diff --git a/demos/with_automatic_differentiation/run_fwi_ad.py b/demos/with_automatic_differentiation/run_fwi_ad.py
index b247b246..e1540f9a 100644
--- a/demos/with_automatic_differentiation/run_fwi_ad.py
+++ b/demos/with_automatic_differentiation/run_fwi_ad.py
@@ -159,4 +159,4 @@ def forward(
                                bounds=(1.5, 3.5),
                                derivative_options={"riesz_representation": 'l2'})
 
-fire.VTKFile("c_optimised.pvd").write(c_optimised)
\ No newline at end of file
+fire.VTKFile("c_optimised.pvd").write(c_optimised)
diff --git a/test/test_gradient_ad.py b/test/test_gradient_ad.py
index 59a9882c..55b8b286 100644
--- a/test/test_gradient_ad.py
+++ b/test/test_gradient_ad.py
@@ -2,6 +2,7 @@
 import firedrake.adjoint as fire_ad
 import spyro
 from numpy.random import rand
+from checkpoint_schedules import Revolve
 
 
 # --- Basid setup to run a forward simulation with AD --- #
@@ -52,6 +53,7 @@
 }
 model["aut_dif"] = {
     "status": True,
+    "checkpointing": False,
 }
 
 model["timeaxis"] = {
@@ -86,6 +88,12 @@ def forward(
 ):
     if annotate:
         fire_ad.continue_annotation()
+        if model["aut_dif"]["checkpointing"]:
+            total_steps = int(model["timeaxis"]["tf"] / model["timeaxis"]["dt"])
+            steps_store = int(total_steps / 10)  # Store 10% of the steps.
+            tape = fire_ad.get_working_tape()
+            tape.progress_bar = fire.ProgressBar
+            tape.enable_checkpointing(Revolve(total_steps, steps_store))
     # source_number based on the ensemble.ensemble_comm.rank
     source_number = ensemble.ensemble_comm.rank
     receiver_data, J = fwd_solver.execute_acoustic(
@@ -131,3 +139,10 @@ def test_taylor():
     h = fire.Function(V)
     h.dat.data[:] = rand(V.dim())
     assert fire_ad.taylor_test(J_hat, c_guess, h) > 1.9
+    fire_ad.get_working_tape().clear_tape()
+    fire_ad.pause_annotation()
+
+
+def test_taylor_checkpointing():
+    model["aut_dif"]["checkpointing"] = True
+    test_taylor()