Agents in Action in Agno Platform

Introduction

As agents become integral to every application, I have explored various methods to learn about them. There are numerous frameworks and applications available. Today, I discovered a lightweight library that is not only easy to use and learn but also offers several cookbooks. Additionally, it provides an intuitive playground user interface that can be run locally to test how your agents work, eliminating the need for frontend development.

Let's have a look at it with a live example of a YouTube agent We will also see how the playground provides an intuitive user interface to interact with this agent and explore the monitoring and observability of agents in action.

What is Agno?

Agno (formerly known as Phidata) is a lightweight library for building multi-modal Agents. Agno is designed with three core principles:

• Simplicity: No graphs (such as LangGraph), chains (LangChain or CrewAI), or convoluted patterns - just pure python.
• Uncompromising Performance: Blazing fast agents with a minimal memory footprint.
• Truly Agnostic: Any model, any provider, any modality. Agno is designed to be the container for AGI.

GitHub Repository -agno-agi/agno: Agno is a lightweight library for building multi-modal Agents

A typical Agno agent is structured as follows: The agent has access to an LLM model, possesses memory (primarily a database), knowledge (web or files), and (search, API calls, etc., or a custom-defined tool).

Examples of Agents in Agno

There are many example agents provided by Agno, which can be explored Introduction - Agno. In the video below, I am exploring a YouTube summarizer agent that takes a video from YouTube and creates a summary of the content.

The next steps, which were more exciting about Ango, involved setting up a playground at my local site with a few simple steps. I then added this YouTube agent to the playground to observe its functionality.

Let's see how it looks and works.

Agent Playground

Agno provides a beautiful Agent UI for interacting with your agents. Instructions to setup this playground are simple and can be found Agent Playground - Agno

It was also easy to add the YouTube Agent that I demonstrated to this playground and observe it functioning. Here is the code snippet illustrating how to incorporate new agents into the playground code.

YouTube Agent in Action

It is now time to observe the functioning of this new agent. As you will see, when I asked the agent to summarize a YouTube video, it adhered to the instructions provided and utilized the tools at its disposal.

Agent Observability

Agno provides built-in monitoring capabilities that track session and performance metrics through app.agno.com

Let's examine the results when we asked the YouTube Agent to summarize the video. In this video, you will notice that each prompt given to the Agent is monitored with details of steps executed, tools utilized, and session information. This ensures the traceability of the Agent's actions, making them easy to understand and record from an audit trail perspective.

Conclusion

Agents and related platforms and are continuously evolving. Innovations such as Agno are rapidly transforming the development process of agents. It is a great area to monitor and learn from such platforms.

A Note to Readers

The purpose of this article is to educate and spread awareness about this evolving topic. While every effort has been made to ensure clarity and accuracy, there is always room for better explanations or more relevant examples. Any misinterpretations are entirely unintentional, as I am also learning alongside you.

The credit for these technological advancements belongs to the brilliant inventors and developers who have made them possible. Let’s appreciate their contributions as we continue to explore these innovations together.