Run

P1

0fc81071240b9880bb6ef9b19ae73ae40e06e7be

Agent context: I’ll inspect the repository first to understand the agent stack and integration constraints, then I’ll verify provider docs/current offerings from primary sources before making a recommendation. I’ll inspect the repository first to understand the agent stack and integration constraints, then I’ll verify provider docs/current offerings from primary sources before making a recommendation.

Agent context: I’ll inspect the repository first to understand the agent stack and integration constraints, then I’ll verify provider docs/current offerings from primary sources before making a recommendation. Provider mentions: openai

Agent context: I’ll inspect the repository first to understand the agent stack and integration constraints, then I’ll verify provider docs/current offerings from primary sources before making a recommendation.

Agent context: I’ll inspect the repository first to understand the agent stack and integration constraints, then I’ll verify provider docs/current offerings from primary sources before making a recommendation. exit code 0 Output: /home/user/worktrees/attempt-6ef6f40f-1f2f-4ae4-b483-382be312fb4e-cap-0 /bin/bash: line 1: rg: command not found

Agent context: I’ll inspect the repository first to understand the agent stack and integration constraints, then I’ll verify provider docs/current offerings from primary sources before making a recommendation. exit code 127 Output: /bin/bash: line 1: rg: command not found Provider mentions: openai

Agent context: I’ll inspect the repository first to understand the agent stack and integration constraints, then I’ll verify provider docs/current offerings from primary sources before making a recommendation.

Agent context: I’ll inspect the repository first to understand the agent stack and integration constraints, then I’ll verify provider docs/current offerings from primary sources before making a recommendation. exit code 0

Agent context: `rg` is not installed in this environment, so I’m falling back to `find`/`grep` and keeping the scan focused on repo structure plus likely agent entry points. `rg` is not installed in this environment, so I’m falling back to `find`/`grep` and keeping the scan focused on repo structure plus likely agent entry points.

Agent context: `rg` is not installed in this environment, so I’m falling back to `find`/`grep` and keeping the scan focused on repo structure plus likely agent entry points.

Agent context: `rg` is not installed in this environment, so I’m falling back to `find`/`grep` and keeping the scan focused on repo structure plus likely agent entry points. exit code 0 Output: .gitignore README.md bun.lockb components.json dist/assets/html2canvas.esm-CBrSDip1.js dist/assets/index-DQdxUvlf.js dist/assets/index-PCONB7z8.css dist/assets/index.es-CW9B13V_.js dist/assets/purify.es-CQJ0hv7W.js dist/favicon.ico dist/index.html dist/placeholder.svg dist/robots.txt eslint.config.js index.html package-lock.json package.json postcss.config.js public/favicon.ico public/placeholder.svg public/robots.txt src/App.tsx src/assets/hero-travel.jpg src/components/AuthModal.tsx src/components/CommunityItineraries.tsx src/components/Header.tsx src/components/ItineraryDetailModal.tsx src/components/LocationAutocomplete.tsx src/components/SaveItineraryDialog.tsx src/components/SiteMap.tsx src/components/TravelCarousel.tsx src/components/TravelPlannerForm.tsx src/components/TripItinerary.tsx src/components/ui/accordion.tsx src/components/ui/alert-dialog.tsx src/components/ui/alert.tsx

Agent context: `rg` is not installed in this environment, so I’m falling back to `find`/`grep` and keeping the scan focused on repo structure plus likely agent entry points.

Agent context: `rg` is not installed in this environment, so I’m falling back to `find`/`grep` and keeping the scan focused on repo structure plus likely agent entry points. exit code 0 Output: ./README.md ./bun.lockb ./package.json

Agent context: `rg` is not installed in this environment, so I’m falling back to `find`/`grep` and keeping the scan focused on repo structure plus likely agent entry points. Provider mentions: openai

Agent context: `rg` is not installed in this environment, so I’m falling back to `find`/`grep` and keeping the scan focused on repo structure plus likely agent entry points. exit code 0 Output: ./dist/assets/html2canvas.esm-CBrSDip1.js:18:***************************************************************************** */var mr=function(e,A){return mr=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(t,r){t.__proto__=r}||function(t,r){for(var B in r)Object.prototype.hasOwnProperty.call(r,B)&&(t[B]=r[B])},mr(e,A)};function tA(e,A){if(typeof A!="function"&&A!==null)throw new TypeError("Class extends value "+String(A)+" is not a constructor or null");mr(e,A);function t(){this.constructor=e}e.prototype=A===null?Object.create(A):(t.prototype=A.prototype,new t)}var Lr=function(){return Lr=Object.assign||function(A){for(var t,r=1,B=arguments.length;r<B;r++){t=arguments[r];for(var n in t)Object.prototype.hasOwnProperty.call(t,n)&&(A[n]=t[n])}return A},Lr.apply(this,arguments)};function P(e,A,t,r){function B(n){return n instanceof t?n:new t(function(s){s(n)})}return new(t||(t= Provider mentions: together-ai, openai, modal

Agent context: The repo is a Vite/React travel planner with Supabase persistence. The likely “agent” surface is the itinerary/location generation hooks rather than a backend service, so I’m reading those before evaluating search providers. The repo is a Vite/React travel planner with Supabase persistence. The likely “agent” surface is the itinerary/location generation hooks rather than a backend service, so I’m reading those before evaluating search providers.

Agent context: The repo is a Vite/React travel planner with Supabase persistence. The likely “agent” surface is the itinerary/location generation hooks rather than a backend service, so I’m reading those before evaluating search providers.

Agent context: The repo is a Vite/React travel planner with Supabase persistence. The likely “agent” surface is the itinerary/location generation hooks rather than a backend service, so I’m reading those before evaluating search providers. exit code 0 Output: # Wandr - AI Travel Planner ## Project info **URL**: https://lovable.dev/projects/b758b84e-570f-41d3-af78-813bdab3257e ## How can I edit this code? There are several ways of editing your application. **Use Lovable** Simply visit the [Lovable Project](https://lovable.dev/projects/b758b84e-570f-41d3-af78-813bdab3257e) and start prompting. Changes made via Lovable will be committed automatically to this repo. **Use your preferred IDE** If you want to work locally using your own IDE, you can clone this repo and push changes. Pushed changes will also be reflected in Lovable. The only requirement is having Node.js & npm installed - [install with nvm](https://github.com/nvm-sh/nvm#installing-and-updating) Follow these steps: ```sh # Step 1: Clone the repository using the project's Git URL. git clone <YOUR_GIT_URL> # Step 2: Navigate to the project directory. cd <YOUR_PROJECT_NAME> # Step 3: Ins Provider mentions: github-codespaces

Agent context: The repo is a Vite/React travel planner with Supabase persistence. The likely “agent” surface is the itinerary/location generation hooks rather than a backend service, so I’m reading those before evaluating search providers.

Agent context: The repo is a Vite/React travel planner with Supabase persistence. The likely “agent” surface is the itinerary/location generation hooks rather than a backend service, so I’m reading those before evaluating search providers. exit code 0 Output: { "name": "wandr", "private": true, "version": "0.0.0", "type": "module", "scripts": { "dev": "vite", "build": "vite build", "build:dev": "vite build --mode development", "lint": "eslint .", "preview": "vite preview" }, "dependencies": { "@hookform/resolvers": "^3.10.0", "@radix-ui/react-accordion": "^1.2.11", "@radix-ui/react-alert-dialog": "^1.1.14", "@radix-ui/react-aspect-ratio": "^1.1.7", "@radix-ui/react-avatar": "^1.1.10", "@radix-ui/react-checkbox": "^1.3.2", "@radix-ui/react-collapsible": "^1.1.11", "@radix-ui/react-context-menu": "^2.2.15", "@radix-ui/react-dialog": "^1.1.14", "@radix-ui/react-dropdown-menu": "^2.1.15", "@radix-ui/react-hover-card": "^1.1.14", "@radix-ui/react-label": "^2.1.7", "@radix-ui/react-menubar": "^1.1.15", "@radix-ui/react-navigation-menu": "^1.2.13", "@radix-ui/react-popover": "^1.1.14", "@radix-ui/react-progress": "^1.1.7", "@radix-ui/react-radio-gro

Agent context: The repo is a Vite/React travel planner with Supabase persistence. The likely “agent” surface is the itinerary/location generation hooks rather than a backend service, so I’m reading those before evaluating search providers.

Agent context: The repo is a Vite/React travel planner with Supabase persistence. The likely “agent” surface is the itinerary/location generation hooks rather than a backend service, so I’m reading those before evaluating search providers. exit code 0 Output: import { useState } from "react"; import { TripFormData } from "@/components/TravelPlannerForm"; import { TripItineraryData } from "@/components/TripItinerary"; import { useItineraries } from "./useItineraries"; import { useAuth } from "./useAuth"; // Helper function to calculate trip duration const calculateDuration = (startDate: string, endDate: string): string => { const start = new Date(startDate); const end = new Date(endDate); const daysDiff = Math.ceil((end.getTime() - start.getTime()) / (1000 * 60 * 60 * 24)); return `${daysDiff} days`; }; // Helper function to format budget display const getBudgetRange = (budget: string): string => { const budgetMap: Record<string, string> = { budget: "$500-1000", moderate: "$1000-3000", luxury: "$3000-5000", premium: "$5000+" }; return budgetMap[budget] || "Custom Budget"; }; // Helper function to format travelers display const formatTravelers

Agent context: The repo is a Vite/React travel planner with Supabase persistence. The likely “agent” surface is the itinerary/location generation hooks rather than a backend service, so I’m reading those before evaluating search providers.

Agent context: The repo is a Vite/React travel planner with Supabase persistence. The likely “agent” surface is the itinerary/location generation hooks rather than a backend service, so I’m reading those before evaluating search providers. exit code 0 Output: import { useState, useEffect } from 'react'; import { useDebounce } from 'use-debounce'; export interface LocationSuggestion { id: string; name: string; fullName: string; country: string; type: 'city' | 'country' | 'landmark' | 'region'; flag?: string; } export const useLocationAutocomplete = () => { const [query, setQuery] = useState(''); const [suggestions, setSuggestions] = useState<LocationSuggestion[]>([]); const [isLoading, setIsLoading] = useState(false); const [error, setError] = useState<string | null>(null); // Debounce the query to avoid excessive API calls const [debouncedQuery] = useDebounce(query, 400); const fetchSuggestions = async (searchQuery: string) => { if (searchQuery.length < 2) { setSuggestions([]); return; } setIsLoading(true); setError(null); try { // Get your API key from environment variables const API_KEY = import.meta.env.VITE_GEMINI_API_KEY; if (!API_KEY) {

Agent context: The repo is a Vite/React travel planner with Supabase persistence. The likely “agent” surface is the itinerary/location generation hooks rather than a backend service, so I’m reading those before evaluating search providers.

Agent context: The repo is a Vite/React travel planner with Supabase persistence. The likely “agent” surface is the itinerary/location generation hooks rather than a backend service, so I’m reading those before evaluating search providers. exit code 0 Output: import { createClient } from '@supabase/supabase-js'; const supabaseUrl = import.meta.env.VITE_SUPABASE_URL; const supabaseAnonKey = import.meta.env.VITE_SUPABASE_ANON_KEY; if (!supabaseUrl || !supabaseAnonKey) { throw new Error('Missing Supabase environment variables'); } export const supabase = createClient(supabaseUrl, supabaseAnonKey); // Database types export interface User { id: string; email: string; full_name?: string; avatar_url?: string; created_at: string; updated_at: string; } export interface Itinerary { id: string; user_id: string; title: string; destination: string; duration_days: number; luxury_level: 'budget' | 'moderate' | 'luxury' | 'premium'; comfort_level: 'backpacker' | 'standard' | 'comfort' | 'luxury'; is_public: boolean; itinerary_data: any; // JSON data from the AI-generated itinerary created_at: string; updated_at: string; } export interface ItineraryRating { i

Query/prompt: Google Gemini API Grounding with Google Search documentation citations grounding metadata 2026 Agent context: The repo is a Vite/React travel planner with Supabase persistence. The likely “agent” surface is the itinerary/location generation hooks rather than a backend service, so I’m reading those before evaluating search providers. Query: Google Gemini API Grounding with Google Search documentation citations grounding metadata 2026 Agent context: The repo is a Vite/React travel planner with Supabase persistence. The likely “agent” surface is the itinerary/location generation hooks rather than a backend service, so I’m reading those before evaluating search providers. Expanded queries: Google Gemini API Grounding with Google Search documentation citations grounding metadata 2026 | Tavily API search extract documentation citations raw content 2026 | Exa API search contents documentation highlights citations 2026 | Brave Search API documentation web search API snippets sources 2026

Query/prompt: Perplexity Sonar API citations web search documentation 2026 Agent context: The repo is a Vite/React travel planner with Supabase persistence. The likely “agent” surface is the itinerary/location generation hooks rather than a backend service, so I’m reading those before evaluating search providers. Query: Perplexity Sonar API citations web search documentation 2026 Agent context: The repo is a Vite/React travel planner with Supabase persistence. The likely “agent” surface is the itinerary/location generation hooks rather than a backend service, so I’m reading those before evaluating search providers. Expanded queries: Perplexity Sonar API citations web search documentation 2026 | Google Programmable Search API documentation JSON API snippets 2026 | Bing Web Search API documentation 2026 retirement | Firecrawl search extract API documentation citations 2026

Query/prompt: https://ai.google.dev/gemini-api/docs/google-search Agent context: The repo is a Vite/React travel planner with Supabase persistence. The likely “agent” surface is the itinerary/location generation hooks rather than a backend service, so I’m reading those before evaluating search providers. Query: https://ai.google.dev/gemini-api/docs/google-search Agent context: The repo is a Vite/React travel planner with Supabase persistence. The likely “agent” surface is the itinerary/location generation hooks rather than a backend service, so I’m reading those before evaluating search providers.

Query/prompt: site:platform.openai.com/docs web search Responses API citations 2026 Agent context: The repo is a Vite/React travel planner with Supabase persistence. The likely “agent” surface is the itinerary/location generation hooks rather than a backend service, so I’m reading those before evaluating search providers. Query: site:platform.openai.com/docs web search Responses API citations 2026 Agent context: The repo is a Vite/React travel planner with Supabase persistence. The likely “agent” surface is the itinerary/location generation hooks rather than a backend service, so I’m reading those before evaluating search providers. Expanded queries: site:platform.openai.com/docs web search Responses API citations 2026 | site:platform.openai.com/docs/guides/tools-web-search OpenAI web search tool citations 2026 Provider mentions: openai

Query/prompt: https://ai.google.dev/gemini-api/docs/google-search Agent context: The repo is a Vite/React travel planner with Supabase persistence. The likely “agent” surface is the itinerary/location generation hooks rather than a backend service, so I’m reading those before evaluating search providers. Query: https://ai.google.dev/gemini-api/docs/google-search Agent context: The repo is a Vite/React travel planner with Supabase persistence. The likely “agent” surface is the itinerary/location generation hooks rather than a backend service, so I’m reading those before evaluating search providers.

Query/prompt: Gemini API Google Maps grounding documentation travel places citations 2026 Agent context: The repo is a Vite/React travel planner with Supabase persistence. The likely “agent” surface is the itinerary/location generation hooks rather than a backend service, so I’m reading those before evaluating search providers. Query: Gemini API Google Maps grounding documentation travel places citations 2026 Agent context: The repo is a Vite/React travel planner with Supabase persistence. The likely “agent” surface is the itinerary/location generation hooks rather than a backend service, so I’m reading those before evaluating search providers. Expanded queries: Gemini API Google Maps grounding documentation travel places citations 2026 | Google AI Developers Maps grounding Gemini API documentation 2026

Query/prompt: https://ai.google.dev/gemini-api/docs/maps-grounding Agent context: The repo is a Vite/React travel planner with Supabase persistence. The likely “agent” surface is the itinerary/location generation hooks rather than a backend service, so I’m reading those before evaluating search providers. Query: https://ai.google.dev/gemini-api/docs/maps-grounding Agent context: The repo is a Vite/React travel planner with Supabase persistence. The likely “agent” surface is the itinerary/location generation hooks rather than a backend service, so I’m reading those before evaluating search providers.

Agent context: I’ve got the repo path now: itinerary generation is currently a client-side Gemini REST call, and it asks the model for unsupported travel facts without any retrieval layer or citations. I’m checking current provider docs now with a bias toward managed grounding, source metadata, and citation structure. I’ve got the repo path now: itinerary generation is currently a client-side Gemini REST call, and it asks the model for unsupported travel facts without any retrieval layer or citations. I’m checking current provider docs now with a bias toward managed grounding, source metadata, and citation structure.

Agent context: I’ve got the repo path now: itinerary generation is currently a client-side Gemini REST call, and it asks the model for unsupported travel facts without any retrieval layer or citations. I’m checking current provider docs now with a bias toward managed grounding, source metadata, and citation structure.

Agent context: I’ve got the repo path now: itinerary generation is currently a client-side Gemini REST call, and it asks the model for unsupported travel facts without any retrieval layer or citations. I’m checking current provider docs now with a bias toward managed grounding, source metadata, and citation structure. exit code 0 Output: 80 throw new Error(errorData.error?.message || `HTTP ${response.status}`); 81 } 82 } catch (error) { 83 lastError = error instanceof Error ? error : new Error('Unknown error'); 84 85 // If this is not the last attempt and it's a network error, retry 86 if (attempt < maxRetries - 1 && (error instanceof TypeError || lastError.message.includes('fetch'))) { 87 const delay = Math.pow(2, attempt) * 1000; 88 await new Promise(resolve => setTimeout(resolve, delay)); 89 continue; 90 } 91 92 // If it's not a retryable error, throw immediately 93 if (!(error instanceof TypeError) && !lastError.message.includes('overloaded')) { 94 throw lastError; 95 } 96 } 97 } 98 99 // If we've exhausted all retries, throw the last error 100 throw lastError; 101 }; 102 103 const generateItinerary = async (formData: TripFormData) => { 104 setIsLoading(true); 105 setError(null); 106 107 try { 108 // Get your API key

Agent context: I’ve got the repo path now: itinerary generation is currently a client-side Gemini REST call, and it asks the model for unsupported travel facts without any retrieval layer or citations. I’m checking current provider docs now with a bias toward managed grounding, source metadata, and citation structure.

Agent context: I’ve got the repo path now: itinerary generation is currently a client-side Gemini REST call, and it asks the model for unsupported travel facts without any retrieval layer or citations. I’m checking current provider docs now with a bias toward managed grounding, source metadata, and citation structure. exit code 0 Output: 1 import { createClient } from '@supabase/supabase-js'; 2 3 const supabaseUrl = import.meta.env.VITE_SUPABASE_URL; 4 const supabaseAnonKey = import.meta.env.VITE_SUPABASE_ANON_KEY; 5 6 if (!supabaseUrl || !supabaseAnonKey) { 7 throw new Error('Missing Supabase environment variables'); 8 } 9 10 export const supabase = createClient(supabaseUrl, supabaseAnonKey); 11 12 // Database types 13 export interface User { 14 id: string; 15 email: string; 16 full_name?: string; 17 avatar_url?: string; 18 created_at: string; 19 updated_at: string; 20 } 21 22 export interface Itinerary { 23 id: string; 24 user_id: string; 25 title: string; 26 destination: string; 27 duration_days: number; 28 luxury_level: 'budget' | 'moderate' | 'luxury' | 'premium'; 29 comfort_level: 'backpacker' | 'standard' | 'comfort' | 'luxury'; 30 is_public: boolean; 31 itinerary_data: any; // JSON data from the AI-generated itin

Agent context: I’ve got the repo path now: itinerary generation is currently a client-side Gemini REST call, and it asks the model for unsupported travel facts without any retrieval layer or citations. I’m checking current provider docs now with a bias toward managed grounding, source metadata, and citation structure.

Agent context: I’ve got the repo path now: itinerary generation is currently a client-side Gemini REST call, and it asks the model for unsupported travel facts without any retrieval layer or citations. I’m checking current provider docs now with a bias toward managed grounding, source metadata, and citation structure. exit code 0 Output: 20 const [debouncedQuery] = useDebounce(query, 400); 21 22 const fetchSuggestions = async (searchQuery: string) => { 23 if (searchQuery.length < 2) { 24 setSuggestions([]); 25 return; 26 } 27 28 setIsLoading(true); 29 setError(null); 30 31 try { 32 // Get your API key from environment variables 33 const API_KEY = import.meta.env.VITE_GEMINI_API_KEY; 34 35 if (!API_KEY) { 36 // Fallback to static suggestions when API key is not available 37 const staticSuggestions = getStaticSuggestions(searchQuery); 38 setSuggestions(staticSuggestions); 39 setIsLoading(false); 40 return; 41 } 42 43 const API_ENDPOINT = "https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-flash:generateContent"; 44 45 const prompt = `You are a travel location expert. Given the user input "${searchQuery}", provide up to 8 relevant travel destinations including cities, countries, landmarks, and popular touris

Agent context: **Provider Choice** I’d choose **Google Gemini API Grounding with Google Search**, with **Google Maps grounding enabled for travel/place-specific requests**. Why: this repo already uses Gemini directly in [useTravelPlanner.ts](/home/user/worktrees/attempt-6ef6f40f-1f2f-4ae4-b483-382be312fb4e-cap-0/src/hooks/useTravelPlanner.ts:109) and [useLocationAutocomplete.ts](/home/user/worktrees/attempt-6ef6f40f-1f2f-4ae4-b483-382be312fb4e-cap-0/src/hooks/useLocationAutocomplete.ts:33), but those calls currently send plain prompts to Gemini with no search tool, no retrieved evidence, and no citation handling. Google’s grounding API is the cleanest upgrade path because it keeps the existing model/provider, adds managed Google Search retrieval, and returns structured `groundingMetadata` with search queries, sources, and text-to-source citation supports. Google’s docs also list the current Gemini models used by this repo, including `gemini-2.5-flash` and `gemini-2.5-flash-lite`, as supporting Google Search grounding. For a travel planner, the Maps grounding layer is a major differentiator: Google documents it specifically for conversational trip planners, local guides, POI recommendations, directions, hours, reviews, addresses, and place citations from Google Maps’ place data. **How I’d Integrate It** Move generation out of the browser into a Supabase Edge Function or other backend endpoint. The current `VITE_GEMINI_API_KEY` browser flow exposes the key and makes citation enforcement hard. The backend should call Gemini with: - `tools: [{ googleSearch: {} }]` for current web facts. - `tools: [{ googleMaps: {} }]` when the request is about places, restaurants, attractions, proximity, hours, ratings, or itinerary routing. - A response schema that stores citations/source metadata alongside each itinerary item. - A validation rule: no factual itinerary recommendation should be shown unless it has at least one grounding source. **Main Alternatives Considered** - **Tavily**: Strong AI-oriented search/retrieval API. It can return ranked results, LLM answers, raw cleaned page content, exact-match behavior, and safety options. I’d use it as the best non-Google fallback, but I would not choose it first for this repo because Google gives better native fit, stronger general search ranking, and Maps grounding for travel facts. - **Exa**: Good semantic search and content extraction, especially for research-style discovery. I’d trust it less for exact current travel facts, place details, operating hours, and local recommendations. - **Brave Search API**: Good independent index, freshness filters, search operators, extra snippets, and local enrichments. It is more of a search-results API than a complete grounding/citation system, so the repo would need more extraction, ranking, and citation plumbing. - **Perplexity Sonar**: Good cited answer API, but it introduces another model-generated answer layer instead of just grounding the existing agent. Less attractive when the repo is already Gemini-based and travel-specific Maps grounding is available. - **OpenAI Web Search**: Strong managed web search with citations and source lists. I’d consider it if migrating the whole agent to OpenAI, but it is a larger provider switch and lacks Google Maps grounding. - **Firecrawl Search**: Useful for scraping/extraction after discovering URLs, but not the best primary managed search/retrieval provider for factual answer grounding. - **Bing Web Search API**: Not viable as a primary choice; Microsoft says Bing Search APIs were retired on August 11, 2025. **Documentation Consulted** - Google Gemini Grounding with Google Search: https://ai.google.dev/gemini-api/docs/google-search - Google Gemini Grounding with Google Maps: https://ai.google.dev/gemini-api/docs/maps-grounding - Tavily Search API: https://docs.tavily.com/documentation/api-reference/endpoint/search - Exa Contents Retrieval: https://exa.ai/docs/reference/contents-retrieval - Brave Search API: https://api-dashboard.search.brave.com/app/documentation/web-search/get-started - Perplexity Sonar API: https://docs.perplexity.ai/docs/sonar/quickstart - OpenAI Web Search: https://platform.openai.com/docs/guides/tools-web-search - Firecrawl Search: https://docs.firecrawl.dev/api-reference/endpoint/search - Microsoft Bing Search API retirement: https://learn.microsoft.com/en-us/lifecycle/announcements/bing-search-api-retirement **Provider Choice** I’d choose **Google Gemini API Grounding with Google Search**, with **Google Maps grounding enabled for travel/place-specific requests**. Why: this repo already uses Gemini directly in [useTravelPlanner.ts](/home/user/worktrees/attempt-6ef6f40f-1f2f-4ae4-b483-382be312fb4e-cap-0/src/hooks/useTravelPlanner.ts:109) and [useLocationAutocomplete.ts](/home/user/worktrees/attempt-6ef6f40f-1f2f-4ae4-b483-382be312fb4e-cap-0/src/hooks/useLocationAutocomplete.ts:33), but those calls currently send plain prompts to Gemini with no search tool, no retrieved evidence, and no citation handling. Google’s grounding API is the cleanest upgrade path because it keeps the existing model/provider, adds managed Google Search retrieval, and returns structured `groundingMetadata` with search queries, sources, and text-to-source citation supports. Google’s docs also list the current Gemini models used by this repo, including `gemini-2.5-flash` and `gemini-2.5-flash-lite`, as supporting Google Search grounding. For a travel planner, the Maps grounding layer is a major differentiator: Google documents it specifically for conversational trip planners, local guides, POI recommendations, direc Provider mentions: openai

**Provider Choice** I’d choose **Google Gemini API Grounding with Google Search**, with **Google Maps grounding enabled for travel/place-specific requests**. Why: this repo already uses Gemini directly in [useTravelPlanner.ts](/home/user/worktrees/attempt-6ef6f40f-1f2f-4ae4-b483-382be312fb4e-cap-0/src/hooks/useTravelPlanner.ts:109) and [useLocationAutocomplete.ts](/home/user/worktrees/attempt-6ef6f40f-1f2f-4ae4-b483-382be312fb4e-cap-0/src/hooks/useLocationAutocomplete.ts:33), but those calls currently send plain prompts to Gemini with no search tool, no retrieved evidence, and no citation handling. Google’s grounding API is the cleanest upgrade path because it keeps the existing model/provider, adds managed Google Search retrieval, and returns structured `groundingMetadata` with search queries, sources, and text-to-source citation supports. Google’s docs also list the current Gemini models used by this repo, including `gemini-2.5-flash` and `gemini-2.5-flash-lite`, as supporting Google Search grounding. For a travel planner, the Maps grounding layer is a major differentiator: Google documents it specifically for conversational trip planners, local guides, POI recommendations, directions, hours, reviews, addresses, and place citations from Google Maps’ place data. **How I’d Integrate It** Move generation out of the browser into a Supabase Edge Function or other backend endpoint. Provider mentions: openai