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9da97ce3ef437ebd56ed8eedaebc3dc37cec4ba9

Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers. I'll research the repository first to understand its architecture, then evaluate web-search providers.

Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers. Tool result summary: /home/user/worktrees/attempt-cdd75772-2d27-4e0a-a54d-7b3ae91aaceb-cap-1/.gitignore /home/user/worktrees/attempt-cdd75772-2d27-4e0a-a54d-7b3ae91aaceb-cap-1/README.md /home/user/worktrees/attempt-cdd75772-2d27-4e0a-a54d-7b3ae91aaceb-cap-1/package.json Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers.

Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers. Tool result summary: 1 # Nuxt 3 Minimal Starter 2 3 Look at the [Nuxt 3 documentation](https://nuxt.com/docs/getting-started/introduction) to learn more. 4 5 ## Setup 6 7 Make sure to install the dependencies: 8 9 ```bash 10 # npm 11 npm install 12 13 # pnpm 14 pnpm install 15 16 # yarn 17 yarn install 18 19 # bun 20 bun install 21 ``` 22 23 ## Development Server 24 25 Start the development server on `http://localhost:3000`: 26 27 ```bas Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers.

Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers. Tool result summary: 1 { 2 "name": "nuxt-app", 3 "private": true, 4 "type": "module", 5 "scripts": { 6 "build": "nuxt build", 7 "dev": "nuxt dev", 8 "start": "nuxt start", 9 "generate": "nuxt generate", 10 "preview": "nuxt preview", 11 "postinstall": "nuxt prepare" 12 }, 13 "devDependencies": { 14 "@nuxt/devtools": "latest", 15 "@types/lodash": "^4.14.199", 16 "nuxt": "^3.8.2", 17 "prisma": "^5.4.2", 18 "vite-plugin-vuetify": "^1.0.2", 1 Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers. Provider mentions: openai

Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers. Tool result summary: total 484 drwxr-xr-x 14 user user 4096 Jun 6 04:48 . drwxr-xr-x 4 user user 4096 Jun 6 04:48 .. -rw-r--r-- 1 user user 170 Jun 6 04:48 .git -rw-r--r-- 1 user user 193 Jun 6 04:48 .gitignore -rw-r--r-- 1 user user 53 Jun 6 04:48 .npmrc drwxr-xr-x 2 user user 4096 Jun 6 04:48 .vscode -rw-r--r-- 1 user user 842 Jun 6 04:48 README.md -rw-r--r-- 1 user user 98 Jun 6 04:48 app.vue drwxr-xr-x 4 user user 4096 Jun 6 04:48 co Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers.

Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers. Tool result summary: 9da97ce update llm call to get values c15b26d update upload to be a csv file 1d2d5f3 update type to category c454899 update type to category 3c19b42 add statement input using string 33d5749 update transaction table to include more information 6c514d0 update the categories for the budget planner b679426 update details to use server data 6fc46c7 update pie to use server data e646b1c add debugging 941f5c9 fix bug with s Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers.

Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers. Tool result summary: 9da97ce3ef437ebd56ed8eedaebc3dc37cec4ba9 update llm call to get values c15b26de65dd862de8ccbcc88f6e85c9b2b18abd update upload to be a csv file 1d2d5f3f6c2a0b6acde5bacaed50661a33232e31 update type to category c454899b6625ceb24207077310011eafecd8734d update type to category 3c19b42e4b33a18047c8f7f814c3cfed18385d1a add statement input using string 33d57495c8ad4cbcb9ed486cdb8f03b7056fcbe9 update transaction table to incl Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers.

Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers. Tool result summary: Found 3 files server/utils/llm.ts package-lock.json package.json Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers.

Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers. Tool result summary: server/api/login/google/callback.get.ts server/api/login/google/index.get.ts server/api/login/index.post.ts server/api/logout.post.ts server/api/signup.post.ts server/api/transaction/many.get.ts server/api/transaction/single.post.ts server/api/transaction/statement.post.ts server/api/transaction/summary.get.ts server/api/user.get.ts server/app.d.ts server/middleware/auth.ts server/utils/llm.ts server/utils/lucia.ts Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers.

Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers. Tool result summary: No files found Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers.

Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers. Tool result summary: 1 import OpenAI from 'openai'; 2 3 const runtimeConfig = useRuntimeConfig(); 4 5 export async function chatCompletion(prompt: string, systemPrompt?: string, history?: { role: string, content: string }[], jsonSchema?: any, name?: string) { 6 // const response = await openai.chat.completions.create({ 7 // messages: [{ role: 'user', content }], 8 // model: 'gpt-3.5-turbo', 9 // }); 10 11 // return response.choices[0].me Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers. Provider mentions: openai

Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers. Tool result summary: /home/user/worktrees/attempt-cdd75772-2d27-4e0a-a54d-7b3ae91aaceb-cap-1/server/utils/llm.ts Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers. Provider mentions: openai

Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers. Tool result summary: 1 import vuetify, { transformAssetUrls } from 'vite-plugin-vuetify' 2 export default defineNuxtConfig({ 3 css: [ 4 'vuetify/lib/styles/main.sass', 5 '@mdi/font/css/materialdesignicons.min.css' 6 ], 7 8 build: { 9 transpile: ['vuetify'], 10 }, 11 12 modules: [ 13 (_options, nuxt) => { 14 nuxt.hooks.hook('vite:extendConfig', (config) => { 15 // @ts-expect-error 16 config.plugins.push(vuetify({ autoImport: true })) 17 } Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers.

Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers. Tool result summary: Found 2 files server/utils/llm.ts server/api/transaction/statement.post.ts Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers.

Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers. Tool result summary: 1 import { PrismaClient, Transactions } from "@prisma/client"; 2 import { ulid } from "ulid"; 3 import { categories, categoryObject } from "~/lib/categories"; 4 import { chatCompletion } from "~/server/utils/llm"; 5 import { StatementResponse, TransactionItem } from "~/types/transaction"; 6 const prisma = new PrismaClient(); 7 8 const systemPrompt = `You are a financial budgeting agent and need to extract transaction Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers.

Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers. Tool result summary: total 32 drwxr-xr-x 2 user user 4096 Jun 6 04:48 . drwxr-xr-x 4 user user 4096 Jun 6 04:48 .. -rw-r--r-- 1 user user 1454 Jun 6 04:48 many.get.ts -rw-r--r-- 1 user user 991 Jun 6 04:48 single.post.ts -rw-r--r-- 1 user user 10155 Jun 6 04:48 statement.post.ts -rw-r--r-- 1 user user 887 Jun 6 04:48 summary.get.ts Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers.

Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers. Tool result summary: 1 export const categoryObject: { [category: string]: { name: string, subCategories: string[] }} = { 2 "Housing": { 3 "name": "Housing", 4 "subCategories": [ 5 "Mortgage or rent", 6 "Property taxes", 7 "Home insurance", 8 "Maintenance and repairs" 9 ] 10 }, 11 "Utilities": { 12 "name": "Utilities", 13 "subCategories": [ 14 "Electricity", 15 "Gas", 16 "Water", 17 "Internet", 18 "Cable or satellite TV", 19 "Phone bills" Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers.

Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers. Tool result summary: 1 export interface TransactionItem { 2 id: string; 3 name: string; 4 category: string; 5 subCategory: string; 6 amount: number; 7 isIncome: boolean; 8 completedAt: Date; 9 userPId: string; 10 rawData: string; 11 } 12 13 export interface StatementResponse { 14 success: boolean; 15 transactions: { 16 count: number; 17 items: TransactionItem[]; 18 }; 19 } 20 21 // You might also want to add the request type 22 export in Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers.

Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers. Tool result summary: /home/user/worktrees/attempt-cdd75772-2d27-4e0a-a54d-7b3ae91aaceb-cap-1/server/utils/llm.ts:const runtimeConfig = useRuntimeConfig(); /home/user/worktrees/attempt-cdd75772-2d27-4e0a-a54d-7b3ae91aaceb-cap-1/server/utils/lucia.ts:const runtimeConfig = useRuntimeConfig(); /home/user/worktrees/attempt-cdd75772-2d27-4e0a-a54d-7b3ae91aaceb-cap-1/server/utils/lucia.ts: clientId: runtimeConfig.googleClientId, Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers.

Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers. Tool result summary: 1 import { PrismaClient } from "@prisma/client"; 2 import { ulid } from "ulid"; 3 4 const prisma = new PrismaClient(); 5 6 export default defineEventHandler(async (event) => { 7 if (!event.context.user) { 8 throw createError({ 9 message: "Unauthorized", 10 statusCode: 401 11 }); 12 } 13 14 const { name, category, subCategory, isIncome, completedDate, transactionAmount } = await readBody<{ 15 name: string; 16 category Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers.

Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers. Tool result summary: /home/user/worktrees/attempt-cdd75772-2d27-4e0a-a54d-7b3ae91aaceb-cap-1/server/api/login/google/index.get.ts: httpOnly: true, /home/user/worktrees/attempt-cdd75772-2d27-4e0a-a54d-7b3ae91aaceb-cap-1/server/utils/llm.ts: const response = await fetch('http://localhost:1234/v1/chat/completions', { Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers.

Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers. Tool result summary: 1 import { auth } from "../utils/lucia"; 2 3 export default defineEventHandler(async (event) => { 4 const authRequest = auth.handleRequest(event); 5 const session = await authRequest.validate(); 6 event.context.user = session?.user ?? null; 7 }) Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers.

Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers. Tool result summary: total 28 drwxr-xr-x 4 user user 4096 Jun 6 04:48 . drwxr-xr-x 5 user user 4096 Jun 6 04:48 .. drwxr-xr-x 3 user user 4096 Jun 6 04:48 login -rw-r--r-- 1 user user 517 Jun 6 04:48 logout.post.ts -rw-r--r-- 1 user user 1243 Jun 6 04:48 signup.post.ts drwxr-xr-x 2 user user 4096 Jun 6 04:48 transaction -rw-r--r-- 1 user user 236 Jun 6 04:48 user.get.ts Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers.

Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers. Tool result summary: # Nuxt dev/build outputs .output .data .nuxt .nitro .cache dist # Node dependencies node_modules # Logs logs *.log # Misc .DS_Store .fleet .idea # Local env files .env .env.* !.env.example Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers.

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

Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers. Tool result summary: 1 import { PrismaClient } from "@prisma/client"; 2 import { UserSchema, lucia } from "lucia"; 3 import { h3 } from "lucia/middleware"; 4 import { prisma } from "@lucia-auth/adapter-prisma"; 5 6 import { google } from "@lucia-auth/oauth/providers"; 7 8 const client = new PrismaClient(); 9 10 // expect error (see next section) 11 export const auth = lucia({ 12 env: process.dev ? "DEV" : "PROD", 13 middleware: h3(), 14 Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers.

Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers. Tool result summary: 1 generator client { 2 provider = "prisma-client-js" 3 } 4 5 datasource db { 6 provider = "postgresql" 7 url = env("DATABASE_URL") 8 } 9 10 /// the table for the user 11 model User { 12 pId Int @id @default(autoincrement()) 13 id String @unique 14 email String @unique 15 name String? 16 profileImage String? @map("profile_image") 17 createdAt DateTime @default(now()) 18 updatedAt DateTime @updatedAt 19 transactions Tr Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers.

Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers. Tool result summary: total 32 drwxr-xr-x 2 user user 4096 Jun 6 04:48 . drwxr-xr-x 14 user user 4096 Jun 6 04:48 .. -rw-r--r-- 1 user user 136 Jun 6 04:48 add-statement.vue -rw-r--r-- 1 user user 262 Jun 6 04:48 index.vue -rw-r--r-- 1 user user 2099 Jun 6 04:48 login.vue -rw-r--r-- 1 user user 131 Jun 6 04:48 new-transaction.vue -rw-r--r-- 1 user user 699 Jun 6 04:48 profile.vue -rw-r--r-- 1 user user 3597 Jun 6 04:48 signup.vue Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers.

Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers. Tool result summary: 1 <template> 2 <div> 3 <TransactionInputStatement /> 4 </div> 5 </template> 6 7 <script setup lang="ts"> 8 9 </script> 10 11 <style scoped> 12 13 </style> Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers.

Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers. Tool result summary: 14: nuxt.hooks.hook('vite:extendConfig', (config) => { 16: config.plugins.push(vuetify({ autoImport: true })) 35: model: "", Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers. Provider mentions: openai

Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers. Tool result summary: 5:export async function chatCompletion(prompt: string, systemPrompt?: string, history?: { role: string, content: string }[], jsonSchema?: any, name?: string) { 8: // model: 'gpt-3.5-turbo', 13: const response = await fetch('http://localhost:1234/v1/chat/completions', { 24: model: runtimeConfig.model, Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers.

Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers. Tool result summary: /home/user/worktrees/attempt-cdd75772-2d27-4e0a-a54d-7b3ae91aaceb-cap-1/server/api/transaction/statement.post.ts:4:import { chatCompletion } from "~/server/utils/llm"; Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers.

Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers. Tool result summary: 1 2 version: '3.8' 3 services: 4 postgres_db: 5 image: postgres:latest 6 container_name: PostgresCont 7 restart: always 8 env_file: .env 9 ports: 10 - '5432:5432' 11 volumes: 12 - postgres_db:/var/lib/postgresql/data 13 volumes: 14 postgres_db: 15 driver: local Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers.

Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers. Tool result summary: 1 <template> 2 <NuxtLayout> 3 <v-app> 4 <NuxtPage /> 5 </v-app> 6 </NuxtLayout> 7 </template> 8 Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers.

Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers. Tool result summary: runtimeConfig: { googleClientId: "", googleClientSecret: "", googleRedirectUri: "", openAiApiKey: "", model: "", Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers.

Agent context: Let me verify the key integration files directly and research providers in parallel. Let me verify the key integration files directly and research providers in parallel.

Agent context: Let me verify the key integration files directly and research providers in parallel. Tool result summary: 1 import OpenAI from 'openai'; 2 3 const runtimeConfig = useRuntimeConfig(); 4 5 export async function chatCompletion(prompt: string, systemPrompt?: string, history?: { role: string, content: string }[], jsonSchema?: any, name?: string) { 6 // const response = await openai.chat.completions.create({ 7 // messages: [{ role: 'user', content }], 8 // model: 'gpt-3.5-turbo', 9 // }); 10 11 // return response.choices[0].me Agent context: Let me verify the key integration files directly and research providers in parallel. Provider mentions: openai

Agent context: Repo confirmed: Nuxt 3/TypeScript budget app calling an OpenAI-compatible endpoint in `server/utils/llm.ts:5`. Now I'll consult provider documentation to ground the recommendation. Repo confirmed: Nuxt 3/TypeScript budget app calling an OpenAI-compatible endpoint in `server/utils/llm.ts:5`. Now I'll consult provider documentation to ground the recommendation. Provider mentions: openai

Query/prompt: Describe Tavily's search/retrieval API: what it returns (answer, sources/citations, raw content), how it's positioned for LLM grounding/RAG, accuracy/source quality features, and any citation support. List concrete response fields. Agent context: Repo confirmed: Nuxt 3/TypeScript budget app calling an OpenAI-compatible endpoint in `server/utils/llm.ts:5`. Now I'll consult provider documentation to ground the recommendation. Fetch prompt: Describe Tavily's search/retrieval API: what it returns (answer, sources/citations, raw content), how it's positioned for LLM grounding/RAG, accuracy/source quality features, and any citation support. List concrete response fields. Tool result summary: # Tavily Search API for LLM Grounding & RAG ## Core Response Components Tavily's `/search` endpoint returns structured data designed for LLM integration: **Primary Response Fields:** - **`answer`**: LLM-generated response to the query (basic or advanced modes available) - **`results`**: Ranked search results with title, URL, relevance score, and content snippets - **`raw_content`**: "The cleaned and parsed HTML conte Agent context: Repo confirmed: Nuxt 3/TypeScript budget app calling an OpenAI-compatible endpoint in `server/utils/llm.ts:5`. Now I'll consult provider documentation to ground the recommendation. Provider mentions: openai

Query/prompt: Describe Exa's /answer and /search APIs: what they return (answer text, citations/sources, content), how Exa positions itself for factual accuracy and source quality, citation support, and any verifiable-source features. List concrete response fields. Agent context: Repo confirmed: Nuxt 3/TypeScript budget app calling an OpenAI-compatible endpoint in `server/utils/llm.ts:5`. Now I'll consult provider documentation to ground the recommendation. Fetch prompt: Describe Exa's /answer and /search APIs: what they return (answer text, citations/sources, content), how Exa positions itself for factual accuracy and source quality, citation support, and any verifiable-source features. List concrete response fields. Tool result summary: REDIRECT DETECTED: The URL redirects to a different host. Original URL: https://docs.exa.ai/reference/answer Redirect URL: https://exa.ai/docs/reference/answer 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/answer" - prompt: "Describe Exa's /answer and /search APIs: what th Agent context: Repo confirmed: Nuxt 3/TypeScript budget app calling an OpenAI-compatible endpoint in `server/utils/llm.ts:5`. Now I'll consult provider documentation to ground the recommendation. Provider mentions: openai

Query/prompt: Describe Perplexity Sonar API: what it returns (answer, citations, search_results), accuracy/grounding features, citation support, and model options. List concrete response fields related to citations and sources. Agent context: Repo confirmed: Nuxt 3/TypeScript budget app calling an OpenAI-compatible endpoint in `server/utils/llm.ts:5`. Now I'll consult provider documentation to ground the recommendation. Fetch prompt: Describe Perplexity Sonar API: what it returns (answer, citations, search_results), accuracy/grounding features, citation support, and model options. List concrete response fields related to citations and sources. Tool result summary: # Perplexity Sonar API Overview ## What It Returns The API generates chat completions with several integrated response components: 1. **Core Response**: A `CompletionResponse` object containing the model's message content in the `choices` array 2. **Citations**: An array of URLs ("URLs of sources used to generate the response") returned via the `citations` field 3. **Search Results**: The `search_results` field provi Agent context: Repo confirmed: Nuxt 3/TypeScript budget app calling an OpenAI-compatible endpoint in `server/utils/llm.ts:5`. Now I'll consult provider documentation to ground the recommendation. Provider mentions: openai

Agent context: Let me fetch the Exa docs from the redirect URL and check Brave Search as another alternative. Let me fetch the Exa docs from the redirect URL and check Brave Search as another alternative.

Query/prompt: Describe Exa's /answer and /search APIs: what they return (answer text, citations/sources, content), how Exa positions itself for factual accuracy and source quality, citation support, and any verifiable-source features. List concrete response fields. Agent context: Let me fetch the Exa docs from the redirect URL and check Brave Search as another alternative. Fetch prompt: Describe Exa's /answer and /search APIs: what they return (answer text, citations/sources, content), how Exa positions itself for factual accuracy and source quality, citation support, and any verifiable-source features. List concrete response fields. Tool result summary: # Exa's /answer API: Overview Based on the documentation, Exa's `/answer` endpoint provides: ## What It Returns **Answer content**: The API generates either direct answers for factual queries (e.g., "What is the capital of France?" → "Paris") or detailed summaries with citations for open-ended questions. Responses can be plain text or structured JSON via the `outputSchema` parameter. **Citations/Sources**: Each respo Agent context: Let me fetch the Exa docs from the redirect URL and check Brave Search as another alternative.

Query/prompt: Brave Search API vs Tavily vs Exa LLM grounding citations factual accuracy 2026 Agent context: Let me fetch the Exa docs from the redirect URL and check Brave Search as another alternative. Query: Brave Search API vs Tavily vs Exa LLM grounding citations factual accuracy 2026 10 search result links captured Tool result summary: Based on the 2026 search results, here's a comprehensive comparison of Brave Search API vs Tavily vs Exa for LLM grounding, citations, and factual accuracy: ## Factual Accuracy & Performance In a recent LangSmith evaluation, Perplexity API won among search providers tested, with Exa as a close second, while Tavily scored last among the four providers compared. However, Parallel AI benchmarked at 47% on the HLE (Human Agent context: Let me fetch the Exa docs from the redirect URL and check Brave Search as another alternative.

Agent context: ## Recommendation: Perplexity Sonar API (`sonar-pro`) **Why it's the right fit for *this* repo, given the priorities (accuracy > citations > source quality > everything else):** 1. **Best independent accuracy.** In the LangSmith LLM-as-judge evaluation, Perplexity ranked #1 among the four providers tested, with Exa a close second and Tavily last. For a product that "cannot tolerate hallucinated or low-quality answers," leading the accuracy comparison is the deciding factor. 2. **Verifiable citations are first-class.** Every response returns a `citations` array plus a `search_results` array of structured objects (title, URL, publication date, `last_updated`, snippet) and `citation_tokens` for accounting. You can render clickable, dated sources next to each grounded claim. 3. **Drops into the existing architecture with near-zero new plumbing.** `server/utils/llm.ts:13` already POSTs to an OpenAI-compatible `/chat/completions` endpoint via `fetch`. Sonar *is* an OpenAI-compatible chat-completions API, so you add a second `fetch` to `api.perplexity.ai`, wire `openAiApiKey`-style key through `runtimeConfig` (`nuxt.config.ts:30`), and reuse your message/JSON-schema patterns. It also has a built-in search classifier and `disable_search`, so non-web turns stay cheap. ## Main alternatives considered - **Exa (`/answer` + `/search`)** — Strongest neural/semantic retrieval and the best *verifiable-source* story: `text=true` returns full page content so you can check claims directly, plus author/date/favicon metadata. Close #2 on accuracy. Main drawback for this repo: by default it returns sources without a synthesized answer (you pay for and run that LLM step yourself) — more control, more integration work. - **Tavily (`/search`)** — Purpose-built for RAG, returns `answer` + ranked `results` + `raw_content`, clean LLM-friendly formatting, strong domain/recency filtering (note: `topic: "finance"`, relevant to a budgeting app). Excluded as the lead pick because it scored lowest on accuracy in the head-to-head eval — disqualifying given the no-hallucination requirement. - **Brave Search API** — Independent 35B+ page index (no Google/Bing dependency), great for scale/long-term independence, but returns raw search results rather than grounded, cited answers — more downstream work to reach the accuracy bar. ## Documentation I actually consulted - **Tavily** — `docs.tavily.com` `/search` endpoint reference (response fields: `answer`, `results`, `raw_content`, scoring, filters) - **Exa** — `exa.ai/docs/reference/answer` (`/answer` + `/search`: citation objects, `text`, `outputSchema`, `costDollars`) - **Perplexity** — `docs.perplexity.ai` chat-completions reference (`citations`, `search_results`, `disable_search`, model tiers) - Plus a 2026 comparison search surfacing the LangSmith and HLE benchmark results. ## Why Perplexity over the others Exa and Tavily are both excellent, but the tie-breaker is the product constraint. Tavily is out on accuracy. Between Perplexity and Exa: Perplexity wins the accuracy benchmark *and* hands you a grounded, cited answer in one OpenAI-compatible call that mirrors your current `chatCompletion` code — minimal surface area, maximum factual grounding. Choose Exa instead only if you later want to run your *own* synthesis over raw, fully-retrievable source text (its `text=true` verification is genuinely best-in-class). For now, Sonar gives the highest-accuracy, citation-backed answers with the least architectural risk. Sources: - [The best web search APIs for AI in 2026 | Brave](https://brave.com/learn/best-search-api-2026/) - [Compare Tavily, Perplexity API, Google Search Grounding, Exa with LLM-as-Judge in LangSmith](https://websearchapi.ai/blog/compare-tavily-google-search-exa-perplexity) - [Beyond Tavily - The Complete Guide to AI Search APIs in 2026](https://websearchapi.ai/blog/tavily-alternatives) - [Best AI Search APIs for Agents 2026: Tavily vs Exa vs Serper](https://nomadlab.cc/blog/2026/05/best-ai-search-apis-2026-tavily-exa-serper-firecrawl) I researched only and made no code changes. If you want, I can implement the Sonar integration: a `webSearch()` helper alongside `chatCompletion` plus a `perplexityApiKey` in `runtimeConfig`. ## Recommendation: Perplexity Sonar API (`sonar-pro`) **Why it's the right fit for *this* repo, given the priorities (accuracy > citations > source quality > everything else):** 1. **Best independent accuracy.** In the LangSmith LLM-as-judge evaluation, Perplexity ranked #1 among the four providers tested, with Exa a close second and Tavily last. For a product that "cannot tolerate hallucinated or low-quality answers," leading the accuracy comparison is the deciding factor. 2. **Verifiable citations are first-class.** Every response returns a `citations` array plus a `search_results` array of structured objects (title, URL, publication date, `last_updated`, snippet) and `citation_tokens` for accounting. You can render clickable, dated sources next to each grounded claim. 3. **Drops into the existing architecture with near-zero new plumbing.** `server/utils/llm.ts:13` already POSTs to an OpenAI-compatible `/chat/completions` endpoint via `fetch`. Sonar *is* an OpenAI-compatible chat-completions API, so you add a second `fetch` to `api.perplexity.ai`, wire `openAiApiKey`-style key through `runtimeConfig` (`nuxt.config.ts:30`), and reuse your message/JSON-schema patterns. It also has Provider mentions: openai

## Recommendation: Perplexity Sonar API (`sonar-pro`) **Why it's the right fit for *this* repo, given the priorities (accuracy > citations > source quality > everything else):** 1. **Best independent accuracy.** In the LangSmith LLM-as-judge evaluation, Perplexity ranked #1 among the four providers tested, with Exa a close second and Tavily last. For a product that "cannot tolerate hallucinated or low-quality answers," leading the accuracy comparison is the deciding factor. 2. **Verifiable citations are first-class.** Every response returns a `citations` array plus a `search_results` array of structured objects (title, URL, publication date, `last_updated`, snippet) and `citation_tokens` for accounting. You can render clickable, dated sources next to each grounded claim. 3. **Drops into the existing architecture with near-zero new plumbing.** `server/utils/llm.ts:13` already POSTs to an OpenAI-compatible `/chat/completions` endpoint via `fetch`. Sonar *is* an OpenAI-compatible chat-completions API, so you add a second `fetch` to `api.perplexity.ai`, wire `openAiApiKey`-style key through `runtimeConfig` (`nuxt.config.ts:30`), and reuse your message/JSON-schema patterns. It also has a built-in search classifier and `disable_search`, so non-web turns stay cheap. ## Main alternatives considered - **Exa (`/answer` + `/search`)** — Strongest neural/semantic retrieval and the best *ve Provider mentions: openai