AI Hedge Fund vs Qlib
Which should you use? A side-by-side look at what each one does, who it suits, what it costs, how hard it is to set up, and where each falls short.
Open source · MIT
Educational AI project that simulates a team of AI investor agents making trading decisions.
Open source · MIT
Open-source Python platform for quant investment research using machine learning.
The short answer
Choose AI Hedge Fund if you need…
- Students learning how AI agents can be applied to investment analysis
- Developers who want a Python codebase to study multi-agent trading workflows
- Researchers wanting to replay simulated funds over historical market data
- Hobbyists comfortable with a terminal who want to experiment with paper trading
Choose Qlib if you need…
- Quant researchers who want a structured ML workflow for stock data
- Python developers experimenting with financial machine learning models
- Students learning how quantitative investment research is organized
- Teams that want to reuse published quant models such as those listed in the README
Side-by-side
| AI Hedge Fund | Qlib | |
|---|---|---|
| Type | AI model / agent | Library |
| Price | Free and open source | Free and open source |
| Licence | MIT | MIT |
| GitHub stars | 64.4k | 49.9k |
| Setup difficulty | Intermediate | Advanced |
| How you get it | Install with a package manager | Install with a package manager |
| Main language | Python | Python |
| Last code update | 4 days ago | 7 days ago |
What each one is
AI Hedge Fund
AI Hedge Fund is a proof-of-concept that uses AI agents, modeled on different investor styles, to explore how AI might make trading decisions. It is for educational purposes only and the README states the system does not actually make any trades. It includes an interactive terminal app with paper trading (simulated funds with a ledger) and backtesting against historical data.
Qlib
Qlib is a Python platform for quantitative investment research, where analysts build models that try to predict market behavior from historical data. It covers the workflow from exploring ideas to running models, and supports supervised learning, market dynamics modeling, and reinforcement learning. The README also says it now connects to RD-Agent, a separate Microsoft project for automating factor mining and model optimization.
Best for
AI Hedge Fund
- Students learning how AI agents can be applied to investment analysis
- Developers who want a Python codebase to study multi-agent trading workflows
- Researchers wanting to replay simulated funds over historical market data
- Hobbyists comfortable with a terminal who want to experiment with paper trading
Qlib
- Quant researchers who want a structured ML workflow for stock data
- Python developers experimenting with financial machine learning models
- Students learning how quantitative investment research is organized
- Teams that want to reuse published quant models such as those listed in the README
Strengths
AI Hedge Fund
- Clear educational framing and explicit disclaimers about its limits
- Simple install via pipx, uv, or pip, with API keys requested on first use
- Paper trading keeps a hash-chained ledger of each session
Qlib
- Open source under the MIT license with a large community (about 49,800 stars at time of analysis)
- Covers multiple modeling approaches in one framework
- Includes documentation, tutorials, and an active CI test badge in the README
Not ideal for
AI Hedge Fund
- Anyone expecting real investment returns or financial advice, since the README says it is not intended for real trading or investment
- Users who need a no-code tool, because it runs from the terminal
- Users without API keys for a market data provider and a supported AI model provider, since the app needs them to function
Qlib
- People looking for a ready-made trading bot, since the README describes a research platform rather than a turnkey strategy
- Anyone who needs guaranteed returns or investment advice; the project does not provide this
- Users who are not comfortable with Python and command-line setup
Trade-offs
AI Hedge Fund
- Explicitly a proof of concept for education, not a validated trading system
- Depends on third-party API keys for market data and AI models
- README is brief on how the agents make decisions, so the internals are unclear from it alone
Qlib
- The README warns of an unreleased breaking change to how saved models and datasets are loaded, which requires reading a migration guide before upgrading
- Recent source builds restrict loading artifacts unless users opt in with trusted=True, so careful review is needed
- Requires a working understanding of quant finance and machine learning to use meaningfully
Getting started
AI Hedge Fund
Install with pipx
pipx install aihfRun the app
aihfFrequently asked questions
What is the difference between AI Hedge Fund and Qlib?
AI Hedge Fund: Educational AI project that simulates a team of AI investor agents making trading decisions. Qlib: Open-source Python platform for quant investment research using machine learning. AI Hedge Fund suits students learning how ai agents can be applied to investment analysis, while Qlib suits quant researchers who want a structured ml workflow for stock data.
Are AI Hedge Fund and Qlib free?
AI Hedge Fund: Free and open source. Qlib: Free and open source.
Which is easier to set up, AI Hedge Fund or Qlib?
AI Hedge Fund is easier to get started with (rated intermediate); Qlib is rated advanced and usually needs more setup.
Is AI Hedge Fund or Qlib more popular?
AI Hedge Fund has more GitHub stars (64.4k) than Qlib (49.9k). Popularity isn't the same as fit — check which one matches your use case above.
What are other alternatives to AI Hedge Fund and Qlib?
Other options people compare them with include FinRL.
Keep comparing
Built from our catalogue entries for both products: open-source details come from each project's README (AI-assisted, refreshed weekly) and GitHub data; AI-tool details are editorial. Spotted something wrong? Let us know from either product's page.