Summary
Highlights
The video introduces an AI agent that researches on Google, crafts an email based on a template, and sends daily updates. The creator outlines his planning process using draw.io to whiteboard the application's flow, identify necessary technologies, and anticipate issues. This involves starting with the desired output (an email summary), choosing a transactional email service (Brevo), defining input (Google search results), and selecting a search API (SerpApi) and a scraping API (Scraping Fish) to handle dynamic web content.
The creator explains the importance of constraining the AI agent's search to specific keywords like 'agentic AI,' 'OpenAI,' 'Meta,' 'Perplexity,' and 'Gemini,' along with social media sites for up-to-date information. Crucially, he emphasizes deciding beforehand what content is desired in the email summary (updates from main AI players, industry applications, news) and what is not (startup blogs, Google ads). This upfront definition helps in crafting effective prompts for the AI.
The agent's design involves using SerpApi to search Google for the defined keywords, retrieving the first 30 results from the last 24 hours. A language model performs an initial relevance check on search result summaries to filter out irrelevant links. Scraping Fish is then used to open and extract content from relevant pages, converting HTML to markdown for easier processing by another language model. This process generates individual page summaries, which are then integrated into a comprehensive email using a custom template.
The core of the agent involves LangGraph, a framework for building stateful, multi-actor applications. Two agents—a 'Report Writer' and a 'Reviewer'—collaborate. The Report Writer drafts the email summary following a predefined template, and the Reviewer checks it, providing feedback for improvements until the summary meets quality standards. This conversational loop ensures consistency and accuracy in the final email.
The video demonstrates the Python implementation in a Jupyter notebook. It covers setting up search queries, using SerpApi for Google searches, and defining a function to check the relevance of search results with a detailed prompt for the language model. The code then uses Scraping Fish to scrape web pages, converts HTML to markdown, and summarizes each article using a separate language model. Error handling is included for robust scraping. The summary generation and review process using LangGraph is detailed, showcasing the structured output for the email summary and reviewer messages.
Finally, the creator shows how to automate email delivery using Brevo's API. The completed AI research assistant, developed in Python, is prepared for deployment on Render as a cron job, allowing it to run daily on a schedule. The deployment process involves setting up environment variables such as API keys for OpenAI, Scraping Fish, Brevo, and SerpApi on the Render platform. The video emphasizes that users can deploy the solution without needing to understand the underlying code by following the provided GitHub repository instructions and setting up required API keys.