diff --git a/Project.toml b/Project.toml
index 8c98c01e6..5ebc4912c 100644
--- a/Project.toml
+++ b/Project.toml
@@ -81,6 +81,7 @@ LinearSolvePETScExt = ["PETSc", "SparseArrays"]
 LinearSolveParUExt = ["ParU_jll", "SparseArrays"]
 LinearSolvePardisoExt = ["Pardiso", "SparseArrays"]
 LinearSolveRecursiveFactorizationExt = "RecursiveFactorization"
+LinearSolveSTRUMPACKExt = "SparseArrays"
 LinearSolveSparseArraysExt = "SparseArrays"
 LinearSolveSparspakExt = ["SparseArrays", "Sparspak"]
 
diff --git a/ext/LinearSolveSTRUMPACKExt.jl b/ext/LinearSolveSTRUMPACKExt.jl
new file mode 100644
index 000000000..26f23cc8e
--- /dev/null
+++ b/ext/LinearSolveSTRUMPACKExt.jl
@@ -0,0 +1,239 @@
+module LinearSolveSTRUMPACKExt
+
+using LinearSolve: LinearSolve, LinearVerbosity, OperatorAssumptions
+using SparseArrays: SparseArrays, AbstractSparseMatrixCSC, getcolptr, rowvals, nonzeros
+using SciMLBase: SciMLBase, ReturnCode
+using SciMLLogging: @SciMLMessage
+using Libdl: Libdl
+
+const STRUMPACK_SUCCESS = Cint(0)
+const STRUMPACK_MATRIX_NOT_SET = Cint(1)
+const STRUMPACK_REORDERING_ERROR = Cint(2)
+const STRUMPACK_ZERO_PIVOT = Cint(3)
+const STRUMPACK_NO_CONVERGENCE = Cint(4)
+const STRUMPACK_INACCURATE_INERTIA = Cint(5)
+
+const STRUMPACK_DOUBLE = Cint(1)
+const STRUMPACK_MT = Cint(0)
+
+const _libstrumpack = Ref{Ptr{Cvoid}}(C_NULL)
+
+function _load_libstrumpack()
+    for name in (
+            "libstrumpack.so",
+            "libstrumpack.so.8",
+            "libstrumpack.so.7",
+            "libstrumpack.dylib",
+            "strumpack",
+        )
+        handle = Libdl.dlopen_e(name)
+        handle != C_NULL && return handle
+    end
+    return C_NULL
+end
+
+function __init__()
+    return _libstrumpack[] = _load_libstrumpack()
+end
+
+strumpack_isavailable() = _libstrumpack[] != C_NULL
+
+mutable struct STRUMPACKCache
+    solver::Ref{Ptr{Cvoid}}
+    rowptr::Vector{Int32}
+    colind::Vector{Int32}
+    nzval::Vector{Float64}
+
+    function STRUMPACKCache()
+        cache = new(Ref{Ptr{Cvoid}}(C_NULL), Int32[], Int32[], Float64[])
+        finalizer(_strumpack_destroy!, cache)
+        return cache
+    end
+end
+
+function _strumpack_destroy!(cache::STRUMPACKCache)
+    _libstrumpack[] == C_NULL && return
+    cache.solver[] == C_NULL && return
+    ccall((:STRUMPACK_destroy, _libstrumpack[]), Cvoid, (Ref{Ptr{Cvoid}},), cache.solver)
+    cache.solver[] = C_NULL
+    return
+end
+
+function _ensure_initialized!(cache::STRUMPACKCache)
+    cache.solver[] != C_NULL && return
+    ccall(
+        (:STRUMPACK_init_mt, _libstrumpack[]),
+        Cvoid,
+        (Ref{Ptr{Cvoid}}, Cint, Cint, Cint, Ptr{Ptr{UInt8}}, Cint),
+        cache.solver,
+        STRUMPACK_DOUBLE,
+        STRUMPACK_MT,
+        Cint(0),
+        Ptr{Ptr{UInt8}}(C_NULL),
+        Cint(0)
+    )
+    return
+end
+
+function _csc_to_csr_0based(A::AbstractSparseMatrixCSC)
+    n = size(A, 1)
+    colptr = getcolptr(A)
+    rowval = rowvals(A)
+    vals = nonzeros(A)
+
+    nnz = length(vals)
+    rowptr = zeros(Int32, n + 1)
+
+    @inbounds for idx in eachindex(rowval)
+        rowptr[Int(rowval[idx]) + 1] += 1
+    end
+
+    @inbounds for i in 1:n
+        rowptr[i + 1] += rowptr[i]
+    end
+
+    nextidx = copy(rowptr)
+    colind = Vector{Int32}(undef, nnz)
+    outvals = Vector{Float64}(undef, nnz)
+
+    @inbounds for j in 1:size(A, 2)
+        for p in colptr[j]:(colptr[j + 1] - 1)
+            row = Int(rowval[p])
+            pos = Int(nextidx[row] + 1)
+            nextidx[row] += 1
+            colind[pos] = Int32(j - 1)
+            outvals[pos] = Float64(vals[p])
+        end
+    end
+
+    return rowptr, colind, outvals
+end
+
+function _retcode_from_strumpack(info::Cint)
+    return if info == STRUMPACK_SUCCESS
+        ReturnCode.Success
+    elseif info == STRUMPACK_ZERO_PIVOT
+        ReturnCode.Infeasible
+    elseif info == STRUMPACK_NO_CONVERGENCE
+        ReturnCode.ConvergenceFailure
+    elseif info == STRUMPACK_INACCURATE_INERTIA
+        ReturnCode.Unstable
+    elseif info == STRUMPACK_MATRIX_NOT_SET || info == STRUMPACK_REORDERING_ERROR
+        ReturnCode.Failure
+    else
+        ReturnCode.Failure
+    end
+end
+
+function LinearSolve.init_cacheval(
+        ::LinearSolve.STRUMPACKFactorization,
+        A::AbstractSparseMatrixCSC{<:AbstractFloat}, b, u, Pl, Pr,
+        maxiters::Int, abstol, reltol,
+        verbose::Union{LinearVerbosity, Bool}, assumptions::OperatorAssumptions
+    )
+    return STRUMPACKCache()
+end
+
+function LinearSolve.init_cacheval(
+        ::LinearSolve.STRUMPACKFactorization,
+        A, b, u, Pl, Pr,
+        maxiters::Int, abstol, reltol,
+        verbose::Union{LinearVerbosity, Bool}, assumptions::OperatorAssumptions
+    )
+    return nothing
+end
+
+function SciMLBase.solve!(
+        cache::LinearSolve.LinearCache,
+        alg::LinearSolve.STRUMPACKFactorization;
+        kwargs...
+    )
+    if _libstrumpack[] == C_NULL
+        error("STRUMPACKFactorization requires a discoverable STRUMPACK shared library (`libstrumpack`)")
+    end
+
+    A = convert(AbstractMatrix, cache.A)
+    if !(A isa AbstractSparseMatrixCSC)
+        error("STRUMPACKFactorization currently supports only sparse CSC matrices")
+    end
+    size(A, 1) == size(A, 2) || error("STRUMPACKFactorization requires a square matrix")
+
+    scache = LinearSolve.@get_cacheval(cache, :STRUMPACKFactorization)
+    if scache === nothing
+        error("STRUMPACKFactorization currently supports `AbstractSparseMatrixCSC{<:AbstractFloat}`")
+    end
+
+    _ensure_initialized!(scache)
+
+    if cache.isfresh
+        scache.rowptr, scache.colind, scache.nzval = _csc_to_csr_0based(A)
+        ccall(
+            (:STRUMPACK_set_csr_matrix, _libstrumpack[]),
+            Cvoid,
+            (Ptr{Cvoid}, Cint, Ref{Cint}, Ref{Cint}, Ref{Cdouble}, Cint),
+            scache.solver[],
+            Cint(size(A, 1)),
+            scache.rowptr,
+            scache.colind,
+            scache.nzval,
+            Cint(0)
+        )
+
+        info = ccall((:STRUMPACK_factor, _libstrumpack[]), Cint, (Ptr{Cvoid},), scache.solver[])
+        if info != STRUMPACK_SUCCESS
+            @SciMLMessage(
+                "STRUMPACK factorization failed (code $(Int(info)))",
+                cache.verbose,
+                :solver_failure
+            )
+            cache.isfresh = false
+            return SciMLBase.build_linear_solution(
+                alg,
+                cache.u,
+                nothing,
+                cache;
+                retcode = _retcode_from_strumpack(info)
+            )
+        end
+        cache.isfresh = false
+    end
+
+    bvec = Float64.(cache.b)
+    xvec = Float64.(cache.u)
+
+    info = ccall(
+        (:STRUMPACK_solve, _libstrumpack[]),
+        Cint,
+        (Ptr{Cvoid}, Ref{Cdouble}, Ref{Cdouble}, Cint),
+        scache.solver[],
+        bvec,
+        xvec,
+        Cint(alg.use_initial_guess)
+    )
+
+    if info != STRUMPACK_SUCCESS
+        @SciMLMessage(
+            "STRUMPACK solve failed (code $(Int(info)))",
+            cache.verbose,
+            :solver_failure
+        )
+        return SciMLBase.build_linear_solution(
+            alg,
+            cache.u,
+            nothing,
+            cache;
+            retcode = _retcode_from_strumpack(info)
+        )
+    end
+
+    copyto!(cache.u, xvec)
+    return SciMLBase.build_linear_solution(
+        alg,
+        cache.u,
+        nothing,
+        cache;
+        retcode = ReturnCode.Success
+    )
+end
+
+end
diff --git a/src/LinearSolve.jl b/src/LinearSolve.jl
index 1307ac648..6b3a62a47 100644
--- a/src/LinearSolve.jl
+++ b/src/LinearSolve.jl
@@ -475,6 +475,7 @@ for alg in (
         :DiagonalFactorization, :CholeskyFactorization, :BunchKaufmanFactorization,
         :CHOLMODFactorization, :LDLtFactorization, :AppleAccelerateLUFactorization,
         :MKLLUFactorization, :MetalLUFactorization, :CUSOLVERRFFactorization, :ParUFactorization,
+        :STRUMPACKFactorization,
     )
     @eval needs_square_A(::$(alg)) = true
 end
@@ -513,7 +514,8 @@ export LUFactorization, SVDFactorization, QRFactorization, GenericFactorization,
     UMFPACKFactorization, KLUFactorization, FastLUFactorization, FastQRFactorization,
     SparspakFactorization, DiagonalFactorization, CholeskyFactorization,
     BunchKaufmanFactorization, CHOLMODFactorization, LDLtFactorization,
-    CUSOLVERRFFactorization, CliqueTreesFactorization, ParUFactorization
+    CUSOLVERRFFactorization, CliqueTreesFactorization, ParUFactorization,
+    STRUMPACKFactorization
 
 export LinearSolveFunction, DirectLdiv!, show_algorithm_choices
 
diff --git a/src/factorization.jl b/src/factorization.jl
index 33cc0114e..71e9b3173 100644
--- a/src/factorization.jl
+++ b/src/factorization.jl
@@ -1482,6 +1482,52 @@ struct SparspakFactorization <: AbstractSparseFactorization
     end
 end
 
+"""
+`STRUMPACKFactorization(; use_initial_guess = false)`
+
+A sparse direct solver based on
+[STRUMPACK](https://github.com/pghysels/STRUMPACK) via the
+`LinearSolveSTRUMPACKExt` extension.
+
+This wrapper targets the single-node (`MT`) sparse interface and currently supports
+real sparse matrices (`AbstractSparseMatrixCSC{<:AbstractFloat}`), solving in
+`Float64` precision.
+
+!!! note
+
+    Using this solver requires:
+    1. `using SparseArrays` (to enable sparse matrix support), and
+    2. a system installation of `libstrumpack` discoverable by the dynamic loader.
+"""
+struct STRUMPACKFactorization <: AbstractSparseFactorization
+    use_initial_guess::Bool
+
+    function STRUMPACKFactorization(; use_initial_guess = false, throwerror = true)
+        ext = Base.get_extension(@__MODULE__, :LinearSolveSTRUMPACKExt)
+        return if throwerror && (ext === nothing || !ext.strumpack_isavailable())
+            error("STRUMPACKFactorization requires a discoverable STRUMPACK shared library (`libstrumpack`) and `using SparseArrays`")
+        else
+            new(use_initial_guess)
+        end
+    end
+end
+
+function init_cacheval(
+        ::STRUMPACKFactorization,
+        ::Union{AbstractMatrix, Nothing, AbstractSciMLOperator}, b, u, Pl, Pr,
+        maxiters::Int, abstol, reltol,
+        verbose::Union{LinearVerbosity, Bool}, assumptions::OperatorAssumptions
+    )
+    return nothing
+end
+
+function init_cacheval(
+        ::STRUMPACKFactorization, ::StaticArray, b, u, Pl, Pr,
+        maxiters::Int, abstol, reltol, verbose::Union{LinearVerbosity, Bool}, assumptions::OperatorAssumptions
+    )
+    return nothing
+end
+
 function init_cacheval(
         alg::SparspakFactorization,
         A::Union{AbstractMatrix, Nothing, AbstractSciMLOperator}, b, u, Pl, Pr,
diff --git a/test/defaults_loading.jl b/test/defaults_loading.jl
index 05369acf1..817828d54 100644
--- a/test/defaults_loading.jl
+++ b/test/defaults_loading.jl
@@ -29,6 +29,13 @@ rhs[begin] = rhs[end] = -2
 prob = LinearProblem(mat, rhs)
 @test_throws ["SparspakFactorization required", "using Sparspak"] sol = solve(prob).u
 
+STRUMPACKExt = Base.get_extension(LinearSolve, :LinearSolveSTRUMPACKExt)
+if STRUMPACKExt === nothing || !STRUMPACKExt.strumpack_isavailable()
+    @test_throws ["STRUMPACKFactorization", "libstrumpack"] STRUMPACKFactorization()
+else
+    @test STRUMPACKFactorization() isa STRUMPACKFactorization
+end
+
 using Sparspak
 sol = solve(prob).u
 @test sol isa Vector{BigFloat}
diff --git a/test/resolve.jl b/test/resolve.jl
index 3782175f5..42987e72d 100644
--- a/test/resolve.jl
+++ b/test/resolve.jl
@@ -4,6 +4,9 @@ using LinearSolve: AbstractDenseFactorization, AbstractSparseFactorization,
     AMDGPUOffloadLUFactorization, AMDGPUOffloadQRFactorization,
     SparspakFactorization
 
+const STRUMPACKExt = Base.get_extension(LinearSolve, :LinearSolveSTRUMPACKExt)
+const HAS_STRUMPACK = STRUMPACKExt !== nothing && STRUMPACKExt.strumpack_isavailable()
+
 # Function to check if an algorithm is mixed precision
 function is_mixed_precision_alg(alg)
     alg_name = string(alg)
@@ -48,6 +51,7 @@ for alg in vcat(
             (!(alg == AppleAccelerate32MixedLUFactorization) || Sys.isapple()) &&
             (!(alg == OpenBLAS32MixedLUFactorization) || LinearSolve.useopenblas) &&
             (!(alg == SparspakFactorization) || false) &&
+            (!(alg == STRUMPACKFactorization) || HAS_STRUMPACK) &&
             (
             !(alg == ParUFactorization) ||
                 Base.get_extension(LinearSolve, :LinearSolveParUExt) !== nothing
@@ -55,7 +59,7 @@ for alg in vcat(
         A = [1.0 2.0; 3.0 4.0]
         alg in [
             KLUFactorization, UMFPACKFactorization, SparspakFactorization,
-            ParUFactorization,
+            ParUFactorization, STRUMPACKFactorization,
         ] &&
             (A = sparse(A))
         A = A' * A
@@ -84,7 +88,7 @@ for alg in vcat(
         A = [1.0 2.0; 3.0 4.0]
         alg in [
             KLUFactorization, UMFPACKFactorization, SparspakFactorization,
-            ParUFactorization,
+            ParUFactorization, STRUMPACKFactorization,
         ] &&
             (A = sparse(A))
         A = A' * A
diff --git a/test/runtests.jl b/test/runtests.jl
index df24888c0..1820e03b8 100644
--- a/test/runtests.jl
+++ b/test/runtests.jl
@@ -53,6 +53,10 @@ if GROUP == "DefaultsLoading"
     @time @safetestset "Defaults Loading Tests" include("defaults_loading.jl")
 end
 
+if GROUP == "All" || GROUP == "LinearSolveSTRUMPACK"
+    @time @safetestset "LinearSolveSTRUMPACK" include("strumpack/strumpack.jl")
+end
+
 if GROUP == "LinearSolveAutotune"
     Pkg.activate(joinpath(dirname(@__DIR__), "lib", GROUP))
     Pkg.test(
diff --git a/test/strumpack/strumpack.jl b/test/strumpack/strumpack.jl
new file mode 100644
index 000000000..be203b242
--- /dev/null
+++ b/test/strumpack/strumpack.jl
@@ -0,0 +1,56 @@
+using LinearSolve, LinearAlgebra, SparseArrays, SciMLBase
+using Test
+
+@testset "STRUMPACK Factorization" begin
+    ext = Base.get_extension(LinearSolve, :LinearSolveSTRUMPACKExt)
+    @test ext !== nothing
+
+    if ext === nothing || !ext.strumpack_isavailable()
+        @test_throws ["STRUMPACKFactorization", "libstrumpack"] STRUMPACKFactorization()
+        @test STRUMPACKFactorization(throwerror = false) isa STRUMPACKFactorization
+
+        A = sparse([4.0 1.0; 2.0 3.0])
+        b = [1.0, -1.0]
+        prob = LinearProblem(A, b)
+        @test_throws ["STRUMPACKFactorization", "libstrumpack"] solve(
+            prob,
+            STRUMPACKFactorization(throwerror = false)
+        )
+    else
+        A = sparse(
+            [
+                7.0 1.0 0.0
+                2.0 8.0 1.0
+                0.0 3.0 9.0
+            ]
+        )
+        b = [1.0, -2.0, 3.0]
+
+        prob = LinearProblem(A, b)
+        sol = solve(prob, STRUMPACKFactorization())
+        @test sol.retcode == ReturnCode.Success
+        @test A * sol.u ≈ b atol = 1.0e-10 rtol = 1.0e-10
+
+        cache = init(prob, STRUMPACKFactorization())
+        sol1 = solve!(cache)
+        @test sol1.retcode == ReturnCode.Success
+        @test A * sol1.u ≈ b atol = 1.0e-10 rtol = 1.0e-10
+
+        A2 = sparse(
+            [
+                8.0 1.0 0.0
+                2.0 9.0 1.0
+                0.0 3.0 10.0
+            ]
+        )
+        cache.A = A2
+        sol2 = solve!(cache)
+        @test sol2.retcode == ReturnCode.Success
+        @test A2 * sol2.u ≈ b atol = 1.0e-10 rtol = 1.0e-10
+
+        prob_guess = LinearProblem(A, b; u0 = fill(1.0, length(b)))
+        sol_guess = solve(prob_guess, STRUMPACKFactorization(use_initial_guess = true))
+        @test sol_guess.retcode == ReturnCode.Success
+        @test A * sol_guess.u ≈ b atol = 1.0e-10 rtol = 1.0e-10
+    end
+end