diff --git a/blackjax/adaptation/window_adaptation.py b/blackjax/adaptation/window_adaptation.py
index cacd0b4a6..63c54bad0 100644
--- a/blackjax/adaptation/window_adaptation.py
+++ b/blackjax/adaptation/window_adaptation.py
@@ -18,7 +18,7 @@
 import jax.numpy as jnp
 
 import blackjax.mcmc as mcmc
-from blackjax.adaptation.base import AdaptationInfo, AdaptationResults
+from blackjax.adaptation.base import AdaptationResults, return_all_adapt_info
 from blackjax.adaptation.mass_matrix import (
     MassMatrixAdaptationState,
     mass_matrix_adaptation,
@@ -249,11 +249,12 @@ def window_adaptation(
     initial_step_size: float = 1.0,
     target_acceptance_rate: float = 0.80,
     progress_bar: bool = False,
+    adaptation_info_fn: Callable = return_all_adapt_info,
     integrator=mcmc.integrators.velocity_verlet,
     **extra_parameters,
 ) -> AdaptationAlgorithm:
     """Adapt the value of the inverse mass matrix and step size parameters of
-    algorithms in the HMC fmaily.
+    algorithms in the HMC fmaily. See Blackjax.hmc_family
 
     Algorithms in the HMC family on a euclidean manifold depend on the value of
     at least two parameters: the step size, related to the trajectory
@@ -280,6 +281,11 @@ def window_adaptation(
         The acceptance rate that we target during step size adaptation.
     progress_bar
         Whether we should display a progress bar.
+    adaptation_info_fn
+        Function to select the adaptation info returned. See return_all_adapt_info
+        and get_filter_adapt_info_fn in blackjax.adaptation.base.  By default all
+        information is saved - this can result in excessive memory usage if the
+        information is unused.
     **extra_parameters
         The extra parameters to pass to the algorithm, e.g. the number of
         integration steps for HMC.
@@ -318,7 +324,7 @@ def one_step(carry, xs):
 
         return (
             (new_state, new_adaptation_state),
-            AdaptationInfo(new_state, info, new_adaptation_state),
+            adaptation_info_fn(new_state, info, new_adaptation_state),
         )
 
     def run(rng_key: PRNGKey, position: ArrayLikeTree, num_steps: int = 1000):