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Agno

What it is

Agno is a lightweight Python framework for building multi-modal agents with memory, knowledge, and tools. It is the successor to Phidata v2 and focuses on high performance, scalability, and ease of use.

What problem it solves

Agno simplifies the transition from a single agent prototype to a production-ready system. It provides a stateless, session-scoped runtime that can be served as a FastAPI backend, making it easy to deploy agents as services.

Where it fits in the stack

[Framework / Agent / Runtime] - A performance-oriented framework for building and serving agentic software.

Typical use cases

  • High-performance multi-modal agents (Vision, Audio, Text)
  • Multi-agent teams coordinating via a shared control plane
  • Production-grade agents served via FastAPI

Strengths

  • Performance: Optimized for low latency and high throughput.
  • Stateless Runtime: Designed to be horizontally scalable out of the box.
  • Multi-modal: Native support for various model modalities.
  • AgentOS: Integration with a control plane for monitoring and managing agents in production.

Limitations

  • New Rebrand: As the successor to Phidata, some documentation and legacy links might still refer to the old name.
  • Python Only: Currently focused on the Python ecosystem.

When to use it

  • When building agents that need to scale horizontally in production.
  • For projects requiring strong multi-modal support.
  • If you liked Phidata but need more production features.

When not to use it

  • If you require a TypeScript-native framework (consider Bee Agent Framework).

CLI examples

# Initialize an Agno project
agno init

# Start the Agno serving environment (FastAPI based)
agno serve

# Manage agent sessions via the CLI
agno sessions list

Getting started

Installation

pip install agno openai duckduckgo-search

Working Example

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

# 1. Create the agent with a tool
agent = Agent(
    model=OpenAIChat(id="gpt-4o"),
    tools=[DuckDuckGo()],
    description="You are a helpful AI assistant that can search the web.",
    markdown=True
)

# 2. Run a query
agent.print_response("Tell me about the Agno framework and its search capabilities.")

Licensing and cost

  • Open Source: Yes (MIT License)
  • Cost: Free
  • Self-hostable: Yes

Sources / References

Contribution Metadata

  • Last reviewed: 2026-03-02
  • Confidence: high