diff --git a/src/graphnet/training/callbacks.py b/src/graphnet/training/callbacks.py
index a66255ca6..c319dccc1 100644
--- a/src/graphnet/training/callbacks.py
+++ b/src/graphnet/training/callbacks.py
@@ -1,20 +1,26 @@
 """Callback class(es) for using during model training."""
 
 import logging
-from typing import Dict, List
+import os
+from typing import Dict, List, TYPE_CHECKING, Any, Optional
 import warnings
 
 import numpy as np
+import torch
 from tqdm.std import Bar
 
 from pytorch_lightning import LightningModule, Trainer
-from pytorch_lightning.callbacks import TQDMProgressBar
+from pytorch_lightning.callbacks import TQDMProgressBar, EarlyStopping
 from pytorch_lightning.utilities import rank_zero_only
 from torch.optim import Optimizer
 from torch.optim.lr_scheduler import _LRScheduler
 
 from graphnet.utilities.logging import Logger
 
+if TYPE_CHECKING:
+    from graphnet.models import Model
+    import pytorch_lightning as pl
+
 
 class PiecewiseLinearLR(_LRScheduler):
     """Interpolate learning rate linearly between milestones."""
@@ -152,3 +158,92 @@ def on_train_epoch_end(
             h.setLevel(logging.ERROR)
             logger.info(str(super().train_progress_bar))
             h.setLevel(level)
+
+
+class GraphnetEarlyStopping(EarlyStopping):
+    """Early stopping callback for graphnet."""
+
+    def __init__(self, save_dir: str, **kwargs: Dict[str, Any]) -> None:
+        """Construct `GraphnetEarlyStopping` Callback.
+
+        Args:
+            save_dir: Path to directory to save best model and config.
+            **kwargs: Keyword arguments to pass to `EarlyStopping`. See
+                `pytorch_lightning.callbacks.EarlyStopping` for details.
+        """
+        self.save_dir = save_dir
+        super().__init__(**kwargs)
+
+    def setup(
+        self,
+        trainer: "pl.Trainer",
+        graphnet_model: "Model",
+        stage: Optional[str] = None,
+    ) -> None:
+        """Call at setup stage of training.
+
+        Args:
+            trainer: The trainer.
+            graphnet_model: The model.
+            stage: The stage of training.
+        """
+        super().setup(trainer, graphnet_model, stage)
+        os.makedirs(self.save_dir, exist_ok=True)
+        graphnet_model.save_config(os.path.join(self.save_dir, "config.yml"))
+
+    def on_train_epoch_end(
+        self, trainer: "pl.Trainer", graphnet_model: "Model"
+    ) -> None:
+        """Call after each train epoch.
+
+        Args:
+            trainer: Trainer object.
+            graphnet_model: Graphnet Model.
+
+        Returns: None.
+        """
+        if not self._check_on_train_epoch_end or self._should_skip_check(
+            trainer
+        ):
+            return
+        current_best = self.best_score
+        self._run_early_stopping_check(trainer)
+        if self.best_score != current_best:
+            graphnet_model.save_state_dict(
+                os.path.join(self.save_dir, "best_model.pth")
+            )
+
+    def on_validation_end(
+        self, trainer: "pl.Trainer", graphnet_model: "Model"
+    ) -> None:
+        """Call after each validation epoch.
+
+        Args:
+            trainer: Trainer object.
+            graphnet_model: Graphnet Model.
+
+        Returns: None.
+        """
+        if self._check_on_train_epoch_end or self._should_skip_check(trainer):
+            return
+        current_best = self.best_score
+        self._run_early_stopping_check(trainer)
+        if self.best_score != current_best:
+            graphnet_model.save_state_dict(
+                os.path.join(self.save_dir, "best_model.pth")
+            )
+
+    def on_fit_end(
+        self, trainer: "pl.Trainer", graphnet_model: "Model"
+    ) -> None:
+        """Call at the end of training.
+
+        Args:
+            trainer: Trainer object.
+            graphnet_model: Graphnet Model.
+
+        Returns: None.
+        """
+        graphnet_model.load_state_dict(
+            os.path.join(self.save_dir, "best_model.pth")
+        )