Agno

A production-focused agent framework that handles the full stack from single agents to multi-agent teams to workflow pipelines. Notable for clean MCP integration patterns (stdio, SSE, and streamable HTTP transports all supported), built-in persistence with SqliteDb or PostgresDb, and agentic memory that lets agents decide when to store context. The LearningMachine component maintains user profiles and entity memory across sessions. Workflow primitives include parallel steps, conditional routing, and loops. If you're moving past prototype agents and need session persistence, structured outputs with Pydantic, or coordinated team behaviors with role delegation, this covers it without requiring you to wire everything yourself.

skills.sh installs: 389
GitHub stars: 26
last updated: Aug 21, 2026

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Install

$ npx -y skills add agno-agi/agno-skills --skill agno --agent claude-code

Installs into .claude/skills of the current project.

Files

Agno Skill

Build production-ready AI agents with Agno - a lightweight, model-agnostic framework for agents, teams, workflows, and MCP integration.

When to Use This Skill

This skill should be triggered when:

Building AI agents with tools, memory, structured outputs, or knowledge
Creating multi-agent teams with role-based delegation
Implementing workflows with sequential, parallel, conditional, or routing steps
Integrating MCP servers (stdio, SSE, or Streamable HTTP)
Deploying agents with AgentOS (FastAPI-based runtime)
Working with the LearningMachine (user profiles, entity memory, session context)
Debugging agent behavior or optimizing performance

Architecture Overview

Agent          - Single autonomous AI unit (model + tools + instructions)
Team           - Multiple agents coordinated by a leader (route/broadcast/tasks modes)
Workflow       - Pipeline-based execution (Step, Parallel, Condition, Loop, Router)
AgentOS        - FastAPI runtime for deploying agents as production APIs
LearningMachine - Persistent learning across sessions (profiles, memory, knowledge)