Deep Research Through an AI Agent Using OpenAI

Conducting thorough research across multiple web sources is time-consuming and overwhelming. AI-powered research assistants automate this by using autonomous agents to iteratively explore topics, refine queries, and synthesize findings into comprehensive reports.

In this project, we'll build a multi-agent research platform using CrewAI, OpenAI GPT models, Firecrawl, and Streamlit that automates deep topic research. Users can enter a research query and configure parameters like breadth (number of sub-queries) and depth (recursion levels) to control exploration scope. The system orchestrates three specialized AI agents: a research agent that fetches relevant data using the Firecrawl search tool, a summarizer agent that condenses findings into structured bullet points using natural language processing, and a presenter agent that formats outputs into clean, readable reports. We'll implement LangChain for agent orchestration, enabling agents to work collaboratively through multi-step workflows.

We'll build a Streamlit interface where users can trigger automated research, preview cleaned summaries, and download professionally formatted PDF reports generated with ReportLab. The backend handles API integration with OpenAI for language processing and Firecrawl for web search and metadata extraction, while CrewAI manages the agent-based workflow. By the end, you'll have a production-ready research automation system demonstrating multi-agent orchestration, OpenAI API usage, web scraping, document generation, and Streamlit app development applicable to any AI automation or knowledge synthesis project.