Qlib vs Vibe-Trading
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
Open-source Python platform for quant investment research using machine learning.
Open source · MIT
An open-source AI trading agent you run yourself, for research, backtesting and paper trading.
The short answer
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
Choose Vibe-Trading if you need…
- Developers who want an AI agent for quantitative trading research
- People who want to test trading strategies with backtesting before risking money
- Students learning quantitative finance with a hands-on tool
- Users who already run an LLM-based agent and want trading tools through the API or MCP
Side-by-side
| Qlib | Vibe-Trading | |
|---|---|---|
| Type | Library | AI model / agent |
| Price | Free and open source | Free and open source |
| Licence | MIT | MIT |
| GitHub stars | 49.9k | 37.4k |
| Setup difficulty | Advanced | Intermediate |
| How you get it | Install with a package manager | Install with a package manager |
| Main language | Python | Python |
| Last code update | 7 days ago | 4 days ago |
What each one is
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.
Vibe-Trading
Vibe-Trading is a Python project that lets you use an AI agent for trading-related work, such as research and backtesting. The README describes it as a personal trading agent that you install with a single pip command and then connect to your own tools. Its topics include backtesting, multi-agent systems, quantitative finance and MCP, and the README mentions paper trading and portfolio connections to brokers like IBKR and Futu.
Best for
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
Vibe-Trading
- Developers who want an AI agent for quantitative trading research
- People who want to test trading strategies with backtesting before risking money
- Students learning quantitative finance with a hands-on tool
- Users who already run an LLM-based agent and want trading tools through the API or MCP
Strengths
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
Vibe-Trading
- Open source under the MIT license
- Actively maintained, with frequent commits listed in the README news section
- Covers several areas in one project: research, backtesting, paper trading and API/MCP access
Not ideal for
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
Vibe-Trading
- People looking for a ready-made system that makes profitable trades; the README does not claim performance and this is research software
- Anyone who needs a polished no-code app, since the README centers on a command-line install and Python setup
- Users who want to connect a wallet or buy tokens; the README warns that related token and memecoin claims are not official
Trade-offs
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
Vibe-Trading
- The README is truncated and does not fully explain every feature, so details may be incomplete
- Many recent changes are marked unreleased and listed as fixes, so behavior may shift between versions
- Trading software carries financial risk, and the README does not provide investment guidance
Getting started
Frequently asked questions
What is the difference between Qlib and Vibe-Trading?
Qlib: Open-source Python platform for quant investment research using machine learning. Vibe-Trading: An open-source AI trading agent you run yourself, for research, backtesting and paper trading. Qlib suits quant researchers who want a structured ml workflow for stock data, while Vibe-Trading suits developers who want an ai agent for quantitative trading research.
Are Qlib and Vibe-Trading free?
Qlib: Free and open source. Vibe-Trading: Free and open source.
Which is easier to set up, Qlib or Vibe-Trading?
Vibe-Trading is easier to get started with (rated intermediate); Qlib is rated advanced and usually needs more setup.
Is Qlib or Vibe-Trading more popular?
Qlib has more GitHub stars (49.9k) than Vibe-Trading (37.4k). Popularity isn't the same as fit — check which one matches your use case above.
What are other alternatives to Qlib and Vibe-Trading?
Other options people compare them with include FinRL, AI Berkshire, DeerFlow, Edict, invisible_dots.
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.