Agno

Architecture Overview

Agno uses an Agent class that wraps a model provider with tools, knowledge (vector databases), memory (chat history + summaries), and optional storage backends. Agents run in a loop where the model generates responses, optionally calling tools, until a final answer is produced. Teams of agents can be organized with a leader agent that delegates to specialized member agents. The framework uses a plugin-like architecture for models, tools, and storage.

When to Use Agno

• Fast agent prototyping with minimal boilerplate
• Multi-modal agents (text, image, audio, video)
• Knowledge-base agents with built-in RAG
• Agent teams with delegation patterns
• Production applications with persistent storage

Strengths & Weaknesses

Quick Start

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()],
    description="You are a helpful research assistant.",
    instructions=["Always provide sources for your information."],
    markdown=True,
    show_tool_calls=True,
)

# Run the agent
agent.print_response(
    "What are the latest developments in AI agents?",
    stream=True,
)

Features at a Glance

Notable Users

Resources