agno

Name: agno
Availability: InStock
Author: metaphorics

Build and deploy AI agents with Agno's MCP integration and AgentOS runtime.

Updated Feb 7, 2026

Search Tags:#multi-agent #mcp #memory #knowledge #agentos #workflows #agno

Ready to run

npx skills add https://github.com/metaphorics/my-skills --skill agno-metaphorics

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: agno
Source: https://github.com/metaphorics/my-skills/tree/main/skills/agno
Command: npx skills add https://github.com/metaphorics/my-skills --skill agno-metaphorics

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

Agno provides a production-ready framework to build, deploy, and orchestrate AI agents and multi-agent systems with MCP integration and AgentOS runtime.

Core Features & Use Cases

MCP integration, including MCPTools and MultiMCPTools for connecting to external servers
AgentOS runtime based on FastAPI for deploying agents as production APIs
Memory and Knowledge: memory, session memory, knowledge bases, and user memories
Team and Workflow patterns for collaborative AI tasks and complex orchestrations
Tools, guardrails, telemetry, and security primitives to support production-grade reliability
Rich reference docs and examples to accelerate development

Quick Start

Instantiate an Agent with tools, memory, and knowledge, then call print_response or run to begin.

Frequently Asked Questions about agno

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I build production-grade AI agents with multi-agent workflows?▼

Build production-grade AI agents by instantiating an Agent with tools, memory, and knowledge, then calling print_response or run. The Agno framework provides team and workflow patterns to orchestrate multi-agent systems.

Does the AgentOS runtime support deploying AI agents as APIs?▼

Yes, the AgentOS runtime deploys AI agents as production APIs. It uses a FastAPI-based runtime to orchestrate agents, teams, and workflows for production-grade reliability.

How do I connect AI agents to external servers using MCP integration?▼

Connect AI agents to external servers using MCP integration. The framework provides MCPTools and MultiMCPTools to establish connections and govern external tool interactions.

Can I add persistent memory and knowledge bases to multi-agent systems?▼

Yes, add persistent memory and knowledge bases to multi-agent systems. The framework supports session memory, user memories, and knowledge bases to retain context across agent interactions.

What's the best way to orchestrate collaborative tasks across multiple AI agents?▼

Orchestrate collaborative AI tasks using the framework's team and workflow patterns. These patterns coordinate multi-agent systems with tools, guardrails, and telemetry for complex orchestrations.

Do I need guardrails and telemetry to deploy production-grade AI agents?▼

Yes, guardrails and telemetry are necessary for production-grade AI agents. The framework includes security primitives, guardrails, and telemetry to support reliable deployment and orchestration.