从用户访谈到 45 个实战样例Composio Examples 样例库的规划设计与落地逻辑【免费下载链接】composioComposio powers 1000 toolkits, tool search, context management, authentication, and a sandboxed workbench to help you build AI agents that turn intent into action.项目地址: https://gitcode.com/GitHub_Trending/co/composio本文基于 Composio 文档仓库中的决策记录 examples.md 展开完整呈现 Composio 文档站 Examples示例库页面的重构规划它的用户调研数据来源、8 大类约 45 个样例的目录结构、四条核心设计原则以及该规划在当前仓库中的实际落地形态示例页面路由、gallery 元数据机制与可运行示例的分级校验体系帮助读者理解一个 Agent 平台如何系统性规划并维护其示例内容生态。规划背景从 50 个真实用例出发该决策记录的核心依据是用户访谈分析团队从 Slack 的#user-interviews频道中提取了50 个真实用例原始材料为 157 条消息其中 77 条具有实质内容并据此设计了示例页面的完整结构。这正是该规划最重要的方法论示例库的内容目录不是拍脑袋的分类而是从真实用户意图中归纳出来的。每个样例都对应一个明确的用户目标如自动分诊 GitHub issue 并指派负责人而不是按技术概念堆砌。该文档在 docs/decisions/ 目录中的定位是cookbook/examples restructuring plan示例库重构规划与同目录的 cookbooks-revamp-plan.md历史任务追踪器共同构成示例内容体系的设计依据。总体结构8 大类、约 45 个样例规划将示例页划分为以下 8 个板块总计约 45 个样例。以下完整继承原文档的目录表格Getting started入门ExampleWhat it demonstratesHello, worldFirst tool execution, basic setupConnect your first app in 60 secondsOAuth flow, connected accountsGuides指南ExampleWhat it demonstratesBuilding a Chat AgentCore agentic loop, conversation contextBuilding a RAG AgentTool router knowledge retrievalBuilding a Slackbot AgentReal-time messaging, event handlingBuilding a Natural Language Data Analysis AgentComplex queries, structured outputGet started with Claude CodeMCP setup, Claude integrationGet started with OpenAI Agents SDKNative tools with OpenAIGet started with Vercel AI SDKStreaming, Next.js integrationGet started with LangChainLangChain tools wrapperGet started with MastraMastra framework integrationGet started with CrewAIMulti-agent with CrewAIAgents智能体ExampleWhat it demonstratesBuild a PR review agent with GitHub and ClaudeMulti-tool (GitHub AI), code contextDeploy an email assistant that drafts responsesEmail integration, response generationCreate a Slack bot with access to 1000 toolsTool router, many toolkitsRun a research agent that searches, scrapes, and summarizesWeb tools, chaining outputsBuild an AI SDR that enriches leads automaticallyCRM web research, data enrichmentBuild an agentic RAG agent over your docsRAG tool calling combinedBuild a data analysis agent with natural language queriesDatabase tools, natural language to SQLBuild a voice agent with real-time tool callingVoice tools, real-time streamingSpawn sub-agents for parallel task executionSub-agents, parallel processingOrchestrate multiple agents on a complex workflowMulti-agent coordination, handoffsSEO data retrieval agentSpecialized data APIs, reportingCode DevOps代码与 DevOpsExampleWhat it demonstratesAuto-triage GitHub issues and assign ownersGitHub API, classification, automationSync Linear tickets to Slack on status changeCross-tool sync, webhooksPost CI failure summaries to DiscordCI integration, notificationsCreate Jira tickets from Slack messagesSlack → Jira, message parsingCommunication Social沟通与社交ExampleWhat it demonstratesSend personalized emails at scale with GmailBulk operations, personalizationBuild a Discord bot that manages your serverDiscord API, bot commandsAuto-respond to Slack DMs with contextSlack events, contextual responsesLinkedIn content strategy agentLinkedIn API, content generationSales CRM销售与客户管理ExampleWhat it demonstratesHubSpot CRM automation: new lead → research → enrichCRM integration, data enrichment pipelineProductivity Data效率与数据ExampleWhat it demonstratesSync databases to Google Sheets automaticallyDatabase Sheets, data syncBuild a meeting notes → Notion pipelineTranscription Notion, structured dataCreate calendar events from natural languageNLP input, calendar APIsDownload attachments and process themFile download, file processingTurn documents into structured outputDocument parsing, structured extractionShopify sales reporting to SlackE-commerce data, scheduled reportsTriggers Background jobs触发器与后台任务ExampleWhat it demonstratesBuild a Shopify customer support agentE-commerce support, always-on agentRun an agent when new emails arriveEmail triggers, event-drivenAuto-review PRs on pushGitHub webhooks, automated reviewDaily digest: Summarize GitHub activity to SlackScheduled jobs, aggregationWeekly business report automationCron-style scheduling, multi-source dataWebhook → process → route to the right toolGeneric webhooks, routing logic从分类分布可以看出规划的意图Agents 与 Productivity Data 是重心各 11 个和 6 个样例Sales CRM 目前仅 1 个样例属于明确标注的待扩展区见下文未来规划。设计原则四条关键决策原文档的 Design Notes 给出了四条设计决策它们解释了为什么目录长成上面这个样子领域分类 行动导向命名风格参考 Modal 的 examples 站点——按业务领域分类而非按 API 或框架分类样例标题直接描述用户要做的事如 Auto-triage GitHub issues and assign owners让读者按目标而非按技术栈检索。Get started with... 板块参考 Vercel AI SDK cookbook 的组织方式把框架快速上手Claude Code、OpenAI Agents SDK、Vercel AI SDK、LangChain、Mastra、CrewAI独立成一个板块覆盖各生态用户的入口习惯。框架作为标签页tabs而非一级分类AI SDK、LangChain 等框架不作为顶层分类出现而是作为同一个示例内部的 tab 展示。这避免了按框架切分后同一用例内容重复三份的维护问题。高级特性嵌入真实用例文件上传/下载、子代理sub-agents等高级功能不单独成节而是内嵌在真实场景示例中自然带出例如Download attachments and process them就是文件处理特性的载体。从规划到落地当前仓库中 Examples 页的实现规划文档是意图当前仓库则展示了现状。二者对照可以看到规划已被部分实现且实现方式对规划做了有意识的收敛。已上线的四个端到端示例当前文档内容目录 docs/content/examples/ 下的meta.jsonmeta.json登记了 4 个示例页面general-agent-with-piPi Composio 通用智能体接入 Slack触发器、按用户会话、共享连接、重定向授权链接与代理standup-slackbot每日站会机器人用白标自有 Slack 应用 tool-router 会话 手动工具执行 代理为每位成员从其已连接工具生成站会草稿local-sandbox-pr-reviewer在自有沙箱中运行 PR 审查器同时通过 Composio 会话调用 GitHub 工具imessage-agent用自定义 toolkit 将本地 iMessage 包装为进程内工具并通过 eve provider 与整个 Composio 工具目录放在同一会话中示例首页index.mdx的定位是End-to-end builds that wire Composio into working agents. Each one is a complete project you can read top to bottom and run.——即每个示例都是一个可从头读到尾、可直接运行的完整项目这与规划中行动导向、领域驱动的原则一致。Gallery 元数据机制规划中Featured 分类的实现页面路由docs/app/(home)/examples/[[...slug]]/page.tsx负责渲染无 slug 时展示自定义 Featured Gallery有 slug 时走标准文档页渲染器。gallery 数据不集中配置而是从每个示例 MDX 的 frontmattergallery块中读取title/description 用页面自身的categories、logos、featured、order由gallery块提供。例如 standup-slackbot.mdx 的 frontmattergallery: categories: [Background agents] logos: [slack, github, linear] featured: true order: 1对应的 examples-gallery.tsx 组件实现了分类筛选与卡片网格。值得注意的是实现层将规划的 8 类收敛为 3 条赛道General agents、Background agents、Coding agents外加一个Featured视图按featured: true过滤order控制排序默认 99。这与规划文档中Future Plans → Featured Section顶部放 5 个 wow 示例的 hero 网格直接呼应——Featured 机制已经落地而 8 类业务分类在落地阶段被让位给了更贴近产品形态的agent 类型三分法。空分类还会显示 More examples in this category are on the way. 的占位提示与规划中随内容增长渐进更新的节奏一致。示例的可持续维护examples-manifest.json 分级校验体系示例内容生态最容易被忽视的成本是示例腐化——文档更新后示例代码跑不通。当前仓库用一套机器可执行的清单来解决这个问题这是对规划文档长期维护诉求的源码级回答。仓库根目录的 examples-manifest.json 是runnable example entrypoints 清单由 harness/run.mjs 消费。文件头部的$comment定义了四级tier执行策略tier 1无人值守可直接跑unattended只需环境变量如OPENAI_API_KEYtier 2需要预置状态provisioned state由scripts/examples-provision.mjs提前准备好账号、auth config 等示例通过ids字段引用如COMPOSIO_EXAMPLES_GMAIL_AUTH_CONFIG_IDtier 3有界执行——输出匹配readiness正则即判定就绪后终止。清单特别强调readiness 正则必须锚定示例成功时打印的带标签输出行例如Visit this URL to authorize: https?://绝不能只用裸https?://防止错误信息里的 URL 被误判为就绪tier X明确排除并给出reason。例如ts/file-handling因设计上会真实发送邮件outbound-email被排除ts/tool-router/webhook-server因依赖外部隧道external-tunnel被排除llmMock: false标记那些 LLM 流量无法走 mock 服务器、只能真实金丝雀验证的条目每个条目还声明了toolkits示例会触碰的工具包如hackernews、gmail、github、timeoutSec和env。从清单覆盖的包来看ts/examples/下 anthropic、langchain、llamaindex、mastra、openai、tool-router、triggers、vercel 等Python 侧为 python/examples/它实际上把规划中Guides 板块的框架入门样例OpenAI / LangChain / Mastra / Vercel 等落实成了可回归测试的一批真实入口点。换言之文档站里的示例与仓库里被 CI 守护的可运行示例是同一套内容资产。未来规划Featured、Templates 与 MCP 入口原文档 Future Plans 部分列出了五个方向其中部分已在仓库中找到对应物Featured Section顶部 hero 网格展示 5 个wow示例类似 Modal 的 featured examples。对应实现即上文featured/order字段与Featured筛选视图。Templates提供可克隆的预置起步模板点名了 AI Email Assistant Template、GitHub Bot Template、Slack Bot Template。更多 Sales CRM 示例Salesforce 自动化、deal tracking agent、pipeline management agent——补上当前仅 1 个样例的短板。MCP 专属板块Connect Composio MCP to Claude Desktop、Use Composio MCP with Cursor 等。文档特别注明这是用户的一个大入口a big entry point for users与 standup-slackbot.mdx 等示例中大量出现的 MCP / tool-router / 会话引用互为印证。文件处理示例处理并总结上传的 PDF、下载并分析附件——文档注明这是访谈中被用户高频提及的需求。此外规划要求整体再增加约 20 个示例以达到目标质量水位且命名要更具体、更行动导向覆盖边缘场景与高级模式。小结docs/decisions/examples.md 这份决策记录的价值在于它完整保留了 Composio 示例内容生态的从需求到结构的推导链用户访谈50 用例→ 8 类约 45 样例的目录 → 四条设计原则领域分类、行动导向、框架做 tab、高级特性嵌入式教学→ 未来扩展路线。而当前仓库展示了这条链路的落地切片docs/content/examples/下的 4 个端到端示例、frontmatter-driven 的 gallery 机制、以及examples-manifest.json harness 的分级可运行性守护。对于正在为自己的 Agent 平台规划文档示例体系的团队这套访谈驱动的目录设计 元数据驱动的页面渲染 清单驱动的可运行性回归的组合是一个可直接参考的工程范式。【免费下载链接】composioComposio powers 1000 toolkits, tool search, context management, authentication, and a sandboxed workbench to help you build AI agents that turn intent into action.项目地址: https://gitcode.com/GitHub_Trending/co/composio创作声明：本文部分内容由AI辅助生成（AIGC），仅供参考