🚀 Build Your First AI Agent with Zero Coding & Zero Cost

This blog takes you on a beginner-friendly journey to create AI agents using open-source tools and a local LLM — all without writing a single line of code.

💡 Introduction

We’re entering an exciting new era where AI doesn’t just assist us—it can actually act on our behalf. Imagine an AI agent that can research a topic, generate a report, and deliver insights, all without you having to type a single line of code. Sounds futuristic? Well, the future is here.

Thanks to open-source tools like CrewAI and Ollama, you can now build and deploy your own AI agents—completely free and entirely code-free.

Let’s dive in.

👥 AI Assistants vs. AI Agents: What’s the Difference?

Before we build, let’s get clear on the terminology.

Put simply: assistants help; agents do. If you ask an assistant to build a web app, it’ll give you code snippets. An agent? It’ll build and deploy the whole thing if you configure it right.

🛠️ Tools We'll Use

1. CrewAI – A lightweight framework for building AI agents with task delegation.

2. Ollama + Llama3.1 – A local large language model (LLM) that runs on your machine.

3. Python (v3.10 - v3.13) – For running the CrewAI environment.

📦 Step-by-Step Guide to Building an AI Agent (No Coding Needed!)

1. Install CrewAI

First, create and activate a Python virtual environment.

# Create a virtual environment named "myenv" (Python 3)
python3 -m venv myenv

# Activate the virtual environment (Linux/macOS)
source myenv/bin/activate

# Activate the virtual environment (Windows)
myenv\Scripts\activate.bat

Then run:

pip install crewai

2. Create a New Project

crew create crew devops_ai_project

You'll be prompted to choose an LLM. Select Ollama and ensure Llama3.1 is installed:

ollama pull llama3.1:latest

3. Understand Your Project Structure

CrewAI will generate:

• src/config/agents.yaml → Define roles like "researcher" or "analyst"

• src/config/task.yaml → Specify what each agent must do

• src/main.py → The main entry point

4. Define Your Agents

Open agents.yaml and customize:

researcher:
  role: >
    {topic} Senior Data Researcher
  goal: >
    Uncover cutting-edge developments in {topic}
  backstory: >
    You're a seasoned researcher with a knack for uncovering the latest
    developments in {topic}. Known for your ability to find the most relevant
    information and present it in a clear and concise manner.

reporting_analyst:
  role: >
    {topic} Reporting Analyst
  goal: >
    Create detailed reports based on {topic} data analysis and research findings
  backstory: >
    You're a meticulous analyst with a keen eye for detail. You're known for
    your ability to turn complex data into clear and concise reports, making
    it easy for others to understand and act on the information you provide.

5. Set the Tasks

In task.yaml, assign duties:

research_task:
  description: >
    Conduct a thorough research about {topic}
    Make sure you find any interesting and relevant information given
    the current year is {current_year}.
  expected_output: >
    A list with 10 bullet points of the most relevant information about {topic}
  agent: researcher

reporting_task:
  description: >
    Review the context you got and expand each topic into a full section for a report.
    Make sure the report is detailed and contains any and all relevant information.
  expected_output: >
    A fully fledged report with the main topics, each with a full section of information.
    Formatted as markdown without '```'
  agent: reporting_analyst

6. Configure the Topic

Open main.py and set the research topic:

topic = "kubernetes"

7. Install Dependencies

crewai install

8. Run Your AI Agents

crewai run

You’ll see your agents collaborating using the local Llama3.1 model, gathering data, and generating a report.

9. Check Your Output

Find reports.md in your project folder.

cat reports.md

🧠 What’s Happening Under the Hood?

Even though we’re not writing code, there’s powerful AI orchestration happening behind the scenes:

• Agents adopt different roles and goals

• CrewAI handles task sequencing and data sharing

• The local LLM (Llama3.1) processes everything on your machine—no cloud costs

⚠️ Limitations to Know

• Local LLMs like Llama3.1 are lightweight, so don’t expect real-time internet updates

• Your machine's performance will impact speed

• Accuracy of data may vary depending on the model’s training data and date

Still, for many DevOps use-cases like research, automation, or summarization—it’s more than enough.

👏 Credits

This blog is heavily inspired by a brilliant walkthrough by Abhishek Veeramalla on YouTube. Check out his original video if you're more of a visual learner. His beginner-friendly breakdown made the entire process fun and approachable.

🚀 Final Thoughts: This is Just the Beginning

You're not just building agents—you’re redefining how you approach DevOps tasks. CrewAI, Ollama, and local LLMs open a door to cost-effective, customizable AI workflows.

Whether you're a student, engineer, or tech enthusiast, this is your chance to experiment with AI agents without needing deep pockets or deep code skills.

So what’s next?

• Try building agents for blog writing, task management, or bug triaging.

• Explore other models available on Ollama.

• Share your project on Hashnode or GitHub!

Let’s automate the future, one agent at a time. 💡