Agno is an open-source framework that empowers anyone to build high-performance AI agents with robust memory, knowledge, tools, and reasoning capabilities. Backed by three core principles - Simplicity, Uncompromising Performance, and True Agnosticism - Agno is designed to be the foundation for your AI endeavors.

With the latest upgrade, Agno is now optimized for performance-driven, agentic systems, significantly outperforming other frameworks like LangChain. It boasts 5,000 times faster agent instantiation and 50 times less memory usage compared to LangChain, while continuously working to minimize execution time and memory footprint.

Agno's Agent UI provides a seamless interface for creating agents with built-in memory, tools, and storage. You can explore the demo agents, such as the Web Search Agent and the Research Agent, to witness their powerful capabilities in action. These agents can leverage various tools, including Google search and domain-specific knowledge, to deliver comprehensive insights and responses.

To get started with Agno, you'll need Python and Git installed. Clone the GitHub repository, explore the Agno Cookbook, and set up a virtual environment. The Cookbook offers a wealth of examples, from basic agents to advanced workflows and full-stack applications, allowing you to easily enhance your agents with features like Retrieval Augmented Generation (RAG).

Agno's uncompromising performance, simplicity, and true agnosticism make it a compelling choice for building your AI agents. Embrace the power of Agno and unlock the potential of memory, knowledge, tools, and reasoning in your AI projects.

Agno, the open-source framework for building high-performance AI agents, is built on three core principles:

1. Simplicity: Agno's design eschews complex graphs, chains, or convoluted patterns, offering a straightforward, pure Python approach.

2. Uncompromising Performance: Agno delivers blazing-fast AI agents with a minimal memory footprint, significantly outperforming other frameworks like LangChain.

3. True Agnosticism: Agno is designed to be the foundation for your AI, supporting any model, provider, or modality, ensuring true flexibility and adaptability.

With the latest upgrade, Agno is now optimized for performance-driven, agentic systems, providing a massive impact even at a small scale. Its ability to reduce memory usage, parallelize tool calls, and minimize execution time makes it a powerful choice for your AI projects.

Agno's Agent UI provides a powerful platform for building and interacting with AI agents that have access to memory, tools, and storage. This UI offers a glimpse into the capabilities that can be created with the Agno framework.

Let's explore the different agents available in the demo agent endpoint:

1. Web Search Agent: This agent can utilize various tools to perform web searches and provide comprehensive responses. For example, you can ask it "What are the five most trending stocks?" and it will deliver the latest trending stock information from multiple sources.

2. Research Agent: The research agent demonstrates how Agno agents can be configured to access a range of tools and knowledge sources to provide in-depth analysis. You can ask it "Give insights on what the World of AI YouTube channel is about," and it will deliver a structured research-style response.

These agents showcase the power of Agno's agent-centric approach, where you can build AI agents with customized capabilities, memory, and access to diverse tools and data sources. This allows for the creation of highly specialized and performant agents that can tackle a wide range of tasks.

To get started with Agno locally, you'll need to have Python and Git installed. Clone the Agno GitHub repository, and explore the Agno Cookbook, which provides ready-made examples and code blocks to help you build your own agents. Start with the basic agent, and then gradually incorporate tools, knowledge, memory, and reasoning capabilities to create more advanced agents.

Agno's focus on simplicity, performance, and agnosticism makes it a compelling choice for building powerful AI agents. With its optimized agent instantiation and reduced memory usage, Agno aims to deliver a significant impact even at a small scale. Explore the resources provided in the description to dive deeper into Agno and unleash the potential of your AI agents.

To get started with Agno, follow these steps:

1. Install Python: Ensure you have Python installed on your system.

2. Install Git: Install Git, as you'll need it to clone the Agno repository.

3. Clone the Agno Repository: Open your command prompt or terminal, navigate to the directory where you want to clone the repository, and run the following command:

   git clone https://github.com/openai/agno.git
   

4. Set Up a Virtual Environment: Create a virtual environment to isolate the Agno dependencies. For Windows, you can use the following commands:

   python -m venv agno-env
   agno-env\Scripts\activate
   

   For other operating systems, the commands may vary slightly.

5. Install Agno: With the virtual environment activated, install the Agno package using pip:

   pip install openai-agno
   

6. Explore the Agno Cookbook: The Agno Cookbook contains various examples and templates to help you get started. Navigate to the agno/cookbook directory and explore the different examples.

7. Run the Basic Agent Example: In the getting-started directory, you'll find a basic_agent.py file. Run this example to see a basic agent in action:

   cd agno/cookbook/getting-started
   python basic_agent.py
   

   This will start the agent, which will respond to the prompt "Tell me about the breaking news story from New York."

8. Explore the Web Search Agent: Another example is the web_search_agent.py file, which demonstrates an agent with web search capabilities. Run this example:

   python web_search_agent.py
   

   You can then ask the agent questions like "What are the five most trending stocks?"

9. Experiment with the Research Agent: The research_agent.py file showcases an agent that can provide in-depth research on a given topic. Run this example:

   python research_agent.py
   

   You can then ask the agent questions like "Give me insights on what the World of AI YouTube channel is about."

These examples should give you a good starting point to understand the capabilities of Agno and how to set up and run your own agents. Explore the Agno Cookbook for more advanced examples and customization options.

Agno, the open-source framework for building high-performance AI agents, offers a wealth of advanced capabilities beyond its core simplicity and performance. In this section, we'll explore how you can leverage Agno to unlock even more powerful AI solutions.

Implement Retrieval-Augmented Generation (RAG)

Agno's cookbook includes a comprehensive example of building a powerful RAG system. This approach combines large language models with knowledge bases to enable agents to search and extract valuable insights from data, such as PDFs. The example comes with a Streamlit-based UI, demonstrating how Agno agents can be seamlessly deployed and integrated into user-facing applications.

Collaborate with Agent Teams

Agno supports the creation of teams of agents that can collaborate on complex workflows. This allows you to build intricate systems where individual agents specialize in different tasks or capabilities, working together to achieve higher-level objectives. The Agno cookbook provides examples of these advanced agent team configurations, giving you a head start in building sophisticated multi-agent systems.

Leverage Memory and Reasoning Capabilities

Agno agents can be equipped with memory and reasoning capabilities, enabling them to maintain context, draw insights, and make more informed decisions. The cookbook includes examples of agents that utilize these advanced features, demonstrating how you can create agents that can learn, remember, and reason over time.

Explore the Agno Ecosystem

The Agno ecosystem extends beyond the core framework, with a growing community of contributors and a wealth of resources. Be sure to check out the Agno Cookbook, which provides a library of pre-built agent examples and code snippets to help you get started quickly. Additionally, stay up-to-date with the latest Agno developments and community contributions by following the project's official channels.

By leveraging Agno's advanced capabilities, you can unlock new frontiers in AI-powered applications, from intelligent search and knowledge extraction to collaborative multi-agent systems. Dive into the Agno ecosystem and start building the next generation of high-performance, versatile AI agents.

The new open-source framework, Agno, is a powerful tool for building high-performance, memory-rich AI agents. With its focus on simplicity, uncompromising performance, and true agnosticism, Agno offers a compelling alternative to other agent frameworks like LangChain.

Key highlights of Agno include:

• Blazing Fast Agents: Agno agents are up to 5,000 times faster in agent instantiation compared to LangChain, with 50 times less memory usage.
• Modular and Flexible: Agno allows you to use any model, provider, or modality, making it a truly agnostic foundation for your AI projects.
• Built-in Agent UI: Agno comes with a user-friendly agent UI that provides access to memory, tools, and storage, simplifying the development of complex agents.
• Comprehensive Cookbook: The Agno Cookbook provides a wealth of examples and code snippets to help you get started quickly with building agents of varying complexity.

Whether you're looking to create a simple news reporting agent or a powerful knowledge extraction system, Agno offers a compelling solution that prioritizes performance, flexibility, and ease of use. With its growing community and continuous improvements, Agno is a framework worth exploring for your AI agent development needs.