feat: Integrate n8n endpoints too.

app/api/generate-options/route.ts

@@ -1,10 +1,6 @@
 import { NextRequest, NextResponse } from "next/server";
-import { OpenRouter } from "@openrouter/sdk";
 import { z } from "zod";
-
-const openrouter = new OpenRouter({
-  apiKey: process.env.OPENROUTER_API_KEY,
-});
+import { chatCompletion, getFastModel } from "@/lib/llm-client";
 
 // Schema for options response
 const OptionsResponseSchema = z.object({
@@ -53,27 +49,17 @@ ${questions.map((q: string, i: number) => `${i}. ${q}`).join("\n")}
 
 Generate 3-4 answer options for each question.`;
 
-    const completion = await openrouter.chat.send({
-      model: "openai/gpt-4o-mini", // Fast enough for simple options, better than flash-lite
+    const content = await chatCompletion({
       messages: [
         { role: "system", content: SYSTEM_PROMPT },
         { role: "user", content: userMessage },
       ],
-      responseFormat: { type: "json_object" },
+      model: getFastModel(),
+      jsonMode: true,
     });
 
-    const content = completion.choices?.[0]?.message?.content;
-
-    if (!content) {
-      return NextResponse.json(
-        { error: "No response from LLM" },
-        { status: 500 }
-      );
-    }
-
     // Parse and validate the response
-    const contentStr = typeof content === "string" ? content : JSON.stringify(content);
-    const parsed = JSON.parse(contentStr);
+    const parsed = JSON.parse(content);
     const validated = OptionsResponseSchema.parse(parsed);
 
     return NextResponse.json(validated);

app/api/plan/route.ts

@@ -1,10 +1,6 @@
 import { NextRequest, NextResponse } from "next/server";
-import { OpenRouter } from "@openrouter/sdk";
 import { z } from "zod";
-
-const openrouter = new OpenRouter({
-  apiKey: process.env.OPENROUTER_API_KEY,
-});
+import { chatCompletion, getDefaultModel } from "@/lib/llm-client";
 
 // Schema for clarifying questions response
 const ClarifyingResponseSchema = z.object({
@@ -100,29 +96,17 @@ export async function POST(request: NextRequest) {
       userMessage = `Original request: ${prompt}\n\nYou asked these questions:\n${questionTexts.map((q: string, i: number) => `${i + 1}. ${q}`).join("\n")}\n\nUser's answers:\n${userResponses.map((a: string, i: number) => `${i + 1}. ${a}`).join("\n")}\n\nNow generate the itinerary based on this information.`;
     }
 
-    // Use high-quality model for trip planning
-    const completion = await openrouter.chat.send({
-      model: "anthropic/claude-sonnet-4", // Better quality for presentations
+    const content = await chatCompletion({
       messages: [
         { role: "system", content: SYSTEM_PROMPT },
         { role: "user", content: userMessage },
       ],
-      responseFormat: { type: "json_object" },
-      max_tokens: 2000, // Limit tokens for faster response
+      model: getDefaultModel(),
+      jsonMode: true,
     });
 
-    const content = completion.choices?.[0]?.message?.content;
-
-    if (!content) {
-      return NextResponse.json(
-        { error: "No response from LLM" },
-        { status: 500 }
-      );
-    }
-
     // Parse and validate the response
-    const contentStr = typeof content === "string" ? content : JSON.stringify(content);
-    const parsed = JSON.parse(contentStr);
+    const parsed = JSON.parse(content);
     const validated = PlanResponseSchema.parse(parsed);
 
     return NextResponse.json(validated);

app/api/refine/route.ts

@@ -1,10 +1,6 @@
 import { NextRequest, NextResponse } from "next/server";
-import { OpenRouter } from "@openrouter/sdk";
 import { z } from "zod";
-
-const openrouter = new OpenRouter({
-  apiKey: process.env.OPENROUTER_API_KEY,
-});
+import { chatCompletion, getDefaultModel } from "@/lib/llm-client";
 
 // Schema for refinement response
 const RefineResponseSchema = z.object({
@@ -113,29 +109,19 @@ ${day.activities.map((a) => `  - ${a.time}: ${a.name} - ${a.description}`).join(
   )
   .join("\n\n")}`;
 
-    const completion = await openrouter.chat.send({
-      model: "anthropic/claude-sonnet-4", // Better quality for presentations
+    const content = await chatCompletion({
       messages: [
         { role: "system", content: SYSTEM_PROMPT },
         { role: "user", content: `Here is my current trip:\n\n${itineraryContext}` },
         ...conversationHistory,
         { role: "user", content: message },
       ],
-      responseFormat: { type: "json_object" },
+      model: getDefaultModel(),
+      jsonMode: true,
     });
 
-    const content = completion.choices?.[0]?.message?.content;
-
-    if (!content) {
-      return NextResponse.json(
-        { error: "No response from LLM" },
-        { status: 500 }
-      );
-    }
-
     // Parse and validate the response
-    const contentStr = typeof content === "string" ? content : JSON.stringify(content);
-    const parsed = JSON.parse(contentStr);
+    const parsed = JSON.parse(content);
 
     // If there's an updatedItinerary, ensure all activities have required fields
     if (parsed.updatedItinerary && parsed.updatedItinerary.days) {

env.example

@@ -3,8 +3,21 @@ CONVEX_DEPLOYMENT=
 
 NEXT_PUBLIC_CONVEX_URL=
 
+# LLM Backend: "openrouter" (default) or "n8n"
+LLM_BACKEND=openrouter
+
+# Required if LLM_BACKEND=openrouter
 OPENROUTER_API_KEY=
 
+# Required if LLM_BACKEND=n8n
+N8N_WEBHOOK_URL=
+# Auth Option 1: Basic Auth
+N8N_USER=
+N8N_PASSWORD=
+# Auth Option 2: Header Auth (takes precedence over Basic Auth)
+N8N_AUTH_HEADER_NAME=
+N8N_AUTH_HEADER_VALUE=
+
 NEXT_PUBLIC_MAPBOX_ACCESS_TOKEN=
 
-ELEVENLABS_API_KEY=
+ELEVENLABS_API_KEY=

lib/llm-client.ts

@@ -0,0 +1,157 @@
+import { OpenRouter } from "@openrouter/sdk";
+
+export type LLMBackend = "openrouter" | "n8n";
+
+interface ChatMessage {
+  role: "system" | "user" | "assistant";
+  content: string;
+}
+
+interface ChatCompletionOptions {
+  messages: ChatMessage[];
+  model?: string;
+  jsonMode?: boolean;
+  sessionId?: string;
+}
+
+interface N8nChatResponse {
+  output?: string | object;
+  response?: string | object;
+  text?: string | object;
+  message?: string | object;
+  [key: string]: unknown;
+}
+
+function getBackend(): LLMBackend {
+  const backend = process.env.LLM_BACKEND || "openrouter";
+  if (backend !== "openrouter" && backend !== "n8n") {
+    console.warn(`Unknown LLM_BACKEND "${backend}", falling back to openrouter`);
+    return "openrouter";
+  }
+  return backend;
+}
+
+function createOpenRouterClient(): OpenRouter {
+  return new OpenRouter({
+    apiKey: process.env.OPENROUTER_API_KEY,
+  });
+}
+
+function getN8nAuthHeaders(): Record<string, string> {
+  const user = process.env.N8N_USER;
+  const password = process.env.N8N_PASSWORD;
+  const headerAuthName = process.env.N8N_AUTH_HEADER_NAME;
+  const headerAuthValue = process.env.N8N_AUTH_HEADER_VALUE;
+
+  const headers: Record<string, string> = {
+    "Content-Type": "application/json",
+  };
+
+  // Option 1: Header Auth (custom header name + value)
+  if (headerAuthName && headerAuthValue) {
+    headers[headerAuthName] = headerAuthValue;
+    return headers;
+  }
+
+  // Option 2: Basic Auth (username + password)
+  if (user && password) {
+    const credentialString = `${user}:${password}`;
+    const credentials = Buffer.from(credentialString).toString("base64");
+    headers["Authorization"] = `Basic ${credentials}`;
+  }
+
+  return headers;
+}
+
+async function callN8nChatTrigger(options: ChatCompletionOptions): Promise<string> {
+  const { messages, sessionId } = options;
+
+  // Format messages into a single prompt for n8n Chat Trigger
+  // Include system message as context, then the conversation
+  const formattedMessages = messages.map((msg) => {
+    if (msg.role === "system") {
+      return `[System Instructions]\n${msg.content}\n[End System Instructions]`;
+    }
+    return `${msg.role === "user" ? "User" : "Assistant"}: ${msg.content}`;
+  }).join("\n\n");
+
+  const baseUrl = process.env.N8N_WEBHOOK_URL;
+
+  if (!baseUrl) {
+    throw new Error("N8N_WEBHOOK_URL is not set. Set it in .env.local");
+  }
+
+  const response = await fetch(baseUrl, {
+    method: "POST",
+    headers: getN8nAuthHeaders(),
+    body: JSON.stringify({
+      chatInput: formattedMessages,
+      sessionId: sessionId || `session-${Date.now()}`,
+    }),
+  });
+
+  if (!response.ok) {
+    const errorText = await response.text().catch(() => "Unknown error");
+    throw new Error(`n8n API error (${response.status}): ${errorText}`);
+  }
+
+  const data: N8nChatResponse = await response.json();
+
+  // n8n Chat Trigger can return response in different fields
+  const content = data.output || data.response || data.text || data.message;
+
+  if (!content) {
+    // If no known field, try to find any string value or stringify the whole response
+    const firstStringValue = Object.values(data).find((v) => typeof v === "string") as string | undefined;
+    if (firstStringValue) {
+      return firstStringValue;
+    }
+    throw new Error("No response content from n8n Chat Trigger");
+  }
+
+  // If content is already an object, stringify it
+  if (typeof content === "object") {
+    return JSON.stringify(content);
+  }
+
+  return content;
+}
+
+export async function chatCompletion(options: ChatCompletionOptions): Promise<string> {
+  const backend = getBackend();
+  const { messages, model, jsonMode = true } = options;
+
+  if (backend === "n8n") {
+    // Use n8n Chat Trigger (direct HTTP call)
+    return callN8nChatTrigger(options);
+  }
+
+  // Default: OpenRouter
+  if (!process.env.OPENROUTER_API_KEY) {
+    throw new Error("OPENROUTER_API_KEY is not set. Set it in .env.local or switch to LLM_BACKEND=n8n");
+  }
+
+  const client = createOpenRouterClient();
+
+  const completion = await client.chat.send({
+    model: model || "openai/gpt-4o-mini",
+    messages,
+    ...(jsonMode && { responseFormat: { type: "json_object" } }),
+  });
+
+  const content = completion.choices?.[0]?.message?.content;
+  if (!content) {
+    throw new Error("No response from OpenRouter");
+  }
+  return typeof content === "string" ? content : JSON.stringify(content);
+}
+
+export function getDefaultModel(): string {
+  const backend = getBackend();
+  return backend === "n8n" ? "gpt-4o-mini" : "openai/gpt-4o-mini";
+}
+
+export function getFastModel(): string {
+  const backend = getBackend();
+  return backend === "n8n" ? "gpt-4o-mini" : "google/gemini-2.0-flash-lite-001";
+}