Build a Working AI Agent in 5 Days: Guide with GPT, n8n, CrewAI & Streamlit

If you're new to AI agents, this guide shows you how to build a functional agent in five days, covering essential tools and practical steps.

1) GPT – Fastest way to build a personal AI assistant

OpenAI GPT lets you create a fully‑featured AI assistant without writing code, hosting servers, or using APIs. Just enter instructions, upload files, and the assistant is ready.

Steps to get started:

Open ChatGPT.

Click "Explore GPT" → "Create GPT".

Write clear instructions, add any knowledge files, and test immediately.

When you finish your coffee, you’ll have a working personal AI agent.

2) n8n – Automation and agent integration

n8n is an open‑source automation tool that connects your AI to other apps, APIs, and data sources. It offers more control than Zapier, can be self‑hosted, and avoids vendor lock‑in.

Examples of what you can do with n8n:

Let your GPT read incoming emails.

Trigger sentiment analysis.

Store results in Airtable.

Send reminders to your team in Slack.

To start:

Install n8n locally or use its cloud service.

Create a simple "Hello World" workflow with one trigger and one action.

Add your GPT as a step in the workflow.

Once you see it work, you’ll realize you can link dozens of tools into a powerful system.

3) CrewAI – Python‑based multi‑agent system

CrewAI is a Python framework for building multi‑agent systems where specialized agents collaborate to achieve a goal.

Example team:

Agent 1 searches the web.

Agent 2 summarizes findings.

Agent 3 writes a report.

CrewAI handles coordination so you can focus on each agent’s task. Basic Python knowledge (virtual environments, running scripts) is sufficient.

Getting started:

Install Python 3.10+.

Run pip install crewai.

Execute the example scripts and replace agents with your own roles.

This turns agents into teammates rather than just chatbots.

4) Cursor + CrewAI – Powerful combination

Cursor is an AI‑driven code editor that can read your codebase and generate new code on demand, offering stronger control than GitHub Copilot.

Prompt Cursor to create a CrewAI project with three agents (researcher, analyst, writer). It will scaffold the entire framework without manual copying.

Steps:

Install Cursor.

Start a new Python project.

Prompt: "Install CrewAI and create three agents: researcher, analyst, writer."

In minutes you’ll have a working multi‑agent system.

5) Streamlit – Quick UI for your agent

Streamlit lets you build a clean, functional web app in minutes using only Python, no HTML/CSS/JS required.

Typical uses:

Chatbot UI for your CrewAI backend.

Dashboard showing each agent’s tasks.

Form to feed input into your GPT.

To build your first agent UI:

Run pip install streamlit.

Create app.py using st.text_input() and st.write().

Connect the UI to your agent logic.

Pro tip: with Cursor, prompt "Build a Streamlit UI for my CrewAI chatbot" and it will generate the full interface.

Your 5‑Tool Stack

The five tools—GPT, n8n, CrewAI, Cursor, and Streamlit—fit together in a consistent architecture: LLM (brain), tools (actions), orchestration, interface, and hosting.

7‑Day AI Agent Challenge

Day 1‑2: Build a GPT that answers domain‑specific questions.

Day 3: Connect GPT to an external tool or API with n8n.

Day 4‑5: Learn CrewAI basics and set up two cooperating agents.

Day 6: Enhance and extend the CrewAI setup using Cursor.

Day 7: Wrap everything in a Streamlit UI.

By the end you’ll have a functional AI agent system that does real work, not just chat.

Sample Modifiable Agent (Python)

import openai</code>
<code>import os</code>
<code>import requests  # Example for a web API tool</code>

<code># --- Brain: Use OpenAI GPT API ---</code>
<code>def ask_gpt(prompt):</code>
<code>    """Calls the OpenAI API to get a response based on the provided prompt."""</code>
<code>    openai.api_key = os.getenv("OPENAI_API_KEY")</code>
<code>    if not openai.api_key:</code>
<code>        return "Error: OpenAI API key not found. Please set the OPENAI_API_KEY environment variable."</code>
<code>    try:</code>
<code>        response = openai.ChatCompletion.create(</code>
<code>            model="gpt-3.5-turbo",
<code>            messages=[{"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": prompt}],
<code>            max_tokens=150,
<code>            temperature=0.7
<code>        )
<code>        return response.choices[0].message["content"].strip()
<code>    except Exception as e:
<code>        return f"Error calling OpenAI API: {e}"

<code># --- Tool: Simple API call (replace with your tool) ---</code>
<code>def get_data(query):</code>
<code>    print(f"Fetching data for query: '{query}'...")</code>
<code>    return f"Placeholder data for '{query}' from my tool."

<code># --- Orchestration: Combine brain and tool ---</code>
<code>def agent_workflow(question):</code>
<code>    print("Starting agent workflow...")</code>
<code>    data_from_tool = get_data(question)</code>
<code>    if data_from_tool.startswith("Error"):
<code>        return data_from_tool
<code>    prompt_for_gpt = f"Use the following data to answer the question: '{question}'

Data:
{data_from_tool}"
<code>    return ask_gpt(prompt_for_gpt)

<code># --- Interface: Command line ---</code>
<code>if __name__ == "__main__":</code>
<code>    print("Welcome to your AI Agent!")</code>
<code>    while True:</code>
<code>        user_input = input("
Ask your agent: ")</code>
<code>        if user_input.lower() in ["quit", "exit"]:</code>
<code>            print("Exiting agent. Goodbye!")
<code>            break
<code>        if not user_input.strip():
<code>            print("Please enter a question.")
<code>            continue
<code>        response = agent_workflow(user_input)
<code>        print("
Agent's Answer:")
<code>        print(response)

Follow the tool order presented, build a rough version quickly, then iterate and improve.

Real learning happens by building, testing, and refining your own AI agent.