diff --git a/.github/workflows/Integration.yml b/.github/workflows/Integration.yml
index 3f7f216873..3338d6a5e8 100644
--- a/.github/workflows/Integration.yml
+++ b/.github/workflows/Integration.yml
@@ -48,6 +48,7 @@ jobs:
           - Bijectors
           - DifferentiationInterface
           - DynamicExpressions
+          - Lux
     steps:
       - uses: actions/checkout@v5
       - uses: julia-actions/setup-julia@v2
diff --git a/test/integration/Lux/Project.toml b/test/integration/Lux/Project.toml
new file mode 100644
index 0000000000..cc6dead153
--- /dev/null
+++ b/test/integration/Lux/Project.toml
@@ -0,0 +1,22 @@
+[deps]
+ComponentArrays = "b0b7db55-cfe3-40fc-9ded-d10e2dbeff66"
+Enzyme = "7da242da-08ed-463a-9acd-ee780be4f1d9"
+EnzymeCore = "f151be2c-9106-41f4-ab19-57ee4f262869"
+Lux = "b2108857-7c20-44ae-9111-449ecde12c47"
+LuxTestUtils = "ac9de150-d08f-4546-94fb-7472b5760531"
+NNlib = "872c559c-99b0-510c-b3b7-b6c96a88d5cd"
+StableRNGs = "860ef19b-820b-49d6-a774-d7a799459cd3"
+Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
+Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"
+
+[sources]
+Enzyme = {path = "../../.."}
+EnzymeCore = {path = "../../../lib/EnzymeCore"}
+
+[compat]
+ComponentArrays = "0.15.29"
+Lux = "1.21"
+LuxTestUtils = "2.0.1"
+NNlib = "0.9.31"
+StableRNGs = "1"
+Zygote = "0.7.10"
diff --git a/test/integration/Lux/runtests.jl b/test/integration/Lux/runtests.jl
new file mode 100644
index 0000000000..d3a870262e
--- /dev/null
+++ b/test/integration/Lux/runtests.jl
@@ -0,0 +1,130 @@
+using Enzyme, Lux, Zygote, Test, NNlib, StableRNGs, ComponentArrays
+using LuxTestUtils: check_approx
+
+generic_loss_function(model, x, ps, st) = sum(abs2, first(model(x, ps, st)))
+
+function compute_enzyme_gradient(model, x, ps, st)
+    return Enzyme.gradient(
+        Enzyme.set_runtime_activity(Reverse),
+        generic_loss_function,
+        Const(model),
+        x,
+        ps,
+        Const(st),
+    )[2:3]
+end
+
+function compute_zygote_gradient(model, x, ps, st)
+    _, dx, dps, _ = Zygote.gradient(generic_loss_function, model, x, ps, st)
+    return dx, dps
+end
+
+function test_enzyme_gradients(model, x, ps, st)
+    dx, dps = compute_enzyme_gradient(model, x, ps, st)
+    dx_zygote, dps_zygote = compute_zygote_gradient(model, x, ps, st)
+    @test check_approx(dx, dx_zygote; atol = 1.0f-3, rtol = 1.0f-3)
+    return @test check_approx(dps, dps_zygote; atol = 1.0f-3, rtol = 1.0f-3)
+end
+
+const MODELS_LIST = Any[
+    (Dense(2, 4), randn(StableRNG(0), Float32, 2, 3)),
+    (Dense(2, 4, gelu), randn(StableRNG(0), Float32, 2, 3)),
+    (Dense(2, 4, gelu; use_bias = false), randn(StableRNG(0), Float32, 2, 3)),
+    (Chain(Dense(2, 4, relu), Dense(4, 3)), randn(StableRNG(0), Float32, 2, 3)),
+    (Scale(2), randn(StableRNG(0), Float32, 2, 3)),
+    (Conv((3, 3), 2 => 3), randn(StableRNG(0), Float32, 3, 3, 2, 2)),
+    (Conv((3, 3), 2 => 3, gelu; pad = SamePad()), randn(StableRNG(0), Float32, 3, 3, 2, 2)),
+    (
+        Conv((3, 3), 2 => 3, relu; use_bias = false, pad = SamePad()),
+        randn(StableRNG(0), Float32, 3, 3, 2, 2),
+    ),
+    (
+        Chain(Conv((3, 3), 2 => 3, gelu), Conv((3, 3), 3 => 1, gelu)),
+        rand(StableRNG(0), Float32, 5, 5, 2, 2),
+    ),
+    (
+        Chain(Conv((4, 4), 2 => 2; pad = SamePad()), MeanPool((5, 5); pad = SamePad())),
+        rand(StableRNG(0), Float32, 5, 5, 2, 2),
+    ),
+    (
+        Chain(Conv((3, 3), 2 => 3, relu; pad = SamePad()), MaxPool((2, 2))),
+        rand(StableRNG(0), Float32, 5, 5, 2, 2),
+    ),
+    (Maxout(() -> Dense(5 => 4, tanh), 3), randn(StableRNG(0), Float32, 5, 2)),
+    (Bilinear((2, 2) => 3), randn(StableRNG(0), Float32, 2, 3)),
+    (SkipConnection(Dense(2 => 2), vcat), randn(StableRNG(0), Float32, 2, 3)),
+    (ConvTranspose((3, 3), 3 => 2; stride = 2), rand(StableRNG(0), Float32, 5, 5, 3, 1)),
+    (StatefulRecurrentCell(RNNCell(3 => 5)), rand(StableRNG(0), Float32, 3, 2)),
+    (StatefulRecurrentCell(RNNCell(3 => 5, gelu)), rand(StableRNG(0), Float32, 3, 2)),
+    (
+        StatefulRecurrentCell(RNNCell(3 => 5, gelu; use_bias = false)),
+        rand(StableRNG(0), Float32, 3, 2),
+    ),
+    (
+        Chain(
+            StatefulRecurrentCell(RNNCell(3 => 5)), StatefulRecurrentCell(RNNCell(5 => 3))
+        ),
+        rand(StableRNG(0), Float32, 3, 2),
+    ),
+    (StatefulRecurrentCell(LSTMCell(3 => 5)), rand(StableRNG(0), Float32, 3, 2)),
+    (
+        Chain(
+            StatefulRecurrentCell(LSTMCell(3 => 5)), StatefulRecurrentCell(LSTMCell(5 => 3))
+        ),
+        rand(StableRNG(0), Float32, 3, 2),
+    ),
+    (StatefulRecurrentCell(GRUCell(3 => 5)), rand(StableRNG(0), Float32, 3, 10)),
+    (
+        Chain(
+            StatefulRecurrentCell(GRUCell(3 => 5)), StatefulRecurrentCell(GRUCell(5 => 3))
+        ),
+        rand(StableRNG(0), Float32, 3, 10),
+    ),
+    (Chain(Dense(2, 4), GroupNorm(4, 2, gelu)), randn(StableRNG(0), Float32, 2, 3)),
+    (Chain(Dense(2, 4), GroupNorm(4, 2)), randn(StableRNG(0), Float32, 2, 3)),
+    (
+        Chain(Conv((3, 3), 2 => 6), GroupNorm(6, 3)),
+        randn(StableRNG(0), Float32, 6, 6, 2, 2),
+    ),
+    (
+        Chain(Conv((3, 3), 2 => 6, tanh), GroupNorm(6, 3)),
+        randn(StableRNG(0), Float32, 6, 6, 2, 2),
+    ),
+    (
+        Chain(Conv((3, 3), 2 => 3, gelu), LayerNorm((1, 1, 3))),
+        randn(StableRNG(0), Float32, 4, 4, 2, 2),
+    ),
+    (
+        Chain(Conv((3, 3), 2 => 6), InstanceNorm(6)),
+        randn(StableRNG(0), Float32, 6, 6, 2, 2),
+    ),
+    (
+        Chain(Conv((3, 3), 2 => 6, tanh), InstanceNorm(6)),
+        randn(StableRNG(0), Float32, 6, 6, 2, 2),
+    ),
+    (Chain(Dense(2, 4), BatchNorm(4)), randn(StableRNG(0), Float32, 2, 3)),
+    (Chain(Dense(2, 4), BatchNorm(4, gelu)), randn(StableRNG(0), Float32, 2, 3)),
+    (
+        Chain(Dense(2, 4), BatchNorm(4, gelu; track_stats = false)),
+        randn(StableRNG(0), Float32, 2, 3),
+    ),
+    (Chain(Conv((3, 3), 2 => 6), BatchNorm(6)), randn(StableRNG(0), Float32, 6, 6, 2, 2)),
+    (
+        Chain(Conv((3, 3), 2 => 6, tanh), BatchNorm(6)),
+        randn(StableRNG(0), Float32, 6, 6, 2, 2),
+    ),
+]
+
+@testset "Enzyme Integration" begin
+    @testset "[$(i)] $(nameof(typeof(model)))" for (i, (model, x)) in enumerate(MODELS_LIST)
+        ps, st = Lux.setup(StableRNG(12345), model)
+        test_enzyme_gradients(model, x, ps, st)
+    end
+end
+
+@testset "Enzyme Integration ComponentArray" begin
+    @testset "[$(i)] $(nameof(typeof(model)))" for (i, (model, x)) in enumerate(MODELS_LIST)
+        ps, st = Lux.setup(StableRNG(12345), model)
+        test_enzyme_gradients(model, x, ComponentArray(ps), st)
+    end
+end