#012: Meet Agno AI: The Lightning-Fast Framework Making AI Agents Actually Usable

In this post I'm going to show you 8 levels of building AI agents using Agno framework from beginner to more advanced (they're all simple!)

If you're building AI agents with frameworks like LangChain or LangGraph, you're probably all too familiar with the frustration: bloated dependencies, and agonizingly slow response times and pure spaghetti code. What should be a seamless experience turns into a resource-hungry monster that is also hard to debug.

That's where Agno AI comes in.

Imagine building sophisticated AI agents that respond instantly, require minimal boilerplate code, and have all the features you need. A framework that handles everything from basic text responses to complex multi-modal teams of agents collaborating on your most challenging problems.

In this post, I'll show you how Agno AI is revolutionizing agent development with a framework that's:

• Much simpler than competing frameworks

• Extremely well organized code base that is easy to comprehend.

• Lightning fast agent creation and response times

• Multi-modal by design - handling text, images, audio and video seamlessly

• Model agnostic - no vendor lock-in, use any AI provider you want

Agno AI

Agno AI (formerly known as Phidata) is an open-source framework designed for building advanced, multi-modal AI agents. Here's what I've discovered about it:

1. Performance

   • Exceptionally fast with agent creation being much faster than competing frameworks like LangGraph

   • Highly memory efficient (50x less memory usage than LangGraph - LangGraph is soo bloated)

   • Designed for high-performance agentic systems at scale. It really does work anywhere.

2. Model Flexibility

   • Model agnostic - works with any AI model or provider without vendor lock-in

   • No dependency on specific LLM providers

3. Multi-Modal Support

   • Native support for text, image, audio, and video processing

   • Can handle various media types in both input and output

4. Tool Integration

   • Supports a wide range of tools like web search (DuckDuckGo), financial data (YFinance), etc.

   • Easy tool integration for extending agent capabilities

   • Most tools are very simple themselves - great starting point for custom development.

5. Agent Architecture

   • Build individual agents or teams of specialized agents

   • Multi-agent teams that can collaborate on complex workflows

   • Different levels of agent complexity from basic to advanced multi-agent systems

   • Incredible number of really simple samples and examples

6. Knowledge Management

   • RAG (Retrieval-Augmented Generation) capabilities via "Agentic RAG"

   • Integration with vector databases for knowledge storage

   • Support for various knowledge sources including PDFs, directories of text files etc.

7. Memory Capabilities

   • Can store and manage agent state in databases

   • Persistent memory across multiple interactions

8. Developer Experience

   • Simple API for creating agents

   • Structured output support (JSON, Pydantic models)

And more..

• Active GitHub repository with 23.9k stars and 3k forks

• Growing community with forum and Discord support

• Open-source with MPL-2.0 license

• Recently hosting an AI Agent Hackathon with prizes up to $20,000

Let's dive in!