Run

P0

0fc81071240b9880bb6ef9b19ae73ae40e06e7be

Agent context: I'll research the repository first to understand its stack and how the agent works. I'll research the repository first to understand its stack and how the agent works.

Agent context: I'll research the repository first to understand its stack and how the agent works. Tool result summary: EISDIR: illegal operation on a directory, read '/home/user/worktrees/attempt-35d8fff8-c6d8-4034-91fc-1476973788d5-cap-0' Agent context: I'll research the repository first to understand its stack and how the agent works.

Agent context: I'll research the repository first to understand its stack and how the agent works. Tool result summary: /home/user/worktrees/attempt-35d8fff8-c6d8-4034-91fc-1476973788d5-cap-0/README.md /home/user/worktrees/attempt-35d8fff8-c6d8-4034-91fc-1476973788d5-cap-0/package.json Agent context: I'll research the repository first to understand its stack and how the agent works.

Agent context: I'll research the repository first to understand its stack and how the agent works. Tool result summary: total 516 drwxr-xr-x 6 user user 4096 Jun 6 04:53 . drwxr-xr-x 3 user user 4096 Jun 6 04:53 .. -rw-r--r-- 1 user user 170 Jun 6 04:53 .git -rw-r--r-- 1 user user 29 Jun 6 04:53 .gitignore -rw-r--r-- 1 user user 2143 Jun 6 04:53 README.md -rw-r--r-- 1 user user 197327 Jun 6 04:53 bun.lockb -rw-r--r-- 1 user user 413 Jun 6 04:53 components.json drwxr-xr-x 3 user user 4096 Jun 6 04:53 dist -rw-r--r-- 1 user user 789 Jun Agent context: I'll research the repository first to understand its stack and how the agent works.

Agent context: I'll research the repository first to understand its stack and how the agent works. 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 first to understand its stack and how the agent works.

Agent context: I'll research the repository first to understand its stack and how the agent works. 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 first to understand its stack and how the agent works.

Agent context: I'll research the repository first to understand its stack and how the agent works. Tool result summary: /home/user/worktrees/attempt-35d8fff8-c6d8-4034-91fc-1476973788d5-cap-0/src/App.tsx /home/user/worktrees/attempt-35d8fff8-c6d8-4034-91fc-1476973788d5-cap-0/src/components/AuthModal.tsx /home/user/worktrees/attempt-35d8fff8-c6d8-4034-91fc-1476973788d5-cap-0/src/components/CommunityItineraries.tsx /home/user/worktrees/attempt-35d8fff8-c6d8-4034-91fc-1476973788d5-cap-0/src/components/Header.tsx /home/user/worktrees/atte Agent context: I'll research the repository first to understand its stack and how the agent works.

Agent context: I'll research the repository first to understand its stack and how the agent works. Tool result summary: total 52 drwxr-xr-x 8 user user 4096 Jun 6 04:53 . drwxr-xr-x 6 user user 4096 Jun 6 04:53 .. -rw-r--r-- 1 user user 1332 Jun 6 04:53 App.tsx drwxr-xr-x 2 user user 4096 Jun 6 04:53 assets drwxr-xr-x 3 user user 4096 Jun 6 04:53 components drwxr-xr-x 2 user user 4096 Jun 6 04:53 hooks -rw-r--r-- 1 user user 4743 Jun 6 04:53 index.css drwxr-xr-x 2 user user 4096 Jun 6 04:53 lib -rw-r--r-- 1 user user 158 Jun 6 04:53 m Agent context: I'll research the repository first to understand its stack and how the agent works.

Agent context: I'll research the repository first to understand its stack and how the agent works. Tool result summary: No files found Agent context: I'll research the repository first to understand its stack and how the agent works.

Agent context: I'll research the repository first to understand its stack and how the agent works. Tool result summary: (Bash completed with no output) Agent context: I'll research the repository first to understand its stack and how the agent works. Provider mentions: openai

Agent context: I'll research the repository first to understand its stack and how the agent works. Tool result summary: 1 import { Toaster } from "@/components/ui/toaster"; 2 import { Toaster as Sonner } from "@/components/ui/sonner"; 3 import { TooltipProvider } from "@/components/ui/tooltip"; 4 import { QueryClient, QueryClientProvider } from "@tanstack/react-query"; 5 import { BrowserRouter, Routes, Route } from "react-router-dom"; 6 import { Header } from "@/components/Header"; 7 import Index from "./pages/Index"; 8 import Communi Agent context: I'll research the repository first to understand its stack and how the agent works.

Agent context: I'll research the repository first to understand its stack and how the agent works. Tool result summary: 1 import { useState } from "react"; 2 import { Button } from "@/components/ui/button"; 3 import { Input } from "@/components/ui/input"; 4 import { Label } from "@/components/ui/label"; 5 import { Card, CardContent, CardDescription, CardHeader, CardTitle } from "@/components/ui/card"; 6 import { Select, SelectContent, SelectItem, SelectTrigger, SelectValue } from "@/components/ui/select"; 7 import { Calendar, Users, D Agent context: I'll research the repository first to understand its stack and how the agent works.

Agent context: I'll research the repository first to understand its stack and how the agent works. Tool result summary: total 52 drwxr-xr-x 8 user user 4096 Jun 6 04:53 . drwxr-xr-x 6 user user 4096 Jun 6 04:53 .. -rw-r--r-- 1 user user 1332 Jun 6 04:53 App.tsx drwxr-xr-x 2 user user 4096 Jun 6 04:53 assets drwxr-xr-x 3 user user 4096 Jun 6 04:53 components drwxr-xr-x 2 user user 4096 Jun 6 04:53 hooks -rw-r--r-- 1 user user 4743 Jun 6 04:53 index.css drwxr-xr-x 2 user user 4096 Jun 6 04:53 lib -rw-r--r-- 1 user user 158 Jun 6 04:53 m Agent context: I'll research the repository first to understand its stack and how the agent works.

Agent context: I'll research the repository first to understand its stack and how the agent works. Tool result summary: total 36 drwxr-xr-x 2 user user 4096 Jun 6 04:53 . drwxr-xr-x 8 user user 4096 Jun 6 04:53 .. -rw-r--r-- 1 user user 306 Jun 6 04:53 Community.tsx -rw-r--r-- 1 user user 7776 Jun 6 04:53 Index.tsx -rw-r--r-- 1 user user 7679 Jun 6 04:53 MyTrips.tsx -rw-r--r-- 1 user user 739 Jun 6 04:53 NotFound.tsx -rw-r--r-- 1 user user 3599 Jun 6 04:53 Settings.tsx Agent context: I'll research the repository first to understand its stack and how the agent works.

Agent context: I'll research the repository first to understand its stack and how the agent works. Tool result summary: total 16 drwxr-xr-x 2 user user 4096 Jun 6 04:53 . drwxr-xr-x 8 user user 4096 Jun 6 04:53 .. -rw-r--r-- 1 user user 6892 Jun 6 04:53 downloadUtils.ts Agent context: I'll research the repository first to understand its stack and how the agent works.

Agent context: I'll research the repository first to understand its stack and how the agent works. Tool result summary: total 48 drwxr-xr-x 2 user user 4096 Jun 6 04:53 . drwxr-xr-x 8 user user 4096 Jun 6 04:53 .. -rw-r--r-- 1 user user 565 Jun 6 04:53 use-mobile.tsx -rw-r--r-- 1 user user 3895 Jun 6 04:53 use-toast.ts -rw-r--r-- 1 user user 2074 Jun 6 04:53 useAuth.ts -rw-r--r-- 1 user user 5771 Jun 6 04:53 useItineraries.ts -rw-r--r-- 1 user user 6156 Jun 6 04:53 useLocationAutocomplete.ts -rw-r--r-- 1 user user 10816 Jun 6 04:53 us Agent context: I'll research the repository first to understand its stack and how the agent works.

Agent context: I'll research the repository first to understand its stack and how the agent works. Tool result summary: total 16 drwxr-xr-x 2 user user 4096 Jun 6 04:53 . drwxr-xr-x 8 user user 4096 Jun 6 04:53 .. -rw-r--r-- 1 user user 1513 Jun 6 04:53 supabase.ts -rw-r--r-- 1 user user 166 Jun 6 04:53 utils.ts Agent context: I'll research the repository first to understand its stack and how the agent works.

Agent context: I'll research the repository first to understand its stack and how the agent works. 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 first to understand its stack and how the agent works.

Agent context: I'll research the repository first to understand its stack and how the agent works. Tool result summary: 1 import { useState } from "react"; 2 import { TravelPlannerForm, type TripFormData } from "@/components/TravelPlannerForm"; 3 import { TripItinerary } from "@/components/TripItinerary"; 4 import { TravelCarousel } from "@/components/TravelCarousel"; 5 import { SiteMap } from "@/components/SiteMap"; 6 import { useTravelPlanner } from "@/hooks/useTravelPlanner"; 7 import { Button } from "@/components/ui/button"; 8 imp Agent context: I'll research the repository first to understand its stack and how the agent works.

Agent context: I'll research the repository first to understand its stack and how the agent works. Tool result summary: /home/user/worktrees/attempt-35d8fff8-c6d8-4034-91fc-1476973788d5-cap-0/src/hooks/useItineraries.ts: const fetchPublicItineraries = async () => { /home/user/worktrees/attempt-35d8fff8-c6d8-4034-91fc-1476973788d5-cap-0/src/hooks/useItineraries.ts: setError(err instanceof Error ? err.message : 'Failed to fetch public itineraries'); /home/user/worktrees/attempt-35d8fff8-c6d8-4034-91fc-1476973788d5-cap-0/src/hooks/useIti Agent context: I'll research the repository first to understand its stack and how the agent works.

Agent context: I'll research the repository first to understand its stack and how the agent works. 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 first to understand its stack and how the agent works.

Agent context: I'll research the repository first to understand its stack and how the agent works. 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 first to understand its stack and how the agent works.

Agent context: I'll research the repository first to understand its stack and how the agent works. Tool result summary: (Bash completed with no output) Agent context: I'll research the repository first to understand its stack and how the agent works.

Agent context: I'll research the repository first to understand its stack and how the agent works. 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 first to understand its stack and how the agent works.

Agent context: I'll research the repository first to understand its stack and how the agent works. Tool result summary: (Bash completed with no output) Agent context: I'll research the repository first to understand its stack and how the agent works.

Agent context: I'll research the repository first to understand its stack and how the agent works. Tool result summary: <tool_use_error>InputValidationError: Grep failed due to the following issue: An unexpected parameter `command` was provided</tool_use_error> Agent context: I'll research the repository first to understand its stack and how the agent works.

Agent context: I'll research the repository first to understand its stack and how the agent works. Tool result summary: node_modules/ .env .env.local Agent context: I'll research the repository first to understand its stack and how the agent works.

Agent context: I'll research the repository first to understand its stack and how the agent works. 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 first to understand its stack and how the agent works.

Agent context: I'll research the repository first to understand its stack and how the agent works. Tool result summary: /home/user/worktrees/attempt-35d8fff8-c6d8-4034-91fc-1476973788d5-cap-0/supabase/migrations/20250906084210_old_sun.sql Agent context: I'll research the repository first to understand its stack and how the agent works.

Agent context: I'll research the repository first to understand its stack and how the agent works. Tool result summary: 1 /* 2 # Travel Planner Database Schema 3 4 1. New Tables 5 - `users` - User profiles and authentication data 6 - `id` (uuid, primary key, references auth.users) 7 - `email` (text, unique) 8 - `full_name` (text) 9 - `avatar_url` (text, optional) 10 - `created_at` (timestamp) 11 - `updated_at` (timestamp) 12 13 - `itineraries` - Travel itineraries created by users 14 - `id` (uuid, primary key) 15 - `user_id` (uuid, fo Agent context: I'll research the repository first to understand its stack and how the agent works.

Agent context: This is a research/recommendation task — let me actually consult provider documentation. I'll load the web tools. This is a research/recommendation task — let me actually consult provider documentation. I'll load the web tools.

Agent context: Now let me consult actual provider documentation. I'll research the strongest candidates in parallel. Now let me consult actual provider documentation. I'll research the strongest candidates in parallel.

Query/prompt: Extract how Google Search grounding works with the Gemini API. How is it enabled (tool config), what models support it, what does the response include (citations, grounding metadata), and what is the pricing? Quote relevant request/response shapes. Agent context: Now let me consult actual provider documentation. I'll research the strongest candidates in parallel. Fetch prompt: Extract how Google Search grounding works with the Gemini API. How is it enabled (tool config), what models support it, what does the response include (citations, grounding metadata), and what is the pricing? Quote relevant request/response shapes. Tool result summary: # Google Search Grounding with Gemini API ## How It's Enabled Grounding with Google Search is configured by passing a `google_search` tool in the request: ```python grounding_tool = types.Tool( google_search=types.GoogleSearch() ) config = types.GenerateContentConfig( tools=[grounding_tool] ) ``` ## How It Works The process follows five steps: 1) user submits a prompt with the tool enabled, 2) the model analyzes whet Agent context: Now let me consult actual provider documentation. I'll research the strongest candidates in parallel.

Query/prompt: Extract how the Tavily Search API works: endpoint, request parameters, auth, response shape, whether it returns LLM-ready answers/content, and any notes on being purpose-built for AI agents/RAG. Include pricing if mentioned. Agent context: Now let me consult actual provider documentation. I'll research the strongest candidates in parallel. Fetch prompt: Extract how the Tavily Search API works: endpoint, request parameters, auth, response shape, whether it returns LLM-ready answers/content, and any notes on being purpose-built for AI agents/RAG. Include pricing if mentioned. Tool result summary: # Tavily Search API Overview ## Endpoint & Authentication **POST** `https://api.tavily.com/search` requires Bearer token authentication using your API key in the format `Bearer tvly-YOUR_API_KEY`. ## Key Request Parameters - **query** (required): Search string - **search_depth**: `advanced` (2 credits), `basic/fast/ultra-fast` (1 credit each) - **max_results**: 0-20 results (default: 5) - **topic**: `general` or `new Agent context: Now let me consult actual provider documentation. I'll research the strongest candidates in parallel.

Query/prompt: Extract how the Exa search/retrieval API works: endpoints (search, contents, answer), auth, request/response shape, neural vs keyword search, and pricing if mentioned. Agent context: Now let me consult actual provider documentation. I'll research the strongest candidates in parallel. Fetch prompt: Extract how the Exa search/retrieval API works: endpoints (search, contents, answer), auth, request/response shape, neural vs keyword search, and pricing if mentioned. 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: "Extract how the Exa s Agent context: Now let me consult actual provider documentation. I'll research the strongest candidates in parallel.

Agent context: Let me get the Exa docs and check two more alternatives (Brave, Perplexity Sonar). Let me get the Exa docs and check two more alternatives (Brave, Perplexity Sonar).

Query/prompt: Extract how the Exa search/retrieval API works: endpoints (search, contents, answer), auth, request/response shape, neural vs keyword search, and pricing if mentioned. Agent context: Let me get the Exa docs and check two more alternatives (Brave, Perplexity Sonar). Fetch prompt: Extract how the Exa search/retrieval API works: endpoints (search, contents, answer), auth, request/response shape, neural vs keyword search, and pricing if mentioned. Tool result summary: # Exa Search/Retrieval API Overview Based on the provided documentation, here's what's available: ## Core Endpoints Exa offers four main functionalities: 1. **Search** - "Find webpages using Exa's embeddings-based search and other intelligent methods." 2. **Contents** - "Obtain clean, up-to-date, parsed HTML from Exa search results." 3. **Answer** - Get direct answers to questions through the Answer API. 4. **Researc Agent context: Let me get the Exa docs and check two more alternatives (Brave, Perplexity Sonar).

Query/prompt: Extract how the Brave Search API works: endpoint, auth header, request params, response shape, and pricing/free tier if mentioned. Is it positioned for AI/LLM grounding? Agent context: Let me get the Exa docs and check two more alternatives (Brave, Perplexity Sonar). Fetch prompt: Extract how the Brave Search API works: endpoint, auth header, request params, response shape, and pricing/free tier if mentioned. Is it positioned for AI/LLM grounding? Tool result summary: # Brave Search API Overview ## Endpoint & Authentication The base endpoint is `https://api.search.brave.com/res/v1/web/search`. Authentication uses a header: `X-Subscription-Token: <YOUR_API_KEY>` ## Key Request Parameters - `q`: Search query (supports operators like `site:`, `filetype:`, quotes for exact matches) - `count`: Results per page (max 20, default 20) - `offset`: Pagination starting position (0-based, max Agent context: Let me get the Exa docs and check two more alternatives (Brave, Perplexity Sonar).

Query/prompt: Extract how the Perplexity Sonar API works: endpoint, auth, request shape, whether it returns answers with citations, models available, and pricing if mentioned. Is it OpenAI-compatible? Agent context: Let me get the Exa docs and check two more alternatives (Brave, Perplexity Sonar). Fetch prompt: Extract how the Perplexity Sonar API works: endpoint, auth, request shape, whether it returns answers with citations, models available, and pricing if mentioned. Is it OpenAI-compatible? Tool result summary: # Perplexity Sonar API Overview Based on the documentation provided, here's what I found: ## Endpoint & Authentication The API uses `https://api.perplexity.ai/v1/agent` with bearer token authentication: `"Authorization: Bearer $PERPLEXITY_API_KEY"` ## Request Structure Requests include a `model` parameter, `input` field for queries, optional `tools` array for web search configuration, and `response_format` for struct Agent context: Let me get the Exa docs and check two more alternatives (Brave, Perplexity Sonar). Provider mentions: openai

Agent context: ## Recommendation **Choose: Tavily Search API** — as the dedicated provider to add. But first, the honest caveat given your stack: you already call **Gemini 2.5 Flash Lite** directly (`useTravelPlanner.ts:154`, `useLocationAutocomplete.ts:65`). The *lowest-effort* grounding is Gemini's **native Google Search grounding** — add one `tools: [{ google_search: {} }]` line to the existing request body; no new provider, no new key, no extra round-trip, and you get `groundingMetadata` (queries, source chunks, inline citation supports) for free. If "minimum change" is the priority, do that. If you want a **provider-agnostic retrieval layer** (decoupled from the LLM, portable if you ever swap Gemini), **Tavily** is the best managed pick. ### Why Tavily over the others - **Purpose-built for LLM/RAG** — self-describes as "a search engine for LLM agents." Returns ranked results *with* clean content snippets and an optional synthesized `answer` in one call, so you feed grounded context straight into your existing Gemini prompt with no HTML scraping. - **One simple REST call** (`POST api.tavily.com/search`, Bearer auth) — fits your native-`fetch` pattern; no SDK needed. - **Credit model is predictable** (1 credit basic / 2 advanced), generous free tier for a hobby travel app. ### Alternatives considered - **Gemini Google Search grounding** — best for minimal integration, but locks retrieval to Gemini and bills per model-issued query. - **Exa** — excellent embeddings/neural search + `answer` endpoint, but more retrieval-engineering surface than this app needs. - **Brave Search API** — strong independent index with an "LLM Context" endpoint, but you assemble context yourself; more plumbing. - **Perplexity Sonar** — returns answers+citations, but it's an opinionated LLM-over-search (overlaps Gemini) rather than a clean retrieval layer. ### Docs I actually consulted - Gemini Google Search grounding — `ai.google.dev/gemini-api/docs/google-search` - Tavily Search — `docs.tavily.com/.../endpoint/search` - Exa — `exa.ai/docs/reference/getting-started` - Brave Search API — `api-dashboard.search.brave.com/.../get-started` - Perplexity Sonar — `docs.perplexity.ai/getting-started/overview` ### One architectural flag Your keys use the `VITE_` prefix, so they ship to the browser. Whatever you pick, proxy the search call through a **Supabase Edge Function** rather than calling it client-side — otherwise the API key is publicly exposed. Want me to wire up Tavily (or the native Gemini one-liner) into the itinerary flow? Sources: - [Gemini API — Grounding with Google Search](https://ai.google.dev/gemini-api/docs/google-search) - [Tavily Search API Reference](https://docs.tavily.com/documentation/api-reference/endpoint/search) - [Exa Docs — Getting Started](https://exa.ai/docs/reference/getting-started) - [Brave Search API — Get Started](https://api-dashboard.search.brave.com/app/documentation/web-search/get-started) - [Perplexity Sonar — Overview](https://docs.perplexity.ai/getting-started/overview) ## Recommendation **Choose: Tavily Search API** — as the dedicated provider to add. But first, the honest caveat given your stack: you already call **Gemini 2.5 Flash Lite** directly (`useTravelPlanner.ts:154`, `useLocationAutocomplete.ts:65`). The *lowest-effort* grounding is Gemini's **native Google Search grounding** — add one `tools: [{ google_search: {} }]` line to the existing request body; no new provider, no new key, no extra round-trip, and you get `groundingMetadata` (queries, source chunks, inline citation supports) for free. If "minimum change" is the priority, do that. If you want a **provider-agnostic retrieval layer** (decoupled from the LLM, portable if you ever swap Gemini), **Tavily** is the best managed pick. ### Why Tavily over the others - **Purpose-built for LLM/RAG** — self-describes as "a search engine for LLM agents." Returns ranked results *with* clean content snippets and an optional synthesized `answer` in one call, so you feed grounded context straight into your existing Gemini prompt with no HTML scraping. - **One simple REST call** (`POST api.tavily.com/search`, Bearer auth) — fits your native-`fetch` pattern; no SDK needed. - **Credit model is predic

## Recommendation **Choose: Tavily Search API** — as the dedicated provider to add. But first, the honest caveat given your stack: you already call **Gemini 2.5 Flash Lite** directly (`useTravelPlanner.ts:154`, `useLocationAutocomplete.ts:65`). The *lowest-effort* grounding is Gemini's **native Google Search grounding** — add one `tools: [{ google_search: {} }]` line to the existing request body; no new provider, no new key, no extra round-trip, and you get `groundingMetadata` (queries, source chunks, inline citation supports) for free. If "minimum change" is the priority, do that. If you want a **provider-agnostic retrieval layer** (decoupled from the LLM, portable if you ever swap Gemini), **Tavily** is the best managed pick. ### Why Tavily over the others - **Purpose-built for LLM/RAG** — self-describes as "a search engine for LLM agents." Returns ranked results *with* clean content snippets and an optional synthesized `answer` in one call, so you feed grounded context straight into your existing Gemini prompt with no HTML scraping. - **One simple REST call** (`POST api.tavily.com/search`, Bearer auth) — fits your native-`fetch` pattern; no SDK needed. - **Credit model is predictable** (1 credit basic / 2 advanced), generous free tier for a hobby travel app. ### Alternatives considered - **Gemini Google Search grounding** — best for minimal integration, but locks retrieval t