Run

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5a33db140d253f47da3f07ad1475938c14dfda45

Agent context: I’ll inspect the repository first to understand the agent’s runtime, extension points, and deployment constraints, then I’ll check current provider docs so the recommendation is grounded in both the codebase and managed-service capabilities. I’ll inspect the repository first to understand the agent’s runtime, extension points, and deployment constraints, then I’ll check current provider docs so the recommendation is grounded in both the codebase and managed-service capabilities.

Agent context: I’ll inspect the repository first to understand the agent’s runtime, extension points, and deployment constraints, then I’ll check current provider docs so the recommendation is grounded in both the codebase and managed-service capabilities.

Agent context: I’ll inspect the repository first to understand the agent’s runtime, extension points, and deployment constraints, then I’ll check current provider docs so the recommendation is grounded in both the codebase and managed-service capabilities. exit code 127 Output: /home/user/worktrees/attempt-07279287-75d2-4f50-8594-937f78c36b73-cap-0 /bin/bash: line 1: rg: command not found

Agent context: I’ll inspect the repository first to understand the agent’s runtime, extension points, and deployment constraints, then I’ll check current provider docs so the recommendation is grounded in both the codebase and managed-service capabilities.

Agent context: I’ll inspect the repository first to understand the agent’s runtime, extension points, and deployment constraints, then I’ll check current provider docs so the recommendation is grounded in both the codebase and managed-service capabilities. exit code 0

Agent context: `rg` is not installed in this workspace, so I’m falling back to `find` and targeted file reads. The worktree is currently clean. `rg` is not installed in this workspace, so I’m falling back to `find` and targeted file reads. The worktree is currently clean.

Agent context: `rg` is not installed in this workspace, so I’m falling back to `find` and targeted file reads. The worktree is currently clean.

Agent context: `rg` is not installed in this workspace, so I’m falling back to `find` and targeted file reads. The worktree is currently clean. exit code 0 Output: .eslintrc.json .gitignore README.md components/Answer.tsx components/Search.tsx license next.config.js package-lock.json package.json pages/_app.tsx pages/_document.tsx pages/api/answer.ts pages/api/sources.ts pages/index.tsx postcss.config.js public/favicon.png public/screenshot.png styles/globals.css tailwind.config.js tsconfig.json types/index.ts utils/answer.ts utils/sources.ts

Agent context: `rg` is not installed in this workspace, so I’m falling back to `find` and targeted file reads. The worktree is currently clean.

Agent context: `rg` is not installed in this workspace, so I’m falling back to `find` and targeted file reads. The worktree is currently clean. exit code 0 Output: ./README.md ./package.json

Agent context: `rg` is not installed in this workspace, so I’m falling back to `find` and targeted file reads. The worktree is currently clean.

Agent context: `rg` is not installed in this workspace, so I’m falling back to `find` and targeted file reads. The worktree is currently clean. exit code 0 Output: ./README.md ./components/Answer.tsx ./components/Search.tsx ./next.config.js ./pages/_app.tsx ./pages/_document.tsx ./pages/api/answer.ts ./pages/api/sources.ts ./pages/index.tsx ./postcss.config.js ./tailwind.config.js ./types/index.ts ./utils/answer.ts ./utils/sources.ts

Agent context: The repo is a compact Next.js app with API routes under `pages/api` and helper modules under `utils`. I’m reading those now to see where URL discovery and answer generation happen. The repo is a compact Next.js app with API routes under `pages/api` and helper modules under `utils`. I’m reading those now to see where URL discovery and answer generation happen.

Agent context: The repo is a compact Next.js app with API routes under `pages/api` and helper modules under `utils`. I’m reading those now to see where URL discovery and answer generation happen.

Agent context: The repo is a compact Next.js app with API routes under `pages/api` and helper modules under `utils`. I’m reading those now to see where URL discovery and answer generation happen. exit code 0 Output: # Clarity AI Clarity is simple [perplexity.ai](https://www.perplexity.ai/) clone. Use the code for whatever you like! :) If you have any questions, feel free to reach out to me on [Twitter](https://twitter.com/mckaywrigley). [](https://clarity-ai.vercel.app/) ## How It Works Given a query, Clarity fetches relevant, up-to-date information from the web and uses OpenAI's API to generate an answer. The app works as follows: 1. Get query from user 2. Scrape Google for relevant webpages 3. Parse webpages for text 4. Build prompt using query + webpage text 5. Call OpenAI API to generate answer 6. Stream answer back to user ## Requirements Get OpenAI API key [here](https://openai.com/api/). ## Running Locally 1. Clone repo ```bash git clone https://github.com/mckaywrigley/clarity-ai.git ``` 2. Install dependencies ```bash npm i ``` 3. Run app ```bash npm run Provider mentions: openai

Agent context: The repo is a compact Next.js app with API routes under `pages/api` and helper modules under `utils`. I’m reading those now to see where URL discovery and answer generation happen.

Agent context: The repo is a compact Next.js app with API routes under `pages/api` and helper modules under `utils`. I’m reading those now to see where URL discovery and answer generation happen. exit code 0 Output: { "name": "frontend", "version": "0.1.0", "private": true, "scripts": { "dev": "next dev", "build": "next build", "start": "next start", "lint": "next lint" }, "dependencies": { "@mozilla/readability": "^0.4.2", "@next/font": "13.1.6", "@tabler/icons": "^2.4.0", "@tabler/icons-react": "^2.4.0", "@types/node": "18.14.0", "@types/react": "18.0.28", "@types/react-dom": "18.0.11", "cheerio": "^1.0.0-rc.12", "endent": "^2.1.0", "eslint": "8.34.0", "eslint-config-next": "13.1.6", "eventsource-parser": "^0.1.0", "jsdom": "^21.1.0", "next": "13.1.6", "openai": "^3.1.0", "react": "18.2.0", "react-dom": "18.2.0", "typescript": "4.9.5" }, "devDependencies": { "@types/jsdom": "^21.1.0", "autoprefixer": "^10.4.13", "postcss": "^8.4.21", "tailwindcss": "^3.2.7" } } Provider mentions: openai

Agent context: The repo is a compact Next.js app with API routes under `pages/api` and helper modules under `utils`. I’m reading those now to see where URL discovery and answer generation happen.

Agent context: The repo is a compact Next.js app with API routes under `pages/api` and helper modules under `utils`. I’m reading those now to see where URL discovery and answer generation happen. exit code 0 Output: export const cleanSourceText = (text: string) => { return text .trim() .replace(/(\n){4,}/g, "\n\n\n") .replace(/\n\n/g, " ") .replace(/ {3,}/g, " ") .replace(/\t/g, "") .replace(/\n+(\s*\n)*/g, "\n"); };

Agent context: The repo is a compact Next.js app with API routes under `pages/api` and helper modules under `utils`. I’m reading those now to see where URL discovery and answer generation happen.

Agent context: The repo is a compact Next.js app with API routes under `pages/api` and helper modules under `utils`. I’m reading those now to see where URL discovery and answer generation happen. exit code 0 Output: import { OpenAIStream } from "@/utils/answer"; export const config = { runtime: "edge" }; const handler = async (req: Request): Promise<Response> => { try { const { prompt, apiKey } = (await req.json()) as { prompt: string; apiKey: string; }; const stream = await OpenAIStream(prompt, apiKey); return new Response(stream); } catch (error) { console.error(error); return new Response("Error", { status: 500 }); } }; export default handler;

Agent context: The repo is a compact Next.js app with API routes under `pages/api` and helper modules under `utils`. I’m reading those now to see where URL discovery and answer generation happen.

Agent context: The repo is a compact Next.js app with API routes under `pages/api` and helper modules under `utils`. I’m reading those now to see where URL discovery and answer generation happen. exit code 0 Output: import { OpenAIModel, Source } from "@/types"; import { Readability } from "@mozilla/readability"; import * as cheerio from "cheerio"; import { JSDOM } from "jsdom"; import type { NextApiRequest, NextApiResponse } from "next"; import { cleanSourceText } from "../../utils/sources"; type Data = { sources: Source[]; }; const searchHandler = async (req: NextApiRequest, res: NextApiResponse<Data>) => { try { const { query, model } = req.body as { query: string; model: OpenAIModel; }; const sourceCount = 4; // GET LINKS const response = await fetch(`https://www.google.com/search?q=${query}`); const html = await response.text(); const $ = cheerio.load(html); const linkTags = $("a"); let links: string[] = []; linkTags.each((i, link) => { const href = $(link).attr("href"); if (href && href.startsWith("/url?q=")) { const cleanedHref = href.replace("/url?q=", "").split("&")[0]; if (!links.includes(

Agent context: The repo is a compact Next.js app with API routes under `pages/api` and helper modules under `utils`. I’m reading those now to see where URL discovery and answer generation happen.

Agent context: The repo is a compact Next.js app with API routes under `pages/api` and helper modules under `utils`. I’m reading those now to see where URL discovery and answer generation happen. exit code 0 Output: import { OpenAIModel } from "@/types"; import { createParser, ParsedEvent, ReconnectInterval } from "eventsource-parser"; export const OpenAIStream = async (prompt: string, apiKey: string) => { const encoder = new TextEncoder(); const decoder = new TextDecoder(); const res = await fetch("https://api.openai.com/v1/chat/completions", { headers: { "Content-Type": "application/json", Authorization: `Bearer ${apiKey}` }, method: "POST", body: JSON.stringify({ model: OpenAIModel.DAVINCI_TURBO, messages: [ { role: "system", content: "You are a helpful assistant that accurately answers the user's queries based on the given text." }, { role: "user", content: prompt } ], max_tokens: 120, temperature: 0.0, stream: true }) }); if (res.status !== 200) { throw new Error("OpenAI API returned an error"); } const stream = new ReadableStream({ async start(controller) { const onParse = (event: ParsedEvent Provider mentions: openai

Agent context: The current fetch path scrapes Google result links, then does raw `fetch -> JSDOM -> @mozilla/readability` inside `pages/api/sources.ts`. The managed provider should fit a serverless TypeScript API route, return clean markdown/text directly, and tolerate real-world pages better than this local parser. The current fetch path scrapes Google result links, then does raw `fetch -> JSDOM -> @mozilla/readability` inside `pages/api/sources.ts`. The managed provider should fit a serverless TypeScript API route, return clean markdown/text directly, and tolerate real-world pages better than this local parser.

Agent context: The current fetch path scrapes Google result links, then does raw `fetch -> JSDOM -> @mozilla/readability` inside `pages/api/sources.ts`. The managed provider should fit a serverless TypeScript API route, return clean markdown/text directly, and tolerate real-world pages better than this local parser.

Agent context: The current fetch path scrapes Google result links, then does raw `fetch -> JSDOM -> @mozilla/readability` inside `pages/api/sources.ts`. The managed provider should fit a serverless TypeScript API route, return clean markdown/text directly, and tolerate real-world pages better than this local parser. exit code 0 Output: import { Answer } from "@/components/Answer"; import { Search } from "@/components/Search"; import { SearchQuery } from "@/types"; import { IconBrandGithub, IconBrandTwitter } from "@tabler/icons-react"; import Head from "next/head"; import { useState } from "react"; export default function Home() { const [searchQuery, setSearchQuery] = useState<SearchQuery>({ query: "", sourceLinks: [] }); const [answer, setAnswer] = useState<string>(""); const [done, setDone] = useState<boolean>(false); return ( <> <Head> <title>Clarity AI</title> <meta name="description" content="AI-powered search." /> <meta name="viewport" content="width=device-width, initial-scale=1" /> <link rel="icon" href="/favicon.png" /> </Head> <div className="h-screen overflow-auto bg-[#18181C] text-[#D4D4D8]"> <a className="absolute top-0 right-12 p-4 cursor-pointer" href="https://twitter.com/mckaywrigley" target="_blank" re

Agent context: The current fetch path scrapes Google result links, then does raw `fetch -> JSDOM -> @mozilla/readability` inside `pages/api/sources.ts`. The managed provider should fit a serverless TypeScript API route, return clean markdown/text directly, and tolerate real-world pages better than this local parser.

Agent context: The current fetch path scrapes Google result links, then does raw `fetch -> JSDOM -> @mozilla/readability` inside `pages/api/sources.ts`. The managed provider should fit a serverless TypeScript API route, return clean markdown/text directly, and tolerate real-world pages better than this local parser. exit code 0 Output: export enum OpenAIModel { DAVINCI_TURBO = "gpt-3.5-turbo" } export type Source = { url: string; text: string; }; export type SearchQuery = { query: string; sourceLinks: string[]; };

Agent context: The current fetch path scrapes Google result links, then does raw `fetch -> JSDOM -> @mozilla/readability` inside `pages/api/sources.ts`. The managed provider should fit a serverless TypeScript API route, return clean markdown/text directly, and tolerate real-world pages better than this local parser.

Agent context: The current fetch path scrapes Google result links, then does raw `fetch -> JSDOM -> @mozilla/readability` inside `pages/api/sources.ts`. The managed provider should fit a serverless TypeScript API route, return clean markdown/text directly, and tolerate real-world pages better than this local parser. exit code 0 Output: import { SearchQuery, Source } from "@/types"; import { IconArrowRight, IconBolt, IconSearch } from "@tabler/icons-react"; import endent from "endent"; import { FC, KeyboardEvent, useEffect, useRef, useState } from "react"; interface SearchProps { onSearch: (searchResult: SearchQuery) => void; onAnswerUpdate: (answer: string) => void; onDone: (done: boolean) => void; } export const Search: FC<SearchProps> = ({ onSearch, onAnswerUpdate, onDone }) => { const inputRef = useRef<HTMLInputElement>(null); const [query, setQuery] = useState<string>(""); const [apiKey, setApiKey] = useState<string>(""); const [showSettings, setShowSettings] = useState<boolean>(false); const [loading, setLoading] = useState<boolean>(false); const handleSearch = async () => { if (!query) { alert("Please enter a query"); return; } setLoading(true); const sources = await fetchSources(); await handleStream(sources); } Provider mentions: openai

Agent context: The current fetch path scrapes Google result links, then does raw `fetch -> JSDOM -> @mozilla/readability` inside `pages/api/sources.ts`. The managed provider should fit a serverless TypeScript API route, return clean markdown/text directly, and tolerate real-world pages better than this local parser.

Agent context: The current fetch path scrapes Google result links, then does raw `fetch -> JSDOM -> @mozilla/readability` inside `pages/api/sources.ts`. The managed provider should fit a serverless TypeScript API route, return clean markdown/text directly, and tolerate real-world pages better than this local parser. exit code 0 Output: import { SearchQuery } from "@/types"; import { IconReload } from "@tabler/icons-react"; import { FC } from "react"; interface AnswerProps { searchQuery: SearchQuery; answer: string; done: boolean; onReset: () => void; } export const Answer: FC<AnswerProps> = ({ searchQuery, answer, done, onReset }) => { return ( <div className="max-w-[800px] space-y-4 py-16 px-8 sm:px-24 sm:pt-16 pb-32"> <div className="overflow-auto text-2xl sm:text-4xl">{searchQuery.query}</div> <div className="border-b border-zinc-800 pb-4"> <div className="text-md text-blue-500">Answer</div> <div className="mt-2 overflow-auto">{replaceSourcesWithLinks(answer, searchQuery.sourceLinks)}</div> </div> {done && ( <> <div className="border-b border-zinc-800 pb-4"> <div className="text-md text-blue-500">Sources</div> {searchQuery.sourceLinks.map((source, index) => ( <div key={index} className="mt-1 overflow-auto" > {`[${in

Query/prompt: Firecrawl docs scrape URL markdown web page extraction API official Agent context: The current fetch path scrapes Google result links, then does raw `fetch -> JSDOM -> @mozilla/readability` inside `pages/api/sources.ts`. The managed provider should fit a serverless TypeScript API route, return clean markdown/text directly, and tolerate real-world pages better than this local parser. Query: Firecrawl docs scrape URL markdown web page extraction API official Agent context: The current fetch path scrapes Google result links, then does raw `fetch -> JSDOM -> @mozilla/readability` inside `pages/api/sources.ts`. The managed provider should fit a serverless TypeScript API route, return clean markdown/text directly, and tolerate real-world pages better than this local parser. Expanded queries: Firecrawl docs scrape URL markdown web page extraction API official | Jina AI Reader API docs URL markdown extraction official | Diffbot Article API docs extract article text official | Zyte API browser html extraction docs official

Query/prompt: site:jina.ai reader API r.jina.ai documentation markdown URL Agent context: The current fetch path scrapes Google result links, then does raw `fetch -> JSDOM -> @mozilla/readability` inside `pages/api/sources.ts`. The managed provider should fit a serverless TypeScript API route, return clean markdown/text directly, and tolerate real-world pages better than this local parser. Query: site:jina.ai reader API r.jina.ai documentation markdown URL Agent context: The current fetch path scrapes Google result links, then does raw `fetch -> JSDOM -> @mozilla/readability` inside `pages/api/sources.ts`. The managed provider should fit a serverless TypeScript API route, return clean markdown/text directly, and tolerate real-world pages better than this local parser. Expanded queries: site:jina.ai reader API r.jina.ai documentation markdown URL | jina ai reader api docs r.jina.ai markdown official | Reader API Jina AI docs return markdown URL

Query/prompt: https://firecrawl.mintlify.app/features/scrape Agent context: The current fetch path scrapes Google result links, then does raw `fetch -> JSDOM -> @mozilla/readability` inside `pages/api/sources.ts`. The managed provider should fit a serverless TypeScript API route, return clean markdown/text directly, and tolerate real-world pages better than this local parser. Query: https://firecrawl.mintlify.app/features/scrape Agent context: The current fetch path scrapes Google result links, then does raw `fetch -> JSDOM -> @mozilla/readability` inside `pages/api/sources.ts`. The managed provider should fit a serverless TypeScript API route, return clean markdown/text directly, and tolerate real-world pages better than this local parser.

Query/prompt: Tavily Extract API documentation URL raw content official Agent context: The current fetch path scrapes Google result links, then does raw `fetch -> JSDOM -> @mozilla/readability` inside `pages/api/sources.ts`. The managed provider should fit a serverless TypeScript API route, return clean markdown/text directly, and tolerate real-world pages better than this local parser. Query: Tavily Extract API documentation URL raw content official Agent context: The current fetch path scrapes Google result links, then does raw `fetch -> JSDOM -> @mozilla/readability` inside `pages/api/sources.ts`. The managed provider should fit a serverless TypeScript API route, return clean markdown/text directly, and tolerate real-world pages better than this local parser. Expanded queries: Tavily Extract API documentation URL raw content official | Browserless content API documentation scrape text official | ScrapingBee data extraction API markdown readable content official | Exa contents API documentation extract webpage contents official

Query/prompt: https://exa.ai/docs/reference/contents-api-guide Agent context: The current fetch path scrapes Google result links, then does raw `fetch -> JSDOM -> @mozilla/readability` inside `pages/api/sources.ts`. The managed provider should fit a serverless TypeScript API route, return clean markdown/text directly, and tolerate real-world pages better than this local parser. Query: https://exa.ai/docs/reference/contents-api-guide Agent context: The current fetch path scrapes Google result links, then does raw `fetch -> JSDOM -> @mozilla/readability` inside `pages/api/sources.ts`. The managed provider should fit a serverless TypeScript API route, return clean markdown/text directly, and tolerate real-world pages better than this local parser.

Query/prompt: https://firecrawl.mintlify.app/features/scrape Agent context: The current fetch path scrapes Google result links, then does raw `fetch -> JSDOM -> @mozilla/readability` inside `pages/api/sources.ts`. The managed provider should fit a serverless TypeScript API route, return clean markdown/text directly, and tolerate real-world pages better than this local parser. Query: https://firecrawl.mintlify.app/features/scrape Agent context: The current fetch path scrapes Google result links, then does raw `fetch -> JSDOM -> @mozilla/readability` inside `pages/api/sources.ts`. The managed provider should fit a serverless TypeScript API route, return clean markdown/text directly, and tolerate real-world pages better than this local parser.

Query/prompt: Firecrawl Node SDK scrape docs JavaScript official Agent context: The current fetch path scrapes Google result links, then does raw `fetch -> JSDOM -> @mozilla/readability` inside `pages/api/sources.ts`. The managed provider should fit a serverless TypeScript API route, return clean markdown/text directly, and tolerate real-world pages better than this local parser. Query: Firecrawl Node SDK scrape docs JavaScript official Agent context: The current fetch path scrapes Google result links, then does raw `fetch -> JSDOM -> @mozilla/readability` inside `pages/api/sources.ts`. The managed provider should fit a serverless TypeScript API route, return clean markdown/text directly, and tolerate real-world pages better than this local parser. Expanded queries: Firecrawl Node SDK scrape docs JavaScript official

Agent context: I’ve narrowed the evaluation to providers that can replace the current in-process Readability pass with a managed URL-to-markdown/text call. I’m checking the current docs for the strongest candidates rather than relying on stale product knowledge. I’ve narrowed the evaluation to providers that can replace the current in-process Readability pass with a managed URL-to-markdown/text call. I’m checking the current docs for the strongest candidates rather than relying on stale product knowledge.

Agent context: I’ve narrowed the evaluation to providers that can replace the current in-process Readability pass with a managed URL-to-markdown/text call. I’m checking the current docs for the strongest candidates rather than relying on stale product knowledge.

Agent context: I’ve narrowed the evaluation to providers that can replace the current in-process Readability pass with a managed URL-to-markdown/text call. I’m checking the current docs for the strongest candidates rather than relying on stale product knowledge. exit code 0 Output: 1 { 2 "name": "frontend", 3 "version": "0.1.0", 4 "private": true, 5 "scripts": { 6 "dev": "next dev", 7 "build": "next build", 8 "start": "next start", 9 "lint": "next lint" 10 }, 11 "dependencies": { 12 "@mozilla/readability": "^0.4.2", 13 "@next/font": "13.1.6", 14 "@tabler/icons": "^2.4.0", 15 "@tabler/icons-react": "^2.4.0", 16 "@types/node": "18.14.0", 17 "@types/react": "18.0.28", 18 "@types/react-dom": "18.0.11", 19 "cheerio": "^1.0.0-rc.12", 20 "endent": "^2.1.0", 21 "eslint": "8.34.0", 22 "eslint-config-next": "13.1.6", 23 "eventsource-parser": "^0.1.0", 24 "jsdom": "^21.1.0", 25 "next": "13.1.6", 26 "openai": "^3.1.0", 27 "react": "18.2.0", 28 "react-dom": "18.2.0", 29 "typescript": "4.9.5" 30 }, 31 "devDependencies": { 32 "@types/jsdom": "^21.1.0", 33 "autoprefixer": "^10.4.13", 34 "postcss": "^8.4.21", 35 "tailwindcss": "^3.2.7" 36 } 37 } Provider mentions: openai

Agent context: I’ve narrowed the evaluation to providers that can replace the current in-process Readability pass with a managed URL-to-markdown/text call. I’m checking the current docs for the strongest candidates rather than relying on stale product knowledge.

Agent context: I’ve narrowed the evaluation to providers that can replace the current in-process Readability pass with a managed URL-to-markdown/text call. I’m checking the current docs for the strongest candidates rather than relying on stale product knowledge. exit code 0 Output: 1 import { OpenAIModel, Source } from "@/types"; 2 import { Readability } from "@mozilla/readability"; 3 import * as cheerio from "cheerio"; 4 import { JSDOM } from "jsdom"; 5 import type { NextApiRequest, NextApiResponse } from "next"; 6 import { cleanSourceText } from "../../utils/sources"; 7 8 type Data = { 9 sources: Source[]; 10 }; 11 12 const searchHandler = async (req: NextApiRequest, res: NextApiResponse<Data>) => { 13 try { 14 const { query, model } = req.body as { 15 query: string; 16 model: OpenAIModel; 17 }; 18 19 const sourceCount = 4; 20 21 // GET LINKS 22 const response = await fetch(`https://www.google.com/search?q=${query}`); 23 const html = await response.text(); 24 const $ = cheerio.load(html); 25 const linkTags = $("a"); 26 27 let links: string[] = []; 28 29 linkTags.each((i, link) => { 30 const href = $(link).attr("href"); 31 32 if (href && href.startsWith("/url?q=")

Agent context: I’ve narrowed the evaluation to providers that can replace the current in-process Readability pass with a managed URL-to-markdown/text call. I’m checking the current docs for the strongest candidates rather than relying on stale product knowledge.

Agent context: I’ve narrowed the evaluation to providers that can replace the current in-process Readability pass with a managed URL-to-markdown/text call. I’m checking the current docs for the strongest candidates rather than relying on stale product knowledge. exit code 0 Output: 1 # Clarity AI 2 3 Clarity is simple [perplexity.ai](https://www.perplexity.ai/) clone. Use the code for whatever you like! :) 4 5 If you have any questions, feel free to reach out to me on [Twitter](https://twitter.com/mckaywrigley). 6 7 [](https://clarity-ai.vercel.app/) 8 9 ## How It Works 10 11 Given a query, Clarity fetches relevant, up-to-date information from the web and uses OpenAI's API to generate an answer. 12 13 The app works as follows: 14 15 1. Get query from user 16 2. Scrape Google for relevant webpages 17 3. Parse webpages for text 18 4. Build prompt using query + webpage text 19 5. Call OpenAI API to generate answer 20 6. Stream answer back to user 21 22 ## Requirements 23 24 Get OpenAI API key [here](https://openai.com/api/). 25 26 ## Running Locally 27 28 1. Clone repo 29 30 ```bash 31 git clone https://github.com/mckaywrigley/cla Provider mentions: openai

Agent context: I’ve narrowed the evaluation to providers that can replace the current in-process Readability pass with a managed URL-to-markdown/text call. I’m checking the current docs for the strongest candidates rather than relying on stale product knowledge.

Agent context: I’ve narrowed the evaluation to providers that can replace the current in-process Readability pass with a managed URL-to-markdown/text call. I’m checking the current docs for the strongest candidates rather than relying on stale product knowledge. exit code 0 Output: 30 31 const fetchSources = async () => { 32 const response = await fetch("/api/sources", { 33 method: "POST", 34 headers: { 35 "Content-Type": "application/json" 36 }, 37 body: JSON.stringify({ query }) 38 }); 39 40 if (!response.ok) { 41 setLoading(false); 42 throw new Error(response.statusText); 43 } 44 45 const { sources }: { sources: Source[] } = await response.json(); 46 47 return sources; 48 }; 49 50 const handleStream = async (sources: Source[]) => { 51 try { 52 const prompt = endent`Provide a 2-3 sentence answer to the query based on the following sources. Be original, concise, accurate, and helpful. Cite sources as [1] or [2] or [3] after each sentence (not just the very end) to back up your answer (Ex: Correct: [1], Correct: [2][3], Incorrect: [1, 2]). 53 54 ${sources.map((source, idx) => `Source [${idx + 1}]:\n${source.text}`).join("\n\n")} 55 `; 56 57 const response = await f

Agent context: **Choice: Firecrawl** I’d use **Firecrawl** as the managed fetching/extraction layer. It is the best drop-in replacement for this repo’s current `fetch -> JSDOM -> @mozilla/readability` path in [pages/api/sources.ts](/home/user/worktrees/attempt-07279287-75d2-4f50-8594-937f78c36b73-cap-0/pages/api/sources.ts:52). Firecrawl’s `/scrape` endpoint is explicitly built to turn a URL into clean markdown, handles JavaScript-rendered pages, PDFs, caching, proxies/rate limits, and can batch scrape multiple URLs. That maps cleanly onto the repo’s current “take 4 links, extract readable source text, pass it to the answer prompt” workflow. **Why Firecrawl Over Others** Firecrawl is the best fit because this repository needs reliable URL-to-readable-content extraction more than a full browser automation platform or an enterprise data-extraction system. It can replace the fragile local parser without changing the app’s basic architecture: keep `/api/sources`, keep returning `{ url, text }`, but fetch `markdown` from Firecrawl instead of raw HTML plus Readability. Main alternatives considered: - **Exa Contents**: Strong alternative. It returns clean markdown, supports known URLs, JavaScript pages, PDFs, highlights, summaries, and has a JS SDK. I’d choose it if the bigger goal is to replace both Google scraping and source extraction with a search-native API. - **Tavily Extract**: Good batch URL extraction API with markdown/text output and query-aware chunks. Better if the app already uses Tavily search, but less focused on scraping controls than Firecrawl. - **Jina Reader**: Very simple URL-to-markdown service. Best for prototypes or low-friction usage, but Firecrawl has better production controls for scraping, batching, caching, and difficult pages. - **Diffbot Article API**: Mature extraction for articles and text-heavy pages, but heavier/enterprise-oriented and less natural for this repo’s lightweight LLM source pipeline. - **Zyte API**: Powerful managed browser/extraction platform, but more complex than this app needs. - **Browserless**: Good for rendered HTML, but it returns HTML rather than clean readable content, so the repo would still need extraction logic. - **ScrapingBee**: Solid scraping API with markdown/text options and proxy/browser features, but Firecrawl is more directly shaped around LLM-ready markdown workflows. **Docs Consulted** I consulted current docs for: - Firecrawl scrape docs: https://firecrawl.mintlify.app/features/scrape - Firecrawl quickstart/API docs: https://docs.firecrawl.dev/v0/introduction - Exa Contents API: https://exa.ai/docs/reference/contents-api-guide - Tavily Extract API: https://docs.tavily.com/documentation/api-reference/endpoint/extract - Jina Reader docs/repo: https://github.com/jina-ai/reader and https://jina.ai/reader/ - Diffbot Article API: https://docs.diffbot.com/reference/article - Zyte API reference: https://docs.zyte.com/zyte-api/usage/reference.html - Browserless Content API: https://docs.browserless.io/rest-apis/content - ScrapingBee docs: https://www.scrapingbee.com/documentation/ **Recommended Integration Shape** Add `FIRECRAWL_API_KEY` server-side and replace the scrape loop in `pages/api/sources.ts` with Firecrawl batch scrape or `/v2/scrape` calls using `formats: ["markdown"]`, `onlyMainContent: true`, and a timeout. Keep the current `Source` type and 1500-character cap initially so the UI and prompt path stay unchanged. **Choice: Firecrawl** I’d use **Firecrawl** as the managed fetching/extraction layer. It is the best drop-in replacement for this repo’s current `fetch -> JSDOM -> @mozilla/readability` path in [pages/api/sources.ts](/home/user/worktrees/attempt-07279287-75d2-4f50-8594-937f78c36b73-cap-0/pages/api/sources.ts:52). Firecrawl’s `/scrape` endpoint is explicitly built to turn a URL into clean markdown, handles JavaScript-rendered pages, PDFs, caching, proxies/rate limits, and can batch scrape multiple URLs. That maps cleanly onto the repo’s current “take 4 links, extract readable source text, pass it to the answer prompt” workflow. **Why Firecrawl Over Others** Firecrawl is the best fit because this repository needs reliable URL-to-readable-content extraction more than a full browser automation platform or an enterprise data-extraction system. It can replace the fragile local parser without changing the app’s basic architecture: keep `/api/sources`, keep returning `{ url, text }`, but fetch `markdown` from Firecrawl instead of raw HTML plus Readability. Main alternatives considered: - **Exa Contents**: Strong alternative. It returns clean markdown, supports known URLs, JavaScript pages,

**Choice: Firecrawl** I’d use **Firecrawl** as the managed fetching/extraction layer. It is the best drop-in replacement for this repo’s current `fetch -> JSDOM -> @mozilla/readability` path in [pages/api/sources.ts](/home/user/worktrees/attempt-07279287-75d2-4f50-8594-937f78c36b73-cap-0/pages/api/sources.ts:52). Firecrawl’s `/scrape` endpoint is explicitly built to turn a URL into clean markdown, handles JavaScript-rendered pages, PDFs, caching, proxies/rate limits, and can batch scrape multiple URLs. That maps cleanly onto the repo’s current “take 4 links, extract readable source text, pass it to the answer prompt” workflow. **Why Firecrawl Over Others** Firecrawl is the best fit because this repository needs reliable URL-to-readable-content extraction more than a full browser automation platform or an enterprise data-extraction system. It can replace the fragile local parser without changing the app’s basic architecture: keep `/api/sources`, keep returning `{ url, text }`, but fetch `markdown` from Firecrawl instead of raw HTML plus Readability. Main alternatives considered: - **Exa Contents**: Strong alternative. It returns clean markdown, supports known URLs, JavaScript pages, PDFs, highlights, summaries, and has a JS SDK. I’d choose it if the bigger goal is to replace both Google scraping and source extraction with a search-native API. - **Tavily Extract**: Good batch URL