Agno — Lightweight AI Agent Framework for Python

What it is

Agno is a lightweight Python framework for building AI agents. It provides model-agnostic agents with tools, memory, knowledge bases, and team coordination capabilities. Agno emphasizes simplicity: you can build a functional agent in as few as 5 lines of Python.

Agno targets developers who want agent capabilities without the complexity of heavier frameworks. It supports OpenAI, Anthropic, Google, Groq, and local models through a unified interface.

How it saves time or tokens

Agno's minimal API means less boilerplate code and faster iteration. The framework handles tool execution, memory management, and model communication with sensible defaults. You focus on defining tools and prompts rather than orchestration plumbing.

The team coordination feature lets you compose specialized agents into collaborative groups, each optimized for specific tasks, reducing the token cost of a single monolithic agent.

Additionally, the project's well-structured documentation and active community mean developers spend less time troubleshooting integration issues. When AI coding assistants generate code for this tool, they can reference established patterns from the documentation, producing correct implementations with fewer iterations and lower token costs.

How to use

1. Install Agno:

pip install agno

1. Create a basic agent:

from agno import Agent

agent = Agent(
    model='claude-sonnet-4-20250514',
    description='You are a helpful coding assistant',
    tools=['web_search', 'file_read']
)
result = agent.run('Find the latest Python release version')
print(result)

1. Add knowledge bases and memory:

from agno import Agent, Knowledge, Memory

agent = Agent(
    model='claude-sonnet-4-20250514',
    knowledge=Knowledge(sources=['./docs']),
    memory=Memory(db='sqlite:///memory.db')
)

1. Create agent teams for complex tasks.

Example

from agno import Agent, Team

researcher = Agent(name='researcher', model='claude-sonnet-4-20250514', tools=['web_search'])
writer = Agent(name='writer', model='claude-sonnet-4-20250514')

team = Team(agents=[researcher, writer])
result = team.run('Research and write a summary about WebAssembly in 2026')

Related on TokRepo

• AI Tools for Agents — AI agent frameworks and tools
• Multi-Agent Frameworks — Agno deep dive and comparison

Common pitfalls

• Overloading a single agent with too many tools. Agents perform better with focused tool sets. Use teams to distribute capabilities across specialized agents.
• Not persisting memory between sessions. Without a database-backed memory, conversation context is lost when the agent stops. Configure SQLite or PostgreSQL memory for persistence.
• Ignoring model-specific limitations. Agno is model-agnostic, but each model has different tool calling capabilities. Test your agent with your target model before deploying.
• Failing to review community discussions and changelogs before upgrading. Breaking changes in major versions can disrupt existing workflows. Pin versions in production and test upgrades in staging first.