How to build multimodal ia agents using the AgNO framework?

While they work in ai de Agent, developers are often browsing compensation between speed, flexibility and resources efficiency. I have been exploring ai Agent's frame and I met Agno (before it was Fis-Data). Agno is a light frame to build multimodal agents. They claim to be ~ 10,000x faster than Langgraph and ~ 50x less memory than Langgraph. It sounds intriguing, right?

AGNO and LANGGRAPH: They offer very different experiences. After putting into practice with AgNO and comparing his performance and architecture with Langgraph, here is a breakdown of how they differ, where each one shines and what agno brings to the table.

TL;

• Trisilization and Marketing Creation Marketing Analyst Agent
• Use agno if you want speed, low memory consumption, multimodal capacities and flexibility with models/tools.
• Use Langgraph if you prefer logic based on the flow, or structured execution routes, or are already linked to the Langchain ecosystem.

The Agno: What does it offer?

AGNO is designed with a laser approach to performance and minimalism. In essence, AGNO is an open source and open source agent frame created for multimodal tasks, which means that he handles text, images, audio and video natively. What makes it unique is how light and fast is under the hood, even when orchestra large amounts of agents with additional complexity such as memory, tools and vector stores.

Key strengths that stand out:

• Burning instantiation speed: The creation of agents in agno watches in approximately 2 μs per agent, which is ~ 10,000x faster than Langgraph.
• Feather memory flow: Agno agents use only ~ 3.75 kib of memory on average: ~ 50x less than Langgraph agents.
• Multimodal native support: No pirates or accessories: AGNO is built from scratch to work without problems with various types of media.
• AGNOSTIC MODEL: Agno does not care if he is using LLMS of Openai, Claude, Gemini or open source. You are not locked in a specific supplier or execution time.
• Real -time monitoring: Agent sessions and performance can be observed live through Lambwhich causes purification and optimization to be much softer.

Ayente with AGNO: Construction Traffic Agent

Using agno feels refreshingly efficient. It can turn entire fleets of agents that not only operate in parallel but also share memory, tools and knowledge bases. These agents can specialize and grouped into teams of multiple agents, and the memory layer admits sessions and storage states in a persistent database.

What is really impressive is how Agno manages complexity without sacrificing performance. It handles the orchestration of the real world agent, such as the chaining of tools, the recovery based on the rag or the structured output generation, without becoming a performance bottleneck.

If you have worked with Langgraph Or similar frames will immediately notice the starting delay and the consumption of resources that Agno avoids. This becomes a critical differentiator on scale. Let's build the triumph agent.

Installation of Libraries required

!pip install -U agno
!pip install duckduckgo-search
!pip install openai
!pip install pycountry

These are Shell commands to install the required Python packages:

• lamb: Central frame used to define and execute ia agents.
• Duckckgo-Search: We allow the agents to use Duckckgo to search the web.
• Opadai: For interfaces with OpenAI models such as GPT-4 or GPT-3.5.
• pycountry: (It probably is not used here, but installed) helps to handle the country's data.

Required imports

from agno.agent import Agent
from agno.models.openai import OpenAIChat
from agno.tools.duckduckgo import DuckDuckGoTools
from agno.tools.googlesearch import GoogleSearchTools
from agno.tools.dalle import DalleTools
from agno.team import Team
from textwrap import dedent

API keys configuration

from getpass import getpass
OPENAI_KEY = getpass('Enter Open ai API Key: ')

import os
os.environ('OPENAI_API_KEY') = OPENAI_KEY

• GetPass (): Sure way to enter your API key (so that it is not visible).
• The key is stored in the environment so that the agno The frame can pick it up by calling Openai API.

Web_agent – Web Search, Writer_agent – Write the article, Image_agent – Crea Visuals

web_agent = Agent(
    name="Web Agent",
    role="Search the web for information on Eiffel tower",
    model=OpenAIChat(id="o3-mini"),
    tools=(DuckDuckGoTools()),
    instructions="Give historical information",
    show_tool_calls=True,
    markdown=True,
)

writer_agent = Agent(
    name="Writer Agent",
    role="Write comprehensive article on the provided topic",
    model=OpenAIChat(id="o3-mini"),
    tools=(GoogleSearchTools()),
    instructions="Use outlines to write articles",
    show_tool_calls=True,
    markdown=True,
)

image_agent = Agent(
    model=OpenAIChat(id="gpt-4o"),
    tools=(DalleTools()),
    description=dedent("""\
        You are an experienced ai artist with expertise in various artistic styles,
        from photorealism to abstract art. You have a deep understanding of composition,
        color theory, and visual storytelling.\
    """),
    instructions=dedent("""\
        As an ai artist, follow these guidelines:
        1. Analyze the user's request carefully to understand the desired style and mood
        2. Before generating, enhance the prompt with artistic details like lighting, perspective, and atmosphere
        3. Use the `create_image` tool with detailed, well-crafted prompts
        4. Provide a brief explanation of the artistic choices made
        5. If the request is unclear, ask for clarification about style preferences

        Always aim to create visually striking and meaningful images that capture the user's vision!\
    """),
    markdown=True,
    show_tool_calls=True,
)

Combine

agent_team = Agent(
    team=(web_agent, writer_agent, image_agent),
    model=OpenAIChat(id="gpt-4o"),
    instructions=("Give historical information", "Use outlines to write articles","Generate Image"),
    show_tool_calls=True,
    markdown=True,
)

Execute everything

agent_team.print_response("Write an article on Eiffel towar and generate image", stream=True)

Production

Continuous output

Continuous output

I have created a realistic image of the Eiffel Tower. The image captures the
tower's full height and design,    ┃
┃ beautifully highlighted by the late afternoon sun. You can view it by
clicking here.

Image output

Ayente with AgNO: Building Analysts of the Market

This market analyst agent is a system based on the equipment used by AGNO, which combines a web agent for real -time information through Duckckgo and a financial financial data agent through Yahoo Finance. Powered by OpenAI modelsIt offers market information and the performance of the artificial intelligence company using tables, brands and content backed by the source for clarity, depth and transparency.

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

web_agent = Agent(
    name="Web Agent",
    role="Search the web for information",
    model=OpenAIChat(id="o3-mini"),
    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="o3-mini"),
    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 top ai companies of the world?", stream=True)

Production

AGNO vs Langgraph: Performance Showdown

Let's get in details and everything is included in the official documentation of AgNO:

• The performance test was performed in an Apple M4 MacBook Pro using Python Tracemalloc for the memory profile.
• Agno measured the average instantiation and the use of memory in more than 1000 executions, isolating the agent code to obtain a clean delta.

This type of speed and memory efficiency is not just about numbers, it is the key to scalability. In real -world agents deployments, where thousands of agents may need to turn simultaneously, every millisecond and Kilobyte are important.

Langgraph, although powerful and more structured for certain flow -based applications, tends to fight under this type of load unless it is very optimized. It is possible that it is not a problem for low -scale applications, but it becomes quickly expensive when executing agents at the production scale.

So … Is Agno better than Langgraph?

Not necessarily. It depends on what you are building:

• If you are working on the logic of the flow -based agent (think: directed graphics of steps with high level control), Langgraph could offer a more expressive structure.
• But if you need an ultra -fast multimodal agents, low footprint, especially in dynamic or high concurrence environments, AGNO wins for a mile.

AGNO clearly favors speed and efficiency at the system level, while Langgraph leans in structured orchestration and reliability. That said, AGNO developers themselves recognize that the precision and reliability reference points are equally important, and are currently in process. Until they are out, we cannot conclude the correction or resilience under edge cases.

Also read: Smolagents vs Langgraph: A complete comparison of the ai ​​agents frames

Conclusion

From a practical perspective, AGNO feels ready for real work loads, especially for teams that build scale agent systems. Its real -time performance monitoring, structured output support and the ability to connect memory + vector knowledge, which makes it a convincing platform to build robust applications quickly.

Langgraph is not out of the race: his strength is in the logic of clear control and flow oriented. But if you are reaching the walls of scale or you need to execute thousands of agents without melting their infrastructure, agno is worth a serious look.