AI Berkshire
ActiveDescription
An AI-era value investing research framework based on Claude Code/Codex, integrating methodologies of four masters (Buffett, Munger, Duan Yongping, Li Lu) with multi-agent parallel research.
Key Features
- Multi-agent parallel - simultaneously call multiple Claude Code/Codex instances to analyze different dimensions in parallel
- Four-master methodology - built-in investment frameworks and decision checklists from Buffett, Munger, Duan Yongping, Li Lu
- Deep financial analysis - auto-parse 10-K, 10-Q, annual reports, extract key financial metrics
- Valuation model integration - built-in DCF, relative valuation, asset valuation three mainstream methods
- Risk assessment - quantitative models evaluate moat, debt ratio, ROIC and other risk indicators
- Decision records - complete investment decision logs for retrospective review
Use Cases
💡 Value investors use AI Berkshire for in-depth single-stock research, generating multi-dimensional investment reports.
💡 Investment researchers use it to batch-track multiple targets, producing weekly full-market scanning reports.
💡 Investment education scenarios as assistant tool, letting students follow multi-Agent to learn master thinking frameworks.
Strengths & Limitations
✅ Strengths
- • Actively maintained, recent updates
- • High community interest (16.1k stars)
- • Permissive open-source license (MIT)
- • Responsive to issues, low backlog
Categories
Quick Start
```bash
# Clone the repo
git clone https://github.com/xbtlin/ai-berkshire.git
cd ai-berkshire
# Load in Claude Code
claude --system-prompt "$(cat prompts/system.md)" \
--tools "$(cat tools/financial.yaml)" \
# Start multi-agent research
./run.sh --stock AAPL --masters all
```