- unwind ai
- Posts
- AI Action Agent for Web Scraping
AI Action Agent for Web Scraping
PLUS: Turn AI agents into MCP servers in seconds, Research mode in Claude
Shubham Saboo & Gargi Gupta
April 16, 2025
Today’s top AI Highlights:
Web Action AI Agent that doesn’t just scrape but finds the data you need
Convert and deploy existing agent projects as MCP servers
Research mode in Claude with new Google Workspace integration
Firecrawl can now automatically detect and detail changes on websites
AI Agent Toolbox that dynamically routes requests to 4000+ MCPs
& so much more!
Read time: 3 mins
AI Tutorial
Financial management is a deeply personal and context-sensitive domain where one-size-fits-all AI solutions fall short. Building truly helpful AI financial advisors requires understanding the interplay between budgeting, saving, and debt management as interconnected rather than isolated concerns.
A multi-agent system provides the perfect architecture for this approach, allowing us to craft specialized agents that collaborate rather than operate in silos, mirroring how human financial advisors actually work.
In this tutorial, we'll build a Multi-Agent Personal Financial Coach application using Google’s newly released Agent Development Kit (ADK) and the Gemini model. Our application will feature specialized agents for budget analysis, savings strategies, and debt reduction working together to provide comprehensive financial advice. The system will offer actionable recommendations with interactive visualizations.
We share hands-on tutorials like this every week, designed to help you stay ahead in the world of AI. If you're serious about leveling up your AI skills and staying ahead of the curve, subscribe now and be the first to access our latest tutorials.
Latest Developments
Firecrawl just released FIRE-1, a web action agent that goes beyond traditional web scraping by autonomously navigating and interacting with websites. This agent doesn't just passively collect data—it actively clicks buttons, fills forms, handles logins, and interacts with dynamic elements to access information that's hidden behind interaction barriers.
FIRE-1 seamlessly integrates with Firecrawl's existing API endpoints, requiring minimal code changes to implement intelligent navigation in your data extraction workflows.
Key Highlights:
Interactive Scraping - FIRE-1 autonomously interacts with web elements like buttons, forms, and dynamic content to access data that traditional scrapers can't reach, eliminating the need for manual intervention in your data extraction pipelines.
Simple Integration - Adding FIRE-1 to your existing Firecrawl implementation requires just a few lines of code—simply include an agent object with your model selection and navigation instructions in your API requests.
Navigation Control - Guide FIRE-1's actions with natural language prompts that specify exactly what data you need and how to find it, whether it's paginating through results, searching for specific content, or navigating complex site structures.
Works With Multiple Endpoints - FIRE-1 integrates with both the /scrape and /extract endpoints, giving you options for different use cases while maintaining the same intelligent navigation capabilities across your entire workflow.
Quick Start - FIRE-1 is available now in preview with straightforward implementation. Current rate limits (10 requests per minute for both endpoints) and credit usage (150 credits for /scrape.
The #1 AI Meeting Assistant
Still taking manual meeting notes in 2025? Let AI handle the tedious work so you can focus on the important stuff.
Fellow is the AI meeting assistant that:
✔️ Auto-joins your Zoom, Google Meet, and Teams calls to take notes for you.
✔️ Tracks action items and decisions so nothing falls through the cracks.
✔️ Answers questions about meetings and searches through your transcripts, like ChatGPT.
Try Fellow today and get unlimited AI meeting notes for 30 days.
AutoMCP is a new open-source library that lets you convert existing AI agent projects into Model Context Protocol (MCP) servers with minimal code changes. Created by Naptha AI, this tool bridges the gap between agent prototyping and deployment by supporting multiple frameworks including CrewAI, LangGraph, Llama Index, OpenAI Agents SDK, and Pydantic AI.
You simply add AutoMCP as a dependency to your existing projects, run a CLI command, make a few edits, and your agent is ready to serve as an MCP server locally or in the cloud.
Key Highlights:
Framework Compatibility - Works with popular agent frameworks including CrewAI, LangGraph, Llama Index, and OpenAI Agents SDK. You can keep using your preferred tools for development while gaining MCP compatibility without rewriting your code or learning new paradigms.
Deployment Pipeline - Think "Vercel for MCP servers" — it lets you deploy directly from GitHub repositories with a single click. Just push your code to GitHub, visit the Naptha platform, add your environment variables, and get a hosted SSE endpoint immediately usable with MCP clients like Cursor.
Minimal Setup Required - AutoMCP generates the boilerplate code needed to wrap your agent in an MCP server. The generated file handles all the protocol details, leaving you to focus on connecting your existing agent code rather than implementing protocol specifications.
Multiple Transport Options - Supports both STDIO (for direct integration with tools like Cursor) and SSE transport modes. STDIO is managed automatically by MCP clients while SSE runs as a standalone server, giving you flexibility in how you integrate your agents with different tools.
Quick Bites
Cohere has released Embed 4, a multimodal embedding model that processes documents up to 128K tokens long with native support for images, tables, and diagrams across 100+ languages. The model targets enterprise needs in regulated industries like finance and healthcare, offering compressed embeddings that reduce storage costs while maintaining search accuracy for RAG applications. Available today on Cohere’s platform, Microsoft Azure AI Foundry, and Amazon SageMaker.
OpenAI is reportedly developing its own X-like social network with features centered around ChatGPT's image generation capabilities. While still in early prototype stages, Sam Altman has been quietly seeking feedback from outside parties about the initiative, which could either launch as a standalone app or integrate directly into ChatGPT. This would put OpenAI in direct competition with Elon Musk's X and Meta, and also help OpenAI in getting real-time feedback on its models at a bigger scale.
Anthropic has released two new capabilties in Claude — Research and a Google Workspace integration that connects your email, calendar, and documents to Claude. With Research enabled, Claude agentically conducts multiple searches, explores different angles of your question, and works on your open questions systematically.
With Google Workspace integration, Claude can securely search emails, review documents, and see your calendar commitments to give you more personalized responses. Both these features are now available in beta to Max, Team, and Enterprise users.
Tools of the Trade
Firecrawl's Change Tracking: Automatically detects and documents website modifications by comparing current scrapes to previous versions, categorizing content as new, unchanged, modified, or removed. It offers both Git-style diff and structured JSON comparison modes.
Smithery Toolbox: A single MCP that dynamically routes to all 4000+ MCPs on Smithery AI’s registry based on your agent's needs. Your AI agents can now access multiple MCPs without knowing which ones are needed in advance. Works with Claude, Cursor, Windsurf, and more.
Mrge: A code review tool that uses AI to analyze pull requests and provide immediate feedback. It learns from your repository context, and comes with intelligent file ordering, stacked PRs, and keyboard shortcuts to help ship higher-quality code faster.
Awesome LLM Apps: Build awesome LLM apps with RAG, AI agents, and more to interact with data sources like GitHub, Gmail, PDFs, and YouTube videos, and automate complex work.
Hot Takes
SF AI boom feels like a tiny group of people, maybe somewhere in the thousands. Can't imagine how small the dot com boom must have felt ~
Jeffrey WangProgramming in natural language is not the end goal of AI. The end goal is programming in new languages that make natural language look like assembly code. ~
Pedro Domingos
That’s all for today! See you tomorrow with more such AI-filled content.
Don’t forget to share this newsletter on your social channels and tag Unwind AI to support us!
PS: We curate this AI newsletter every day for FREE, your support is what keeps us going. If you find value in what you read, share it with at least one, two (or 20) of your friends 😉
Reply