AI Berkshire is an open-source investment research framework that applies the methodologies of Buffett, Munger, Duan Yongping, and Li Lu through multi-agent analysis on Claude Code or Codex. The /investment-team command launches four independent agents that each conduct complete research — web searches, data cross-validation, and independent conclusions — before a Team Lead synthesizes the final investment call. The adversarial design means each agent tries to break the others' thesis, surfacing risks that single-agent analysis misses.
AI Berkshire brings institutional-grade investment research to individual investors through a multi-agent framework built on Claude Code and Codex. Rather than asking a single AI for stock analysis, AI Berkshire launches four independent analyst agents — each applying a different value investing methodology — that conduct parallel research, cross-validate data, and reach independent conclusions. A Team Lead agent then synthesizes the results, explicitly tracking where analysts agree and disagree, producing research reports that surface risks a single-pass analysis would miss.
The /investment-team command is the core interface. It spins up four agents, each conducting complete research: searching the web for financials, news, and filings; analyzing the data through their assigned methodology lens; and writing an independent assessment. This is not prompt splitting — each agent runs a full research loop. The adversarial design is intentional: agents are prompted to find weaknesses in bullish theses and strengths in bearish ones. The Team Lead weights the analyses and produces a final recommendation with confidence intervals and explicit risk factors.
AI Berkshire is designed for individual investors who want professional-quality research without the cost of analyst teams. Common workflows include deep-dive analysis of individual stocks before major positions, screening a watchlist for red flags, comparing companies within a sector, and stress-testing an existing portfolio thesis. The framework can also be pointed at free models through OpenRouter or OmniRoute, making it accessible to hobbyist investors who don’t want to pay for frontier model API access for every research run.
AI Berkshire is an educational and research tool, not financial advice. The framework’s quality depends heavily on the underlying model’s capability — frontier models like Claude Sonnet produce significantly better analysis than free alternatives, though free models handle routine data gathering well. Running four parallel agents consumes more tokens than single-agent analysis, so cost-conscious users should consider routing through free model providers for the research phase. The framework requires Claude Code or Codex CLI to run.
Individual investors, finance students, and research hobbyists who want structured, multi-perspective investment analysis. Developers interested in multi-agent adversarial architectures will also find AI Berkshire instructive as a reference implementation of the pattern. Not suitable for automated trading — it produces research reports, not trade signals.
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