Phidata (Agno) Tutorial: Build Powerful AI Agents with a Simple Framework

Building intelligent and autonomous AI agents is now easier than ever with Phidata (now known as Agno). This framework offers a simpler approach compared to CrewAI or LangGraph, while remaining powerful enough for various use cases from simple chatbots to complex multi-agent systems.

In this tutorial, we will learn how to build AI agents using Phidata/Agno from scratch, culminating in building a research agent that can search the web and write reports automatically.

What is Phidata/Agno?

Phidata is an open-source framework for building AI agents with Python. It is designed with a simplicity-first philosophy, where you can create a functional agent with just a few lines of code. Phidata recently rebranded to Agno, but the concepts and API remain consistent.

Key advantages of Phidata/Agno:

• Easy to learn: Intuitive API and comprehensive documentation
• Built-in tools: Web search, file operations, SQL, and much more
• Knowledge bases: Integration with vector databases for RAG
• Memory and storage: Agents that remember conversation context
• Multi-agent teams: Orchestrate multiple agents simultaneously
• Structured outputs: Type-safe outputs with Pydantic models
• Streaming: Real-time streaming response support

Installation and Setup

Package Installation

# Install Phidata/Agno
pip install agno

Or using the legacy name
pip install phidata

Additional dependencies for tools
pip install openai duckduckgo-search sqlalchemy pgvector

API Key Configuration

# Set environment variable for OpenAI
export OPENAIAPIKEY="sk-your-api-key-here"

Or use a .env file
echo 'OPENAIAPIKEY=sk-your-api-key-here' > .env

# Or set directly in Python
import os
os.environ["OPENAIAPIKEY"] = "sk-your-api-key-here"

Creating Your First Agent

Simple Agent

from agno.agent import Agent
from agno.models.openai import OpenAIChat

Create a simple agent
agent = Agent(
    model=OpenAIChat(id="gpt-4o-mini"),
    description="You are a helpful and friendly assistant.",
    instructions=[
        "Answer clearly and concisely",
        "Provide well-structured responses"
    ],
    markdown=True
)

Run the agent
agent.printresponse("Explain what machine learning is in 3 paragraphs")

agent = Agent(
    model=OpenAIChat(id="gpt-4o"),
    description="You are a senior data scientist with 10 years of experience.",
    instructions=[
        "Provide technical but easy-to-understand answers",
        "Include Python code examples when relevant",
        "Use analogies to explain complex concepts",
        "Always recommend best practices"
    ],
    markdown=True,
    showtoolcalls=True

)

response = agent.run("How do I handle imbalanced datasets?")
print(response.content)

from agno.agent import Agent
from agno.models.openai import OpenAIChat
from agno.tools.duckduckgo import DuckDuckGoTools

Agent with web search capability
webagent = Agent(
    model=OpenAIChat(id="gpt-4o-mini"),
    tools=[DuckDuckGoTools()],
    description="You are a research assistant that can search the internet.",
    instructions=[
        "Always search for the latest information before answering",
        "Include information sources",
        "Provide well-structured summaries"
    ],
    showtoolcalls=True,
    markdown=True
)

webagent.printresponse("What are the latest AI developments in Southeast Asia in 2026?")