diff --git a/Project.toml b/Project.toml
index af43fc925..d7ea297cc 100644
--- a/Project.toml
+++ b/Project.toml
@@ -23,6 +23,8 @@ Adapt = "79e6a3ab-5dfb-504d-930d-738a2a938a0e"
 AMDGPU = "21141c5a-9bdb-4563-92ae-f87d6854732e"
 CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba"
 ChainRulesCore = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4"
+Enzyme = "7da242da-08ed-463a-9acd-ee780be4f1d9"
+EnzymeTestUtils = "12d8515a-0907-448a-8884-5fe00fdf1c5a"
 FiniteDifferences = "26cc04aa-876d-5657-8c51-4c34ba976000"
 Mooncake = "da2b9cff-9c12-43a0-ae48-6db2b0edb7d6"
 cuTENSOR = "011b41b2-24ef-40a8-b3eb-fa098493e9e1"
@@ -32,6 +34,8 @@ TensorKitAdaptExt = "Adapt"
 TensorKitAMDGPUExt = "AMDGPU"
 TensorKitCUDAExt = ["CUDA", "cuTENSOR"]
 TensorKitChainRulesCoreExt = "ChainRulesCore"
+TensorKitEnzymeExt = "Enzyme"
+TensorKitEnzymeTestUtilsExt = "EnzymeTestUtils"
 TensorKitFiniteDifferencesExt = "FiniteDifferences"
 TensorKitMooncakeExt = "Mooncake"
 
@@ -44,6 +48,8 @@ AMDGPU = "2"
 CUDA = "6"
 ChainRulesCore = "1"
 Dictionaries = "0.4"
+Enzyme = "0.13.146"
+EnzymeTestUtils = "0.2.5"
 FiniteDifferences = "0.12"
 LRUCache = "1.0.2"
 LinearAlgebra = "1"
@@ -55,8 +61,11 @@ Random = "1"
 ScopedValues = "1.3.0"
 Strided = "2"
 TensorKitSectors = "0.3.7"
-TensorOperations = "5.1"
+TensorOperations = "5.5.2"
 TupleTools = "1.5"
 VectorInterface = "0.4.8, 0.5"
 cuTENSOR = "6"
 julia = "1.10"
+
+[sources]
+VectorInterface = {url = "https://github.com/QuantumKitHub/VectorInterface.jl", rev = "main"}
diff --git a/ext/TensorKitEnzymeExt/TensorKitEnzymeExt.jl b/ext/TensorKitEnzymeExt/TensorKitEnzymeExt.jl
new file mode 100644
index 000000000..338f92137
--- /dev/null
+++ b/ext/TensorKitEnzymeExt/TensorKitEnzymeExt.jl
@@ -0,0 +1,9 @@
+module TensorKitEnzymeExt
+
+using Enzyme
+using TensorKit
+import TensorKit as TK
+
+include("utility.jl")
+
+end
diff --git a/ext/TensorKitEnzymeExt/utility.jl b/ext/TensorKitEnzymeExt/utility.jl
new file mode 100644
index 000000000..3106861da
--- /dev/null
+++ b/ext/TensorKitEnzymeExt/utility.jl
@@ -0,0 +1,38 @@
+# Projection
+# ----------
+pullback_dα(α::Const, C::Const, A) = nothing
+pullback_dα(α::Const, C::Annotation, A) = nothing
+pullback_dα(α::Annotation, C::Const, A) = zero(α.val)
+pullback_dα(α::Annotation, C::Annotation, A) = project_scalar(α.val, inner(A, C.dval))
+
+pullback_dβ(β::Const, C::Const, Ccache) = nothing
+pullback_dβ(β::Const, C::Annotation, Ccache) = nothing
+pullback_dβ(β::Annotation, C::Const, Ccache) = zero(β.val)
+pullback_dβ(β::Annotation, C::Annotation, Ccache) = project_scalar(β.val, inner(Ccache, C.dval))
+
+pullback_dC!(ΔC, β::Number) = scale!(ΔC, conj(β))
+
+"""
+    project_scalar(x::Number, dx::Number)
+
+Project a computed tangent `dx` onto the correct tangent type for `x`.
+For example, we might compute a complex `dx` but only require the real part.
+"""
+project_scalar(x::Number, dx::Number) = oftype(x, dx)
+project_scalar(x::Real, dx::Complex) = project_scalar(x, real(dx))
+
+# Ignore derivatives
+# ------------------
+
+@inline EnzymeRules.inactive_type(::Type{<:TensorKit.FusionTree}) = true
+@inline EnzymeRules.inactive_type(::Type{<:TensorKit.GenericTreeTransformer}) = true
+@inline EnzymeRules.inactive_type(::Type{<:TensorKit.VectorSpace}) = true
+
+@inline EnzymeRules.inactive(::typeof(TensorKit.sectorstructure), ::Any) = nothing
+@inline EnzymeRules.inactive(::typeof(TensorKit.degeneracystructure), ::Any) = nothing
+@inline EnzymeRules.inactive(::typeof(TensorKit.select), s::HomSpace, i::Index2Tuple) = nothing
+@inline EnzymeRules.inactive(::typeof(TensorKit.flip), s::HomSpace, i::Any) = nothing
+@inline EnzymeRules.inactive(::typeof(TensorKit.permute), s::HomSpace, i::Index2Tuple) = nothing
+@inline EnzymeRules.inactive(::typeof(TensorKit.braid), s::HomSpace, i::Index2Tuple, ::IndexTuple) = nothing
+@inline EnzymeRules.inactive(::typeof(TensorKit.compose), s1::HomSpace, s2::HomSpace) = nothing
+@inline EnzymeRules.inactive(::typeof(TensorOperations.tensorcontract), c::HomSpace, p::Index2Tuple, α::Bool, b::HomSpace, q::Index2Tuple, β::Bool, pq::Index2Tuple) = nothing
diff --git a/ext/TensorKitEnzymeTestUtilsExt.jl b/ext/TensorKitEnzymeTestUtilsExt.jl
new file mode 100644
index 000000000..ca0a42e89
--- /dev/null
+++ b/ext/TensorKitEnzymeTestUtilsExt.jl
@@ -0,0 +1,60 @@
+module TensorKitEnzymeTestUtilsExt
+
+using TensorKit
+using EnzymeTestUtils
+using EnzymeTestUtils: Enzyme
+import EnzymeTestUtils: to_vec, from_vec, rand_tangent
+
+function EnzymeTestUtils.to_vec(x::TensorMap, seen_vecs::EnzymeTestUtils.AliasDict)
+    has_seen = haskey(seen_vecs, x)
+    is_const = Enzyme.Compiler.guaranteed_const(Core.Typeof(x))
+    if has_seen || is_const
+        x_vec = Float32[]
+    else
+        vec_of_vecs = [b * TensorKit.sqrtdim(c) for (c, b) in blocks(x)]
+        x_vec, back = to_vec(vec_of_vecs)
+        seen_vecs[x] = x_vec
+    end
+    function TensorMap_from_vec(x_vec_new::AbstractVector, seen_xs::EnzymeTestUtils.AliasDict)
+        if xor(has_seen, haskey(seen_xs, x))
+            throw(ErrorException("Arrays must be reconstructed in the same order as they are vectorized."))
+        end
+        has_seen && return seen_xs[x]
+        is_const && return x
+
+        x_new = similar(x)
+        xvec_of_vecs = back(x_vec_new)
+        for (i, (c, b)) in enumerate(blocks(x_new))
+            scale!(b, xvec_of_vecs[i], TensorKit.invsqrtdim(c))
+        end
+        if Core.Typeof(x_new) != Core.Typeof(x)
+            x_new = Core.Typeof(x)(x_new)
+        end
+        seen_xs[x] = x_new
+        return x_new
+    end
+    return x_vec, TensorMap_from_vec
+end
+function EnzymeTestUtils.to_vec(t::TensorKit.AdjointTensorMap, seen_vecs::EnzymeTestUtils.AliasDict)
+    parent_vec, parent_t = to_vec(parent(t), seen_vecs)
+    return parent_vec, adjoint ∘ parent_t
+end
+function EnzymeTestUtils.to_vec(t::TensorKit.DiagonalTensorMap, seen_vecs::EnzymeTestUtils.AliasDict)
+    parent_vec, parent_t = to_vec(TensorMap(t), seen_vecs)
+    return parent_vec, TensorKit.DiagonalTensorMap ∘ parent_t
+end
+
+# generate random tangents for testing
+function EnzymeTestUtils.rand_tangent(rng, t::TensorMap)
+    return TensorMap(EnzymeTestUtils.rand_tangent(rng, t.data), space(t))
+end
+
+function EnzymeTestUtils.rand_tangent(rng, t::TensorKit.AdjointTensorMap)
+    return adjoint(rand_tangent(rng, parent(t)))
+end
+
+function EnzymeTestUtils.rand_tangent(rng, t::DiagonalTensorMap)
+    return DiagonalTensorMap(EnzymeTestUtils.rand_tangent(rng, t.data), space(t, 1))
+end
+
+end
diff --git a/test/Project.toml b/test/Project.toml
index 18af8af80..56d11900a 100644
--- a/test/Project.toml
+++ b/test/Project.toml
@@ -9,6 +9,8 @@ CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba"
 ChainRulesCore = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4"
 ChainRulesTestUtils = "cdddcdb0-9152-4a09-a978-84456f9df70a"
 Combinatorics = "861a8166-3701-5b0c-9a16-15d98fcdc6aa"
+Enzyme = "7da242da-08ed-463a-9acd-ee780be4f1d9"
+EnzymeTestUtils = "12d8515a-0907-448a-8884-5fe00fdf1c5a"
 FiniteDifferences = "26cc04aa-876d-5657-8c51-4c34ba976000"
 GPUArrays = "0c68f7d7-f131-5f86-a1c3-88cf8149b2d7"
 JET = "c3a54625-cd67-489e-a8e7-0a5a0ff4e31b"
@@ -36,6 +38,8 @@ AllocCheck = "0.2"
 ChainRulesTestUtils = "1"
 Combinatorics = "1"
 GPUArrays = "11.3.1"
+Enzyme = "0.13.134"
+EnzymeTestUtils = "0.2.5"
 JET = "0.9, 0.10, 0.11"
 ParallelTestRunner = "2"
 Test = "1"
diff --git a/test/enzyme-vectorinterface/add.jl b/test/enzyme-vectorinterface/add.jl
new file mode 100644
index 000000000..7dc3c76b0
--- /dev/null
+++ b/test/enzyme-vectorinterface/add.jl
@@ -0,0 +1,44 @@
+using Test, TestExtras
+using TensorKit, Enzyme, EnzymeTestUtils
+using TensorOperations
+using Random
+
+spacelist = ad_spacelist(fast_tests)
+eltypes = (Float64, ComplexF64)
+
+@testset "Enzyme - VectorInterface (add!) $(TensorKit.type_repr(sectortype(eltype(V)))) ($T)" for V in spacelist, T in eltypes
+    atol = default_tol(T)
+    rtol = default_tol(T)
+
+    C = randn(T, V[1] ⊗ V[2] ← V[3] ⊗ V[4] ⊗ V[5])
+    A = randn(T, V[1] ⊗ V[2] ← V[3] ⊗ V[4] ⊗ V[5])
+    α = randn(T)
+    β = randn(T)
+
+    for TC in (Duplicated, Const), TA in (Duplicated, Const)
+        C = randn(T, V[1] ⊗ V[2] ← V[3] ⊗ V[4] ⊗ V[5])
+        A = randn(T, V[1] ⊗ V[2] ← V[3] ⊗ V[4] ⊗ V[5])
+        EnzymeTestUtils.test_reverse(add!, TC, (C, TC), (A, TA); atol, rtol, testset_name = "add! TC $TC TA $TA no α no β")
+        EnzymeTestUtils.test_forward(add!, TC, (C, TC), (A, TA); atol, rtol, testset_name = "add! TC $TC TA $TA no α no β")
+        for Tα in (Active, Const)
+            C = randn(T, V[1] ⊗ V[2] ← V[3] ⊗ V[4] ⊗ V[5])
+            A = randn(T, V[1] ⊗ V[2] ← V[3] ⊗ V[4] ⊗ V[5])
+            EnzymeTestUtils.test_reverse(add!, TC, (C, TC), (A, TA), (α, Tα); atol, rtol, testset_name = "add! TC $TC TA $TA Tα $Tα no β")
+            for Tβ in (Active, Const)
+                C = randn(T, V[1] ⊗ V[2] ← V[3] ⊗ V[4] ⊗ V[5])
+                A = randn(T, V[1] ⊗ V[2] ← V[3] ⊗ V[4] ⊗ V[5])
+                EnzymeTestUtils.test_reverse(add!, TC, (C, TC), (A, TA), (α, Tα), (β, Tβ); atol, rtol, testset_name = "add! TC $TC TA $TA Tα $Tα Tβ $Tβ")
+            end
+        end
+        for Tα in (Duplicated, Const)
+            C = randn(T, V[1] ⊗ V[2] ← V[3] ⊗ V[4] ⊗ V[5])
+            A = randn(T, V[1] ⊗ V[2] ← V[3] ⊗ V[4] ⊗ V[5])
+            EnzymeTestUtils.test_forward(add!, TC, (C, TC), (A, TA), (α, Tα); atol, rtol, testset_name = "add! TC $TC TA $TA Tα $Tα no β")
+            for Tβ in (Duplicated, Const)
+                C = randn(T, V[1] ⊗ V[2] ← V[3] ⊗ V[4] ⊗ V[5])
+                A = randn(T, V[1] ⊗ V[2] ← V[3] ⊗ V[4] ⊗ V[5])
+                EnzymeTestUtils.test_forward(add!, TC, (C, TC), (A, TA), (α, Tα), (β, Tβ); atol, rtol, testset_name = "add! TC $TC TA $TA Tα $Tα Tβ $Tβ")
+            end
+        end
+    end
+end
diff --git a/test/enzyme-vectorinterface/inner.jl b/test/enzyme-vectorinterface/inner.jl
new file mode 100644
index 000000000..b2cb17e2d
--- /dev/null
+++ b/test/enzyme-vectorinterface/inner.jl
@@ -0,0 +1,25 @@
+using Test, TestExtras
+using TensorKit
+using TensorOperations
+using Enzyme, EnzymeTestUtils
+using Random, FiniteDifferences
+
+spacelist = ad_spacelist(fast_tests)
+eltypes = (Float64, ComplexF64)
+
+@testset "Enzyme - VectorInterface" begin
+    @timedtestset "$(TensorKit.type_repr(sectortype(eltype(V)))) ($T)" for V in spacelist, T in eltypes
+        @testset for TC in (Duplicated, Const), TA in (Duplicated, Const), f in (identity, adjoint)
+            atol = default_tol(T)
+            rtol = default_tol(T)
+            C = randn(T, V[1] ⊗ V[2] ← V[3] ⊗ V[4] ⊗ V[5])
+            A = randn(T, V[1] ⊗ V[2] ← V[3] ⊗ V[4] ⊗ V[5])
+            for RT in (Active, Const)
+                EnzymeTestUtils.test_reverse(inner, RT, (f(C), TC), (f(A), TA); atol, rtol)
+            end
+            for RT in (Duplicated, Const)
+                EnzymeTestUtils.test_forward(inner, RT, (f(C), TC), (f(A), TA); atol, rtol)
+            end
+        end
+    end
+end
diff --git a/test/enzyme-vectorinterface/scale.jl b/test/enzyme-vectorinterface/scale.jl
new file mode 100644
index 000000000..622cca252
--- /dev/null
+++ b/test/enzyme-vectorinterface/scale.jl
@@ -0,0 +1,40 @@
+using Test, TestExtras
+using TensorKit
+using TensorOperations
+using Enzyme, EnzymeTestUtils
+using Random
+
+spacelist = ad_spacelist(fast_tests)
+eltypes = (Float64, ComplexF64)
+
+@testset "Enzyme - VectorInterface (scale!)" begin
+    @timedtestset "$(TensorKit.type_repr(sectortype(eltype(V)))) ($T)" for V in spacelist, T in eltypes
+        atol = default_tol(T)
+        rtol = default_tol(T)
+        α = randn(T)
+        @testset for TC in (Duplicated,)
+            for Tα in (Active, Const)
+                C = randn(T, V[1] ⊗ V[2] ← V[3] ⊗ V[4] ⊗ V[5])
+                EnzymeTestUtils.test_reverse(scale!, TC, (C, TC), (α, Tα); atol, rtol)
+                C = randn(T, V[1] ⊗ V[2] ← V[3] ⊗ V[4] ⊗ V[5])
+                EnzymeTestUtils.test_reverse(scale!, TC, (C', TC), (α, Tα); atol, rtol)
+                @testset for TA in (Duplicated,), (fc, fa) in ((identity, identity), (adjoint, adjoint))
+                    C = randn(T, V[1] ⊗ V[2] ← V[3] ⊗ V[4] ⊗ V[5])
+                    A = randn(T, V[1] ⊗ V[2] ← V[3] ⊗ V[4] ⊗ V[5])
+                    EnzymeTestUtils.test_reverse(scale!, TC, (fc(C), TC), (fa(A), TA), (α, Tα); atol, rtol)
+                end
+            end
+            for Tα in (Duplicated, Const)
+                C = randn(T, V[1] ⊗ V[2] ← V[3] ⊗ V[4] ⊗ V[5])
+                EnzymeTestUtils.test_forward(scale!, TC, (C, TC), (α, Tα); atol, rtol)
+                C = randn(T, V[1] ⊗ V[2] ← V[3] ⊗ V[4] ⊗ V[5])
+                EnzymeTestUtils.test_forward(scale!, TC, (C', TC), (α, Tα); atol, rtol)
+                @testset for TA in (Duplicated,), (fc, fa) in ((identity, identity), (adjoint, adjoint))
+                    C = randn(T, V[1] ⊗ V[2] ← V[3] ⊗ V[4] ⊗ V[5])
+                    A = randn(T, V[1] ⊗ V[2] ← V[3] ⊗ V[4] ⊗ V[5])
+                    EnzymeTestUtils.test_forward(scale!, TC, (fc(C), TC), (fa(A), TA), (α, Tα); atol, rtol)
+                end
+            end
+        end
+    end
+end
diff --git a/test/runtests.jl b/test/runtests.jl
index 881d538a8..7e76a60e7 100644
--- a/test/runtests.jl
+++ b/test/runtests.jl
@@ -23,6 +23,7 @@ end
 if (Sys.isapple() && get(ENV, "CI", "false") == "true") || !isempty(VERSION.prerelease)
     filter!(!startswith("chainrules") ∘ first, testsuite)
     filter!(!startswith("mooncake") ∘ first, testsuite)
+    filter!(!startswith("enzyme") ∘ first, testsuite)
 end
 
 args = parse_args(ARGS; custom = ["fast"])