Agno is a lightweight and versatile library designed for building Reasoning Agents equipped with memory, knowledge, tools, and native multi-modal support. It allows the creation of various types of agents, including Reasoning Agents, Multi-Modal Agents, Teams of Agents, and Agentic Workflows. The library supports multiple models and providers, ensuring no lock-in and high flexibility.
Agno stands out for its speed and efficiency, with agents instantiating 10,000 times faster and using 50 times less memory compared to LangGraph. The library supports text, image, audio, and video, making it natively multi-modal. It also features an industry-leading multi-agent architecture with modes for routing, collaboration, and coordination. Additionally, Agno includes built-in support for long-term memory and domain knowledge, enabling agents to perform complex tasks autonomously.
Agents built with Agno can dynamically adapt their approach based on context and tool results, making them highly autonomous and capable of solving various problems. The library provides structured outputs, real-time monitoring, and extensive documentation to help users get started quickly.
Agno emphasizes performance, ensuring minimal execution time and memory usage, which is crucial for scaling agentic systems. The library's benchmarks show significant performance advantages over other frameworks, making it a robust choice for building high-performance agents.
