How to Build a Research Agent From Scratch

Your Personal Research Assistant

A research agent is an AI helper that can search the web, read pages, and write a summary for you. Instead of opening 20 tabs and skimming each one, the agent does the clicking, reading, and note-taking on its own.

Think of it like a curious intern who never gets tired. You give it a question — "What are the best laptops under $1000?" or "What does the science say about creatine?" — and it comes back a few minutes later with a clear answer based on real sources it actually read.

Hours of Reading, Done in Minutes

Research takes time. A lot of it. If you've ever spent an hour reading reviews before buying a laptop, or two hours comparing vacation spots, you know how much time research eats up. A research agent does that boring work for you — and it doesn't get bored halfway through article seven.

This matters even more at work. A marketer writing a competitive analysis, a student working on a paper, or a founder exploring a new market can save hours every week. The agent handles the searching and reading. You just review the final report and make the decisions.

How a Research Agent Thinks

A research agent works in a loop, the same way a person does research. It searches the web, reads the best results, takes notes, and asks itself: do I have enough? If yes, it writes the report. If no, it searches again with a better query.

Behind the scenes, the agent is using three simple tools: a search tool (like Google) to find good sources, a reader tool to open and understand each page, and the AI itself to think, take notes, and write the final summary.

A Simple Research Agent in Python

Here's a stripped-down version. Real research agents add error handling, citation tracking, and fancier search — but the bones are the same. Three tools, one loop, one answer.

import requests
from openai import OpenAI

# Tool 1: search the web
def search_web(query):
    # Pretend this calls a search API
    return [
        "https://example.com/article-1",
        "https://example.com/article-2",
        "https://example.com/article-3",
    ]

# Tool 2: read a web page
def read_page(url):
    response = requests.get(url)
    return response.text[:2000]  # first 2000 chars

# The agent itself
def research_agent(question):
    # Step 1: search
    sources = search_web(question)

    # Step 2: read each source
    notes = []
    for url in sources:
        notes.append(read_page(url))

    # Step 3: ask the AI to write the report
    client = OpenAI()
    response = client.chat.completions.create(
        model="gpt-4o",
        messages=[{
            "role": "user",
            "content": f"Question: {question}\n\nNotes: {notes}"
        }]
    )
    return response.choices[0].message.content

# Use the agent
answer = research_agent("What is photosynthesis?")
print(answer)

That's the whole thing. Search. Read. Summarize. The magic isn't in any one piece — it's in combining them with an AI that knows how to think.

Knowledge Check

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