# AI Berkshire — Value Investing Research Framework for AI Agents

> A multi-agent investment research framework inspired by Berkshire Hathaway methodology. Combines Buffett, Munger, Duan Yongping, and Li Lu's analytical approaches with LLM-powered adversarial analysis.

## Install

Save as a script file and run:

# AI Berkshire — Value Investing Research Framework for AI Agents

## Quick Use
```bash
git clone https://github.com/xbtlin/ai-berkshire.git
cd ai-berkshire
pip install -r requirements.txt
cp .env.example .env   # add your LLM API key
python main.py --ticker AAPL
```

## Introduction
AI Berkshire is a multi-agent investment research framework that applies value investing methodology through LLM-powered analysis. It models distinct investor perspectives—Buffett, Munger, Duan Yongping, and Li Lu—as separate agents that independently evaluate a stock, then subjects their conclusions to adversarial debate to surface blind spots.

## What AI Berkshire Does
- Runs multiple analyst agents, each embodying a different value investing philosophy
- Fetches fundamental data including financial statements, valuation ratios, and industry metrics
- Produces structured research reports with bull and bear cases for a given stock
- Uses adversarial analysis where agents challenge each other's assumptions and conclusions
- Generates a synthesis report aggregating findings and highlighting agreement and disagreement

## Architecture Overview
The framework follows a multi-agent pipeline. A data layer fetches financials from public APIs. Each analyst agent applies its own lens—margin of safety, competitive moats, capital allocation, or growth-at-reasonable-price. After independent analysis, a debate module orchestrates adversarial exchange. A synthesis agent produces the final report, weighting conclusions by evidence strength.

## Self-Hosting & Configuration
- Requires Python 3.10+ and an LLM API key (supports OpenAI, Anthropic, and local models)
- Financial data sources are configurable; defaults to free APIs for US equities
- Analyst agent personas and their evaluation criteria are defined in YAML config files
- Debate rounds, temperature settings, and output format are adjustable via CLI flags
- Reports can be output as Markdown, JSON, or HTML

## Key Features
- Multi-perspective analysis that avoids single-viewpoint bias in research
- Adversarial debate mechanism that stress-tests investment theses before conclusion
- Configurable analyst personas so users can add or modify investment frameworks
- Structured output with clear bull/bear cases and confidence levels
- Support for both cloud and local LLMs to keep research workflows private

## Comparison with Similar Tools
- **FinRobot** — general financial AI agent; AI Berkshire focuses specifically on value investing with adversarial multi-agent debate
- **OpenBB** — financial data terminal; AI Berkshire adds LLM-powered analytical reasoning on top of raw data
- **GPT Researcher** — general research agent; AI Berkshire is domain-specific with investor persona modeling
- **Stock Analysis AI agents** — typically single-agent; AI Berkshire uses multi-agent adversarial review

## FAQ
**Q: Does it provide buy or sell recommendations?**
A: It produces research reports with bull and bear cases. It is a research tool, not financial advice.

**Q: Which financial data sources does it use?**
A: It defaults to free public APIs for US equities. Users can configure additional data sources in the settings.

**Q: Can I add my own analyst persona?**
A: Yes. Analyst agents are defined in YAML files. You can add new personas with custom evaluation criteria.

**Q: Does it support non-US stocks?**
A: Yes. Configure the appropriate data source adapters for other markets.

## Sources
- https://github.com/xbtlin/ai-berkshire

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Source: https://tokrepo.com/en/workflows/asset-2d7a5795
Author: Script Depot