diff --git a/sdk/python/feast/type_map.py b/sdk/python/feast/type_map.py
index 9d9921a2bf9..4a540eb4953 100644
--- a/sdk/python/feast/type_map.py
+++ b/sdk/python/feast/type_map.py
@@ -176,6 +176,16 @@ def python_type_to_feast_value_type(
         item_type = type_map[str(value.dtype)]
         return ValueType[item_type.name + "_LIST"]
 
+    # Special handling for pandas 'object' dtype - infer from actual value type
+    # This must come after array handling to avoid intercepting array types
+    if type_name == "object" and not isinstance(value, (list, np.ndarray)):
+        if value is not None:
+            actual_type = type(value).__name__.lower()
+            if actual_type in type_map:
+                return type_map[actual_type]
+        # Default to STRING for object dtype (pandas commonly uses object for strings)
+        return ValueType.STRING
+
     if isinstance(value, (list, np.ndarray)):
         # if the value's type is "ndarray" and we couldn't infer from "value.dtype"
         # this is most probably array of "object",
diff --git a/sdk/python/tests/unit/test_on_demand_feature_view.py b/sdk/python/tests/unit/test_on_demand_feature_view.py
index 505146aa612..a3286846dd4 100644
--- a/sdk/python/tests/unit/test_on_demand_feature_view.py
+++ b/sdk/python/tests/unit/test_on_demand_feature_view.py
@@ -17,6 +17,7 @@
 import pandas as pd
 import pytest
 
+from feast.data_source import RequestSource
 from feast.feature_view import FeatureView
 from feast.field import Field
 from feast.infra.offline_stores.file_source import FileSource
@@ -26,7 +27,7 @@
     PythonTransformation,
     on_demand_feature_view,
 )
-from feast.types import Float32
+from feast.types import Float32, String
 
 
 def udf1(features_df: pd.DataFrame) -> pd.DataFrame:
@@ -418,3 +419,40 @@ def another_transform(features_df: pd.DataFrame) -> pd.DataFrame:
 
     deserialized = OnDemandFeatureView.from_proto(proto)
     assert deserialized.name == CUSTOM_FUNCTION_NAME
+
+
+def test_on_demand_string_features():
+    """Test that on-demand feature views work with STRING type inputs from RequestSource."""
+    # Define a request data source with STRING type fields
+    input_request = RequestSource(
+        name="vals_to_add",
+        schema=[
+            Field(name="val_to_add", dtype=String),
+            Field(name="val_to_add_2", dtype=String),
+        ],
+    )
+
+    # Use input data to create new features in Pandas mode
+    @on_demand_feature_view(
+        sources=[input_request],
+        schema=[
+            Field(name="conv_rate_plus_val1", dtype=String),
+            Field(name="conv_rate_plus_val2", dtype=String),
+        ],
+        mode="pandas",
+    )
+    def transformed_conv_rate(features_df: pd.DataFrame) -> pd.DataFrame:
+        df = pd.DataFrame()
+        df["conv_rate_plus_val1"] = features_df["val_to_add"]
+        df["conv_rate_plus_val2"] = features_df["val_to_add_2"]
+        return df
+
+    # This should work without raising ValueError about 'object' type
+    transformed_conv_rate.infer_features()
+
+    # Verify the inferred features have the correct type
+    assert len(transformed_conv_rate.features) == 2
+    assert transformed_conv_rate.features[0].name == "conv_rate_plus_val1"
+    assert transformed_conv_rate.features[0].dtype == String
+    assert transformed_conv_rate.features[1].name == "conv_rate_plus_val2"
+    assert transformed_conv_rate.features[1].dtype == String