diff --git a/tests/utils/test_anomaly_evaluation.py b/tests/utils/test_anomaly_evaluation.py
new file mode 100644
index 0000000000..dec30aaed5
--- /dev/null
+++ b/tests/utils/test_anomaly_evaluation.py
@@ -0,0 +1,47 @@
+#!/usr/bin/env python3
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+# pyre-strict
+
+import math
+import unittest
+
+from torchtnt.utils.anomaly_evaluation import IsNaNEvaluator, ThresholdEvaluator
+
+
+class TestAnomalyLogger(unittest.TestCase):
+
+    def test_threshold(self) -> None:
+        threshold = ThresholdEvaluator(min_val=0.5, max_val=0.9)
+        self.assertFalse(threshold.is_anomaly())
+
+        threshold.update(0.4)
+        self.assertTrue(threshold.is_anomaly())
+
+        threshold.update(0.6)
+        self.assertFalse(threshold.is_anomaly())
+
+        threshold.update(0.95)
+        self.assertTrue(threshold.is_anomaly())
+
+        threshold = ThresholdEvaluator(max_val=1)
+
+        threshold.update(100.0)
+        self.assertTrue(threshold.is_anomaly())
+
+        threshold.update(-500.0)
+        self.assertFalse(threshold.is_anomaly())
+
+    def test_isnan(self) -> None:
+        isnan = IsNaNEvaluator()
+        self.assertFalse(isnan.is_anomaly())
+
+        isnan.update(0.4)
+        self.assertFalse(isnan.is_anomaly())
+
+        isnan.update(math.nan)
+        self.assertTrue(isnan.is_anomaly())
diff --git a/torchtnt/utils/__init__.py b/torchtnt/utils/__init__.py
index 397ec67d26..fb8098c360 100644
--- a/torchtnt/utils/__init__.py
+++ b/torchtnt/utils/__init__.py
@@ -6,6 +6,7 @@
 
 # pyre-strict
 
+from .anomaly_evaluation import IsNaNEvaluator, ThresholdEvaluator
 from .checkpoint import (
     BestCheckpointConfig,
     CheckpointManager,
@@ -88,6 +89,8 @@
 )
 
 __all__ = [
+    "IsNaNEvaluator",
+    "ThresholdEvaluator",
     "CheckpointPath",
     "MetricData",
     "get_best_checkpoint_path",
diff --git a/torchtnt/utils/anomaly_evaluation.py b/torchtnt/utils/anomaly_evaluation.py
index 97fed6cb5d..08b2d4a195 100644
--- a/torchtnt/utils/anomaly_evaluation.py
+++ b/torchtnt/utils/anomaly_evaluation.py
@@ -9,7 +9,9 @@
 
 
 import logging
+import math
 from abc import ABC, abstractmethod
+from math import inf
 
 _logger: logging.Logger = logging.getLogger(__name__)
 
@@ -49,3 +51,49 @@ def is_anomaly(self) -> bool:
         an anomaly detection algorithm.
         """
         pass
+
+
+class ThresholdEvaluator(MetricAnomalyEvaluator):
+    """
+    Evaluates whether a metric value is anomalous based on a predefined threshold.
+    """
+
+    def __init__(
+        self,
+        *,
+        min_val: float = -inf,
+        max_val: float = inf,
+    ) -> None:
+        """
+        Args:
+            min_val: Minimum allowed value. Default value is -inf.
+            max_val: Maximum allowed value. Default value is inf.
+            warmup_steps: Number of steps to ignore before evaluating anomalies. Default value is 0.
+            evaluate_every_n_steps: Step interval to wait in between anomaly evaluations. Default value is 1.
+        """
+        self.min_val = min_val
+        self.max_val = max_val
+        self.curr_val: float = min_val
+
+    def update(self, value: float) -> None:
+        self.curr_val = value
+
+    def is_anomaly(self) -> bool:
+        return not self.min_val <= self.curr_val <= self.max_val
+
+
+class IsNaNEvaluator(MetricAnomalyEvaluator):
+    """
+    Evaluates whether a metric value is NaN.
+    """
+
+    def __init__(
+        self,
+    ) -> None:
+        self.curr_val: float = 0
+
+    def update(self, value: float) -> None:
+        self.curr_val = value
+
+    def is_anomaly(self) -> bool:
+        return math.isnan(self.curr_val)