Agno is a modern AI agent framework designed for building production-ready multi-agent systems, featuring an AgentOS runtime and deep Model Context Protocol (MCP) integration.

Does it support Model Context Protocol (MCP)?

Absolutely. It covers connecting to single or multiple MCP servers via stdio, SSE, or Streamable HTTP transports for extended tool capabilities.

Can I use this for production deployments?

Yes, the skill includes detailed guidance on using AgentOS with FastAPI, JWT authentication, and persistent database storage for enterprise-grade environments.

How do I persist agent conversations?

The skill demonstrates how to integrate session memory with databases like PostgreSQL or SQLite to maintain conversation state and history across interactions.

How does this skill help with multi-agent teams?

It provides implementation patterns for role-based delegation, allowing Claude to help you coordinate multiple agents that collaborate on complex tasks with shared memory.

Agno AI Agent Framework

Name: Agno AI Agent Framework
Author: delorenj

bydelorenj

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Data Science & ML

Builds production-grade multi-agent systems, AgentOS runtimes, and complex agentic workflows with native MCP integration.

About

The Agno skill provides comprehensive architectural guidance and implementation patterns for building sophisticated AI agent systems. It enables Claude to assist you in designing autonomous agents, multi-agent teams with role-based delegation, and robust workflows featuring conditional branching and memory persistence. Whether you are connecting to Model Context Protocol (MCP) servers, deploying scalable APIs via AgentOS, or implementing RAG-powered knowledge bases, this skill ensures your agentic applications are built using production-ready best practices and modern orchestration patterns.

Key Features

5 GitHub stars
Deep Model Context Protocol (MCP) integration for tool-use and transports
Production-ready AgentOS deployment patterns using FastAPI and JWT
Multi-agent team orchestration with autonomous role delegation
Advanced workflow management with conditional logic, loops, and branching
Structured output validation and input schema enforcement using Pydantic

Use Cases

Developing automated agentic pipelines with multi-step orchestration and error handling
Deploying scalable AI microservices with persistent session storage and database backends
Architecting collaborative AI research teams that share memory and specialized tools

About

Key Features

5 GitHub stars
Deep Model Context Protocol (MCP) integration for tool-use and transports
Production-ready AgentOS deployment patterns using FastAPI and JWT
Multi-agent team orchestration with autonomous role delegation
Advanced workflow management with conditional logic, loops, and branching
Structured output validation and input schema enforcement using Pydantic

Use Cases

Developing automated agentic pipelines with multi-step orchestration and error handling
Deploying scalable AI microservices with persistent session storage and database backends
Architecting collaborative AI research teams that share memory and specialized tools