diff --git a/Project.toml b/Project.toml
index a92b7e4ff..b31689afd 100644
--- a/Project.toml
+++ b/Project.toml
@@ -27,6 +27,7 @@ SciMLLogging = "a6db7da4-7206-11f0-1eab-35f2a5dbe1d1"
 SciMLOperators = "c0aeaf25-5076-4817-a8d5-81caf7dfa961"
 Setfield = "efcf1570-3423-57d1-acb7-fd33fddbac46"
 StaticArraysCore = "1e83bf80-4336-4d27-bf5d-d5a4f845583c"
+TriangularSolve = "d5829a12-d9aa-46ab-831f-fb7c9ab06edf"
 UnPack = "3a884ed6-31ef-47d7-9d2a-63182c4928ed"
 
 [weakdeps]
@@ -124,7 +125,7 @@ PrecompileTools = "1.2"
 Preferences = "1.4"
 Random = "1.10"
 RecursiveArrayTools = "3.37"
-RecursiveFactorization = "0.2.23"
+RecursiveFactorization = "0.2.26"
 Reexport = "1.2.2"
 SafeTestsets = "0.1"
 SciMLBase = "2.70"
@@ -137,6 +138,7 @@ StableRNGs = "1.0"
 StaticArrays = "1.9"
 StaticArraysCore = "1.4.3"
 Test = "1.10"
+TriangularSolve = "0.2.1"
 UnPack = "1.0.2"
 Zygote = "0.7"
 blis_jll = "0.9.0"
diff --git a/benchmarks/lu.jl b/benchmarks/lu.jl
index 896ee952e..d75e838f3 100644
--- a/benchmarks/lu.jl
+++ b/benchmarks/lu.jl
@@ -1,7 +1,8 @@
 using BenchmarkTools, Random, VectorizationBase
 using LinearAlgebra, LinearSolve, MKL_jll
+using RecursiveFactorization
+
 nc = min(Int(VectorizationBase.num_cores()), Threads.nthreads())
-BLAS.set_num_threads(nc)
 BenchmarkTools.DEFAULT_PARAMETERS.seconds = 0.5
 
 function luflop(m, n = m; innerflop = 2)
@@ -24,10 +25,10 @@ algs = [
     RFLUFactorization(),
     MKLLUFactorization(),
     FastLUFactorization(),
-    SimpleLUFactorization()
+    SimpleLUFactorization(),
+    ButterflyFactorization(Val(true))
 ]
 res = [Float64[] for i in 1:length(algs)]
-
 ns = 4:8:500
 for i in 1:length(ns)
     n = ns[i]
@@ -65,3 +66,4 @@ p
 
 savefig("lubench.png")
 savefig("lubench.pdf")
+
diff --git a/ext/LinearSolveRecursiveFactorizationExt.jl b/ext/LinearSolveRecursiveFactorizationExt.jl
index a5c62b4c0..2bd0b0f73 100644
--- a/ext/LinearSolveRecursiveFactorizationExt.jl
+++ b/ext/LinearSolveRecursiveFactorizationExt.jl
@@ -1,11 +1,12 @@
 module LinearSolveRecursiveFactorizationExt
 
 using LinearSolve: LinearSolve, userecursivefactorization, LinearCache, @get_cacheval,
-                   RFLUFactorization, RF32MixedLUFactorization, default_alias_A,
-                   default_alias_b, LinearVerbosity
+                   RFLUFactorization, ButterflyFactorization, RF32MixedLUFactorization, 
+                   default_alias_A, default_alias_b, LinearVerbosity
 using LinearSolve.LinearAlgebra, LinearSolve.ArrayInterface, RecursiveFactorization
 using SciMLBase: SciMLBase, ReturnCode
 using SciMLLogging: @SciMLMessage
+using TriangularSolve
 
 LinearSolve.userecursivefactorization(A::Union{Nothing, AbstractMatrix}) = true
 
@@ -20,7 +21,6 @@ function SciMLBase.solve!(cache::LinearSolve.LinearCache, alg::RFLUFactorization
         end
         fact = RecursiveFactorization.lu!(A, ipiv, Val(P), Val(T), check = false)
         cache.cacheval = (fact, ipiv)
-
         if !LinearAlgebra.issuccess(fact)
             @SciMLMessage("Solver failed", cache.verbose, :solver_failure)
             return SciMLBase.build_linear_solution(
@@ -107,4 +107,41 @@ function SciMLBase.solve!(
         alg, cache.u, nothing, cache; retcode = ReturnCode.Success)
 end
 
+function SciMLBase.solve!(cache::LinearSolve.LinearCache, alg::ButterflyFactorization;
+        kwargs...)
+    cache_A = cache.A
+    cache_A = convert(AbstractMatrix, cache_A)
+    cache_b = cache.b
+    M, N = size(cache_A)
+    workspace = cache.cacheval[1]
+    thread = alg.thread
+
+    if cache.isfresh
+        @assert M==N "A must be square"
+        if (size(workspace.A, 1) != M)
+            workspace = RecursiveFactorization.🦋workspace(cache_A, cache_b)    
+        end
+        (;A, b, ws, U, V, out, tmp, n) = workspace
+        RecursiveFactorization.🦋mul!(A, ws)
+        F = RecursiveFactorization.lu!(A, Val(false), thread)
+        cache.cacheval = (workspace, F)
+        cache.isfresh = false
+    end
+
+    workspace, F = cache.cacheval
+    (;A, b, ws, U, V, out, tmp, n) = workspace
+    b[1:M] .= cache_b
+    mul!(tmp, U', b)
+    TriangularSolve.ldiv!(F, tmp, thread)
+    mul!(b, V, tmp)
+    out .= @view b[1:n]
+    SciMLBase.build_linear_solution(alg, out, nothing, cache)
+end
+
+function LinearSolve.init_cacheval(alg::ButterflyFactorization, A, b, u, Pl, Pr, maxiters::Int,
+        abstol, reltol, verbose::Bool, assumptions::LinearSolve.OperatorAssumptions)
+    ws = RecursiveFactorization.🦋workspace(A, b), RecursiveFactorization.lu!(rand(1, 1), Val(false), alg.thread)
 end
+
+end
+
diff --git a/src/LinearSolve.jl b/src/LinearSolve.jl
index fa2eadaa0..4c94150df 100644
--- a/src/LinearSolve.jl
+++ b/src/LinearSolve.jl
@@ -446,7 +446,7 @@ for kralg in (Krylov.lsmr!, Krylov.craigmr!)
 end
 for alg in (:LUFactorization, :FastLUFactorization, :SVDFactorization,
     :GenericFactorization, :GenericLUFactorization, :SimpleLUFactorization,
-    :RFLUFactorization, :UMFPACKFactorization, :KLUFactorization, :SparspakFactorization,
+    :RFLUFactorization, :ButterflyFactorization, :UMFPACKFactorization, :KLUFactorization, :SparspakFactorization,
     :DiagonalFactorization, :CholeskyFactorization, :BunchKaufmanFactorization,
     :CHOLMODFactorization, :LDLtFactorization, :AppleAccelerateLUFactorization,
     :MKLLUFactorization, :MetalLUFactorization, :CUSOLVERRFFactorization)
@@ -480,7 +480,7 @@ cudss_loaded(A) = false
 is_cusparse(A) = false
 
 export LUFactorization, SVDFactorization, QRFactorization, GenericFactorization,
-       GenericLUFactorization, SimpleLUFactorization, RFLUFactorization,
+       GenericLUFactorization, SimpleLUFactorization, RFLUFactorization, ButterflyFactorization,
        NormalCholeskyFactorization, NormalBunchKaufmanFactorization,
        UMFPACKFactorization, KLUFactorization, FastLUFactorization, FastQRFactorization,
        SparspakFactorization, DiagonalFactorization, CholeskyFactorization,
diff --git a/src/extension_algs.jl b/src/extension_algs.jl
index 51cdb901f..70d373ad2 100644
--- a/src/extension_algs.jl
+++ b/src/extension_algs.jl
@@ -254,6 +254,29 @@ function RFLUFactorization(; pivot = Val(true), thread = Val(true), throwerror =
     RFLUFactorization(pivot, thread; throwerror)
 end
 
+"""
+`ButterflyFactorization()`
+
+A fast pure Julia LU-factorization implementation
+using RecursiveFactorization.jl. This method utilizes a butterly 
+factorization approach rather than pivoting. 
+"""
+struct ButterflyFactorization{T} <: AbstractDenseFactorization
+    thread::Val{T}
+    function ButterflyFactorization(::Val{T}; throwerror = true) where {T}
+        if !userecursivefactorization(nothing)
+            throwerror &&
+                error("ButterflyFactorization requires that RecursiveFactorization.jl is loaded, i.e. `using RecursiveFactorization`")
+        end
+        new{T}()
+    end
+end
+
+function ButterflyFactorization(; thread = Val(true), throwerror = true)
+    ButterflyFactorization(thread; throwerror)
+end
+
+
 # There's no options like pivot here.
 # But I'm not sure it makes sense as a GenericFactorization
 # since it just uses `LAPACK.getrf!`.
diff --git a/test/butterfly.jl b/test/butterfly.jl
new file mode 100644
index 000000000..9e10ae43d
--- /dev/null
+++ b/test/butterfly.jl
@@ -0,0 +1,35 @@
+using LinearAlgebra, LinearSolve
+using Test
+using RecursiveFactorization
+
+@testset "Random Matricies" begin
+    for i in 490 : 510
+        A = rand(i, i)
+        b = rand(i)
+        prob = LinearProblem(A, b)
+        x = solve(prob, ButterflyFactorization())
+        @test norm(A * x .- b) <= 1e-6
+    end
+end
+
+function wilkinson(N)
+    A = zeros(N, N)
+    A[1:(N+1):N*N] .= 1
+    A[:, end] .= 1
+    for n in 1:(N - 1)
+        for r in (n + 1):N
+            @inbounds A[r, n] = -1
+        end
+    end
+    A
+end
+
+@testset "Wilkinson" begin
+    for i in 790 : 810
+        A = wilkinson(i)
+        b = rand(i)
+        prob = LinearProblem(A, b)
+        x = solve(prob, ButterflyFactorization())
+        @test norm(A * x .- b) <= 1e-10
+    end
+end