{"id":10986,"date":"2025-03-11T17:50:44","date_gmt":"2025-03-11T17:50:44","guid":{"rendered":"https:\/\/wealthrevelation.com\/data-science\/2025\/03\/11\/how-to-build-financial-agent-with-agno-groq-405\/"},"modified":"2025-03-11T17:50:44","modified_gmt":"2025-03-11T17:50:44","slug":"how-to-build-financial-agent-with-agno-groq-405","status":"publish","type":"post","link":"https:\/\/wealthrevelation.com\/data-science\/2025\/03\/11\/how-to-build-financial-agent-with-agno-groq-405\/","title":{"rendered":"How To Build Financial Agent with Agno &amp; Groq"},"content":{"rendered":"<div id=\"tve_editor\" data-post-id=\"12525\">\n<div class=\"thrv_wrapper tve_image_caption img_style_rounded_corners\" data-css=\"tve-u-1952e1dbbb7\"><span class=\"tve_image_frame\"><img src=\"https:\/\/dataaspirant.com\/wp-content\/plugins\/lazy-load\/images\/1x1.trans.gif\" data-lazy-src=\"https:\/\/dataaspirant.com\/wp-content\/uploads\/2025\/02\/1-1.png\" class=\"tve_image wp-image-12526\" alt=\"Building a Financial Agent with Agno &amp; Groq\" data-id=\"12526\" width=\"834\" data-init-width=\"1920\" height=\"469\" data-init-height=\"1080\" title=\"Building a Financial Agent with Agno &amp; Groq\" data-width=\"834\" data-height=\"469\" loading=\"lazy\"><img class=\"tve_image wp-image-12526\" alt=\"Building a Financial Agent with Agno &amp; Groq\" data-id=\"12526\" width=\"834\" data-init-width=\"1920\" height=\"469\" data-init-height=\"1080\" title=\"Building a Financial Agent with Agno &amp; Groq\" src=\"https:\/\/dataaspirant.com\/wp-content\/uploads\/2025\/02\/1-1.png\" data-width=\"834\" data-height=\"469\" loading=\"lazy\"><\/span><\/div>\n<div class=\"thrv_wrapper thrv_text_element\">\n<p data-pm-slice=\"1 1 []\">After a long and busy Friday at work, I was unwinding by surfing YouTube before heading to bed. My recommendations were filled with videos on building AI agents, and one particular video by <strong>Krish Naik<\/strong> caught my attention. He demonstrated how to create an agent for stock analysis and financial recommendations using the <strong>Phidata agent framework.\u00a0<\/strong>Which has now evolved into <strong>Agno<\/strong>.<\/p>\n<p>Watching the video got me thinking:<\/p>\n<blockquote class=\"\"><p><em>Why not build my own stock analysis agent using the latest Agno framework?<\/em> Inspired by this, I decided to give it a shot.<\/p><\/blockquote>\n<p>What started as an exciting implementation took just <strong>30 minutes to script,\u00a0<\/strong>but making it actually work took <strong>the entire night<\/strong>! Debugging, setting up APIs, and fine-tuning the agent was a challenge, but the results were worth it.<\/p>\n<p>In this article, I will walk you through building a <strong>financial agent<\/strong> using:<\/p>\n<ul data-spread=\"false\" class=\"\">\n<li><a href=\"https:\/\/docs.agno.com\/introduction\" target=\"_blank\" rel=\"noopener\"><strong>Agno<\/strong><\/a> for agent-based automation,<\/li>\n<li><a href=\"https:\/\/console.groq.com\/docs\/overview\" target=\"_blank\" class=\"\" rel=\"noopener\"><strong>Groq<\/strong><\/a> for high-speed LLM inference,<\/li>\n<li><a href=\"https:\/\/platform.openai.com\/docs\/overview\" target=\"_blank\" rel=\"noopener\"><strong>OpenAI<\/strong><\/a> for language model integration.<\/li>\n<\/ul>\n<p>So grab your <strong>laptop<\/strong>, follow along, and let&#8217;s build your first AI-powered financial agent together!<\/p>\n<\/div>\n<div class=\"thrv_wrapper thrv_tw_qs tve_clearfix\" data-url=\"https:\/\/twitter.com\/intent\/tweet\" data-via=\"\" data-use_custom_url=\"\" data-element-name=\"Click to Tweet\">\n<div class=\"thrv_tw_qs_container\">\n<div class=\"thrv_tw_quote\">\n<p class=\"\">Building a Financial Agent with Agno &amp; Groq<\/p>\n<\/p><\/div>\n<p>\n\t\t\t<span><br \/>\n\t\t\t\t<i><\/i><br \/>\n\t\t\t\t<span class=\"thrv_tw_qs_button_text thrv-inline-text tve_editable\">Click to Tweet<\/span><br \/>\n\t\t\t<\/span>\n\t\t<\/p>\n<\/p><\/div>\n<\/div>\n<div class=\"thrv_wrapper thrv_text_element\">\n<p data-end=\"687\" data-start=\"386\">Before jumping into the implementation, it\u2019s essential to understand <strong data-end=\"549\" data-start=\"444\">what an agent is, its key components, and <\/strong>the best practices to follow when developing one. A well-structured agent can <strong data-end=\"686\" data-start=\"579\">a<\/strong>utonomously make decisions, interact with multiple data sources, and deliver highly optimized outputs.<\/p>\n<p data-end=\"812\" data-start=\"689\">Let&#8217;s first <strong data-end=\"743\" data-start=\"701\">break down the core concepts of agents<\/strong> and their fundamental components before moving on to implementation.<\/p>\n<\/div>\n<div class=\"thrv_wrapper thrv_text_element\">\n<h2 data-end=\"848\" data-start=\"819\" id=\"t-1740235415110\" class=\"\"><strong data-end=\"846\" data-start=\"822\">What is an AI Agent?<\/strong><\/h2>\n<p data-end=\"1099\" data-start=\"850\">An <strong data-end=\"865\" data-start=\"853\">AI agent<\/strong> is a system that perceives its environment, <a href=\"https:\/\/dataaspirant.com\/streamlining-big-data-processing\/\" target=\"_blank\" rel=\"noopener\" data-wpil-monitor-id=\"67\" class=\"\" data-css=\"tve-u-1952e898fdf\">processes data<\/a>, makes decisions, and takes actions to achieve a predefined goal. It acts autonomously or semi-autonomously, <a href=\"https:\/\/dataaspirant.com\/transfer-learning\/\" target=\"_blank\" rel=\"noopener\" data-wpil-monitor-id=\"71\" class=\"\" data-css=\"tve-u-1952e89994e\">leveraging AI models<\/a>, tools, and APIs to perform specific tasks.<\/p>\n<p data-end=\"1274\" data-start=\"1101\">In the financial world, AI agents are particularly useful for tasks like<\/p>\n<ul class=\"\">\n<li class=\"\" data-end=\"1274\" data-start=\"1101\"><strong data-end=\"1273\" data-start=\"1174\">Automated stock analysis, <\/strong><\/li>\n<li class=\"\" data-end=\"1274\" data-start=\"1101\"><strong data-end=\"1273\" data-start=\"1174\">Investment recommendations, <\/strong><\/li>\n<li class=\"\" data-end=\"1274\" data-start=\"1101\"><strong data-end=\"1273\" data-start=\"1174\">Risk assessment, <\/strong><\/li>\n<li class=\"\" data-end=\"1274\" data-start=\"1101\"><strong data-end=\"1273\" data-start=\"1174\">Portfolio management<\/strong>.<\/li>\n<\/ul>\n<\/div>\n<div class=\"thrv_wrapper tve_image_caption img_style_rounded_corners\" data-css=\"tve-u-1952e6f0a1f\"><span class=\"tve_image_frame\"><img src=\"https:\/\/dataaspirant.com\/wp-content\/plugins\/lazy-load\/images\/1x1.trans.gif\" data-lazy-src=\"https:\/\/dataaspirant.com\/wp-content\/uploads\/2025\/02\/Types-of-AI-Agents.png\" class=\"tve_image wp-image-12556\" alt=\"Types of AI Agents\" data-id=\"12556\" width=\"834\" data-init-width=\"1920\" height=\"469\" data-init-height=\"1080\" title=\"Types of AI Agents\" data-width=\"834\" data-height=\"469\" loading=\"lazy\"><img class=\"tve_image wp-image-12556\" alt=\"Types of AI Agents\" data-id=\"12556\" width=\"834\" data-init-width=\"1920\" height=\"469\" data-init-height=\"1080\" title=\"Types of AI Agents\" src=\"https:\/\/dataaspirant.com\/wp-content\/uploads\/2025\/02\/Types-of-AI-Agents.png\" data-width=\"834\" data-height=\"469\" loading=\"lazy\"><\/span><\/div>\n<div class=\"thrv_wrapper thrv_text_element\">\n<h3 data-end=\"1304\" data-start=\"1276\" id=\"t-1740235415111\" class=\"\"><strong data-end=\"1302\" data-start=\"1280\">Types of AI Agents<\/strong><\/h3>\n<p data-end=\"1384\" data-start=\"1305\">There are different types of AI agents based on complexity and functionality:<\/p>\n<ul data-end=\"1758\" data-start=\"1385\" class=\"\">\n<li data-end=\"1469\" data-start=\"1385\"><strong data-end=\"1406\" data-start=\"1387\">Reactive Agents<\/strong>: Respond directly to inputs without memory (e.g., chatbots).<\/li>\n<li data-end=\"1572\" data-start=\"1470\"><strong data-end=\"1495\" data-start=\"1472\">Deliberative Agents<\/strong>: Use reasoning and planning for decision-making (e.g., AI stock analysts).<\/li>\n<li data-end=\"1641\" data-start=\"1573\"><strong data-end=\"1594\" data-start=\"1575\">Learning Agents<\/strong>: Improve over time through <a href=\"https:\/\/dataaspirant.com\/machine-learning\/\" target=\"_blank\" rel=\"noopener\" data-wpil-monitor-id=\"65\" class=\"\" data-css=\"tve-u-1952e89ab04\">machine learning<\/a>.<\/li>\n<li data-end=\"1758\" data-start=\"1642\"><strong data-end=\"1667\" data-start=\"1644\">Multi-Agent Systems<\/strong>: Multiple agents working together to achieve a goal (e.g., finance + web search agents).<\/li>\n<\/ul>\n<h2 data-end=\"1803\" data-start=\"1765\" id=\"t-1740235415112\" class=\"\"><strong data-end=\"1801\" data-start=\"1768\">Key Components of an AI Agent<\/strong><\/h2>\n<p data-end=\"1908\" data-start=\"1805\">A well-structured agent consists of several important components. Here\u2019s a <strong data-end=\"1893\" data-start=\"1880\">deep dive<\/strong> into each one:<\/p>\n<h3 data-end=\"1955\" data-start=\"1910\" id=\"t-1740235415113\" class=\"\"><strong data-end=\"1955\" data-start=\"1914\">1. Perception Layer (Input Mechanism)<\/strong><\/h3>\n<ul data-end=\"2249\" data-start=\"1956\" class=\"\">\n<li data-end=\"2033\" data-start=\"1956\">This is how an agent <strong data-end=\"2011\" data-start=\"1979\">understands and gathers data<\/strong> from its environment.<\/li>\n<li data-end=\"2153\" data-start=\"2034\">Data can come from <strong data-end=\"2136\" data-start=\"2055\">APIs (financial market feeds), web scraping (news sites), real-time databases<\/strong>, or user inputs.<\/li>\n<li data-end=\"2249\" data-start=\"2154\">Example: Fetching <strong data-end=\"2221\" data-start=\"2174\">Apple stock price &amp; analyst recommendations<\/strong> from <strong data-end=\"2248\" data-start=\"2227\">Yahoo Finance API<\/strong>.<\/li>\n<\/ul>\n<h3 data-end=\"2290\" data-start=\"2251\" id=\"t-1740235415114\" class=\"\"><strong data-end=\"2290\" data-start=\"2255\">2. Decision-Making &amp; Processing<\/strong><\/h3>\n<ul data-end=\"2614\" data-start=\"2291\" class=\"\">\n<li data-end=\"2386\" data-start=\"2291\">This is the agent\u2019s <strong data-end=\"2322\" data-start=\"2313\">brain<\/strong>, where it analyzes data, applies logic, and determines actions.<\/li>\n<li data-end=\"2614\" data-start=\"2387\">It involves:\n<ul data-end=\"2614\" data-start=\"2404\">\n<li data-end=\"2465\" data-start=\"2404\"><strong data-end=\"2426\" data-start=\"2406\">Predefined Rules<\/strong> (if stock drops 5%, trigger an alert).<\/li>\n<li data-end=\"2526\" data-start=\"2468\"><strong data-end=\"2483\" data-start=\"2470\">AI Models<\/strong> (Groq-powered LLM for <a href=\"https:\/\/dataaspirant.com\/twitter-sentiment-analysis-using-r\/\" target=\"_blank\" rel=\"noopener\" data-wpil-monitor-id=\"69\" class=\"\" data-css=\"tve-u-1952e89bc08\">sentiment analysis<\/a>).<\/li>\n<li data-end=\"2614\" data-start=\"2529\"><strong data-end=\"2552\" data-start=\"2531\">Context Awareness<\/strong> (combining financial data + news trends for better insights).<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<h3 data-end=\"2664\" data-start=\"2616\" id=\"t-1740235415115\" class=\"\"><strong data-end=\"2664\" data-start=\"2620\">3. Action Mechanism (Output &amp; Execution)<\/strong><\/h3>\n<ul data-end=\"2928\" data-start=\"2665\" class=\"\">\n<li data-end=\"2720\" data-start=\"2665\">The agent <strong data-end=\"2719\" data-start=\"2677\">executes actions based on its analysis<\/strong>.<\/li>\n<li data-end=\"2928\" data-start=\"2721\">This can be:\n<ul data-end=\"2928\" data-start=\"2738\">\n<li data-end=\"2806\" data-start=\"2738\">Displaying results (printing stock insights in structured tables).<\/li>\n<li data-end=\"2864\" data-start=\"2809\">Sending alerts (notifying users about stock changes).<\/li>\n<li data-end=\"2928\" data-start=\"2867\">Triggering API calls (executing automated trading actions).<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<h3 data-end=\"2961\" data-start=\"2930\" id=\"t-1740235415116\" class=\"\"><strong data-end=\"2961\" data-start=\"2934\">4. Tools &amp; Integrations<\/strong><\/h3>\n<ul data-end=\"3228\" data-start=\"2962\" class=\"\">\n<li data-end=\"3023\" data-start=\"2962\">Agents require external tools to <strong data-end=\"3022\" data-start=\"2997\">enhance functionality<\/strong>.<\/li>\n<li data-end=\"3228\" data-start=\"3024\">Example tools:\n<ul data-end=\"3228\" data-start=\"3043\">\n<li data-end=\"3097\" data-start=\"3043\"><strong data-end=\"3070\" data-start=\"3045\">DuckDuckGo Search API<\/strong> (fetching financial news).<\/li>\n<li data-end=\"3155\" data-start=\"3100\"><strong data-end=\"3120\" data-start=\"3102\">YFinance Tools<\/strong> (retrieving real-time stock data).<\/li>\n<li data-end=\"3228\" data-start=\"3158\"><strong data-end=\"3173\" data-start=\"3160\">Groq LLMs<\/strong> (processing and summarizing large financial datasets).<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<h3 data-end=\"3269\" data-start=\"3230\" id=\"t-1740235415117\" class=\"\"><strong data-end=\"3269\" data-start=\"3234\">5. Memory &amp; Learning (Optional)<\/strong><\/h3>\n<ul data-end=\"3491\" data-start=\"3270\" class=\"\">\n<li data-end=\"3340\" data-start=\"3270\">Some agents store past interactions and improve their responses.<\/li>\n<li data-end=\"3416\" data-start=\"3341\">Can use vector databases or logs to track financial trends over time.<\/li>\n<li data-end=\"3491\" data-start=\"3417\">Useful for long-term investment tracking &amp; historical data analysis.<\/li>\n<\/ul>\n<h2 data-end=\"3545\" data-start=\"3498\" id=\"t-1740235415118\" class=\"\"><strong data-end=\"3543\" data-start=\"3501\">Best Practices When Building AI Agents<\/strong><\/h2>\n<p data-end=\"3634\" data-start=\"3547\">Building a reliable agent requires careful planning. Keep these key principles in mind:<\/p>\n<h3 data-end=\"3683\" data-start=\"3636\" id=\"t-1740235415119\" class=\"\"><strong data-end=\"3683\" data-start=\"3640\">1. Clearly Define the Agent\u2019s Objective<\/strong><\/h3>\n<ul data-end=\"3823\" data-start=\"3684\" class=\"\">\n<li data-end=\"3762\" data-start=\"3684\">Identify <strong data-end=\"3732\" data-start=\"3695\">what problem the agent is solving<\/strong> (e.g., stock trend analysis).<\/li>\n<li data-end=\"3823\" data-start=\"3763\">Keep the agent <strong data-end=\"3791\" data-start=\"3780\">focused<\/strong> (avoid unnecessary complexity).<\/li>\n<\/ul>\n<h3 data-end=\"3869\" data-start=\"3825\" id=\"t-1740235415120\" class=\"\"><strong data-end=\"3869\" data-start=\"3829\">2. Choose the Right AI Model &amp; Tools<\/strong><\/h3>\n<ul data-end=\"4040\" data-start=\"3870\" class=\"\">\n<li data-end=\"3925\" data-start=\"3870\">Use <strong data-end=\"3911\" data-start=\"3876\">Groq for low-latency processing<\/strong> of LLM tasks.<\/li>\n<li data-end=\"3987\" data-start=\"3926\">Select <strong data-end=\"3986\" data-start=\"3935\">YFinance or Alpha Vantage for stock market data<\/strong>.<\/li>\n<li data-end=\"4040\" data-start=\"3988\">Combine <strong data-end=\"4019\" data-start=\"3998\">multiple AI tools<\/strong> for richer insights.<\/li>\n<\/ul>\n<h3 data-end=\"4085\" data-start=\"4042\" id=\"t-1740235415121\" class=\"\"><strong data-end=\"4085\" data-start=\"4046\">3. Optimize for Performance &amp; Speed<\/strong><\/h3>\n<ul data-end=\"4279\" data-start=\"4086\" class=\"\">\n<li data-end=\"4153\" data-start=\"4086\">Ensure <strong data-end=\"4118\" data-start=\"4095\">fast response times<\/strong> by reducing unnecessary API calls.<\/li>\n<li data-end=\"4221\" data-start=\"4154\">Use <strong data-end=\"4182\" data-start=\"4160\">caching strategies<\/strong> for repetitive financial data lookups.<\/li>\n<li data-end=\"4279\" data-start=\"4222\">Deploy the agent <strong data-end=\"4262\" data-start=\"4241\">on a cloud server<\/strong> for scalability.<\/li>\n<\/ul>\n<h3 data-end=\"4318\" data-start=\"4281\" id=\"t-1740235415122\" class=\"\"><strong data-end=\"4318\" data-start=\"4285\">4. Handle Errors &amp; Edge Cases<\/strong><\/h3>\n<h3 data-end=\"4581\" data-start=\"4549\" id=\"t-1740235415123\" class=\"\"><strong data-end=\"4581\" data-start=\"4553\">5. Keep Security in Mind<\/strong><\/h3>\n<ul data-end=\"4738\" data-start=\"4582\" class=\"\">\n<li data-end=\"4633\" data-start=\"4582\"><strong data-end=\"4609\" data-start=\"4584\">Never expose API keys<\/strong> in public repositories.<\/li>\n<li data-end=\"4680\" data-start=\"4634\">Store <strong data-end=\"4679\" data-start=\"4642\">sensitive financial data securely<\/strong>.<\/li>\n<li data-end=\"4738\" data-start=\"4681\">Use <strong data-end=\"4715\" data-start=\"4687\">encrypted communications<\/strong> for user transactions.<\/li>\n<\/ul>\n<p data-end=\"4936\" data-start=\"4768\">Now that you have a solid understanding of what an AI agent is, its key components, and best practices for building one, it\u2019s time to jump into implementation!<\/p>\n<p data-end=\"5150\" data-start=\"4938\">In the next section, we will set up the development environment, initialize Agno and Groq, and build a fully functional <strong>financial agent <\/strong>that can analyze stocks, fetch real-time data, and generate insights!\u00a0<\/p>\n<\/div>\n<h2 data-pm-slice=\"1 3 []\" class=\"\" id=\"t-1740235415074\"><strong>What is Role of Financial Agent?<\/strong><\/h2>\n<div class=\"thrv_wrapper tve_image_caption\" data-css=\"tve-u-1952e1f66ed\"><span class=\"tve_image_frame\"><img src=\"https:\/\/dataaspirant.com\/wp-content\/plugins\/lazy-load\/images\/1x1.trans.gif\" data-lazy-src=\"https:\/\/dataaspirant.com\/wp-content\/uploads\/2025\/02\/2-1.png\" class=\"tve_image wp-image-12530\" alt=\"What is Role of Financial Agent?\" data-id=\"12530\" width=\"834\" data-init-width=\"1920\" height=\"469\" data-init-height=\"1080\" title=\"What is Role of Financial Agent?\" data-width=\"834\" data-height=\"469\" loading=\"lazy\"><img class=\"tve_image wp-image-12530\" alt=\"What is Role of Financial Agent?\" data-id=\"12530\" width=\"834\" data-init-width=\"1920\" height=\"469\" data-init-height=\"1080\" title=\"What is Role of Financial Agent?\" src=\"https:\/\/dataaspirant.com\/wp-content\/uploads\/2025\/02\/2-1.png\" data-width=\"834\" data-height=\"469\" loading=\"lazy\"><\/span><\/div>\n<div class=\"thrv_wrapper thrv_text_element\">\n<p>A financial agent is an AI-driven system that helps with financial analysis, decision-making, and automation. These agents can:<\/p>\n<ul data-spread=\"false\" class=\"\">\n<li>Analyze stock market trends.<\/li>\n<li>Provide investment insights.<\/li>\n<li>Fetch real-time financial news.<\/li>\n<li>Offer portfolio recommendations.<\/li>\n<\/ul>\n<h3 data-pm-slice=\"1 1 []\" id=\"t-1740235415075\" class=\"\"><strong>Traditional vs. AI-Powered Financial Agents<\/strong><\/h3>\n<table class=\"\">\n<tbody>\n<tr>\n<th>\n<p>Feature<\/p>\n<\/th>\n<th>\n<p>Traditional Systems<\/p>\n<\/th>\n<th>\n<p>AI-Powered Financial Agents<\/p>\n<\/th>\n<\/tr>\n<tr>\n<td>\n<p>Data Processing<\/p>\n<\/td>\n<td>\n<p>Manual, rule-based<\/p>\n<\/td>\n<td>\n<p>Automated, AI-driven<\/p>\n<\/td>\n<\/tr>\n<tr>\n<td>\n<p>Real-Time Insights<\/p>\n<\/td>\n<td>\n<p>Limited<\/p>\n<\/td>\n<td>\n<p>Yes<\/p>\n<\/td>\n<\/tr>\n<tr>\n<td>\n<p>Context Awareness<\/p>\n<\/td>\n<td>\n<p>No<\/p>\n<\/td>\n<td>\n<p>Yes<\/p>\n<\/td>\n<\/tr>\n<tr>\n<td>\n<p>Decision-Making<\/p>\n<\/td>\n<td>\n<p>Human-led<\/p>\n<\/td>\n<td>\n<p>AI-assisted<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>With AI, financial agents can process large datasets in real time, improving accuracy and efficiency.<\/p>\n<h2 data-pm-slice=\"1 3 []\" id=\"t-1740235415076\" class=\"\"><strong>Understanding Agno &amp; Groq<\/strong><\/h2>\n<h3 id=\"t-1740235415077\" class=\"\"><strong>Agno: The Agent-Oriented Framework<\/strong><\/h3>\n<p><strong>Agno<\/strong> provides a structured approach to creating AI agents. It allows developers to build multi-agent systems that can work collaboratively to process financial data and provide meaningful insights.<\/p>\n<h4 class=\"\"><strong>Key Features of Agno<\/strong><\/h4>\n<ul data-spread=\"false\" class=\"\">\n<li>Supports multiple AI models within a single system.<\/li>\n<li>Integrates various tools like <strong>web search, stock analysis, and financial news fetchers<\/strong>.<\/li>\n<li>Provides structured agent-based communication.<\/li>\n<\/ul>\n<h3 id=\"t-1740235415078\" class=\"\"><strong>Groq: The High-Speed AI Accelerator<\/strong><\/h3>\n<p><strong>Groq<\/strong> enables ultra-fast AI inference, making it ideal for processing large volumes of financial data with low latency.<\/p>\n<h4 class=\"\"><strong>Advantages of Groq in Financial AI<\/strong><\/h4>\n<ul data-spread=\"false\" class=\"\">\n<li>Optimized for AI model inference.<\/li>\n<li>Faster than traditional cloud compute solutions.<\/li>\n<li>Scalable for real-time financial applications.<\/li>\n<\/ul>\n<h2 data-pm-slice=\"1 1 []\" id=\"t-1740235415080\" class=\"\"><strong>Setting Up the Development Environment<\/strong><\/h2>\n<h3 id=\"t-1740235415081\" class=\"\"><strong>Prerequisites<\/strong><\/h3>\n<p>Before you begin, ensure you have:<\/p>\n<ul data-spread=\"false\" class=\"\">\n<li>Python installed.<\/li>\n<li><a href=\"https:\/\/dataaspirant.com\/access-openai-api-keys\/\" target=\"_blank\" rel=\"noopener\" data-wpil-monitor-id=\"61\" class=\"\" data-css=\"tve-u-1952e89f259\">OpenAI API<\/a> and Groq API access.<\/li>\n<li>Agno and Groq SDKs installed.<\/li>\n<\/ul>\n<h3 class=\"\" id=\"t-1740235415082\"><strong>Installing Required Libraries<\/strong><\/h3>\n<p>Run the following command to install the necessary dependencies:<\/p>\n<\/div>\n<div class=\"thrv_wrapper thrv_custom_html_shortcode\">\n<h3 id=\"t-1740235415083\">\ud83d\udcbb Installation Commands<\/h3>\n<p>Install Agno:<\/p>\n<pre>pip install agno\n<\/pre>\n<p>Install Groq:<\/p>\n<pre>pip install groq\n<\/pre>\n<p>Install OpenAI:<\/p>\n<pre>pip install openai\n<\/pre>\n<\/div>\n<h2 data-pm-slice=\"1 1 []\" id=\"t-1740235415084\" class=\"\"><strong>Step-by-Step Guide: Building the Financial Agent<\/strong><\/h2>\n<h3 id=\"t-1740235415085\" class=\"\"><strong>Step 1: Initialize Environment &amp; Import Dependencies<\/strong><\/h3>\n<div class=\"thrv_wrapper thrv_custom_html_shortcode\">\n<p>\n<code><br \/>\n<span>import<\/span> os<br \/>\n<span>from<\/span> dotenv <span>import<\/span> load_dotenv<br \/>\n<span>from<\/span> agno.agent <span>import<\/span> Agent<br \/>\n<span>from<\/span> agno.models.groq <span>import<\/span> Groq<br \/>\n<span>from<\/span> agno.tools.duckduckgo <span>import<\/span> DuckDuckGoTools<br \/>\n<span>from<\/span> agno.tools.yfinance <span>import<\/span> YFinanceTools<\/p>\n<p><span># Load environment variables<\/span><br \/>\nload_dotenv()<br \/>\n<\/code>\n<\/p>\n<\/div>\n<div class=\"thrv_wrapper thrv_custom_html_shortcode\">\n<h3 id=\"t-1740235415091\">\ud83d\udccc Understanding the Code<\/h3>\n<ul>\n<li><strong>Importing Required Libraries:<\/strong>\n<ul>\n<li><code>os<\/code> \u2013 Access system-level functionalities, like environment variables.<\/li>\n<li><code>load_dotenv<\/code> \u2013 Loads API keys and sensitive data from a <code>.env<\/code> file.<\/li>\n<\/ul>\n<\/li>\n<li><strong>Importing AI Agent &amp; Tools:<\/strong>\n<ul>\n<li><code>Agent<\/code> \u2013 Defines an AI-powered assistant.<\/li>\n<li><code>Groq<\/code> \u2013 Integrates AI models for processing queries.<\/li>\n<li><code>DuckDuckGoTools<\/code> \u2013 Enables web searches using DuckDuckGo.<\/li>\n<li><code>YFinanceTools<\/code> \u2013 Fetches financial data from Yahoo Finance.<\/li>\n<\/ul>\n<\/li>\n<li><strong>Loading Environment Variables:<\/strong>\n<ul>\n<li><code>load_dotenv()<\/code> \u2013 Ensures API keys are securely stored and accessed.<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<h3 id=\"t-1740235415092\">\ud83d\udd39 Next Steps<\/h3>\n<p>This setup prepares the environment to create an AI-powered agent that can retrieve stock market insights, perform web searches, and process AI-driven queries.<\/p>\n<\/div>\n<div class=\"thrv_wrapper thrv_text_element\">\n<h3 data-pm-slice=\"1 1 []\" class=\"\" id=\"t-1740235415087\"><strong>Step 2: Create a Web Search Agent<\/strong><\/h3>\n<p>This agent will fetch financial information from the web.<\/p>\n<\/div>\n<div class=\"thrv_wrapper thrv_custom_html_shortcode\">\n<p>\n<code><br \/>\nweb_search_agent = Agent(<br \/>\n    name=<span>\"Web Search Agent\"<\/span>,<br \/>\n    role=<span>\"Search the web for financial information\"<\/span>,<br \/>\n    model=Groq(id=<span>\"llama-3.2-3b-preview\"<\/span>),<br \/>\n    tools=[DuckDuckGoTools()],<br \/>\n    instructions=[<span>\"Always include sources\"<\/span>],<br \/>\n    show_tool_calls=<span>True<\/span>,<br \/>\n    markdown=<span>True<\/span>,<br \/>\n)<br \/>\n<\/code>\n<\/p>\n<\/div>\n<div class=\"thrv_wrapper thrv_custom_html_shortcode\">\n<h3 id=\"t-1740235415093\">\ud83d\udccc Understanding the Code<\/h3>\n<ul>\n<li><strong>Creating an AI Agent:<\/strong>\n<ul>\n<li><code>Agent<\/code> \u2013 Defines an AI-powered assistant for web searches.<\/li>\n<li><code>name=\"Web Search Agent\"<\/code> \u2013 Assigns a name to the agent.<\/li>\n<li><code>role=\"Search the web for financial information\"<\/code> \u2013 Specifies the agent\u2019s purpose.<\/li>\n<\/ul>\n<\/li>\n<li><strong>Using an AI Model:<\/strong>\n<ul>\n<li><code>model=Groq(id=\"llama-3.2-3b-preview\")<\/code> \u2013 Uses Groq&#8217;s AI model for processing queries.<\/li>\n<\/ul>\n<\/li>\n<li><strong>Integrating Search Tools:<\/strong>\n<ul>\n<li><code>tools=[DuckDuckGoTools()]<\/code> \u2013 Enables web searches using DuckDuckGo.<\/li>\n<\/ul>\n<\/li>\n<li><strong>Defining Instructions:<\/strong>\n<ul>\n<li><code>instructions=[\"Always include sources\"]<\/code> \u2013 Ensures that search results include sources.<\/li>\n<li><code>show_tool_calls=True<\/code> \u2013 Displays how the AI interacts with tools.<\/li>\n<li><code>markdown=True<\/code> \u2013 Formats output in Markdown for better readability.<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<h3 id=\"t-1740235415094\">\ud83d\udd39 Next Steps<\/h3>\n<p>This setup prepares the AI agent to search the web for financial information, process responses using Groq\u2019s AI model, and display well-structured results with sources.<\/p>\n<\/div>\n<div class=\"thrv_wrapper thrv_text_element\">\n<h3 data-pm-slice=\"1 1 []\" id=\"t-1740235415088\" class=\"\"><strong>Step 3: Create a Financial Analysis Agent<\/strong><\/h3>\n<p>This agent will analyze stocks, fetch real-time data, and generate insights.<\/p>\n<\/div>\n<div class=\"thrv_wrapper thrv_custom_html_shortcode\">\n<p>\n<code><br \/>\nfinance_agent = Agent(<br \/>\n    name=<span>\"Finance AI Agent\"<\/span>,<br \/>\n    role=<span>\"Analyze the given stock\"<\/span>,<br \/>\n    model=Groq(id=<span>\"llama-3.2-11b-vision-preview\"<\/span>),<br \/>\n    tools=[<br \/>\n        YFinanceTools(<br \/>\n            stock_price=<span>True<\/span>,<br \/>\n            analyst_recommendations=<span>True<\/span>,<br \/>\n            stock_fundamentals=<span>True<\/span>,<br \/>\n            company_news=<span>True<\/span><br \/>\n        )<br \/>\n    ],<br \/>\n    instructions=[<span>\"Use tables to display data\"<\/span>],<br \/>\n    show_tool_calls=<span>True<\/span>,<br \/>\n    markdown=<span>True<\/span>,<br \/>\n)<br \/>\n<\/code>\n<\/p>\n<\/div>\n<div class=\"thrv_wrapper thrv_custom_html_shortcode\">\n<h3 id=\"t-1740235415095\">\ud83d\udccc Understanding the Code<\/h3>\n<ul>\n<li><strong>Creating a Finance AI Agent:<\/strong>\n<ul>\n<li><code>Agent<\/code> \u2013 Defines an AI-powered assistant for financial analysis.<\/li>\n<li><code>name=\"Finance AI Agent\"<\/code> \u2013 Assigns a name to the agent.<\/li>\n<li><code>role=\"Analyze the given stock\"<\/code> \u2013 Specifies that the agent will analyze stock data.<\/li>\n<\/ul>\n<\/li>\n<li><strong>Using an AI Model:<\/strong>\n<ul>\n<li><code>model=Groq(id=\"llama-3.2-11b-vision-preview\")<\/code> \u2013 Utilizes Groq\u2019s AI model for financial analysis.<\/li>\n<\/ul>\n<\/li>\n<li><strong>Integrating Financial Tools:<\/strong>\n<ul>\n<li><code>YFinanceTools()<\/code> \u2013 Fetches stock market data from Yahoo Finance, including:<\/li>\n<ul>\n<li><code>stock_price=True<\/code> \u2013 Retrieves the latest stock price.<\/li>\n<li><code>analyst_recommendations=True<\/code> \u2013 Collects expert recommendations on the stock.<\/li>\n<li><code>stock_fundamentals=True<\/code> \u2013 Analyzes company fundamentals.<\/li>\n<li><code>company_news=True<\/code> \u2013 Fetches the latest company-related news.<\/li>\n<\/ul>\n<\/ul>\n<\/li>\n<li><strong>Defining Instructions:<\/strong>\n<ul>\n<li><code>instructions=[\"Use tables to display data\"]<\/code> \u2013 Ensures structured data presentation.<\/li>\n<li><code>show_tool_calls=True<\/code> \u2013 Displays the tools used in the analysis.<\/li>\n<li><code>markdown=True<\/code> \u2013 Formats output in Markdown for better readability.<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<h3 id=\"t-1740235415096\">\ud83d\udd39 Next Steps<\/h3>\n<p>This setup prepares the AI agent to analyze stock data, fetch real-time market insights, and present structured financial reports with recommendations.<\/p>\n<\/div>\n<div class=\"thrv_wrapper thrv_text_element\">\n<h3 data-pm-slice=\"1 1 []\" class=\"\" id=\"t-1740235415089\"><strong>Step 4: Aggregate the Agents<\/strong><\/h3>\n<p>We combine multiple agents to work together efficiently.<\/p>\n<\/div>\n<div class=\"thrv_wrapper thrv_custom_html_shortcode\">\n<p>\n<code><br \/>\nmulti_ai_agent = Agent(<br \/>\n    team=[web_search_agent, finance_agent],<br \/>\n    instructions=[<br \/>\n        <span>\"Always include sources\"<\/span>,<br \/>\n        <span>\"Use tables to display data\"<\/span><br \/>\n    ],<br \/>\n    show_tool_calls=<span>True<\/span>,<br \/>\n    markdown=<span>True<\/span>,<br \/>\n)<br \/>\n<\/code>\n<\/p>\n<\/div>\n<div class=\"thrv_wrapper thrv_custom_html_shortcode\">\n<h3 id=\"t-1740235415097\">\ud83d\udccc Understanding the Code<\/h3>\n<ul>\n<li><strong>Creating a Multi-AI Agent:<\/strong>\n<ul>\n<li><code>Agent<\/code> \u2013 Defines an AI-powered assistant capable of handling multiple tasks.<\/li>\n<li><code>team=[web_search_agent, finance_agent]<\/code> \u2013 Combines two specialized agents:<\/li>\n<ul>\n<li><strong>Web Search Agent<\/strong> \u2013 Searches the web for financial information.<\/li>\n<li><strong>Finance AI Agent<\/strong> \u2013 Analyzes stock data using Yahoo Finance tools.<\/li>\n<\/ul>\n<\/ul>\n<\/li>\n<li><strong>Defining Instructions:<\/strong>\n<ul>\n<li><code>instructions=[\"Always include sources\", \"Use tables to display data\"]<\/code><\/li>\n<ul>\n<li>Ensures all responses include proper sources for credibility.<\/li>\n<li>Formats financial data in tables for better readability.<\/li>\n<\/ul>\n<\/ul>\n<\/li>\n<li><strong>Enabling Additional Features:<\/strong>\n<ul>\n<li><code>show_tool_calls=True<\/code> \u2013 Displays how the agent interacts with external tools.<\/li>\n<li><code>markdown=True<\/code> \u2013 Ensures well-structured output with Markdown formatting.<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<h3 id=\"t-1740235415098\">\ud83d\udd39 Next Steps<\/h3>\n<p>This setup allows the AI agent to combine web search and financial analysis, enabling it to provide well-researched, structured insights for investment decisions.<\/p>\n<\/div>\n<div class=\"thrv_wrapper thrv_text_element\">\n<h3 data-pm-slice=\"1 1 []\" class=\"\" id=\"t-1740235415090\"><strong>Step 5: Execute the Financial Query<\/strong><\/h3>\n<p>Run the agent to fetch the latest stock analysis and news.<\/p>\n<\/div>\n<div class=\"thrv_wrapper thrv_custom_html_shortcode\">\n<p>\n<code><br \/>\nmulti_ai_agent.print_response(<br \/>\n    <span>\"Summarize analyst recommendations and share the latest news for Apple\"<\/span>,<br \/>\n    stream=<span>True<\/span><br \/>\n)<br \/>\n<\/code>\n<\/p>\n<\/div>\n<div class=\"thrv_wrapper thrv_custom_html_shortcode\">\n<h3 id=\"t-1740235415099\">\ud83d\udccc Understanding the Code<\/h3>\n<ul>\n<li><strong>Executing an AI Query:<\/strong>\n<ul>\n<li><code>multi_ai_agent.print_response()<\/code> \u2013 Calls the AI agent to generate a response.<\/li>\n<li><code>\"Summarize analyst recommendations and share the latest news for Apple\"<\/code> \u2013 Provides the query that the AI should process.<\/li>\n<\/ul>\n<\/li>\n<li><strong>Defining Output Behavior:<\/strong>\n<ul>\n<li><code>stream=True<\/code> \u2013 Streams the response in real-time instead of waiting for full processing.<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<h3 id=\"t-1740235415100\">\ud83d\udd39 Next Steps<\/h3>\n<p>This command prompts the multi-agent AI system to fetch and summarize stock analyst recommendations and latest news about Apple, displaying the results in a structured, real-time manner.<\/p>\n<\/div>\n<div class=\"thrv_wrapper thrv_text_element\">\n<p data-end=\"305\" data-start=\"0\">Now that we&#8217;ve explored the <strong data-end=\"53\" data-start=\"28\">individual components<\/strong> of the financial agent, it&#8217;s time to bring everything together into a <strong data-end=\"166\" data-start=\"124\">fully integrated and functional script<\/strong>. Below is the <strong data-end=\"212\" data-start=\"181\">complete aggregated version<\/strong> of our financial agent, combining <strong data-end=\"302\" data-start=\"247\">stock analysis, web search, and AI-powered insights<\/strong>.<\/p>\n<p data-end=\"476\" data-is-last-node=\"\" data-is-only-node=\"\" data-start=\"307\">You can <strong data-end=\"349\" data-start=\"315\">run this script on your system<\/strong> and experiment with its capabilities firsthand. Feel free to tweak it, add more features, and enhance it as per your needs!<\/p>\n<\/div>\n<div class=\"thrv_wrapper thrv_custom_html_shortcode\">\n<p>\n<code><br \/>\n<span>import<\/span> os<\/p>\n<p><span>from<\/span> dotenv <span>import<\/span> load_dotenv<br \/>\n<span>from<\/span> agno.agent <span>import<\/span> Agent<br \/>\n<span>from<\/span> agno.models.groq <span>import<\/span> Groq<br \/>\n<span>from<\/span> agno.tools.duckduckgo <span>import<\/span> DuckDuckGoTools<br \/>\n<span>from<\/span> agno.tools.yfinance <span>import<\/span> YFinanceTools<\/p>\n<p><span>## Initializations<\/span><br \/>\nload_dotenv()<\/p>\n<p><span>## Web Search Agent Creation<\/span><br \/>\nweb_search_agent = Agent(<br \/>\n    name=<span>\"Web Search Agent\"<\/span>,<br \/>\n    role=<span>\"Search the web for the information\"<\/span>,<br \/>\n    model=Groq(id=<span>\"llama-3.2-3b-preview\"<\/span>),<br \/>\n    tools=[DuckDuckGoTools()],<br \/>\n    instructions=[<span>\"Always include sources\"<\/span>],<br \/>\n    show_tool_calls=<span>True<\/span>,<br \/>\n    markdown=<span>True<\/span><br \/>\n)<\/p>\n<p><span>## Financial Agent<\/span><br \/>\nfinance_agent = Agent(<br \/>\n    name=<span>\"Finance AI Agent\"<\/span>,<br \/>\n    role=<span>\"Analyse the given stock\"<\/span>,<br \/>\n    model=Groq(id=<span>\"llama-3.2-11b-vision-preview\"<\/span>),<br \/>\n    tools=[<br \/>\n        YFinanceTools(<br \/>\n            stock_price=<span>True<\/span>,<br \/>\n            analyst_recommendations=<span>True<\/span>,<br \/>\n            stock_fundamentals=<span>True<\/span>,<br \/>\n            company_news=<span>True<\/span><br \/>\n        )<br \/>\n    ],<br \/>\n    instructions=[<span>\"Use tables to display the data\"<\/span>],<br \/>\n    show_tool_calls=<span>True<\/span>,<br \/>\n    markdown=<span>True<\/span><br \/>\n)<\/p>\n<p><span>## Aggregating Agents<\/span><br \/>\nmulti_ai_agent = Agent(<br \/>\n    team=[web_search_agent, finance_agent],<br \/>\n    instructions=[<br \/>\n        <span>\"Always include sources\"<\/span>,<br \/>\n        <span>\"Use tables to display the data\"<\/span><br \/>\n    ],<br \/>\n    show_tool_calls=<span>True<\/span>,<br \/>\n    markdown=<span>True<\/span><br \/>\n)<\/p>\n<p>multi_ai_agent.print_response(<br \/>\n    <span>\"Summarize analyst recommendation and share the latest news for Apple\"<\/span>,<br \/>\n    stream=<span>True<\/span><br \/>\n)<br \/>\n<\/code>\n<\/p>\n<\/div>\n<div class=\"thrv_wrapper thrv_text_element\">\n<p data-end=\"246\" data-start=\"0\">If you successfully run the script, you should see <strong data-end=\"71\" data-start=\"51\">a similar output<\/strong>, displaying real-time <strong data-end=\"151\" data-start=\"94\">stock analysis, market trends, and financial insights<\/strong> in a structured format. This confirms that your financial agent is functioning correctly!\u00a0<\/p>\n<p data-end=\"378\" data-is-last-node=\"\" data-is-only-node=\"\" data-start=\"248\">Feel free to <strong data-end=\"282\" data-start=\"261\">modify the script<\/strong>, test it with different stocks, and explore additional features to enhance its capabilities.\u00a0<\/p>\n<\/div>\n<div class=\"thrv_wrapper tve_image_caption img_style_rounded_corners\" data-css=\"tve-u-1952e3e5574\"><span class=\"tve_image_frame\"><img src=\"https:\/\/dataaspirant.com\/wp-content\/plugins\/lazy-load\/images\/1x1.trans.gif\" data-lazy-src=\"https:\/\/dataaspirant.com\/wp-content\/uploads\/2025\/02\/Screenshot-2025-02-21-at-12.34.22\u202fPM.png\" class=\"tve_image wp-image-12542\" alt=\"Financial Agent Result\" data-id=\"12542\" width=\"834\" data-init-width=\"1666\" height=\"513\" data-init-height=\"1026\" title=\"Financial Agent Result\" data-width=\"834\" data-height=\"513\" loading=\"lazy\"><img class=\"tve_image wp-image-12542\" alt=\"Financial Agent Result\" data-id=\"12542\" width=\"834\" data-init-width=\"1666\" height=\"513\" data-init-height=\"1026\" title=\"Financial Agent Result\" src=\"https:\/\/dataaspirant.com\/wp-content\/uploads\/2025\/02\/Screenshot-2025-02-21-at-12.34.22%E2%80%AFPM.png\" data-width=\"834\" data-height=\"513\" loading=\"lazy\"><\/span><\/div>\n<div class=\"thrv_wrapper thrv_custom_html_shortcode\">\n<div>\n<h3 id=\"t-1739611944195\">\ud83d\udcbb Full Code Available on GitHub<\/h3>\n<p>You can find the complete code for this post in my GitHub repository. Click the link below to explore the code and dive deeper into building Agents:<\/p>\n<p><a href=\"https:\/\/github.com\/saimadhu-polamuri\/generative-ai-projects\/tree\/main\/agents\/financial-agent-with-phidata\" target=\"_blank\" rel=\"noopener\">\ud83d\udc49 View Code on GitHub<\/a>\n<\/div>\n<\/div>\n<div class=\"thrv_wrapper thrv_text_element\">\n<h2 id=\"t-1740235415103\" class=\"\"><strong>Conclusion<\/strong><\/h2>\n<p data-end=\"381\" data-start=\"34\">Building financial agents with Agno &amp; Groq opens up a world of AI-driven financial intelligence, enabling automated investment analysis, budgeting insights, and real-time market trend predictions. By leveraging LLMs, data-driven decision-making, and automation, you can create a robust financial assistant tailored to your needs.<\/p>\n<p data-end=\"567\" data-start=\"383\">With this guide, you now have a solid foundation to develop, test, and deploy your own financial AI solutions. The next step? Experiment, iterate, and enhance your agent!<\/p>\n<ul class=\"\">\n<li data-end=\"772\" data-start=\"569\">Try running the script with different stocks.<\/li>\n<li data-end=\"772\" data-start=\"569\">Expand functionalities by integrating additional financial APIs.<\/li>\n<li data-end=\"772\" data-start=\"569\">Optimize the agent for faster response times and better insights.<\/li>\n<\/ul>\n<blockquote data-end=\"891\" data-is-last-node=\"\" data-is-only-node=\"\" data-start=\"774\" class=\"\"><p>The possibilities are limitless start building today and revolutionize your financial decision-making with AI!\u00a0<\/p><\/blockquote>\n<h2 id=\"t-1740235415104\" class=\"\"><strong>FAQs<\/strong><\/h2>\n<h3 id=\"t-1740235415105\" class=\"\"><strong>What makes Agno different from other AI agent frameworks?<\/strong><\/h3>\n<p>Agno allows structured multi-agent collaboration, making it ideal for AI-powered financial applications.<\/p>\n<h3 id=\"t-1740235415106\" class=\"\"><strong>How does Groq improve AI inference speed?<\/strong><\/h3>\n<p>Groq optimizes inference by reducing latency and increasing model efficiency, outperforming traditional cloud computing solutions.<\/p>\n<h3 id=\"t-1740235415107\" class=\"\"><strong>Can I integrate additional data sources?<\/strong><\/h3>\n<p>Yes, you can add APIs like Bloomberg, Yahoo Finance, or custom data feeds.<\/p>\n<h3 id=\"t-1740235415108\" class=\"\"><strong>How secure is the financial agent?<\/strong><\/h3>\n<p>Always store API keys securely and implement encryption for sensitive financial data.<\/p>\n<h3 id=\"t-1740235415109\" class=\"\"><strong>Can I deploy this agent to production?<\/strong><\/h3>\n<p>Yes! With cloud deployment and monitoring, this financial agent can be production-ready for fintech applications.<\/p>\n<\/div>\n<h4 class=\"\">Recommended Courses<\/h4>\n<div class=\"thrv_wrapper thrv-page-section thrv-lp-block\" data-inherit-lp-settings=\"1\" data-css=\"tve-u-1952e1dab20\" data-keep-css_id=\"1\">\n<div class=\"tve-page-section-in tve_empty_dropzone  \" data-css=\"tve-u-17481b960b8\">\n<div class=\"thrv_wrapper thrv-columns dynamic-group-kbt3q0q7\" data-css=\"tve-u-17481b95e2b\">\n<div class=\"tcb-flex-row v-2 tcb--cols--3 tcb-medium-no-wrap tcb-mobile-wrap m-edit\" data-css=\"tve-u-1952e1dab21\">\n<div class=\"tcb-flex-col\">\n<div class=\"tcb-col dynamic-group-kbt3pyfd\" data-css=\"tve-u-17481b95e2d\">\n<div class=\"thrv_wrapper thrv_contentbox_shortcode thrv-content-box tve-elem-default-pad dynamic-group-kbt3pwhk\" data-css=\"tve-u-1952e1dab38\">\n<div class=\"tve-cb\">\n<div class=\"thrv_wrapper tve_image_caption dynamic-group-kbt3pu4z\" data-css=\"tve-u-1952e1dab3b\"><span class=\"tve_image_frame\"><img src=\"https:\/\/dataaspirant.com\/wp-content\/plugins\/lazy-load\/images\/1x1.trans.gif\" data-lazy-src=\"https:\/\/dataaspirant.com\/wp-content\/uploads\/2025\/02\/Coursers-Images-1-1-1.png\" class=\"tve_image wp-image-12447\" alt data-id=\"12447\" width=\"245\" data-init-width=\"200\" height=\"245\" data-init-height=\"200\" title=\"Coursers Images (1) (1)\" data-width=\"245\" data-height=\"245\" data-css=\"tve-u-1952e1dab3c\" loading=\"lazy\"><img class=\"tve_image wp-image-12447\" alt=\"\" data-id=\"12447\" width=\"245\" data-init-width=\"200\" height=\"245\" data-init-height=\"200\" title=\"Coursers Images (1) (1)\" src=\"https:\/\/dataaspirant.com\/wp-content\/uploads\/2025\/02\/Coursers-Images-1-1-1.png\" data-width=\"245\" data-height=\"245\" data-css=\"tve-u-1952e1dab3c\" loading=\"lazy\"><span class=\"tve-image-overlay\"><\/span><\/span><\/div>\n<h4 class=\"\" data-css=\"tve-u-1952e1dab23\">GenAI Course<\/h4>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"tcb-flex-col\">\n<div class=\"tcb-col dynamic-group-kbt3pyfd\" data-css=\"tve-u-17481b95e2d\">\n<div class=\"thrv_wrapper thrv_contentbox_shortcode thrv-content-box tve-elem-default-pad dynamic-group-kbt3pwhk\" data-css=\"tve-u-1952e1dab39\">\n<div class=\"tve-cb\">\n<div class=\"thrv_wrapper tve_image_caption dynamic-group-kbt3pu4z\" data-css=\"tve-u-1952e1dab47\"><span class=\"tve_image_frame\"><img src=\"https:\/\/dataaspirant.com\/wp-content\/plugins\/lazy-load\/images\/1x1.trans.gif\" data-lazy-src=\"https:\/\/dataaspirant.com\/wp-content\/uploads\/2023\/10\/9-2.png\" class=\"tve_image wp-image-11367\" alt=\"Machine Learning\" data-id=\"11367\" width=\"245\" data-init-width=\"200\" height=\"245\" data-init-height=\"200\" title=\"Machine Learning\" data-width=\"245\" data-height=\"245\" data-css=\"tve-u-1952e1dab48\" loading=\"lazy\"><img class=\"tve_image wp-image-11367\" alt=\"Machine Learning\" data-id=\"11367\" width=\"245\" data-init-width=\"200\" height=\"245\" data-init-height=\"200\" title=\"Machine Learning\" src=\"https:\/\/dataaspirant.com\/wp-content\/uploads\/2023\/10\/9-2.png\" data-width=\"245\" data-height=\"245\" data-css=\"tve-u-1952e1dab48\" loading=\"lazy\"><span class=\"tve-image-overlay\"><\/span><\/span><\/div>\n<h4 class=\"\" data-css=\"tve-u-1952e1dab2a\">Machine Learning Course<\/h4>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"tcb-flex-col\">\n<div class=\"tcb-col dynamic-group-kbt3pyfd\" data-css=\"tve-u-17481b95e2d\">\n<div class=\"thrv_wrapper thrv_contentbox_shortcode thrv-content-box tve-elem-default-pad dynamic-group-kbt3pwhk\" data-css=\"tve-u-1952e1dab3a\">\n<div class=\"tve-cb\">\n<div class=\"thrv_wrapper tve_image_caption dynamic-group-kbt3pu4z\" data-css=\"tve-u-1952e1dab49\"><span class=\"tve_image_frame\"><img src=\"https:\/\/dataaspirant.com\/wp-content\/plugins\/lazy-load\/images\/1x1.trans.gif\" data-lazy-src=\"https:\/\/dataaspirant.com\/wp-content\/uploads\/2023\/10\/10-2.png\" class=\"tve_image wp-image-11368\" alt=\"Deep Learning\" data-id=\"11368\" width=\"245\" data-init-width=\"200\" height=\"245\" data-init-height=\"200\" title=\"Deep Learning\" data-width=\"245\" data-height=\"245\" data-css=\"tve-u-1952e1dab4a\" loading=\"lazy\"><img class=\"tve_image wp-image-11368\" alt=\"Deep Learning\" data-id=\"11368\" width=\"245\" data-init-width=\"200\" height=\"245\" data-init-height=\"200\" title=\"Deep Learning\" src=\"https:\/\/dataaspirant.com\/wp-content\/uploads\/2023\/10\/10-2.png\" data-width=\"245\" data-height=\"245\" data-css=\"tve-u-1952e1dab4a\" loading=\"lazy\"><span class=\"tve-image-overlay\"><\/span><\/span><\/div>\n<h4 class=\"\" data-css=\"tve-u-1952e1dab32\">Deep Learning Course<\/h4>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"thrv_wrapper tve_wp_shortcode\">\n<div class=\"tve_shortcode_rendered\">\n<div>\n<h4>\ud83c\udf1f Follow Us<\/h4>\n<p>\n<strong>\ud83d\udcac I hope you like this post!<\/strong> If you have any questions or want me to write an article on a specific topic, <span>feel free to comment below<\/span>.\n<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>https:\/\/dataaspirant.com\/building-financial-agent-agno-groq\/<\/p>\n","protected":false},"author":0,"featured_media":10987,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[2],"tags":[],"_links":{"self":[{"href":"https:\/\/wealthrevelation.com\/data-science\/wp-json\/wp\/v2\/posts\/10986"}],"collection":[{"href":"https:\/\/wealthrevelation.com\/data-science\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/wealthrevelation.com\/data-science\/wp-json\/wp\/v2\/types\/post"}],"replies":[{"embeddable":true,"href":"https:\/\/wealthrevelation.com\/data-science\/wp-json\/wp\/v2\/comments?post=10986"}],"version-history":[{"count":0,"href":"https:\/\/wealthrevelation.com\/data-science\/wp-json\/wp\/v2\/posts\/10986\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/wealthrevelation.com\/data-science\/wp-json\/wp\/v2\/media\/10987"}],"wp:attachment":[{"href":"https:\/\/wealthrevelation.com\/data-science\/wp-json\/wp\/v2\/media?parent=10986"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/wealthrevelation.com\/data-science\/wp-json\/wp\/v2\/categories?post=10986"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/wealthrevelation.com\/data-science\/wp-json\/wp\/v2\/tags?post=10986"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}