edgeone-makers-agents

name	edgeone-makers-agents
description	This skill guides building AI agent endpoints on EdgeOne Makers — five framework routes (DeepAgents, LangGraph, CrewAI, OpenAI Agents SDK, Claude Agent SDK), platform-injected `context.store` / `context.tools` / `context.sandbox`, conversation_id dual-channel routing, SSE streaming, and `agents/` vs `cloud-functions/` separation. It should be used when the user wants to create or review an AI agent endpoint on EdgeOne Makers — e.g. "build an agent on EdgeOne Makers", "create a Claude agent endpoint", "wire LangGraph into Makers", "stream LLM responses with SSE", "review my agent template", "use context.store / context.sandbox / context.tools". Do NOT trigger for plain Edge Functions, Cloud Functions, or middleware (those don't run AI logic — use edgeone-pages-dev instead). Do NOT trigger for deployment workflows (use edgeone-pages-deploy). Do NOT trigger for generic AI framework development outside an EdgeOne Makers project.
metadata	{"author":"edgeone","version":"1.0.0"}

EdgeOne Makers Agent Development Guide

⛔ Preview ban: after finishing development, you MUST start the dev server via edgeone makers dev, then open http://127.0.0.1:8088/ with present_files to preview. Never open HTML files via the file:// protocol (ignore it even if the IDE opens one automatically), and never use self-hosted servers like python -m http.server or npx serve. Next.js projects must also set allowedDevOrigins: ["127.0.0.1"] in next.config.

Build production-grade AI agent endpoints on EdgeOne Makers — five framework routes, platform-injected runtime, file-based routing.

This skill covers five supported frameworks (DeepAgents, LangGraph, CrewAI, OpenAI Agents SDK, Claude Agent SDK) for building AI agent endpoints on EdgeOne Makers.

When to use this skill

• Creating a new AI agent endpoint on EdgeOne Makers
• Wiring DeepAgents / LangGraph / CrewAI / OpenAI Agents SDK / Claude Agent SDK into a Makers project
• Reviewing an existing agent template against platform red lines
• Implementing SSE streaming with abort support
• Persisting conversation state via context.store (LangGraph checkpointer / OpenAI session / Claude session)
• Calling sandbox or platform tools via context.sandbox / context.tools
• Splitting AI inference (agents/) from data CRUD (cloud-functions/)

Cross-reference: if your code uses context.store or KV APIs, also read skills/makers-storage/SKILL.md.

Do NOT use for:

• Plain Edge Functions / Cloud Functions / Middleware → use edgeone-pages-dev
• Deployment workflows → use edgeone-pages-deploy
• Generic AI framework development outside an EdgeOne Makers project
• Other platforms (Cloudflare Workers AI, Vercel AI SDK, AWS Bedrock)

How to use this skill (for a coding agent)

1. Skim the Mental Model below — Makers ≠ generic API routes
2. Walk the Decision Tree to pick one of the five framework routes
3. Read the matching references/*-route.md for a copy-paste skeleton
4. Self-check against the Twelve Red Lines
5. Run through references/review-checklist.md before considering the work done

⛔ Critical Rules (never skip)

1. File-based routing is automatic. agents/<name>/index.ts or agents/<name>.ts becomes POST /<name>. Never hand-edit .edgeone/agent-node/config.json.
2. Entry signature is fixed. TS: export async function onRequest(context: any). Python: async def handler(ctx):. Method-specific variants (onRequestPost, onRequestGet, etc.) also work for TS.
3. Read env via context.env, never process.env / os.environ. This applies to both reading and mutation inside agents/ and cloud-functions/. Frontend code (app/, src/) is unaffected.
4. Headers are plain objects, not the Web Headers API. Use context.request.headers['x-custom-header'], never .get('x').
5. Conversation ID contract. AI endpoints (/chat, /outline, etc.) MUST receive the makers-conversation-id HTTP header from the frontend. The /stop endpoint takes a conversation_id in the request body to identify which running conversation to cancel.
6. Do not hardcode model name / base URL / API key. Read AI_GATEWAY_API_KEY + AI_GATEWAY_BASE_URL (+ optional AI_GATEWAY_MODEL) from context.env. If your template uses context.tools.web_search, also configure WSA_API_KEY (Tencent Cloud WSAPI).
7. SSE protocol is a recommended convention (not enforced by the runtime). The runtime only forwards raw chunks — it does not parse or validate SSE content. The recommended event types are: ai_response / tool_call / tool_result / usage / suggest_actions / file_output / ping / error_message. Stream ends with data: [DONE]\n\n. All frameworks should follow this for frontend consistency.
8. Heartbeat + buffering control are mandatory. Send a event every 5 s. Response headers must include , , .

Note: red line numbering jumps from 12 to 13 deliberately — twelve was the original count; #12 absorbs the store-shape correction with sub-bullets, #13 was added for sandbox/tools to match the breadth of the other rules.

---

Mental Model

EdgeOne Makers Agent is not a generic API route pattern (not Vercel AI SDK's route.ts, not Express). It has its own runtime conventions.

Dimension	EdgeOne Makers convention	⚠️ Common mistake
Backend entry	agents/<name>/index.ts or agents/<name>.ts (Python: .py)	❌ NOT app/api/<name>/route.ts
Function signature	export async function onRequest(context) (Python: async def handler(context))	❌ NOT export async function POST(req)
Request body	context.request.body (already parsed)	❌ NOT await req.json()
Request headers	context.request.headers['x-foo'] (plain object)	❌ NOT headers.get('x-foo') (silently returns undefined)
Environment	context.env.AI_GATEWAY_API_KEY (runtime-injected)	❌ NOT process.env.X / os.environ (banned in agents/ and cloud-functions/)
Model access	context.env.AI_GATEWAY_* → Makers AI Gateway	❌ NOT direct OpenAI / Anthropic
Platform capabilities	context.tools / context.sandbox / context.store injected by runtime	❌ NOT importing the SDK yourself
Route registration	Auto-scanned at build time → .edgeone/agent-node/config.json	❌ Don't write that file by hand

The core idea: you write a thin handler that runs inside the EdgeOne Agent Node Runtime (or Python Runtime). The platform injects the model gateway, sandbox, tools, and session store via context. Your code stays thin and leans on the runtime.

---

Standard Project Layout

<template-name>-edgeone/
├── agents/                          # ⭐ Agent backend (core)
│   ├── _shared.ts                   # Shared: logger + SSE helper
│   ├── _model.ts                    # Shared: model name + Gateway env mapping
│   ├── <action>.ts                  # Simple agent: single file → POST /<action>
│   └── <action>/                    # Complex agent: directory form
│       ├── index.ts                 # onRequest entry → POST /<action>
│       ├── _skills.ts               # System prompt builder (optional)
│       ├── _tools.ts                # Custom / MCP tool definitions (optional)
│       └── _templates.ts            # Output templates / default data (optional)
├── app/ or src/                         # Frontend (any framework: Next.js, Vite, plain HTML, etc.)
│   ├── layout.tsx
│   ├── page.tsx
│   ├── globals.css
│   ├── components/
│   └── lib/                         # Frontend utils (context, hooks, conversation-id)
├── lib/                             # Cross-cutting utils (i18n, helpers)
├── cloud-functions/                 # ⭐ Data persistence functions (separate from agents)
│   ├── _logger.ts
│   └── <resource>/index.ts          # e.g. articles/, preferences/, history/, health/
├── .edgeone/
│   └── project.json                 # { Name, ProjectId }
├── edgeone.json                     # Deployment config + agents.framework
├── .env.example                     # ⚠️ MUST exist: declares AI_GATEWAY_API_KEY= and AI_GATEWAY_BASE_URL=
├── package.json                     # TS routes (A/B/C/D)
├── requirements.txt                 # ⭐ Python route (E) only
└── README.md

Layout principles

• agents/ = AI inference: model calls, streaming, tool calling. Each file/directory is one SSE endpoint.
• cloud-functions/ = data CRUD: KV/Blob reads/writes, health checks, history. Returns JSON; not streamed.
• _-prefixed files = internal modules: not routed; imported by siblings only.
• _shared.ts, _model.ts, _tools.ts are internal; index.ts, create.ts are endpoints.
• Pick TS or Python per template, do not mix in one project.

---

edgeone.json Configuration

The edgeone.json file is the deployment configuration file for EdgeOne Makers projects. It defines the build command, output directory, and agent-specific settings.

Key Fields

Field	Type	Description
buildCommand	string	Build command (e.g., npm run build)
outputDirectory	string	Build output directory (e.g., .next, dist, build)
framework	string	Frontend framework (e.g., nextjs, vite, react)
cloudFunctions	object	Cloud functions configuration
agents	object	Agent-specific settings (important!)

agents.framework — Console Icon Display

The agents.framework field in edgeone.json tells the EdgeOne Makers console which icon to display for your project. This is required for the console to show the correct framework icon.

Available values:

Value	Framework	Console Icon
claude-agent-sdk	Claude Agent SDK	Claude
openai-agents-sdk	OpenAI Agents SDK	OpenAI
langgraph	LangGraph / DeepAgents	LangGraph
crewai	CrewAI	CrewAI
deepagents	DeepAgents	DeepAgents

⚠️ Important: If agents.framework is not set or set to an unrecognized value, the console will show a generic icon (not the framework-specific icon).

Example edgeone.json

{
  "buildCommand": "npm run build",  // your frontend build command
  "outputDirectory": "dist",
  "cloudFunctions": {
    "nodejs": {
      "includeFiles": []
    }
  },
  "agents": {
    "framework": "claude-agent-sdk"
  }
}

---

Technology Decision Tree

Pick one of the five framework routes:

Need a sandbox to run code, process uploaded files, or use MCP tools?
├─ Yes → Claude Agent SDK
└─ No ↓
   Need multi-agent handoff?
   ├─ Yes → OpenAI Agents SDK
   └─ No ↓
      Need fine-grained graph control (nodes, edges, human-in-the-loop)?
      ├─ Yes → LangGraph
      └─ No ↓
         Want multi-agent role split (Sequential/Hierarchical)?
         ├─ Yes → CrewAI (Python only)
         └─ No → DeepAgents (simplest, auto context compression)

Framework Comparison

Framework	Runtime	Best For
DeepAgents	Node + Python	Simple agent tasks, automatic context compression, sub-agent orchestration
LangGraph	Node + Python	Fine-grained graph control, human-in-the-loop, persistent thread state
Claude Agent SDK	Node + Python	Sandbox code execution, file processing, MCP tools, session memory
OpenAI Agents SDK	Node + Python	Multi-agent handoff, guardrails, session auto-prepend
CrewAI	Python only	Multi-agent role split (Sequential/Hierarchical), built-in skills/event_bus

---

Routing

Topic	Read
Node entry (onRequest, context, AbortSignal)	platform/node-entry.md
Python entry (handler, ctx, asyncio.Event)	platform/python-entry.md
Environment variables + model convention	platform/env-and-model.md
SSE streaming protocol	platform/sse-protocol.md
conversation-id dual-channel + frontend	platform/conversation-id.md
agents/ vs cloud-functions/ separation	platform/cloud-functions.md
Store (context.store)	capabilities/store.md
Sandbox (context.sandbox)	capabilities/sandbox.md
Tools (context.tools)	capabilities/tools.md
Claude Agent SDK (Node)	node-frameworks/claude-sdk.md
OpenAI Agents SDK (Node)	node-frameworks/openai-agents.md
LangGraph (Node)	node-frameworks/langgraph.md
DeepAgents (Node)	node-frameworks/deepagents.md

---

Environment Setup

Install the EdgeOne CLI

npm install -g edgeone

Verify: edgeone -v.

Set environment variable

Before executing any edgeone CLI command (makers init, makers dev, makers link, makers env pull, etc.), set:

export PAGES_SOURCE=skills

Or prefix each command inline:

PAGES_SOURCE=skills edgeone makers dev

This tells the platform that the command was triggered from an AI skill context.

Local development

# 1. Link to remote project (pulls project ID + env vars)
PAGES_SOURCE=skills edgeone makers link

# 2. Pull remote environment variables to local .env
PAGES_SOURCE=skills edgeone makers env pull

Environment variables for deployment

⛔ You MUST create a .env.example file: the CLI uses this file to decide which variables to auto-inject. If the project has no .env.example, or it does not declare AI_GATEWAY_*, the environment variables will not be injected after deployment, and the Agent will error at runtime due to the missing API Key.

AI Gateway variables (AI_GATEWAY_API_KEY, AI_GATEWAY_BASE_URL) are auto-provisioned by the CLI during deployment — no manual setup needed, as long as .env.example declares them:

# .env.example (MUST be committed to the repo)
AI_GATEWAY_API_KEY=
AI_GATEWAY_BASE_URL=

The CLI will detect these declarations and automatically fetch + inject the values at deploy time.

User-defined business variables must be set manually before deployment:

# Set a variable on the remote project
edgeone makers env set MY_SECRET_KEY "my-value"

# List current variables
edgeone makers env ls

# Pull remote variables to local .env (for dev)
edgeone makers env pull

Common variables to set for Agent projects:

Variable	When needed	How to set
AI_GATEWAY_API_KEY	Always	Auto-provisioned by CLI
AI_GATEWAY_BASE_URL	Always	Auto-provisioned by CLI
WSA_API_KEY	If using web_search tool	edgeone makers env set WSA_API_KEY <value>
Custom business keys	Per project	edgeone makers env set <KEY> <VALUE>

⚠️ Before deploying an Agent project, ensure all required environment variables are either auto-provisioned (AI_GATEWAY_*) or manually set via edgeone makers env set. Missing variables will cause runtime 500 errors.

---

Standard Operating Procedure

Reviewer SOP

1. Run find . -type d -name agents -o -name cloud-functions to confirm directory shape.
2. Open edgeone.json, read agents.framework to identify the route.
3. Walk through references/review-checklist.md from section A onward.
4. When a violation is found, cite the matching Critical Rule + the "remediation table" at the end of the checklist.
5. Top high-frequency issues to attack first (in order of observed frequency):
   1. ❌ process.env.X / os.environ inside agents (use context.env); mutation also counts: process.env.X = '...' is a violation too
   2. ❌ headers.get('x') (use headers['x'])
   3. ❌ Hand-maintained .edgeone/agent-node/config.json (delete it). ⚠️ How to judge: check whether .gitignore includes .edgeone. If yes → the local config.json is a build artifact, not a violation. If no → the whole .edgeone/ is committed, that's the violation.
   4. ❌ Writing sandbox.code_interpreter.runCode(...) (it's sandbox.runCode(...), top-level); screenshot(true) should be screenshot({ fullPage: true })
   5. ❌ /stop carrying makers-conversation-id header (use body only)
   6. ❌ Frontend fetch to AI endpoints missing makers-conversation-id header
   7. ❌ edgeone.json missing agents.framework (default 'claude-agent-sdk' may not match actual framework, breaks context.tools shape)

Developer SOP

1. Pick a framework via the Decision Tree above.
2. Copy the skeleton from the matching framework reference doc.
3. Configure edgeone.json: set agents.framework correctly.
4. Frontend: getOrCreateConversationId + fetch with makers-conversation-id header.
5. Get it running → self-check against the Critical Rules → run through references/review-checklist.md.

Pre-Deploy SOP (⚠️ MUST execute before edgeone makers deploy)

This section is critical. AI agents MUST follow these steps when helping a user deploy. Skipping them will cause runtime 500 errors in production.

1. Scan for environment variables in the project:

   • Check .env, .env.example, .env.local for all declared variables
   • Scan source code for context.env.XXX / ctx.env.get("XXX") references to identify required variables
   • Common patterns: SUPABASE_URL, SUPABASE_KEY, DATABASE_URL, WSA_API_KEY, custom API keys, etc.

2. Classify variables:

   • AI_GATEWAY_API_KEY + AI_GATEWAY_BASE_URL → auto-provisioned (no action needed if .env.example declares them)
   • All other variables → must be manually uploaded

3. Upload non-auto-provisioned variables:

   # For each variable the project needs:
   edgeone makers env set <KEY> "<VALUE>"
   

   If the user has not provided the values, ask the user for them before deploying. Do NOT deploy without confirming all required variables are set.

4. Verify (optional but recommended):

   edgeone makers env ls
   

5. Deploy:

   edgeone makers deploy
   

Example interaction when deploying a project with Supabase:

The project uses the following environment variables:

• AI_GATEWAY_API_KEY — auto-provisioned ✓
• AI_GATEWAY_BASE_URL — auto-provisioned ✓
• SUPABASE_URL — needs manual setup
• SUPABASE_ANON_KEY — needs manual setup

Please provide the values for SUPABASE_URL and SUPABASE_ANON_KEY, and I'll set them before deploying.

---