Agno

Agents from Agno ..

Agno, previously known as Phidata, is an open-source framework for building agentic systems that allows developers to build, ship, and monitor AI agents with memory, knowledge, tools, and reasoning capabilities. It helps create domain-specific agents, integrate with any LLM, and manage agents' state, memory, and knowledge.

With Agno, you can build teams of agents, chat with them using an Agent UI, and monitor, evaluate, and optimize your agents. It's designed for performance and scale, supporting any model provider and allowing you to run your systems in your own cloud.

Follow the WSL + Docker instructions to configure environment.

Ensure Ollama is installed & configured and the required models - deepseek-llm:latest & llama3.2b:latest - are downloaded.

The following examples and workshops illustrate haw Agents can help with various tasks ..

This example demonstrates how to design an AI agent with a distinctive journalistic personality. We'll craft a news reporter character that blends authentic New York City attitude with creative storytelling elements.

Example Prompts

Try engaging with the agent using these prompts:

"What's the latest scoop from Central Park?"

"Tell me about a breaking story from Wall Street"

"What's happening at the Yankees game right now?"

"Give me the buzz about a new Broadway show"

Take a look at the .env to set environmental variables.

As everything is being run locally no need for API keys. 

# Import the dedent function to format multi-line strings without extra indentation
from textwrap import dedent

# Import necessary components from the agno library
# Agent: Core class for creating AI agents
# RunResponse: Used for handling responses from the agent (marked with noqa to ignore linting)
from agno.agent import Agent, RunResponse  # noqa
# Import the Ollama model class which provides access to local LLM models
from agno.models.ollama import Ollama

# Create a new Agent instance with a news reporter personality
# This instantiates our AI agent with specific model and behavior instructions
agent = Agent(
    # Specify which model to use - here we're using deepseek-llm from Ollama
    # Ollama is a tool for running local LLMs, and deepseek-llm is a specific model available through it
    model=Ollama(id="deepseek-llm:latest"), 
    
    # Define the agent's personality and behavior using a multi-line string
    # dedent() removes the leading indentation from the multi-line string to improve readability
    instructions=dedent("""\
        You are an enthusiastic news reporter with a flair for storytelling! 🗽
        Think of yourself as a mix between a witty comedian and a sharp journalist.

        Your style guide:
        - Start with an attention-grabbing headline using emoji
        - Share news with enthusiasm and NYC attitude
        - Keep your responses concise but entertaining
        - Throw in local references and NYC slang when appropriate
        - End with a catchy sign-off like 'Back to you in the studio!' or 'Reporting live from the Big Apple!'

        Remember to verify all facts while keeping that NYC energy high!\
    """),
    
    # Enable markdown formatting in the agent's responses
    # This allows for rich text formatting like bold, italics, and headings
    markdown=True,
)

# Execute the agent with a test prompt
# This will print the agent's response to the console in real-time (streaming)
agent.print_response(
    "Tell me about a breaking news story happening in Times Square.", 
    stream=True  # Enable streaming mode to see responses character-by-character
)

# A commented-out section containing additional example prompts
# These are suggestions for further testing the agent with different scenarios
"""
Try these fun scenarios:
1. "What's the latest food trend taking over Brooklyn?"
2. "Tell me about a peculiar incident on the subway today"
3. "What's the scoop on the newest rooftop garden in Manhattan?"
4. "Report on an unusual traffic jam caused by escaped zoo animals"
5. "Cover a flash mob wedding proposal at Grand Central"
"""

Script Breakdown

Once you've mastered the Agno agent structure, give the Workshop a go ..!

from textwrap import dedent
from agno.agent import Agent, RunResponse  # noqa
from agno.models.ollama import Ollama

  • dedent from textwrap: Removes common leading whitespace from multi-line strings, making the code more readable while preserving the final output format

  • Agent from agno.agent: The main class for creating an AI agent

  • RunResponse (marked with # noqa to ignore linting warnings): A class for response handling

  • Ollama from agno.models.ollama: A wrapper for accessing Ollama LLM models

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