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Deep Research Through an AI Agent Using OpenAI

PROJECT

Deep Research Through an AI Agent Using OpenAI

In this project, we’ll learn to build a fully functional AI-powered deep research assistant using Python, Streamlit, CrewAI, LangChain, Firecrawl, and OpenAI’s GPT models. This intelligent research platform will help automate deep web research, summarization, and report generation.

You will learn to:

Define and configure agents (with tools, goals, and LLMs) to simulate division of labor.

Integrate an API in a Streamlit application.

Develop interactive user interfaces using Streamlit components

Understand the principles behind multi-agent orchestration using CrewAI.

Understand how to scaffold a Python project using the MVC (Model-View-Controller) pattern.

Skills

Large Language Models (LLMs)

API Integration

Python Programming

MVC Architecture

Generative AI

Prerequisites

Intermediate Python coding skills

Basic understanding of large language models

Basic understanding of Web API usage

Understanding of OpenAI API key

Technologies

Python

OpenAI

Streamlit

Project Description

In today’s information-rich world, conducting deep, focused research across the web can be time-consuming and overwhelming.

In this project, we will design and implement an AI-powered research assistant that automates this process through intelligent agents, real-time web search, and advanced language models. The assistant simulates how a human researcher might iteratively explore a topic by asking refined questions, reading across multiple sources, summarizing insights, and compiling a comprehensive report.

We will use tools like CrewAI, Firecrawl, LangChain, and OpenAI’s GPT models to manage search, summarization, and report generation workflows.

We’ll build a research platform with the following capabilities:

1. Perform deep topic research

Users can:

Enter a topic or query for research.
Configure parameters such as breadth (number of sub-queries) and depth (levels of recursion).
Trigger an automated multi-step research process powered by agents.

Under the hood:

A research agent fetches relevant data using the Firecrawl search tool.
A summarizer agent condenses findings into structured bullet points.
A presenter agent formats the output into a clean, readable report.

2. Generate and view research reports

Users can:

Preview cleaned research summaries within the UI.
Download a professionally formatted PDF of their research findings.

Tech stack

This AI research assistant is built using:

Streamlit for the frontend interface
Python with CrewAI and LangChain for agent-based task orchestration
OpenAI API for natural language processing
Firecrawl API for web-based search and metadata extraction
ReportLab for dynamic PDF report generation

Project Tasks

Project Setup and Configuration

Task 0: Get Started

Task 1: Set Up the OpenAPI Key

Task 2: Set the Firecrawl Key

External Tooling and Utilities

Task 3: Implement the Firecrawl Search Tool

Task 4: Clean the Markdown Output

CrewAI Agents and Workflow

Task 5: Set Up CrewAI Agents and Tasks for Multi-Stage Research

Task 6: Generate Research PDF Reports

Task 7: Run Deep Research and Generate the Report

Streamlit Interface

Task 8: Build the Streamlit GUI for the Deep Research Tool

Wrap Up

Congratulations!

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