from agno.agent import Agent
from agno.tools.duckduckgo import DuckDuckGoTools

agent = Agent(
    tools=[DuckDuckGoTools()],
    markdown=True,
)

agent.print_response("Search for the latest AI news", stream=True)

from agno.agent import Agent
from pydantic import BaseModel, Field


class MovieScript(BaseModel):
    name: str = Field(..., description="Movie title")
    genre: str = Field(..., description="Movie genre")
    storyline: str = Field(..., description="3 sentence storyline")


agent = Agent(
    description="You help people write movie scripts.",
    output_schema=MovieScript,
)

result = agent.run("Write a sci-fi thriller")
print(result.content.name)  # Access structured output

import asyncio
from agno.agent import Agent
from agno.tools.mcp import MCPTools


async def run_agent(message: str) -> None:
    mcp_tools = MCPTools(command="uvx mcp-server-git")
    await mcp_tools.connect()

    try:
        agent = Agent(tools=[mcp_tools])
        await agent.aprint_response(message, stream=True)
    finally:
        await mcp_tools.close()


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

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


async def run_agent(message: str) -> None:
    env = {
        **os.environ,
        "GOOGLE_MAPS_API_KEY": os.getenv("GOOGLE_MAPS_API_KEY"),
    }

    mcp_tools = MultiMCPTools(
        commands=[
            "npx -y @openbnb/mcp-server-airbnb --ignore-robots-txt",
            "npx -y @modelcontextprotocol/server-google-maps",
        ],
        env=env,
    )
    await mcp_tools.connect()

    try:
        agent = Agent(tools=[mcp_tools], markdown=True)
        await agent.aprint_response(message, stream=True)
    finally:
        await mcp_tools.close()

from agno.agent import Agent
from agno.team import Team
from agno.tools.duckduckgo import DuckDuckGoTools
from agno.tools.hackernews import HackerNewsTools

research_agent = Agent(
    name="Research Specialist",
    role="Gather information on topics",
    tools=[DuckDuckGoTools()],
    instructions=["Find comprehensive information", "Cite sources"],
)

news_agent = Agent(
    name="News Analyst",
    role="Analyze tech news",
    tools=[HackerNewsTools()],
    instructions=["Focus on trending topics", "Summarize key points"],
)

team = Team(
    members=[research_agent, news_agent],
    instructions=["Delegate research tasks to appropriate agents"],
)

team.print_response("Research AI trends and latest HN discussions", stream=True)

from agno.agent import Agent
from agno.workflow.workflow import Workflow
from agno.workflow.router import Router
from agno.workflow.step import Step
from agno.tools.duckduckgo import DuckDuckGoTools
from agno.tools.hackernews import HackerNewsTools

simple_researcher = Agent(
    name="Simple Researcher",
    tools=[DuckDuckGoTools()],
)

deep_researcher = Agent(
    name="Deep Researcher",
    tools=[HackerNewsTools()],
)

workflow = Workflow(
    steps=[
        Router(
            routes={
                "simple_topics": Step(agent=simple_researcher),
                "complex_topics": Step(agent=deep_researcher),
            }
        )
    ]
)

workflow.run("Research quantum computing")

from agno.agent import Agent
from agno.db.postgres import PostgresDb

db = PostgresDb(
    db_url="postgresql://user:pass@localhost:5432/agno", schema="agno_sessions"
)

agent = Agent(
    db=db,
    session_id="user-123",  # Persistent session
    add_history_to_messages=True,
)

undefined

from fastapi import FastAPI
from agno.agent import Agent
from agno.models.openai import OpenAIChat
from agno.os import AgentOS

undefined

from agno.agent import Agent
from agno.tools.hackernews import HackerNewsTools

agent = Agent(
    tools=[HackerNewsTools()],
    debug_mode=True,  # Enable detailed logging
    # debug_level=2,  # More verbose output
)

undefined

from typing import List
from agno.agent import Agent
from agno.workflow.workflow import Workflow
from agno.workflow.step import Step
from pydantic import BaseModel, Field


class ResearchTopic(BaseModel):
    """Structured research topic with specific requirements"""

    topic: str
    focus_areas: List[str] = Field(description="Specific areas to focus on")
    target_audience: str = Field(description="Who this research is for")
    sources_required: int = Field(description="Number of sources needed", default=5)


workflow = Workflow(
    input_schema=ResearchTopic,  # Validate inputs
    steps=[Step(agent=Agent(instructions=["Research based on focus areas"]))],
)

undefined

from agno.agent import Agent
from agno.tools.duckduckgo import DuckDuckGoTools

agent = Agent(tools=[DuckDuckGoTools()])
agent.print_response("Your question here")

from agno.team import Team

team = Team(
    members=[researcher, analyst, writer],
    instructions=["Delegate tasks based on agent roles"],
)

from agno.os import AgentOS

agent_os = AgentOS(
    agents=[agent1, agent2], db=PostgresDb(...), base_app=custom_fastapi_app
)
agent_os.serve()

async def run_with_mcp():
    mcp_tools = MCPTools(command="uvx mcp-server-git")
    await mcp_tools.connect()  # Always connect before use

    try:
        agent = Agent(tools=[mcp_tools])
        await agent.aprint_response("Your query")
    finally:
        await mcp_tools.close()  # Always close when done

from agno.agent import Agent
from agno.db.postgres import PostgresDb

db = PostgresDb(db_url="postgresql://...")

agent = Agent(
    db=db,
    session_id="unique-user-id",
    add_history_to_messages=True,  # Include conversation history
)

from agno.workflow.router import Router

workflow = Workflow(
    steps=[
        Router(
            routes={
                "route_a": Step(agent=agent_a),
                "route_b": Step(agent=b),
            }
        )
    ]
)