Run

P1

6b010650109bf32003ff0e0910127d6cfd5d23a5

Agent context: I’ll inspect the repository first to understand the agent’s architecture and where web content would enter the workflow, then I’ll verify provider capabilities against current primary docs. I’ll inspect the repository first to understand the agent’s architecture and where web content would enter the workflow, then I’ll verify provider capabilities against current primary docs.

Agent context: I’ll inspect the repository first to understand the agent’s architecture and where web content would enter the workflow, then I’ll verify provider capabilities against current primary docs.

Agent context: I’ll inspect the repository first to understand the agent’s architecture and where web content would enter the workflow, then I’ll verify provider capabilities against current primary docs. exit code 0 Output: /home/user/worktrees/attempt-290dbd24-6aaf-4364-9637-261162d7af9e-cap-1 /bin/bash: line 1: rg: command not found

Agent context: I’ll inspect the repository first to understand the agent’s architecture and where web content would enter the workflow, then I’ll verify provider capabilities against current primary docs.

Agent context: I’ll inspect the repository first to understand the agent’s architecture and where web content would enter the workflow, then I’ll verify provider capabilities against current primary docs. exit code 0

Agent context: I’ll inspect the repository first to understand the agent’s architecture and where web content would enter the workflow, then I’ll verify provider capabilities against current primary docs.

Agent context: I’ll inspect the repository first to understand the agent’s architecture and where web content would enter the workflow, then I’ll verify provider capabilities against current primary docs. exit code 127 Output: /bin/bash: line 1: rg: command not found

Agent context: `rg` is not installed in this environment, so I’m falling back to `find`/`grep` and keeping the scan scoped to source files. `rg` is not installed in this environment, so I’m falling back to `find`/`grep` and keeping the scan scoped to source files.

Agent context: `rg` is not installed in this environment, so I’m falling back to `find`/`grep` and keeping the scan scoped to source files.

Agent context: `rg` is not installed in this environment, so I’m falling back to `find`/`grep` and keeping the scan scoped to source files. exit code 0 Output: .env.example .eslintrc.json .git .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/

Agent context: `rg` is not installed in this environment, so I’m falling back to `find`/`grep` and keeping the scan scoped to source files.

Agent context: `rg` is not installed in this environment, so I’m falling back to `find`/`grep` and keeping the scan scoped to source files. exit code 0 Output: ./.eslintrc.json:2: "extends": "next/core-web-vitals" ./LICENSE:126:System Libraries, or general-purpose tools or generally available free ./LICENSE:652:get its source. For example, if your program is a web application, its ./README.md:54:5. Open [http://localhost:3000](http://localhost:3000) in your browser to see the app. ./README.md:61:2. Update the `tools` api in `api` to include the new plugin function call. ./package.json:19: "@reduxjs/toolkit": "^2.2.3", ./package.json:53: "@types/mixpanel-browser": "^2.49.0", ./public/Logo.svg:2:<g clip-path="url(#clip0_1_24)"> ./public/Logo.svg:4:<path d="M88.16 130.58C88.09 125.23 80.36 121.19 70.16 121.19C59.96 121.19 52.16 125.19 52.16 130.58V130.65C52.22 136 59.96 140.04 70.16 140.04C80.36 140.04 88.16 136.04 88.16 130.65V130.58ZM70.16 135.58C62.08 135.58 56.91 132.66 56.86 130.58C56.81 128.5 62.08 125.58 70.16 125.58C78.24 125.58 83.41 128. Provider mentions: together-ai, openai

Agent context: `rg` is not installed in this environment, so I’m falling back to `find`/`grep` and keeping the scan scoped to source files.

Agent context: `rg` is not installed in this environment, so I’m falling back to `find`/`grep` and keeping the scan scoped to source files. exit code 0 Output: ./README.md ./package.json

Agent context: The repo is a Next.js chat/search app. I found an existing `/api/scrape` route that fetches raw HTML and strips tags manually, plus a Bing search flow that scrapes the top three result URLs before answering. The repo is a Next.js chat/search app. I found an existing `/api/scrape` route that fetches raw HTML and strips tags manually, plus a Bing search flow that scrapes the top three result URLs before answering.

Agent context: The repo is a Next.js chat/search app. I found an existing `/api/scrape` route that fetches raw HTML and strips tags manually, plus a Bing search flow that scrapes the top three result URLs before answering.

Agent context: The repo is a Next.js chat/search app. I found an existing `/api/scrape` route that fetches raw HTML and strips tags manually, plus a Bing search flow that scrapes the top three result URLs before answering. 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

Agent context: The repo is a Next.js chat/search app. I found an existing `/api/scrape` route that fetches raw HTML and strips tags manually, plus a Bing search flow that scrapes the top three result URLs before answering.

Agent context: The repo is a Next.js chat/search app. I found an existing `/api/scrape` route that fetches raw HTML and strips tags manually, plus a Bing search flow that scrapes the top three result URLs before answering. 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 chat/search app. I found an existing `/api/scrape` route that fetches raw HTML and strips tags manually, plus a Bing search flow that scrapes the top three result URLs before answering.

Agent context: The repo is a Next.js chat/search app. I found an existing `/api/scrape` route that fetches raw HTML and strips tags manually, plus a Bing search flow that scrapes the top three result URLs before answering. 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 chat/search app. I found an existing `/api/scrape` route that fetches raw HTML and strips tags manually, plus a Bing search flow that scrapes the top three result URLs before answering.

Agent context: The repo is a Next.js chat/search app. I found an existing `/api/scrape` route that fetches raw HTML and strips tags manually, plus a Bing search flow that scrapes the top three result URLs before answering. exit code 0 Output: import { Timestamp } from "firebase/firestore"; import { Chat, Citation, Message } from "./types"; export const getDomain = (url: string): string => { try { return new URL(url).hostname; } catch (error) { console.error("An error occurred while parsing the URL:", error); return "Invalid URL"; } }; export const getSecondLevelDomain = (url: string): string => { try { const hostname = new URL(url).hostname; const parts = hostname.split("."); let domain = parts.length > 2 ? parts[parts.length - 2] : parts[0]; if (domain === "www" && parts.length > 2) { domain = parts[parts.length - 3]; } return domain.charAt(0).toUpperCase() + domain.slice(1); } catch (error) { console.error("An error occurred while parsing the URL:", error); return "Invalid URL"; } }; export const getCurrentDateUTC = (): string => { const days = [ "Sunday", "Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday", Provider mentions: together-ai

Agent context: The repo is a Next.js chat/search app. I found an existing `/api/scrape` route that fetches raw HTML and strips tags manually, plus a Bing search flow that scrapes the top three result URLs before answering.

Agent context: The repo is a Next.js chat/search app. I found an existing `/api/scrape` route that fetches raw HTML and strips tags manually, plus a Bing search flow that scrapes the top three result URLs before answering. exit code 0 Output: chatIdCounterRef.current++; } }; processChatThread(); }, [ 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

Agent context: The repo is a Next.js chat/search app. I found an existing `/api/scrape` route that fetches raw HTML and strips tags manually, plus a Bing search flow that scrapes the top three result URLs before answering.

Agent context: The repo is a Next.js chat/search app. I found an existing `/api/scrape` route that fetches raw HTML and strips tags manually, plus a Bing search flow that scrapes the top three result URLs before answering. 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

Query/prompt: Tavily Extract API documentation raw content source citations official Agent context: The repo is a Next.js chat/search app. I found an existing `/api/scrape` route that fetches raw HTML and strips tags manually, plus a Bing search flow that scrapes the top three result URLs before answering. Query: Tavily Extract API documentation raw content source citations official Agent context: The repo is a Next.js chat/search app. I found an existing `/api/scrape` route that fetches raw HTML and strips tags manually, plus a Bing search flow that scrapes the top three result URLs before answering. Expanded queries: Tavily Extract API documentation raw content source citations official | Exa API contents endpoint documentation extract webpage contents official | Firecrawl scrape API documentation markdown official | Diffbot Analyze Article API documentation official

Query/prompt: site:docs.tavily.com Extract API raw content Tavily official documentation Agent context: The repo is a Next.js chat/search app. I found an existing `/api/scrape` route that fetches raw HTML and strips tags manually, plus a Bing search flow that scrapes the top three result URLs before answering. Query: site:docs.tavily.com Extract API raw content Tavily official documentation Agent context: The repo is a Next.js chat/search app. I found an existing `/api/scrape` route that fetches raw HTML and strips tags manually, plus a Bing search flow that scrapes the top three result URLs before answering. Expanded queries: site:docs.tavily.com Extract API raw content Tavily official documentation | Tavily Extract API documentation include images extract depth official | Jina AI Reader API documentation official r.jina.ai URL markdown | Bright Data Web Scraper API documentation markdown extract URL official

Query/prompt: site:jina.ai reader API documentation r.jina.ai official Agent context: The repo is a Next.js chat/search app. I found an existing `/api/scrape` route that fetches raw HTML and strips tags manually, plus a Bing search flow that scrapes the top three result URLs before answering. Query: site:jina.ai reader API documentation r.jina.ai official Agent context: The repo is a Next.js chat/search app. I found an existing `/api/scrape` route that fetches raw HTML and strips tags manually, plus a Bing search flow that scrapes the top three result URLs before answering. Expanded queries: site:jina.ai reader API documentation r.jina.ai official | site:docs.jina.ai Reader API r.jina.ai markdown URL official | Jina AI Reader API URL markdown official docs

Query/prompt: https://exa.ai/docs/reference/contents-api-guide Agent context: The repo is a Next.js chat/search app. I found an existing `/api/scrape` route that fetches raw HTML and strips tags manually, plus a Bing search flow that scrapes the top three result URLs before answering. Query: https://exa.ai/docs/reference/contents-api-guide Agent context: The repo is a Next.js chat/search app. I found an existing `/api/scrape` route that fetches raw HTML and strips tags manually, plus a Bing search flow that scrapes the top three result URLs before answering.

Query/prompt: Tavily Search API documentation chunks score raw_content citations source quality official Agent context: The repo is a Next.js chat/search app. I found an existing `/api/scrape` route that fetches raw HTML and strips tags manually, plus a Bing search flow that scrapes the top three result URLs before answering. Query: Tavily Search API documentation chunks score raw_content citations source quality official Agent context: The repo is a Next.js chat/search app. I found an existing `/api/scrape` route that fetches raw HTML and strips tags manually, plus a Bing search flow that scrapes the top three result URLs before answering. Expanded queries: Tavily Search API documentation chunks score raw_content citations source quality official | Tavily API Search documentation include answer include raw content chunks official | Exa search API documentation highlights text source quality official | Perplexity API search citations documentation official

Agent context: I’ve finished the repo pass and am comparing providers against primary docs now. The key architectural constraint is that the app already stores Bing result URLs and renders citations from those URLs, so the provider should return source-grounded page text/highlights, not an opaque generated answer. I’ve finished the repo pass and am comparing providers against primary docs now. The key architectural constraint is that the app already stores Bing result URLs and renders citations from those URLs, so the provider should return source-grounded page text/highlights, not an opaque generated answer.

Agent context: I’ve finished the repo pass and am comparing providers against primary docs now. The key architectural constraint is that the app already stores Bing result URLs and renders citations from those URLs, so the provider should return source-grounded page text/highlights, not an opaque generated answer.

Agent context: I’ve finished the repo pass and am comparing providers against primary docs now. The key architectural constraint is that the app already stores Bing result URLs and renders citations from those URLs, so the provider should return source-grounded page text/highlights, not an opaque generated answer. 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: I’ve finished the repo pass and am comparing providers against primary docs now. The key architectural constraint is that the app already stores Bing result URLs and renders citations from those URLs, so the provider should return source-grounded page text/highlights, not an opaque generated answer.

Agent context: I’ve finished the repo pass and am comparing providers against primary docs now. The key architectural constraint is that the app already stores Bing result URLs and renders citations from those URLs, so the provider should return source-grounded page text/highlights, not an opaque generated answer. exit code 0 Output: 292 const handleSearch = async (chatIndex: number) => { 293 const chat = chatThread?.chats[chatIndex]; 294 setIsLoading(true); 295 setIsCompleted(false); 296 297 try { 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)

Agent context: I’ve finished the repo pass and am comparing providers against primary docs now. The key architectural constraint is that the app already stores Bing result URLs and renders citations from those URLs, so the provider should return source-grounded page text/highlights, not an opaque generated answer.

Agent context: I’ve finished the repo pass and am comparing providers against primary docs now. The key architectural constraint is that the app already stores Bing result URLs and renders citations from those URLs, so the provider should return source-grounded page text/highlights, not an opaque generated answer. exit code 0 Output: 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 "You have the ability to search and will be given websites and the scarped data from them and you will have to make u Provider mentions: together-ai

Query/prompt: https://exa.ai/docs/reference/contents-retrieval Agent context: I’ve finished the repo pass and am comparing providers against primary docs now. The key architectural constraint is that the app already stores Bing result URLs and renders citations from those URLs, so the provider should return source-grounded page text/highlights, not an opaque generated answer. Query: https://exa.ai/docs/reference/contents-retrieval Agent context: I’ve finished the repo pass and am comparing providers against primary docs now. The key architectural constraint is that the app already stores Bing result URLs and renders citations from those URLs, so the provider should return source-grounded page text/highlights, not an opaque generated answer.

Query/prompt: 'include_raw_content' in https://docs.tavily.com/documentation/api-reference/endpoint/search Agent context: I’ve finished the repo pass and am comparing providers against primary docs now. The key architectural constraint is that the app already stores Bing result URLs and renders citations from those URLs, so the provider should return source-grounded page text/highlights, not an opaque generated answer. Query: 'include_raw_content' in https://docs.tavily.com/documentation/api-reference/endpoint/search Agent context: I’ve finished the repo pass and am comparing providers against primary docs now. The key architectural constraint is that the app already stores Bing result URLs and renders citations from those URLs, so the provider should return source-grounded page text/highlights, not an opaque generated answer.

Query/prompt: 'onlyMainContent' Agent context: I’ve finished the repo pass and am comparing providers against primary docs now. The key architectural constraint is that the app already stores Bing result URLs and renders citations from those URLs, so the provider should return source-grounded page text/highlights, not an opaque generated answer. Query: 'onlyMainContent' Agent context: I’ve finished the repo pass and am comparing providers against primary docs now. The key architectural constraint is that the app already stores Bing result URLs and renders citations from those URLs, so the provider should return source-grounded page text/highlights, not an opaque generated answer.

Query/prompt: https://docs.firecrawl.dev/api-reference/endpoint/scrape Agent context: I’ve finished the repo pass and am comparing providers against primary docs now. The key architectural constraint is that the app already stores Bing result URLs and renders citations from those URLs, so the provider should return source-grounded page text/highlights, not an opaque generated answer. Query: https://docs.firecrawl.dev/api-reference/endpoint/scrape Agent context: I’ve finished the repo pass and am comparing providers against primary docs now. The key architectural constraint is that the app already stores Bing result URLs and renders citations from those URLs, so the provider should return source-grounded page text/highlights, not an opaque generated answer.

Query/prompt: Diffbot Article API documentation text field html official Agent context: I’ve finished the repo pass and am comparing providers against primary docs now. The key architectural constraint is that the app already stores Bing result URLs and renders citations from those URLs, so the provider should return source-grounded page text/highlights, not an opaque generated answer. Query: Diffbot Article API documentation text field html official Agent context: I’ve finished the repo pass and am comparing providers against primary docs now. The key architectural constraint is that the app already stores Bing result URLs and renders citations from those URLs, so the provider should return source-grounded page text/highlights, not an opaque generated answer. Expanded queries: Diffbot Article API documentation text field html official | Diffbot Article API extract articles text tags sentiment official docs

Agent context: **Provider Chosen** I’d choose **Exa Contents API**. The repo already has the right shape for this: Bing finds URLs, [Chat.tsx](/home/user/worktrees/attempt-290dbd24-6aaf-4364-9637-261162d7af9e-cap-1/src/components/Chat/Chat.tsx:322) sends the top three URLs to `/api/scrape`, and citations are generated from those URLs in [utils.ts](/home/user/worktrees/attempt-290dbd24-6aaf-4364-9637-261162d7af9e-cap-1/src/utils/utils.ts:154). The weak point is [route.ts](/home/user/worktrees/attempt-290dbd24-6aaf-4364-9637-261162d7af9e-cap-1/src/app/api/scrape/route.ts:5): it does a plain `fetch`, regex-strips tags, truncates at 5,000 chars, silently returns empty strings, and has no real handling for JavaScript pages, PDFs, boilerplate, extraction failures, or source-level metadata. Exa is the best fit because its `/contents` endpoint is explicitly for “known URLs,” returns clean structured content, handles JavaScript-rendered pages, PDFs, and complex layouts, and supports full text, query-targeted highlights, and summaries from the same URL input. For this product’s accuracy requirements, I would use **Exa extractive `text` and/or `highlights`**, not LLM summaries, as the answer evidence. Exa documents that full text is markdown extracted from the page source while filtering peripheral text, and highlights are also extractive source excerpts. That matters more than speed or setup because the agent can cite the original URL and inspect source-derived passages instead of relying on generated summaries. Sources: Exa Contents guide, Exa Contents Retrieval, Exa Search Best Practices. https://exa.ai/docs/reference/contents-api-guide https://exa.ai/docs/reference/contents-retrieval https://exa.ai/docs/reference/search-best-practices **Main Alternatives Considered** **Tavily** was the closest alternative. Tavily Extract supports URL extraction to markdown/text and has an `advanced` mode for more complete extraction, including tables and embedded content. Tavily Search also has source snippets and raw content. I would choose it if replacing the whole Bing search layer were the goal. For this repo’s current need, Exa’s known-URL content model, extractive highlights, freshness controls, and explicit source-grounding fit better. https://docs.tavily.com/documentation/api-reference/endpoint/extract https://docs.tavily.com/documentation/api-reference/endpoint/search **Firecrawl** is strong for scraping, crawling, dynamic pages, browser actions, PDFs, proxy retries, and main-content markdown. It may be the best fallback for hard-to-fetch pages. I would not make it the primary accuracy layer because it is more of a scraping/browser automation product; its optional extra cleaning can involve an LLM pass, and it does not add as much source-quality or citation-oriented retrieval structure as Exa. https://docs.firecrawl.dev/api-reference/endpoint/scrape **Diffbot** is mature for structured extraction from articles, products, discussions, and other page types. It is attractive for news/product pages, but less ideal as the general-purpose readable-content layer for arbitrary URLs from search results, docs pages, PDFs, and mixed web pages. https://docs.diffbot.com/reference/extract-analyze https://docs.diffbot.com/reference/article https://docs.diffbot.com/docs/getting-started-with-diffbot-extract **Jina Reader** is simple and useful for URL-to-LLM-friendly text. I would not choose it here because the repo needs higher assurance, error handling, source metadata, query-relevant excerpts, and freshness controls. https://jina.ai/en-US/reader/ **Bright Data** is excellent for large-scale, anti-bot-heavy, site-specific structured scraping, with many prebuilt scrapers and managed services. It is too heavyweight and scraper-centric for this app’s general “read the URLs we found and answer with citations” workflow. https://docs.brightdata.com/datasets/scrapers/overview https://docs.brightdata.com/api-reference/web-scraper-api I also checked Perplexity’s Search API docs, but I would not treat it as the page-extraction provider here; it is primarily search/answer infrastructure, while this repo needs URL content extraction that can feed its own citation pipeline. https://docs.perplexity.ai/docs/search/quickstart **Recommendation** Use **Exa Contents API** as the primary managed extraction provider. Keep each URL’s extracted text/highlights separate, preserve title/published date/author/status metadata where available, and pass source-numbered excerpts into the answer prompt. For high-stakes freshness, force live crawling with Exa’s freshness controls; for hard blocked/dynamic pages, consider Firecrawl as a secondary fallback rather than the default. **Provider Chosen** I’d choose **Exa Contents API**. The repo already has the right shape for this: Bing finds URLs, [Chat.tsx](/home/user/worktrees/attempt-290dbd24-6aaf-4364-9637-261162d7af9e-cap-1/src/components/Chat/Chat.tsx:322) sends the top three URLs to `/api/scrape`, and citations are generated from those URLs in [utils.ts](/home/user/worktrees/attempt-290dbd24-6aaf-4364-9637-261162d7af9e-cap-1/src/utils/utils.ts:154). The weak point is [route.ts](/home/user/worktrees/attempt-290dbd24-6aaf-4364-9637-261162d7af9e-cap-1/src/app/api/scrape/route.ts:5): it does a plain `fetch`, regex-strips tags, truncates at 5,000 chars, silently returns empty strings, and has no real handling for JavaScript pages, PDFs, boilerplate, extraction failures, or source-level metadata. Exa is the best fit because its `/contents` endpoint is explicitly for “known URLs,” returns clean structured content, handles JavaScript-rendered pages, PDFs, and complex layouts, and supports full text, query-targeted highlights, and summaries from the same URL input. For this product’s accuracy requirements, I would use **Exa extractive `text` and/or `highlights`**, not LLM summaries, as the answer evidence. Exa d

**Provider Chosen** I’d choose **Exa Contents API**. The repo already has the right shape for this: Bing finds URLs, [Chat.tsx](/home/user/worktrees/attempt-290dbd24-6aaf-4364-9637-261162d7af9e-cap-1/src/components/Chat/Chat.tsx:322) sends the top three URLs to `/api/scrape`, and citations are generated from those URLs in [utils.ts](/home/user/worktrees/attempt-290dbd24-6aaf-4364-9637-261162d7af9e-cap-1/src/utils/utils.ts:154). The weak point is [route.ts](/home/user/worktrees/attempt-290dbd24-6aaf-4364-9637-261162d7af9e-cap-1/src/app/api/scrape/route.ts:5): it does a plain `fetch`, regex-strips tags, truncates at 5,000 chars, silently returns empty strings, and has no real handling for JavaScript pages, PDFs, boilerplate, extraction failures, or source-level metadata. Exa is the best fit because its `/contents` endpoint is explicitly for “known URLs,” returns clean structured content, handles JavaScript-rendered pages, PDFs, and complex layouts, and supports full text, query-targeted highlights, and summaries from the same URL input. For this product’s accuracy requirements, I would use **Exa extractive `text` and/or `highlights`**, not LLM summaries, as the answer evidence. Exa documents that full text is markdown extracted from the page source while filtering peripheral text, and highlights are also extractive source excerpts. That matters more than speed or setup because th