Someone has transformed Buffett and Munger into agents and then open-sourced them…

Author: Quantum Bit

Before you know it, Charlie Munger and Warren Buffett have been refined into investment agents, and now everyone can use them.

This is AI Hedge Fund, one of the hottest projects on GitHub recently.

Twelve world-class investment experts are now online anytime to help you analyze stocks and refine your trading strategies; six analysts summarize their views, make the final decision, and place the order.

This agent army, "refined" by legendary investors, not only provides real-time analysis but also has a built-in backtesting module.

You can run the strategy through historical data first, and then decide whether to use real money.

Quite comprehensive.

In terms of deployment, the project has a very low barrier to entry, is compatible with 13 large models such as OpenAI, Anthropic, Groq, and DeepSeek, and can run locally without any problems.

Currently, this project, created by independent developer Virat Singh , has quickly climbed the GitHub Trending list after being open-sourced, garnering 51.7k stars and 9k+ forks .

One netizen concluded immediately after reading: Whether it's profitable or not, I don't know. But at least I learned some knowledge about Agent frameworks.

Will it make money or not? Maybe we can lose less.

Bringing legendary investors back into the spotlight

Frankly speaking, most retail investors are far from being large enough to warrant top investors personally managing their portfolios, and quantitative models heavily rely on data and computing power, making them difficult for ordinary people to master.

The core concept of AI Hedge Fund is to encode investment philosophy into an agent, giving small investors a "master model" .

Each investment guru agent is infused with the corresponding figure's signature stock-picking logic and risk preferences. When faced with the same stock, each agent makes an independent judgment, which is then aggregated and decided by the portfolio manager agent, who outputs buy, sell, or hold signals.

The system currently has 18 built-in dedicated agents , divided into two main types:

First, there's the legendary investor agent legion :

Warren Buffett – the Oracle of Omaha, seeks out high-quality companies with wide moats and reasonable prices.
Charlie Munger – Warren Buffett's golden partner, only buys excellent businesses at reasonable prices, and values management quality and predictability.
Ben Graham – the father of value investing, strictly adheres to the margin of safety and specializes in hunting undervalued hidden gems.
Bill Ackman – an activist investor who dares to make large bets and drive corporate change.
Cathie Wood – the queen of growth investing, a firm believer in disruptive innovation and technological change.
Michael Burry – the inspiration for "The Big Short," a contrarian thinker who focuses on in-depth value discovery.
Peter Lynch – a master of investing for ordinary people, discovering ten-bagger stocks in everyday life.
Phil Fisher – a growth stock expert, known for his scuttlebutt research method.
Stanley Druckenmiller – a macro legend who specializes in seeking highly asymmetric offensive opportunities.
Mohnish Pabrai – Dhandho investor, low-risk betting with high odds.
Nassim Taleb, author of "The Black Swan," focuses on tail risks and antifragility.
Aswath Damodaran – a valuation master who prices all assets using rigorous financial modeling.

Then there's the professional analysis agent team :

Valuation Agent: Calculates intrinsic value and generates valuation trading signals.
Fundamentals Agent: Interpreting financial data and generating fundamental signals.
Technicals Agent: Analyzes technical indicators to capture trends and momentum.
Sentiment Agent: Tracking market sentiment and quantifying the long-short game.
Risk Manager: Calculates risk exposure and sets position limits.
Portfolio Manager: Summarizes all signals and makes the final trading decision.

Twelve experts offered their opinions, while six analysts provided calm and thorough evaluation. And just like that, a Wall Street dream team was assembled.

Technical Architecture

In terms of technical architecture, AI Hedge Fund adopts a three-tier architecture design that separates the front-end and back-end.

The front-end is built on React 18 + TypeScript, and its core highlight is the integration of the React Flow visual workflow editor.

Users can drag and drop different Agent nodes to form an investment strategy graph, just like building blocks, and intuitively design their own investment committee.

The backend is powered by Python + FastAPI and orchestrates multi-agent workflows with LangGraph .

All Agents share the same AgentState data dictionary, and information flows and is transmitted between nodes, which not only ensures state consistency but also allows the analysis results of each Agent to be dynamically referenced by downstream nodes.

The data layer connects to multiple external APIs, supporting unified access to real-time market data, financial statements, market sentiment, and other data. It can also access professional financial data sources through "FINANCIAL_DATASETS_API_KEY".

The entire system supports 13 main LLM providers and can also connect to local large models via the --ollama parameter, allowing the complete inference process to run without an internet connection.

The backtesting module mentioned earlier can be started with a single command: `poetry run python src/backtester.py —ticker AAPL,MSFT,NVDA`

The system will automatically call each agent to analyze stocks on a daily basis within the historical period, and finally output the historical return curve and key performance indicators of the strategy.

How to deploy

In terms of deployment, AI Hedge Fund offers both command-line and web application options.

Let's look at the command-line method first:

The first step is to clone the repository: `git clone https://github.com/virattt/ai-hedge-fund.gitcdai-hedge-fund`

The second step is to install dependencies (using Poetry): curl-sSL https://install.python-poetry.org|python3-poetry install

Step 3: Configure the API Key:

Copy `.env.example` to `.env`, and enter the key for at least one LLM service, for example: `OPENAI_API_KEY=your_key_here` `FINANCIAL_DATASETS_API_KEY=your_key_here`

Step 4, start the analysis: `poetry run pythonsrc/main.py --ticker AAPL,MSFT,NVDA`

To use a local large model, simply add the `--ollama` parameter.

After startup, his example looks like this.

For those who are not familiar with the command line, web applications provide a visual interface.

First, start the backend service: `cdapp/backend poetry run uvicorn main:app --reload`

Then, start the frontend interface (open a new terminal): cdapp/frontend pnpm install pnpm dev

Finally, visit http://localhost:3000 to access the visual Agent workflow editor and drag and drop to build your own AI investment committee.

One more thing

To be fair, there have been quite a few investment agents claiming to be "refining masters" lately.

For example, Li Dan's "Shrimp" released his own Buffett-Hulan investment skill, which included the investment strategies of Duan Yongping, Buffett, Munger, and Hulan.

Meanwhile, more and more open-source projects, such as AI Hedge Fund, are integrating various investment methodologies, and the "agentization" of investment masters is becoming a growing trend.

However, it is worth noting that most of these frameworks do not yet have a definite rate of return on investment, nor have they been tested in actual trading. Small investors who want to try them should keep the risks in mind.

The netizens' comments on this were also very genuine.

Some people directly criticized: "Sister Wood!"

Many people aspire to be like Simmons, earning a stable income.