🚀 Agentic AI: Building Free Autonomous Agents - No Cost, No API Key, No Catch

If you've ever dreamed of building your own AI assistant but were stopped by expensive APIs, cloud costs, or complex setups — this guide is for you.

Thanks to open-source models and local inference tools, you can now build powerful Agentic AI systems without spending a single rupee.

In this blog, you’ll learn how to:

• Understand what Agentic AI is,

• Run powerful language models locally using Ollama,

• Use the fast and free Mistral 7B model,

• And create your own autonomous AI agent for zero cost.

Let’s dive in. 🧠

---

Artificial Intelligence is rapidly evolving from simple prompt-based tasks to autonomous, goal-driven systems. This new wave is called Agentic AI — and it’s revolutionizing how we interact with machines.

In this blog, I’ll demystify what Agentic AI is, and then walk you step-by-step through how to build your own AI agent using Ollama and Mistral, completely free.

---

🌐 What is Agentic AI?

Unlike traditional LLM prompts that just respond and forget, Agentic AI goes further. It’s about AI systems that:

• Plan actions across multiple steps,

• Use memory to recall past decisions,

• Make autonomous decisions,

• And use external tools or APIs to achieve goals.

Think: an AI assistant that doesn’t just answer questions but gets work done — research, summarization, scheduling, and more.

You’ve seen it in tools like AutoGPT, CrewAI, LangGraph, and Autogen — but many of these rely on costly APIs like OpenAI or Google.

---

🛠️ What You'll Use (For Free!)

Here’s the secret sauce — everything in this blog runs 100% free and locally:

⚠️ No OpenAI key. No cloud bill. No rate limits. Just your machine.

---

🧪 Demo: Build a Research Assistant Agent (Locally)

We’ll create a news research agent that:

1. Takes a topic input from you,

2. Searches the web for the latest updates,

3. Summarizes the findings using a local Mistral LLM.

---

✅ Step 1: Install Ollama

Ollama is the tool that lets you run large language models like Mistral directly on your machine.

• Go to https://ollama.com/download

• Download the version for your OS (macOS, Windows (WSL), or Linux)

• Follow the installation instructions (very minimal setup)

---

✅ Step 2: Pull the Mistral 7B Model

Once Ollama is installed, open your terminal and run:

ollama run mistral

This will automatically download the Mistral 7B model to your system (about 4GB) and start it. This model is open-source, fast, and runs well on most modern laptops.

---

✅ Step 3: Set Up Python

Make sure you have Python 3.10 or later installed on your system.

To check:

python --version

If not installed, download it from https://python.org/downloads

---

✅ Step 4: Install Required Libraries

You’ll use a Python script to:

1. Search news from the web

2. Summarize it using the local Mistral model via Ollama

Install the helper libraries:

pip install requests duckduckgo-search

These help:

• Make web searches (using DuckDuckGo)

• Communicate with the local model running in Ollama

---

Refer to the GitHub repository for the complete code details : Ollama News Agent

---

🧠 What Happens in the Code — Step by Step

When you run newsynth.py, the agent performs the following steps:

🔹 Step 1: Takes Input from the User

• The script prompts you with:

    Enter a topic to research:
  

• You type a topic like: AI regulations 2025

• This input is stored as the query for news search

---

🔹 Step 2: Searches the Web for Recent News

• The agent uses the duckduckgo_search library.

• It sends the user’s topic to DuckDuckGo to fetch real-time search results.

• From those results, it extracts:

  • The title of the article

  • The URL link

• The top 5 headlines + links are compiled into a plain text list like:

    EU proposes new AI law - https://news.example.com/ai-law
    OpenAI faces scrutiny - https://ai.example.org/scrutiny
    ...
  

---

🔹 Step 3: Prepares a System Prompt for Mistral

• The agent defines a system role (like giving the AI an identity):

    You are a research assistant AI. Summarize the key news points clearly.
  

• This ensures the model responds in a professional, summarized format.

---

🔹 Step 4: Sends Data to Mistral LLM (via Ollama)

• The agent sends a POST request to:

    http://localhost:11434/v1/chat/completions
  

• This is Ollama's local API endpoint.

• The payload includes:

  • The system prompt (defining the AI role)

  • The user input (the 5 news headlines)

  • The model to use ("mistral")

• Ollama routes the request to Mistral 7B, which processes it entirely on your machine (no internet/cloud needed).

---

🔹 Step 5: Receives and Displays the Summary

• Mistral generates a natural-language summary based on the headlines.

• The response comes back from Ollama in JSON format.

• The agent extracts the summarized text and prints it in your terminal.

Example output:

✅ Summary:

- The EU is proposing a new AI law focused on transparency and accountability.
- OpenAI faces regulatory scrutiny amid growing concerns over model safety.
- Google is partnering with universities to develop responsible AI frameworks.
...

---

🧠 Visual Flow

User Input → DuckDuckGo Search → 5 Headlines → System Prompt +
→ Sent to Ollama (Mistral) → AI-generated Summary → Printed Output

---

🧩 Behind the Scenes

🎉 That’s Agentic AI for free — no login, no keys, no credit card required.

---

⚡️ Why This Matters

• Privacy-first: Everything runs on your machine

• Developer control: Modify every part of the agent

• Zero cost: Perfect for students, indie hackers, and hobbyists

You don’t need GPT-4 to build smart agents anymore. Mistral + Ollama + Python = your personal AI lab.

---

🎁 Bonus: What You Didn't Need to Pay For

---

✨ Final Words

Agentic AI is here — and it's free to use, explore, and build upon. Stop waiting for access tokens or pricing tiers. Start building AI systems on your terms.

If this inspired you, don’t forget to follow me here on Hashnode and drop a comment on what agent you'd like to build next.

Happy Programming!