],{},"Jules CLI Reference：",[696,13370,13371],{"href":13371,"rel":13372},"https:\u002F\u002Fjules.google\u002Fdocs\u002Fcli\u002Freference",[1009],[725,13374,13375],{},"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: 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.sVt8B{--shiki-default:#24292E;--shiki-dark:#E1E4E8}",{"title":331,"searchDepth":363,"depth":363,"links":13377},[13378,13379,13380,13381,13382,13383,13384],{"id":5007,"depth":353,"text":5007},{"id":13017,"depth":353,"text":13017},{"id":13095,"depth":353,"text":13095},{"id":5142,"depth":353,"text":5142},{"id":13314,"depth":353,"text":13314},{"id":12926,"depth":353,"text":12927},{"id":2163,"depth":353,"text":2163},"Google 发布 Jules Tools（@google\u002Fjules），让开发者可以在终端中创建、查看、拉取 Jules 云端 coding session，把异步 Coding Agent 接入脚本、GitHub issue 和现有开发流水线。",{},"\u002Fnews\u002F2026\u002Fjules-tools-cli",{"title":12984,"description":13385},"Google Developers Blog","news\u002F2026\u002Fjules-tools-cli","1kyxy10LD9ImC1gL3FGG4ndZ9lE9b_6yxXCmWIu-EMc",1784288923377]