Edgeone Makers Migration

What it does

When your agent uses it

• The user wants to adapt a standard agent project to run on EdgeOne Makers
• Convert Express/Next.js API routes to Makers handlers
• Add platform capabilities (context.tools
• Context.sandbox

Example prompts

• “/edgeone-makers-migration”

Requirements

• Python 3
• A credential in WSA_API_KEY
• A credential in OPENAI_API_KEY

Workflow steps

7 steps, taken from the first numbered list in SKILL.md.

1. 1Replace Flask/FastAPI entry with async def handler(ctx)
2. 2Read env from ctx.env, never os.environ
3. 3Use LLM(provider="openai", api_key=ctx.env["AI_GATEWAY_API_KEY"], base_url=ctx.env["AI_GATEWAY_BASE_URL"])
4. 4Set Crew(memory=False, verbose=False)
5. 5Wrap crew.kickoff() in asyncio.to_thread()
6. 6Replace custom tools with ctx.tools.to_crewai_tools(BaseTool)
7. 7Return SSE via ctx.utils.stream_sse(gen())

What it can do on your machine

Read from SKILL.md and the folder at commit e6ead94. It shows what the files ask for, not the result of running them.

• Tool permissions

  Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

  From allowed-tools in the SKILL.md frontmatter.

• Runs code

  No scripts in the folder and no shell commands in SKILL.md (its code samples are json, typescript and python).

  From the folder's file list and the shell code blocks in SKILL.md.

• Network

  No URLs in SKILL.md.

  From URLs in SKILL.md, links to its own repository left out.

• Credentials

  Names these keys or tokens, usually read from environment variables:

  • AI_GATEWAY_API_KEY
  • OPENAI_API_KEY
  • WSA_API_KEY
  • ANTHROPIC_API_KEY

  From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Edgeone Makers Migration loads about 4.1k tokens when it runs, and up to ~14k if it reads all its reference files. Until then it costs about 113 tokens; SKILL.md has 798 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it

~113

When it runs · the whole SKILL.md, loaded when a task matches

~4.1k

With references · SKILL.md plus every file in references/, read only if the agent opens them

~14k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from TencentEdgeOne/edgeone-makers-tools at commit e6ead94, republished under its MIT licence (© TencentEdgeOne). 798 words, ~4,125 tokens.

name

edgeone-makers-migration

description

Migrate existing AI agent projects (LangChain, LangGraph, OpenAI Agents SDK, Claude Agent SDK, CrewAI) to EdgeOne Makers platform conventions. Use when the user wants to adapt a standard agent project to run on EdgeOne Makers, convert Express/Next.js API routes to Makers handlers, or add platform capabilities (context.tools, context.sandbox, context.store). Do NOT trigger for new agent projects (use makers-agents instead).

pathPatterns

agents/**, cloud-functions/**

metadata.author

edgeone

metadata.version

1.0.0

What type of project are you migrating?
├── Python project
│   ├── Using CrewAI → See §2 CrewAI
│   ├── Using LangChain/LangGraph/DeepAgents → See §3 LangGraph (Python)
│   ├── Using OpenAI Agents SDK → See §4 OpenAI Agents (Python)
│   └── Using Claude Agent SDK → See §5 Claude SDK (Python)
└── Node/TS project
    ├── Using Express/Next.js API routes → See §6 Express → Makers
    ├── Using LangGraph/DeepAgents → See §3 LangGraph (Node)
    ├── Using OpenAI Agents SDK → See §4 OpenAI Agents (Node)
    └── Using Claude Agent SDK → See §5 Claude SDK (Node)

// ❌ Before: Next.js API route (app/api/chat/route.ts)
export async function POST(req: Request) {
  const body = await req.json();
  const headers = req.headers;
  const apiKey = process.env.OPENAI_API_KEY;
  // ... LLM call ...
  return Response.json({ data: result });
}

// ✅ After: Makers agent handler (agents/chat/index.ts)
export async function onRequest(context: any) {
  const body = context.request.body;               // already parsed
  const conversationId = context.conversation_id;   // auto-injected from header
  const env = context.env;                          // context.env, never process.env
  // ... LLM call via AI_GATEWAY_* ...
  return new Response(JSON.stringify({ data: result }), {
    headers: { 'Content-Type': 'application/json' },
  });
}

# ❌ Before: Flask route
@app.route('/chat', methods=['POST'])
def chat():
    body = request.get_json()
    api_key = os.environ.get('OPENAI_API_KEY')
    # ... LLM call ...
    return jsonify({'data': result})

# ✅ After: Makers agent handler (agents/chat/index.py)
async def handler(ctx):
    body = ctx.request.body
    conversation_id = ctx.conversation_id
    api_key = ctx.env.get("AI_GATEWAY_API_KEY")
    # ... LLM call via AI_GATEWAY_* ...
    return {"data": result}

{
  "buildCommand": "",
  "outputDirectory": "",
  "agents": {
    "framework": "crewai"
  }
}

crewai>=1.14.5
openai>=1.50.0

{
  "agents": {
    "framework": "langgraph"
  }
}

{
  "buildCommand": "",
  "outputDirectory": "",
  "agents": {
    "framework": "langgraph"
  }
}

{
  "agents": {
    "framework": "openai-agents-sdk"
  }
}

{
  "buildCommand": "",
  "outputDirectory": "",
  "agents": {
    "framework": "openai-agents-sdk"
  }
}

{
  "agents": {
    "framework": "claude-agent-sdk"
  }
}

{
  "buildCommand": "",
  "outputDirectory": "",
  "agents": {
    "framework": "claude-agent-sdk"
  }
}

// ❌ Before: Next.js (app/api/chat/route.ts)
import { NextRequest } from 'next/server';
import OpenAI from 'openai';

export async function POST(req: NextRequest) {
  const { message } = await req.json();
  const client = new OpenAI({ apiKey: process.env.OPENAI_API_KEY });
  const response = await client.chat.completions.create({
    model: 'gpt-4o',
    messages: [{ role: 'user', content: message }],
    stream: true,
  });
  // ... stream back as Response
}

// ✅ After: EdgeOne Makers (agents/chat/index.ts)
import { createLogger, sseEvent, createSSEResponse } from '../_shared';

export async function onRequest(context: any) {
  const { message } = context.request.body ?? {};
  if (!message) return new Response('Missing message', { status: 400 });

  const signal = context.request.signal as AbortSignal;

  return createSSEResponse(async function* (sig) {
    const response = await fetch(context.env.AI_GATEWAY_BASE_URL + '/v1/chat/completions', {
      method: 'POST',
      headers: {
        'Content-Type': 'application/json',
        'Authorization': `Bearer ${context.env.AI_GATEWAY_API_KEY}`,
      },
      body: JSON.stringify({
        model: context.env.AI_GATEWAY_MODEL || '@makers/deepseek-v4-flash',
        messages: [{ role: 'user', content: message }],
        stream: true,
      }),
      signal: sig,
    });
    // ... proxy SSE chunks ...
    yield 'data: [DONE]\n\n';
  }, signal);
}

// ❌ Before: plain fetch without conversation-id
const response = await fetch('/api/chat', {
  method: 'POST',
  headers: { 'Content-Type': 'application/json' },
  body: JSON.stringify({ message }),
});

// ✅ After: with makers-conversation-id header
const conversationId = getOrCreateConversationId(); // crypto.randomUUID() + localStorage
const response = await fetch('/chat', {
  method: 'POST',
  headers: {
    'Content-Type': 'application/json',
    'makers-conversation-id': conversationId,  // ⭐ required for all AI endpoints
  },
  body: JSON.stringify({ message }),
});

// ✅ Always pass conversation_id in body for /stop
await fetch('/stop', {
  method: 'POST',
  headers: { 'Content-Type': 'application/json' },
  body: JSON.stringify({ conversation_id: conversationId }),
});

const reader = response.body!.getReader();
const decoder = new TextDecoder();
let buffer = '';

while (true) {
  const { done, value } = await reader.read();
  if (done) break;
  buffer += decoder.decode(value, { stream: true });

  const lines = buffer.split('\n\n');
  buffer = lines.pop() || '';
  for (const line of lines) {
    if (line.startsWith('data: ')) {
      const data = line.slice(6);
      if (data === '[DONE]') return;
      try {
        const event = JSON.parse(data);
        if (event.type === 'ai_response') { /* display text */ }
        if (event.type === 'tool_call') { /* show tool call */ }
        if (event.type === 'ping') { /* ignore heartbeat */ }
      } catch { /* skip non-JSON */ }
    }
  }
}

© TencentEdgeOne, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 6 other files (references) in skills/edgeone-makers-tools/references/makers-migration of TencentEdgeOne/edgeone-makers-tools.

• SKILL.md
• references/api-route-to-makers.md
• references/claude-agent-sdk-to-makers.md
• references/crewai-to-makers.md
• references/deepagents-to-makers.md
• references/langgraph-to-makers.md
• references/openai-agents-to-makers.md

Open the folder on GitHubat commit e6ead94

Used in 1 other repository

We found 3 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in TencentEdgeOne/edgeone-makers-tools, which our catalogue first saw on October 7, 2026.

• infometa/workbuddyskillsexperts/edgeone-makers-experts/skills/makers-migrationconnectors/edgeone-pages/skills/references/makers-migration+1 more