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.
Installer avec Codex ou Claude Copiez ce prompt, collez-le dans Codex, Claude ou un autre assistant, puis laissez-le vérifier la page du skill et l'installer pour vous.
Une commande directe contourne le prompt de vérification. Examinez la source avant de l'exécuter.
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.
pathPatterns
["agents/**"]
validate
[{"pattern":"process\\.env|os\\.environ","message":"Read env via context.env inside agents/ and cloud-functions/, never process.env or os.environ (Critical Rule 3)."},{"pattern":"headers\\s*\\.\\s*get\\s*\\(","message":"Headers are plain objects here: context.request.headers['x-name'], not .get('x-name') (Critical Rule 4)."},{"pattern":"langgraphStore\\s*\\?\\?\\s*store","message":"Never write `store?.langgraphStore ?? store` — in cloud-function context it falls back to a store with no .get and crashes (Critical Rule 12)."}]
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 / conversation-scoped state / claudeSessionBinding)
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 ../makers-storage/SKILL.md.
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)
Skim the Mental Model below — Makers ≠ generic API routes
Walk the Decision Tree to pick one of the five framework routes
Read the matching references/*-route.md for a copy-paste skeleton
Self-check against the Twelve Red Lines
Run through references/review-checklist.md before considering the work done
⛔ Critical Rules (never skip)
File-based routing is automatic.agents/<name>/index.ts or agents/<name>.ts becomes POST /<name>. Never hand-edit .edgeone/agent-node/config.json.
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.
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.
Headers are plain objects, not the Web Headers API. Use context.request.headers['x-custom-header'], never .get('x').
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.
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).
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.
Heartbeat + buffering control are mandatory. Send a ping event every 5 s. Response headers must include X-Accel-Buffering: no, Cache-Control: no-cache, Connection: keep-alive.
Always honor context.request.signal. Check signal?.aborted (TS) or signal.is_set() (Python) inside loops; exit gracefully on abort, do not throw.
Cap your loops. Manual bind-tools loops use a hard turn limit (e.g. for (let i = 0; i < 4; i++)); SDK routes set maxTurns. No unbounded "until model says stop" loops.
Errors must not crash the stream. Wrap every model / tool call in try/catch. Swallow AbortError silently. Emit other errors as error_message events without ending the stream prematurely.
Pick the right store entry point — they are NOT shape-equivalent.
context.store (agent endpoints, agents/<name>/): full AgentMemory, includes all adapters (openaiSession, claudeSessionStore, langgraphCheckpointer, langgraphStore, conversation state, claudeSessionBinding).
context.agent.store (cloud-function endpoints, cloud-functions/<name>/): runtime stripslanggraphCheckpointer and langgraphStore. Only generic message API + openaiSession + claudeSessionStore (+ state / claudeSessionBinding) are available.
Consequence: any endpoint that needs langgraphStore.get/put MUST live under agents/. Putting it in cloud-functions/ will throw kv.get is not a function at runtime.
Never write store?.langgraphStore ?? store as a fake fallback — in cloud-function context this falls back to the store itself, which has no .get, and crashes.
Use injected context.sandbox / context.tools. Do not hand-write /v1/sandbox/* calls or parse tokens. context.tools shape is determined by edgeone.json's agents.framework (claude-agent-sdk / openai-agents-sdk / langgraph / crewai / deepagents — there is no basic). Use context.tools.all(), .get(name), .files(), .browser(). Sandbox: sandbox.runCode(...) is top-level (not code_interpreter.runCode); screenshot({ fullPage: true }) takes an object, not a boolean; timeout is in seconds.
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.
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)
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 envset MY_SECRET_KEY "my-value"# List current variables
edgeone makers envls# 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
Run find . -type d -name agents -o -name cloud-functions to confirm directory shape.
Open edgeone.json, read agents.framework to identify the route.
Walk through references/review-checklist.md from section A onward.
When a violation is found, cite the matching Critical Rule + the "remediation table" at the end of the checklist.
Top high-frequency issues to attack first (in order of observed frequency):
❌ process.env.X / os.environ inside agents (use context.env); mutation also counts: process.env.X = '...' is a violation too
❌ headers.get('x') (use headers['x'])
❌ 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.
❌ Writing sandbox.code_interpreter.runCode(...) (it's sandbox.runCode(...), top-level); screenshot(true) should be screenshot({ fullPage: true })
❌ /stop carrying makers-conversation-id header (use body only)
❌ Frontend fetch to AI endpoints missing makers-conversation-id header
❌ edgeone.json missing agents.framework (default 'claude-agent-sdk' may not match actual framework, breaks context.tools shape)
Developer SOP
Pick a framework via the Decision Tree above.
Copy the skeleton from the matching framework reference doc.
Configure edgeone.json: set agents.framework correctly.
Frontend: getOrCreateConversationId + fetch with makers-conversation-id header.
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.
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.
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
Upload non-auto-provisioned variables:
# For each variable the project needs:
edgeone makers envset <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.
Verify (optional but recommended):
edgeone makers envls
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.
