diff --git a/tools/python/dump_ort_model.py b/tools/python/dump_ort_model.py
new file mode 100644
index 0000000000..da737b754c
--- /dev/null
+++ b/tools/python/dump_ort_model.py
@@ -0,0 +1,145 @@
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License.
+
+import argparse
+import os
+import sys
+import typing
+
+from util.ort_format_model.types import FbsTypeInfo
+# the import of FbsTypeInfo sets up the path so we can import ort_flatbuffers_py
+import ort_flatbuffers_py.experimental.fbs as fbs
+
+
+class OrtFormatModelDumper:
+    'Class to dump an ORT format model.'
+
+    def __init__(self, model_path: str):
+        '''
+        Initialize ORT format model dumper
+        :param model_path: Path to model
+        '''
+        self._file = open(model_path, 'rb').read()
+        self._buffer = bytearray(self._file)
+        if not fbs.InferenceSession.InferenceSession.InferenceSessionBufferHasIdentifier(self._buffer, 0):
+            raise RuntimeError("File does not appear to be a valid ORT format model: '{}'".format(model_path))
+        self._model = fbs.InferenceSession.InferenceSession.GetRootAsInferenceSession(self._buffer, 0).Model()
+
+    def _dump_initializers(self, graph: fbs.Graph):
+        print('Initializers:')
+        for idx in range(0, graph.InitializersLength()):
+            tensor = graph.Initializers(idx)
+            dims = []
+            for dim in range(0, tensor.DimsLength()):
+                dims.append(tensor.Dims(dim))
+
+            print(f'{tensor.Name().decode()} data_type={tensor.DataType()} dims={dims}')
+        print('--------')
+
+    def _dump_nodeargs(self, graph: fbs.Graph):
+        print('NodeArgs:')
+        for idx in range(0, graph.NodeArgsLength()):
+            node_arg = graph.NodeArgs(idx)
+            type = node_arg.Type()
+            if not type:
+                # NodeArg for optional value that does not exist
+                continue
+
+            type_str = FbsTypeInfo.typeinfo_to_str(type)
+            value_type = type.ValueType()
+            value = type.Value()
+            dims = None
+            if value_type == fbs.TypeInfoValue.TypeInfoValue.tensor_type:
+                tensor_type_and_shape = fbs.TensorTypeAndShape.TensorTypeAndShape()
+                tensor_type_and_shape.Init(value.Bytes, value.Pos)
+                shape = tensor_type_and_shape.Shape()
+                if shape:
+                    dims = []
+                    for dim in range(0, shape.DimLength()):
+                        d = shape.Dim(dim).Value()
+                        if d.DimType() == fbs.DimensionValueType.DimensionValueType.VALUE:
+                            dims.append(str(d.DimValue()))
+                        elif d.DimType() == fbs.DimensionValueType.DimensionValueType.PARAM:
+                            dims.append(d.DimParam().decode())
+                        else:
+                            dims.append('?')
+            else:
+                dims = None
+
+            print(f'{node_arg.Name().decode()} type={type_str} dims={dims}')
+        print('--------')
+
+    def _dump_node(self, node: fbs.Node):
+        optype = node.OpType().decode()
+        domain = node.Domain().decode() or 'ai.onnx'  # empty domain defaults to ai.onnx
+
+        inputs = [node.Inputs(i).decode() for i in range(0, node.InputsLength())]
+        outputs = [node.Outputs(i).decode() for i in range(0, node.OutputsLength())]
+        print(f'{node.Index()}:{node.Name().decode()}({domain}:{optype}) '
+              f'inputs=[{",".join(inputs)} outputs=[{",".join(outputs)}]')
+
+    def _dump_graph(self, graph: fbs.Graph):
+        '''
+        Process one level of the Graph, descending into any subgraphs when they are found
+        '''
+
+        self._dump_initializers(graph)
+        self._dump_nodeargs(graph)
+        print('Nodes:')
+        for i in range(0, graph.NodesLength()):
+            node = graph.Nodes(i)
+            self._dump_node(node)
+
+            # Read all the attributes
+            for j in range(0, node.AttributesLength()):
+                attr = node.Attributes(j)
+                attr_type = attr.Type()
+                if attr_type == fbs.AttributeType.AttributeType.GRAPH:
+                    print(f'## Subgraph for {node.OpType().decode()}.{attr.Name().decode()} ##')
+                    self._dump_graph(attr.G())
+                    print(f'## End {node.OpType().decode()}.{attr.Name().decode()} Subgraph ##')
+                elif attr_type == fbs.AttributeType.AttributeType.GRAPHS:
+                    # the ONNX spec doesn't currently define any operators that have multiple graphs in an attribute
+                    # so entering this 'elif' isn't currently possible
+                    print(f'## Subgraphs for {node.OpType().decode()}.{attr.Name().decode()} ##')
+                    for k in range(0, attr.GraphsLength()):
+                        print(f'## Subgraph {k} ##')
+                        self._dump_graph(attr.Graphs(k))
+                        print(f'## End Subgraph {k} ##')
+
+    def dump(self, output: typing.IO):
+        graph = self._model.Graph()
+
+        original_stdout = sys.stdout
+        sys.stdout = output
+        self._dump_graph(graph)
+        sys.stdout = original_stdout
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(os.path.basename(__file__),
+                                     description='Dump an ORT format model. Output is to <model_path>.txt')
+    parser.add_argument('--stdout', action='store_true', help='Dump to stdout instead of writing to file.')
+    parser.add_argument('model_path', help='Path to ORT format model')
+    args = parser.parse_args()
+
+    if not os.path.isfile(args.model_path):
+        parser.error(f'{args.model_path} is not a file.')
+
+    return args
+
+
+def main():
+    args = parse_args()
+    d = OrtFormatModelDumper(args.model_path)
+
+    if args.stdout:
+        d.dump(sys.stdout)
+    else:
+        output_filename = args.model_path + ".txt"
+        with open(output_filename, "w", encoding="utf-8") as ofile:
+            d.dump(ofile)
+
+
+if __name__ == '__main__':
+    main()