xbtlin / ai-berkshire

2026-04-07 · Source: Github Trending: All languages · Field: Finance & Economics — Capital Markets & Investment Management, FinTech & Digital Financial Services · Depth: Intermediate, extended

Summary

AI Berkshire is an investment research framework built on Claude Code, designed to enhance the depth and efficiency of investment analysis. It systematizes the methodologies of four value investing masters: Warren Buffett, Charlie Munger, Duan Yongping, and Li Lu, to achieve professional-grade research through AI Agents. The framework boasts a real track record, with a +69.29% return in 2024 and +66.38% year-to-date in 2025, significantly outperforming major indices like the S&P 500 and Hang Seng Index. Unlike direct AI queries, AI Berkshire provides structured outputs like "Pass/Fail/Gray Area" recommendations, incorporates multi-perspective contention, employs anti-bias mechanisms, ensures financial data accuracy using Python's `decimal.Decimal`, and offers a reproducible research process through 16 distinct "Skills."

Key takeaway

For investors seeking to elevate their due diligence beyond generic AI outputs, AI Berkshire offers a structured, multi-perspective framework to move from analysis to actionable investment decisions. You should consider integrating this Claude Code-based system to enhance your research quality and discipline, leveraging its proven outperformance and rigorous data verification to avoid common AI pitfalls and make more informed choices.

Key insights

AI Berkshire transforms raw AI analysis into structured, decision-ready investment research by integrating master methodologies and anti-bias mechanisms.

Principles

• Value investing masters' methods enhance AI analysis.
• Multi-agent contention reveals investment blind spots.
• Structured anti-bias mechanisms improve AI reliability.

Method

AI Berkshire employs a three-layer design: Skill Layer (16 entry points), Agent Layer (4 parallel agents for research and challenge), and Tool Layer (precise calculation, cross-verification, and rigor).

In practice

• Use `/investment-team` for parallel company research.
• Apply `/quality-screen` for rapid company exclusion.
• Verify market cap with `financial_rigor.py` tool.

Topics

• AI Agents
• Value Investing
• Investment Research
• Financial Analysis
• Large Language Models
• Portfolio Management
• Quantitative Finance

Code references

• xbtlin/ai-berkshire
• xbtlin/ai-berkshire

Best for: Entrepreneur, AI Engineer, Data Scientist, Investor

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Editorial summary, takeaway, and curation by AIssential. Original article published by Github Trending: All languages.