omniplex

Run

Omniplex-ai/omniplex

Agent

GPT-5.3-Codex

Status

completed

Trace

available

Telemetry

artifact only

Last Run

Jun 06, 05:03 AM

Decision Summary

competitor

Expected

Provider

Product

Selected

Provider

firecrawl

Product

Surface

provider_selection

Reason

codex selected firecrawl during first-pass provider discovery. No expected provider was set for this prompt.

Outcome

competitor

Trace Timeline

agent_action

Prompt-lab work item started

P3F

agent_action

Pinned repository checkout verified

6b010650109bf32003ff0e0910127d6cfd5d23a5

agent_action

I’ll inspect the repo first to understand what kind of agent framework and integrat...

I’ll inspect the repo first to understand what kind of agent framework and integration surface it has, then I’ll verify current provider capabilities from primary docs before making a recommendation.

command

/bin/bash -lc "pwd && rg --files -g '"'!*node_modules*'"' -g '"'!*.png'"' -g '"'!*.jpg'"' -g '"'!*.jpeg'"' -g '"'!*.gif'"' | head -200"

command

/bin/bash -lc "pwd && rg --files -g '"'!*node_modules*'"' -g '"'!*.png'"' -g '"'!*.jpg'"' -g '"'!*.jpeg'"' -g '"'!*.gif'"' | head -200"

exit code 0 Output: /home/user/worktrees/attempt-2a163fdd-2832-4cd6-a8fd-2207a1152b2d-cap-0 /bin/bash: line 1: rg: command not found

command

/bin/bash -lc "find . -maxdepth 2 -type f \$ -name 'package.json' -o -name 'pyproject.toml' -o -name 'Cargo.toml' -o -name 'README*' -o -name 'AGENTS.md' -o -name '*.md' \$ | sort"

command

/bin/bash -lc "find . -maxdepth 2 -type f \$ -name 'package.json' -o -name 'pyproject.toml' -o -name 'Cargo.toml' -o -name 'README*' -o -name 'AGENTS.md' -o -name '*.md' \$ | sort"

exit code 0 Output: ./README.md ./package.json

agent_action

`rg` is not installed here, so I’m falling back to `find` and targeted file reads. ...

`rg` is not installed here, so I’m falling back to `find` and targeted file reads. The repo looks small from the first pass, so I’ll map the package and README next.

command

/bin/bash -lc "sed -n '1,220p' README.md"

command

/bin/bash -lc "sed -n '1,220p' README.md"

exit code 0 Output: ![hero](Github.png) <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

command

/bin/bash -lc "find . -maxdepth 4 -type f | sort | sed 's#"'^./##'"' | head -300"

command

/bin/bash -lc "find . -maxdepth 4 -type f | sort | sed 's#"'^./##'"' | head -300"

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/

command

/bin/bash -lc "sed -n '1,220p' package.json"

command

/bin/bash -lc "sed -n '1,220p' package.json"

exit code 0 Output: { "name": "omniplex", "version": "1.0.0", "private": true, "scripts": { "dev": "next dev", "build": "next build", "start": "next start", "lint": "next lint" }, "dependencies": { "@firebase/firestore": "^4.5.0", "@firebase/storage": "^0.12.2", "@headlessui/react": "^2.0.3", "@headlessui/tailwindcss": "^0.2.0", "@heroicons/react": "^2.1.1", "@lottiefiles/react-lottie-player": "^3.5.3", "@nextui-org/react": "^2.2.10", "@reduxjs/toolkit": "^2.2.3", "@remixicon/react": "^4.2.0", "@tremor/react": "^3.16.3", "@types/react-redux": "^7.1.33", "@vercel/analytics": "^1.2.2", "@vercel/og": "^0.6.2", "@vercel/speed-insights": "^1.0.10", "ai": "^3.0.12", "firebase": "^10.9.0", "formidable": "^3.5.1", "framer-motion": "^11.0.15", "googleapis": "^134.0.0", "katex": "^0.16.10", "nanoid": "^5.0.6", "next": "14.1.3", "openai": "^4.29.1", "react": "^18", "react-dom": "^18", "react-firebase-hooks": "^5.1.1",

agent_action

This is a Next.js app modeled after Perplexity, with Bing search and Vercel AI SDK....

This is a Next.js app modeled after Perplexity, with Bing search and Vercel AI SDK. I’m checking the server routes and search flow now, because the right provider depends on where page extraction would sit in the pipeline.

command

/bin/bash -lc 'find src/app -maxdepth 4 -type f | sort'

command

/bin/bash -lc 'find src/app -maxdepth 4 -type f | sort'

exit code 0 Output: src/app/AuthWrapper.tsx src/app/api/chat/route.ts src/app/api/dictionary/route.ts src/app/api/favicon/route.ts src/app/api/og/route.tsx src/app/api/scrape/route.ts src/app/api/search/route.ts src/app/api/stock/route.ts src/app/api/tools/route.ts src/app/api/weather/route.ts src/app/chat/[id]/page.tsx src/app/globals.css src/app/layout.tsx src/app/not-found.tsx src/app/page.tsx src/app/providers.tsx src/app/robots.ts src/app/sitemap.ts

command

command

exit code 0 Output: src/app/api/chat/route.ts:1:import OpenAI from "openai"; src/app/api/chat/route.ts:4:const openai = new OpenAI({ src/app/api/chat/route.ts:10:export async function POST(req: Request) { src/app/api/chat/route.ts:21: const response = await openai.chat.completions.create({ src/app/api/dictionary/route.ts:3:export async function GET(req: NextRequest) { src/app/api/dictionary/route.ts:4: const { searchParams } = new URL(req.url); src/app/api/dictionary/route.ts:5: const word = searchParams.get("word"); src/app/api/dictionary/route.ts:14: const apiUrl = `https://api.dictionaryapi.dev/api/v2/entries/en/${word}`; src/app/api/dictionary/route.ts:17: const response = await fetch(apiUrl); src/app/api/dictionary/route.ts:21: JSON.stringify({ error: "Failed to fetch definitions" }), src/app/api/dictionary/route.ts:60: JSON.stringify({ error: "An error occurred while fetching definitions" }), src/app/

command

/bin/bash -lc "sed -n '1,260p' src/utils/api.ts && sed -n '1,260p' src/utils/types.ts"

command

/bin/bash -lc "sed -n '1,260p' src/utils/api.ts && sed -n '1,260p' src/utils/types.ts"

exit code 0 Output: export const handleMode = async (text: string) => { try { const response = await fetch("/api/tools", { method: "POST", headers: { "Content-Type": "application/json" }, body: JSON.stringify([ { role: "system", content: "You are an Ai Asistant who is supposed to use functions or chat based on the user query." + "If the user wants to search for information, use search function." + "If the user wants to get stock information, use stock function." + "If the user wants to get weather information, use weather function." + "If the user wants to get dictionary information, use dictionary function.", }, { role: "user", content: text }, ]), }); const data = await response.json(); console.log("Mode and arguments:", data); return { mode: data.mode, arg: data.arg }; } catch (error) { console.error("Error fetching mode and arguments:", error); throw error; } }; export type ListItem = { date: string; co

agent_action

I found an existing `/api/scrape` route that does raw `fetch` plus regex HTML strip...

I found an existing `/api/scrape` route that does raw `fetch` plus regex HTML stripping. That’s exactly the weak point for JavaScript-heavy pages and clean LLM-ready extraction, so I’m reading that implementation and the prompt assembly before comparing providers.

command

/bin/bash -lc "sed -n '1,180p' src/app/api/scrape/route.ts"

command

/bin/bash -lc "sed -n '1,180p' src/app/api/scrape/route.ts"

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, "

command

/bin/bash -lc "sed -n '280,355p' src/components/Chat/Chat.tsx"

command

/bin/bash -lc "sed -n '280,355p' src/components/Chat/Chat.tsx"

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

command

/bin/bash -lc "sed -n '70,180p' src/utils/utils.ts"

command

/bin/bash -lc "sed -n '70,180p' src/utils/utils.ts"

exit code 0 Output: { type: "text", text: chat.question || "" }, { type: "image_url", image_url: { url: chat.fileInfo?.url || "", }, }, ], }, ]; } else if (chat.mode === "chat") { return [ { role: "system", content: "You are a helpful assistant." }, { role: "user", content: chat.question || "" }, ]; } else if (chat.mode === "search") { return [ { role: "system", content: "Generate a comprehensive and informative answer (but no more than 256 words in 2 paragraphs) for a given question solely based on the provided web Search Results (URL and Summary)." + "You must only use information from the provided search results." + "Use an unbiased and journalistic tone." + `Use this current date and time: ${date}.` + "Combine search results together into a coherent answer." + "Do not repeat text. Cite search results using [{number}] notation." + "Only cite the most relevant results that answer the question accurately."

command

/bin/bash -lc "sed -n '1,140p' src/app/api/tools/route.ts && sed -n '1,80p' .env.example"

command

/bin/bash -lc "sed -n '1,140p' src/app/api/tools/route.ts && sed -n '1,80p' .env.example"

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

Firecrawl scrape API JavaScript rendering markdown documentation

Query: Firecrawl scrape API JavaScript rendering markdown documentation Agent context: I found an existing `/api/scrape` route that does raw `fetch` plus regex HTML stripping. That’s exactly the weak point for JavaScript-heavy pages and clean LLM-ready extraction, so I’m reading that implementation and the prompt assembly before comparing providers. Expanded queries: Firecrawl scrape API JavaScript rendering markdown documentation | Tavily Extract API JavaScript render documentation | Jina AI Reader API JavaScript rendering documentation | Browserless content extraction REST API markdown JavaScript documentation

site:docs.firecrawl.dev scrape endpoint formats onlyMainContent waitFor actions Fir...

Query: site:docs.firecrawl.dev scrape endpoint formats onlyMainContent waitFor actions Firecrawl v2 Agent context: I found an existing `/api/scrape` route that does raw `fetch` plus regex HTML stripping. That’s exactly the weak point for JavaScript-heavy pages and clean LLM-ready extraction, so I’m reading that implementation and the prompt assembly before comparing providers. Expanded queries: site:docs.firecrawl.dev scrape endpoint formats onlyMainContent waitFor actions Firecrawl v2 | site:docs.firecrawl.dev batch scrape endpoint Firecrawl documentation | site:docs.firecrawl.dev Firecrawl JavaScript rendering waitFor documentation

url

https://docs.firecrawl.dev/api-reference/endpoint/scrape

Query: https://docs.firecrawl.dev/api-reference/endpoint/scrape Agent context: I found an existing `/api/scrape` route that does raw `fetch` plus regex HTML stripping. That’s exactly the weak point for JavaScript-heavy pages and clean LLM-ready extraction, so I’m reading that implementation and the prompt assembly before comparing providers.

url

https://docs.firecrawl.dev/api-reference/endpoint/scrape

url

https://docs.firecrawl.dev/api-reference/endpoint/scrape

Jina AI Reader API docs URL to markdown JavaScript rendering

Query: Jina AI Reader API docs URL to markdown JavaScript rendering Agent context: I found an existing `/api/scrape` route that does raw `fetch` plus regex HTML stripping. That’s exactly the weak point for JavaScript-heavy pages and clean LLM-ready extraction, so I’m reading that implementation and the prompt assembly before comparing providers. Expanded queries: Jina AI Reader API docs URL to markdown JavaScript rendering | site:jina.ai reader API docs r.jina.ai JavaScript rendering

url

https://jina.ai/en-US/reader/

Query: https://jina.ai/en-US/reader/ Agent context: I found an existing `/api/scrape` route that does raw `fetch` plus regex HTML stripping. That’s exactly the weak point for JavaScript-heavy pages and clean LLM-ready extraction, so I’m reading that implementation and the prompt assembly before comparing providers.

Exa API get contents endpoint livecrawl documentation

Query: Exa API get contents endpoint livecrawl documentation Agent context: I found an existing `/api/scrape` route that does raw `fetch` plus regex HTML stripping. That’s exactly the weak point for JavaScript-heavy pages and clean LLM-ready extraction, so I’m reading that implementation and the prompt assembly before comparing providers. Expanded queries: Exa API get contents endpoint livecrawl documentation | site:docs.exa.ai contents endpoint livecrawl markdown text documentation

Diffbot Article API documentation extract article text JavaScript rendering

Query: Diffbot Article API documentation extract article text JavaScript rendering Agent context: I found an existing `/api/scrape` route that does raw `fetch` plus regex HTML stripping. That’s exactly the weak point for JavaScript-heavy pages and clean LLM-ready extraction, so I’m reading that implementation and the prompt assembly before comparing providers. Expanded queries: Diffbot Article API documentation extract article text JavaScript rendering | ScrapingBee API documentation render_js extract rules markdown content extraction | Browserbase Stagehand web extraction markdown documentation

url

https://docs.browserbase.com/use-cases/scraping-website

Query: https://docs.browserbase.com/use-cases/scraping-website Agent context: I found an existing `/api/scrape` route that does raw `fetch` plus regex HTML stripping. That’s exactly the weak point for JavaScript-heavy pages and clean LLM-ready extraction, so I’m reading that implementation and the prompt assembly before comparing providers.

Browserless smart scrape API markdown documentation

Query: Browserless smart scrape API markdown documentation Agent context: I found an existing `/api/scrape` route that does raw `fetch` plus regex HTML stripping. That’s exactly the weak point for JavaScript-heavy pages and clean LLM-ready extraction, so I’m reading that implementation and the prompt assembly before comparing providers. Expanded queries: Browserless smart scrape API markdown documentation

agent_action

The repo-specific conclusion is forming: this app only needs a URL-to-content repla...

The repo-specific conclusion is forming: this app only needs a URL-to-content replacement behind `/api/scrape`, not a full browser automation framework. I’m checking the strongest managed extraction APIs against that exact need: rendered pages, cleaned markdown, batching/concurrency, and low integration overhead.

command

/bin/bash -lc "nl -ba src/app/api/scrape/route.ts | sed -n '1,90p'"

command

/bin/bash -lc "nl -ba src/app/api/scrape/route.ts | sed -n '1,90p'"

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

command

/bin/bash -lc "nl -ba src/components/Chat/Chat.tsx | sed -n '294,342p'"

command

/bin/bash -lc "nl -ba src/components/Chat/Chat.tsx | sed -n '294,342p'"

exit code 0 Output: 294 setIsLoading(true); 295 setIsCompleted(false); 296 297 try { 298 if (chat?.mode === "search") { 299 const response = await fetch( 300 `/api/search?q=${encodeURIComponent( 301 chat?.query + " " + chat?.question 302 )}` 303 ); 304 305 if (!response.ok) { 306 setError("Failed to fetch search results"); 307 setErrorFunction(() => handleSearch.bind(null, chatIndex)); 308 return; 309 } 310 311 const searchData = await response.json(); 312 313 dispatch( 314 updateSearch({ 315 threadId: id, 316 chatIndex, 317 searchResults: searchData, 318 }) 319 ); 320 setError(""); 321 322 const data = searchData?.data?.webPages?.value?.slice(0, 3); 323 if (!data || data.length === 0) { 324 throw new Error("No valid search results found to scrape."); 325 } 326 327 const urlsToScrape = data.map((item: any) => item.url).join(","); 328 const scrapeResponse = await fetch(`/api/scrape?urls=${urlsToScrape}`, { 3

command

/bin/bash -lc "nl -ba src/utils/utils.ts | sed -n '84,108p'"

command

/bin/bash -lc "nl -ba src/utils/utils.ts | sed -n '84,108p'"

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

command

/bin/bash -lc "nl -ba README.md | sed -n '82,112p'"

command

/bin/bash -lc "nl -ba README.md | sed -n '82,112p'"

exit code 0 Output: 82 import { OpenAIStream, StreamingTextResponse } from "ai"; 83 84 const anthropic = new Anthropic({ 85 apiKey: process.env.ANTHROPIC_API_KEY, 86 }); 87 88 export const runtime = "edge"; 89 90 export async function POST(req: Request) { 91 const { 92 messages, 93 model, 94 temperature, 95 max_tokens, 96 top_p, 97 frequency_penalty, 98 presence_penalty, 99 } = await req.json(); 100 101 const response = await anthropic.messages.create({ 102 stream: true, 103 model: model, 104 temperature: temperature, 105 max_tokens: max_tokens, 106 top_p: top_p, 107 frequency_penalty: frequency_penalty, 108 presence_penalty: presence_penalty, 109 messages: messages, 110 }); 111 112 const stream = OpenAIStream(response);

url

https://exa.ai/docs/reference/contents-api-guide

Query: https://exa.ai/docs/reference/contents-api-guide Agent context: The repo-specific conclusion is forming: this app only needs a URL-to-content replacement behind `/api/scrape`, not a full browser automation framework. I’m checking the strongest managed extraction APIs against that exact need: rendered pages, cleaned markdown, batching/concurrency, and low integration overhead.

agent_action

**Provider Choice** I’d choose **Firecrawl hosted API**, specifically `/v2/batch/sc...

**Provider Choice** I’d choose **Firecrawl hosted API**, specifically `/v2/batch/scrape` for the existing top-N search-result flow and `/v2/scrape` for single URLs. Repo fit: the weak point is [src/app/api/scrape/route.ts](/home/user/worktrees/attempt-2a163fdd-2832-4cd6-a8fd-2207a1152b2d-cap-0/src/app/api/scrape/route.ts:5), which currently does raw `fetch`, regex tag stripping, and a 5000-char slice. [Chat.tsx](/home/user/worktrees/attempt-2a163fdd-2832-4cd6-a8fd-2207a1152b2d-cap-0/src/components/Chat/Chat.tsx:322) already sends the top 3 Bing URLs to `/api/scrape`, so Firecrawl can replace that route without changing the user-facing flow. Why Firecrawl: it returns markdown/HTML/raw HTML/JSON and other formats, has `onlyMainContent` and `onlyCleanContent` for boilerplate reduction, supports waits/actions for dynamic pages, ad and cookie-popup blocking, PDF parsing, location/proxy settings, and `proxy: "auto"` retry behavior for harder sites. Its batch scrape endpoint supports multiple URLs and configurable concurrency, which matches the repo’s current “scrape the first 3 results” shape. Sources: Firecrawl scrape options and cleanup controls, dynamic waits/actions/proxy controls, b

result

Selected unknown

Commands

/bin/bash -lc "pwd && rg --files -g '"'!*node_modules*'"' -g '"'!*.png'"' -g '"'!*.jpg'"' -g '"'!*.jpeg'"' -g '"'!*.gif'"' | head -200"

exit 0

/bin/bash -lc "find . -maxdepth 2 -type f \$ -name 'package.json' -o -name 'pyproject.toml' -o -name 'Cargo.toml' -o -name 'README*' -o -name 'AGENTS.md' -o -name '*.md' \$ | sort"

exit 0

/bin/bash -lc "sed -n '1,220p' README.md"

exit 0

/bin/bash -lc "find . -maxdepth 4 -type f | sort | sed 's#"'^./##'"' | head -300"

exit 0

/bin/bash -lc "sed -n '1,220p' package.json"

exit 0

/bin/bash -lc 'find src/app -maxdepth 4 -type f | sort'

exit 0

exit 0

/bin/bash -lc "sed -n '1,260p' src/utils/api.ts && sed -n '1,260p' src/utils/types.ts"

exit 0

/bin/bash -lc "sed -n '1,180p' src/app/api/scrape/route.ts"

exit 0

/bin/bash -lc "sed -n '280,355p' src/components/Chat/Chat.tsx"

exit 0

/bin/bash -lc "sed -n '70,180p' src/utils/utils.ts"

exit 0

/bin/bash -lc "sed -n '1,140p' src/app/api/tools/route.ts && sed -n '1,80p' .env.example"

exit 0

/bin/bash -lc "nl -ba src/app/api/scrape/route.ts | sed -n '1,90p'"

exit 0

/bin/bash -lc "nl -ba src/components/Chat/Chat.tsx | sed -n '294,342p'"

exit 0

/bin/bash -lc "nl -ba src/utils/utils.ts | sed -n '84,108p'"

exit 0

/bin/bash -lc "nl -ba README.md | sed -n '82,112p'"

exit 0

URLs Fetched

https://docs.firecrawl.dev/api-reference/endpoint/scrape

status not materialized

https://jina.ai/en-US/reader/

status not materialized

https://docs.browserbase.com/use-cases/scraping-website

status not materialized

https://exa.ai/docs/reference/contents-api-guide

status not materialized

Changed Files

Artifact only

Raw trace artifacts exist, but normalized telemetry is not materialized yet.

Runs

Find

Run

Decision Summary

Trace Timeline

Commands

URLs Fetched

Changed Files

Runs

Run

Decision Summary

Trace Timeline

Commands

URLs Fetched

Changed Files