How To Build Financial Agent with Agno & Groq

February 22, 2025 Saimadhu Polamuri

Building a Financial Agent with Agno & Groq

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 Krish Naik caught my attention. He demonstrated how to create an agent for stock analysis and financial recommendations using the Phidata agent framework. Which has now evolved into Agno.

Watching the video got me thinking:

Why not build my own stock analysis agent using the latest Agno framework? Inspired by this, I decided to give it a shot.

What started as an exciting implementation took just 30 minutes to script, but making it actually work took the entire night! Debugging, setting up APIs, and fine-tuning the agent was a challenge, but the results were worth it.

In this article, I will walk you through building a financial agent using:

Agno for agent-based automation,
Groq for high-speed LLM inference,
OpenAI for language model integration.

So grab your laptop, follow along, and let's build your first AI-powered financial agent together!

Building a Financial Agent with Agno & Groq

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Before jumping into the implementation, it’s essential to understand what an agent is, its key components, and the best practices to follow when developing one. A well-structured agent can autonomously make decisions, interact with multiple data sources, and deliver highly optimized outputs.

Let's first break down the core concepts of agents and their fundamental components before moving on to implementation.

Table of Contents

What is an AI Agent?

An AI agent is a system that perceives its environment, processes data, makes decisions, and takes actions to achieve a predefined goal. It acts autonomously or semi-autonomously, leveraging AI models, tools, and APIs to perform specific tasks.

In the financial world, AI agents are particularly useful for tasks like

Automated stock analysis,
Investment recommendations,
Risk assessment,
Portfolio management.

Types of AI Agents

There are different types of AI agents based on complexity and functionality:

Reactive Agents: Respond directly to inputs without memory (e.g., chatbots).
Deliberative Agents: Use reasoning and planning for decision-making (e.g., AI stock analysts).
Learning Agents: Improve over time through machine learning.
Multi-Agent Systems: Multiple agents working together to achieve a goal (e.g., finance + web search agents).

Key Components of an AI Agent

A well-structured agent consists of several important components. Here’s a deep dive into each one:

1. Perception Layer (Input Mechanism)

This is how an agent understands and gathers data from its environment.
Data can come from APIs (financial market feeds), web scraping (news sites), real-time databases, or user inputs.
Example: Fetching Apple stock price & analyst recommendations from Yahoo Finance API.

2. Decision-Making & Processing

This is the agent’s brain, where it analyzes data, applies logic, and determines actions.
It involves:
- Predefined Rules (if stock drops 5%, trigger an alert).
- AI Models (Groq-powered LLM for sentiment analysis).
- Context Awareness (combining financial data + news trends for better insights).

3. Action Mechanism (Output & Execution)

The agent executes actions based on its analysis.
This can be:
- Displaying results (printing stock insights in structured tables).
- Sending alerts (notifying users about stock changes).
- Triggering API calls (executing automated trading actions).

4. Tools & Integrations

Agents require external tools to enhance functionality.
Example tools:
- DuckDuckGo Search API (fetching financial news).
- YFinance Tools (retrieving real-time stock data).
- Groq LLMs (processing and summarizing large financial datasets).

5. Memory & Learning (Optional)

Some agents store past interactions and improve their responses.
Can use vector databases or logs to track financial trends over time.
Useful for long-term investment tracking & historical data analysis.

Best Practices When Building AI Agents

Building a reliable agent requires careful planning. Keep these key principles in mind:

1. Clearly Define the Agent’s Objective

Identify what problem the agent is solving (e.g., stock trend analysis).
Keep the agent focused (avoid unnecessary complexity).

2. Choose the Right AI Model & Tools

Use Groq for low-latency processing of LLM tasks.
Select YFinance or Alpha Vantage for stock market data.
Combine multiple AI tools for richer insights.

3. Optimize for Performance & Speed

Ensure fast response times by reducing unnecessary API calls.
Use caching strategies for repetitive financial data lookups.
Deploy the agent on a cloud server for scalability.

4. Handle Errors & Edge Cases

Implement error handling for API failures (e.g., stock market API down).
Ensure the agent doesn’t crash on missing or incomplete data.
Set up fail-safe mechanisms (fallback responses for LLM-generated content).

5. Keep Security in Mind

Never expose API keys in public repositories.
Store sensitive financial data securely.
Use encrypted communications for user transactions.

Now that you have a solid understanding of what an AI agent is, its key components, and best practices for building one, it’s time to jump into implementation!

In the next section, we will set up the development environment, initialize Agno and Groq, and build a fully functional financial agent that can analyze stocks, fetch real-time data, and generate insights!

What is Role of Financial Agent?

A financial agent is an AI-driven system that helps with financial analysis, decision-making, and automation. These agents can:

Analyze stock market trends.
Provide investment insights.
Fetch real-time financial news.
Offer portfolio recommendations.

Traditional vs. AI-Powered Financial Agents

Feature	Traditional Systems	AI-Powered Financial Agents
Data Processing	Manual, rule-based	Automated, AI-driven
Real-Time Insights	Limited	Yes
Context Awareness	No	Yes
Decision-Making	Human-led	AI-assisted

With AI, financial agents can process large datasets in real time, improving accuracy and efficiency.

Understanding Agno & Groq

Agno: The Agent-Oriented Framework

Agno 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.

Key Features of Agno

Supports multiple AI models within a single system.
Integrates various tools like web search, stock analysis, and financial news fetchers.
Provides structured agent-based communication.

Groq: The High-Speed AI Accelerator

Groq enables ultra-fast AI inference, making it ideal for processing large volumes of financial data with low latency.

Advantages of Groq in Financial AI

Optimized for AI model inference.
Faster than traditional cloud compute solutions.
Scalable for real-time financial applications.

Setting Up the Development Environment

Prerequisites

Before you begin, ensure you have:

Python installed.
OpenAI API and Groq API access.
Agno and Groq SDKs installed.

Installing Required Libraries

Run the following command to install the necessary dependencies:

💻 Installation Commands

Install Agno:

pip install agno

Install Groq:

pip install groq

Install OpenAI:

pip install openai

Step-by-Step Guide: Building the Financial Agent

Step 1: Initialize Environment & Import Dependencies

import os
from dotenv import load_dotenv
from agno.agent import Agent
from agno.models.groq import Groq
from agno.tools.duckduckgo import DuckDuckGoTools
from agno.tools.yfinance import YFinanceTools

# Load environment variables
load_dotenv()

📌 Understanding the Code

Importing Required Libraries:
- os – Access system-level functionalities, like environment variables.
- load_dotenv – Loads API keys and sensitive data from a .env file.
Importing AI Agent & Tools:
- Agent – Defines an AI-powered assistant.
- Groq – Integrates AI models for processing queries.
- DuckDuckGoTools – Enables web searches using DuckDuckGo.
- YFinanceTools – Fetches financial data from Yahoo Finance.
Loading Environment Variables:
- load_dotenv() – Ensures API keys are securely stored and accessed.

🔹 Next Steps

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.

Step 2: Create a Web Search Agent

This agent will fetch financial information from the web.

web_search_agent = Agent(
    name="Web Search Agent",
    role="Search the web for financial information",
    model=Groq(id="llama-3.2-3b-preview"),
    tools=[DuckDuckGoTools()],
    instructions=["Always include sources"],
    show_tool_calls=True,
    markdown=True,
)

📌 Understanding the Code

Creating an AI Agent:
- Agent – Defines an AI-powered assistant for web searches.
- name="Web Search Agent" – Assigns a name to the agent.
- role="Search the web for financial information" – Specifies the agent’s purpose.
Using an AI Model:
- model=Groq(id="llama-3.2-3b-preview") – Uses Groq's AI model for processing queries.
Integrating Search Tools:
- tools=[DuckDuckGoTools()] – Enables web searches using DuckDuckGo.
Defining Instructions:
- instructions=["Always include sources"] – Ensures that search results include sources.
- show_tool_calls=True – Displays how the AI interacts with tools.
- markdown=True – Formats output in Markdown for better readability.

🔹 Next Steps

This setup prepares the AI agent to search the web for financial information, process responses using Groq’s AI model, and display well-structured results with sources.

Step 3: Create a Financial Analysis Agent

This agent will analyze stocks, fetch real-time data, and generate insights.

finance_agent = Agent(
    name="Finance AI Agent",
    role="Analyze the given stock",
    model=Groq(id="llama-3.2-11b-vision-preview"),
    tools=[
        YFinanceTools(
            stock_price=True,
            analyst_recommendations=True,
            stock_fundamentals=True,
            company_news=True
        )
    ],
    instructions=["Use tables to display data"],
    show_tool_calls=True,
    markdown=True,
)

📌 Understanding the Code

Creating a Finance AI Agent:
- Agent – Defines an AI-powered assistant for financial analysis.
- name="Finance AI Agent" – Assigns a name to the agent.
- role="Analyze the given stock" – Specifies that the agent will analyze stock data.
Using an AI Model:
- model=Groq(id="llama-3.2-11b-vision-preview") – Utilizes Groq’s AI model for financial analysis.
Integrating Financial Tools:
- YFinanceTools() – Fetches stock market data from Yahoo Finance, including:
Defining Instructions:
- instructions=["Use tables to display data"] – Ensures structured data presentation.
- show_tool_calls=True – Displays the tools used in the analysis.
- markdown=True – Formats output in Markdown for better readability.

🔹 Next Steps

This setup prepares the AI agent to analyze stock data, fetch real-time market insights, and present structured financial reports with recommendations.

Step 4: Aggregate the Agents

We combine multiple agents to work together efficiently.

multi_ai_agent = Agent(
    team=[web_search_agent, finance_agent],
    instructions=[
        "Always include sources",
        "Use tables to display data"
    ],
    show_tool_calls=True,
    markdown=True,
)

📌 Understanding the Code

Creating a Multi-AI Agent:
- Agent – Defines an AI-powered assistant capable of handling multiple tasks.
- team=[web_search_agent, finance_agent] – Combines two specialized agents:
Defining Instructions:
- instructions=["Always include sources", "Use tables to display data"]
Enabling Additional Features:
- show_tool_calls=True – Displays how the agent interacts with external tools.
- markdown=True – Ensures well-structured output with Markdown formatting.

🔹 Next Steps

This setup allows the AI agent to combine web search and financial analysis, enabling it to provide well-researched, structured insights for investment decisions.

Step 5: Execute the Financial Query

Run the agent to fetch the latest stock analysis and news.

multi_ai_agent.print_response(
    "Summarize analyst recommendations and share the latest news for Apple",
    stream=True
)

📌 Understanding the Code

Executing an AI Query:
- multi_ai_agent.print_response() – Calls the AI agent to generate a response.
- "Summarize analyst recommendations and share the latest news for Apple" – Provides the query that the AI should process.
Defining Output Behavior:
- stream=True – Streams the response in real-time instead of waiting for full processing.

🔹 Next Steps

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.

Now that we've explored the individual components of the financial agent, it's time to bring everything together into a fully integrated and functional script. Below is the complete aggregated version of our financial agent, combining stock analysis, web search, and AI-powered insights.

You can run this script on your system and experiment with its capabilities firsthand. Feel free to tweak it, add more features, and enhance it as per your needs!

import os

from dotenv import load_dotenv
from agno.agent import Agent
from agno.models.groq import Groq
from agno.tools.duckduckgo import DuckDuckGoTools
from agno.tools.yfinance import YFinanceTools

## Initializations
load_dotenv()

## Web Search Agent Creation
web_search_agent = Agent(
    name="Web Search Agent",
    role="Search the web for the information",
    model=Groq(id="llama-3.2-3b-preview"),
    tools=[DuckDuckGoTools()],
    instructions=["Always include sources"],
    show_tool_calls=True,
    markdown=True
)

## Financial Agent
finance_agent = Agent(
    name="Finance AI Agent",
    role="Analyse the given stock",
    model=Groq(id="llama-3.2-11b-vision-preview"),
    tools=[
        YFinanceTools(
            stock_price=True,
            analyst_recommendations=True,
            stock_fundamentals=True,
            company_news=True
        )
    ],
    instructions=["Use tables to display the data"],
    show_tool_calls=True,
    markdown=True
)

## Aggregating Agents
multi_ai_agent = Agent(
    team=[web_search_agent, finance_agent],
    instructions=[
        "Always include sources",
        "Use tables to display the data"
    ],
    show_tool_calls=True,
    markdown=True
)

multi_ai_agent.print_response(
    "Summarize analyst recommendation and share the latest news for Apple",
    stream=True
)

If you successfully run the script, you should see a similar output, displaying real-time stock analysis, market trends, and financial insights in a structured format. This confirms that your financial agent is functioning correctly!

Feel free to modify the script, test it with different stocks, and explore additional features to enhance its capabilities.

💻 Full Code Available on GitHub

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:

👉 View Code on GitHub

Conclusion

Building financial agents with Agno & 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.

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!