148 lines
6.2 KiB
TypeScript
148 lines
6.2 KiB
TypeScript
/**
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* CopilotKit API Route with A2A Middleware
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*
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* Sets up the connection between:
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* - Frontend (CopilotKit) → A2A Middleware → Orchestrator → A2A Agents
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*
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* KEY CONCEPTS:
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* - AG-UI Protocol: Agent-UI communication (CopilotKit ↔ Orchestrator)
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* - A2A Protocol: Agent-to-agent communication (Orchestrator ↔ Specialized Agents)
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* - A2A Middleware: Injects send_message_to_a2a_agent tool to bridge AG-UI and A2A
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*/
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import {
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CopilotRuntime,
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ExperimentalEmptyAdapter,
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copilotRuntimeNextJSAppRouterEndpoint,
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} from "@copilotkit/runtime";
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import { HttpAgent } from "@ag-ui/client";
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import { A2AMiddlewareAgent } from "@ag-ui/a2a-middleware";
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import { NextRequest } from "next/server";
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export async function POST(request: NextRequest) {
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// STEP 1: Define A2A agent URLs
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const itineraryAgentUrl =
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process.env.ITINERARY_AGENT_URL || "http://localhost:9001";
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const budgetAgentUrl =
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process.env.BUDGET_AGENT_URL || "http://localhost:9002";
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const restaurantAgentUrl =
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process.env.RESTAURANT_AGENT_URL || "http://localhost:9003";
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const weatherAgentUrl =
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process.env.WEATHER_AGENT_URL || "http://localhost:9005";
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// STEP 2: Define orchestrator URL (speaks AG-UI Protocol)
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const orchestratorUrl =
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process.env.ORCHESTRATOR_URL || "http://localhost:9000";
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// STEP 3: Wrap orchestrator with HttpAgent (AG-UI client)
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const orchestrationAgent = new HttpAgent({
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url: orchestratorUrl,
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});
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// STEP 4: Create A2A Middleware Agent
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// This bridges AG-UI and A2A protocols by:
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// 1. Wrapping the orchestrator
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// 2. Registering all A2A agents
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// 3. Injecting send_message_to_a2a_agent tool
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// 4. Routing messages between orchestrator and A2A agents
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const a2aMiddlewareAgent = new A2AMiddlewareAgent({
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description:
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"Travel planning assistant with 4 specialized agents: Itinerary and Restaurant (LangGraph), Weather and Budget (ADK)",
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agentUrls: [
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itineraryAgentUrl, // LangGraph + OpenAI
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restaurantAgentUrl, // ADK + Gemini
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budgetAgentUrl, // ADK + Gemini
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weatherAgentUrl, // ADK + Gemini
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],
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orchestrationAgent,
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// Workflow instructions (middleware auto-adds routing info)
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instructions: `
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You are a travel planning assistant that orchestrates between 4 specialized agents.
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AVAILABLE AGENTS:
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- Itinerary Agent (LangGraph): Creates day-by-day travel itineraries with activities
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- Restaurant Agent (LangGraph): Recommends breakfast, lunch, dinner for each day
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- Weather Agent (ADK): Provides weather forecasts and packing advice
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- Budget Agent (ADK): Estimates travel costs and creates budget breakdowns
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WORKFLOW STRATEGY (SEQUENTIAL - ONE AT A TIME):
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0. **FIRST STEP - Gather Trip Requirements**:
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- Before doing ANYTHING else, call 'gather_trip_requirements' to collect essential trip information
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- Try to extract any mentioned details from the user's message (city, days, people, budget level)
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- Pass any extracted values as parameters to pre-fill the form:
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* city: Extract destination city if mentioned (e.g., "Paris", "Tokyo")
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* numberOfDays: Extract if mentioned (e.g., "5 days", "a week")
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* numberOfPeople: Extract if mentioned (e.g., "2 people", "family of 4")
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* budgetLevel: Extract if mentioned (e.g., "budget", "luxury") -> map to Economy/Comfort/Premium
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- Wait for the user to submit the complete requirements
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- Use the returned values for all subsequent agent calls
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1. Itinerary Agent - Create the base itinerary using the trip requirements
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- Pass: city, numberOfDays from trip requirements
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- The itinerary will have empty meals initially
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2. Weather Agent - Get forecast to inform planning
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- Pass: city and numberOfDays from trip requirements
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3. Restaurant Agent - Get day-by-day meal recommendations
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- Pass: city and numberOfDays from trip requirements
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- The meals will populate the itinerary display
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4. Budget Agent - Create cost estimate
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- Pass: city, numberOfDays, numberOfPeople, budgetLevel from trip requirements
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- This creates an accurate budget based on all the information
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5. **IMPORTANT**: Use 'request_budget_approval' tool for budget approval
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- Pass the budget JSON data to this tool
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- Wait for the user's decision before proceeding
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6. Present complete plan to user
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CRITICAL RULES:
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- **ALWAYS START by calling 'gather_trip_requirements' FIRST before any agent calls**
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- Call tools/agents ONE AT A TIME - never make multiple tool calls simultaneously
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- After making a tool call, WAIT for the result before making the next call
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- Pass information from trip requirements and earlier agents to later agents
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- You MUST call 'request_budget_approval' after receiving the budget
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- After receiving approval, present a complete summary to the user
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TRIP REQUIREMENTS EXTRACTION EXAMPLES:
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- "Plan a trip to Paris" -> city: "Paris"
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- "5 day trip to Tokyo for 2 people" -> city: "Tokyo", numberOfDays: 5, numberOfPeople: 2
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- "Budget vacation to Bali" -> city: "Bali", budgetLevel: "Economy"
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- "Luxury 3-day getaway for my family of 4" -> numberOfDays: 3, numberOfPeople: 4, budgetLevel: "Premium"
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Human-in-the-Loop (HITL):
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- Always gather trip requirements using 'gather_trip_requirements' at the start
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- Always request budget approval using 'request_budget_approval' after budget is created
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- Wait for user responses before proceeding
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Additional Rules:
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- Once you have received information from an agent, do not call that agent again
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- Each agent returns structured JSON - acknowledge and build on the information
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- Always provide a final response that synthesizes ALL gathered information
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`,
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});
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// STEP 5: Create CopilotKit Runtime
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const runtime = new CopilotRuntime({
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agents: {
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a2a_chat: a2aMiddlewareAgent, // Must match frontend: <CopilotKit agent="a2a_chat">
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},
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});
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// STEP 6: Set up Next.js endpoint handler
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const { handleRequest } = copilotRuntimeNextJSAppRouterEndpoint({
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runtime,
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serviceAdapter: new ExperimentalEmptyAdapter(),
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endpoint: "/api/copilotkit",
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});
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return handleRequest(request);
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}
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