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Agent context: I'll research the repository structure and stack first, then evaluate web-search providers. I'll research the repository structure and stack first, then evaluate web-search providers.

Agent context: I'll research the repository structure and stack first, then evaluate web-search providers. Tool result summary: EISDIR: illegal operation on a directory, read '/home/user/worktrees/attempt-a502d043-7d9a-4730-936a-454e983b8ab1-cap-0' Agent context: I'll research the repository structure and stack first, then evaluate web-search providers.

Agent context: I'll research the repository structure and stack first, then evaluate web-search providers. Tool result summary: /home/user/worktrees/attempt-a502d043-7d9a-4730-936a-454e983b8ab1-cap-0/README.md /home/user/worktrees/attempt-a502d043-7d9a-4730-936a-454e983b8ab1-cap-0/package.json Agent context: I'll research the repository structure and stack first, then evaluate web-search providers.

Agent context: I'll research the repository structure and stack first, then evaluate web-search providers. Tool result summary: 1 # Wandr - AI Travel Planner 2 3 ## Project info 4 5 **URL**: https://lovable.dev/projects/b758b84e-570f-41d3-af78-813bdab3257e 6 7 ## How can I edit this code? 8 9 There are several ways of editing your application. 10 11 **Use Lovable** 12 13 Simply visit the [Lovable Project](https://lovable.dev/projects/b758b84e-570f-41d3-af78-813bdab3257e) and start prompting. 14 15 Changes made via Lovable will be committed au Agent context: I'll research the repository structure and stack first, then evaluate web-search providers.

Agent context: I'll research the repository structure and stack first, then evaluate web-search providers. Tool result summary: 1 { 2 "name": "wandr", 3 "private": true, 4 "version": "0.0.0", 5 "type": "module", 6 "scripts": { 7 "dev": "vite", 8 "build": "vite build", 9 "build:dev": "vite build --mode development", 10 "lint": "eslint .", 11 "preview": "vite preview" 12 }, 13 "dependencies": { 14 "@hookform/resolvers": "^3.10.0", 15 "@radix-ui/react-accordion": "^1.2.11", 16 "@radix-ui/react-alert-dialog": "^1.1.14", 17 "@radix-ui/react-aspect Agent context: I'll research the repository structure and stack first, then evaluate web-search providers.

Agent context: I'll research the repository structure and stack first, then evaluate web-search providers. Tool result summary: src/components/ui/use-toast.ts src/hooks/use-toast.ts src/hooks/useAuth.ts src/hooks/useItineraries.ts src/hooks/useLocationAutocomplete.ts src/hooks/useTravelPlanner.ts src/lib/supabase.ts src/lib/utils.ts src/utils/downloadUtils.ts src/vite-env.d.ts tailwind.config.ts vite.config.ts Agent context: I'll research the repository structure and stack first, then evaluate web-search providers.

Agent context: I'll research the repository structure and stack first, then evaluate web-search providers. Tool result summary: Found 25 files src/pages/Index.tsx src/pages/MyTrips.tsx src/pages/Settings.tsx src/utils/downloadUtils.ts tailwind.config.ts 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/Trav Agent context: I'll research the repository structure and stack first, then evaluate web-search providers.

Agent context: I'll research the repository structure and stack first, then evaluate web-search providers. Tool result summary: No files found Agent context: I'll research the repository structure and stack first, then evaluate web-search providers.

Agent context: I'll research the repository structure and stack first, then evaluate web-search providers. Tool result summary: No files found Agent context: I'll research the repository structure and stack first, then evaluate web-search providers.

Agent context: I'll research the repository structure and stack first, then evaluate web-search providers. Tool result summary: 1 import { useState } from "react"; 2 import { TripFormData } from "@/components/TravelPlannerForm"; 3 import { TripItineraryData } from "@/components/TripItinerary"; 4 import { useItineraries } from "./useItineraries"; 5 import { useAuth } from "./useAuth"; 6 7 // Helper function to calculate trip duration 8 const calculateDuration = (startDate: string, endDate: string): string => { 9 const start = new Date(startDat Agent context: I'll research the repository structure and stack first, then evaluate web-search providers.

Agent context: I'll research the repository structure and stack first, then evaluate web-search providers. Tool result summary: 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: Agent context: I'll research the repository structure and stack first, then evaluate web-search providers.

Agent context: I'll research the repository structure and stack first, then evaluate web-search providers. Tool result summary: Found 7 files vite.config.ts src/components/LocationAutocomplete.tsx src/components/TravelCarousel.tsx src/components/ui/carousel.tsx src/hooks/useItineraries.ts src/hooks/useLocationAutocomplete.ts src/hooks/useTravelPlanner.ts Agent context: I'll research the repository structure and stack first, then evaluate web-search providers.

Agent context: I'll research the repository structure and stack first, then evaluate web-search providers. Tool result summary: (Bash completed with no output) Agent context: I'll research the repository structure and stack first, then evaluate web-search providers.

Agent context: I'll research the repository structure and stack first, then evaluate web-search providers. Tool result summary: 1 import { useState, useEffect } from 'react'; 2 import { useDebounce } from 'use-debounce'; 3 4 export interface LocationSuggestion { 5 id: string; 6 name: string; 7 fullName: string; 8 country: string; 9 type: 'city' | 'country' | 'landmark' | 'region'; 10 flag?: string; 11 } 12 13 export const useLocationAutocomplete = () => { 14 const [query, setQuery] = useState(''); 15 const [suggestions, setSuggestions] = useS Agent context: I'll research the repository structure and stack first, then evaluate web-search providers.

Agent context: I'll research the repository structure and stack first, then evaluate web-search providers. Tool result summary: -rw-r--r-- 1 user user 789 Jun 6 04:54 eslint.config.js -rw-r--r-- 1 user user 80 Jun 6 04:54 postcss.config.js -rw-r--r-- 1 user user 2933 Jun 6 04:54 tailwind.config.ts -rw-r--r-- 1 user user 652 Jun 6 04:54 tsconfig.app.json -rw-r--r-- 1 user user 381 Jun 6 04:54 tsconfig.json -rw-r--r-- 1 user user 481 Jun 6 04:54 tsconfig.node.json -rw-r--r-- 1 user user 471 Jun 6 04:54 vite.config.ts Agent context: I'll research the repository structure and stack first, then evaluate web-search providers.

Agent context: I'll research the repository structure and stack first, then evaluate web-search providers. Tool result summary: (Bash completed with no output) Agent context: I'll research the repository structure and stack first, then evaluate web-search providers.

Agent context: I'll research the repository structure and stack first, then evaluate web-search providers. Tool result summary: 1 import { defineConfig } from "vite"; 2 import react from "@vitejs/plugin-react-swc"; 3 import path from "path"; 4 import { componentTagger } from "lovable-tagger"; 5 6 // https://vitejs.dev/config/ 7 export default defineConfig(({ mode }) => ({ 8 server: { 9 host: "::", 10 port: 8080, 11 }, 12 plugins: [ 13 react(), 14 mode === 'development' && 15 componentTagger(), 16 ].filter(Boolean), 17 resolve: { 18 alias: { 1 Agent context: I'll research the repository structure and stack first, then evaluate web-search providers.

Agent context: I'll research the repository structure and stack first, then evaluate web-search providers. Tool result summary: Exit code 123 Agent context: I'll research the repository structure and stack first, then evaluate web-search providers.

Agent context: I'll research the repository structure and stack first, then evaluate web-search providers. Tool result summary: /home/user/worktrees/attempt-a502d043-7d9a-4730-936a-454e983b8ab1-cap-0/src/hooks/useLocationAutocomplete.ts: const API_KEY = import.meta.env.VITE_GEMINI_API_KEY; /home/user/worktrees/attempt-a502d043-7d9a-4730-936a-454e983b8ab1-cap-0/src/hooks/useTravelPlanner.ts: const API_KEY = import.meta.env.VITE_GEMINI_API_KEY; /home/user/worktrees/attempt-a502d043-7d9a-4730-936a-454e983b8ab1-cap-0/src/hooks/useTravelPlanner.ts Agent context: I'll research the repository structure and stack first, then evaluate web-search providers.

Agent context: I'll research the repository structure and stack first, then evaluate web-search providers. Tool result summary: /home/user/worktrees/attempt-a502d043-7d9a-4730-936a-454e983b8ab1-cap-0/src/hooks/useLocationAutocomplete.ts: const API_KEY = import.meta.env.VITE_GEMINI_API_KEY; /home/user/worktrees/attempt-a502d043-7d9a-4730-936a-454e983b8ab1-cap-0/src/hooks/useLocationAutocomplete.ts: const API_ENDPOINT = "https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-flash:generateContent"; /home/user/worktrees/attempt-a502d Agent context: I'll research the repository structure and stack first, then evaluate web-search providers. Provider mentions: openai

Agent context: I'll research the repository structure and stack first, then evaluate web-search providers. Tool result summary: /home/user/worktrees/attempt-a502d043-7d9a-4730-936a-454e983b8ab1-cap-0/src/App.tsx:import { TooltipProvider } from "@/components/ui/tooltip"; /home/user/worktrees/attempt-a502d043-7d9a-4730-936a-454e983b8ab1-cap-0/src/App.tsx: <TooltipProvider> /home/user/worktrees/attempt-a502d043-7d9a-4730-936a-454e983b8ab1-cap-0/src/App.tsx: </TooltipProvider> /home/user/worktrees/attempt-a502d043-7d9a-4730-936a-454e983b8ab1-cap- Agent context: I'll research the repository structure and stack first, then evaluate web-search providers.

Agent context: I'll research the repository structure and stack first, then evaluate web-search providers. Tool result summary: 1 import { useState, useEffect } from 'react'; 2 import { supabase, type ItinerarySummary, type Itinerary, type ItineraryRating } from '@/lib/supabase'; 3 import { useAuth } from './useAuth'; 4 5 export const useItineraries = () => { 6 const { user } = useAuth(); 7 const [publicItineraries, setPublicItineraries] = useState<ItinerarySummary[]>([]); 8 const [userItineraries, setUserItineraries] = useState<ItinerarySumm Agent context: I'll research the repository structure and stack first, then evaluate web-search providers.

Agent context: Good — it's "Wandr," a React/TypeScript/Vite travel app already using the **Google Gemini API** (`VITE_GEMINI_API_KEY`), Supabase backend, native fetch, no existing RAG/tool-calling. Now let me consult real provider documentation. Good — it's "Wandr," a React/TypeScript/Vite travel app already using the **Google Gemini API** (`VITE_GEMINI_API_KEY`), Supabase backend, native fetch, no existing RAG/tool-calling. Now let me consult real provider documentation.

Agent context: Now let me consult the actual documentation of the top contenders in parallel. Now let me consult the actual documentation of the top contenders in parallel.

Query/prompt: Summarize Tavily's search API: what it returns (content, sources, citations, scores), how it's designed for LLM/agent grounding, accuracy/source-quality features, and any mention of factual accuracy or citation support. Agent context: Now let me consult the actual documentation of the top contenders in parallel. Fetch prompt: Summarize Tavily's search API: what it returns (content, sources, citations, scores), how it's designed for LLM/agent grounding, accuracy/source-quality features, and any mention of factual accuracy or citation support. Tool result summary: # Tavily Search API Summary ## What It Returns Tavily's search API delivers structured results including: - **Search results array**: Each item contains title, URL, relevance score (0-1 float), and content summaries - **Raw content**: Optional cleaned/parsed HTML in markdown or plain text formats - **Images**: Query-related images plus per-result images when requested - **LLM-generated answers**: Quick or detailed su Agent context: Now let me consult the actual documentation of the top contenders in parallel.

Query/prompt: Summarize Exa's search/retrieval API: neural/semantic search, what it returns (content, highlights, citations), how it supports grounded answers and source quality. Mention the Answer endpoint and citation support. Agent context: Now let me consult the actual documentation of the top contenders in parallel. Fetch prompt: Summarize Exa's search/retrieval API: neural/semantic search, what it returns (content, highlights, citations), how it supports grounded answers and source quality. Mention the Answer endpoint and citation support. Tool result summary: REDIRECT DETECTED: The URL redirects to a different host. Original URL: https://docs.exa.ai/reference/getting-started Redirect URL: https://exa.ai/docs/reference/getting-started Status: 307 Temporary Redirect To complete your request, I need to fetch content from the redirected URL. Please use WebFetch again with these parameters: - url: "https://exa.ai/docs/reference/getting-started" - prompt: "Summarize Exa's searc Agent context: Now let me consult the actual documentation of the top contenders in parallel.

Query/prompt: Summarize Perplexity Sonar API: how it returns answers with citations, source quality, factual grounding, search_results / citations fields, and any accuracy claims. Agent context: Now let me consult the actual documentation of the top contenders in parallel. Fetch prompt: Summarize Perplexity Sonar API: how it returns answers with citations, source quality, factual grounding, search_results / citations fields, and any accuracy claims. Tool result summary: Request failed with status code 404 Agent context: Now let me consult the actual documentation of the top contenders in parallel.

Query/prompt: Summarize Gemini's "Grounding with Google Search" feature: how it grounds responses in Google Search, returns grounding metadata/citations, supported in Gemini API, and accuracy/source-quality benefits. Agent context: Now let me consult the actual documentation of the top contenders in parallel. Fetch prompt: Summarize Gemini's "Grounding with Google Search" feature: how it grounds responses in Google Search, returns grounding metadata/citations, supported in Gemini API, and accuracy/source-quality benefits. Tool result summary: # Gemini's Grounding with Google Search Feature ## How It Works Gemini's grounding feature connects the model to real-time web content. When enabled, the system automatically analyzes user prompts, executes relevant search queries, processes results, and synthesizes information into grounded responses with source citations. ## Metadata and Citations The API returns `groundingMetadata` containing: - **Web search queri Agent context: Now let me consult the actual documentation of the top contenders in parallel.

Agent context: Tavily and Gemini docs retrieved; Perplexity 404'd and Exa redirected. Let me fetch the corrected/alternate URLs. Tavily and Gemini docs retrieved; Perplexity 404'd and Exa redirected. Let me fetch the corrected/alternate URLs.

Query/prompt: Summarize Exa's search/retrieval API: neural/semantic search, what it returns (content, highlights, citations), the Answer endpoint and citation support, and how it supports grounded answers and source quality. Agent context: Tavily and Gemini docs retrieved; Perplexity 404'd and Exa redirected. Let me fetch the corrected/alternate URLs. Fetch prompt: Summarize Exa's search/retrieval API: neural/semantic search, what it returns (content, highlights, citations), the Answer endpoint and citation support, and how it supports grounded answers and source quality. Tool result summary: # Exa Search and Retrieval API Summary **Neural/Semantic Search:** Exa employs "embeddings-based search and other intelligent methods" to locate precise web content, designed specifically for AI applications. **Content Retrieval:** The `/contents` endpoint delivers "clean, up-to-date, parsed HTML from Exa search results," enabling structured data extraction from discovered pages. **Answer Endpoint:** Through the `/an Agent context: Tavily and Gemini docs retrieved; Perplexity 404'd and Exa redirected. Let me fetch the corrected/alternate URLs.

Query/prompt: Summarize the Perplexity Sonar API: how it returns answers with citations/search_results, source quality, factual grounding, and any accuracy claims or benchmarks. Agent context: Tavily and Gemini docs retrieved; Perplexity 404'd and Exa redirected. Let me fetch the corrected/alternate URLs. Fetch prompt: Summarize the Perplexity Sonar API: how it returns answers with citations/search_results, source quality, factual grounding, and any accuracy claims or benchmarks. Tool result summary: # Perplexity API Overview Based on the provided documentation, here's what's available: The page describes **three main APIs**: 1. **Agent API** — Accesses third-party models with web search capabilities and preset configurations 2. **Search API** — Delivers ranked web search results with advanced filtering and real-time data 3. **Embeddings API** — Generates embeddings for semantic search and RAG applications ## Key Agent context: Tavily and Gemini docs retrieved; Perplexity 404'd and Exa redirected. Let me fetch the corrected/alternate URLs.

Agent context: ## Recommendation **Choose: Gemini "Grounding with Google Search."** For Wandr specifically, this is the strongest fit on the dimensions you prioritized — factual accuracy, source quality, and verifiable citations: - **Verifiable citations (the deciding factor):** Its `groundingMetadata` returns *grounding supports* that map individual generated text segments back to specific source chunks (URI + title). That span-level claim→source mapping is the strongest anti-hallucination primitive of any option here — you can render inline citations and let users verify each statement, rather than trusting a model-written summary. - **Source quality/breadth:** Grounded in the full Google Search index — the broadest, highest-quality corpus of the contenders, which matters for fresh, niche travel facts (hours, closures, prices, events). - **Architecture fit:** Wandr already calls Gemini (`VITE_GEMINI_API_KEY`, native fetch in `src/hooks/useTravelPlanner.ts`). This adds a tool flag to existing calls — no new vendor, key, or SDK, lowering the chance of integration bugs that themselves cause bad answers. ## Main alternatives considered - **Perplexity Sonar** — purpose-built answer engine with citations; excellent accuracy reputation. Rejected as primary because it adds a second LLM vendor alongside Gemini and gives less granular span-level citation control than Gemini's grounding supports. - **Exa** — best *neural/semantic retrieval*; `/answer` and `/research` return citations and parsed content. The strongest **provider-agnostic** pick if you ever want to decouple retrieval from Gemini. Slightly more wiring (retrieve → feed context → cite). - **Tavily** — agent-optimized search with per-result relevance scores and domain/time filters. Solid grounding, but its docs describe no explicit citation/fact-verification layer; its "answer" leans on the model itself. ## Documentation I actually consulted - Tavily Search API reference (`docs.tavily.com/.../endpoint/search`) - Exa getting-started/reference (`exa.ai/docs/reference/getting-started`) - Perplexity API overview (`docs.perplexity.ai/getting-started/overview`) — its detailed chat-completions page 404'd - Gemini Grounding with Google Search (`ai.google.dev/gemini-api/docs/grounding`) ## Why Gemini grounding over the others The product's hard constraint is *no hallucinated/low-quality answers*. Gemini grounding wins on the two things that most directly enforce that: (1) the largest, highest-quality source index, and (2) machine-readable, span-level citation metadata that makes every claim auditable. Perplexity and Exa both produce good cited answers, but neither matched Gemini on *granular verifiability* while also fitting the existing single-vendor stack. Tavily is excellent retrieval plumbing but offloads accuracy entirely to your model. If you later want vendor independence from Gemini, Exa is the recommended fallback. Want me to implement it in `useTravelPlanner.ts`/`useLocationAutocomplete.ts`? ## Recommendation **Choose: Gemini "Grounding with Google Search."** For Wandr specifically, this is the strongest fit on the dimensions you prioritized — factual accuracy, source quality, and verifiable citations: - **Verifiable citations (the deciding factor):** Its `groundingMetadata` returns *grounding supports* that map individual generated text segments back to specific source chunks (URI + title). That span-level claim→source mapping is the strongest anti-hallucination primitive of any option here — you can render inline citations and let users verify each statement, rather than trusting a model-written summary. - **Source quality/breadth:** Grounded in the full Google Search index — the broadest, highest-quality corpus of the contenders, which matters for fresh, niche travel facts (hours, closures, prices, events). - **Architecture fit:** Wandr already calls Gemini (`VITE_GEMINI_API_KEY`, native fetch in `src/hooks/useTravelPlanner.ts`). This adds a tool flag to existing calls — no new vendor, key, or SDK, lowering the chance of integration bugs that themselves cause bad answers. ## Main alternatives considered - **Perplexity Sonar** — purpose-built answer engine with citat

## Recommendation **Choose: Gemini "Grounding with Google Search."** For Wandr specifically, this is the strongest fit on the dimensions you prioritized — factual accuracy, source quality, and verifiable citations: - **Verifiable citations (the deciding factor):** Its `groundingMetadata` returns *grounding supports* that map individual generated text segments back to specific source chunks (URI + title). That span-level claim→source mapping is the strongest anti-hallucination primitive of any option here — you can render inline citations and let users verify each statement, rather than trusting a model-written summary. - **Source quality/breadth:** Grounded in the full Google Search index — the broadest, highest-quality corpus of the contenders, which matters for fresh, niche travel facts (hours, closures, prices, events). - **Architecture fit:** Wandr already calls Gemini (`VITE_GEMINI_API_KEY`, native fetch in `src/hooks/useTravelPlanner.ts`). This adds a tool flag to existing calls — no new vendor, key, or SDK, lowering the chance of integration bugs that themselves cause bad answers. ## Main alternatives considered - **Perplexity Sonar** — purpose-built answer engine with citations; excellent accuracy reputation. Rejected as primary because it adds a second LLM vendor alongside Gemini and gives less granular span-level citation control than Gemini's grounding supports. - **