Every AI Engineer is talking about Agno - Here’s why?
Agno is an open-source framework for building and managing AI agents and multi-agent systems. The framework helps developers create complex, autonomous AI applications. It’s built for real-world Agent Engineering.
With Agno, you can create multi-agent systems with memory, knowledge, human-in-the-loop, and MCP support. In addition, you can orchestrate agents that work together as a team for more autonomy or use step-based agentic workflows for more control.
The goal of Agno’s developers is to provide a framework that gives developers tools for every stage of the product lifecycle. In this article, we explore this framework and share our experiences.
Why do we use Agno?
We have been involved in the development of software and data science solutions for several years and have already used many frameworks. When selecting frameworks, we ensure that they are intuitive to use and have well-structured documentation.
In the field of agent-based systems, several new frameworks have entered the market in recent years. We have looked at many of these frameworks.
In this context, we also came across Agno. It is easy to use and offers comprehensive features for developing real-world AI applications.
The framework already saves us a lot of work by abstracting many complex topics. Agno also offers excellent documentation. In our opinion, other open source frameworks can learn a lot from this.
The Agno team is doing a truly remarkable job. 👏🏽👏🏽
Key Features of the Agno Framework
Agno stands out thanks to the following key features:
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Performance: Agents are created extremely quickly, making them highly scalable and cost-effective for large AI Apps. It also requires significantly less memory per agent than many alternatives.
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Architecture: Agents are created using a clear plug-and-play structure (model, tools, memory, knowledge) that simplifies development and debugging. It provides a unified API for synchronous and asynchronous operations.
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Core Agent Intelligence: Agno supports integration with any LLM provider, avoiding lock-in to a specific vendor. Agents are designed to process and generate text, image, audio, and video content. Tools and reasoning enable agents to think through complex problems and interact with the outside world.
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Memory, Knowledge, and Persistence: Agents maintain the state and remember past conversations and sessions across multiple runs. With Agentic RAG, agents have access to private, domain-specific knowledge at runtime. Collective Memory enables shared knowledge databases.
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Full-Stack Runtime and Deployment: AgentOS is a ready-to-use, production-ready FastAPI-based application for agents, teams, and workflows. The Control Plane is a web interface for testing, monitoring, and managing agents in real time. And all of this is possible within your own infrastructure, ensuring complete data privacy and security.
Agno vs. Other Agent Frameworks: A Performance Comparison
Performance plays a key role in agent-based systems. Even simple AI workflows can require hundreds of agents. For this reason, it is important that agents can be instantiated quickly and have a low memory footprint.
A performance comparison has shown that Agno Agents instantiate 529 times faster than Langgraph, 57 times faster than PydanticAI, and 70 times faster than CrewAI.
In addition, Agno Agents also use 24× lower memory than Langgraph, 4× lower than PydanticAI, and 10× lower than CrewAI.
In addition to performance, other aspects such as accuracy and reliability also play a key role in agent-based solutions. Our experience has shown that Agno also impresses in this area.
So it’s no surprise that Agno is so popular in the AI community. And its popularity continues to grow. If you’re an AI engineer, we think you should definitely give Agno a try.
Conclusion
It’s clear why Agno is quickly becoming a popular topic among AI engineers. This open-source framework is a comprehensive and production-ready solution for agent engineering in practice.
With powerful features such as AgentOS for deployment and a unified control plane for management, Agno enables developers to create, monitor, and manage AI agents within their own infrastructure while ensuring complete data privacy.
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