Lime
Explains black box classifiers with two or more classes.
Pricing
Free tier
Flat rate
Adoption
↘CoolingLicense
Open Source
Data freshness
Aging · Jun 8, 2026Overview
What is Lime?
Lime is a tool for explaining machine learning models by providing insights into how these models make decisions, particularly useful for understanding complex and opaque models.
Key differentiator
“Lime stands out by providing local explanations for any machine learning model, making it a versatile tool for enhancing the interpretability of complex models without requiring access to their internal structure.”
Capability profile
Capability Radar
Honest assessment
Strengths & Weaknesses
↑ Strengths
↓ Weaknesses
API requires Python-specific patterns, TypeScript SDK is community-maintained
v0.1 to v0.2 migration required rewriting chain definitions
Lime focuses on post-hoc explanations rather than continuous model observability
Computational complexity increases with the size of input data and model intricacy
Fit analysis
Who is it for?
✓ Best for
Teams needing to understand complex machine learning models without access to the model's internal workings.
Projects where transparency and explainability are critical for regulatory compliance.
✕ Not a fit for
Scenarios requiring real-time explanations of predictions due to computational overhead.
Use cases that do not require understanding individual prediction rationales, focusing solely on overall accuracy.
Cost structure
Pricing
Free Tier
Available
Open source — free to use
Starts at
$0
Model
Flat rate
Enterprise
None
Performance benchmarks
How Fast Is It?
Ecosystem
Relationships
Works well with
Next step
Get Started with Lime
Step-by-step setup guide with code examples and common gotchas.