Go from a blank screen to a working agent in 5 days with this step-by-step starter pack If you’re brand new to AI agents, you’re in the right place. Everyone in this field started from scratch. I’ve been building AI agents and automations for years, and I also write content for an AI/ML startup, so I spend my time both building the tech and explaining it in plain language. In this guide, I’ll skip the fluff, skip the hype, and show you exactly which tools you should start with if you want to build your first AI agent fast. This is the stuff I’d hand you if you showed up today and said, “I want to go from zero to working agent by the end of the week.” Your First 5 Tools (From 0 to Dangerous) 1. GPTs — The Fastest Way to Build a Personal AI Assistant Start here. OpenAI GPTs are the easiest way to get something working quickly. You can build a functional AI assistant without touching code, hosting servers, or messing with APIs. Just give it instructions, upload files it needs, and it’s ready. Are there “better” models out there? Sure. Can you squeeze out a little more performance if you spend weeks coding a custom solution? Maybe. But if the goal is to get something done now, GPTs get you there faster than anything else. Here’s your first move: Open ChatGPT.
Click “Explore GPTs” → “Create GPT.”
Write clear instructions, add any knowledge files, and test it immediately. Open ChatGPT. Click “Explore GPTs” → “Create GPT.” Write clear instructions, add any knowledge files, and test it immediately. By the time you finish your coffee, you’ll have a working personal AI agent. 2. n8n — Automations and Agents That Use Tools Once your agent can talk, you’ll want it to do. do n8n is an open-source automation tool that connects your AI to other apps, APIs, and data sources. It’s like Zapier, but with way more control, the option to self-host, and no vendor lock-in. For example, you could: Have your GPT read incoming emails.
Trigger sentiment analysis with n8n.
Store results in Airtable.
Alert your team in Slack. Have your GPT read incoming emails. Trigger sentiment analysis with n8n. Store results in Airtable. Alert your team in Slack. You’ve just built an AI-powered workflow without writing a huge backend from scratch. Try this to get started: Install n8n locally or use their cloud service.
Create a “Hello World” workflow with one trigger and one action.
Add your GPT as a step in the chain. Install n8n locally or use their cloud service. Create a “Hello World” workflow with one trigger and one action. Add your GPT as a step in the chain. Once you see it work, you’ll realize you can chain dozens of tools into something powerful. If you want a deeper walkthrough, I’ve written a full guide on how to use n8n, which you can find here. here 3. CrewAI — Multi-Agent Systems in Python When you’re ready to code, CrewAI is a great first Python framework for multi-agent systems. These are setups where several specialized agents work together toward one goal. Picture a “research crew”: Agent 1 searches the web.
Agent 2 summarizes findings.
Agent 3 writes a report. Agent 1 searches the web. Agent 2 summarizes findings. Agent 3 writes a report. CrewAI handles the coordination so you can focus on what the agents actually do. You don’t need advanced ML knowledge, just basic Python skills like setting up a virtual environment and running scripts. Begin with this: Install Python 3.10+.
pip install crewai
Run the example from the docs and swap in your own agent roles. Install Python 3.10+. pip install crewai Run the example from the docs and swap in your own agent roles. This will make you think about agents as teammates instead of just chatbots. 4. Cursor + CrewAI — The Power Combo Cursor is an AI-powered code editor that works directly inside your development environment. It can read your codebase and generate new code on demand, like GitHub Copilot, but with more control. Here’s where it gets fun: tell Cursor to set up a CrewAI project with three agents that handle research, summarizing, and writing. It will scaffold the whole thing inside your project. No jumping between docs, no endless copy-pasting from tutorials. To try it: Install Cursor.
Start a new Python project.
Prompt: “Install CrewAI and create three agents: researcher, analyst, writer.” Install Cursor. Start a new Python project. Prompt: “Install CrewAI and create three agents: researcher, analyst, writer.” In minutes, you’ll have a functioning multi-agent system without manually wiring every piece together. 5. Streamlit — Quick UIs for Your Agents Sometimes, you need a simple interface for people to use your agent, whether that’s for a demo, internal testing, or a public launch. Streamlit is perfect for that. It’s a Python library that lets you create a clean, functional web app in minutes. No HTML, CSS, or JavaScript required. Just write Python, run it, and it’s live in your browser. For example, you could build: A chatbot UI for your CrewAI backend.
A dashboard showing what each agent is working on.
A form that feeds input into your GPT. A chatbot UI for your CrewAI backend. A dashboard showing what each agent is working on. A form that feeds input into your GPT. To build your first one: pip install streamlit
Create app.py with st.text_input() and st.write().
Connect it to your agent logic. pip install streamlit Create app.py with st.text_input() and st.write(). Connect it to your agent logic. Pro tip: If you’re using Cursor, prompt it to “Build a Streamlit UI for my CrewAI chatbot” and it’ll write the whole interface for you. The Only Mental Model You Need The Mindset Strip away the buzzwords. Agent are just code running on a server, using language models and calling tools. No magic, just engineering. When you stop treating them like mysterious black boxes, you’ll build faster. You’ll also stop wasting time chasing “the most advanced framework” and focus on getting something live. Think of every agent project like this: LLM for thinking/talking.
Tools for doing.
Orchestration for coordinating.
Hosting to make it live. LLM for thinking/talking. Tools for doing. Orchestration for coordinating. Hosting to make it live. Everything in this list: GPTs, n8n, CrewAI, Cursor, Streamlit, fits neatly into that structure. Start small, layer in complexity, and keep moving. Your Reusable AI Agent Recipe Think of this as a blueprint for building any AI agent, no matter which tools you use. You break your project into these core pieces: Brain: The LLM that generates language, makes decisions, or answers questions. Examples: OpenAI GPT, Anthropic Claude, or open-source models like Llama.
Tools: The external apps, APIs, or databases your agent needs to interact with to do stuff. Examples: n8n workflows, Google Sheets API, Slack bots, web scrapers, custom Python functions.
Orchestration: The logic that coordinates your agent’s thinking and doing, including multi-agent communication. Examples: CrewAI for Python multi-agent systems, n8n for workflow orchestration, or simple scripts.
Interface: How users interact with your agent. Examples: ChatGPT UI, Streamlit web apps, Slack or Discord bots, or simple command line tools.
Hosting: Where your code lives and runs. Examples: Cloud services like AWS, DigitalOcean, n8n cloud, or your own server. Brain: The LLM that generates language, makes decisions, or answers questions. Examples: OpenAI GPT, Anthropic Claude, or open-source models like Llama. Brain: Examples Tools: The external apps, APIs, or databases your agent needs to interact with to do stuff. Examples: n8n workflows, Google Sheets API, Slack bots, web scrapers, custom Python functions. Tools: do Examples Orchestration: The logic that coordinates your agent’s thinking and doing, including multi-agent communication. Examples: CrewAI for Python multi-agent systems, n8n for workflow orchestration, or simple scripts. Orchestration: Examples Interface: How users interact with your agent. Examples: ChatGPT UI, Streamlit web apps, Slack or Discord bots, or simple command line tools. Interface: Examples Hosting: Where your code lives and runs. Examples: Cloud services like AWS, DigitalOcean, n8n cloud, or your own server. Hosting: Examples How to Use This Recipe How to Use This Recipe Start by picking your tools for each piece. For example: Brain: OpenAI GPT
Tools: n8n workflow calling APIs
Orchestration: n8n itself or CrewAI if you’re coding
Interface: Streamlit app for user input/output
Hosting: Your own cloud VM or n8n’s cloud Brain: OpenAI GPT Brain Tools: n8n workflow calling APIs Tools Orchestration: n8n itself or CrewAI if you’re coding Orchestration Interface: Streamlit app for user input/output Interface Hosting: Your own cloud VM or n8n’s cloud Hosting Then build step-by-step: Get the Brain talking (test GPT or your LLM).
Connect the Brain to a Tool and verify actions.
Add Orchestration to coordinate multiple tasks or agents.
Build a simple Interface for easy use.
Deploy on Hosting so it’s accessible. Get the Brain talking (test GPT or your LLM). Connect the Brain to a Tool and verify actions. Add Orchestration to coordinate multiple tasks or agents. Build a simple Interface for easy use. Deploy on Hosting so it’s accessible. Your 7-Day Agent Challenge Want a working AI agent system in a week? Follow this exactly: Day 1–2: Build a GPT that answers a specific set of questions for your domain.
Day 3: Use n8n to connect your GPT to one external tool or API.
Day 4–5: Learn CrewAI basics and set up two agents working together.
Day 6: Use Cursor to improve and expand your CrewAI setup.
Day 7: Wrap everything in a Streamlit UI. Day 1–2: Build a GPT that answers a specific set of questions for your domain. Day 3: Use n8n to connect your GPT to one external tool or API. Day 4–5: Learn CrewAI basics and set up two agents working together. Day 6: Use Cursor to improve and expand your CrewAI setup. Day 7: Wrap everything in a Streamlit UI. By the end, you’ll move beyond simle chat and start getting real tasks done. A Simple Agent You Can Steal Here’s a minimal example you can modify in Python: import openai import os import requests # Example for a web API tool import openai
import os
import requests # Example for a web API tool

# --- Brain: Use OpenAI GPT API ---
def ask_gpt(prompt):
    """
    Calls the OpenAI API to get a response based on the provided prompt.
    """
    # Ensure you have your OpenAI API key set as an environment variable
    # export OPENAI_API_KEY='your-api-key'
    openai.api_key = os.getenv("OPENAI_API_KEY")

    if not openai.api_key:
        return "Error: OpenAI API key not found. Please set the OPENAI_API_KEY environment variable."

    try:
        # Using ChatCompletion for newer models like gpt-3.5-turbo or gpt-4
        response = openai.ChatCompletion.create(
            model="gpt-3.5-turbo", # Or "gpt-4" if you have access
            messages=[
                {"role": "system", "content": "You are a helpful assistant."},
                {"role": "user", "content": prompt}
            ],
            max_tokens=150, # Adjust as needed
            temperature=0.7 # Adjust for creativity
        )
        return response.choices[0].message['content'].strip()
    except Exception as e:
        return f"Error calling OpenAI API: {e}"

# --- Tool: Simple API call (replace with your tool) ---
def get_data(query):
    """
    Fetches data from an external source based on the query.
    Replace this with your actual tool implementation.
    """
    print(f"Fetching data for query: '{query}'...")
    # --- Example: Using a hypothetical public API ---
    # Let's assume there's an API that returns information based on a search term.
    # You would replace this with your actual API endpoint and parameters.

    # Example using a dummy API that just returns the query in a string
    # In a real scenario, you'd make a request like:
    # try:
    #     api_url = f"https://api.example.com/data?q={query}"
    #     response = requests.get(api_url)
    #     response.raise_for_status() # Raise an exception for bad status codes
    #     data = response.json() # Assuming API returns JSON
    #     return str(data) # Convert to string for the prompt
    # except requests.exceptions.RequestException as e:
    #     return f"Error fetching data from API: {e}"

    # For this example, we'll just return a placeholder string
    return f"Placeholder data for '{query}' from my tool."
    # --- End of Example ---

# --- Orchestration: Combine brain and tool ---
def agent_workflow(question):
    """
    Orchestrates the workflow: gets data from the tool and then asks the
    GPT brain to respond using that data.
    """
    print("Starting agent workflow...")
    data_from_tool = get_data(question)

    if data_from_tool.startswith("Error"):
        return data_from_tool # Return the error from the tool

    print(f"Data received from tool: '{data_from_tool}'")

    # Construct the prompt for the GPT model
    prompt_for_gpt = f"Use the following data to answer the question: '{question}'\n\nData:\n{data_from_tool}"

    response_from_gpt = ask_gpt(prompt_for_gpt)
    return response_from_gpt

# --- Interface: Command line ---
if __name__ == "__main__":
    print("Welcome to your AI Agent!")
    print("Type 'quit' or 'exit' to stop.")

    while True:
        user_input = input("\nAsk your agent: ")
        if user_input.lower() in ["quit", "exit"]:
            print("Exiting agent. Goodbye!")
            break

        if not user_input.strip():
            print("Please enter a question.")
            continue

        agent_response = agent_workflow(user_input)
        print("\nAgent's Answer:")
        print(agent_response)Swap in your actual API calls, hosting, and UI as needed. import openai
import os
import requests # Example for a web API tool

# --- Brain: Use OpenAI GPT API ---
def ask_gpt(prompt):
    """
    Calls the OpenAI API to get a response based on the provided prompt.
    """
    # Ensure you have your OpenAI API key set as an environment variable
    # export OPENAI_API_KEY='your-api-key'
    openai.api_key = os.getenv("OPENAI_API_KEY")

    if not openai.api_key:
        return "Error: OpenAI API key not found. Please set the OPENAI_API_KEY environment variable."

    try:
        # Using ChatCompletion for newer models like gpt-3.5-turbo or gpt-4
        response = openai.ChatCompletion.create(
            model="gpt-3.5-turbo", # Or "gpt-4" if you have access
            messages=[
                {"role": "system", "content": "You are a helpful assistant."},
                {"role": "user", "content": prompt}
            ],
            max_tokens=150, # Adjust as needed
            temperature=0.7 # Adjust for creativity
        )
        return response.choices[0].message['content'].strip()
    except Exception as e:
        return f"Error calling OpenAI API: {e}"

# --- Tool: Simple API call (replace with your tool) ---
def get_data(query):
    """
    Fetches data from an external source based on the query.
    Replace this with your actual tool implementation.
    """
    print(f"Fetching data for query: '{query}'...")
    # --- Example: Using a hypothetical public API ---
    # Let's assume there's an API that returns information based on a search term.
    # You would replace this with your actual API endpoint and parameters.

    # Example using a dummy API that just returns the query in a string
    # In a real scenario, you'd make a request like:
    # try:
    #     api_url = f"https://api.example.com/data?q={query}"
    #     response = requests.get(api_url)
    #     response.raise_for_status() # Raise an exception for bad status codes
    #     data = response.json() # Assuming API returns JSON
    #     return str(data) # Convert to string for the prompt
    # except requests.exceptions.RequestException as e:
    #     return f"Error fetching data from API: {e}"

    # For this example, we'll just return a placeholder string
    return f"Placeholder data for '{query}' from my tool."
    # --- End of Example ---

# --- Orchestration: Combine brain and tool ---
def agent_workflow(question):
    """
    Orchestrates the workflow: gets data from the tool and then asks the
    GPT brain to respond using that data.
    """
    print("Starting agent workflow...")
    data_from_tool = get_data(question)

    if data_from_tool.startswith("Error"):
        return data_from_tool # Return the error from the tool

    print(f"Data received from tool: '{data_from_tool}'")

    # Construct the prompt for the GPT model
    prompt_for_gpt = f"Use the following data to answer the question: '{question}'\n\nData:\n{data_from_tool}"

    response_from_gpt = ask_gpt(prompt_for_gpt)
    return response_from_gpt

# --- Interface: Command line ---
if __name__ == "__main__":
    print("Welcome to your AI Agent!")
    print("Type 'quit' or 'exit' to stop.")

    while True:
        user_input = input("\nAsk your agent: ")
        if user_input.lower() in ["quit", "exit"]:
            print("Exiting agent. Goodbye!")
            break

        if not user_input.strip():
            print("Please enter a question.")
            continue

        agent_response = agent_workflow(user_input)
        print("\nAgent's Answer:")
        print(agent_response)Swap in your actual API calls, hosting, and UI as needed. Final Word Don’t wait for perfect. Start by building a simple AI agent that works. You will encounter problems and rewrite parts as you go. That’s how you figure out what your use case really needs. Follow the tool sequ--

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The Zero-to-Agent Playbook

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