Building AI Agents with Agno-Phidata (Github +18.5k Stars)

#agno #phidata #aiagent #ai

I would like to talk about Agno Framework having +18.5k Stars on Github, so far. I will deep dive and make an analysis, listing features and some tutorial. Benchmark Video on Github (Agno vs. LangGraph) I've uploaded to Youtube is below.

Introduction to Agno

Agno is a cutting-edge framework for building multi-modal AI agents, previously known as Phidata. It is designed with simplicity, performance, and model agnosticism in mind, allowing developers to create highly efficient AI agents that can work with multiple models and data sources.

Why Agno?

Agno is structured around three core principles:

Simplicity: Avoids complex graphs, chains, or dependency-heavy architectures.
Performance: Claims to be 5000x faster in agent instantiation and 50x more memory efficient than LangGraph.
Agnostic Compatibility: Supports multiple AI models and providers without vendor lock-in.

Key Features of Agno

Performance Optimization
- Agent instantiation is measured at <5μs, significantly outperforming competing frameworks.
- Memory usage is optimized to 50x lower than LangGraph, reducing system load and increasing scalability.
Multi-Modality Support
- Agno natively supports text, image, audio, and video inputs.
- Agents can process information from diverse sources efficiently.
Modular Agent Design
- Agents can work individually or in teams, distributing tasks based on domain expertise.
- Users can integrate structured outputs, memory management, and tool calls into agents.
Vector Database and Knowledge Store Integration
- Supports Agentic RAG (Retrieval Augmented Generation) for improved knowledge recall.
- Can integrate with vector databases like LanceDB for long-term memory storage.
Monitoring & Debugging
- Offers real-time tracking of agent performance and session logs.
- Agent sessions can be monitored through Agno’s Agent Monitoring Platform.

Getting Started with Agno

Installation

To install Agno, run the following command:

pip install -U agno

Additional dependencies may be required based on agent functionality, such as:

pip install openai duckduckgo-search lancedb tantivy pypdf yfinance

Creating a Basic Agent

The following Python script initializes a simple Agno agent using OpenAI's GPT-4 model:

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

agent = Agent(
    model=OpenAIChat(id="gpt-4o"),
    description="You are an enthusiastic news reporter!",
    markdown=True
)
agent.print_response("Tell me about a breaking news story from New York.", stream=True)

Steps to run:

Install dependencies (pip install openai agno).
Export your OpenAI API key (export OPENAI_API_KEY=sk-xxxx).
Run the script (python basic_agent.py).

Creating an Agent with External Tools

Agents can integrate external tools like web search to enhance their knowledge base:

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

agent = Agent(
    model=OpenAIChat(id="gpt-4o"),
    description="You are an enthusiastic news reporter!",
    tools=[DuckDuckGoTools()],
    show_tool_calls=True,
    markdown=True
)
agent.print_response("Tell me about a breaking news story from New York.", stream=True)

Steps to run:

Install duckduckgo-search.
Execute the script (python agent_with_tools.py).

Advanced Example: Agents with Knowledge Base

Agno supports knowledge storage in vector databases for efficient information retrieval:

from agno.agent import Agent
from agno.models.openai import OpenAIChat
from agno.embedder.openai import OpenAIEmbedder
from agno.knowledge.pdf_url import PDFUrlKnowledgeBase
from agno.vectordb.lancedb import LanceDb, SearchType

agent = Agent(
    model=OpenAIChat(id="gpt-4o"),
    description="You are a Thai cuisine expert!",
    instructions=[
        "Search your knowledge base for Thai recipes.",
        "If needed, search the web to fill in gaps.",
        "Prefer your knowledge base over web results."
    ],
    knowledge=PDFUrlKnowledgeBase(
        urls=["https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
        vector_db=LanceDb(
            uri="tmp/lancedb",
            table_name="recipes",
            search_type=SearchType.hybrid,
            embedder=OpenAIEmbedder(id="text-embedding-3-small"),
        ),
    ),
    markdown=True
)

Multi-Agent Systems

Agno allows multiple agents to work in coordination:

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

web_agent = Agent(
    name="Web Agent",
    model=OpenAIChat(id="gpt-4o"),
    tools=[DuckDuckGoTools()],
    markdown=True,
)

finance_agent = Agent(
    name="Finance Agent",
    model=OpenAIChat(id="gpt-4o"),
    tools=[YFinanceTools(stock_price=True)],
    markdown=True,
)

agent_team = Agent(team=[web_agent, finance_agent], model=OpenAIChat(id="gpt-4o"))
agent_team.print_response("What's the market outlook for AI stocks?", stream=True)

Performance Benchmarks

Agno significantly outperforms LangGraph in both speed and memory efficiency:

Instantiation time: ~5000x faster than LangGraph.
Memory usage: ~50x lower memory consumption.

https://www.youtube.com/watch?v=bUCMErB5CEs

How to test Agno’s performance:

# Setup virtual environment
./scripts/perf_setup.sh
source .venvs/perfenv/bin/activate

# Run Agno evaluation
python evals/performance/instantiation_with_tool.py

# Compare with LangGraph
python evals/performance/other/langgraph_instantiation.py

Why Agno is Transforming AI Applications

At the core of Agno lies its unparalleled speed, efficiency, and flexibility. With its truly model-agnostic architecture, Agno allows businesses and developers to integrate any AI model, from OpenAI’s GPT series to custom LLMs, making it a future-proof solution for any industry.

Real-World Use Cases: How Agno is Changing the Game

✅ AI-Powered Customer Support – Businesses can deploy 24/7 AI agents trained to understand customer queries, resolve issues instantly, and provide personalized recommendations—cutting response times and improving user satisfaction.

✅ Smart Travel Assistants – With multi-modal capabilities, Agno agents can curate tailored travel itineraries, book flights and hotels, and even provide real-time updates on weather, currency exchange rates, and local events.

✅ Next-Gen Coding Assistants – Agno enables developers to integrate AI coding helpers that analyze existing codebases, suggest optimizations, auto-generate boilerplate code, and even debug issues—streamlining software development.

✅ Financial & Market Analysis – With seamless integration into financial databases, an Agno-powered agent can monitor stock trends, provide investment insights, and generate in-depth reports in seconds.

✅ AI for Research & Data Processing – Academic and corporate researchers can use Agno to process large datasets, extract insights, and generate well-structured reports, making knowledge discovery faster and more efficient.

A Future Built on AI Collaboration

The beauty of Agno lies in its ability to combine multiple agents into a powerful AI workforce. Businesses can design an autonomous team of AI agents specializing in different tasks—marketing, finance, support, and analytics—working seamlessly together to enhance productivity and decision-making.

With its open-source foundation and an upcoming Agent Monitoring Platform, Agno is paving the way for AI-driven automation at scale. Whether you're an entrepreneur, developer, or enterprise looking to integrate AI into your workflow, Agno offers limitless possibilities—without complexity.

Building AI Agents with Agno(Phidata) Conclusion

Agno provides an efficient, scalable, and flexible framework for building AI-powered agents. With its simplicity, modularity, and multi-modal capabilities, Agno is one of the excellent choices for developers seeking high-performance AI frameworks.