Build Your Own Warren Buffett Agent in 5 Minutes: A Chatbot That Thinks Like the Oracle of Omaha

 

What if you could ask Warren Buffett—the Oracle of Omaha—what he thinks about a stock, a market trend, or the value of a business… anytime you wanted?

While we can’t clone Buffett himself, we can create something remarkably close: an AI agent that interacts like him, drawing from his core investing principles, using real-time stock data, and helping users think long-term—just like Buffett would.

With reports suggesting that Buffett may soon step down as CEO of Berkshire Hathaway, now is the perfect moment to celebrate his legendary mindset by building something that lives on: a Warren Buffett-inspired chatbot.

This blog will show you how to build that agent from scratch in under 5 minutes using Streamlit, LangChain, and a few handy APIs.

🧭 Disclaimer: This agent won’t give you financial advice—but it will help you apply Buffett-style thinking to your investment research.

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🎯 Project Goal and Architecture

Goal: Build a conversational chatbot that emulates Warren Buffett’s investing philosophy while fetching real-time stock data and news to analyze companies through his lens.

Architecture Overview:

• 🧠 LLM core: GPT-4 or GPT-3.5-turbo via OpenAI

• 💬 Interface: Streamlit chatbot UI

• 🔧 Tools: Stock price & news fetchers

• 🧑‍💼 Agent Personality: Customized to mimic Buffett’s tone and logic

• 🔗 LangChain: For building the agent and orchestrating responses

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🧰 Step 1: Prepare Your Environment

Install the required Python libraries:

pip install streamlit langchain openai yfinance requests

You’ll also need an OpenAI API key:

export OPENAI_API_KEY="your-key-here"

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▶️ Step 2: Start the Script and Import Libraries

Create a file named buffett_agent.py and start with these imports:

import streamlit as st
import yfinance as yf
import requests
from langchain.agents import initialize_agent, Tool
from langchain.chat_models import ChatOpenAI
from langchain.agents.agent_types import AgentType
from langchain.prompts import PromptTemplate

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💻 Step 3: Set Up the Streamlit Interface

st.set_page_config(page_title="Warren Buffett Agent", layout="centered")
st.title("💬 Ask Warren Buffett (AI Edition)")

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👔 Step 4: Define Core Settings and the Buffett Persona

Use a prompt template to define how the AI should think and speak like Buffett:

buffett_prompt = PromptTemplate.from_template("""
You are Warren Buffett, legendary value investor. You respond with wisdom, clarity, and wit. 
You prioritize fundamentals, long-term thinking, and intrinsic value. Never recommend short-term speculation.

Current conversation:
{input}
""")

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📈 Step 5: Create Data Fetching Tools

Define tools the agent can call to fetch stock data and news.

def get_stock_price(ticker: str):
    stock = yf.Ticker(ticker)
    price = stock.history(period="1d")["Close"][0]
    return f"The current price of {ticker.upper()} is ${price:.2f}."

def get_company_summary(ticker: str):
    stock = yf.Ticker(ticker)
    info = stock.info
    return f"{info.get('longBusinessSummary', 'No summary available')}"

tools = [
    Tool(
        name="Get Stock Price",
        func=get_stock_price,
        description="Use to get the latest stock price for a company by ticker symbol (e.g., AAPL)"
    ),
    Tool(
        name="Get Company Summary",
        func=get_company_summary,
        description="Use to understand what the company does, based on its ticker symbol"
    )
]

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🧠 Step 6: Assemble the LangChain Agent

llm = ChatOpenAI(temperature=0.5, model="gpt-3.5-turbo")

agent = initialize_agent(
    tools, llm,
    agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
    verbose=True
)

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💬 Step 7: Implement the Chat Interaction Loop

if "messages" not in st.session_state:
    st.session_state.messages = []

for msg in st.session_state.messages:
    st.chat_message(msg["role"]).write(msg["content"])

user_input = st.chat_input("Ask me about a stock or investing principle...")

if user_input:
    st.chat_message("user").write(user_input)
    st.session_state.messages.append({"role": "user", "content": user_input})

    with st.spinner("Warren is thinking..."):
        response = agent.run(buffett_prompt.format(input=user_input))

    st.chat_message("assistant").write(response)
    st.session_state.messages.append({"role": "assistant", "content": response})

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🚀 Step 8: Run the Warren Buffett Agent

Run the chatbot with:

streamlit run buffett_agent.py

Now you’ve got your very own Buffett-style advisor who can:

• Give timeless investment wisdom

• Analyze any stock via real-time data

• Explain complex financials in simple terms

• Help you think long-term—like the Oracle himself

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🧪 Analyzing the Output

Try asking:

• “What do you think about Amazon stock?”

• “Is it a good time to invest in banks?”

• “How do you value a tech company?”

• “Tell me about Coca-Cola as an investment.”

You’ll get responses like:

“Well, I don’t focus much on timing the market, but on buying great businesses at fair prices. If Amazon has durable competitive advantages and strong free cash flow, it’s worth a look.”

The language is patient, grounded, and value-driven—just like Buffett.

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📌

Warren Buffett once said, “I am a better investor because I am a businessman, and a better businessman because I am an investor.” With this project, you can tap into that dual mindset—through a chatbot that helps you think, evaluate, and reflect like Buffett.

By combining LLMs, LangChain, Streamlit, and real-time financial tools, we’ve built more than just a Q&A bot—we’ve built a digital investing mentor.

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