Leaves
Pure Go implementation for GBRT prediction including XGBoost and LightGBM.
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Free tier
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Open Source
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Aging · Jun 8, 2026Overview
What is Leaves?
Leaves is a pure Go library that implements the prediction part of Gradient Boosting Regression Trees (GBRTs), supporting models like XGBoost and LightGBM. It's designed to be efficient and easy to integrate into Go applications for machine learning tasks.
Key differentiator
“Leaves stands out by offering a pure Go solution for GBRT model predictions, making it an ideal choice for developers who need to integrate these models into their applications without the overhead of external dependencies or complex setup processes.”
Capability profile
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Honest assessment
Strengths & Weaknesses
↑ Strengths
↓ Weaknesses
Leaves is a pure Go library, limiting its use to projects that are already in Go or willing to integrate with it.
Integrating Leaves into applications written in other languages requires significant effort due to the lack of native support and the need for inter-language communication mechanisms.
As an open-source library, Leaves may have a smaller user base and fewer contributors compared to more established frameworks like XGBoost or LightGBM in Python.
While efficient, the Go implementation might not match the optimized C++ backends of XGBoost and LightGBM when dealing with very large datasets or complex models.
Fit analysis
Who is it for?
✓ Best for
Go developers who need to integrate machine learning predictions into their applications without external dependencies
Projects requiring efficient and lightweight GBRT model deployment in a Go environment
✕ Not a fit for
Developers looking for a full-featured ML framework that includes training capabilities, as Leaves only supports prediction
Teams preferring languages other than Go for machine learning tasks
Cost structure
Pricing
Free Tier
Available
Open source — free to use
Starts at
$0
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None
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Next step
Get Started with Leaves
Step-by-step setup guide with code examples and common gotchas.