Flare

Dynamic Tensor Graph library in Clojure for deep learning

DecliningOpen SourceLow lock-in

Pricing

Free tier

Flat rate

Adoption

Cooling

License

Open Source

Data freshness

Aging · Jun 8, 2026

Overview

What is Flare?

Flare is a dynamic tensor graph library written in Clojure, offering flexibility and power similar to PyTorch or DynNet. It's ideal for developers looking to leverage the functional programming benefits of Clojure while working on deep learning projects.

Key differentiator

Flare stands out as the only deep learning framework that integrates seamlessly with Clojure, offering developers the unique ability to leverage functional programming principles in their AI projects.

Capability profile

Capability Radar

Ease of StartEcosystemValueMaturityFlexibilityScale Ready

Honest assessment

Strengths & Weaknesses

↑ Strengths

Dynamic tensor graph constructionmedium

Functional programming paradigm in Clojuremedium

Flexibility similar to PyTorch or DynNetmedium

↓ Weaknesses

Limited language supporthigh

Flare is primarily written in Clojure, which has a smaller community compared to Python or other mainstream languages used for deep learning.

Complex setup and configurationmedium

Setting up Flare requires familiarity with the Leiningen build tool and managing dependencies within the Clojure ecosystem, which can be challenging for developers new to Clojure.

Smaller community and limited third-party integrationshigh

The smaller user base of Flare means fewer community contributions, tutorials, and third-party libraries compared to more popular frameworks like PyTorch or TensorFlow.

Performance overhead due to Clojure's dynamic naturemedium

Clojure is a dynamically typed language which can introduce performance penalties in comparison to statically-typed alternatives commonly used for deep learning tasks.

Fit analysis

Who is it for?

✓ Best for

Developers who prefer the functional programming paradigm in their deep learning projects

Teams working on research and prototyping with a preference for Clojure

Projects that require dynamic tensor graph construction capabilities

✕ Not a fit for

Teams requiring real-time streaming or high-frequency data processing (Flare's focus is more on flexibility than performance optimization)

Developers looking for a wide range of pre-built models and extensive community support (as Flare is niche)

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

Next step

Get Started with Flare

Step-by-step setup guide with code examples and common gotchas.

View Setup Guide →