diff --git a/keras_core/metrics/__init__.py b/keras_core/metrics/__init__.py
index 19213822c..6ec09b5b3 100644
--- a/keras_core/metrics/__init__.py
+++ b/keras_core/metrics/__init__.py
@@ -8,6 +8,7 @@ from keras_core.metrics.accuracy_metrics import TopKCategoricalAccuracy
 from keras_core.metrics.confusion_metrics import FalseNegatives
 from keras_core.metrics.confusion_metrics import FalsePositives
 from keras_core.metrics.confusion_metrics import Precision
+from keras_core.metrics.confusion_metrics import Recall
 from keras_core.metrics.confusion_metrics import TrueNegatives
 from keras_core.metrics.confusion_metrics import TruePositives
 from keras_core.metrics.hinge_metrics import CategoricalHinge
@@ -39,6 +40,7 @@ ALL_OBJECTS = {
     FalseNegatives,
     FalsePositives,
     Precision,
+    Recall,
     TrueNegatives,
     TruePositives,
     # Hinge
diff --git a/keras_core/metrics/confusion_metrics.py b/keras_core/metrics/confusion_metrics.py
index e074d6bb9..be9461bf5 100644
--- a/keras_core/metrics/confusion_metrics.py
+++ b/keras_core/metrics/confusion_metrics.py
@@ -14,9 +14,9 @@ class _ConfusionMatrixConditionCount(Metric):
         confusion_matrix_cond: One of `metrics_utils.ConfusionMatrix`
             conditions.
         thresholds: (Optional) Defaults to 0.5. A float value or a python list /
-            tuple of float threshold values in [0, 1]. A threshold is compared
+            tuple of float threshold values in `[0, 1]`. A threshold is compared
             with prediction values to determine the truth value of predictions
-            (i.e., above the threshold is `true`, below is `false`). One metric
+            (i.e., above the threshold is `True`, below is `False`). One metric
             value is generated for each threshold value.
         name: (Optional) string name of the metric instance.
         dtype: (Optional) data type of the metric result.
@@ -85,9 +85,9 @@ class FalsePositives(_ConfusionMatrixConditionCount):
 
     Args:
         thresholds: (Optional) Defaults to 0.5. A float value, or a Python
-            list/tuple of float threshold values in [0, 1]. A threshold is
+            list/tuple of float threshold values in `[0, 1]`. A threshold is
             compared with prediction values to determine the truth value of
-            predictions (i.e., above the threshold is `true`, below is `false`).
+            predictions (i.e., above the threshold is `True`, below is `False`).
             If used with a loss function that sets `from_logits=True` (i.e. no
             sigmoid applied to predictions), `thresholds` should be set to 0.
             One metric value is generated for each threshold value.
@@ -129,9 +129,9 @@ class FalseNegatives(_ConfusionMatrixConditionCount):
 
     Args:
         thresholds: (Optional) Defaults to 0.5. A float value, or a Python
-            list/tuple of float threshold values in [0, 1]. A threshold is
+            list/tuple of float threshold values in `[0, 1]`. A threshold is
             compared with prediction values to determine the truth value of
-            predictions (i.e., above the threshold is `true`, below is `false`).
+            predictions (i.e., above the threshold is `True`, below is `False`).
             If used with a loss function that sets `from_logits=True` (i.e. no
             sigmoid applied to predictions), `thresholds` should be set to 0.
             One metric value is generated for each threshold value.
@@ -173,9 +173,9 @@ class TrueNegatives(_ConfusionMatrixConditionCount):
 
     Args:
         thresholds: (Optional) Defaults to 0.5. A float value, or a Python
-            list/tuple of float threshold values in [0, 1]. A threshold is
+            list/tuple of float threshold values in `[0, 1]`. A threshold is
             compared with prediction values to determine the truth value of
-            predictions (i.e., above the threshold is `true`, below is `false`).
+            predictions (i.e., above the threshold is `True`, below is `False`).
             If used with a loss function that sets `from_logits=True` (i.e. no
             sigmoid applied to predictions), `thresholds` should be set to 0.
             One metric value is generated for each threshold value.
@@ -217,9 +217,9 @@ class TruePositives(_ConfusionMatrixConditionCount):
 
     Args:
         thresholds: (Optional) Defaults to 0.5. A float value, or a Python
-            list/tuple of float threshold values in [0, 1]. A threshold is
+            list/tuple of float threshold values in `[0, 1]`. A threshold is
             compared with prediction values to determine the truth value of
-            predictions (i.e., above the threshold is `true`, below is `false`).
+            predictions (i.e., above the threshold is `True`, below is `False`).
             If used with a loss function that sets `from_logits=True` (i.e. no
             sigmoid applied to predictions), `thresholds` should be set to 0.
             One metric value is generated for each threshold value.
@@ -272,13 +272,13 @@ class Precision(Metric):
 
     Args:
         thresholds: (Optional) A float value, or a Python list/tuple of float
-            threshold values in [0, 1]. A threshold is compared with prediction
-            values to determine the truth value of predictions (i.e., above the
-            threshold is `true`, below is `false`). If used with a loss function
-            that sets `from_logits=True` (i.e. no sigmoid applied to
-            predictions), `thresholds` should be set to 0. One metric value is
-            generated for each threshold value. If neither thresholds nor top_k
-            are set, the default is to calculate precision with
+            threshold values in `[0, 1]`. A threshold is compared with
+            prediction values to determine the truth value of predictions (i.e.,
+            above the threshold is `True`, below is `False`). If used with a
+            loss function that sets `from_logits=True` (i.e. no sigmoid applied
+            to predictions), `thresholds` should be set to 0. One metric value
+            is generated for each threshold value. If neither thresholds nor
+            top_k are set, the default is to calculate precision with
             `thresholds=0.5`.
         top_k: (Optional) Unset by default. An int value specifying the top-k
             predictions to consider when calculating precision.
@@ -326,7 +326,7 @@ class Precision(Metric):
 
     ```python
     model.compile(optimizer='adam',
-                  loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),
+                  loss=keras_core.losses.BinaryCrossentropy(from_logits=True),
                   metrics=[keras_core.metrics.Precision(thresholds=0)])
     ```
     """
@@ -369,7 +369,7 @@ class Precision(Metric):
                 Can be a `Tensor` whose rank is either 0, or the same rank as
                 `y_true`, and must be broadcastable to `y_true`.
         """
-        return metrics_utils.update_confusion_matrix_variables(
+        metrics_utils.update_confusion_matrix_variables(
             {
                 metrics_utils.ConfusionMatrix.TRUE_POSITIVES: self.true_positives,  # noqa: E501
                 metrics_utils.ConfusionMatrix.FALSE_POSITIVES: self.false_positives,  # noqa: E501
@@ -403,3 +403,143 @@ class Precision(Metric):
         }
         base_config = super().get_config()
         return dict(list(base_config.items()) + list(config.items()))
+
+
+@keras_core_export("keras_core.metrics.Recall")
+class Recall(Metric):
+    """Computes the recall of the predictions with respect to the labels.
+
+    This metric creates two local variables, `true_positives` and
+    `false_negatives`, that are used to compute the recall. This value is
+    ultimately returned as `recall`, an idempotent operation that simply divides
+    `true_positives` by the sum of `true_positives` and `false_negatives`.
+
+    If `sample_weight` is `None`, weights default to 1.
+    Use `sample_weight` of 0 to mask values.
+
+    If `top_k` is set, recall will be computed as how often on average a class
+    among the labels of a batch entry is in the top-k predictions.
+
+    If `class_id` is specified, we calculate recall by considering only the
+    entries in the batch for which `class_id` is in the label, and computing the
+    fraction of them for which `class_id` is above the threshold and/or in the
+    top-k predictions.
+
+    Args:
+        thresholds: (Optional) A float value, or a Python list/tuple of float
+            threshold values in `[0, 1]`. A threshold is compared with
+            prediction values to determine the truth value of predictions (i.e.,
+            above thethreshold is `True`, below is `False`). If used with a loss
+            function that sets `from_logits=True` (i.e. no sigmoid applied to
+            predictions), `thresholds` should be set to 0. One metric value is
+            generated for each threshold value. If neither thresholds nor top_k
+            are set, the default is to calculate recall with `thresholds=0.5`.
+        top_k: (Optional) Unset by default. An int value specifying the top-k
+            predictions to consider when calculating recall.
+        class_id: (Optional) Integer class ID for which we want binary metrics.
+            This must be in the half-open interval `[0, num_classes)`, where
+            `num_classes` is the last dimension of predictions.
+        name: (Optional) string name of the metric instance.
+        dtype: (Optional) data type of the metric result.
+
+    Standalone usage:
+
+    >>> m = keras_core.metrics.Recall()
+    >>> m.update_state([0, 1, 1, 1], [1, 0, 1, 1])
+    >>> m.result()
+    0.6666667
+
+    >>> m.reset_state()
+    >>> m.update_state([0, 1, 1, 1], [1, 0, 1, 1], sample_weight=[0, 0, 1, 0])
+    >>> m.result()
+    1.0
+
+    Usage with `compile()` API:
+
+    ```python
+    model.compile(optimizer='sgd',
+                  loss='mse',
+                  metrics=[keras_core.metrics.Recall()])
+    ```
+
+    Usage with a loss with `from_logits=True`:
+
+    ```python
+    model.compile(optimizer='adam',
+                  loss=keras_core.losses.BinaryCrossentropy(from_logits=True),
+                  metrics=[keras_core.metrics.Recall(thresholds=0)])
+    ```
+    """
+
+    def __init__(
+        self, thresholds=None, top_k=None, class_id=None, name=None, dtype=None
+    ):
+        super().__init__(name=name, dtype=dtype)
+        self.init_thresholds = thresholds
+        self.top_k = top_k
+        self.class_id = class_id
+
+        default_threshold = 0.5 if top_k is None else metrics_utils.NEG_INF
+        self.thresholds = metrics_utils.parse_init_thresholds(
+            thresholds, default_threshold=default_threshold
+        )
+        self._thresholds_distributed_evenly = (
+            metrics_utils.is_evenly_distributed_thresholds(self.thresholds)
+        )
+        self.true_positives = self.add_variable(
+            shape=(len(self.thresholds),),
+            initializer=initializers.Zeros(),
+            name="true_positives",
+        )
+        self.false_negatives = self.add_variable(
+            shape=(len(self.thresholds),),
+            initializer=initializers.Zeros(),
+            name="false_negatives",
+        )
+
+    def update_state(self, y_true, y_pred, sample_weight=None):
+        """Accumulates true positive and false negative statistics.
+
+        Args:
+            y_true: The ground truth values, with the same dimensions as
+                `y_pred`. Will be cast to `bool`.
+            y_pred: The predicted values. Each element must be in the range
+                `[0, 1]`.
+            sample_weight: Optional weighting of each example. Defaults to 1.
+                Can be a `Tensor` whose rank is either 0, or the same rank as
+                `y_true`, and must be broadcastable to `y_true`.
+        """
+        metrics_utils.update_confusion_matrix_variables(
+            {
+                metrics_utils.ConfusionMatrix.TRUE_POSITIVES: self.true_positives,  # noqa: E501
+                metrics_utils.ConfusionMatrix.FALSE_NEGATIVES: self.false_negatives,  # noqa: E501
+            },
+            y_true,
+            y_pred,
+            thresholds=self.thresholds,
+            thresholds_distributed_evenly=self._thresholds_distributed_evenly,
+            top_k=self.top_k,
+            class_id=self.class_id,
+            sample_weight=sample_weight,
+        )
+
+    def result(self):
+        result = ops.divide(
+            self.true_positives,
+            self.true_positives + self.false_negatives + backend.epsilon(),
+        )
+        return result[0] if len(self.thresholds) == 1 else result
+
+    def reset_state(self):
+        num_thresholds = len(to_list(self.thresholds))
+        self.true_positives.assign(ops.zeros((num_thresholds,)))
+        self.false_negatives.assign(ops.zeros((num_thresholds,)))
+
+    def get_config(self):
+        config = {
+            "thresholds": self.init_thresholds,
+            "top_k": self.top_k,
+            "class_id": self.class_id,
+        }
+        base_config = super().get_config()
+        return dict(list(base_config.items()) + list(config.items()))
diff --git a/keras_core/metrics/confusion_metrics_test.py b/keras_core/metrics/confusion_metrics_test.py
index fb3b9fb1a..6a544bfe4 100644
--- a/keras_core/metrics/confusion_metrics_test.py
+++ b/keras_core/metrics/confusion_metrics_test.py
@@ -534,3 +534,158 @@ class PrecisionTest(testing.TestCase):
         self.assertAlmostEqual(1, result)
         self.assertAlmostEqual(1, p_obj.true_positives)
         self.assertAlmostEqual(0, p_obj.false_positives)
+
+
+class RecallTest(testing.TestCase):
+    def test_config(self):
+        r_obj = metrics.Recall(
+            name="my_recall", thresholds=[0.4, 0.9], top_k=15, class_id=12
+        )
+        self.assertEqual(r_obj.name, "my_recall")
+        self.assertLen(r_obj.variables, 2)
+        self.assertEqual(
+            [v.name for v in r_obj.variables],
+            ["true_positives", "false_negatives"],
+        )
+        self.assertEqual(r_obj.thresholds, [0.4, 0.9])
+        self.assertEqual(r_obj.top_k, 15)
+        self.assertEqual(r_obj.class_id, 12)
+
+        # Check save and restore config
+        r_obj2 = metrics.Recall.from_config(r_obj.get_config())
+        self.assertEqual(r_obj2.name, "my_recall")
+        self.assertLen(r_obj2.variables, 2)
+        self.assertEqual(r_obj2.thresholds, [0.4, 0.9])
+        self.assertEqual(r_obj2.top_k, 15)
+        self.assertEqual(r_obj2.class_id, 12)
+
+    def test_unweighted(self):
+        r_obj = metrics.Recall()
+        y_pred = np.array([1, 0, 1, 0])
+        y_true = np.array([0, 1, 1, 0])
+        self.assertAlmostEqual(0.5, r_obj(y_true, y_pred))
+
+    def test_unweighted_all_incorrect(self):
+        r_obj = metrics.Recall(thresholds=[0.5])
+        inputs = np.random.randint(0, 2, size=(100, 1))
+        y_pred = np.array(inputs)
+        y_true = np.array(1 - inputs)
+        self.assertAlmostEqual(0, r_obj(y_true, y_pred))
+
+    def test_weighted(self):
+        r_obj = metrics.Recall()
+        y_pred = np.array([[1, 0, 1, 0], [0, 1, 0, 1]])
+        y_true = np.array([[0, 1, 1, 0], [1, 0, 0, 1]])
+        result = r_obj(
+            y_true,
+            y_pred,
+            sample_weight=np.array([[1, 2, 3, 4], [4, 3, 2, 1]]),
+        )
+        weighted_tp = 3.0 + 1.0
+        weighted_t = (2.0 + 3.0) + (4.0 + 1.0)
+        expected_recall = weighted_tp / weighted_t
+        self.assertAlmostEqual(expected_recall, result)
+
+    def test_div_by_zero(self):
+        r_obj = metrics.Recall()
+        y_pred = np.array([0, 0, 0, 0])
+        y_true = np.array([0, 0, 0, 0])
+        self.assertEqual(0, r_obj(y_true, y_pred))
+
+    def test_unweighted_with_threshold(self):
+        r_obj = metrics.Recall(thresholds=[0.5, 0.7])
+        y_pred = np.array([1, 0, 0.6, 0])
+        y_true = np.array([0, 1, 1, 0])
+        self.assertAllClose([0.5, 0.0], r_obj(y_true, y_pred), 0)
+
+    def test_weighted_with_threshold(self):
+        r_obj = metrics.Recall(thresholds=[0.5, 1.0])
+        y_true = np.array([[0, 1], [1, 0]])
+        y_pred = np.array([[1, 0], [0.6, 0]], dtype="float32")
+        weights = np.array([[1, 4], [3, 2]], dtype="float32")
+        result = r_obj(y_true, y_pred, sample_weight=weights)
+        weighted_tp = 0 + 3.0
+        weighted_positives = (0 + 3.0) + (4.0 + 0.0)
+        expected_recall = weighted_tp / weighted_positives
+        self.assertAllClose([expected_recall, 0], result, 1e-3)
+
+    def test_multiple_updates(self):
+        r_obj = metrics.Recall(thresholds=[0.5, 1.0])
+        y_true = np.array([[0, 1], [1, 0]])
+        y_pred = np.array([[1, 0], [0.6, 0]], dtype="float32")
+        weights = np.array([[1, 4], [3, 2]], dtype="float32")
+        for _ in range(2):
+            r_obj.update_state(y_true, y_pred, sample_weight=weights)
+
+        weighted_tp = (0 + 3.0) + (0 + 3.0)
+        weighted_positives = ((0 + 3.0) + (4.0 + 0.0)) + (
+            (0 + 3.0) + (4.0 + 0.0)
+        )
+        expected_recall = weighted_tp / weighted_positives
+        self.assertAllClose([expected_recall, 0], r_obj.result(), 1e-3)
+
+    def test_unweighted_top_k(self):
+        r_obj = metrics.Recall(top_k=3)
+        y_pred = np.array([0.2, 0.1, 0.5, 0, 0.2])
+        y_true = np.array([0, 1, 1, 0, 0])
+        self.assertAlmostEqual(0.5, r_obj(y_true, y_pred))
+
+    def test_weighted_top_k(self):
+        r_obj = metrics.Recall(top_k=3)
+        y_pred1 = np.array([[0.2, 0.1, 0.4, 0, 0.2]])
+        y_true1 = np.array([[0, 1, 1, 0, 1]])
+        r_obj(y_true1, y_pred1, sample_weight=np.array([[1, 4, 2, 3, 5]]))
+
+        y_pred2 = np.array([0.2, 0.6, 0.4, 0.2, 0.2])
+        y_true2 = np.array([1, 0, 1, 1, 1])
+        result = r_obj(y_true2, y_pred2, sample_weight=np.array(3))
+
+        tp = (2 + 5) + (3 + 3)
+        positives = (4 + 2 + 5) + (3 + 3 + 3 + 3)
+        expected_recall = tp / positives
+        self.assertAlmostEqual(expected_recall, result)
+
+    def test_unweighted_class_id(self):
+        r_obj = metrics.Recall(class_id=2)
+
+        y_pred = np.array([0.2, 0.1, 0.6, 0, 0.2])
+        y_true = np.array([0, 1, 1, 0, 0])
+        self.assertAlmostEqual(1, r_obj(y_true, y_pred))
+        self.assertAlmostEqual(1, r_obj.true_positives)
+        self.assertAlmostEqual(0, r_obj.false_negatives)
+
+        y_pred = np.array([0.2, 0.1, 0, 0, 0.2])
+        y_true = np.array([0, 1, 1, 0, 0])
+        self.assertAlmostEqual(0.5, r_obj(y_true, y_pred))
+        self.assertAlmostEqual(1, r_obj.true_positives)
+        self.assertAlmostEqual(1, r_obj.false_negatives)
+
+        y_pred = np.array([0.2, 0.1, 0.6, 0, 0.2])
+        y_true = np.array([0, 1, 0, 0, 0])
+        self.assertAlmostEqual(0.5, r_obj(y_true, y_pred))
+        self.assertAlmostEqual(1, r_obj.true_positives)
+        self.assertAlmostEqual(1, r_obj.false_negatives)
+
+    def test_unweighted_top_k_and_class_id(self):
+        r_obj = metrics.Recall(class_id=2, top_k=2)
+
+        y_pred = np.array([0.2, 0.6, 0.3, 0, 0.2])
+        y_true = np.array([0, 1, 1, 0, 0])
+        self.assertAlmostEqual(1, r_obj(y_true, y_pred))
+        self.assertAlmostEqual(1, r_obj.true_positives)
+        self.assertAlmostEqual(0, r_obj.false_negatives)
+
+        y_pred = np.array([1, 1, 0.9, 1, 1])
+        y_true = np.array([0, 1, 1, 0, 0])
+        self.assertAlmostEqual(0.5, r_obj(y_true, y_pred))
+        self.assertAlmostEqual(1, r_obj.true_positives)
+        self.assertAlmostEqual(1, r_obj.false_negatives)
+
+    def test_unweighted_top_k_and_threshold(self):
+        r_obj = metrics.Recall(thresholds=0.7, top_k=2)
+
+        y_pred = np.array([0.2, 0.8, 0.6, 0, 0.2])
+        y_true = np.array([1, 1, 1, 0, 1])
+        self.assertAlmostEqual(0.25, r_obj(y_true, y_pred))
+        self.assertAlmostEqual(1, r_obj.true_positives)
+        self.assertAlmostEqual(3, r_obj.false_negatives)