Run

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6b010650109bf32003ff0e0910127d6cfd5d23a5

Agent context: I’ll inspect the repo first to understand what kind of agent this is and where web content ingestion would fit, then I’ll check current provider docs before making a recommendation. I’ll inspect the repo first to understand what kind of agent this is and where web content ingestion would fit, then I’ll check current provider docs before making a recommendation.

Agent context: I’ll inspect the repo first to understand what kind of agent this is and where web content ingestion would fit, then I’ll check current provider docs before making a recommendation.

Agent context: I’ll inspect the repo first to understand what kind of agent this is and where web content ingestion would fit, then I’ll check current provider docs before making a recommendation. exit code 0 Output: /home/user/worktrees/attempt-460f8564-0609-44c6-acc1-3447c04d6125-cap-0 /bin/bash: line 1: rg: command not found

Agent context: I’ll inspect the repo first to understand what kind of agent this is and where web content ingestion would fit, then I’ll check current provider docs before making a recommendation.

Agent context: I’ll inspect the repo first to understand what kind of agent this is and where web content ingestion would fit, then I’ll check current provider docs before making a recommendation. exit code 0

Agent context: `rg` is not installed in this environment, so I’m falling back to `find`/`grep`. The worktree is clean so far. `rg` is not installed in this environment, so I’m falling back to `find`/`grep`. The worktree is clean so far.

Agent context: `rg` is not installed in this environment, so I’m falling back to `find`/`grep`. The worktree is clean so far.

Agent context: `rg` is not installed in this environment, so I’m falling back to `find`/`grep`. The worktree is clean so far. exit code 0 Output: .env.example .eslintrc.json .gitignore Github.png LICENSE README.md firebaseConfig.js next.config.mjs package.json postcss.config.js public/Apple-Icon.png public/Logo.png public/Logo.svg public/OGImage.png public/favicon.ico public/lottie/Audio.json public/lottie/Love.json public/svgs/Arrow.svg public/svgs/Bin.svg public/svgs/Check.svg public/svgs/Clip.svg public/svgs/Copy.svg public/svgs/Cross.svg public/svgs/CrossRed.svg public/svgs/CrossWhite.svg public/svgs/Doc.svg public/svgs/File.svg public/svgs/FileActive.svg public/svgs/Filter.svg public/svgs/Folder.svg public/svgs/Fork.svg public/svgs/Google.svg public/svgs/Info.svg public/svgs/Link.svg public/svgs/Menu.svg public/svgs/NextArrow.svg public/svgs/Pen.svg public/svgs/Picture.svg public/svgs/Play.svg public/svgs/Plus.svg public/svgs/PrevArrow.svg public/svgs/Redirect.svg public/svgs/Retry.svg public/svgs/Return.svg public/svgs/Rewri

Agent context: `rg` is not installed in this environment, so I’m falling back to `find`/`grep`. The worktree is clean so far.

Agent context: `rg` is not installed in this environment, so I’m falling back to `find`/`grep`. The worktree is clean so far. exit code 0 Output: ./README.md ./package.json

Agent context: The repo is a Next.js app with existing API routes for chat/search/scrape. I’m going to read those next because the best provider choice depends on whether this is just search snippets, browser automation, or clean article extraction for agent context. The repo is a Next.js app with existing API routes for chat/search/scrape. I’m going to read those next because the best provider choice depends on whether this is just search snippets, browser automation, or clean article extraction for agent context.

Agent context: The repo is a Next.js app with existing API routes for chat/search/scrape. I’m going to read those next because the best provider choice depends on whether this is just search snippets, browser automation, or clean article extraction for agent context.

Agent context: The repo is a Next.js app with existing API routes for chat/search/scrape. I’m going to read those next because the best provider choice depends on whether this is just search snippets, browser automation, or clean article extraction for agent context. exit code 0 Output:  <p align="center"> <h1 align="center"><b>Omniplex</b></h1> <p align="center"> Open-Source Perplexity <br /> <br /> <a href="https://omniplex.ai">Website</a> · <a href="https://discord.gg/87Mh7q5ZSd">Discord</a> · <a href="https://www.reddit.com/r/omniplex_ai">Reddit</a> </p> </p> # :construction: Under Active Development > Our focus is on establishing core functionality and essential features. As we continue to develop Omniplex, we are committed to implementing best practices, refining the codebase, and introducing new features to enhance the user experience. ## Get started To run the project, modify the code in the Chat component to use the `// Development Code`. 1. Fork & Clone the repository ```bash git clone git@github.com:[YOUR_GITHUB_ACCOUNT]/omniplex.git ``` 2. Install the dependencies ```bash yarn ``` 3. Fill out secrets in `.env.local` ```bash BING_API_KEY= O Provider mentions: openai

Agent context: The repo is a Next.js app with existing API routes for chat/search/scrape. I’m going to read those next because the best provider choice depends on whether this is just search snippets, browser automation, or clean article extraction for agent context.

Agent context: The repo is a Next.js app with existing API routes for chat/search/scrape. I’m going to read those next because the best provider choice depends on whether this is just search snippets, browser automation, or clean article extraction for agent context. exit code 0 Output: import { NextRequest, NextResponse } from "next/server"; const BING_API_KEY = process.env.BING_API_KEY; const BING_SEARCH_URL = "https://api.bing.microsoft.com/v7.0/search"; export const runtime = "edge"; export async function GET(req: NextRequest) { const { searchParams } = new URL(req.url); const q = searchParams.get("q"); if (!q || typeof q !== "string") { return new NextResponse( JSON.stringify({ message: 'Query parameter "q" is required and must be a string.', }), { status: 400 } ); } if (!BING_API_KEY) { console.error( "Bing API key is undefined. Please check your .env.local file." ); return new NextResponse( JSON.stringify({ message: "Bing API key is not configured." }), { status: 500 } ); } try { const response = await fetch( `${BING_SEARCH_URL}?q=${encodeURIComponent(q)}`, { method: "GET", headers: new Headers({ "Ocp-Apim-Subscription-Key": BING_API_KEY, }), } ); if (!response.o

Agent context: The repo is a Next.js app with existing API routes for chat/search/scrape. I’m going to read those next because the best provider choice depends on whether this is just search snippets, browser automation, or clean article extraction for agent context.

Agent context: The repo is a Next.js app with existing API routes for chat/search/scrape. I’m going to read those next because the best provider choice depends on whether this is just search snippets, browser automation, or clean article extraction for agent context. exit code 0 Output: import type { NextRequest } from "next/server"; export const runtime = "edge"; async function scrapeText(url: string): Promise<string> { try { const response = await fetch(url); if (!response.ok) { throw new Error(`HTTP error! status: ${response.status}`); } const html = await response.text(); const text = extractBodyText(html); return text; } catch (error) { console.error(`Error fetching URL ${url}:`, error); return ""; } } function extractBodyText(html: string): string { const bodyStartTag = "<body"; const bodyEndTag = "</body>"; const bodyStartIndex = html.indexOf(bodyStartTag); const bodyEndIndex = html.indexOf(bodyEndTag, bodyStartIndex); if (bodyStartIndex !== -1 && bodyEndIndex !== -1) { const bodyContent = html.slice( bodyStartIndex, bodyEndIndex + bodyEndTag.length ); const bodyText = bodyContent .replace(/<script[\s\S]*?<\/script>/gi, "") .replace(/<style[\s\S]*?<\/style>/gi, "

Agent context: The repo is a Next.js app with existing API routes for chat/search/scrape. I’m going to read those next because the best provider choice depends on whether this is just search snippets, browser automation, or clean article extraction for agent context.

Agent context: The repo is a Next.js app with existing API routes for chat/search/scrape. I’m going to read those next because the best provider choice depends on whether this is just search snippets, browser automation, or clean article extraction for agent context. exit code 0 Output: { "name": "omniplex", "version": "1.0.0", "private": true, "scripts": { "dev": "next dev", "build": "next build", "start": "next start", "lint": "next lint" }, "dependencies": { "@firebase/firestore": "^4.5.0", "@firebase/storage": "^0.12.2", "@headlessui/react": "^2.0.3", "@headlessui/tailwindcss": "^0.2.0", "@heroicons/react": "^2.1.1", "@lottiefiles/react-lottie-player": "^3.5.3", "@nextui-org/react": "^2.2.10", "@reduxjs/toolkit": "^2.2.3", "@remixicon/react": "^4.2.0", "@tremor/react": "^3.16.3", "@types/react-redux": "^7.1.33", "@vercel/analytics": "^1.2.2", "@vercel/og": "^0.6.2", "@vercel/speed-insights": "^1.0.10", "ai": "^3.0.12", "firebase": "^10.9.0", "formidable": "^3.5.1", "framer-motion": "^11.0.15", "googleapis": "^134.0.0", "katex": "^0.16.10", "nanoid": "^5.0.6", "next": "14.1.3", "openai": "^4.29.1", "react": "^18", "react-dom": "^18", "react-firebase-hooks": "^5.1.1", Provider mentions: openai, modal

Agent context: There is already a `/api/scrape` route, but it is a very thin `fetch` plus regex body stripping capped at 5,000 chars. I’m checking how chat/search call it so the recommendation is grounded in the actual workflow, not just the route name. There is already a `/api/scrape` route, but it is a very thin `fetch` plus regex body stripping capped at 5,000 chars. I’m checking how chat/search call it so the recommendation is grounded in the actual workflow, not just the route name.

Agent context: There is already a `/api/scrape` route, but it is a very thin `fetch` plus regex body stripping capped at 5,000 chars. I’m checking how chat/search call it so the recommendation is grounded in the actual workflow, not just the route name.

Agent context: There is already a `/api/scrape` route, but it is a very thin `fetch` plus regex body stripping capped at 5,000 chars. I’m checking how chat/search call it so the recommendation is grounded in the actual workflow, not just the route name. exit code 0 Output: import OpenAI from "openai"; import { OpenAIStream, StreamingTextResponse } from "ai"; const openai = new OpenAI({ apiKey: process.env.OPENAI_API_KEY, }); export const runtime = "edge"; export async function POST(req: Request) { const { messages, model, temperature, max_tokens, top_p, frequency_penalty, presence_penalty, } = await req.json(); const response = await openai.chat.completions.create({ stream: true, model: model, temperature: temperature, max_tokens: max_tokens, top_p: top_p, frequency_penalty: frequency_penalty, presence_penalty: presence_penalty, messages: messages, }); const stream = OpenAIStream(response); return new StreamingTextResponse(stream); } Provider mentions: openai

Agent context: There is already a `/api/scrape` route, but it is a very thin `fetch` plus regex body stripping capped at 5,000 chars. I’m checking how chat/search call it so the recommendation is grounded in the actual workflow, not just the route name.

Agent context: There is already a `/api/scrape` route, but it is a very thin `fetch` plus regex body stripping capped at 5,000 chars. I’m checking how chat/search call it so the recommendation is grounded in the actual workflow, not just the route name. exit code 0 Output: import { useState } from "react"; import { useDispatch, useSelector } from "react-redux"; import { updateAnswer, addMessage, updateMessage, selectChatThread, } from "@/store/chatSlice"; import { Chat as ChatType, ChatThread, Message } from "../utils/types"; import { getInitialMessages } from "../utils/utils"; import { selectUserDetailsState } from "@/store/authSlice"; import { selectAI } from "@/store/aiSlice"; import { store } from "@/store/store"; import { doc, updateDoc } from "@firebase/firestore"; import { db } from "../../firebaseConfig"; type UseChatAnswerProps = { threadId: string; chatThread: ChatThread; setError: (error: string) => void; setErrorFunction: (fn: Function | null) => void; setIsStreaming: (isStreaming: boolean) => void; setIsLoading: (isLoading: boolean) => void; setIsCompleted: (isCompleted: boolean) => void; }; const useChatAnswer = ({ threadId, chatThread, setEr

Agent context: There is already a `/api/scrape` route, but it is a very thin `fetch` plus regex body stripping capped at 5,000 chars. I’m checking how chat/search call it so the recommendation is grounded in the actual workflow, not just the route name.

Agent context: There is already a `/api/scrape` route, but it is a very thin `fetch` plus regex body stripping capped at 5,000 chars. I’m checking how chat/search call it so the recommendation is grounded in the actual workflow, not just the route name. exit code 0 Output: src/app/api/dictionary/route.ts:4: const { searchParams } = new URL(req.url); src/app/api/dictionary/route.ts:5: const word = searchParams.get("word"); src/app/api/favicon/route.ts:6: const { searchParams } = new URL(req.url); src/app/api/favicon/route.ts:7: const url = searchParams.get("url"); src/app/api/og/route.tsx:8: const { searchParams } = new URL(request.url); src/app/api/og/route.tsx:9: const id = searchParams.get("id") || "none"; src/app/api/scrape/route.ts:5:async function scrapeText(url: string): Promise<string> { src/app/api/scrape/route.ts:44: const urlParams = new URL(req.url).searchParams; src/app/api/scrape/route.ts:48: JSON.stringify({ error: "Please provide URLs to scrape" }), src/app/api/scrape/route.ts:61: const text = await scrapeText(url); src/app/api/scrape/route.ts:67: .map((result, index) => `${urls[index]}\nWebsite data: ${result}`) src/app/api/scrape/route.ts: Provider mentions: together-ai

Agent context: There is already a `/api/scrape` route, but it is a very thin `fetch` plus regex body stripping capped at 5,000 chars. I’m checking how chat/search call it so the recommendation is grounded in the actual workflow, not just the route name.

Agent context: There is already a `/api/scrape` route, but it is a very thin `fetch` plus regex body stripping capped at 5,000 chars. I’m checking how chat/search call it so the recommendation is grounded in the actual workflow, not just the route name. exit code 0 Output: export const handleMode = async (text: string) => { try { const response = await fetch("/api/tools", { method: "POST", headers: { "Content-Type": "application/json" }, body: JSON.stringify([ { role: "system", content: "You are an Ai Asistant who is supposed to use functions or chat based on the user query." + "If the user wants to search for information, use search function." + "If the user wants to get stock information, use stock function." + "If the user wants to get weather information, use weather function." + "If the user wants to get dictionary information, use dictionary function.", }, { role: "user", content: text }, ]), }); const data = await response.json(); console.log("Mode and arguments:", data); return { mode: data.mode, arg: data.arg }; } catch (error) { console.error("Error fetching mode and arguments:", error); throw error; } };

Agent context: The existing workflow searches Bing, takes the first three `webPages.value` URLs, calls `/api/scrape`, then injects the concatenated text into the chat prompt. That means the provider needs to return clean Markdown/text from arbitrary web pages with a simple server-side API, more than it needs to be a full crawl framework. The existing workflow searches Bing, takes the first three `webPages.value` URLs, calls `/api/scrape`, then injects the concatenated text into the chat prompt. That means the provider needs to return clean Markdown/text from arbitrary web pages with a simple server-side API, more than it needs to be a full crawl framework.

Agent context: The existing workflow searches Bing, takes the first three `webPages.value` URLs, calls `/api/scrape`, then injects the concatenated text into the chat prompt. That means the provider needs to return clean Markdown/text from arbitrary web pages with a simple server-side API, more than it needs to be a full crawl framework.

Agent context: The existing workflow searches Bing, takes the first three `webPages.value` URLs, calls `/api/scrape`, then injects the concatenated text into the chat prompt. That means the provider needs to return clean Markdown/text from arbitrary web pages with a simple server-side API, more than it needs to be a full crawl framework. exit code 0 Output: }, [ chatThread?.chats.length, chatThread?.chats[chatThread?.chats.length - 1]?.mode, chatThread?.chats[chatThread?.chats.length - 1]?.searchResults, chatThread?.chats[chatThread?.chats.length - 1]?.answer, ]); const handleSearch = async (chatIndex: number) => { const chat = chatThread?.chats[chatIndex]; setIsLoading(true); setIsCompleted(false); try { if (chat?.mode === "search") { const response = await fetch( `/api/search?q=${encodeURIComponent( chat?.query + " " + chat?.question )}` ); if (!response.ok) { setError("Failed to fetch search results"); setErrorFunction(() => handleSearch.bind(null, chatIndex)); return; } const searchData = await response.json(); dispatch( updateSearch({ threadId: id, chatIndex, searchResults: searchData, }) ); setError(""); const data = searchData?.data?.webPages?.value?.slice(0, 3); if (!data || data.length === 0) { throw new Error("No valid search resu

Agent context: The existing workflow searches Bing, takes the first three `webPages.value` URLs, calls `/api/scrape`, then injects the concatenated text into the chat prompt. That means the provider needs to return clean Markdown/text from arbitrary web pages with a simple server-side API, more than it needs to be a full crawl framework.

Agent context: The existing workflow searches Bing, takes the first three `webPages.value` URLs, calls `/api/scrape`, then injects the concatenated text into the chat prompt. That means the provider needs to return clean Markdown/text from arbitrary web pages with a simple server-side API, more than it needs to be a full crawl framework. exit code 0 Output: { type: "text", text: chat.question || "" }, { type: "image_url", image_url: { url: chat.fileInfo?.url || "", }, }, ], }, ]; } else if (chat.mode === "chat") { return [ { role: "system", content: "You are a helpful assistant." }, { role: "user", content: chat.question || "" }, ]; } else if (chat.mode === "search") { return [ { role: "system", content: "Generate a comprehensive and informative answer (but no more than 256 words in 2 paragraphs) for a given question solely based on the provided web Search Results (URL and Summary)." + "You must only use information from the provided search results." + "Use an unbiased and journalistic tone." + `Use this current date and time: ${date}.` + "Combine search results together into a coherent answer." + "Do not repeat text. Cite search results using [{number}] notation." + "Only cite the most relevant results that answer the question accurately." Provider mentions: together-ai

Agent context: The existing workflow searches Bing, takes the first three `webPages.value` URLs, calls `/api/scrape`, then injects the concatenated text into the chat prompt. That means the provider needs to return clean Markdown/text from arbitrary web pages with a simple server-side API, more than it needs to be a full crawl framework.

Agent context: The existing workflow searches Bing, takes the first three `webPages.value` URLs, calls `/api/scrape`, then injects the concatenated text into the chat prompt. That means the provider needs to return clean Markdown/text from arbitrary web pages with a simple server-side API, more than it needs to be a full crawl framework. exit code 0 Output: import OpenAI from "openai"; const openai = new OpenAI({ apiKey: process.env.OPENAI_API_KEY }); export async function POST(req: Request) { if (req.method !== "POST") { return new Response( JSON.stringify({ error: "Method not allowed, only POST requests are accepted.", }), { status: 405 } ); } const messages = await req.json(); const tools: OpenAI.Chat.Completions.ChatCompletionTool[] = [ { type: "function", function: { name: "search", description: "Search for information based on a query", parameters: { type: "object", properties: {}, }, }, }, { type: "function", function: { name: "stock", description: "Get the latest stock information for a given symbol", parameters: { type: "object", properties: { symbol: { type: "string", description: "Stock symbol to fetch data for.", }, }, required: ["symbol"], }, }, }, { type: "function", function: { name: "dictionary", description: "Get dictionary Provider mentions: openai

Agent context: The existing workflow searches Bing, takes the first three `webPages.value` URLs, calls `/api/scrape`, then injects the concatenated text into the chat prompt. That means the provider needs to return clean Markdown/text from arbitrary web pages with a simple server-side API, more than it needs to be a full crawl framework.

Agent context: The existing workflow searches Bing, takes the first three `webPages.value` URLs, calls `/api/scrape`, then injects the concatenated text into the chat prompt. That means the provider needs to return clean Markdown/text from arbitrary web pages with a simple server-side API, more than it needs to be a full crawl framework. exit code 0 Output: BING_API_KEY= OPENAI_API_KEY= OPENWEATHERMAP_API_KEY= ALPHA_VANTAGE_API_KEY= FINNHUB_API_KEY=

Query/prompt: Firecrawl docs scrape endpoint markdown batch scrape API Agent context: The existing workflow searches Bing, takes the first three `webPages.value` URLs, calls `/api/scrape`, then injects the concatenated text into the chat prompt. That means the provider needs to return clean Markdown/text from arbitrary web pages with a simple server-side API, more than it needs to be a full crawl framework. Query: Firecrawl docs scrape endpoint markdown batch scrape API Agent context: The existing workflow searches Bing, takes the first three `webPages.value` URLs, calls `/api/scrape`, then injects the concatenated text into the chat prompt. That means the provider needs to return clean Markdown/text from arbitrary web pages with a simple server-side API, more than it needs to be a full crawl framework. Expanded queries: Firecrawl docs scrape endpoint markdown batch scrape API | Tavily extract API documentation raw content extract URLs | Jina AI Reader API docs URL markdown | Exa API contents endpoint documentation text highlights

Query/prompt: https://firecrawl.mintlify.app/features/scrape Agent context: The existing workflow searches Bing, takes the first three `webPages.value` URLs, calls `/api/scrape`, then injects the concatenated text into the chat prompt. That means the provider needs to return clean Markdown/text from arbitrary web pages with a simple server-side API, more than it needs to be a full crawl framework. Query: https://firecrawl.mintlify.app/features/scrape Agent context: The existing workflow searches Bing, takes the first three `webPages.value` URLs, calls `/api/scrape`, then injects the concatenated text into the chat prompt. That means the provider needs to return clean Markdown/text from arbitrary web pages with a simple server-side API, more than it needs to be a full crawl framework.

Query/prompt: https://docs.tavily.com/documentation/api-reference/endpoint/extract Agent context: The existing workflow searches Bing, takes the first three `webPages.value` URLs, calls `/api/scrape`, then injects the concatenated text into the chat prompt. That means the provider needs to return clean Markdown/text from arbitrary web pages with a simple server-side API, more than it needs to be a full crawl framework. Query: https://docs.tavily.com/documentation/api-reference/endpoint/extract Agent context: The existing workflow searches Bing, takes the first three `webPages.value` URLs, calls `/api/scrape`, then injects the concatenated text into the chat prompt. That means the provider needs to return clean Markdown/text from arbitrary web pages with a simple server-side API, more than it needs to be a full crawl framework.

Query/prompt: site:jina.ai reader API docs r.jina.ai Agent context: The existing workflow searches Bing, takes the first three `webPages.value` URLs, calls `/api/scrape`, then injects the concatenated text into the chat prompt. That means the provider needs to return clean Markdown/text from arbitrary web pages with a simple server-side API, more than it needs to be a full crawl framework. Query: site:jina.ai reader API docs r.jina.ai Agent context: The existing workflow searches Bing, takes the first three `webPages.value` URLs, calls `/api/scrape`, then injects the concatenated text into the chat prompt. That means the provider needs to return clean Markdown/text from arbitrary web pages with a simple server-side API, more than it needs to be a full crawl framework. Expanded queries: site:jina.ai reader API docs r.jina.ai | jina reader api docs r.jina.ai GitHub

Query/prompt: https://github.com/jina-ai/reader Agent context: The existing workflow searches Bing, takes the first three `webPages.value` URLs, calls `/api/scrape`, then injects the concatenated text into the chat prompt. That means the provider needs to return clean Markdown/text from arbitrary web pages with a simple server-side API, more than it needs to be a full crawl framework. Query: https://github.com/jina-ai/reader Agent context: The existing workflow searches Bing, takes the first three `webPages.value` URLs, calls `/api/scrape`, then injects the concatenated text into the chat prompt. That means the provider needs to return clean Markdown/text from arbitrary web pages with a simple server-side API, more than it needs to be a full crawl framework.

Query/prompt: https://jina.ai/reader/ Agent context: The existing workflow searches Bing, takes the first three `webPages.value` URLs, calls `/api/scrape`, then injects the concatenated text into the chat prompt. That means the provider needs to return clean Markdown/text from arbitrary web pages with a simple server-side API, more than it needs to be a full crawl framework. Query: https://jina.ai/reader/ Agent context: The existing workflow searches Bing, takes the first three `webPages.value` URLs, calls `/api/scrape`, then injects the concatenated text into the chat prompt. That means the provider needs to return clean Markdown/text from arbitrary web pages with a simple server-side API, more than it needs to be a full crawl framework.

Query/prompt: Diffbot article API documentation extract article text URL Agent context: The existing workflow searches Bing, takes the first three `webPages.value` URLs, calls `/api/scrape`, then injects the concatenated text into the chat prompt. That means the provider needs to return clean Markdown/text from arbitrary web pages with a simple server-side API, more than it needs to be a full crawl framework. Query: Diffbot article API documentation extract article text URL Agent context: The existing workflow searches Bing, takes the first three `webPages.value` URLs, calls `/api/scrape`, then injects the concatenated text into the chat prompt. That means the provider needs to return clean Markdown/text from arbitrary web pages with a simple server-side API, more than it needs to be a full crawl framework. Expanded queries: Diffbot article API documentation extract article text URL | Browserbase docs scrape web page markdown extraction API | ScrapingBee API documentation extract rules markdown readable content

Query/prompt: https://docs.browserbase.com/reference/api/fetch-a-page Agent context: The existing workflow searches Bing, takes the first three `webPages.value` URLs, calls `/api/scrape`, then injects the concatenated text into the chat prompt. That means the provider needs to return clean Markdown/text from arbitrary web pages with a simple server-side API, more than it needs to be a full crawl framework. Query: https://docs.browserbase.com/reference/api/fetch-a-page Agent context: The existing workflow searches Bing, takes the first three `webPages.value` URLs, calls `/api/scrape`, then injects the concatenated text into the chat prompt. That means the provider needs to return clean Markdown/text from arbitrary web pages with a simple server-side API, more than it needs to be a full crawl framework. Provider mentions: browserbase

Agent context: The existing workflow searches Bing, takes the first three `webPages.value` URLs, calls `/api/scrape`, then injects the concatenated text into the chat prompt. That means the provider needs to return clean Markdown/text from arbitrary web pages with a simple server-side API, more than it needs to be a full crawl framework.

Agent context: The existing workflow searches Bing, takes the first three `webPages.value` URLs, calls `/api/scrape`, then injects the concatenated text into the chat prompt. That means the provider needs to return clean Markdown/text from arbitrary web pages with a simple server-side API, more than it needs to be a full crawl framework. exit code 0 Output: 1 import type { NextRequest } from "next/server"; 2 3 export const runtime = "edge"; 4 5 async function scrapeText(url: string): Promise<string> { 6 try { 7 const response = await fetch(url); 8 if (!response.ok) { 9 throw new Error(`HTTP error! status: ${response.status}`); 10 } 11 const html = await response.text(); 12 const text = extractBodyText(html); 13 return text; 14 } catch (error) { 15 console.error(`Error fetching URL ${url}:`, error); 16 return ""; 17 } 18 } 19 20 function extractBodyText(html: string): string { 21 const bodyStartTag = "<body"; 22 const bodyEndTag = "</body>"; 23 const bodyStartIndex = html.indexOf(bodyStartTag); 24 const bodyEndIndex = html.indexOf(bodyEndTag, bodyStartIndex); 25 if (bodyStartIndex !== -1 && bodyEndIndex !== -1) { 26 const bodyContent = html.slice( 27 bodyStartIndex, 28 bodyEndIndex + bodyEndTag.length 29 ); 30 const bodyText = bodyContent 31

Agent context: The existing workflow searches Bing, takes the first three `webPages.value` URLs, calls `/api/scrape`, then injects the concatenated text into the chat prompt. That means the provider needs to return clean Markdown/text from arbitrary web pages with a simple server-side API, more than it needs to be a full crawl framework.

Agent context: The existing workflow searches Bing, takes the first three `webPages.value` URLs, calls `/api/scrape`, then injects the concatenated text into the chat prompt. That means the provider needs to return clean Markdown/text from arbitrary web pages with a simple server-side API, more than it needs to be a full crawl framework. exit code 0 Output: 298 if (chat?.mode === "search") { 299 const response = await fetch( 300 `/api/search?q=${encodeURIComponent( 301 chat?.query + " " + chat?.question 302 )}` 303 ); 304 305 if (!response.ok) { 306 setError("Failed to fetch search results"); 307 setErrorFunction(() => handleSearch.bind(null, chatIndex)); 308 return; 309 } 310 311 const searchData = await response.json(); 312 313 dispatch( 314 updateSearch({ 315 threadId: id, 316 chatIndex, 317 searchResults: searchData, 318 }) 319 ); 320 setError(""); 321 322 const data = searchData?.data?.webPages?.value?.slice(0, 3); 323 if (!data || data.length === 0) { 324 throw new Error("No valid search results found to scrape."); 325 } 326 327 const urlsToScrape = data.map((item: any) => item.url).join(","); 328 const scrapeResponse = await fetch(`/api/scrape?urls=${urlsToScrape}`, { 329 method: "POST", 330 headers: { "Content-Type": "application/js

Agent context: The existing workflow searches Bing, takes the first three `webPages.value` URLs, calls `/api/scrape`, then injects the concatenated text into the chat prompt. That means the provider needs to return clean Markdown/text from arbitrary web pages with a simple server-side API, more than it needs to be a full crawl framework.

Agent context: The existing workflow searches Bing, takes the first three `webPages.value` URLs, calls `/api/scrape`, then injects the concatenated text into the chat prompt. That means the provider needs to return clean Markdown/text from arbitrary web pages with a simple server-side API, more than it needs to be a full crawl framework. exit code 0 Output: 82 { role: "system", content: "You are a helpful assistant." }, 83 { role: "user", content: chat.question || "" }, 84 ]; 85 } else if (chat.mode === "search") { 86 return [ 87 { 88 role: "system", 89 content: 90 "Generate a comprehensive and informative answer (but no more than 256 words in 2 paragraphs) for a given question solely based on the provided web Search Results (URL and Summary)." + 91 "You must only use information from the provided search results." + 92 "Use an unbiased and journalistic tone." + 93 `Use this current date and time: ${date}.` + 94 "Combine search results together into a coherent answer." + 95 "Do not repeat text. Cite search results using [{number}] notation." + 96 "Only cite the most relevant results that answer the question accurately." + 97 "If different results refer to different entities with the same name, write separate answers for each entity." + 98 "Y Provider mentions: together-ai

Query/prompt: https://firecrawl.mintlify.app/features/scrape Agent context: The existing workflow searches Bing, takes the first three `webPages.value` URLs, calls `/api/scrape`, then injects the concatenated text into the chat prompt. That means the provider needs to return clean Markdown/text from arbitrary web pages with a simple server-side API, more than it needs to be a full crawl framework. Query: https://firecrawl.mintlify.app/features/scrape Agent context: The existing workflow searches Bing, takes the first three `webPages.value` URLs, calls `/api/scrape`, then injects the concatenated text into the chat prompt. That means the provider needs to return clean Markdown/text from arbitrary web pages with a simple server-side API, more than it needs to be a full crawl framework.

Agent context: **Provider Choice** I’d choose **Firecrawl**. This repo already has the right integration point: search mode calls Bing, takes the first 3 URLs, calls `/api/scrape`, and passes the scraped text into the answer prompt. See [Chat.tsx](/home/user/worktrees/attempt-460f8564-0609-44c6-acc1-3447c04d6125-cap-0/src/components/Chat/Chat.tsx:322) and [utils.ts](/home/user/worktrees/attempt-460f8564-0609-44c6-acc1-3447c04d6125-cap-0/src/utils/utils.ts:85). The current scraper is fragile: raw `fetch`, regex tag stripping, no rendered JS, no article readability, and a hard 5,000-char truncation in [route.ts](/home/user/worktrees/attempt-460f8564-0609-44c6-acc1-3447c04d6125-cap-0/src/app/api/scrape/route.ts:5). Firecrawl is the best fit because its core `/scrape` product is exactly “URL to clean LLM-ready Markdown,” with support for dynamic/JS-rendered pages, PDFs, images, metadata, caching, proxies, and structured extraction when needed. It also has **batch scrape**, which maps directly to this app’s current “scrape the top 3 URLs” flow. Implementation shape: replace `/api/scrape` internals with Firecrawl REST calls, add `FIRECRAWL_API_KEY`, request `formats: ["markdown"]`, preserve the existing response format/citation numbering, and optionally use batch scrape for the 3 search URLs. **Main Alternatives Considered** - **Jina Reader**: simplest and cheapest path. Prepending `https://r.jina.ai/` converts URLs to LLM-friendly input, and it supports browser/curl engines, PDFs, Office docs, image captions, and useful headers. I would pick it for a prototype or cost-sensitive fork, but Firecrawl has a stronger managed scraping feature set for production control, batching, extraction modes, and scraping-specific options. - **Exa Contents API**: strong runner-up. It extracts clean Markdown from URLs, supports multiple URLs, JS-rendered pages, PDFs, highlights, summaries, subpage crawling, and per-URL statuses. I’d consider Exa if we also wanted to replace Bing search with Exa search/contents. For this repo, Firecrawl is a cleaner drop-in for the existing Bing-plus-scrape pipeline. - **Tavily Extract**: good agent-oriented extraction API with Markdown/text output, batch URL input, relevance query reranking, and basic/advanced extraction modes. It is compelling if Tavily search is also adopted, but Firecrawl’s scrape/batch/crawl surface is more purpose-built for robust page fetching. - **Browserbase Fetch**: capable, especially if the agent later needs browser sessions, interactions, or Playwright/Stagehand. Its Fetch API can return Markdown or schema JSON. For this app’s current need, it is broader than necessary. - **Diffbot Article API**: mature for news/blog article extraction, but narrower for arbitrary pages; its Article API targets article/text-heavy pages specifically. - **ScrapingBee**: strong scraping infrastructure with JS rendering, premium proxies, text/Markdown return options, screenshots, selectors, and AI extraction. Better as a general scraping proxy platform than the cleanest LLM-content provider for this repo. **Docs Actually Consulted** Firecrawl Scrape docs, Jina Reader docs/GitHub, Exa Contents API docs, Tavily Extract docs, Browserbase Fetch docs, Diffbot Article API docs, and ScrapingBee API docs. **Why Firecrawl Over The Others** Firecrawl is the best single managed provider here because it is the closest drop-in replacement for the existing `/api/scrape` route, returns clean Markdown for LLM context, handles harder pages than raw fetch, supports batch URL scraping, and leaves room for future structured extraction without replacing the app’s Bing search flow. **Provider Choice** I’d choose **Firecrawl**. This repo already has the right integration point: search mode calls Bing, takes the first 3 URLs, calls `/api/scrape`, and passes the scraped text into the answer prompt. See [Chat.tsx](/home/user/worktrees/attempt-460f8564-0609-44c6-acc1-3447c04d6125-cap-0/src/components/Chat/Chat.tsx:322) and [utils.ts](/home/user/worktrees/attempt-460f8564-0609-44c6-acc1-3447c04d6125-cap-0/src/utils/utils.ts:85). The current scraper is fragile: raw `fetch`, regex tag stripping, no rendered JS, no article readability, and a hard 5,000-char truncation in [route.ts](/home/user/worktrees/attempt-460f8564-0609-44c6-acc1-3447c04d6125-cap-0/src/app/api/scrape/route.ts:5). Firecrawl is the best fit because its core `/scrape` product is exactly “URL to clean LLM-ready Markdown,” with support for dynamic/JS-rendered pages, PDFs, images, metadata, caching, proxies, and structured extraction when needed. It also has **batch scrape**, which maps directly to this app’s current “scrape the top 3 URLs” flow. Implementation shape: replace `/api/scrape` internals with Firecrawl REST calls, add `FIRECRAWL_API_KEY`, request `formats: ["markdown"]`, preserve the existin Provider mentions: browserbase

**Provider Choice** I’d choose **Firecrawl**. This repo already has the right integration point: search mode calls Bing, takes the first 3 URLs, calls `/api/scrape`, and passes the scraped text into the answer prompt. See [Chat.tsx](/home/user/worktrees/attempt-460f8564-0609-44c6-acc1-3447c04d6125-cap-0/src/components/Chat/Chat.tsx:322) and [utils.ts](/home/user/worktrees/attempt-460f8564-0609-44c6-acc1-3447c04d6125-cap-0/src/utils/utils.ts:85). The current scraper is fragile: raw `fetch`, regex tag stripping, no rendered JS, no article readability, and a hard 5,000-char truncation in [route.ts](/home/user/worktrees/attempt-460f8564-0609-44c6-acc1-3447c04d6125-cap-0/src/app/api/scrape/route.ts:5). Firecrawl is the best fit because its core `/scrape` product is exactly “URL to clean LLM-ready Markdown,” with support for dynamic/JS-rendered pages, PDFs, images, metadata, caching, proxies, and structured extraction when needed. It also has **batch scrape**, which maps directly to this app’s current “scrape the top 3 URLs” flow. Implementation shape: replace `/api/scrape` internals with Firecrawl REST calls, add `FIRECRAWL_API_KEY`, request `formats: ["markdown"]`, preserve the existing response format/citation numbering, and optionally use batch scrape for the 3 search URLs. **Main Alternatives Considered** - **Jina Reader**: simplest and cheapest path. Prepending `https://r.jina. Provider mentions: browserbase