makers-migration

name	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).
metadata	{"author":"edgeone","version":"1.0.0"}

EdgeOne Makers Migration Guide

Migrate existing AI agent projects to the EdgeOne Makers platform format. Covers structural conversion, API adaptation, and platform capability injection.

---

Migration Decision Tree

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)

---

⚠️ Migration Checklist (common to all frameworks)

Before starting framework-specific changes, check these global items:

• Create edgeone.json with correct agents.framework and buildCommand/outputDirectory
• Move backend code from Express routes / Next.js API routes into agents/ directory
• Replace process.env / os.environ with context.env / ctx.env
• Replace req.headers.get('x') with context.request.headers['x'] (Node) or plain dict access (Python)
• Replace await req.json() with context.request.body (already parsed)
• Replace direct model API calls (OpenAI, Anthropic) with AI_GATEWAY_* env vars
• Add SSE streaming for AI endpoints (replace res.json() / return {"data": ...})
• Add makers-conversation-id header to frontend fetch calls
• Wire platform tools through context.tools instead of custom tool implementations
• Wire conversation history through context.store instead of in-memory or custom DB
• If using web_search, set WSA_API_KEY env var and use context.tools.get("web_search")
• Set up edgeone makers dev for local development

---

§1. Standard API Route → Makers Handler

This is the most common migration pattern. Applies to Express/Next.js API routes, plain HTTP handlers, etc.

Node (Express/Next.js → Makers)

// ❌ 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' },
  });
}

Python (Flask/FastAPI → Makers)

# ❌ 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}

---

§2. CrewAI (Python)

Key changes

Before	After
os.environ.get("OPENAI_API_KEY")	ctx.env.get("AI_GATEWAY_API_KEY")
LLM(provider="openai", ...) — LiteLLM dispatch	LLM(provider="openai", base_url=ctx.env["AI_GATEWAY_BASE_URL"], ...) — bypass LiteLLM
memory=True on Crew	memory=False + use ctx.store
verbose=True	verbose=False (events go through crewai_event_bus)
crew.kickoff() (blocking)	await asyncio.to_thread(crew.kickoff)
Custom search tools	Use ctx.tools.to_crewai_tools(BaseTool)
Flask/FastAPI handler	async def handler(ctx): → ctx.utils.stream_sse(gen())

edgeone.json

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

Requirements

crewai>=1.14.5
openai>=1.50.0

Migration steps

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

See makers-agents/skills/python-frameworks/crewai.md for the complete pattern. Detailed before/after: references/crewai-to-makers.md

---

§3. LangGraph / DeepAgents (Node + Python)

Key changes

Before	After
Direct model creation (new ChatOpenAI(...))	Use AI_GATEWAY_* for apiKey/baseURL
MemorySaver (in-memory checkpointer)	context.store.langgraphCheckpointer (persistent)
Custom tool functions	context.tools.toLangChainTools(tool)
agent.stream()	SSE via createSSEResponse(gen, signal) (Node) or ctx.utils.stream_sse(gen()) (Python)
thread_id manual management	thread_id = context.conversation_id

Node — edgeone.json

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

Python — edgeone.json

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

Migration steps

1. Move handler into agents/<name>/index.ts (or .py)
2. Replace model initialization: use AI_GATEWAY_API_KEY + AI_GATEWAY_BASE_URL
3. Replace checkpointer: context.store.langgraphCheckpointer instead of MemorySaver
4. Replace store: context.store.langgraphStore
5. Replace tools: context.tools.toLangChainTools(tool) instead of custom tool functions
6. Set thread_id: { configurable: { thread_id: context.conversation_id } }
7. Replace response with SSE streaming pattern

Node: makers-agents/skills/node-frameworks/langgraph.md Python: makers-agents/skills/python-frameworks/langgraph.md DeepAgents: makers-agents/skills/node-frameworks/deepagents.md Detailed before/after: references/langgraph-to-makers.md, references/deepagents-to-makers.md

---

§4. OpenAI Agents SDK (Node + Python)

Key changes

Before	After
new OpenAI({ apiKey, baseURL })	Read AI_GATEWAY_* from context.env / ctx.env
Runner.run(agent, input, { tools })	Tools from context.tools.all() (already OpenAI function format)
Session management	context.store.openaiSession(convId) (Node)
Express route response	SSE via createSSEResponse(gen, signal) (Node) or ctx.utils.stream_sse(gen()) (Python)
Model name hardcoded	`ctx.env.AI_GATEWAY_MODEL

Node — edgeone.json

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

Python — edgeone.json

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

Migration steps

1. Move handler into agents/<name>/index.ts (or .py)
2. Create OpenAI client from context.env (not process.env)
3. Replace tools with context.tools.all() (returns OpenAI function tools)
4. Use context.store.openaiSession(conversationId) for session (Node)
5. Map stream events to SSE: output_text_delta → ai_response, tool_called → tool_call

Node: makers-agents/skills/node-frameworks/openai-agents.md Python: makers-agents/skills/python-frameworks/openai-agents.md Detailed before/after: references/openai-agents-to-makers.md

---

§5. Claude Agent SDK (Node + Python)

Key changes

Before	After
ANTHROPIC_API_KEY env var	Mapped from AI_GATEWAY_* via collectGatewayEnv()
process.env	context.env injected into query().options.env
Custom MCP tools	context.tools.toClaudeMcpServer()
Session	context.store.claudeSessionStore() (Node)
Stdout EPIPE crash	Swallow EPIPE on process.stdout (Node)
No writable config dir	Set CLAUDE_CONFIG_DIR=/tmp/claude-agent-sdk, CLAUDE_CODE_TMPDIR=/tmp

Node — edgeone.json

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

Python — edgeone.json

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

Migration steps

1. Move handler into agents/<name>/index.ts (or .py)
2. Map AI_GATEWAY_* → ANTHROPIC_* via collectGatewayEnv(context.env)
3. Inject env into query({ options: { env: collectGatewayEnv(...) } })
4. Replace MCP tools: context.tools.toClaudeMcpServer('edgeone', { alwaysLoad: true })
5. Node only: swallow EPIPE on process.stdout
6. Set writable config dirs: CLAUDE_CONFIG_DIR=/tmp/claude-agent-sdk, CLAUDE_CODE_TMPDIR=/tmp

Node: makers-agents/skills/node-frameworks/claude-sdk.md Python: makers-agents/skills/python-frameworks/claude-sdk.md Detailed before/after: references/claude-agent-sdk-to-makers.md

---

§6. Express / Next.js API Routes (Node)

General migration for any Express-based or Next.js API route agent.

The 7-step conversion

Step	Before	After
1. File location	app/api/chat/route.ts or server/routes/chat.ts	agents/chat/index.ts
2. Entry signature	export async function POST(req) or app.post('/chat', handler)	export async function onRequest(context)
3. Body parsing	await req.json()	context.request.body (already parsed)
4. Headers	req.headers.get('x-foo')	context.request.headers['x-foo']
5. Abort signal	req.signal	context.request.signal (AbortSignal)
6. Model access	process.env.OPENAI_API_KEY → direct call	context.env.AI_GATEWAY_* → AI Gateway
7. Response	res.json() or return Response.json()	SSE stream via createSSEResponse(gen, signal)

Example: Next.js API route → Makers

// ❌ 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. ?? {};
   (!message)   (, { :  });

   signal = context..  ;

   ( * (sig) {
     response =  (context.. + , {
      : ,
      : {
        : ,
        : ,
      },
      : .({
        : context.. || ,
        : [{ : , : message }],
        : ,
      }),
      : sig,
    });
    
     ;
  }, signal);
}

---

§7. Client-Side Migration

Frontend fetch calls

// ❌ 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 }),
});

/stop endpoint

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

SSE parsing

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 */ }
      }  {  }
    }
  }
}

---

§8. Post-Migration Verification

After migration, verify these items before deploying:

• edgeone makers dev starts without errors
• /chat endpoint returns SSE stream (not JSON)
• AI responses work end-to-end (frontend → agent → model → frontend)
• context.env is used everywhere (grep for process.env / os.environ — none should remain)
• edgeone.json has correct agents.framework
• Platform tools (context.tools) work in at least one framework
• Conversation history persists across requests (via context.store)
• /stop endpoint cancels active runs
• Frontend sends makers-conversation-id header

---

See Also

• Agent development guide: makers-agents/SKILL.md
• Platform conventions: makers-agents/references/platform/
• CLI commands: makers-cli/SKILL.md
• Deploy guide: makers-deploy/SKILL.md

Detailed before/after reference files

• references/api-route-to-makers.md — generic API route → Makers handler (no-framework fallback; Makers supports framework-less onRequest/handler)
• references/langgraph-to-makers.md — LangGraph (Node + Python)
• references/deepagents-to-makers.md — DeepAgents (Node + Python)
• references/openai-agents-to-makers.md — OpenAI Agents SDK (Node + Python)
• references/claude-agent-sdk-to-makers.md — Claude Agent SDK (Node + Python)