{"id":153687,"date":"2025-03-24T13:02:04","date_gmt":"2025-03-24T13:02:04","guid":{"rendered":"https:\/\/peraltafinancing.com\/analytics\/how-to-build-multimodal-ai-agents-using-agno-framework\/"},"modified":"2025-03-24T13:02:04","modified_gmt":"2025-03-24T13:02:04","slug":"how-to-build-multimodal-ai-agents-using-agno-framework","status":"publish","type":"post","link":"https:\/\/fivemor.com\/?p=153687","title":{"rendered":"How to Build MultiModal AI Agents Using Agno Framework?"},"content":{"rendered":"<p> <br \/>\n<\/p>\n<div id=\"article-start\">\n<p>While working on Agentic AI, developers often find themselves navigating the trade-offs between speed, flexibility, and resource efficiency. I have been exploring the Agentic AI framework and came across Agno (earlier it was Phi-data). Agno is a lightweight framework for building multi-modal Agents. They are claiming to be ~10,000x faster than LangGraph and ~50x less memory than LangGraph. Sound intriguing right? <\/p>\n<p>Agno and LangGraph\u2014offer very different experiences. After going hands-on with Agno and comparing its performance and architecture to LangGraph, here\u2019s a breakdown of how they differ, where each one shines, and what Agno brings to the table.<\/p>\n<p><strong>TL;DR<\/strong><\/p>\n<ul class=\"wp-block-list\">\n<li>Building TriSage and Marketing Analyst Agent<\/li>\n<li>Use Agno if you want speed, low memory use, multimodal capabilities, and flexibility with models\/tools.<\/li>\n<li>Use <a href=\"https:\/\/www.analyticsvidhya.com\/blog\/2025\/03\/enhancing-code-quality-with-langgraph-reflection\/\" target=\"_blank\" rel=\"noreferrer noopener\">LangGraph<\/a> if you prefer flow-based logic, or structured execution paths, or are already tied into LangChain\u2019s ecosystem.<\/li>\n<\/ul>\n<h2 class=\"wp-block-heading\" id=\"h-the-agno-what-it-offers\">The Agno: What it Offers?<\/h2>\n<p>Agno is designed with a laser focus on performance and minimalism. At its core, Agno is an open-source, model-agnostic <a href=\"https:\/\/www.analyticsvidhya.com\/blog\/2024\/07\/ai-agent-frameworks\/\" target=\"_blank\" rel=\"noreferrer noopener\">agent framework<\/a> built for multimodal tasks\u2014meaning it handles text, images, audio, and video natively. What makes it unique is how light and fast it is under the hood, even when orchestrating large numbers of agents with added complexity like memory, tools, and vector stores.<\/p>\n<p>Key strengths that stand out:<\/p>\n<ul class=\"wp-block-list\">\n<li><strong>Blazing Instantiation Speed:<\/strong> Agent creation in Agno clocks in at about 2\u03bcs per agent, which is ~10,000x faster than LangGraph.<\/li>\n<li><strong>Featherlight Memory Footprint:<\/strong> Agno agents use just ~3.75 KiB of memory on average\u2014~50x less than LangGraph agents.<\/li>\n<li><strong>Multimodal Native Support:<\/strong> No hacks or plugins\u2014Agno is built from the ground up to work seamlessly with various media types.<\/li>\n<li><strong>Model Agnostic:<\/strong> Agno doesn\u2019t care if you\u2019re using OpenAI, Claude, Gemini, or open-source LLMs. You\u2019re not locked into a specific provider or runtime.<\/li>\n<li><strong>Real-Time Monitoring:<\/strong> Agent sessions and performance can be observed live via <a href=\"http:\/\/Agno.com\" target=\"_blank\" rel=\"noreferrer noopener nofollow\">Agno<\/a>, which makes debugging and optimization much smoother.<\/li>\n<\/ul>\n<h2 class=\"wp-block-heading\" id=\"h-hands-on-with-agno-building-trisage-agent\">Hands-On with Agno: Building TriSage Agent<\/h2>\n<p>Using Agno feels refreshingly efficient. You can spin up entire fleets of agents that not only operate in parallel but also share memory, tools, and knowledge bases. These agents can be specialized and grouped into multi-agent teams, and the memory layer supports storing sessions and states in a persistent database.<\/p>\n<p>What\u2019s really impressive is how Agno manages complexity without sacrificing performance. It handles real-world agent orchestration\u2014like tool chaining, <a href=\"https:\/\/www.analyticsvidhya.com\/blog\/2025\/03\/top-rag-frameworks-for-ai-applications\/\" target=\"_blank\" rel=\"noreferrer noopener\">RAG<\/a>-based retrieval, or structured output generation\u2014without becoming a performance bottleneck.<\/p>\n<p>If you\u2019ve worked with <a href=\"https:\/\/www.langchain.com\/langgraph\" target=\"_blank\" rel=\"noreferrer noopener nofollow\">LangGraph<\/a> or similar frameworks, you\u2019ll immediately notice the startup lag and resource consumption that Agno avoids. This becomes a critical differentiator at scale. Let\u2019s build the TriSage Agent.<\/p>\n<h3 class=\"wp-block-heading\" id=\"h-installing-required-libraries\">Installing Required Libraries<\/h3>\n<pre class=\"wp-block-code\"><code>!pip install -U agno\n!pip install duckduckgo-search\n!pip install openai\n!pip install pycountry<\/code><\/pre>\n<p>These are shell commands to install the required Python packages:<\/p>\n<ul class=\"wp-block-list\">\n<li><strong>agno<\/strong>: Core framework used to define and run AI agents.<\/li>\n<li><strong>duckduckgo-search<\/strong>: Lets agents use DuckDuckGo to search the web.<\/li>\n<li><strong>openai<\/strong>: For interfacing with OpenAI\u2019s models like GPT-4 or GPT-3.5.<\/li>\n<li><strong>pycountry<\/strong>: (Probably not used here, but installed) helps handle country data.<\/li>\n<\/ul>\n<h3 class=\"wp-block-heading\" id=\"h-required-imports\">Required Imports<\/h3>\n<pre class=\"wp-block-code\"><code>from agno.agent import Agent\nfrom agno.models.openai import OpenAIChat\nfrom agno.tools.duckduckgo import DuckDuckGoTools\nfrom agno.tools.googlesearch import GoogleSearchTools\nfrom agno.tools.dalle import DalleTools\nfrom agno.team import Team\nfrom textwrap import dedent<\/code><\/pre>\n<h3 class=\"wp-block-heading\" id=\"h-api-key-setup\">API Key Setup<\/h3>\n<pre class=\"wp-block-code\"><code>from getpass import getpass\nOPENAI_KEY = getpass('Enter Open AI API Key: ')\n\nimport os\nos.environ['OPENAI_API_KEY'] = OPENAI_KEY<\/code><\/pre>\n<ul class=\"wp-block-list\">\n<li><strong>getpass()<\/strong>: Secure way to enter your API key (so it\u2019s not visible).<\/li>\n<li>The key is then stored in the environment so that the <code>agno<\/code> framework can pick it up when calling OpenAI\u2019s API.<\/li>\n<\/ul>\n<h3 class=\"wp-block-heading\" id=\"h-web-agent-searches-the-web-writer-agent-writes-the-article-image-agent-creates-visuals\">web_agent \u2013 Searches the Web, writer_agent \u2013 Writes the Article, image_agent \u2013 Creates Visuals<\/h3>\n<pre class=\"wp-block-code\"><code>web_agent = Agent(\n    name=\"Web Agent\",\n    role=\"Search the web for information on Eiffel tower\",\n    model=OpenAIChat(id=\"o3-mini\"),\n    tools=[DuckDuckGoTools()],\n    instructions=\"Give historical information\",\n    show_tool_calls=True,\n    markdown=True,\n)\n\nwriter_agent = Agent(\n    name=\"Writer Agent\",\n    role=\"Write comprehensive article on the provided topic\",\n    model=OpenAIChat(id=\"o3-mini\"),\n    tools=[GoogleSearchTools()],\n    instructions=\"Use outlines to write articles\",\n    show_tool_calls=True,\n    markdown=True,\n)\n\nimage_agent = Agent(\n    model=OpenAIChat(id=\"gpt-4o\"),\n    tools=[DalleTools()],\n    description=dedent(\"\"\"\\\n        You are an experienced AI artist with expertise in various artistic styles,\n        from photorealism to abstract art. You have a deep understanding of composition,\n        color theory, and visual storytelling.\\\n    \"\"\"),\n    instructions=dedent(\"\"\"\\\n        As an AI artist, follow these guidelines:\n        1. Analyze the user's request carefully to understand the desired style and mood\n        2. Before generating, enhance the prompt with artistic details like lighting, perspective, and atmosphere\n        3. Use the `create_image` tool with detailed, well-crafted prompts\n        4. Provide a brief explanation of the artistic choices made\n        5. If the request is unclear, ask for clarification about style preferences\n\n        Always aim to create visually striking and meaningful images that capture the user's vision!\\\n    \"\"\"),\n    markdown=True,\n    show_tool_calls=True,\n)<\/code><\/pre>\n<h3 class=\"wp-block-heading\" id=\"h-combine-into-a-team\">Combine into a Team<\/h3>\n<pre class=\"wp-block-code\"><code>agent_team = Agent(\n    team=[web_agent, writer_agent, image_agent],\n    model=OpenAIChat(id=\"gpt-4o\"),\n    instructions=[\"Give historical information\", \"Use outlines to write articles\",\"Generate Image\"],\n    show_tool_calls=True,\n    markdown=True,\n)<\/code><\/pre>\n<h3 class=\"wp-block-heading\" id=\"h-run-the-whole-thing\">Run the Whole Thing<\/h3>\n<pre class=\"wp-block-code\"><code>agent_team.print_response(\"Write an article on Eiffel towar and generate image\", stream=True)<\/code><\/pre>\n<h3 class=\"wp-block-heading\" id=\"h-output\">Output<\/h3>\n<figure class=\"wp-block-image size-full is-resized\"><img fetchpriority=\"high\" decoding=\"async\" width=\"978\" height=\"1076\" src=\"https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2025\/03\/image-109.webp\" alt=\"Output\" class=\"wp-image-227962\" style=\"width:840px;height:auto\" srcset=\"https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2025\/03\/image-109.webp 978w, https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2025\/03\/image-109-273x300.webp 273w, https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2025\/03\/image-109-768x845.webp 768w, https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2025\/03\/image-109-150x165.webp 150w\" sizes=\"(max-width: 978px) 100vw, 978px\"\/><\/figure>\n<h4 class=\"wp-block-heading\" id=\"h-continued-output\">Continued Output<\/h4>\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"984\" height=\"1080\" src=\"https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2025\/03\/image-110.webp\" alt=\"Output\" class=\"wp-image-227963\" srcset=\"https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2025\/03\/image-110.webp 984w, https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2025\/03\/image-110-273x300.webp 273w, https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2025\/03\/image-110-768x843.webp 768w, https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2025\/03\/image-110-150x165.webp 150w\" sizes=\"auto, (max-width: 984px) 100vw, 984px\"\/><\/figure>\n<h4 class=\"wp-block-heading\" id=\"h-continued-output-0\">Continued Output<\/h4>\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"977\" height=\"641\" src=\"https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2025\/03\/image-111.webp\" alt=\"Output\" class=\"wp-image-227964\" srcset=\"https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2025\/03\/image-111.webp 977w, https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2025\/03\/image-111-300x197.webp 300w, https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2025\/03\/image-111-768x504.webp 768w, https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2025\/03\/image-111-150x98.webp 150w\" sizes=\"auto, (max-width: 977px) 100vw, 977px\"\/><\/figure>\n<pre class=\"wp-block-preformatted\">I have created a realistic image of the Eiffel Tower. The image captures the<br\/>tower's full height and design,    \u2503<br\/>\u2503 beautifully highlighted by the late afternoon sun. You can view it by<br\/>clicking here.<\/pre>\n<h4 class=\"wp-block-heading\" id=\"h-image-output\">Image Output<\/h4>\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"1024\" src=\"https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2025\/03\/image-112.webp\" alt=\"Image Output\" class=\"wp-image-227965\" srcset=\"https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2025\/03\/image-112.webp 1024w, https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2025\/03\/image-112-300x300.webp 300w, https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2025\/03\/image-112-150x150.webp 150w, https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2025\/03\/image-112-768x768.webp 768w, https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2025\/03\/image-112-96x96.webp 96w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\"\/><\/figure>\n<h2 class=\"wp-block-heading\" id=\"h-hands-on-with-agno-building-market-analyst-agent\">Hands-On with Agno: Building Market Analyst Agent<\/h2>\n<p>This Market Analyst Agent is a team-based system using Agno, combining a Web Agent for real-time info via DuckDuckGo and a Finance Agent for financial data via Yahoo Finance. Powered by <a href=\"https:\/\/platform.openai.com\/docs\/models\" target=\"_blank\" rel=\"noreferrer noopener nofollow\">OpenAI models<\/a>, it delivers market insights and AI company performance using tables, markdown, and source-backed content for clarity, depth, and transparency.<\/p>\n<pre class=\"wp-block-code\"><code>from agno.agent import Agent\nfrom agno.models.openai import OpenAIChat\nfrom agno.tools.duckduckgo import DuckDuckGoTools\nfrom agno.tools.yfinance import YFinanceTools\nfrom agno.team import Team\n\nweb_agent = Agent(\n    name=\"Web Agent\",\n    role=\"Search the web for information\",\n    model=OpenAIChat(id=\"o3-mini\"),\n    tools=[DuckDuckGoTools()],\n    instructions=\"Always include sources\",\n    show_tool_calls=True,\n    markdown=True,\n)\n\nfinance_agent = Agent(\n    name=\"Finance Agent\",\n    role=\"Get financial data\",\n    model=OpenAIChat(id=\"o3-mini\"),\n    tools=[YFinanceTools(stock_price=True, analyst_recommendations=True, company_info=True)],\n    instructions=\"Use tables to display data\",\n    show_tool_calls=True,\n    markdown=True,\n)\n\nagent_team = Agent(\n    team=[web_agent, finance_agent],\n    model=OpenAIChat(id=\"gpt-4o\"),\n    instructions=[\"Always include sources\", \"Use tables to display data\"],\n    show_tool_calls=True,\n    markdown=True,\n)\n\nagent_team.print_response(\"What's the market outlook and financial performance of top AI companies of the world?\", stream=True)<\/code><\/pre>\n<h3 class=\"wp-block-heading\" id=\"h-output-0\">Output<\/h3>\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"989\" height=\"1079\" src=\"https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2025\/03\/image-113.webp\" alt=\"Output\" class=\"wp-image-227966\" srcset=\"https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2025\/03\/image-113.webp 989w, https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2025\/03\/image-113-275x300.webp 275w, https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2025\/03\/image-113-768x838.webp 768w, https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2025\/03\/image-113-150x164.webp 150w\" sizes=\"auto, (max-width: 989px) 100vw, 989px\"\/><\/figure>\n<h2 class=\"wp-block-heading\" id=\"h-agno-vs-langgraph-performance-showdown\">Agno vs LangGraph: Performance Showdown<\/h2>\n<p>Let\u2019s get into specifics and it is all included in the official documentation of Agno:<\/p>\n<figure class=\"wp-block-table\">\n<table class=\"table table-bordered border-black table-striped\">\n<thead>\n<tr>\n<th><strong>Metric<\/strong><\/th>\n<th><strong>Agno<\/strong><\/th>\n<th><strong>LangGraph<\/strong><\/th>\n<th><strong>Factor<\/strong><\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Agent Instantiation Time<\/td>\n<td>~2\u03bcs<\/td>\n<td>~20ms<\/td>\n<td>~10,000x faster<\/td>\n<\/tr>\n<tr>\n<td>Memory Usage per Agent<\/td>\n<td>~3.75 KiB<\/td>\n<td>~137 KiB<\/td>\n<td>~50x lighter<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/figure>\n<ul class=\"wp-block-list\">\n<li>The performance testing was done on an Apple M4 MacBook Pro using Python\u2019s tracemalloc for memory profiling.<\/li>\n<li>Agno measured the average instantiation and memory usage over 1000 runs, isolating the Agent code to get a clean delta.<\/li>\n<\/ul>\n<p>This kind of speed and memory efficiency isn\u2019t just about numbers\u2014it\u2019s the key to scalability. In real-world agent deployments, where thousands of agents may need to spin up concurrently, every millisecond and kilobyte matters.<\/p>\n<p>LangGraph, while powerful and more structured for certain flow-based applications, tends to struggle under this kind of load unless heavily optimized. That might not be an issue for low-scale apps, but it becomes expensive fast when running production-scale agents.<\/p>\n<p>\n<iframe src=\"https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2025\/03\/407806244-ba466d45-75dd-45ac-917b-0a56c5742e23.mp4\" loading=\"lazy\" title=\"YouTube video\" allowfullscreen=\"\"><\/iframe>\n<\/p>\n<h2 class=\"wp-block-heading\" id=\"h-so-is-agno-better-than-langgraph\">So\u2026 Is Agno Better Than LangGraph?<\/h2>\n<p>Not necessarily. It depends on what you\u2019re building:<\/p>\n<ul class=\"wp-block-list\">\n<li>If you\u2019re working on flow-based agent logic (think: directed graphs of steps with high-level control), LangGraph might offer a more expressive structure.<\/li>\n<li>But if you need ultra-fast, low-footprint, multimodal agent execution, especially in high-concurrency or dynamic environments, Agno wins by a mile.<\/li>\n<\/ul>\n<p>Agno clearly favours speed and system-level efficiency, whereas LangGraph leans into structured orchestration and reliability. That said, Agno\u2019s developers themselves acknowledge that accuracy and reliability benchmarks are just as important\u2014and they\u2019re currently in the works. Until those are out, we can\u2019t conclude correctness or resilience under edge cases.<\/p>\n<p>Also read: <a href=\"https:\/\/www.analyticsvidhya.com\/blog\/2025\/01\/smolagents-vs-langgraph\/\" target=\"_blank\" rel=\"noreferrer noopener\">Smolagents vs LangGraph: A Comprehensive Comparison of AI Agent Frameworks<\/a><\/p>\n<h2 class=\"wp-block-heading\" id=\"h-conclusion\">Conclusion<\/h2>\n<p>From a hands-on perspective, Agno feels ready for real workloads, especially for teams building agentic systems at scale. It\u2019s real-time performance monitoring, support for structured output, and ability to plug in memory + vector knowledge make it a compelling platform for building robust applications quickly.<\/p>\n<p>LangGraph isn\u2019t out of the race\u2014its strength lies in clear, flow-oriented control logic. But if you\u2019re hitting scaling walls or need to run thousands of agents without melting your infrastructure, Agno is worth a serious look.<\/p>\n<div class=\"border-top py-3 author-info my-4\">\n<div class=\"author-card d-flex align-items-center\">\n<div class=\"flex-shrink-0 overflow-hidden\">\n                                    <a href=\"https:\/\/www.analyticsvidhya.com\/blog\/author\/pankaj9786\/\" class=\"text-decoration-none active-avatar\"><br \/>\n                                                                       <img decoding=\"async\" src=\"https:\/\/av-eks-lekhak.s3.amazonaws.com\/media\/lekhak-profile-images\/converted_image_Lb7Lh0T.webp\" width=\"48\" height=\"48\" alt=\"Pankaj Singh\" loading=\"lazy\" class=\"rounded-circle\"\/><\/p>\n<p>                                <\/a>\n                                <\/div>\n<\/p><\/div>\n<p>                Hi, I am Pankaj Singh Negi &#8211; Senior Content Editor | Passionate about storytelling and crafting compelling narratives that transform ideas into impactful content. I love reading about technology revolutionizing our lifestyle.                 <\/p>\n<\/p><\/div>\n<\/p><\/div>\n<p><h4 class=\"fs-24 text-dark\">Login to continue reading and enjoy expert-curated content.<\/h4>\n<p>                        <button class=\"btn btn-primary mx-auto d-table\" data-bs-toggle=\"modal\" data-bs-target=\"#loginModal\" id=\"readMoreBtn\">Keep Reading for Free<\/button>\n                    <\/p>\n\n","protected":false},"excerpt":{"rendered":"<p>While working on Agentic AI, developers often find themselves navigating the trade-offs between speed, flexibility, and resource efficiency. I have been exploring the Agentic AI framework and came across Agno (earlier it was Phi-data). Agno is a lightweight framework for building multi-modal Agents. They are claiming to be ~10,000x faster than LangGraph and ~50x less [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":153688,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[12033],"tags":[11530,63779,5293,13782,20383],"dealstore":[],"offerexpiration":[],"class_list":["post-153687","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-analytics","tag-agents","tag-agno","tag-build","tag-framework","tag-multimodal"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.4 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>How to Build MultiModal AI Agents Using Agno Framework? - Som2ny Network<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/fivemor.com\/?p=153687\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"How to Build MultiModal AI Agents Using Agno Framework? - Som2ny Network\" \/>\n<meta property=\"og:description\" content=\"While working on Agentic AI, developers often find themselves navigating the trade-offs between speed, flexibility, and resource efficiency. 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