Agno is a lightweight library for building Multimodal Agents. It exposes LLMs as a unified API and gives them superpowers like memory, knowledge, tools and reasoning.

• Build lightning-fast Agents that can generate text, image, audio and video.
• Add memory, knowledge, tools and reasoning as needed.
• Run anywhere, Agno is open-source.

Here’s an Agent that can search the web:

from agno.agent import Agent
from agno.models.openai import OpenAIChat
from agno.tools.duckduckgo import DuckDuckGoTools

agent = Agent(
    model=OpenAIChat(id="gpt-4o"),
    tools=[DuckDuckGoTools()],
    markdown=True
)
agent.print_response("What's happening in New York?", stream=True)

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Key features

Agno is simple, fast and model agnostic. Here are some key features:

• Lightning Fast: Agent creation is 10,000x faster than LangGraph (see performance).
• Model Agnostic: Use any model, any provider, no lock-in.
• Multi Modal: Native support for text, image, audio and video.
• Multi Agent: Build teams of specialized agents.
• Memory Management: Store agent sessions and state in a database.
• Knowledge Stores: Use vector databases for RAG or dynamic few-shot learning.
• Structured Outputs: Make Agents respond in a structured format.
• Monitoring: Track agent sessions and performance in real-time on agno.com.

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Get Started

If you’re new to Agno, start by building your first Agent.

After that, checkout the Examples Gallery and build real-world applications using Agno.

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Dive deeper

Agno is a battle-tested framework with best-in-class performance, checkout the following guides to dive-in: