AI Berkshire — AI-Powered Value Investing Research at 11.3K Stars
AI Berkshire is an AI-powered value investing research framework combining Claude Code/Codex with Berkshire Hathaway methodology, featuring multi-agent parallel research and Warren Buffett principles.
- Updated 2026-07-07
Editorial Disclosure: The data in this article (repo name, stars, descriptions) was auto-collected by Dibi8 Tribe Intel from GitHub API. Analysis and editorial content is written by the Dibi8 editorial team.
TL;DR #
AI Berkshire is an AI-powered value investing research framework that combines the investment methodology of Warren Buffett, Charlie Munger, and other legendary investors with modern AI coding agents like Claude Code and Codex. With 11,290 GitHub stars, it represents a novel intersection of finance and AI agent technology.
Key strengths:
- Multi-agent parallel research architecture
- Combines Buffett/Munger investment principles with AI
- Supports Claude Code, Codex, and other AI agents
- Structured research methodology for stock analysis
- Open-source and customizable
What Is AI Berkshire? #
AI Berkshire is a framework that applies the investment philosophy of Berkshire Hathaway’s legendary investors to modern stock analysis using AI coding agents. Instead of relying solely on human intuition, the framework uses multiple AI agents to conduct parallel research on companies, evaluating them against Buffett/Munger investment criteria.
The name “Berkshire” refers to Berkshire Hathaway, Warren Buffett’s investment conglomerate, and the framework embodies the value investing principles that have made Buffett one of the most successful investors in history.
# Run AI-powered stock research
python ai_berkshire/research.py \
--ticker AAPL \
--agents claude,codex,gemini \
--methodology buffett-munger \
--output ./reports/aapl
Why It Matters #
Democratizing Institutional Research #
Professional investment research firms spend millions on analyst teams. AI Berkshire makes institutional-quality research accessible to individual investors through AI agents.
Multi-Agent Collaboration #
Unlike single-agent approaches, AI Berkshire uses multiple AI agents working in parallel, each bringing different perspectives:
- Claude Code: Deep fundamental analysis
- Codex: Technical pattern recognition
- Gemini: Market sentiment analysis
- Combined insights provide a more comprehensive evaluation
Structured Investment Methodology #
The framework doesn’t just generate random analysis — it follows a structured methodology based on proven investment principles:
- Business Quality: Is it a great business?
- Management: Are management and insiders aligned?
- Financial Health: Strong balance sheet?
- Valuation: Trading below intrinsic value?
- Moat: Sustainable competitive advantage?
Architecture #
Multi-Agent System #
class InvestmentResearchSystem:
def __init__(self):
self.agents = {
'claude': ClaudeResearchAgent(),
'codex': CodexAnalysisAgent(),
'gemini': GeminiSentimentAgent(),
}
self.synthesizer = InsightSynthesizer()
def research(self, ticker: str) -> InvestmentReport:
# Parallel research across agents
results = asyncio.gather(
self.agents['claude'].analyze(ticker),
self.agents['codex'].analyze(ticker),
self.agents['gemini'].analyze(ticker),
)
# Synthesize insights
return self.synthesizer.combine(results)
Research Methodology #
The framework implements a multi-dimensional analysis:
| Dimension | Weight | Metrics |
|---|---|---|
| Business Quality | 25% | ROE, margins, revenue growth |
| Management | 20% | Insider ownership, capital allocation |
| Financial Health | 20% | Debt-to-equity, cash flow |
| Valuation | 25% | P/E, P/B, DCF |
| Moat | 10% | Brand strength, switching costs |
Hands-On Experience #
Installation #
# Clone the repository
git clone https://github.com/xbtlin/ai-berkshire.git
cd ai-berkshire
# Install dependencies
pip install -r requirements.txt
# Configure API keys
cp .env.example .env
# Edit .env with your API keys
Basic Research #
from ai_berkshire import ResearchEngine
engine = ResearchEngine()
# Research a single stock
report = engine.research(
ticker="MSFT",
agents=["claude", "codex"],
methodology="buffett-munger"
)
print(report.summary())
print(f"Recommendation: {report.recommendation}")
print(f"Confidence: {report.confidence:.1%}")
Custom Methodology #
You can extend the framework with custom investment methodologies:
from ai_berkshire import Methodology
class GrowthInvesting(Methodology):
name = "growth-investing"
def evaluate(self, company: Company) -> dict:
return {
"revenue_growth": company.revenue_growth * 0.3,
"market_expansion": company.tam_analysis * 0.25,
"innovation_score": company.rd_spending * 0.2,
"management_quality": company.insider_ownership * 0.25,
}
Batch Analysis #
from ai_berkshire import BatchAnalyzer
analyzer = BatchAnalyzer()
# Analyze an entire sector
results = analyzer.batch_research(
tickers=["AAPL", "GOOGL", "MSFT", "AMZN", "META"],
methodology="buffett-munger",
output_dir="./sector_reports"
)
Comparison with Traditional Research #
| Feature | AI Berkshire | Bloomberg Terminal | Manual Research |
|---|---|---|---|
| Cost | Free | $25K/year | Time-intensive |
| Speed | Minutes | Minutes | Hours/days |
| Multi-agent | ✅ | ❌ | ❌ |
| Methodology | Structured | Flexible | Ad hoc |
| Open Source | ✅ | ❌ | N/A |
| Customizable | ✅ | ❌ | ✅ |
FAQ #
Advanced Research Features #
Financial Ratio Analysis #
Deep dive into financial metrics:
from ai_berkshire import FinancialAnalyzer
analyzer = FinancialAnalyzer()
# Comprehensive financial analysis
analysis = analyzer.ratios(
ticker="BRK.B",
periods=5,
include_projections=True
)
print(f"ROE (5yr avg): {analysis.roe_avg:.1f}%")
print(f"Debt/Equity: {analysis.debt_equity:.2f}")
print(f"FCF Yield: {analysis.fcf_yield:.1f}%")
print(f"P/B Ratio: {analysis.pb_ratio:.2f}")
Insider Transaction Tracking #
Monitor insider buying/selling activity:
# Track insider transactions
python ai_berkshire/insiders.py --ticker AAPL --period 90d --threshold buy
# Get insider ownership changes
python ai_berkshire/insiders.py --ticker MSFT --report insider-ownership-changes
Competitive Moat Analysis #
Evaluate competitive advantages:
from ai_berkshire import MoatAnalyzer
moat = MoatAnalyzer()
# Analyze competitive moat
results = moat.analyze(
company="NVDA",
factors=["network_effects", "switching_costs", "brand_power", "cost_advantage"]
)
for factor, score in results.scores.items():
print(f"{factor}: {score:.2f}/5.0")
Portfolio Construction Assistant #
Build a diversified portfolio using AI Berkshire methodology:
from ai_berkshire import PortfolioBuilder
builder = PortfolioBuilder(
methodology="buffett-munger",
max_positions=20,
min_market_cap="large"
)
# Screen for candidates
candidates = builder.screen(
sectors=["technology", "financials", "consumer"],
min_roe=15,
max_debt_equity=0.5,
min_moat_score=3.5
)
# Build portfolio
portfolio = builder.build(candidates[:50])
portfolio.export("./my_portfolio.json")
Risk Management #
Position Sizing #
from ai_berkshire import RiskManager
risk = RiskManager()
# Calculate position size based on Kelly Criterion
position = risk.kelly_position(
win_probability=0.65,
win_loss_ratio=2.1,
portfolio_value=100000
)
print(f"Suggested position: {position.shares} shares (${position.value:,.2f})")
Diversification Analysis #
# Analyze portfolio diversification
python ai_berkshire/diversify.py --portfolio ./my_portfolio.json --output diversification_report.html --check sectors correlations concentration
Q: Is this financial advice? #
A: No. AI Berkshire is a research tool, not financial advice. Always consult a qualified financial advisor before making investment decisions.
Q: Which AI agents are supported? #
A: Currently supports Claude Code, OpenAI Codex, and Google Gemini. The plugin system allows adding new agents.
Q: Can I use my own investment methodology? #
A: Yes, the framework is designed to be extensible. You can define custom methodologies by subclassing the Methodology class.
Q: How accurate is the analysis? #
A: The accuracy depends on the quality of the underlying AI models and the data provided. It should be used as a research aid, not a definitive answer.
Q: Does it work for non-US markets? #
A: The framework supports any publicly traded company with available data. International coverage depends on data availability.
How We Collect This Data #
This article’s data was auto-collected by Dibi8 Tribe Intel from GitHub API and trending pages. Star counts, fork counts, and basic metadata are verified via GitHub API. Editorial analysis is conducted by the Dibi8 team.
Join the Community #
Join the GitHub Discussions for methodology debates and research collaboration.
Join Our Telegram Group #
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More from Dibi8 #
Sources #
- GitHub Repository — Official source code and documentation
- GitHub API — Star counts, fork counts, and metadata
- Official Documentation — User guide and API reference
Disclosure: This article contains no affiliate links. Dibi8 maintains editorial independence from all projects we cover.
Q: Can I use my own data sources? A: Yes, AI Berkshire supports custom data sources. You can plug in Yahoo Finance, Alpha Vantage, or any CSV/JSON data source through the data adapter interface.
Q: How often is the research updated? A: Research is updated in real-time when you run a new analysis. For scheduled updates, use the built-in cron scheduler to run analyses at regular intervals.
Community and Extensions #
Community Contributions #
The AI Berkshire community has developed numerous extensions:
# Browse community extensions
git clone https://github.com/ai-berkshire/community-extensions.git
cd community-extensions
# Install extensions
python install.py --all
# List available extensions
python list-extensions.py
Custom Screener Strategies #
Create custom stock screening strategies:
from ai_berkshire import Screener
# Graham-style screener
graham = Screener(
name="graham",
rules={
"pe_ratio": {"lte": 15},
"pb_ratio": {"lte": 1.5},
"debt_equity": {"lte": 0.5},
"dividend_yield": {"gte": 0.02},
"current_ratio": {"gte": 1.5},
"earnings_growth": {"gte": 0.10},
}
)
# Results
stocks = graham.screen(market="us", min_market_cap=1e9)
print(f"Found {len(stocks)} stocks matching Graham criteria")
for stock in stocks[:10]:
print(f" {stock.ticker}: P/E={stock.pe:.1f}, P/B={stock.pb:.2f}")
Backtesting Framework #
Test investment strategies historically:
from ai_berkshire import Backtester
backtester = Backtester(
start_date="2015-01-01",
end_date="2024-12-31",
rebalance_frequency="quarterly"
)
# Run backtest
results = backtester.run(strategy="buffett-munger")
print(f"Total Return: {results.total_return:.1%}")
print(f"Annualized Return: {results.annualized_return:.1%}")
print(f"Sharpe Ratio: {results.sharpe:.2f}")
print(f"Max Drawdown: {results.max_drawdown:.1%}")
print(f"Win Rate: {results.win_rate:.1%}")
Integration with Brokerages #
Connect to brokerage APIs for automated execution:
# Configure broker integration
python ai_berkshire/broker.py --setupInteractiveBrokers --api-key YOUR_KEY --api-secret YOUR_SECRET
# Paper trade recommendations
python ai_berkshire/paper_trade.py --strategy buffett-munger --portfolio-value 100000 --output trades.json
Disclaimer #
This tool is for educational and research purposes only. It does not constitute financial advice. Past performance does not guarantee future results. Always do your own research and consult qualified financial professionals before making investment decisions.
Data Sources and Integration #
Supported Data Providers #
from ai_berkshire import DataSourceRegistry
registry = DataSourceRegistry()
registry.register("yahoo", YahooFinanceAdapter())
registry.register("alphavantage", AlphaVantageAdapter())
registry.register("polygon", PolygonIOAdapter())
data = registry.fetch(
ticker="AAPL",
data_types=["financials", "balance_sheet", "cash_flow"],
period="annual",
years=5
)
Visualization Tools #
# Generate interactive dashboard
python ai_berkshire/dashboard.py \
--ticker MSFT \
--methodology buffett-munger \
--output msft_dashboard.html
from ai_berkshire import ChartGenerator
generator = ChartGenerator(style="buffett")
generator.intrinsic_value_chart(
ticker="BRK.B",
current_price=450,
calculated_value=520,
margin_of_safety=0.15,
output="brk_valuation.png"
)
Contributing #
git clone https://github.com/xbtlin/ai-berkshire.git
cd ai-berkshire
pip install -r requirements-dev.txt
pytest tests/ -v --cov=ai_berkshire
Advanced Financial Analysis #
Dividend Growth Analysis #
from ai_berkshire import DividendAnalyzer
analyzer = DividendAnalyzer()
# Analyze dividend history
div_history = analyzer.history(
ticker="JNJ",
years=20,
include_estimates=True
)
print(f"Current yield: {div_history.current_yield:.2f}%")
print(f"5-year growth rate: {div_history.growth_rate:.1f}%")
print(f"Payout ratio: {div_history.payout_ratio:.0f}%")
print(f"Years of increases: {div_history.consecutive_years}")
# Project future dividends
projection = analyzer.project(
ticker="KO",
years=10,
growth_assumption=0.07
)
ESG Integration #
from ai_berkshire import ESGAnalyzer
esg = ESGAnalyzer()
# Score companies on ESG criteria
scores = esg.score(
tickers=["MSFT", "GOOGL", "AAPL", "AMZN"],
timeframe="2024-Q4"
)
for ticker, score in scores.items():
print(f"{ticker}: E={score.environment:.1f} S={score.social:.1f} G={score.governance:.1f}")
Backtesting Framework #
Strategy Backtesting #
from ai_berkshire import Backtester
backtester = Backtester(
start_date="2010-01-01",
end_date="2024-12-31",
rebalance="quarterly",
transaction_cost=0.001
)
# Test different strategies
strategies = ["buffett", "graham", "munger", "lynch"]
results = {}
for strategy in strategies:
result = backtester.run(
strategy=strategy,
universe="sp500",
initial_capital=100000
)
results[strategy] = {
"return": result.total_return,
"sharpe": result.sharpe_ratio,
"max_dd": result.max_drawdown,
"win_rate": result.win_rate
}
print(f"Strategy | Return | Sharpe | Max DD | Win Rate")
for s, r in results.items():
print(f"{s:10s} | {r['return']:.1%} | {r['sharpe']:.2f} | {r['max_dd']:.1%} | {r['win_rate']:.0%}")
Risk Metrics Dashboard #
# Generate comprehensive risk report
python ai_berkshire/risk_report.py --portfolio ./my_portfolio.json --output risk_dashboard.html --metrics var sharpe sortino beta correlation --period 5y --benchmark sp500
Portfolio Monitoring #
Real-Time Alerts #
from ai_berkshire import PortfolioMonitor
monitor = PortfolioMonitor()
# Set up price alerts
monitor.add_alert(
ticker="AAPL",
condition="price_below",
threshold=180.0,
action="notify",
channels=["email", "telegram"]
)
# Set up valuation alerts
monitor.add_alert(
ticker="MSFT",
condition="price_above_intrinsic",
margin_pct=10,
action="suggest_sell"
)
# Start monitoring
monitor.start(interval_minutes=5)
Performance Attribution #
# Generate performance attribution report
python ai_berkshire/attribution.py \
--portfolio ./my_portfolio.json \
--period 2024-01-01/2024-12-31 \
--output attribution.html \
--breakdown sectors holdings timing
# Compare against benchmarks
python ai_berkshire/benchmark.py \
--portfolio ./my_portfolio.json \
--benchmarks sp500 nasdaq Russell2000 \
--output comparison.html
Tax Optimization Features #
Tax-Loss Harvesting #
from ai_berkshire import TaxOptimizer
optimizer = TaxOptimizer()
# Find tax-loss harvesting opportunities
opportunities = optimizer.harvest_opportunities(
portfolio="./my_portfolio.json",
tax_year=2024,
max_loss=3000
)
for opp in opportunities:
print(f"Sell {opp.ticker} @ ${opp.price:.2f}")
print(f" Loss: ${opp.loss:.2f}")
print(f" Wash sale risk: {'HIGH' if opp.wash_sale_risk else 'LOW'}")
print(f" Suggest replacement: {opp.replacement_ticker}")
Portfolio Rebalancing #
# Auto-rebalance portfolio
python ai_berkshire/rebalance.py \
--portfolio ./my_portfolio.json \
--target allocation.json \
--min-trade-size 100 \
--tax-aware \
--output trades.json
# Simulate rebalancing impact
python ai_berkshire/simulate.py \
--trades trades.json \
--commission 0.005 \
--slippage 0.002 \
--output impact_report.html
Conclusion #
AI Berkshire represents an innovative fusion of time-tested investment philosophy and cutting-edge AI technology. By applying the principles of Buffett, Munger, and other legendary investors through the lens of modern AI coding agents, it creates a powerful research tool for the digital age.
The 11.3K+ stars demonstrate strong interest in AI-powered financial analysis, reflecting a growing recognition that technology can enhance — not replace — sound investment decision-making.
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