Build Your Own AI Hedge Fund: The Open-Source Project That Simulates 14 Legendary Investors as AI Agents
What if you could put Warren Buffett, Charlie Munger, Peter Lynch, Cathie Wood, Michael Burry, and Nassim Taleb in a single room and ask them to analyze a stock? A new open-source project called AI Hedge Fund does exactly that ??by simulating all twelve legendary investors as separate AI agents, each with their own philosophy, methodology, and investment style, all working together to generate a consensus trading signal.
The project, available on GitHub, is a proof-of-concept that explores the use of multi-agent AI systems in financial analysis. It is explicitly not intended for real trading ??the system does not actually execute any trades ??but it is a fascinating demonstration of how AI agents with distinct personas and reasoning frameworks can collaborate on complex analytical tasks.
How the System Works
The AI Hedge Fund employs a hierarchical multi-agent architecture. At the top level are twelve investor agents, each modeled on a real legendary investor:
- Warren Buffett Agent ??seeks wonderful companies at a fair price
- Charlie Munger Agent ??only buys wonderful businesses at fair prices
- Ben Graham Agent ??the godfather of value investing, only buys hidden gems with a margin of safety
- Peter Lynch Agent ??seeks ten-baggers in everyday businesses
- Cathie Wood Agent ??believes in the power of innovation and disruption
- Michael Burry Agent ??the Big Short contrarian who hunts for deep value
- Nassim Taleb Agent ??focuses on tail risk, antifragility, and asymmetric payoffs
- Bill Ackman Agent ??activist investor who takes bold positions
- Phil Fisher Agent ??meticulous growth investor who uses deep scuttlebutt research
- Mohnish Pabrai Agent ??the Dhandho investor who looks for doubles at low risk
- Rakesh Jhunjhunwala Agent ??the Big Bull of India
- Stanley Druckenmiller Agent ??macro legend who hunts for asymmetric opportunities
Below the investor agents are four specialist agents ??Valuation Agent, Sentiment Agent, Fundamentals Agent, and Technical Analysis Agent ??that feed data into the system. A Risk Manager calculates risk metrics and sets position limits, and a Portfolio Manager makes final trading decisions.
The Agent Architecture
Each investor agent is powered by a large language model (GPT-4o, Claude, or any OpenAI-compatible model) and is instructed to reason and speak exactly like the investor it represents. The Aswath Damodaran Agent, for instance, focuses on story, numbers, and disciplined valuation. The Nassim Taleb Agent focuses exclusively on tail risk and asymmetric payoffs. The Charlie Munger Agent only buys wonderful businesses at a fair price ??and has strong opinions about what wonderful actually means.
When a user submits a stock for analysis, the system queries each investor agent, collects their opinions, synthesizes the inputs from the four specialist agents, runs risk calculations, and produces a final portfolio recommendation ??complete with reasoning from each investor in the room.
What This Tells Us About AI Agents
The project is a compelling proof-of-concept for multi-agent AI systems in general. It demonstrates how agents with distinct system prompts, reasoning frameworks, and evaluation criteria can be orchestrated to produce richer, more nuanced outputs than any single agent could achieve alone. This is the fundamental promise of agentic AI: specialized agents that each do their own thing well, coordinated by a higher-level system that synthesizes their collective intelligence.
The financial domain makes this especially vivid, because the right answer in investing is genuinely contested. Different investors look at the same data and reach different conclusions based on fundamentally different worldviews. A multi-agent system that simulates those different worldviews is arguably more honest about the nature of financial analysis than a single model that produces a false air of certainty.
Getting Started
The project runs from the command line or as a full-stack web application. Users need to configure API keys for at least one LLM provider (OpenAI, Anthropic, Groq, or any compatible service) and a financial data provider. Installation is straightforward: clone the repository, copy the environment file, add your keys, and run.
The web application provides a visual interface for submitting stock analysis requests and watching the agents debate in real time ??which is as entertaining as it is informative.
Limitations and Disclaimers
The project makes no pretense of being a production trading system. The README is explicit: “This project is for educational and research purposes only. Not intended for real trading or investment. No investment advice or guarantees provided. Consult a financial advisor for investment decisions.” The system does not connect to any brokerage and cannot execute trades.
But as a window into both the potential and the limitations of multi-agent AI systems ??and into the philosophy of twelve of history’s most influential investors ??it is genuinely remarkable work.