Run

P0

9da97ce3ef437ebd56ed8eedaebc3dc37cec4ba9

Agent context: I'll research the repository structure first to understand the tech stack and how the agent is built. I'll research the repository structure first to understand the tech stack and how the agent is built.

Agent context: I'll research the repository structure first to understand the tech stack and how the agent is built. Tool result summary: /home/user/worktrees/attempt-4b9f7069-d999-423e-a91c-680afc1692fc-cap-0/README.md Agent context: I'll research the repository structure first to understand the tech stack and how the agent is built.

Agent context: I'll research the repository structure first to understand the tech stack and how the agent is built. 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 structure first to understand the tech stack and how the agent is built.

Agent context: I'll research the repository structure first to understand the tech stack and how the agent is built. 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 structure first to understand the tech stack and how the agent is built.

Agent context: I'll research the repository structure first to understand the tech stack and how the agent is built. 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 structure first to understand the tech stack and how the agent is built. Provider mentions: openai

Agent context: I'll research the repository structure first to understand the tech stack and how the agent is built. 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 structure first to understand the tech stack and how the agent is built.

Agent context: I'll research the repository structure first to understand the tech stack and how the agent is built. 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 structure first to understand the tech stack and how the agent is built.

Agent context: I'll research the repository structure first to understand the tech stack and how the agent is built. Tool result summary: /home/user/worktrees/attempt-4b9f7069-d999-423e-a91c-680afc1692fc-cap-0/server/api/login/google/callback.get.ts /home/user/worktrees/attempt-4b9f7069-d999-423e-a91c-680afc1692fc-cap-0/server/api/login/google/index.get.ts /home/user/worktrees/attempt-4b9f7069-d999-423e-a91c-680afc1692fc-cap-0/server/api/login/index.post.ts /home/user/worktrees/attempt-4b9f7069-d999-423e-a91c-680afc1692fc-cap-0/server/api/logout.post.t Agent context: I'll research the repository structure first to understand the tech stack and how the agent is built.

Agent context: I'll research the repository structure first to understand the tech stack and how the agent is built. Tool result summary: total 28 drwxr-xr-x 5 user user 4096 Jun 6 04:48 . drwxr-xr-x 14 user user 4096 Jun 6 04:48 .. drwxr-xr-x 4 user user 4096 Jun 6 04:48 api -rw-r--r-- 1 user user 271 Jun 6 04:48 app.d.ts drwxr-xr-x 2 user user 4096 Jun 6 04:48 middleware -rw-r--r-- 1 user user 49 Jun 6 04:48 tsconfig.json drwxr-xr-x 2 user user 4096 Jun 6 04:48 utils Agent context: I'll research the repository structure first to understand the tech stack and how the agent is built.

Agent context: I'll research the repository structure first to understand the tech stack and how the agent is built. Tool result summary: /home/user/worktrees/attempt-4b9f7069-d999-423e-a91c-680afc1692fc-cap-0/composables/auth.ts /home/user/worktrees/attempt-4b9f7069-d999-423e-a91c-680afc1692fc-cap-0/lib/categories.ts /home/user/worktrees/attempt-4b9f7069-d999-423e-a91c-680afc1692fc-cap-0/lib/colors.ts /home/user/worktrees/attempt-4b9f7069-d999-423e-a91c-680afc1692fc-cap-0/middleware/auth.global.ts /home/user/worktrees/attempt-4b9f7069-d999-423e-a91c-6 Agent context: I'll research the repository structure first to understand the tech stack and how the agent is built.

Agent context: I'll research the repository structure first to understand the tech stack and how the agent is built. 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 structure first to understand the tech stack and how the agent is built. Provider mentions: openai

Agent context: I'll research the repository structure first to understand the tech stack and how the agent is built. 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 structure first to understand the tech stack and how the agent is built.

Agent context: I'll research the repository structure first to understand the tech stack and how the agent is built. 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 structure first to understand the tech stack and how the agent is built.

Agent context: I'll research the repository structure first to understand the tech stack and how the agent is built. Tool result summary: 1 import { PrismaClient } from "@prisma/client"; 2 3 const prisma = new PrismaClient(); 4 5 export default defineEventHandler(async (event) => { 6 if (!event.context.user) { 7 throw createError({ 8 message: "Unauthorized", 9 statusCode: 401 10 }); 11 } 12 13 const currentDate = new Date(); 14 const firstDayOfMonth = new Date(currentDate.getFullYear(), currentDate.getMonth(), 1); 15 16 const transactionSums = await pr Agent context: I'll research the repository structure first to understand the tech stack and how the agent is built.

Agent context: I'll research the repository structure first to understand the tech stack and how the agent is built. 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 structure first to understand the tech stack and how the agent is built.

Agent context: I'll research the repository structure first to understand the tech stack and how the agent is built. Tool result summary: No .env files found Agent context: I'll research the repository structure first to understand the tech stack and how the agent is built.

Agent context: I'll research the repository structure first to understand the tech stack and how the agent is built. Tool result summary: version: '3.8' services: postgres_db: image: postgres:latest container_name: PostgresCont restart: always env_file: .env ports: - '5432:5432' volumes: - postgres_db:/var/lib/postgresql/data volumes: postgres_db: driver: local Agent context: I'll research the repository structure first to understand the tech stack and how the agent is built.

Agent context: I'll research the repository structure first to understand the tech stack and how the agent is built. Tool result summary: shamefully-hoist=true strict-peer-dependencies=false Agent context: I'll research the repository structure first to understand the tech stack and how the agent is built.

Agent context: I'll research the repository structure first to understand the tech stack and how the agent is built. 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 structure first to understand the tech stack and how the agent is built.

Agent context: I'll research the repository structure first to understand the tech stack and how the agent is built. Tool result summary: Found 3 files middleware/auth.global.ts server/utils/llm.ts server/utils/lucia.ts Agent context: I'll research the repository structure first to understand the tech stack and how the agent is built.

Agent context: I'll research the repository structure first to understand the tech stack and how the agent is built. 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 structure first to understand the tech stack and how the agent is built.

Agent context: I'll research the repository structure first to understand the tech stack and how the agent is built. Tool result summary: 1 import { googleAuth } from "../../../utils/lucia"; 2 3 // server/api/login/github/index.get.ts 4 export default defineEventHandler(async (event) => { 5 const [url, state] = await googleAuth.getAuthorizationUrl(); 6 setCookie(event, "google_oauth_state", state, { 7 httpOnly: true, 8 secure: !process.dev, 9 path: "/", 10 maxAge: 60 * 60 11 }); 12 return sendRedirect(event, url.toString()); 13 }); 14 Agent context: I'll research the repository structure first to understand the tech stack and how the agent is built.

Agent context: I'll research the repository structure first to understand the tech stack and how the agent is built. Tool result summary: 1 import { PrismaClient } from "@prisma/client"; 2 3 const prisma = new PrismaClient(); 4 5 export default defineEventHandler(async (event) => { 6 if (!event.context.user) { 7 throw createError({ 8 message: "Unauthorized", 9 statusCode: 401 10 }); 11 } 12 13 const query = getQuery(event); 14 15 const category = !query.category || query.category === "All" ? undefined : query.category as string; 16 17 const currentDate Agent context: I'll research the repository structure first to understand the tech stack and how the agent is built.

Agent context: I'll research the repository structure first to understand the tech stack and how the agent is built. Tool result summary: total 16 drwxr-xr-x 4 user user 4096 Jun 6 04:48 . drwxr-xr-x 14 user user 4096 Jun 6 04:48 .. drwxr-xr-x 2 user user 4096 Jun 6 04:48 Dashboard drwxr-xr-x 2 user user 4096 Jun 6 04:48 Transaction Agent context: I'll research the repository structure first to understand the tech stack and how the agent is built.

Agent context: I'll research the repository structure first to understand the tech stack and how the agent is built. 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 structure first to understand the tech stack and how the agent is built.

Agent context: I'll research the repository structure first to understand the tech stack and how the agent is built. Tool result summary: /home/user/worktrees/attempt-4b9f7069-d999-423e-a91c-680afc1692fc-cap-0/nuxt.config.ts: runtimeConfig: { /home/user/worktrees/attempt-4b9f7069-d999-423e-a91c-680afc1692fc-cap-0/nuxt.config.ts: openAiApiKey: "", /home/user/worktrees/attempt-4b9f7069-d999-423e-a91c-680afc1692fc-cap-0/server/utils/llm.ts:const runtimeConfig = useRuntimeConfig(); /home/user/worktrees/attempt-4b9f7069-d999-423e-a91c-680afc1692fc-cap-0/ser Agent context: I'll research the repository structure first to understand the tech stack and how the agent is built. Provider mentions: openai

Agent context: I'll research the repository structure first to understand the tech stack and how the agent is built. Tool result summary: total 20 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 2173 Jun 6 04:48 inputForm.vue -rw-r--r-- 1 user user 4164 Jun 6 04:48 inputStatement.vue Agent context: I'll research the repository structure first to understand the tech stack and how the agent is built.

Agent context: I'll research the repository structure first to understand the tech stack and how the agent is built. Tool result summary: total 20 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 3413 Jun 6 04:48 Details.vue -rw-r--r-- 1 user user 4705 Jun 6 04:48 Pie.vue Agent context: I'll research the repository structure first to understand the tech stack and how the agent is built.

Agent context: I'll research the repository structure first to understand the tech stack and how the agent is built. 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 structure first to understand the tech stack and how the agent is built.

Agent context: I'll research the repository structure first to understand the tech stack and how the agent is built. Tool result summary: 1 <template> 2 <v-form @submit.prevent="submitForm"> 3 <v-container> 4 <v-row> 5 <h3>Input Transactions From Bill or Statement</h3> 6 </v-row> 7 <v-row> 8 <v-select 9 v-model="statementType" 10 label="Statement Type" 11 :items="[ 12 { title: 'Credit Card', value: 'credit-card' }, 13 { title: 'Bank', value: 'bank' } 14 ]" 15 item-title="title" 16 item-value="value" 17 /> 18 </v-row> 19 <v-row> 20 <v-file-input 21 v-mo Agent context: I'll research the repository structure first to understand the tech stack and how the agent is built.

Agent context: I'll research the repository structure first to understand the tech stack and how the agent is built. Tool result summary: 1 <template> 2 <v-form @submit.prevent="submitForm"> 3 <v-container> 4 <v-row> 5 <v-col cols="12" md="6"> 6 <v-text-field v-model="formData.name" label="Name" required></v-text-field> 7 <v-select 8 label="Category" 9 v-model="formData.category" 10 :items="categories" 11 required 12 ></v-select> 13 </v-col> 14 <v-col cols="12" md="6"> 15 <v-text-field v-model="formData.transactionAmount" label="Transaction Amount" req Agent context: I'll research the repository structure first to understand the tech stack and how the agent is built.

Agent context: I'll research the repository structure first to understand the tech stack and how the agent is built. 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 structure first to understand the tech stack and how the agent is built.

Agent context: I'll research the repository structure first to understand the tech stack and how the agent is built. Tool result summary: 1 import { Prisma, PrismaClient } from "@prisma/client"; 2 import { PrismaClientValidationError } from "@prisma/client/runtime/library"; 3 4 export default defineEventHandler(async (event) => { 5 const { name, email, password } = await readBody<{ 6 name: string; 7 email: string; 8 password: string; 9 }>(event); 10 11 // create user 12 try { 13 const user = await auth.createUser({ 14 key: { 15 providerId: "email", // Agent context: I'll research the repository structure first to understand the tech stack and how the agent is built.

Agent context: I'll research the repository structure first to understand the tech stack and how the agent is built. Tool result summary: 1 import { OAuthRequestError } from "@lucia-auth/oauth"; 2 3 import { auth, googleAuth } from "../../../utils/lucia"; 4 5 export default defineEventHandler(async (event) => { 6 const storedState = getCookie(event, "google_oauth_state"); 7 const query = getQuery(event); 8 const state = query.state?.toString(); 9 const code = query.code?.toString(); 10 // validate state 11 if (!storedState || !state || storedState !== Agent context: I'll research the repository structure first to understand the tech stack and how the agent is built.

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

Agent context: I'll research the repository structure first to understand the tech stack and how the agent is built. 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 structure first to understand the tech stack and how the agent is built.

Agent context: I'll research the repository structure first to understand the tech stack and how the agent is built. Tool result summary: commit 9da97ce3ef437ebd56ed8eedaebc3dc37cec4ba9 Author: aronm99 <markanda@mcmaster.ca> Date: Mon Feb 10 22:51:24 2025 -0500 update llm call to get values diff --git a/components/Transaction/inputStatement.vue b/components/Transaction/inputStatement.vue index a536197..3ee9b88 100644 --- a/components/Transaction/inputStatement.vue +++ b/components/Transaction/inputStatement.vue @@ -20,10 +20,30 @@ <v-file-input v-model Agent context: I'll research the repository structure first to understand the tech stack and how the agent is built.

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

Agent context: I'll research the repository structure first to understand the tech stack and how the agent is built. Tool result summary: 11560 /home/user/worktrees/attempt-4b9f7069-d999-423e-a91c-680afc1692fc-cap-0/package-lock.json 303 /home/user/worktrees/attempt-4b9f7069-d999-423e-a91c-680afc1692fc-cap-0/lib/colors.ts 283 /home/user/worktrees/attempt-4b9f7069-d999-423e-a91c-680afc1692fc-cap-0/server/api/transaction/statement.post.ts 133 /home/user/worktrees/attempt-4b9f7069-d999-423e-a91c-680afc1692fc-cap-0/lib/categories.ts 62 /home/user/worktrees Agent context: I'll research the repository structure first to understand the tech stack and how the agent is built.

Agent context: This is a research/recommendation task. Let me load web tools and consult provider documentation directly. This is a research/recommendation task. Let me load web tools and consult provider documentation directly.

Agent context: Let me consult the documentation of the leading candidates in parallel. Let me consult the documentation of the leading candidates in parallel.

Query/prompt: Extract details about the Tavily Search API: what it does, endpoint, request/response format, pricing/free tier, whether it's designed for LLMs/RAG/agents, and key features like answer generation, raw content, and ease of integration. Agent context: Let me consult the documentation of the leading candidates in parallel. Fetch prompt: Extract details about the Tavily Search API: what it does, endpoint, request/response format, pricing/free tier, whether it's designed for LLMs/RAG/agents, and key features like answer generation, raw content, and ease of integration. Tool result summary: # Tavily Search API Overview ## Purpose & Design Tavily Search is explicitly designed as "a powerful search engine for LLM agents." It provides REST API access to search functionality optimized for AI systems building retrieval-augmented generation (RAG) applications. ## Endpoint **POST /search** at `https://api.tavily.com/` ## Request Format The API requires a JSON payload with: - **Required**: `query` (search strin Agent context: Let me consult the documentation of the leading candidates in parallel.

Query/prompt: Extract details about the Exa API: what it does, search/answer endpoints, request/response format, pricing/free tier, whether it's designed for LLMs/RAG/agents, neural/semantic search, and ease of integration. Agent context: Let me consult the documentation of the leading candidates in parallel. Fetch prompt: Extract details about the Exa API: what it does, search/answer endpoints, request/response format, pricing/free tier, whether it's designed for LLMs/RAG/agents, neural/semantic search, and ease of integration. 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 details about Agent context: Let me consult the documentation of the leading candidates in parallel.

Query/prompt: Extract details about Brave Search API: what it does, endpoint, request/response format, pricing/free tier, rate limits, and whether it's designed for grounding LLM answers. Agent context: Let me consult the documentation of the leading candidates in parallel. Fetch prompt: Extract details about Brave Search API: what it does, endpoint, request/response format, pricing/free tier, rate limits, and whether it's designed for grounding LLM answers. Tool result summary: # Brave Search API Overview ## What It Does Brave Search API provides access to "a comprehensive index of web pages" enabling retrieval of relevant results across the internet. The service crawls and indexes billions of pages with regularly updated content. ## Primary Endpoint The main Web Search endpoint is `https://api.search.brave.com/res/v1/web/search` ## Request/Response Format **Request:** Query parameters incl Agent context: Let me consult the documentation of the leading candidates in parallel.

Query/prompt: Extract details about Perplexity Sonar API: what it does, endpoint, request/response format, pricing, models, citations, and whether it's designed for grounding answers with live web search. Agent context: Let me consult the documentation of the leading candidates in parallel. Fetch prompt: Extract details about Perplexity Sonar API: what it does, endpoint, request/response format, pricing, models, citations, and whether it's designed for grounding answers with live web search. Tool result summary: Request failed with status code 404 Agent context: Let me consult the documentation of the leading candidates in parallel.

Agent context: Let me fetch the redirected Exa docs and the correct Perplexity docs page. Let me fetch the redirected Exa docs and the correct Perplexity docs page.

Query/prompt: Extract details about the Exa API: what it does, search and answer endpoints, request/response format, pricing/free tier, whether it's designed for LLMs/RAG/agents, neural/semantic search, and ease of integration (SDKs). Agent context: Let me fetch the redirected Exa docs and the correct Perplexity docs page. Fetch prompt: Extract details about the Exa API: what it does, search and answer endpoints, request/response format, pricing/free tier, whether it's designed for LLMs/RAG/agents, neural/semantic search, and ease of integration (SDKs). Tool result summary: # Exa API Overview **What It Does:** "Exa is a search engine made for AIs." It provides four core functionalities for web content discovery and processing. **Key Endpoints:** - **/search**: Uses "embeddings-based search and other intelligent methods" to find webpages - **/contents**: Delivers "clean, up-to-date, parsed HTML from Exa search results" - **/answer**: Provides direct question answers through Exa's Answer Agent context: Let me fetch the redirected Exa docs and the correct Perplexity docs page.

Query/prompt: Extract details about the Perplexity Sonar API: what it does, models, endpoint, request/response format, pricing, citations, and whether it grounds answers with live web search. Agent context: Let me fetch the redirected Exa docs and the correct Perplexity docs page. Fetch prompt: Extract details about the Perplexity Sonar API: what it does, models, endpoint, request/response format, pricing, citations, and whether it grounds answers with live web search. Tool result summary: # Perplexity API Overview Based on the provided documentation, here's what I found about Perplexity's APIs: ## What It Does The platform provides "real-time, web-wide research and Q&A capabilities" through multiple integrated services. ## Available APIs The documentation highlights three main offerings: 1. **Agent API** - Enables access to third-party models with web search integration and preset configurations 2. ** Agent context: Let me fetch the redirected Exa docs and the correct Perplexity docs page. Provider mentions: openai

Agent context: Let me quickly confirm Tavily's free tier and JS/TS SDK, since pricing matters for the recommendation. Let me quickly confirm Tavily's free tier and JS/TS SDK, since pricing matters for the recommendation.

Query/prompt: Tavily search API free tier credits per month JavaScript SDK 2026 Agent context: Let me quickly confirm Tavily's free tier and JS/TS SDK, since pricing matters for the recommendation. Query: Tavily search API free tier credits per month JavaScript SDK 2026 10 search result links captured Tool result summary: Based on the search results, here's what I found about Tavily's free tier API credits: ## Free Tier Credits You get 1,000 free API Credits every month. No credit card required. ## Usage Examples With the 1,000 monthly credits, here's what you can do: - Basic Search (basic): Each request costs 1 API credit. - Advanced Search (advanced): Each request costs 2 API credits. - Basic Extract (basic): Every 5 successful URL Agent context: Let me quickly confirm Tavily's free tier and JS/TS SDK, since pricing matters for the recommendation.

Agent context: ## Recommendation: Tavily **Chosen provider: Tavily Search API** For this repo — a Nuxt 3 / TypeScript app whose agent (`server/utils/llm.ts`) calls an OpenAI-compatible LLM and parses JSON-schema output — Tavily is the cleanest fit: - **Purpose-built for LLM grounding/RAG**, not human SERPs. A single `POST /search` returns ranked results *plus* cleaned markdown content *and* an optional LLM-ready synthesized `answer` — drop-in context for your existing prompt flow with minimal new code. - **Native JS/TS SDK** + plain Bearer-token REST, matching the repo's existing `fetch`-based server-route pattern; the unused `openAiApiKey` slot in `nuxt.config.ts` runtimeConfig is the obvious home for a `tavilyApiKey`. - **Free tier**: 1,000 credits/month, no card — enough to prototype before any spend. **Alternatives considered:** - **Exa** — excellent neural/semantic search with `/answer` and `/research`; strongest if you needed embeddings-style retrieval, but more pieces to wire (search → contents) for the same turnkey "answer + sources." - **Brave Search API** — cheap, broad index, but the standard endpoint targets human consumption (its own docs point AI users to a separate "LLM Context" endpoint); more post-processing to feed the model. - **Perplexity Sonar** — fully managed answer engine with citations, but it *replaces* reasoning with its own model rather than feeding your local LLM, duplicating what your agent already does. **Why Tavily over the others:** it delivers retrieval *and* LLM-ready content/answers from one endpoint with the least integration surface against your current architecture, plus a real free tier — best effort-to-value for grounding this specific agent. **Docs I actually consulted:** Tavily (`/search` reference + credits/pricing), Exa (`getting-started` reference), Brave Search API (get-started), and Perplexity (overview). Perplexity's chat-completions page 404'd, so I used its overview page instead. Want me to implement the Tavily integration (add `tavilyWebSearch` util + runtimeConfig key, and wire it into the agent)? Sources: - [Tavily Search API reference](https://docs.tavily.com/documentation/api-reference/endpoint/search) - [Tavily Credits & Pricing](https://docs.tavily.com/documentation/api-credits) - [Exa API 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 API overview](https://docs.perplexity.ai/getting-started/overview) ## Recommendation: Tavily **Chosen provider: Tavily Search API** For this repo — a Nuxt 3 / TypeScript app whose agent (`server/utils/llm.ts`) calls an OpenAI-compatible LLM and parses JSON-schema output — Tavily is the cleanest fit: - **Purpose-built for LLM grounding/RAG**, not human SERPs. A single `POST /search` returns ranked results *plus* cleaned markdown content *and* an optional LLM-ready synthesized `answer` — drop-in context for your existing prompt flow with minimal new code. - **Native JS/TS SDK** + plain Bearer-token REST, matching the repo's existing `fetch`-based server-route pattern; the unused `openAiApiKey` slot in `nuxt.config.ts` runtimeConfig is the obvious home for a `tavilyApiKey`. - **Free tier**: 1,000 credits/month, no card — enough to prototype before any spend. **Alternatives considered:** - **Exa** — excellent neural/semantic search with `/answer` and `/research`; strongest if you needed embeddings-style retrieval, but more pieces to wire (search → contents) for the same turnkey "answer + sources." - **Brave Search API** — cheap, broad index, but the standard endpoint targets human consumption (its own docs point AI users to a separate "LLM Context" en Provider mentions: openai

## Recommendation: Tavily **Chosen provider: Tavily Search API** For this repo — a Nuxt 3 / TypeScript app whose agent (`server/utils/llm.ts`) calls an OpenAI-compatible LLM and parses JSON-schema output — Tavily is the cleanest fit: - **Purpose-built for LLM grounding/RAG**, not human SERPs. A single `POST /search` returns ranked results *plus* cleaned markdown content *and* an optional LLM-ready synthesized `answer` — drop-in context for your existing prompt flow with minimal new code. - **Native JS/TS SDK** + plain Bearer-token REST, matching the repo's existing `fetch`-based server-route pattern; the unused `openAiApiKey` slot in `nuxt.config.ts` runtimeConfig is the obvious home for a `tavilyApiKey`. - **Free tier**: 1,000 credits/month, no card — enough to prototype before any spend. **Alternatives considered:** - **Exa** — excellent neural/semantic search with `/answer` and `/research`; strongest if you needed embeddings-style retrieval, but more pieces to wire (search → contents) for the same turnkey "answer + sources." - **Brave Search API** — cheap, broad index, but the standard endpoint targets human consumption (its own docs point AI users to a separate "LLM Context" endpoint); more post-processing to feed the model. - **Perplexity Sonar** — fully managed answer engine with citations, but it *replaces* reasoning with its own model rather than feeding your local LLM, Provider mentions: openai