Hello World AI Agent

Build your first AI agent using Python! This project demonstrates how to create a simple conversational AI agent using modern tools and frameworks.

Overview

flowchart TB
    subgraph User["User Interaction Layer"]
        UI[User Input]
        OR[Output Response]
    end

    subgraph Agent["AI Agent Core"]
        LLM["Language Model
        (Understanding & Generation)"]
        CT[(Context Storage
        - Memory & History
        - Instructions
        - Knowledge)]
        TM{"Tool Manager
        - Tool Selection
        - Task Routing"}
    end

    subgraph Tools["Available Tools"]
        MT[("Storage Systems
        - Databases
        - Files")]
        CT1{{Computation Tools
        - Math & Analysis}}
        ET>External Tools
        - Web & APIs]
        OB(((Observability)))
    end

    UI --> LLM
    LLM <--> CT
    LLM <--> TM
    TM <--> MT
    TM <--> CT1
    TM <--> ET
    TM <--> OB
    LLM --> OR

    classDef primary fill:#e1f5fe,stroke:#01579b,color:black
    classDef secondary fill:#f3e5f5,stroke:#4a148c,color:black
    classDef tertiary fill:#e8f5e9,stroke:#1b5e20,color:black
    
    class UI,OR primary
    class LLM,CT,TM secondary
    class MT,CT1,ET,OB tertiary

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This project uses:

• Agno - A powerful framework for building AI agents
• ollama - Local LLM provider for AI capabilities

Prerequisites

Before you begin, ensure you have the following installed:

• git
• Python 3.10+
• uv - Modern Python package installer and environment manager
• ollama - Local LLM provider
• Your favorite text editor (Vim, VSCode, Notepad++, Windsurf, Cursor, etc)

Then run:

git clone https://github.com/tobalo/ai-agent-hello-world.git
cd ai-agent-hello-world # navigate into the cloned repository

Installation

Quick Setup

1. Download and install Ollama from ollama.com/download
2. Install uv:

# macOS/Linux
curl -LsSf https://astral.sh/uv/install.sh | sh

# Windows (PowerShell)
powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"

Project Setup

1. Create a virtual environment:

uv venv

2. Activate the virtual environment:

# macOS/Linux
source .venv/bin/activate

# Windows
.venv\Scripts\activate

3. Install dependencies:

uv pip install agno ollama

4. Start Ollama:

ollama run llama3.2:1b

5. Launch the agent:

python3 agent.py

6. Launch the web search agent:

python3 websearch-agent.py

Next Steps

Now that your AI agent is running, you can:

• Customize its behavior and responses
• Add new capabilities using Agno's toolkits
• Experiment with different LLM models available through Ollama

Resources

• Agno Documentation
• Ollama Blog
• Project Issues