Your first Agents

What are Agents?

Agents are AI programs that operate autonomously.

The core of an Agent is the model, tools and instructions:

• Model: is the brain of an Agent, helping it reason, act, and respond to the user.
• Tools: are the body of an Agent, enabling it to interact with the real world.
• Instructions: guide the Agent’s behavior. Better the model, better it is at following instructions.

Agents also have memory, knowledge, storage and the ability to reason:

• Reasoning: enables Agents to “think” before responding and “analyze” the results of their actions (i.e. tool calls), this improves the Agents’ ability to solve problems that require sequential tool calls.
• Knowledge: is domain-specific information that the Agent can search on demand to make better decisions and provide accurate responses. Knowledge is stored in a vector database and this search on demand pattern is known as Agentic RAG.
• Storage: is used by Agents to save session history and state in a database. Model APIs are stateless and storage enables us to continue conversations from where they left off. This makes Agents stateful, enabling multi-turn conversations.
• Memory: gives Agents the ability to store and recall information from previous interactions, allowing them to learn user preferences and personalize their responses.

Let’s build a few Agents to see how they work.

Basic Agent

The simplest Agent only contains a model and calls the model API to generate a response.

Agno provides a unified interface to 23+ model providers, so you can test different providers and switch models as needed.

basic_agent.py

from agno.agent import Agent
from agno.models.anthropic import Claude

agent = Agent(model=Claude(id="claude-3-7-sonnet-latest"), markdown=True)
agent.print_response("What is the stock price of Apple?", stream=True)

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

1. Setup your virtual environment

   Mac

   uv venv --python 3.12
   source .venv/bin/activate
   

2. Install dependencies

   Mac

   uv pip install -U agno anthropic
   

3. Export your Anthropic key

   Mac

   export ANTHROPIC_API_KEY=sk-***
   

4. Run the agent

   python basic_agent.py
   

This Agent will not be able to give you the latest stock price because it doesn’t have access to it.

Agent with tools

Lets give the Agent a tool to fetch the latest stock price using the yfinance library.

agent_with_tools.py

from agno.agent import Agent
from agno.models.anthropic import Claude
from agno.tools.yfinance import YFinanceTools

agent = Agent(
    model=Claude(id="claude-3-7-sonnet-latest"),
    tools=[YFinanceTools(stock_price=True)],
    markdown=True,
)
agent.print_response("What is the stock price of Apple?", stream=True)

Install dependencies and run the Agent

1. Install new dependencies

   Mac

   uv pip install -U yfinance
   

2. Run the agent

   python agent_with_tools.py
   

Now the Agent will be able to give you the latest stock price.

Agent with instructions

The Agent will give you the latest stock price, but it will also yap along with it. To control the Agent’s output, we can and should add instructions.

agent_with_instructions.py

from agno.agent import Agent
from agno.models.anthropic import Claude
from agno.tools.yfinance import YFinanceTools

agent = Agent(
    model=Claude(id="claude-3-7-sonnet-latest"),
    tools=[YFinanceTools(stock_price=True)],
    instructions=[
        "Use tables to display data.",
        "Only include the table in your response. No other text.",
    ],
    markdown=True,
)
agent.print_response("What is the stock price of Apple?", stream=True)

Run the Agent

python agent_with_instructions.py

This will give you a much more concise response.

Set debug_mode=True or export AGNO_DEBUG=true to see the system prompt, user messages and tool calls.

Agent with reasoning

Agents can also “think” & “analyze” to solve problems that require more than one step. The ReasoningTools is one of the best “hacks” to improve the Agents’s response quality.

agent_with_reasoning.py

from agno.agent import Agent
from agno.models.anthropic import Claude
from agno.tools.reasoning import ReasoningTools
from agno.tools.yfinance import YFinanceTools

agent = Agent(
    model=Claude(id="claude-3-7-sonnet-latest"),
    tools=[
        ReasoningTools(add_instructions=True),
        YFinanceTools(
            stock_price=True,
            analyst_recommendations=True,
            company_info=True,
            company_news=True,
        ),
    ],
    instructions=[
        "Use tables to display data.",
        "Include sources in your response.",
        "Only include the report in your response. No other text.",
    ],
    markdown=True,
)
agent.print_response(
    "Write a report on NVDA",
    stream=True,
    show_full_reasoning=True,
    stream_intermediate_steps=True,
)

Run the Agent

python agent_with_reasoning.py

Agent with knowledge

While models have a large amount of training data, we almost always need to give them domain-specific information to help them achieve their task. This knowledge isn’t just used for RAG, an emerging use case is to dynamically provide few-shot examples to the model.

Dynamic Few-Shot Learning: Text2Sql Agent

Example: You’re building a Text2Sql Agent and for best results, you’ll need to give the Agent table schemas, column names, data types, example queries, common “gotchas”, etc.

You’re not going to put this all in the system prompt, instead you’ll store this information in a vector database and let the Agent query it at runtime, based on the user’s question.

Using this information, the Agent can then generate the best-possible SQL query. This is called dynamic few-shot learning.

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

Here’s how the following example works:

• The UrlKnowledge will download the Agno documentation and load it into a LanceDB vector database, using OpenAI for embeddings
• At runtime, the Agent will search the knowledge base for the most relevant information and use the ReasoningTools to reason about the user’s question.

agent_with_knowledge.py

from agno.agent import Agent
from agno.embedder.openai import OpenAIEmbedder
from agno.knowledge.url import UrlKnowledge
from agno.models.anthropic import Claude
from agno.tools.reasoning import ReasoningTools
from agno.vectordb.lancedb import LanceDb, SearchType

# Load Agno documentation in a knowledge base
knowledge = UrlKnowledge(
    urls=["https://docs.agno.com/introduction/agents.md"],
    vector_db=LanceDb(
        uri="tmp/lancedb",
        table_name="agno_docs",
        search_type=SearchType.hybrid,
        # Use OpenAI for embeddings
        embedder=OpenAIEmbedder(id="text-embedding-3-small", dimensions=1536),
    ),
)

agent = Agent(
    name="Agno Assist",
    model=Claude(id="claude-3-7-sonnet-latest"),
    instructions=[
        "Use tables to display data.",
        "Include sources in your response.",
        "Search your knowledge before answering the question.",
        "Only include the output in your response. No other text.",
    ],
    knowledge=knowledge,
    tools=[ReasoningTools(add_instructions=True)],
    add_datetime_to_instructions=True,
    markdown=True,
)

if __name__ == "__main__":
    # Load the knowledge base, comment out after first run
    # Set recreate to True to recreate the knowledge base if needed
    agent.knowledge.load(recreate=False)
    agent.print_response(
        "What are Agents?",
        stream=True,
        show_full_reasoning=True,
        stream_intermediate_steps=True,
    )

Install dependencies, export your OPENAI_API_KEY and run the Agent

1. Install new dependencies

   Mac

   uv pip install -U lancedb tantivy openai
   

2. Run the agent

   python agent_with_knowledge.py
   

Agent with storage

Storage drivers will help you save Agent sessions and state in a database. Model APIs are stateless and storage enables us to continue conversations from where they left off, by storing chat history and state in a database.

In this example, we’ll use the SqliteStorage driver to save the Agent’s session history and state in a database.

We’ll also set the session_id to a fixed value to demo persistence. Run this example multiple times to see the conversation continue from where it left off.

agent_with_storage.py

from agno.agent import Agent
from agno.models.anthropic import Claude
from agno.storage.sqlite import SqliteStorage
from agno.tools.duckduckgo import DuckDuckGoTools
from rich.pretty import pprint

agent = Agent(
    # This session_id is usually auto-generated
    # But for this example, we can set it to a fixed value
    # This session will now forever continue as a very long chat
    session_id="agent_session_which_is_autogenerated_if_not_set",
    model=Claude(id="claude-3-7-sonnet-latest"),
    storage=SqliteStorage(table_name="agent_sessions", db_file="tmp/agents.db"),
    tools=[DuckDuckGoTools()],
    add_history_to_messages=True,
    num_history_runs=3,
    add_datetime_to_instructions=True,
    markdown=True,
)

if __name__ == "__main__":
    print(f"Session id: {agent.session_id}")
    agent.print_response("How many people live in Canada?")
    agent.print_response("What is their national anthem?")
    agent.print_response("List my messages one by one")

    # Print all messages in this session
    messages_in_session = agent.get_messages_for_session()
    pprint(messages_in_session)

Install dependencies and run the Agent

1. Install new dependencies

   Mac

   uv pip install -U sqlalchemy duckduckgo-search
   

2. Run the agent

   python agent_with_storage.py
   

Agent with memory

Memory drivers enable Agents to store and recall information about users from previous interactions, allowing them to learn user preferences and personalize their responses.

In this example, we’ll use the v2 Memory driver to store user memories in a Sqlite database.

Because memories are tied to a user, we’ll set the user_id to a fixed value to build a persona for the user.

agent_with_memory.py

from agno.agent import Agent
from agno.memory.v2.db.sqlite import SqliteMemoryDb
from agno.memory.v2.manager import MemoryManager
from agno.memory.v2.memory import Memory
from agno.models.anthropic import Claude
from agno.models.openai import OpenAIChat
from rich.pretty import pprint

user_id = "peter_rabbit"
memory = Memory(
    db=SqliteMemoryDb(table_name="memory", db_file="tmp/memory.db"),
    model=OpenAIChat(id="gpt-4o-mini"),
)
memory.clear()

agent = Agent(
    model=Claude(id="claude-3-7-sonnet-latest"),
    user_id=user_id,
    memory=memory,
    # Enable the Agent to dynamically create and manage user memories
    enable_agentic_memory=True,
    add_datetime_to_instructions=True,
    markdown=True,
)

if __name__ == "__main__":
    agent.print_response("My name is Peter Rabbit and I like to eat carrots.")
    memories = memory.get_user_memories(user_id=user_id)
    print(f"Memories about {user_id}:")
    pprint(memories)
    agent.print_response("What is my favorite food?")
    agent.print_response("My best friend is Jemima Puddleduck.")
    print(f"Memories about {user_id}:")
    pprint(memories)
    agent.print_response("Recommend a good lunch meal, who should i invite?")

Run the Agent

python agent_with_memory.py

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.

Agno provides an industry leading multi-agent Architecture that allows you to build Reasoning Agent Teams. You can run the team in 3 modes: route, coordinate and collaborate.

In this example, we’ll build a team of 2 agents to analyze the semiconductor market performance, reasoning step by step.

agent_team.py

from textwrap import dedent

from agno.agent import Agent
from agno.models.anthropic import Claude
from agno.models.openai import OpenAIChat
from agno.team.team import Team
from agno.tools.duckduckgo import DuckDuckGoTools
from agno.tools.reasoning import ReasoningTools
from agno.tools.yfinance import YFinanceTools

web_agent = Agent(
    name="Web Search Agent",
    role="Handle web search requests",
    model=OpenAIChat(id="gpt-4o-mini"),
    tools=[DuckDuckGoTools()],
    instructions="Always include sources.",
    add_datetime_to_instructions=True,
)

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

team_leader = Team(
    name="Reasoning Finance Team Leader",
    mode="coordinate",
    model=Claude(id="claude-3-7-sonnet-latest"),
    members=[web_agent, finance_agent],
    tools=[ReasoningTools(add_instructions=True)],
    instructions=[
        "Use tables to display data.",
        "Only respond with the final answer, no other text.",
    ],
    markdown=True,
    show_members_responses=True,
    enable_agentic_context=True,
    add_datetime_to_instructions=True,
    success_criteria="The team has successfully completed the task.",
)

task = """\
Analyze the semiconductor market performance focusing on:
- NVIDIA (NVDA)
- AMD (AMD)
- Intel (INTC)
- Taiwan Semiconductor (TSM)
Compare their market positions, growth metrics, and future outlook."""

team_leader.print_response(
    task,
    stream=True,
    stream_intermediate_steps=True,
    show_full_reasoning=True,
)

Install dependencies and run the Agent team

1. Install dependencies

   Mac

   uv pip install -U duckduckgo-search yfinance
   

2. Run the agent

   python agent_team.py
   

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