Can I integrate local tools with Agno agents?

Yes, Agno supports the Model Context Protocol (MCP), allowing agents to connect to local tools and servers via stdio, SSE, or Streamable HTTP transports.

What is the Agno framework?

Agno is a modern AI agent framework designed for building production-ready multi-agent systems, structured workflows, and agentic applications with built-in runtime support.

How does Agno handle multi-agent collaboration?

Agno uses a 'Team' structure where multiple agents with specific roles, instructions, and tools collaborate on tasks through automated delegation and shared context.

Is Agno suitable for production AI applications?

Absolutely. Agno includes AgentOS, a FastAPI-based runtime that provides features like JWT authentication, telemetry, and persistent session storage for scalable deployments.

Does Agno support Retrieval-Augmented Generation (RAG)?

Yes, Agno features a dedicated Knowledge Base system for RAG, supporting document processing and vector embeddings with various database backends.

Agno AI Agent Framework

Name: Agno AI Agent Framework
Author: OutlineDriven

byOutlineDriven

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データサイエンスとML

Builds production-grade multi-agent systems, AgentOS runtimes, and MCP server integrations for advanced agentic AI development.

The Agno skill provides comprehensive assistance for developers building sophisticated agentic AI applications. It facilitates the creation of autonomous agents with specialized tools and memory, orchestrates multi-agent teams with role-based delegation, and manages complex workflows featuring conditional logic and loops. With built-in support for Model Context Protocol (MCP) servers and the AgentOS runtime, it enables the seamless deployment of agents as scalable FastAPI applications with persistent session storage and RAG-powered knowledge bases.

主な機能

01Production deployment via AgentOS with FastAPI and JWT middleware

028 GitHub stars

03Multi-agent team orchestration with role-based delegation

04Workflow management with conditional branching, loops, and parallel execution

05Model Context Protocol (MCP) integration for local and remote toolsets

06Persistent memory systems using PostgreSQL, SQLite, or cloud storage

ユースケース

01Building specialized AI research teams that collaborate on complex data analysis

02Deploying scalable AI assistants with persistent user sessions and RAG capabilities

03Connecting agents to local developer tools and file systems via MCP servers

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add outlinedriven/odin-codex-plugin agno

For use in Claude.ai and ChatGPT

Download Skill