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)

Quick Reference

1. Basic Agent with Tools

from agno.agent import Agent
from agno.models.google import Gemini
from agno.tools.yfinance import YFinanceTools

agent = Agent(
    name="Finance Agent",
    model=Gemini(id="gemini-3-flash-preview"),
    tools=[YFinanceTools()],
    add_datetime_to_context=True,
    markdown=True,
)

agent.print_response("Give me a quick brief on NVIDIA", stream=True)

2. Structured Output with Pydantic

from typing import List, Optional
from agno.agent import Agent
from agno.models.google import Gemini
from agno.tools.yfinance import YFinanceTools
from pydantic import BaseModel, Field

class StockAnalysis(BaseModel):
    ticker: str = Field(..., description="Stock ticker symbol")
    company_name: str = Field(..., description="Full company name")
    current_price: float = Field(..., description="Current price in USD")
    summary: str = Field(..., description="One-line summary")
    key_drivers: List[str] = Field(..., description="2-3 key growth drivers")
    recommendation: str = Field(..., description="Buy, Hold, or Sell")

agent = Agent(
    model=Gemini(id="gemini-3-flash-preview"),
    tools=[YFinanceTools()],
    output_schema=StockAnalysis,
)

response = agent.run("Analyze NVIDIA")
analysis: StockAnalysis = response.content
print(f"{analysis.company_name}: {analysis.recommendation}")

3. Agent with Storage (Session Persistence)

from agno.agent import Agent
from agno.db.sqlite import SqliteDb
from agno.models.google import Gemini

agent = Agent(
    model=Gemini(id="gemini-3-flash-preview"),
    db=SqliteDb(db_file="tmp/agents.db"),
    add_history_to_context=True,
    num_history_runs=5,
    markdown=True,
)

# Same session_id = continuous conversation across runs
agent.print_response("Analyze NVDA", session_id="my-session", stream=True)
agent.print_response("Compare that to Tesla", session_id="my-session", stream=True)

4. Agent with Memory (User Preferences)

from agno.agent import Agent
from agno.db.sqlite import SqliteDb
from agno.memory import MemoryManager
from agno.models.google import Gemini

db = SqliteDb(db_file="tmp/agents.db")

agent = Agent(
    model=Gemini(id="gemini-3-flash-preview"),
    db=db,
    memory_manager=MemoryManager(
        model=Gemini(id="gemini-3-flash-preview"),
        db=db,
    ),
    enable_agentic_memory=True,  # Agent decides when to store/recall
    markdown=True,
)

# Agent remembers user preferences across sessions
agent.print_response(
    "I'm interested in AI stocks. My risk tolerance is moderate.",
    user_id="alice@example.com",
    stream=True,
)

5. Multi-Agent Team

from agno.agent import Agent
from agno.models.google import Gemini
from agno.team.team import Team
from agno.tools.yfinance import YFinanceTools

bull = Agent(
    name="Bull Analyst",
    role="Make the investment case FOR a stock",
    model=Gemini(id="gemini-3-flash-preview"),
    tools=[YFinanceTools()],
)

bear = Agent(
    name="Bear Analyst",
    role="Make the investment case AGAINST a stock",
    model=Gemini(id="gemini-3-flash-preview"),
    tools=[YFinanceTools()],
)

team = Team(
    name="Investment Research",
    model=Gemini(id="gemini-3-flash-preview"),
    members=[bull, bear],
    instructions=["Get both perspectives, then synthesize a balanced recommendation"],
    show_members_responses=True,
    markdown=True,
)

team.print_response("Should I invest in NVIDIA?", stream=True)

6. Sequential Workflow

from agno.agent import Agent
from agno.models.google import Gemini
from agno.tools.yfinance import YFinanceTools
from agno.workflow import Step, Workflow

data_agent = Agent(name="Data Gatherer", model=Gemini(id="gemini-3-flash-preview"), tools=[YFinanceTools()])
analyst = Agent(name="Analyst", model=Gemini(id="gemini-3-flash-preview"))
writer = Agent(name="Report Writer", model=Gemini(id="gemini-3-flash-preview"), markdown=True)

workflow = Workflow(
    name="Research Pipeline",
    steps=[
        Step(name="Gather Data", agent=data_agent),
        Step(name="Analyze", agent=analyst),
        Step(name="Write Report", agent=writer),
    ],
)

workflow.print_response("Analyze NVIDIA for investment", stream=True)

7. MCP Server Integration (stdio)

import asyncio
from agno.agent import Agent
from agno.models.anthropic import Claude
from agno.tools.mcp import MCPTools

async def run_agent(message: str) -> None:
    async with MCPTools(command="uvx mcp-server-git") as mcp_tools:
        agent = Agent(model=Claude(id="claude-sonnet-4-5-20250929"), tools=[mcp_tools])
        await agent.aprint_response(message, stream=True)

asyncio.run(run_agent("What is the license for this project?"))

8. MCP Server (Streamable HTTP)

import asyncio
from agno.agent import Agent
from agno.models.anthropic import Claude
from agno.tools.mcp import MCPTools

async def run_agent(message: str) -> None:
    async with MCPTools(
        transport="streamable-http",
        url="https://docs.agno.com/mcp",
    ) as mcp_tools:
        agent = Agent(model=Claude(id="claude-sonnet-4-5-20250929"), tools=[mcp_tools], markdown=True)
        await agent.aprint_response(message, stream=True)

asyncio.run(run_agent("What is Agno?"))

9. Multiple MCP Servers

import asyncio
from os import getenv
from agno.agent import Agent
from agno.tools.mcp import MultiMCPTools

async def run_agent(message: str) -> None:
    mcp_tools = MultiMCPTools(
        commands=["npx -y @openbnb/mcp-server-airbnb --ignore-robots-txt"],
        urls=["http://localhost:8000/mcp"],
        urls_transports=["streamable-http"],
        timeout_seconds=30,
    )
    await mcp_tools.connect()
    agent = Agent(tools=[mcp_tools], markdown=True)
    await agent.aprint_response(message, stream=True)
    await mcp_tools.close()

asyncio.run(run_agent("Find listings in Barcelona"))

10. LearningMachine (Persistent Learning)

from agno.agent import Agent
from agno.db.postgres import PostgresDb
from agno.learn import LearningMachine, LearningMode, UserProfileConfig
from agno.models.openai import OpenAIResponses

db = PostgresDb(db_url="postgresql+psycopg://ai:ai@localhost:5532/ai")

agent = Agent(
    model=OpenAIResponses(id="gpt-5.2"),
    db=db,
    learning=LearningMachine(
        user_profile=UserProfileConfig(mode=LearningMode.ALWAYS),
    ),
    markdown=True,
)

agent.print_response("Hi! I'm Alice, call me Ali.", user_id="alice@example.com", stream=True)
# Profile fields (name, preferred_name) captured automatically

Key Patterns

Pattern: MCP Connection Lifecycle

Always close MCP connections. Use async context managers or try/finally:

# Preferred: context manager
async with MCPTools(command="uvx mcp-server-git") as tools:
    agent = Agent(tools=[tools])
    await agent.aprint_response("query")

# Alternative: manual lifecycle
tools = MCPTools(command="uvx mcp-server-git")
await tools.connect()
try:
    agent = Agent(tools=[tools])
    await agent.aprint_response("query")
finally:
    await tools.close()

Pattern: Production Database (PostgreSQL)

from agno.db.postgres import PostgresDb
db = PostgresDb(db_url="postgresql+psycopg://user:pass@localhost:5432/agno")
agent = Agent(db=db, add_history_to_context=True)

Pattern: Debug Mode

agent = Agent(debug_mode=True)  # Detailed logs of messages, tools, tokens

Pattern: Custom Tools

from agno.tools.decorator import tool

@tool
def get_weather(city: str) -> str:
    """Get current weather for a city."""
    return f"Weather in {city}: 72F, sunny"

agent = Agent(tools=[get_weather])

Important Rules

• Never create agents in loops - reuse agents for performance
• Use output_schema for structured responses (not free-form parsing)
• PostgreSQL for production, SQLite only for development
• Both sync and async - all public methods have async variants (prefix with a)
• Always close MCP connections - use try/finally or async context managers
• Enable debug_mode=True when troubleshooting

Reference Files

Detailed documentation is available in references/:

• agents.md - Agent parameters, configuration, tools, memory, knowledge, guardrails
• teams.md - Team modes (route/broadcast/tasks), member coordination
• workflows.md - Step types (Step, Parallel, Condition, Loop, Router)
• mcp.md - MCP integration (stdio, SSE, Streamable HTTP), MultiMCPTools
• tools.md - Built-in tools list, custom tool creation, tool hooks
• learning.md - LearningMachine stores (profile, memory, session, knowledge, entity)
• models.md - Supported model providers and configuration

Resources

• Documentation: https://docs.agno.com
• GitHub: https://github.com/agno-agi/agno
• Cookbook Examples: https://github.com/agno-agi/agno/tree/main/cookbook
• Install: pip install agno