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What are Agents?

Agents are autonomous programs that use language models to achieve tasks. They solve problems by running tools, accessing knowledge and memory to improve responses.

Instead of a rigid binary definition, let’s think of Agents in terms of agency and autonomy.

  • Level 0: Agents with no tools (basic inference tasks).
  • Level 1: Agents with tools for autonomous task execution.
  • Level 2: Agents with knowledge, combining memory and reasoning.
  • Level 3: Teams of agents collaborating on complex workflows.

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Basic Agent

basic_agent.py
from agno.agent import Agent
from agno.models.openai import OpenAIChat

agent = Agent(
    model=OpenAIChat(id="gpt-4o"),
    description="You are an enthusiastic news reporter with a flair for storytelling!",
    markdown=True
)
agent.print_response("Tell me about a breaking news story from New York.", stream=True)

To run the agent, install dependencies and export your OPENAI_API_KEY.

1

Setup your virtual environment

2

Install dependencies

3

Export your OpenAI key

4

Run the agent

python basic_agent.py

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Agent with tools

This basic agent will obviously make up a story, lets give it a tool to search the web.

agent_with_tools.py
from agno.agent import Agent
from agno.models.openai import OpenAIChat
from agno.tools.duckduckgo import DuckDuckGoTools

agent = Agent(
    model=OpenAIChat(id="gpt-4o"),
    description="You are an enthusiastic news reporter with a flair for storytelling!",
    tools=[DuckDuckGoTools()],
    show_tool_calls=True,
    markdown=True
)
agent.print_response("Tell me about a breaking news story from New York.", stream=True)

Install dependencies and run the Agent

1

Install dependencies

2

Run the agent

python agent_with_tools.py

Now you should see a much more relevant result.

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Agent with knowledge

Agents can store knowledge in a vector database and use it for RAG or dynamic few-shot learning.

Agno agents use Agentic RAG by default, which means they will search their knowledge base for the specific information they need to achieve their task.

agent_with_knowledge.py
from agno.agent import Agent
from agno.models.openai import OpenAIChat
from agno.embedder.openai import OpenAIEmbedder
from agno.tools.duckduckgo import DuckDuckGoTools
from agno.knowledge.pdf_url import PDFUrlKnowledgeBase
from agno.vectordb.lancedb import LanceDb, SearchType

agent = Agent(
    model=OpenAIChat(id="gpt-4o"),
    description="You are a Thai cuisine expert!",
    instructions=[
        "Search your knowledge base for Thai recipes.",
        "If the question is better suited for the web, search the web to fill in gaps.",
        "Prefer the information in your knowledge base over the web results."
    ],
    knowledge=PDFUrlKnowledgeBase(
        urls=["https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
        vector_db=LanceDb(
            uri="tmp/lancedb",
            table_name="recipes",
            search_type=SearchType.hybrid,
            embedder=OpenAIEmbedder(id="text-embedding-3-small"),
        ),
    ),
    tools=[DuckDuckGoTools()],
    show_tool_calls=True,
    markdown=True
)

# Comment out after the knowledge base is loaded
if agent.knowledge is not None:
    agent.knowledge.load()

agent.print_response("How do I make chicken and galangal in coconut milk soup", stream=True)
agent.print_response("What is the history of Thai curry?", stream=True)

Install dependencies and run the Agent

1

Install dependencies

2

Run the agent

python agent_with_knowledge.py

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Multi Agent Teams

Agents work best when they have a singular purpose, a narrow scope and a small number of tools. When the number of tools grows beyond what the language model can handle or the tools belong to different categories, use a team of agents to spread the load.

agent_team.py
from agno.agent import Agent
from agno.models.openai import OpenAIChat
from agno.tools.duckduckgo import DuckDuckGoTools
from agno.tools.yfinance import YFinanceTools

web_agent = Agent(
    name="Web Agent",
    role="Search the web for information",
    model=OpenAIChat(id="gpt-4o"),
    tools=[DuckDuckGoTools()],
    instructions="Always include sources",
    show_tool_calls=True,
    markdown=True,
)

finance_agent = Agent(
    name="Finance Agent",
    role="Get financial data",
    model=OpenAIChat(id="gpt-4o"),
    tools=[YFinanceTools(stock_price=True, analyst_recommendations=True, company_info=True)],
    instructions="Use tables to display data",
    show_tool_calls=True,
    markdown=True,
)

agent_team = Agent(
    team=[web_agent, finance_agent],
    model=OpenAIChat(id="gpt-4o"),
    instructions=["Always include sources", "Use tables to display data"],
    show_tool_calls=True,
    markdown=True,
)

agent_team.print_response("What's the market outlook and financial performance of AI semiconductor companies?", stream=True)

Install dependencies and run the Agent team

1

Install dependencies

2

Run the agent

python agent_team.py

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Debugging

Want to see the system prompt, user messages and tool calls?

Agno includes a built-in debugger that will print debug logs in the terminal. Set debug_mode=True on any agent or set AGNO_DEBUG=true in your environment.

debugging.py
from agno.agent import Agent

agent = Agent(markdown=True, debug_mode=True)
agent.print_response("Share a 2 sentence horror story")

Run the agent to view debug logs in the terminal:

python debugging.py
Welcome to AgnoAgent Playground