Comparison
xgboost vs LightGBM
Verdict
Pick xgboost if xgboost: Scalable, portable gradient boosting library in C++ under Apache-2 license; pick LightGBM if lightGBM offers a blend of speed, memory efficiency, and high accuracy with support for parallel, distributed, and GPU learning.
Markdown twin · xgboost alternatives · LightGBM alternatives
GraphCanon updated Sep 27, 2026
11views this month
Trust & integrity
| Signal | xgboost | LightGBM |
|---|---|---|
| Maintenance | Very active (0d since push) As of Sep 27, 2026 · github_public_v1 | Very active (1d since push) As of Sep 26, 2026 · github_public_v1 |
| Provenance | Not a fork · Organization account As of Sep 27, 2026 · github_public_v1 | Not a fork · Organization account As of Sep 26, 2026 · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of Jul 11, 2026 · osv@v1 | No lockfile (source not queried) As of Jul 11, 2026 · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- xgboost
- Scalable, Portable and Distributed Gradient Boosting Library
- LightGBM
- A fast, distributed, high performance gradient boosting framework based on decision tree algorithms.
Stars
- xgboost
- 29k
- LightGBM
- 19k
Forks
- xgboost
- 8.9k
- LightGBM
- 4.1k
Open issues
- xgboost
- 442
- LightGBM
- 522
Language
- xgboost
- C++
- LightGBM
- C++
Adopt for
- xgboost
- xgboost: Scalable, portable gradient boosting library in C++ under Apache-2 license
- LightGBM
- LightGBM offers a blend of speed, memory efficiency, and high accuracy with support for parallel, distributed, and GPU learning.
Persona
- xgboost
- -
- LightGBM
- library
Runtime
- xgboost
- -
- LightGBM
- -
License
- xgboost
- Apache-2.0 license allows free use, modification and distribution but requires preservation of copyright notices from source files and reproduction of license grants into any copyings of the codebase
- LightGBM
- MIT
Last pushed
- xgboost
- Sep 26, 2026
- LightGBM
- Sep 25, 2026
Categories
- xgboost
- Model Training
- LightGBM
- Model Training
Trust and health
Days since push
- xgboost
- 0d
- LightGBM
- 1d
Open issues (now)
- xgboost
- 442
- LightGBM
- 522
Stars delta
- xgboost
- +176 (30d)
- LightGBM
- +157 (30d)
Open issues delta
- xgboost
- +26 (30d)
- LightGBM
- +13 (30d)
Full report
- xgboost
- Trust report
- LightGBM
- Trust report
Choose xgboost if…
- License: xgboost is Apache-2.0, LightGBM is MIT.
- Tags unique to xgboost: distributed-systems, machine-learning, xgboost.
- Highly efficient for large datasets over billions of examples due to optimizations for speed and memory use.
When NOT to use xgboost
- Avoid if ease-of-use and quick model training are more important than fine-tuning or extreme scalability.
- Not suitable when the dataset fits comfortably in memory on a single node, where other simpler tools may exceed.
- Steer clear if your project does not require high-performance gradient boosting models for regression or classification.
Choose LightGBM if…
- License: LightGBM is MIT, xgboost is Apache-2.0.
- Requirements: Min 4 GB RAM.
- Tags unique to LightGBM: data-mining, decision-trees, distributed, gradient-boosting.
- When you need fast training speeds and efficient memory use, as LightGBM is specifically optimized to handle large datasets quickly.
When NOT to use LightGBM
- If your task requires a framework that natively integrates with deep learning libraries such as TensorFlow or PyTorch without the need for external hooks.
- For use cases demanding extreme interpretability of models, where LightGBM's efficiency comes at a slight cost to model interpretation compared to other decision tree implementations.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (dmlc/xgboost) · observed Sep 27, 2026
- GitHub forks (dmlc/xgboost) · observed Sep 27, 2026
- Last push (dmlc/xgboost) · observed Sep 26, 2026
- License file (Apache-2.0) · observed Sep 27, 2026
- Decision facts (enrichment) · observed Jul 16, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (lightgbm-org/LightGBM) · observed Sep 26, 2026
- GitHub forks (lightgbm-org/LightGBM) · observed Sep 26, 2026
- Last push (lightgbm-org/LightGBM) · observed Sep 25, 2026
- License file (MIT) · observed Sep 26, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: xgboost 29k · LightGBM 19k (synced Sep 27, 2026).
Common questions
- What is the difference between xgboost and LightGBM?
- xgboost: Scalable, Portable and Distributed Gradient Boosting Library. LightGBM: A fast, distributed, high performance gradient boosting framework based on decision tree algorithms.. See the comparison table for live GitHub stats and shared categories.
- When should I choose xgboost over LightGBM?
- Choose xgboost over LightGBM when License: xgboost is Apache-2.0, LightGBM is MIT; Tags unique to xgboost: distributed-systems, machine-learning, xgboost; Highly efficient for large datasets over billions of examples due to optimizations for speed and memory use.
- When should I choose LightGBM over xgboost?
- Choose LightGBM over xgboost when License: LightGBM is MIT, xgboost is Apache-2.0; Requirements: Min 4 GB RAM; Tags unique to LightGBM: data-mining, decision-trees, distributed, gradient-boosting; When you need fast training speeds and efficient memory use, as LightGBM is specifically optimized to handle large datasets quickly.
- When should I avoid xgboost?
- Avoid if ease-of-use and quick model training are more important than fine-tuning or extreme scalability. Not suitable when the dataset fits comfortably in memory on a single node, where other simpler tools may exceed. Steer clear if your project does not require high-performance gradient boosting models for regression or classification.
- When should I avoid LightGBM?
- If your task requires a framework that natively integrates with deep learning libraries such as TensorFlow or PyTorch without the need for external hooks. For use cases demanding extreme interpretability of models, where LightGBM's efficiency comes at a slight cost to model interpretation compared to other decision tree implementations.
- Is xgboost or LightGBM more popular on GitHub?
- xgboost has more GitHub stars (28,796 vs 18,813). Stars measure visibility, not whether either tool fits your constraints.
- Are xgboost and LightGBM open source?
- Yes - both are open-source projects on GitHub (xgboost: Apache-2.0, LightGBM: MIT).
- Where can I find alternatives to xgboost or LightGBM?
- GraphCanon lists graph-backed alternatives at xgboost alternatives and LightGBM alternatives (xgboost markdown twin, LightGBM markdown twin), ranked by typed relationship edges rather than popularity votes.
- Is there a machine-readable version of this comparison?
- Yes. The markdown twin at this comparison mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.
- Which is better maintained, xgboost or LightGBM?
- xgboost: Very active. LightGBM: Very active. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance.
- Where are the full trust reports for xgboost and LightGBM?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: xgboost trust report; LightGBM trust report.