diff --git a/lib/SimpleNonlinearSolve/src/SimpleNonlinearSolve.jl b/lib/SimpleNonlinearSolve/src/SimpleNonlinearSolve.jl
index b4fc1d202..c8df31e51 100644
--- a/lib/SimpleNonlinearSolve/src/SimpleNonlinearSolve.jl
+++ b/lib/SimpleNonlinearSolve/src/SimpleNonlinearSolve.jl
@@ -11,7 +11,7 @@ using PrecompileTools: @compile_workload, @setup_workload
 using Reexport: @reexport
 @reexport using SciMLBase  # I don't like this but needed to avoid a breaking change
 using SciMLBase: AbstractNonlinearAlgorithm, NonlinearProblem, ReturnCode
-using StaticArraysCore: StaticArray, SVector
+using StaticArraysCore: StaticArray, SArray, SVector, MArray
 
 # AD Dependencies
 using ADTypes: AbstractADType, AutoFiniteDiff, AutoForwardDiff, AutoPolyesterForwardDiff
@@ -107,7 +107,7 @@ export AutoFiniteDiff, AutoForwardDiff, AutoPolyesterForwardDiff
 
 export Alefeld, Bisection, Brent, Falsi, ITP, Ridder
 
-export SimpleBroyden, SimpleKlement
+export SimpleBroyden, SimpleKlement, SimpleLimitedMemoryBroyden
 export SimpleDFSane
 export SimpleGaussNewton, SimpleNewtonRaphson, SimpleTrustRegion
 export SimpleHalley
diff --git a/lib/SimpleNonlinearSolve/src/lbroyden.jl b/lib/SimpleNonlinearSolve/src/lbroyden.jl
index 8b1378917..2a9ace6dc 100644
--- a/lib/SimpleNonlinearSolve/src/lbroyden.jl
+++ b/lib/SimpleNonlinearSolve/src/lbroyden.jl
@@ -1 +1,328 @@
+"""
+    SimpleLimitedMemoryBroyden(; threshold::Union{Val, Int} = Val(27),
+        linesearch = Val(false), alpha = nothing)
 
+A limited memory implementation of Broyden. This method applies the L-BFGS scheme to
+Broyden's method.
+
+If the threshold is larger than the problem size, then this method will use `SimpleBroyden`.
+
+### Keyword Arguments:
+
+  - `linesearch`: If `linesearch` is `Val(true)`, then we use the `LiFukushimaLineSearch`
+    line search else no line search is used. For advanced customization of the line search,
+    use `Broyden` from `NonlinearSolve.jl`.
+  - `alpha`: Scale the initial jacobian initialization with `alpha`. If it is `nothing`, we
+    will compute the scaling using `2 * norm(fu) / max(norm(u), true)`.
+
+!!! warning
+
+    Currently `alpha` is only used for StaticArray problems. This will be fixed in the
+    future.
+"""
+@concrete struct SimpleLimitedMemoryBroyden <: AbstractSimpleNonlinearSolveAlgorithm
+    linesearch <: Union{Val{false}, Val{true}}
+    threshold <: Val
+    alpha
+end
+
+function SimpleLimitedMemoryBroyden(; threshold::Union{Val, Int} = Val(27),
+        linesearch::Union{Bool, Val{true}, Val{false}} = Val(false), alpha = nothing)
+    linesearch = linesearch isa Bool ? Val(linesearch) : linesearch
+    threshold = threshold isa Int ? Val(threshold) : threshold
+    return SimpleLimitedMemoryBroyden(linesearch, threshold, alpha)
+end
+
+function SciMLBase.__solve(
+        prob::ImmutableNonlinearProblem, alg::SimpleLimitedMemoryBroyden,
+        args...; termination_condition = nothing, kwargs...)
+    if prob.u0 isa SArray
+        if termination_condition === nothing ||
+           termination_condition isa AbsNormTerminationMode
+            return internal_static_solve(
+                prob, alg, args...; termination_condition, kwargs...)
+        end
+        @warn "Specifying `termination_condition = $(termination_condition)` for \
+               `SimpleLimitedMemoryBroyden` with `SArray` is not non-allocating. Use \
+               either `termination_condition = AbsNormTerminationMode()` or \
+               `termination_condition = nothing`." maxlog=1
+    end
+    return internal_generic_solve(prob, alg, args...; termination_condition, kwargs...)
+end
+
+@views function internal_generic_solve(
+        prob::ImmutableNonlinearProblem, alg::SimpleLimitedMemoryBroyden,
+        args...; abstol = nothing, reltol = nothing, maxiters = 1000,
+        alias_u0 = false, termination_condition = nothing, kwargs...)
+    x = Utils.maybe_unaliased(prob.u0, alias_u0)
+    η = min(SciMLBase._unwrap_val(alg.threshold), maxiters)
+
+    # For scalar problems / if the threshold is larger than problem size just use Broyden
+    if x isa Number || length(x) ≤ η
+        return SciMLBase.__solve(prob, SimpleBroyden(; alg.linesearch), args...; abstol,
+            reltol, maxiters, termination_condition, kwargs...)
+    end
+
+    fx = Utils.get_fx(prob, x)
+
+    U, Vᵀ = init_low_rank_jacobian(x, fx, x isa StaticArray ? alg.threshold : Val(η))
+
+    abstol, reltol, tc_cache = NonlinearSolveBase.init_termination_cache(
+        prob, abstol, reltol, fx, x, termination_condition, Val(:simple))
+
+    @bb xo = copy(x)
+    @bb δx = copy(fx)
+    @bb δx .*= -1
+    @bb fo = copy(fx)
+    @bb δf = copy(fx)
+
+    @bb vᵀ_cache = copy(x)
+    Tcache = lbroyden_threshold_cache(x, x isa StaticArray ? alg.threshold : Val(η))
+    @bb mat_cache = copy(x)
+
+    if alg.linesearch === Val(true)
+        ls_alg = LiFukushimaLineSearch(; nan_maxiters = nothing)
+        ls_cache = init(prob, ls_alg, fx, x)
+    else
+        ls_cache = nothing
+    end
+
+    for i in 1:maxiters
+        if ls_cache === nothing
+            α = true
+        else
+            ls_sol = solve!(ls_cache, xo, δx)
+            α = ls_sol.step_size # Ignores the return code for now
+        end
+
+        @bb @. x = xo + α * δx
+        fx = Utils.eval_f(prob, fx, x)
+        @bb @. δf = fx - fo
+
+        # Termination Checks
+        solved, retcode, fx_sol, x_sol = Utils.check_termination(tc_cache, fx, x, xo, prob)
+        solved && return SciMLBase.build_solution(prob, alg, x_sol, fx_sol; retcode)
+
+        Uₚ = selectdim(U, 2, 1:min(η, i - 1))
+        Vᵀₚ = selectdim(Vᵀ, 1, 1:min(η, i - 1))
+
+        vᵀ = rmatvec!!(vᵀ_cache, Tcache, Uₚ, Vᵀₚ, δx)
+        mvec = matvec!!(mat_cache, Tcache, Uₚ, Vᵀₚ, δf)
+        d = dot(vᵀ, δf)
+        @bb @. δx = (δx - mvec) / d
+
+        selectdim(U, 2, mod1(i, η)) .= Utils.safe_vec(δx)
+        selectdim(Vᵀ, 1, mod1(i, η)) .= Utils.safe_vec(vᵀ)
+
+        Uₚ = selectdim(U, 2, 1:min(η, i))
+        Vᵀₚ = selectdim(Vᵀ, 1, 1:min(η, i))
+        δx = matvec!!(δx, Tcache, Uₚ, Vᵀₚ, fx)
+        @bb @. δx *= -1
+
+        @bb copyto!(xo, x)
+        @bb copyto!(fo, fx)
+    end
+
+    return SciMLBase.build_solution(prob, alg, x, fx; retcode = ReturnCode.MaxIters)
+end
+
+# Non-allocating StaticArrays version of SimpleLimitedMemoryBroyden is actually quite
+# finicky, so we'll implement it separately from the generic version
+# Ignore termination_condition. Don't pass things into internal functions
+function internal_static_solve(
+        prob::ImmutableNonlinearProblem{<:SArray}, alg::SimpleLimitedMemoryBroyden,
+        args...; abstol = nothing, maxiters = 1000, kwargs...)
+    x = prob.u0
+    fx = Utils.get_fx(prob, x)
+
+    U, Vᵀ = init_low_rank_jacobian(vec(x), vec(fx), alg.threshold)
+
+    abstol = NonlinearSolveBase.get_tolerance(x, abstol, eltype(x))
+
+    xo, δx, fo, δf = x, -fx, fx, fx
+
+    if alg.linesearch === Val(true)
+        ls_alg = LiFukushimaLineSearch(; nan_maxiters = nothing)
+        ls_cache = init(prob, ls_alg, fx, x)
+    else
+        ls_cache = nothing
+    end
+
+    T = promote_type(eltype(x), eltype(fx))
+    if alg.alpha === nothing
+        fx_norm = L2_NORM(fx)
+        x_norm = L2_NORM(x)
+        init_α = ifelse(fx_norm ≥ 1e-5, max(x_norm, T(true)) / (2 * fx_norm), T(true))
+    else
+        init_α = inv(alg.alpha)
+    end
+
+    converged, res = internal_unrolled_lbroyden_initial_iterations(
+        prob, xo, fo, δx, abstol, U, Vᵀ, alg.threshold, ls_cache, init_α)
+
+    converged && return SciMLBase.build_solution(
+        prob, alg, res.x, res.fx; retcode = ReturnCode.Success)
+
+    xo, fo, δx = res.x, res.fx, res.δx
+
+    for i in 1:(maxiters - SciMLBase._unwrap_val(alg.threshold))
+        if ls_cache === nothing
+            α = true
+        else
+            ls_sol = solve!(ls_cache, xo, δx)
+            α = ls_sol.step_size # Ignores the return code for now
+        end
+
+        x = xo + α * δx
+        fx = Utils.eval_f(prob, fx, x)
+        δf = fx - fo
+
+        maximum(abs, fx) ≤ abstol &&
+            return SciMLBase.build_solution(prob, alg, x, fx; retcode = ReturnCode.Success)
+
+        vᵀ = Utils.restructure(x, rmatvec!!(U, Vᵀ, vec(δx), init_α))
+        mvec = Utils.restructure(x, matvec!!(U, Vᵀ, vec(δf), init_α))
+
+        d = dot(vᵀ, δf)
+        δx = @. (δx - mvec) / d
+
+        U = Base.setindex(U, vec(δx), mod1(i, SciMLBase._unwrap_val(alg.threshold)))
+        Vᵀ = Base.setindex(Vᵀ, vec(vᵀ), mod1(i, SciMLBase._unwrap_val(alg.threshold)))
+
+        δx = -Utils.restructure(fx, matvec!!(U, Vᵀ, vec(fx), init_α))
+
+        xo, fo = x, fx
+    end
+
+    return SciMLBase.build_solution(prob, alg, xo, fo; retcode = ReturnCode.MaxIters)
+end
+
+@generated function internal_unrolled_lbroyden_initial_iterations(
+        prob, xo, fo, δx, abstol, U, Vᵀ, ::Val{threshold},
+        ls_cache, init_α) where {threshold}
+    calls = []
+    for i in 1:threshold
+        static_idx, static_idx_p1 = Val(i - 1), Val(i)
+        push!(calls, quote
+            α = ls_cache === nothing ? true : ls_cache(xo, δx)
+            x = xo .+ α .* δx
+            fx = prob.f(x, prob.p)
+            δf = fx - fo
+
+            maximum(abs, fx) ≤ abstol && return true, (; x, fx, δx)
+
+            Uₚ = first_n_getindex(U, $(static_idx))
+            Vᵀₚ = first_n_getindex(Vᵀ, $(static_idx))
+
+            vᵀ = Utils.restructure(x, rmatvec!!(Uₚ, Vᵀₚ, vec(δx), init_α))
+            mvec = Utils.restructure(x, matvec!!(Uₚ, Vᵀₚ, vec(δf), init_α))
+
+            d = dot(vᵀ, δf)
+            δx = @. (δx - mvec) / d
+
+            U = Base.setindex(U, vec(δx), $(i))
+            Vᵀ = Base.setindex(Vᵀ, vec(vᵀ), $(i))
+
+            Uₚ = first_n_getindex(U, $(static_idx_p1))
+            Vᵀₚ = first_n_getindex(Vᵀ, $(static_idx_p1))
+            δx = -Utils.restructure(fx, matvec!!(Uₚ, Vᵀₚ, vec(fx), init_α))
+
+            x0, fo = x, fx
+        end)
+    end
+    push!(calls, quote
+        # Termination Check
+        maximum(abs, fx) ≤ abstol && return true, (; x, fx, δx)
+
+        return false, (; x, fx, δx)
+    end)
+    return Expr(:block, calls...)
+end
+
+function rmatvec!!(y, xᵀU, U, Vᵀ, x)
+    # xᵀ × (-I + UVᵀ)
+    η = size(U, 2)
+    if η == 0
+        @bb @. y = -x
+        return y
+    end
+    x_ = vec(x)
+    xᵀU_ = view(xᵀU, 1:η)
+    @bb xᵀU_ = transpose(U) × x_
+    @bb y = transpose(Vᵀ) × vec(xᵀU_)
+    @bb @. y -= x
+    return y
+end
+
+rmatvec!!(::Nothing, Vᵀ, x, init_α) = -x .* init_α
+rmatvec!!(U, Vᵀ, x, init_α) = fast_mapTdot(fast_mapdot(x, U), Vᵀ) .- x .* init_α
+
+function matvec!!(y, Vᵀx, U, Vᵀ, x)
+    # (-I + UVᵀ) × x
+    η = size(U, 2)
+    if η == 0
+        @bb @. y = -x
+        return y
+    end
+    x_ = vec(x)
+    Vᵀx_ = view(Vᵀx, 1:η)
+    @bb Vᵀx_ = Vᵀ × x_
+    @bb y = U × vec(Vᵀx_)
+    @bb @. y -= x
+    return y
+end
+
+@inline matvec!!(::Nothing, Vᵀ, x, init_α) = -x .* init_α
+@inline matvec!!(U, Vᵀ, x, init_α) = fast_mapTdot(fast_mapdot(x, Vᵀ), U) .- x .* init_α
+
+function fast_mapdot(x::SVector{S1}, Y::SVector{S2, <:SVector{S1}}) where {S1, S2}
+    return map(Base.Fix1(dot, x), Y)
+end
+@generated function fast_mapTdot(
+        x::SVector{S1}, Y::SVector{S1, <:SVector{S2}}) where {S1, S2}
+    calls = []
+    syms = [gensym("m$(i)") for i in 1:S1]
+    for i in 1:S1
+        push!(calls, :($(syms[i]) = x[$(i)] .* Y[$i]))
+    end
+    push!(calls, :(return .+($(syms...))))
+    return Expr(:block, calls...)
+end
+
+@generated function first_n_getindex(x::SVector{L, T}, ::Val{N}) where {L, T, N}
+    @assert N ≤ L
+    getcalls = ntuple(i -> :(x[$i]), N)
+    N == 0 && return :(return nothing)
+    return :(return SVector{$N, $T}(($(getcalls...))))
+end
+
+lbroyden_threshold_cache(x, ::Val{threshold}) where {threshold} = similar(x, threshold)
+function lbroyden_threshold_cache(x::StaticArray, ::Val{threshold}) where {threshold}
+    return zeros(MArray{Tuple{threshold}, eltype(x)})
+end
+lbroyden_threshold_cache(::SArray, ::Val{threshold}) where {threshold} = nothing
+
+function init_low_rank_jacobian(u::StaticArray{S1, T1}, fu::StaticArray{S2, T2},
+        ::Val{threshold}) where {S1, S2, T1, T2, threshold}
+    T = promote_type(T1, T2)
+    fuSize, uSize = Size(fu), Size(u)
+    Vᵀ = MArray{Tuple{threshold, prod(uSize)}, T}(undef)
+    U = MArray{Tuple{prod(fuSize), threshold}, T}(undef)
+    return U, Vᵀ
+end
+@generated function init_low_rank_jacobian(u::SVector{Lu, T1}, fu::SVector{Lfu, T2},
+        ::Val{threshold}) where {Lu, Lfu, T1, T2, threshold}
+    T = promote_type(T1, T2)
+    inner_inits_Vᵀ = [:(zeros(SVector{$Lu, $T})) for i in 1:threshold]
+    inner_inits_U = [:(zeros(SVector{$Lfu, $T})) for i in 1:threshold]
+    return quote
+        Vᵀ = SVector($(inner_inits_Vᵀ...))
+        U = SVector($(inner_inits_U...))
+        return U, Vᵀ
+    end
+end
+function init_low_rank_jacobian(u, fu, ::Val{threshold}) where {threshold}
+    Vᵀ = similar(u, threshold, length(u))
+    U = similar(u, length(fu), threshold)
+    return U, Vᵀ
+end