Run

P3F

5a33db140d253f47da3f07ad1475938c14dfda45

Agent context: I'll research the repository first to understand its architecture and how the agent works. I'll research the repository first to understand its architecture and how the agent works.

Agent context: I'll research the repository first to understand its architecture and how the agent works. Tool result summary: EISDIR: illegal operation on a directory, read '/home/user/worktrees/attempt-0ee2cdd9-4f48-47cd-bb8e-c67ee313fce6-cap-0' Agent context: I'll research the repository first to understand its architecture and how the agent works.

Agent context: I'll research the repository first to understand its architecture and how the agent works. Tool result summary: /home/user/worktrees/attempt-0ee2cdd9-4f48-47cd-bb8e-c67ee313fce6-cap-0/README.md /home/user/worktrees/attempt-0ee2cdd9-4f48-47cd-bb8e-c67ee313fce6-cap-0/package.json /home/user/worktrees/attempt-0ee2cdd9-4f48-47cd-bb8e-c67ee313fce6-cap-0/tsconfig.json Agent context: I'll research the repository first to understand its architecture and how the agent works.

Agent context: I'll research the repository first to understand its architecture and how the agent works. Tool result summary: 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 u Agent context: I'll research the repository first to understand its architecture and how the agent works. Provider mentions: openai

Agent context: I'll research the repository first to understand its architecture and how the agent works. Tool result summary: 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. Agent context: I'll research the repository first to understand its architecture and how the agent works. Provider mentions: openai

Agent context: I'll research the repository first to understand its architecture and how the agent works. Tool result summary: components/Answer.tsx components/Search.tsx pages/_app.tsx pages/_document.tsx pages/index.tsx Agent context: I'll research the repository first to understand its architecture and how the agent works.

Agent context: I'll research the repository first to understand its architecture and how the agent works. Tool result summary: /home/user/worktrees/attempt-0ee2cdd9-4f48-47cd-bb8e-c67ee313fce6-cap-0/components /home/user/worktrees/attempt-0ee2cdd9-4f48-47cd-bb8e-c67ee313fce6-cap-0/pages /home/user/worktrees/attempt-0ee2cdd9-4f48-47cd-bb8e-c67ee313fce6-cap-0/pages/api Agent context: I'll research the repository first to understand its architecture and how the agent works.

Agent context: I'll research the repository first to understand its architecture and how the agent works. Tool result summary: Found 1 file package-lock.json Agent context: I'll research the repository first to understand its architecture and how the agent works.

Agent context: I'll research the repository first to understand its architecture and how the agent works. Tool result summary: /home/user/worktrees/attempt-0ee2cdd9-4f48-47cd-bb8e-c67ee313fce6-cap-0/pages/api/answer.ts /home/user/worktrees/attempt-0ee2cdd9-4f48-47cd-bb8e-c67ee313fce6-cap-0/pages/api/sources.ts Agent context: I'll research the repository first to understand its architecture and how the agent works.

Agent context: I'll research the repository first to understand its architecture and how the agent works. Tool result summary: No files found Agent context: I'll research the repository first to understand its architecture and how the agent works. Provider mentions: openai

Agent context: I'll research the repository first to understand its architecture and how the agent works. Tool result summary: 1 import { OpenAIStream } from "@/utils/answer"; 2 3 export const config = { 4 runtime: "edge" 5 }; 6 7 const handler = async (req: Request): Promise<Response> => { 8 try { 9 const { prompt, apiKey } = (await req.json()) as { 10 prompt: string; 11 apiKey: string; 12 }; 13 14 const stream = await OpenAIStream(prompt, apiKey); 15 16 return new Response(stream); 17 } catch (error) { 18 console.error(error); 19 return ne Agent context: I'll research the repository first to understand its architecture and how the agent works.

Agent context: I'll research the repository first to understand its architecture and how the agent works. Tool result summary: 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 Agent context: I'll research the repository first to understand its architecture and how the agent works.

Agent context: I'll research the repository first to understand its architecture and how the agent works. Tool result summary: 1 import { SearchQuery, Source } from "@/types"; 2 import { IconArrowRight, IconBolt, IconSearch } from "@tabler/icons-react"; 3 import endent from "endent"; 4 import { FC, KeyboardEvent, useEffect, useRef, useState } from "react"; 5 6 interface SearchProps { 7 onSearch: (searchResult: SearchQuery) => void; 8 onAnswerUpdate: (answer: string) => void; 9 onDone: (done: boolean) => void; 10 } 11 12 export const Search: Agent context: I'll research the repository first to understand its architecture and how the agent works.

Agent context: I'll research the repository first to understand its architecture and how the agent works. Tool result summary: /home/user/worktrees/attempt-0ee2cdd9-4f48-47cd-bb8e-c67ee313fce6-cap-0/utils/answer.ts /home/user/worktrees/attempt-0ee2cdd9-4f48-47cd-bb8e-c67ee313fce6-cap-0/utils/sources.ts Agent context: I'll research the repository first to understand its architecture and how the agent works.

Agent context: I'll research the repository first to understand its architecture and how the agent works. Tool result summary: 1 import { SearchQuery } from "@/types"; 2 import { IconReload } from "@tabler/icons-react"; 3 import { FC } from "react"; 4 5 interface AnswerProps { 6 searchQuery: SearchQuery; 7 answer: string; 8 done: boolean; 9 onReset: () => void; 10 } 11 12 export const Answer: FC<AnswerProps> = ({ searchQuery, answer, done, onReset }) => { 13 return ( 14 <div className="max-w-[800px] space-y-4 py-16 px-8 sm:px-24 sm:pt-16 pb- Agent context: I'll research the repository first to understand its architecture and how the agent works.

Agent context: I'll research the repository first to understand its architecture and how the agent works. Tool result summary: 1 import { OpenAIModel } from "@/types"; 2 import { createParser, ParsedEvent, ReconnectInterval } from "eventsource-parser"; 3 4 export const OpenAIStream = async (prompt: string, apiKey: string) => { 5 const encoder = new TextEncoder(); 6 const decoder = new TextDecoder(); 7 8 const res = await fetch("https://api.openai.com/v1/chat/completions", { 9 headers: { 10 "Content-Type": "application/json", 11 Authorization Agent context: I'll research the repository first to understand its architecture and how the agent works. Provider mentions: openai

Agent context: I'll research the repository first to understand its architecture and how the agent works. Tool result summary: 1 export const cleanSourceText = (text: string) => { 2 return text 3 .trim() 4 .replace(/( ){4,}/g, " ") 5 .replace(/ /g, " ") 6 .replace(/ {3,}/g, " ") 7 .replace(/\t/g, "") 8 .replace(/ +(\s* )*/g, " "); 9 }; 10 Agent context: I'll research the repository first to understand its architecture and how the agent works.

Agent context: I'll research the repository first to understand its architecture and how the agent works. Tool result summary: (Bash completed with no output) Agent context: I'll research the repository first to understand its architecture and how the agent works.

Agent context: I'll research the repository first to understand its architecture and how the agent works. Tool result summary: 1 import { Answer } from "@/components/Answer"; 2 import { Search } from "@/components/Search"; 3 import { SearchQuery } from "@/types"; 4 import { IconBrandGithub, IconBrandTwitter } from "@tabler/icons-react"; 5 import Head from "next/head"; 6 import { useState } from "react"; 7 8 export default function Home() { 9 const [searchQuery, setSearchQuery] = useState<SearchQuery>({ query: "", source }); 10 const [answer, Agent context: I'll research the repository first to understand its architecture and how the agent works.

Agent context: I'll research the repository first to understand its architecture and how the agent works. Tool result summary: /home/user/worktrees/attempt-0ee2cdd9-4f48-47cd-bb8e-c67ee313fce6-cap-0/types/index.ts Agent context: I'll research the repository first to understand its architecture and how the agent works.

Agent context: I'll research the repository first to understand its architecture and how the agent works. Tool result summary: No files found Agent context: I'll research the repository first to understand its architecture and how the agent works.

Agent context: I'll research the repository first to understand its architecture and how the agent works. Tool result summary: 1 export enum OpenAIModel { 2 DAVINCI_TURBO = "gpt-3.5-turbo" 3 } 4 5 export type Source = { 6 url: string; 7 text: string; 8 }; 9 10 export type SearchQuery = { 11 query: string; 12 sourceLinks: string[]; 13 }; 14 Agent context: I'll research the repository first to understand its architecture and how the agent works.

Agent context: I'll research the repository first to understand its architecture and how the agent works. Tool result summary: /home/user/worktrees/attempt-0ee2cdd9-4f48-47cd-bb8e-c67ee313fce6-cap-0 /home/user/worktrees/attempt-0ee2cdd9-4f48-47cd-bb8e-c67ee313fce6-cap-0/components /home/user/worktrees/attempt-0ee2cdd9-4f48-47cd-bb8e-c67ee313fce6-cap-0/pages /home/user/worktrees/attempt-0ee2cdd9-4f48-47cd-bb8e-c67ee313fce6-cap-0/pages/api /home/user/worktrees/attempt-0ee2cdd9-4f48-47cd-bb8e-c67ee313fce6-cap-0/public /home/user/worktrees/attem Agent context: I'll research the repository first to understand its architecture and how the agent works.

Agent context: I'll research the repository first to understand its architecture and how the agent works. Tool result summary: /home/user/worktrees/attempt-0ee2cdd9-4f48-47cd-bb8e-c67ee313fce6-cap-0/next.config.js /home/user/worktrees/attempt-0ee2cdd9-4f48-47cd-bb8e-c67ee313fce6-cap-0/postcss.config.js /home/user/worktrees/attempt-0ee2cdd9-4f48-47cd-bb8e-c67ee313fce6-cap-0/tailwind.config.js Agent context: I'll research the repository first to understand its architecture and how the agent works.

Agent context: Let me look at the current web fetching implementation directly. Let me look at the current web fetching implementation directly.

Agent context: Let me look at the current web fetching implementation directly. Tool result summary: 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 Agent context: Let me look at the current web fetching implementation directly.

Agent context: The current approach uses raw `fetch()` + JSDOM/Readability — which can't render JS-heavy/dynamic pages and gets blocked or returns noise. Let me research managed providers by consulting their actual documentation. The current approach uses raw `fetch()` + JSDOM/Readability — which can't render JS-heavy/dynamic pages and gets blocked or returns noise. Let me research managed providers by consulting their actual documentation.

Agent context: I'll consult the actual documentation of the leading managed providers. Let me fetch their docs in parallel. I'll consult the actual documentation of the leading managed providers. Let me fetch their docs in parallel.

Query/prompt: Summarize how Firecrawl's scrape endpoint works: does it render JavaScript/dynamic pages? What output formats does it return (markdown, LLM-ready)? How does it handle boilerplate/noise removal? What are the main parameters and any batch/scale capabilities? Note pricing model if mentioned. Agent context: I'll consult the actual documentation of the leading managed providers. Let me fetch their docs in parallel. Fetch prompt: Summarize how Firecrawl's scrape endpoint works: does it render JavaScript/dynamic pages? What output formats does it return (markdown, LLM-ready)? How does it handle boilerplate/noise removal? What are the main parameters and any batch/scale capabilities? Note pricing model if mentioned. Tool result summary: # Firecrawl Scrape Endpoint Summary ## JavaScript & Dynamic Content Handling Firecrawl's `/scrape` endpoint actively handles dynamic content, including "js-rendered sites" and complex websites. It manages infrastructure challenges like proxies, caching, and rate limits automatically. ## Output Formats The endpoint returns multiple LLM-ready formats: - **Markdown** (primary, cleaned format) - **HTML** (cleaned or raw) Agent context: I'll consult the actual documentation of the leading managed providers. Let me fetch their docs in parallel.

Query/prompt: Summarize how Jina AI Reader (r.jina.ai) works: does it render JavaScript/dynamic pages? What output does it return (clean markdown, LLM-ready)? How does it handle boilerplate removal? How is it called (URL prefix, API)? Pricing and rate limits? Agent context: I'll consult the actual documentation of the leading managed providers. Let me fetch their docs in parallel. Fetch prompt: Summarize how Jina AI Reader (r.jina.ai) works: does it render JavaScript/dynamic pages? What output does it return (clean markdown, LLM-ready)? How does it handle boilerplate removal? How is it called (URL prefix, API)? Pricing and rate limits? Tool result summary: # Jina AI Reader (r.jina.ai) Summary ## How It Works Reader uses a proxy to fetch URLs and renders content in a browser, extracting high-quality main content from webpages. This browser-based approach handles dynamic content rendering. ## Output Format The service converts webpage content into "clean, LLM-friendly text" delivered as JSON responses containing the URL, title, content, and timestamp. It returns markdown Agent context: I'll consult the actual documentation of the leading managed providers. Let me fetch their docs in parallel.

Query/prompt: Summarize Tavily's Extract API: does it render JavaScript/dynamic pages? What content does it return (clean, LLM-ready)? Parameters, batch support, and pricing model? Is it designed for LLM agents? Agent context: I'll consult the actual documentation of the leading managed providers. Let me fetch their docs in parallel. Fetch prompt: Summarize Tavily's Extract API: does it render JavaScript/dynamic pages? What content does it return (clean, LLM-ready)? Parameters, batch support, and pricing model? Is it designed for LLM agents? Tool result summary: # Tavily Extract API Summary ## JavaScript/Dynamic Content Rendering The documentation doesn't explicitly state whether the API renders JavaScript or handles dynamic pages. No mention of headless browser technology or JavaScript execution is provided. ## Content Return Format The API returns "content in markdown format" or "plain text" depending on your format parameter selection. It's optimized for LLM consumption, Agent context: I'll consult the actual documentation of the leading managed providers. Let me fetch their docs in parallel.

Agent context: Let me consult two more alternatives to round out the comparison: Exa and ScrapingBee. Let me consult two more alternatives to round out the comparison: Exa and ScrapingBee.

Query/prompt: Summarize Exa's get-contents/contents API: does it render JavaScript/dynamic pages with a live crawl? What content does it return (clean text, markdown, LLM-ready)? Is it primarily a search engine with extraction, or a standalone page fetcher? Batch support and pricing? Agent context: Let me consult two more alternatives to round out the comparison: Exa and ScrapingBee. Fetch prompt: Summarize Exa's get-contents/contents API: does it render JavaScript/dynamic pages with a live crawl? What content does it return (clean text, markdown, LLM-ready)? Is it primarily a search engine with extraction, or a standalone page fetcher? Batch support and pricing? Tool result summary: REDIRECT DETECTED: The URL redirects to a different host. Original URL: https://docs.exa.ai/reference/get-contents Redirect URL: https://exa.ai/docs/reference/get-contents Status: 307 Temporary Redirect To complete your request, I need to fetch content from the redirected URL. Please use WebFetch again with these parameters: - url: "https://exa.ai/docs/reference/get-contents" - prompt: "Summarize Exa's get-contents/c Agent context: Let me consult two more alternatives to round out the comparison: Exa and ScrapingBee.

Query/prompt: Summarize ScrapingBee: does it render JavaScript with headless browsers? Does it return clean LLM-ready/markdown content or just raw HTML? Is it a general-purpose scraping proxy or a content-extraction/LLM-focused service? Pricing model and scale? Agent context: Let me consult two more alternatives to round out the comparison: Exa and ScrapingBee. Fetch prompt: Summarize ScrapingBee: does it render JavaScript with headless browsers? Does it return clean LLM-ready/markdown content or just raw HTML? Is it a general-purpose scraping proxy or a content-extraction/LLM-focused service? Pricing model and scale? Tool result summary: # ScrapingBee Overview ## JavaScript Rendering Yes, ScrapingBee renders JavaScript with headless browsers by default. The documentation states: *"By default, ScrapingBee fetches the URL to scrape via a headless browser that will execute the JavaScript code on the page."* This is useful for Single Page Applications built with React, Angular, or Vue. However, you can disable this with `render_js=false` to reduce costs. Agent context: Let me consult two more alternatives to round out the comparison: Exa and ScrapingBee.

Agent context: Let me fetch the redirected Exa docs URL. Let me fetch the redirected Exa docs URL.

Query/prompt: Summarize Exa's get-contents/contents API: does it render JavaScript/dynamic pages with a live crawl? What content does it return (clean text, markdown, LLM-ready)? Is it primarily a search engine with extraction, or a standalone page fetcher? Batch support and pricing? Agent context: Let me fetch the redirected Exa docs URL. Fetch prompt: Summarize Exa's get-contents/contents API: does it render JavaScript/dynamic pages with a live crawl? What content does it return (clean text, markdown, LLM-ready)? Is it primarily a search engine with extraction, or a standalone page fetcher? Batch support and pricing? Tool result summary: # Exa Contents API Summary ## JavaScript/Dynamic Rendering Yes, the API supports live crawling. It offers a `maxAgeHours` parameter where setting it to `0` "fetches fresh content" and applies "text rendering options to newly fetched pages." The `livecrawl` parameter (deprecated) previously controlled fetch behavior with options like "always" and "fallback." ## Content Return Format The API returns multiple content ty Agent context: Let me fetch the redirected Exa docs URL.

Agent context: ## Recommendation: **Firecrawl** (`/scrape` endpoint) ### Why Firecrawl It's the closest match to every requirement in the brief, and it's a drop-in replacement for what `pages/api/sources.ts:53-67` does today (raw `fetch` → JSDOM → Readability, which silently fails on JS-rendered/dynamic pages and gets blocked or returns noise): - **Renders JS/dynamic pages** out of the box, and manages proxies, caching, and rate limits for you — the exact gap in the current code. - **LLM-ready by default**: returns cleaned markdown with `onlyMainContent`, stripping nav/ads/boilerplate. That replaces the hand-rolled `cleanSourceText` + `.slice(0, 1500)` truncation in `sources.ts:62,72`. - **Scale**: `batch_scrape` fits the repo's "scrape N source links in parallel" pattern (`sources.ts:53`) with one async job instead of N fragile `fetch` calls. - **Simple pricing**: 1 credit/page baseline; predictable for this workload. - **`actions`** (wait/click) handle stubborn dynamic pages — something JSDOM fundamentally cannot do. ### Main alternatives considered - **Jina AI Reader** — strongest runner-up. Browser-rendered, clean markdown, dead-simple (`r.jina.ai/<url>`), generous free tier (10M tokens). Lighter than Firecrawl but fewer scale/interaction controls (no native batch, no click/wait actions). Best pick if you want minimal integration effort. - **Tavily Extract** — explicitly LLM-agent-focused, query-aware chunk reranking, but docs don't confirm JS rendering, which is a core requirement here. Better as a *search* API than a page renderer. - **Exa Contents** — good live-crawl + highlights/summaries, batch up to 100 URLs, but it's oriented around its neural search engine; standalone fetching is secondary. - **ScrapingBee** — reliable headless-browser rendering, but it's a general-purpose scraping *proxy* returning raw HTML by default; markdown is an add-on, not the product. More plumbing for less LLM-ready output. ### Documentation I actually consulted - Firecrawl Scrape — `docs.firecrawl.dev/features/scrape` - Jina Reader — `jina.ai/reader` - Tavily Extract — `docs.tavily.com/.../endpoint/extract` - Exa Contents — `exa.ai/docs/reference/get-contents` - ScrapingBee — `scrapingbee.com/documentation` ### Why Firecrawl over the others Tavily and Exa are fundamentally search products with extraction bolted on; ScrapingBee is a raw-HTML proxy needing extra cleaning. Only **Firecrawl and Jina** lead with "render the page, return clean markdown." Between them, Firecrawl wins on **batch + interaction controls at scale** — matching this repo's multi-source parallel scrape — while Jina is the better choice if you prioritize cheapest/simplest integration. I'd ship Firecrawl. Want me to wire Firecrawl into `pages/api/sources.ts` next? ## Recommendation: **Firecrawl** (`/scrape` endpoint) ### Why Firecrawl It's the closest match to every requirement in the brief, and it's a drop-in replacement for what `pages/api/sources.ts:53-67` does today (raw `fetch` → JSDOM → Readability, which silently fails on JS-rendered/dynamic pages and gets blocked or returns noise): - **Renders JS/dynamic pages** out of the box, and manages proxies, caching, and rate limits for you — the exact gap in the current code. - **LLM-ready by default**: returns cleaned markdown with `onlyMainContent`, stripping nav/ads/boilerplate. That replaces the hand-rolled `cleanSourceText` + `.slice(0, 1500)` truncation in `sources.ts:62,72`. - **Scale**: `batch_scrape` fits the repo's "scrape N source links in parallel" pattern (`sources.ts:53`) with one async job instead of N fragile `fetch` calls. - **Simple pricing**: 1 credit/page baseline; predictable for this workload. - **`actions`** (wait/click) handle stubborn dynamic pages — something JSDOM fundamentally cannot do. ### Main alternatives considered - **Jina AI Reader** — strongest runner-up. Browser-rendered, clean markdown, dead-simple (`r.jina.ai/<url>`), generous free tier (10M tokens). Lig

## Recommendation: **Firecrawl** (`/scrape` endpoint) ### Why Firecrawl It's the closest match to every requirement in the brief, and it's a drop-in replacement for what `pages/api/sources.ts:53-67` does today (raw `fetch` → JSDOM → Readability, which silently fails on JS-rendered/dynamic pages and gets blocked or returns noise): - **Renders JS/dynamic pages** out of the box, and manages proxies, caching, and rate limits for you — the exact gap in the current code. - **LLM-ready by default**: returns cleaned markdown with `onlyMainContent`, stripping nav/ads/boilerplate. That replaces the hand-rolled `cleanSourceText` + `.slice(0, 1500)` truncation in `sources.ts:62,72`. - **Scale**: `batch_scrape` fits the repo's "scrape N source links in parallel" pattern (`sources.ts:53`) with one async job instead of N fragile `fetch` calls. - **Simple pricing**: 1 credit/page baseline; predictable for this workload. - **`actions`** (wait/click) handle stubborn dynamic pages — something JSDOM fundamentally cannot do. ### Main alternatives considered - **Jina AI Reader** — strongest runner-up. Browser-rendered, clean markdown, dead-simple (`r.jina.ai/<url>`), generous free tier (10M tokens). Lighter than Firecrawl but fewer scale/interaction controls (no native batch, no click/wait actions). Best pick if you want minimal integration effort. - **Tavily Extract** — explicitly LLM-agent-focused