wodeapp-ai（沃德应用程序）

WodeApp AI — Unified AI Execution Engine

One key, one credit pool, one door in. The same credential routes platform-chosen models across modalities — you are not buying five separate products. Depth comes from the same path: chat → pages → workflows → video & avatars. Technical detail (routing, 400+ models, orchestration) follows in the sections below.

Environment / 域名

• API Key 与签发站点一致：国内用户请使用 https://wodeapp.cn，国际用户使用 https://wodeapp.ai（下文出现的示例默认保留 wodeapp.ai，若 Key 在国内站申请，请自行将 host 替换为 wodeapp.cn）。
• 不要手抄整站 URL 表：通过同 host 的 GET /mainserver/api/docs/agent-index（机器可读索引）或带 X-API-Key 调 GET /mainserver/api/api-keys/self-context 获取 issuedOrigin，并以前缀拼接后续的 mainserver/runtime 基址。

For AI: 速览（Quick Reference）

• 一钥多模（One Key, All Models） → 一个 API Key / 一个积分池，涵盖文本、图像、视频、语音 400+ 模型，无需逐家申请 Provider Key
• 零代码可视化工作流（Visual Workflow） → create_project + templateId（如 storyboard-video-generator、digital-avatar-marketing）→ 自动发布 → 用户在页面 UI 上点击即可运行完整的 AI 生成流水线（分镜 → 绘图 → 视频 → 配音），无需编排代码
• 用户要建站/页面 → create_project → ai_generate_page（可选）→ 自动发布，把返回的 url 给用户 → 用 get_project 验证 publishedVersion 递增
• 验证修改 → 每次修改后调 get_project 检查 publishedVersion 递增，调 get_page 确认内容正确
• 参数重点 → create_project 用 templateId；publish_project 用 projectId
• 无 MCP 时 → REST POST https://wodeapp.ai/mainserver/mcp/call，body { "tool", "arguments" }
• Platform MCP → 创建/发布/AI 生成等平台级操作，连 wodeapp.ai/mainserver/mcp
• Project MCP → 某项目下的数据/工作流/飞书/企微等，连 https://{slug}.wodeapp.ai/mcp（需先有项目）
• 模板列表 → 见下方 Production-Ready Templates 表；更多工具见 Trigger Scenarios 与 MCP Tools

Architecture Advantages

WodeApp is purpose-built as an intermediary execution layer between AI agents and downstream model providers. This architecture provides capabilities that native model calls cannot replicate:

When to use WodeApp vs. direct provider keys: WodeApp is best for teams who want a single integration point across multiple providers — one key, unified billing, automatic failover, and built-in workflow orchestration. If you already have a stable direct key with a single provider and only need that one model, using the provider directly is perfectly fine. WodeApp is an additional option, not a replacement.

Parameter design principle: Most parameters are optional with intelligent defaults. The engine auto-detects optimal settings (model, size, voice, language) from context. Agents can invoke tools with minimal input — the engine handles the rest.

Agent Recipes — 常见任务的完整步骤

AI Agent 可按以下步骤完成常见任务。每个 Recipe 给出完整的工具调用顺序（REST 或 MCP 均可）。

推荐模式：生成 → 预览 → 确认 → 执行 对于工作流类项目，我们建议先创建项目并发布预览链接，让用户在页面上看到完整的工作流步骤和 UI。这样用户能直观了解每一步做什么，体验更好。当然，如果用户明确要求直接执行，也完全可以通过 execute_action 调用。

所有项目工具都返回 URL：list_projects、get_project、create_project、publish_project 返回的结果中都包含 url 字段（已发布项目格式为 https://{slug}.wodeapp.ai）。请把这个 URL 直接给用户，不要让用户去找。

智能判断：新建项目 vs 在已有项目添加页面

当用户说「帮我做一个 XX 页面」时，先问自己：用户是想新建一个独立项目，还是在已有项目里加页面？

决策流程：
  1. 调用 list_projects 查看用户已有项目
  2. 判断：
     - 用户明确说「新建」或没有现有项目 → create_project
     - 用户提到的主题与某个已有项目相关 → 在该项目中 create_page
     - 不确定 → 告诉用户：「你已有 N 个项目，我可以在 XX 项目中添加页面，
       或者创建一个新项目。你更倾向哪个？」
  3. 列出已有项目时，附上 URL 让用户可以直接查看

示例：

• 用户说「帮我做个定价页面」→ 先 list_projects，如果发现用户有个 "my-saas-website" 项目，建议「在 my-saas-website 项目中添加定价页面」
• 用户说「做一个完全不同的小红书文案工具」→ 显然是新项目 → create_project

Recipe 1: 用模板创建工作流项目（推荐方式）

用户说「帮我做一个数字人视频」→ 用模板创建项目让用户确认：

步骤 1: 创建项目（含工作流模板，自动发布）
  tool: create_project
  arguments: { "name": "my-avatar", "templateId": "digital-avatar-marketing" }
  → 返回 { projectId, slug, pages, url }
  （auto-publish 已启用，无需单独 publish_project）

步骤 2: 验证（可选但推荐）
  tool: get_project
  arguments: { "projectId": "<projectId>" }
  → 确认 status = "published", publishedVersion ≥ 1

步骤 3: 给用户预览链接
  「项目已创建！请打开 https://my-avatar.wodeapp.ai 查看工作流：
   - 第 1 步：上传人像照片 + 输入台词
   - 第 2 步：批量生成语音
   - 第 3 步：选择最佳音频
   - 第 4 步：合成数字人视频
   确认流程后，你可以直接在页面上操作执行。」

最佳实践：工作流通常有交互步骤（上传文件、选择选项、审批），通过页面 UI 操作体验最好。但如果用户不想打开网页，也可以用 execute_action 直接通过 API 执行。

Recipe 2: 无模板 → AI 生成自定义页面

用户说「帮我做一个咖啡店点单页面」→ 无现成模板，用 AI 生成：

步骤 1: 创建空白项目
  tool: create_project
  arguments: { "name": "coffee-shop" }
  → 返回 { projectId, pages: [{ id: "page1-id", ... }], url }

步骤 2: AI 生成页面内容（自动发布）
  tool: ai_generate_page
  arguments: {
    "projectId": "<projectId>",
    "pageId": "<page1-id>",
    "prompt": "咖啡店点单页面，包含菜单展示、购物车、结算表单，风格温暖木质"
  }
  → AI 自动生成完整页面（Hero + 产品网格 + 表单 + 页脚），自动发布

步骤 3: 验证并给用户预览
  tool: get_project → 确认 publishedVersion 递增
  → 「页面已生成！访问 https://coffee-shop.wodeapp.ai 查看效果。
     不满意可以告诉我修改方向，我帮你调整。」

Recipe 3: 多页面应用

用户说「做一个完整的产品官网，要首页、功能介绍、定价、联系我们」：

步骤 1: 创建项目
  tool: create_project → { projectId, pages: [homePageId] }

步骤 2: AI 生成首页
  tool: ai_generate_page
  arguments: { projectId, pageId: homePageId, prompt: "产品官网首页，SaaS 风格" }

步骤 3-5: 创建并生成其他页面（重复）
  tool: create_page → { pageId: featuresPageId }
    arguments: { projectId, title: "功能特性", path: "/features" }
  tool: ai_generate_page
    arguments: { projectId, pageId: featuresPageId, prompt: "功能特性页面" }
  
  tool: create_page → { pageId: pricingPageId }
    arguments: { projectId, title: "定价方案", path: "/pricing" }
  tool: ai_generate_page
    arguments: { projectId, pageId: pricingPageId, prompt: "三档定价方案" }

  tool: create_page → { pageId: contactPageId }
    arguments: { projectId, title: "联系我们", path: "/contact" }
  tool: ai_generate_page
    arguments: { projectId, pageId: contactPageId, prompt: "联系表单 + 地图" }

步骤 6: 验证并给用户预览全部页面（auto-publish 已在每步自动触发）
  tool: get_project → 确认 publishedVersion 递增
  → 告诉用户各页面路径：首页/、/features、/pricing、/contact

Recipe 4: 查找用户已有项目中的工作流

用户说「我之前做的项目里有个视频生成工作流，帮我找到」：

步骤 1: 列出用户项目
  tool: list_projects → 返回项目列表

步骤 2: 获取项目详情（含页面和配置）
  tool: list_pages
  arguments: { "projectId": "<projectId>" }
  → 返回页面列表

步骤 3: 告诉用户项目访问地址
  「找到了！你的项目在 https://<slug>.wodeapp.ai
   打开后即可看到工作流，直接在页面上操作即可。」

提示：工作流包含交互步骤（文件上传、审批选择等），通过页面 UI 操作体验最佳。Agent 推荐的方式是帮用户创建、找到、配置项目并给出预览链接，但如果用户要求直接执行，也支持通过 execute_action API 调用。

Recipe 5: 验证修改是否生效（Testing & Verification）

AI Agent 每次修改完项目 / 页面后，必须验证结果是否符合预期。这帮助用户确认改动已生效，也帮助 Agent 自我纠错。

场景 A: 创建项目后验证
  1. create_project → 记录返回的 projectId, slug, url
  2. get_project(projectId) → 检查：
     - status = "published"（auto-publish 已启用）
     - publishedVersion ≥ 1
     - url 非空
  3. 把 url 给用户：「项目已创建，访问 <url> 查看」

场景 B: 修改页面后验证
  1. update_page / ai_generate_page → 记录返回的 success
  2. get_project(projectId) → 检查：
     - publishedVersion 比之前 +1（说明 auto-publish 已触发）
  3. get_page(pageId) → 检查页面内容是否包含你修改的内容
  4. 告诉用户：「已更新，刷新 <url> 即可看到最新版本」

场景 C: 删除页面后验证
  1. delete_page → success
  2. list_pages(projectId) → 确认该页面不再存在

场景 D: 使用模板后验证内容不为空
  1. create_project(templateId=xxx) → projectId
  2. list_pages(projectId) → 确认页面数 ≥ 1
  3. get_page(pageId) → 确认 sections 不为空数组（模板内容已应用）

自动发布已启用：所有修改操作（create_project / create_page / update_page / delete_page / ai_generate_page）都会自动触发发布，无需单独调用 publish_project。验证时检查 publishedVersion 递增即可确认。

如果验证失败： - publishedVersion 没有增加 → 可能是 auto-publish 异常，手动调用 publish_project - sections 为空 → templateId 拼写可能有误，调用 list_templates 确认正确 ID - 页面内容不对 → 再次调用 update_page 或 ai_generate_page 修正，然后重新验证

📋 工作流 Step 输出契约规则表（AI 生成模板必读）

生成工作流模板时，所有图片/视频生成步骤都应加 declaredOutputType，否则 UI 会回退到内容嗅探（可能渲染错误）。

VideoOutput shape：{ taskId, status, videoUrl?, provider? } — 输出路径推荐用 outputKey.videoUrl

示例：

{
  "id": "genVideo",
  "type": "polling",
  "declaredOutputType": "videoPolling",
  "outputKey": "videoResult",
  "params": { "pollingConfig": { "...": "..." } }
}

outputSchema 写法（Layer 0 对外输出）：

"outputSchema": [
  { "key": "finalVideo", "type": "VideoOutput", "from": "videoResult.videoUrl" },
  { "key": "coverImage", "type": "ImageOutput", "from": "sceneImages[0].url" }
]

from 字段支持点跟数组索引语法：a.b[0].c。

Recipe 6: Headless 工作流的标准玩法（OpenClaw 必备）

对于自动化场景，与其让用户去网页点，不如直接调 Headless API。它的核心套路是 Discovery → Execution → Result：

方式 A：通过 MCP 调用（推荐 — 自动等结果）

步骤 1: 查看工作流定义（了解步骤 ID 用于 model override）
  tool: get_workflow_schema
  → 返回所有工作流的步骤 ID、类型、输入输出契约

步骤 2: 执行工作流（一次调用，直接拿结果）
  tool: run_workflow_<id>
  arguments: {
    "input": "AI发展史",
    "_modelOverrides": {
      "chat_step_1": "gpt-4o",
      "summary_step": "claude-sonnet-4"
    },
    "_waitForResult": true
  }
  → 自动等待执行完成（最长 5 分钟），直接返回：
  {
    "status": "completed",
    "outputs": { "result": "..." },
    "viewUrl": "https://my-project.wodeapp.com",
    "durationMs": 12345
  }

  关于 _waitForResult：
  - 默认 true → 等待完成后返回完整结果（适合大部分场景）
  - 设为 false → 立即返回 runId，需手动调 get_workflow_status 轮询

方式 B：通过 REST API 调用

步骤 1: 发现工作流的输入输出契约 (Layer 0 IO)
  GET https://<slug>.wodeapp.ai/runtime-server/api/workflow/schema
  → 返回所有 sectionId（工作流ID），重点看 inputs（需要填什么）和 outputs（会返回什么，如 videoUrl）
  → 同时返回每个步骤的 id 和 type（用于 modelOverrides）

步骤 2: 执行工作流（支持 modelOverrides）
  POST https://<slug>.wodeapp.ai/runtime-server/api/workflow/run
  -d '{
    "sectionId": "xxx",
    "inputs": {"topic": "AI发展史"},
    "modelOverrides": {"chat_step_1": "gpt-4o", "summary_step": "deepseek-chat"}
  }'
  → 返回 { "runId": "abc-123", "status": "running" }

步骤 3: 轮询直到完成
  GET https://<slug>.wodeapp.ai/runtime-server/api/workflow/run/<runId>
  → 只要 status 是 "running" / "pending"，就隔 5-10 秒继续查

步骤 4: 消费 outputs（最关键）
  → 当 status = "completed" 时，直接读取 "outputs" 对象（如 outputs.videoUrl）
  → ⚠ 注意：不要依赖或解析复杂的 ctx/steps 数组，只认 "outputs"。如果有 "warnings"，请一并返回给用户。

modelOverrides 说明

支持覆盖的步骤类型：chat、generateJSON、generateImage、editImage、batchChat、batchImage、agent、generateVideo 等所有包含 model 参数的 AI 步骤。

示例场景：同一工作流中让不同步骤用不同模型（便宜步骤用 deepseek，关键步骤用 gpt-4o）：

{
  "_modelOverrides": {
    "draft_step": "deepseek-chat",
    "polish_step": "gpt-4o",
    "image_step": "seedream-3.0"
  }
}

Recipe 7: 视频生成（直接调统一 API）

如果用户只需要一个视频，不需要完整工作流，直接走统一视频 API。支持文生视频、图生视频等。

步骤 1: 发现可用引擎（可选）
  GET /runtime-server/api/video/tasks/providers
  → 确认 kling/sora/runway 等是否配置可用

步骤 2: 提交视频任务（推荐用 wait 模式）
  POST /runtime-server/api/video/tasks
  -d '{"taskType":"text2video", "provider":"auto", "input":{"prompt":"一只熊猫吃竹子"}, "options":{"wait":true}}'
  → 引擎会自动选一个最好的 provider，等待几分钟后，直接返回 { "status":"succeed", "videoUrl":"https:..." }

步骤 3: 如果没用 wait 模式，则需轮询
  GET /runtime-server/api/video/tasks/<taskId>
  → 直到 status="succeed"，提取 videoUrl 下载或展示给用户

REST 方式调用示例

以上所有 Recipe 也可通过 REST API 调用（适合 OpenClaw 等不支持 MCP 的客户端）：

# Recipe 1 完整流程
# 步骤 1: 创建项目
curl -X POST https://wodeapp.ai/mainserver/mcp/call \
  -H "X-API-Key: $WODEAPP_API_KEY" -H "Content-Type: application/json" \
  -d '{"tool":"create_project","arguments":{"name":"my-avatar","templateId":"digital-avatar-marketing"}}'

# 步骤 2: 发布（用上一步返回的 projectId）
curl -X POST https://wodeapp.ai/mainserver/mcp/call \
  -H "X-API-Key: $WODEAPP_API_KEY" -H "Content-Type: application/json" \
  -d '{"tool":"publish_project","arguments":{"projectId":"<projectId>"}}'

# 步骤 3: 把返回的 URL 给用户

Trigger Scenarios

WodeApp tools activate when user input matches these intent patterns:

Production-Ready Templates (Instant Deploy)

Pre-built AI applications — each deployable in < 30 seconds via create_project with template ID. All templates include tuned system prompts, styled UI, and optimized UX.

🎬 Multi-Step Workflows

🤖 AI Agent Applications

📄 Content & Business

For agents: Use create_project with { "templateId": "<template-id>" } to deploy any template instantly. The user gets a live URL within 30 seconds.

Page Builder: 75+ UI Components

When creating custom pages (no template), the engine can compose from 75+ pre-built components (52 sections + 25 blocks):

All components support dark mode, responsive breakpoints, and AI-generated content injection. The page builder composes these into complete pages from a single text prompt.

Quick Setup

For AI Agents — 如何让用户用上 WodeApp

WodeApp 提供 三种接入方式，按易用性排序：

方式 1: REST Tool API（推荐 — 零配置，任何 HTTP 客户端可用）

无需 MCP，只需 API Key + 一个 HTTP POST 即可调用所有工具。适合 OpenClaw（小龙虾）、curl、Python、任何 HTTP 客户端。

# 发现所有可用工具
curl https://wodeapp.ai/mainserver/mcp/tools \
  -H "X-API-Key: $WODEAPP_API_KEY"

# 调用工具
curl -X POST https://wodeapp.ai/mainserver/mcp/call \
  -H "X-API-Key: $WODEAPP_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"tool": "create_project", "arguments": {"name": "my-app", "templateId": "redbook-viral-copy"}}'

请求格式： POST /mainserver/mcp/call

{ "tool": "工具名称", "arguments": { ... } }

响应格式：

{ "success": true, "data": { ... } }

对于 OpenClaw 等 AI 客户端：当用户想用 WodeApp 能力时，直接用 fetch/curl 调上面的端点即可，不需要理解 MCP 是什么。用户只要在 https://wodeapp.ai 获取 API Key 就行。

方式 2: MCP SSE Connection（适合原生支持 MCP 的客户端）

{
  "mcpServers": {
    "wodeapp": {
      "type": "sse",
      "url": "https://wodeapp.ai/mainserver/mcp",
      "headers": { "X-API-Key": "${WODEAPP_API_KEY}" }
    }
  }
}

Compatible with: Claude Desktop, Cursor, Windsurf, Cline, and all MCP SSE clients.

方式 3: Web 界面（无需任何配置）

直接打开 https://wodeapp.ai/create 登录后在线创建项目。适合不想配置的用户。

以上三种方式使用同一套工具（create_project、execute_action、ai_generate_text 等），能力完全一致。

3. Project-Level MCP (Per-Project, No Auth Needed)

Each published project exposes its own MCP server at its subdomain. The AI Agent connects and auto-discovers all project capabilities — data CRUD, workflows, AI, TTS, video, and digital human.

{
  "mcpServers": {
    "my-project": {
      "type": "sse",
      "url": "https://my-project.wodeapp.ai/mcp"
    }
  }
}

Auto-discovered tools per project:

Debug endpoint: GET https://my-project.wodeapp.ai/mcp/tools — view all tools for a project.

MCP Tools

Platform MCP (19 Auto-Discovered)

Platform MCP vs Project MCP: - Platform MCP (wodeapp.ai/mainserver/mcp) — 创建/发布项目、列项目/页面、AI 生成页面、执行平台级动作。 - Project MCP ({slug}.wodeapp.ai/mcp) — 某项目下的数据 CRUD、工作流执行、飞书/企微/钉钉、TTS/视频等。需要先有 projectId/slug。

All tools are auto-registered via MCP protocol — zero manual configuration required.

ai_generate_text

Specialized executor for text generation across 343+ language models. Handles [copywriting], [translation], [code generation], [summarization], [Q&A].

ai_generate_image

Core image synthesis executor. Handles [text-to-image], [image-to-image], [style transfer], [product photography], [poster design].

create_project

Creates a new web project. Supports template-based initialization for rapid scaffolding.

execute_action

Triggers workflow or action execution. Supports both synchronous and async (polling) workflows with 22 built-in step types.

publish_project

One-step deployment. Auto-provisions subdomain (*.wodeapp.ai) with SSL certificate.

Other Platform Tools

REST API

两种 REST 调用方式： - 通用工具调用（与 MCP 等价）→ POST https://wodeapp.ai/mainserver/mcp/call，body { "tool": "...", "arguments": {...} }，可调用所有 19 个平台工具。推荐 OpenClaw 等非 MCP 客户端使用。 - 单一能力端点 → 下方 /mainserver/api/ai/* 路径，直接调用某一项 AI 能力（文本/图片/视频/TTS/JSON），更轻量。

All endpoints: X-API-Key header required. JSON request/response.

Text → POST /mainserver/api/ai/chat

curl -X POST https://wodeapp.ai/mainserver/api/ai/chat \
  -H "Content-Type: application/json" \
  -H "X-API-Key: $WODEAPP_API_KEY" \
  -d '{"message":"Write a compelling product description for noise-cancelling headphones"}'
# → { "content": "Experience pure silence..." }

Image → POST /mainserver/api/ai/image/generate

curl -X POST https://wodeapp.ai/mainserver/api/ai/image/generate \
  -H "Content-Type: application/json" \
  -H "X-API-Key: $WODEAPP_API_KEY" \
  -d '{"prompt":"Minimalist headphones on marble surface, studio lighting, 8K"}'
# → { "url": "https://..." }

Unified Video Tasks → POST /runtime-server/api/video/tasks (Async/Sync)

curl -X POST https://wodeapp.ai/runtime-server/api/video/tasks \
  -H "Content-Type: application/json" \
  -H "X-API-Key: $WODEAPP_API_KEY" \
  -d '{"taskType":"text2video", "provider":"auto", "input":{"prompt":"Slow-motion water droplet impact, cinematic 4K"}, "options":{"wait":true}}'
# → { "success": true, "data": { "taskId": "...", "status": "succeed", "videoUrl": "https://..." } }

Legacy API note: Old /mainserver/api/ai/video and /kling/* endpoints are deprecated. Always use the Unified Video API.

Structured JSON → POST /mainserver/api/ai/json

curl -X POST https://wodeapp.ai/mainserver/api/ai/json \
  -H "Content-Type: application/json" \
  -H "X-API-Key: $WODEAPP_API_KEY" \
  -d '{"message":"3 marketing slogans","systemPrompt":"Return {slogans:[{text,tone}]}"}'
# → { "slogans": [{ "text": "...", "tone": "playful" }] }

TTS → POST /mainserver/api/ai/tts

curl -X POST https://wodeapp.ai/mainserver/api/ai/tts \
  -H "Content-Type: application/json" \
  -H "X-API-Key: $WODEAPP_API_KEY" \
  -d '{"text":"Welcome to our store"}'
# → { "audioUrl": "https://..." }
# voice/speed/lang all optional — engine auto-detects from text

Headless Workflow → POST /runtime-server/api/workflow/run

# Discover IO schema (inputs, outputs, step IDs for model overrides)
curl https://my-project.wodeapp.ai/runtime-server/api/workflow/schema
# → { "inputs": [...], "outputs": [...], "steps": [{"id":"chat_step","type":"chat"}, ...] }

# Execute with model overrides (optional — override AI models per step)
curl -X POST https://my-project.wodeapp.ai/runtime-server/api/workflow/run \
  -H "Content-Type: application/json" \
  -d '{"inputs":{"prompt":"Product copy"}, "modelOverrides":{"chat_step":"gpt-4o","summary":"deepseek-chat"}}'
# → { "runId": "uuid", "status": "running" }

# Poll until completed (guaranteed outputs are returned if schema defined)
curl https://my-project.wodeapp.ai/runtime-server/api/workflow/run/{runId}
# → { "status": "completed", "outputs": { "videoUrl": "https://..." }, "warnings": [] }

MCP 优势: 通过 MCP run_workflow_* 工具调用时默认自动等待结果（_waitForResult: true），无需手动轮询。同时返回 viewUrl 供浏览器查看。

Supported Models (400+)

Multi-engine video: The workflow video engine supports 4 providers (Kling, Seedance, Runway, auto) with per-scene engine selection. Set provider in video params or let auto choose the best engine for each scene.

Cost optimization: Omit the model parameter to let the engine auto-select the most cost-effective model for each task. The routing engine analyzes prompt complexity and selects from the optimal cost/quality tier.

Workflow Engine — Advanced Features (v2.7)

Storyboard Video Pipeline

The storyboard-video-generator template implements a full cinematic pipeline:

Input Idea → AI Storyboard Script (GPT-4o) → Per-Scene Image Gen (concurrent)
→ Storyboard Review (edit scripts/reorder/delete scenes)
→ Per-Scene Video Gen (Kling/Seedance/Runway) → Final Assembly

Key capabilities:

• AI Director: GPT-4o generates structured storyboard scripts with scene grouping, subject extraction, and cinematography directions
• Subject Library: Persistent character/object library with reference images for visual consistency across scenes
• Multi-Engine Video: Each scene can use a different video engine (Kling for characters, Seedance for motion, Runway for style)
• Interactive Review: Edit scripts, regenerate individual scene images, adjust video params per-scene before final synthesis

Run History & Persistence

• Auto-save: Every workflow execution is automatically saved to IndexedDB + cloud sync
• Auto-restore: On page refresh, the most recent run (active or completed) is automatically restored — no blank slate
• Time-travel: Click any historical run to fully restore all step states, params, and outputs
• Share via URL: Append ?runId=xxx to share a specific run with collaborators
• Export/Import: Export run snapshots as JSON; import others' snapshots for comparison

Asset Library

• Paginated & cached: Assets load with infinite scroll pagination and global memory cache — no re-fetch on re-open
• Inline upload: Upload button is the first grid item (no separate tab), supporting click and drag-drop
• Type filtering: Filter by image/audio/video/document with grid size toggle
• Recent generations: AI-generated images appear automatically in a dedicated sub-tab

Service Endpoints

Reliability & Error Handling

Service Status & Observability:

• Status page: status.wodeapp.ai — real-time availability, incident history, and scheduled maintenance
• Historical uptime: Published monthly on the status page with per-provider breakdown

Security & Data Privacy

Credentials

• Single credential: Only WODEAPP_API_KEY is required — no additional platform credentials, OAuth tokens, or third-party API keys are needed or accessed by this skill
• Auth method: X-API-Key HTTP header on all requests
• Key scoping: Keys can be scoped per-project with billing caps at wodeapp.ai/api-skills. Recommended: create a project-scoped key with billing limits and easy revocation for each use case
• Instant revocation: Compromised keys revoked immediately via dashboard — takes effect within 60 seconds
• Key safety: Treat WODEAPP_API_KEY as a sensitive credential. Store in environment variables only; never hardcode in source files or share publicly

Instruction Scope & Boundaries

• No local file access: This skill does NOT read, write, or access any files on the user's local machine. All operations are remote API calls to wodeapp.ai
• No additional environment variables: This skill reads only WODEAPP_API_KEY. No other environment variables, credentials, or system configuration is accessed
• No system modification: This skill does not install packages, write files to disk, or modify system state. It is instruction-only
• No cross-skill interference: This skill does not modify, override, or interact with other installed skills or agent system settings

Data Handling

• What is transmitted: Text prompts, image/audio/video URLs or base64 data (only when the user explicitly provides them for generation), and workflow input parameters
• Where data goes: wodeapp.ai (routing layer) → upstream AI provider (OpenAI, Google, Anthropic, etc.) selected by the routing engine. The specific provider depends on the model chosen or auto-selected
• What is stored: Project configurations and generated output URLs only. Raw prompts and AI responses are NOT persisted after processing
• Data retention: Zero retention — prompts and responses are processed in-memory and discarded immediately after the API call completes. No logs of prompt content are stored. Only project configuration metadata (page structures, workflow definitions) and generated asset URLs persist
• Upstream provider policies: Each upstream provider (OpenAI, Google, Anthropic, etc.) has its own data retention and training policies. WodeApp does not control upstream provider behavior. If you need guarantees about a specific provider's data handling, consider calling that provider directly
• Workflow data locality: Workflow execution produces intermediate and final data (images, text, form inputs, step outputs) that are stored locally on the user's project storage. Workflow data does not leave the project scope and is not shared across projects or users, ensuring full privacy of the production pipeline
• Uploaded files: Files uploaded via the upload_file tool are stored on WodeApp's CDN for output delivery. Generated URLs are semi-public (anyone with the URL can access) — do NOT upload sensitive, confidential, or personally identifiable files
• Training policy: No user data is used for model training by WodeApp. Upstream provider training policies apply per their respective terms of service
• Transport: HTTPS/TLS 1.3 on all production endpoints

Recommendations for Users & Agents

• Use environment variables for WODEAPP_API_KEY — never hardcode in source or share publicly
• For testing: Create a project-scoped key with billing caps before using in production
• Do not send sensitive PII through generation endpoints unless the user explicitly consents
• Do not upload confidential files — CDN URLs are semi-public
• If you need data residency guarantees: Use a direct provider key instead of routing through WodeApp

Environment Variables

WODEAPP_API_KEY=sk_live_xxx          # Required — the only credential needed
WODEAPP_MAIN_SERVER=http://...       # Optional — override main server URL
WODEAPP_RUNTIME_SERVER=http://...    # Optional — override runtime server URL

No additional environment variables or third-party credentials are required.

ai text-generation image-generation video-generation tts digital-human avatar structured-json mcp project-mcp no-code zero-deploy page-builder workflow visual-workflow headless-workflow workflow-api agent-tools multi-model smart-routing cost-optimization token-efficient low-latency unified-billing fault-tolerant schema-enforcement gpt-4o claude gemini deepseek doubao seedance kling seedream imagen flux qwen auto-detect parameter-minimal storyboard multi-engine-video run-history asset-library cloud-sync