Firecrawl

We're opening up a new job role for Firecrawl This time humans aren't allowed to apply, AI Agents only. If you think your Agent can do the job, apply below 👇

Google’s new Gemini 2.0 Flash Thinking model is out 👀 It leads OpenAI's o1 on the Chatbot Arena leaderboard and is incredibly fast. Head to head with a complex visual reasoning problem, it got the answer right and beat o1 in time:

Introducing o1 Web Extractor ⚡️ Ask any information about a company and it will search the web, and extract structured data for you using Firecrawl’s new /extract endpoint. Built with OpenAI’s new o1 structured outputs API and Firecrawl_dev.

I just created an agentic-workflow to automatically write and publish content for me! It's powered by CrewAI Flow and Llama 3.2, running 100% locally. Tech stack: - CrewAI to build an agentic workflow - FireCrawl for web scraping - Typefully for scheduling Here's how it works: - You provide a link to a website. - It scrapes and saves the data as markdown. - A router triggers the desired Crew of agents. - The Crew prepares a ready-to-publish draft. - Finally, use Typefully to post it to your socials. Totally hands-off and 100% automated! In this video, I provide a deep dive into how it actually works! Find all the code here: https://lnkd.in/gY2RvBiA Enjoy the video! 🥂

We just crossed 20,000 stars on Github 🚀 What an incredible way to end 2024, we are all so excited to continue building! Thank you to all of our contributors, community, and team 🧡

Announcing /extract beta 🔥 Extract structured data from single URLs, multiple URLs, or even entire websites! This new endpoint crawls entire websites for you and then extracts data based on a prompt. Check it out:

I gave o1 pro a budget of $1000 and told it to build me a revenue generating SAAS (pt 1) My only requirement was to use Firecrawl web data somewhere in the service. It came up with FireScope, a competitive insights tool for small eCommerce businesses that uses real-time web data from Firecrawl to track competitors’ pricing and product strategies. It thinks I should spend the $1000 on a low tech prototype. Here are the highlights: • $150: Landing page (Webflow) to collect leads. • $600: Marketing (LinkedIn, Reddit, influencers). • $100: Typeform + Zapier for a prototype Should I build FireScope? Stay tuned for pt 2 👨🍳

Turn any website into LLM-ready data in just 2 mins! That too with an open-source tool👇 AI systems thrive on clean, structured data—Markdown, HTML etc. But getting there is often messy and time-consuming. Introducing Firecrawl an open-source framework that takes a URL, crawls it, and converts it into a clean markdown or structured format. Why it's a game changer? • LLM-ready formats → Markdown, HTML Structured data • Handles complexity → Proxies, anti-bots, dynamic content • Customizable → Exclude tags, headers, depth • Reliable → Accurate, consistent results • Batching → Scrape thousands of URLs • Media parsing → PDFs, DOCX, images • Actions → Click, scroll, input, wait FireCrawl GitHub (20k+ 🌟): https://lnkd.in/gPJt6ZRZ Don't forget to star the repo! ✨ If you prefer their managed service, you can use the code "DDODS" for a 10% discount code.

Companies need a data strategy for Generative AI 🎯 A year ago, Generative AI powered search seemed like the perfect quick win for companies taking their first steps with AI. After building AI search for companies like MongoDB, Coinbase, and Snap at Mendable, we learned the nuances that make the difference between a demo and a production-ready system that actually drives value. Before we came in, almost every company had a few engineers creating prototypes yet most fell short following contact with real users. All of these failures came back to a simple truth: the system is only as good as the data going in. As you scale, here’s why simple approaches break: - Context crowding: Correct context for a given query gets crowded out by bad context.  - Outdated data: Information and processes constantly iterate, and documentation is not always maintained. - Data cleanliness: If data isn't clean, performance worsens and costs soar.  - Data access: Accessing a variety of data sources is often critical for companies, but it introduces a host of challenges. To mitigate these issues, companies building these apps should have a data strategy with the goal of curating and maintaining quality data. Here are some practical suggestions to guide your strategy: - Metadata Management: Good metadata is your first defense against context crowding. Every piece of content should be tagged with essential details like product association, who created it, and who can access it. This enables advanced filtering and more accurate responses. - Data Maintenance: To keep data fresh and reliable, the teams that create content should be responsible for regular reviews and updates. When underlying information changes, the corresponding documentation needs to change with it. - Data Sanitation: Raw data rarely arrives in ideal form. Before ingestion, strip away unnecessary formatting and information while preserving the essential details.  - Data Access & Integration: Build the infrastructure to access your data sources seamlessly. You'll need continuous data flow from knowledge bases, ticketing systems, websites, and more. The industry is still in the early stages of solving these complex issues, there's also significant opportunity for innovative companies to emerge and tackle various aspects of this problem. Startups like Glean, unstructured.io, and our own Firecrawl have made some incredible progress on these problems, but no one has solved it all. No matter what tools emerge to make the process easier, having a robust data strategy is foundational to building production ready Generative AI Apps. That's all for now! If you want to see more content like this, give me a follow. If you are interested in more details, check out the full blog post here: https://lnkd.in/eQNdMcwg

30 Open-Source Developer Tools in 30 Posts! As a developer, I’m constantly amazed by the incredible open-source tools out there, each solving unique problems and making our lives easier. To explore this ecosystem, I’m launching a blog series where I’ll dive into 30 open-source developer tools, one post at a time. These tools are game-changers for developers, and I can’t wait to share my experience with each of them! Here's the very first blog - Firecrawl Firecrawl is a tool that helps turn entire websites into markdown files or structured data ready for large language models (LLMs). It makes web scraping and data extraction so much easier! Features of Firecrawl Scrape: Scrapes a URL and gets its content in LLM-ready format (markdown, structured data via LLM Extract, screenshot, HTML). Crawl: Scrapes all the URLs of a webpage and returns content in LLM-ready format. Map: Inputs a website and retrieves all the website URLs In my blog, I share how I used Firecrawl + LLM to simplify scraping and extract data efficiently. Link to blog post - https://lnkd.in/dSVZDf4b If you know a cool open-source tool, drop it in the comments—maybe it’ll be part of the series! #OpenSource #DevTools #WebScraping