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Basic Information

Agno is a full-stack Python framework for building multi-agent systems that incorporate memory, knowledge and reasoning. It is designed to let developers create agents at five levels of capability, from simple tool-using agents to agent teams and stateful agentic workflows. The README shows examples such as a reasoning finance agent that uses YFinance and a coordinated multi-agent team that uses web search and finance tools. Agno supports serving agents via pre-built FastAPI routes and provides monitoring and examples for getting started. The project includes installation instructions, a cookbook of examples, and guidance for integrating model providers and tools, enabling developers to prototype, run and monitor agentic applications in production-ready configurations.

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Features
Agno highlights a unified, model-agnostic interface to 23+ model providers and native multi-modal I/O for text, image, audio and video. Reasoning is first-class, with three supported approaches: reasoning models, ReasoningTools, and a custom chain-of-thought option. The framework provides Agent Teams for advanced multi-agent architectures and built-in Agentic Search across 20+ vector databases for runtime retrieval-augmented generation. Agents include built-in Storage and Memory drivers for session and long-term memory, support structured outputs or json_mode, and ship with pre-built tools like YFinance and DuckDuckGo. Agno emphasizes performance with agent instantiation around ~3μs and ~6.5Kib average memory, plus pre-built FastAPI routes and monitoring support.
Use Cases
Agno reduces boilerplate and accelerates development of reliable, scalable agentic systems by providing building blocks for tools, memory, reasoning and team coordination. It helps developers move from prototypes to production with examples, a cookbook, and pre-built API routes for deployment. Performance optimizations allow many agents to be instantiated and run efficiently, which matters when spawning thousands of agents or parallelizing tool calls. Built-in search, storage and structured output features make it easier to implement retrieval-augmented workflows, long-term memory, and deterministic agentic workflows. Documentation, community resources and telemetry controls are provided to support adoption and debugging while allowing telemetry to be disabled.

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