diff --git a/examples/04-advanced/14-incremental_evaluation.py b/examples/04-advanced/14-incremental_evaluation.py
new file mode 100644
index 0000000000..60ab0a2f8a
--- /dev/null
+++ b/examples/04-advanced/14-incremental_evaluation.py
@@ -0,0 +1,64 @@
+"""
+.. _ref_incremental_evaluation:
+
+Use incremental evaluation helper
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+This example shows you how to use the incremental evaluation helper.
+"""
+
+# Import necessary modules
+from ansys.dpf import core as dpf
+from ansys.dpf.core import examples
+
+
+#######################################################################################
+# Retrieve an example to instantiate a DataSources object
+path = examples.download_transient_result()
+ds = dpf.DataSources(path)
+
+# From the DataSources object we can retrieve the scoping
+# In this example we want to compute the min/max for all the time sets
+tf_provider = dpf.operators.metadata.time_freq_provider(data_sources=ds)
+tf_support = tf_provider.get_output(output_type=dpf.types.time_freq_support)
+scoping = dpf.time_freq_scoping_factory.scoping_on_all_time_freqs(tf_support)
+
+# If you don't need to reuse TimeFreqSupport you could also use the DataSources
+# scoping = dpf.time_freq_scoping_factory.scoping_on_all_time_freqs(ds)
+
+#######################################################################################
+# Defining the workflow to exploit
+
+# Instantiating a streams_provider is important when dealing with incremental evaluation
+# due to multiple reuses of operators
+streams_provider = dpf.operators.metadata.streams_provider(data_sources=ds)
+
+# Defining the main workflow
+result_op = dpf.operators.result.stress(
+    data_sources=ds, time_scoping=scoping, streams_container=streams_provider
+)
+norm_fc = dpf.operators.math.norm_fc(result_op)
+final_op = dpf.operators.min_max.min_max_fc_inc(norm_fc)
+
+#######################################################################################
+# Obtain a new operator to retrieve outputs from
+
+# Workflow is adapted from the first and the last operator in the current workflow
+# Scoping is important to split the workload into chunks
+new_end_op = dpf.split_workflow_in_chunks(result_op, final_op, scoping)
+
+
+# Obtain results on the same pin numbers
+min = new_end_op.get_output(0, dpf.types.field)
+max = new_end_op.get_output(1, dpf.types.field)
+
+# Plot results
+import matplotlib.pyplot as plt
+
+x = tf_support.time_frequencies.data
+plt.plot(x, min.data, "b", label="Min")
+plt.plot(x, max.data, "r", label="Max")
+plt.xlabel("Time")
+plt.ylabel("Stress")
+plt.legend()
+plt.show()
diff --git a/src/ansys/dpf/core/__init__.py b/src/ansys/dpf/core/__init__.py
index e6932c12ed..39132638cd 100644
--- a/src/ansys/dpf/core/__init__.py
+++ b/src/ansys/dpf/core/__init__.py
@@ -87,9 +87,11 @@
     LicenseContextManager
 )
 from ansys.dpf.core.unit_system import UnitSystem, unit_systems
+from ansys.dpf.core.incremental import IncrementalHelper, split_workflow_in_chunks
+from ansys.dpf.core.any import Any
 from ansys.dpf.core.mesh_info import MeshInfo
 from ansys.dpf.core.generic_data_container import GenericDataContainer
-from ansys.dpf.core.any import Any
+
 
 # for matplotlib
 # solves "QApplication: invalid style override passed, ignoring it."
diff --git a/src/ansys/dpf/core/incremental.py b/src/ansys/dpf/core/incremental.py
new file mode 100644
index 0000000000..d8043ea3f9
--- /dev/null
+++ b/src/ansys/dpf/core/incremental.py
@@ -0,0 +1,318 @@
+"""
+.. _ref_incremental:
+
+Incremental
+===========
+"""
+
+from ansys.dpf import core
+
+from typing import Dict, Any
+
+
+class IncrementalHelper:
+    """Provides an API to transform an existing workflow into an incrementally evaluating one.
+
+    It works by plugging operators into an incomplete workflow.
+
+    Example
+    -------
+    >>> from ansys.dpf import core as dpf
+    >>> from ansys.dpf.core import examples
+    >>> path = examples.find_msup_transient()
+    >>> ds = dpf.DataSources(path)
+    >>> scoping = dpf.time_freq_scoping_factory.scoping_on_all_time_freqs(ds)
+    >>>
+    >>> result_op = dpf.operators.result.displacement(data_sources=ds, time_scoping=scoping)
+    >>> minmax_op = dpf.operators.min_max.min_max_fc_inc(result_op)
+    >>>
+    >>> new_op = dpf.split_workflow_in_chunks(result_op, minmax_op, scoping, chunk_size=5)
+    >>> min_field = new_op.get_output(0, dpf.types.field)
+    >>> max_field = new_op.get_output(1, dpf.types.field)
+    """
+
+    def __init__(
+        self,
+        start_op: core.Operator,
+        end_op: core.Operator,
+        scoping: core.Scoping,
+        scoping_pin: int = None,
+    ):
+        """Constructs an IncrementalHelper object.
+
+        Given the first and the last operator of a workflow, as well as the scoping.
+
+        This class can be used to simplify the use of incremental operators, and automatically
+        be enabled to incrementally evaluate a workflow.
+
+        Under the constraint that the end_op supports incremental evaluation.
+
+        Parameters
+        ----------
+        start_op : Operator
+            First operator in the workflow to convert
+        end_op : Operator
+            Last operator in the workflow to convert (Operator providing the meaningful output)
+        scoping : Scoping
+            Scoping used to chunk the data
+        scoping_pin : int, optional
+            Pin number of the scoping on the first operator, otherwise it is deduced with types
+        """
+        # Input operator should accept a scoping
+        # Last operator should support incremental evaluation
+        # but as we don't have a consistent method to check,
+        # it should be permissive in the case the specification isn't up to date
+        self._start_op = start_op
+        self._end_op = self._map_to_incremental(end_op)
+
+        self._scoping = scoping
+        self._scoping_pin = self._find_scoping_pin(scoping_pin)
+
+    def estimate_size(self, max_bytes: int, _dict_inputs: Dict[int, Any] = {}) -> int:
+        """Estimates the chunk size from the estimated number of bytes outputted in one iteration.
+
+        Estimation is based on the size of the output for one ID of the given time_scoping,
+        so it will run the operator for only one iteration.
+
+        It only supports Field and FieldContainer.
+        For other types, you should specify chunk_size argument in the split() method.
+
+        Parameters
+        ----------
+            max_bytes : int
+                Max allowed size of an output from the first operator, for one iteration (in bytes).
+            _dict_inputs: dict[int,any]
+                Dictionary associating pin number to inputs, for evaluating output of one iteration.
+        """
+        # Evaluate for the first element to try to guess memory consumption
+        # It is best to use with a lot of elements
+        first_id = self._scoping.ids[0]
+        srv = self._scoping._server
+        loc = self._scoping.location
+        _dict_inputs[self._scoping_pin] = core.Scoping(server=srv, ids=[first_id], location=loc)
+
+        outputs = self._prerun(_dict_inputs)
+
+        _outputs = outputs._outputs
+        data = map(lambda o: o.get_data(), _outputs)
+        # Output sizes of all inputs for one iteration
+        sizes = map(lambda obj: self._compute_size(obj), data)
+
+        # Total size for one ID in the scoping
+        size_for_one = sum(sizes)
+        # total_size = size_for_one * self._scoping.size
+
+        num_iter = int(max_bytes / size_for_one)
+        num_iter = min(max(num_iter, 1), self._scoping.size)  # clamp(num_iter, 1, scoping size)
+        return num_iter  # math.gcd(num_iter, self._scoping.size)
+
+    def _compute_size(self, obj):
+        if isinstance(obj, core.FieldsContainer):
+            fc = obj
+            return self._compute_size(fc[0])
+        elif isinstance(obj, core.Field):
+            field = obj
+            # Double = 8 bytes assumption
+            return field.size * 8
+
+        raise NotImplementedError()
+
+    def _prerun(self, _dict_inputs: Dict[int, Any]):
+        """"""
+
+        for pin_idx, val in _dict_inputs.items():
+            self._start_op.connect(pin_idx, val)
+        self._start_op.run()
+        return self._start_op.outputs
+
+    # Transforms a user workflow:
+    #
+    #           +----------+    +---------------+    +---------+
+    # scoping ->| start_op | -> | middle ops... | -> | end_op  | ->
+    #           +----------+    +---------------+    +---------+
+    #
+    # Into a new workflow like this:
+    #
+    #           +----------+    +---------------+    +---------+
+    # scoping ->| start_op | -> | middle ops... | -> | end_op  |
+    #   \       +----------+    +---------------+    +---------+
+    #    \           \                                  |
+    #     \           \   +------------------+          |                    (pins remaps)
+    #      \           \> |                  |          |   +----------+    +-----------+
+    #       \ scop_pin -> | chunk_in         |          +-> |          | -> | forward   | -> final
+    #        +----------> |   for_each_range |  iterables   |          |    | (new end) |    outputs
+    #       chunk_size -> |                  | -----------> | for_each |    +-----------+
+    #                     +------------------+              |          |
+    #                                     end_input_pin-->  |          |
+    #                                                       +----------+
+    def split(
+        self, chunk_size: int, end_input_pin: int = 0, rescope: bool = False
+    ) -> core.Operator:
+        """Integrate given operators into a new workflow enabling incremental evaluation.
+
+        Given a chunk size (multiple of given scoping), it will provide a new operator to retrieve
+        outputs from, and enable incremental evaluation, notably reducing peak memory usage.
+
+        Parameters
+        ----------
+            chunk_size : int
+                Number of iterations per run
+            end_input_pin : int, optional
+                Pin number of the output to use from the first operator (default = 0)
+            rescope : bool, optional
+                Rescope all the outputs based on the given scoping (default = False)
+        """
+        # Enables incremental evaluation:
+        # Using for_each, chunk_in_for_each_range and incremental version of the last operator
+        # by returning two operators with remapped inputs and outputs to other operators
+
+        _server = self._start_op._server
+
+        for_each = core.Operator("for_each", server=_server)
+        split_in_range = core.Operator("chunk_in_for_each_range", server=_server)
+        forward = core.Operator("forward", server=_server)
+
+        split_in_range.connect_operator_as_input(1, self._start_op)
+        split_in_range.connect(2, self._scoping_pin)
+        split_in_range.connect(3, self._scoping)
+        split_in_range.connect(4, chunk_size)
+
+        for_each.connect(0, split_in_range, 0)
+        for_each.connect(2, end_input_pin)
+
+        # connect inputs
+        dict_outputs = core.Operator.operator_specification(
+            op_name=self._end_op.name, server=_server
+        ).outputs
+        if not dict_outputs:
+            # temporary patch for incremental:: operators
+            dict_outputs = {0: None}
+
+        fe_pin_idx = 3  # see doc of for_each
+        for pin_idx in dict_outputs.keys():
+            # connect end_op to for_each
+            for_each.connect(fe_pin_idx, self._end_op, pin_idx)
+            # remap
+            forward.connect(pin_idx, for_each, fe_pin_idx)
+            fe_pin_idx += 1
+
+        output = forward
+
+        if rescope:
+            new_forward = core.Operator("forward")
+            for pin_idx in dict_outputs.keys():
+                rescope = core.Operator("Rescope")
+                rescope.connect(0, forward, pin_idx)
+                rescope.connect(1, self._scoping)
+                new_forward.connect(pin_idx, rescope, 0)
+
+            output = new_forward
+        return output
+
+    def _map_to_incremental(self, end_op: core.Operator):
+        # The goal of this function is to validate that a given operator is indeed incremental.
+        # If an operator is found to not support incremental evaluation, it must not be strict
+        # it should only output warnings
+        # because this function -by design- may be outdated.
+        inc_operators = [
+            "accumulate_level_over_label_fc",
+            "accumulate_min_over_label_fc",
+            "accumulate_over_label_fc",
+            "average_over_label_fc",
+            "min_max_inc",
+            "min_max_fc_inc",
+            "max_over_time_by_entity",
+            "min_max_over_time_by_entity",
+            "min_max_by_time",
+            "min_over_time_by_entity",
+            "time_of_max_by_entity",
+            "time_of_min_by_entity",
+            "incremental::merge::property_field",
+            "incremental::merge::mesh",
+            "incremental::merge::field",
+            "incremental::merge::fields_container",
+        ]
+
+        map_to_inc = {"min_max": "min_max_inc", "min_max_fc": "min_max_fc_inc"}
+
+        if end_op.name not in inc_operators:
+            print(f"WARNING: Operator named {end_op.name} may not support incremental evaluation")
+            if end_op.name in map_to_inc.keys():
+                print(
+                    f"An operator named {map_to_inc[end_op.name]} supports incremental evaluation"
+                )
+
+        if "incremental" in end_op.config.available_config_options:
+            end_op.config.set_config_option("incremental", True)
+
+        return end_op
+
+    def _find_scoping_pin(self, pin_idx):
+        dict_inputs = self._start_op.inputs._dict_inputs
+        # validate given pin_idx
+        if pin_idx != None and pin_idx in dict_inputs:
+            pin_spec = dict_inputs[pin_idx]
+            if "scoping" in pin_spec.type_names:
+                return pin_idx
+
+        # look for scoping pin
+        for pin_idx, spec in dict_inputs.items():
+            if "scoping" in spec.type_names:
+                return pin_idx
+
+        raise Exception(
+            f"Scoping pin could not be found in start_op with name '{self._start_op.name}'"
+        )
+
+
+def split_workflow_in_chunks(
+    start_op: core.Operator,
+    end_op: core.Operator,
+    scoping: core.Scoping,
+    rescope: bool = False,
+    max_bytes: int = 1024**3,
+    dict_inputs: Dict[int, Any] = {},
+    chunk_size: int = None,
+    scoping_pin: int = None,
+    end_input_pin: int = 0,
+):
+    """Transforms a workflow into an incrementally evaluating one.
+
+    It wraps in one method the functionality of the IncrementalHelper class as well
+    as the estimation of the chunk size.
+
+    If no chunk_size is specified, the function will attempt to estimate the value
+    by calling IncrementalHelper.estimate_size(max_bytes, dict_inputs).
+
+    If no scoping_pin is specified, the function will attempt to deduce the correct pin,
+    which would be the first input pin matching a scoping type.
+
+    Parameters
+    ----------
+        start_op : Operator
+            Initial operator of the workflow to convert
+        end_op : Operator
+            Last operator of the workflow to convert
+        scoping : Scoping
+            Scoping to split across multiple evaluation
+        rescope : bool, optional
+            If enabled, will rescope final outputs with the given scoping (default = False)
+        max_bytes : int, optional
+            Max allowed size for the output from the first operator (default = 1024**3)
+        dict_inputs : dict[int, any], optional
+            Inputs to pass to the first operator, used only for the estimation run (default = {})
+        chunk_size = int, optional
+            Maximum number of scoping elements to process in an iteration (default = None)
+        scoping_pin : int, optional
+            The pin number on the first operator to bind the scoping (default = None)
+        end_input_pin : int, optional
+            Pin number of the output to use from the first operator(default = 0)
+    """
+    splitter = IncrementalHelper(start_op, end_op, scoping, scoping_pin)
+
+    if chunk_size == None:
+        print(f"Estimating chunk_size with max_bytes: {max_bytes}")
+        chunk_size = splitter.estimate_size(max_bytes, dict_inputs)
+        print(f"Done. chunk_size set to {chunk_size} (scoping size: {scoping.size})")
+
+    return splitter.split(chunk_size, end_input_pin, rescope)
diff --git a/src/ansys/dpf/core/time_freq_scoping_factory.py b/src/ansys/dpf/core/time_freq_scoping_factory.py
index d6c6a82b3c..49e83f22e0 100644
--- a/src/ansys/dpf/core/time_freq_scoping_factory.py
+++ b/src/ansys/dpf/core/time_freq_scoping_factory.py
@@ -9,6 +9,12 @@
 from ansys.dpf.core import errors as dpf_errors
 from ansys.dpf.core.common import locations
 from ansys.dpf.core.model import Model
+from ansys.dpf.core.time_freq_support import TimeFreqSupport
+from ansys.dpf.core.data_sources import DataSources
+from ansys.dpf.core.operators.metadata import time_freq_provider
+
+from ansys.dpf.core import types
+from typing import Union
 
 
 def scoping_by_load_step(load_step: int, server=None):
@@ -164,7 +170,7 @@ def scoping_by_step_and_substep_from_model(load_step_id, subset_id, model, serve
     return scoping_by_step_and_substep(load_step_id, subset_id, model.metadata.time_freq_support)
 
 
-def scoping_on_all_time_freqs(tf_support_or_model):
+def scoping_on_all_time_freqs(obj: Union[TimeFreqSupport, Model, DataSources]):
     """Create a specific :class:`ansys.dpf.core.Scoping` with all time or
     frequency sets of a :class:`ansys.dpf.core.TimeFreqSupport` or a class:`ansys.dpf.core.Model`
 
@@ -177,10 +183,21 @@ def scoping_on_all_time_freqs(tf_support_or_model):
     scoping : Scoping
         Scoping with all time or frequency sets IDs.
     """
-    if isinstance(tf_support_or_model, Model):
-        tf_support_or_model = tf_support_or_model.metadata.time_freq_support
+    tf_support = None
+    if isinstance(obj, TimeFreqSupport):
+        tf_support = obj
+    elif isinstance(obj, Model):
+        tf_support = obj.metadata.time_freq_support
+    elif isinstance(obj, DataSources):
+        tf_provider = time_freq_provider(data_sources=obj, server=obj._server)
+        tf_support = tf_provider.get_output(output_type=types.time_freq_support)
+
+    if tf_support == None:
+        supported_types = f"{TimeFreqSupport}, {Model}, {DataSources}"
+        raise TypeError(f"Given type was {type(obj)} while accepted types are {supported_types}")
+
     return Scoping(
-        ids=range(1, len(tf_support_or_model.time_frequencies) + 1),
+        ids=range(1, len(tf_support.time_frequencies) + 1),
         location=locations.time_freq,
-        server=tf_support_or_model._server,
+        server=tf_support._server,
     )
diff --git a/tests/test_incremental.py b/tests/test_incremental.py
new file mode 100644
index 0000000000..6df9e76b8e
--- /dev/null
+++ b/tests/test_incremental.py
@@ -0,0 +1,159 @@
+from ansys.dpf import core
+from ansys.dpf.core import common
+import numpy as np
+import pytest
+import conftest
+
+
+@pytest.mark.skipif(
+    not conftest.SERVERS_VERSION_GREATER_THAN_OR_EQUAL_TO_6_0,
+    reason="for_each not implemented below 6.0",
+)
+def test_incremental_results(server_type, plate_msup):
+    ds = core.DataSources(plate_msup, server=server_type)
+    scoping = core.time_freq_scoping_factory.scoping_on_all_time_freqs(ds)
+
+    def create_wf():
+        res_op = core.operators.result.displacement(
+            data_sources=ds, time_scoping=scoping, server=server_type
+        )
+        concat_op = core.Operator("incremental::merge::fields_container", server=server_type)
+        concat_op.connect(0, res_op, 0)
+
+        return (res_op, concat_op)
+
+    # incremental
+    (start_op, end_op) = create_wf()
+    inc_op = core.split_workflow_in_chunks(start_op, end_op, scoping, chunk_size=5)
+
+    (_, ref_op) = create_wf()
+
+    inc_fc = inc_op.get_output(0, core.types.fields_container)
+    ref_fc = ref_op.get_output(0, core.types.fields_container)
+
+    assert len(inc_fc.get_time_scoping()) == len(ref_fc.get_time_scoping())
+    for id in ref_fc.get_time_scoping().ids:
+        ref_field = ref_fc.get_field_by_time_id(id)
+        inc_field = inc_fc.get_field_by_time_id(id)
+        assert np.isclose(ref_field.data, inc_field.data).all()
+
+
+@pytest.mark.skipif(
+    not conftest.SERVERS_VERSION_GREATER_THAN_OR_EQUAL_TO_6_0,
+    reason="for_each not implemented below 6.0",
+)
+def test_incremental_minmax(server_type, plate_msup):
+    ds = core.DataSources(plate_msup, server=server_type)
+    scoping = core.time_freq_scoping_factory.scoping_on_all_time_freqs(ds)
+
+    def create_wf():
+        res_op = core.operators.result.displacement(
+            data_sources=ds, time_scoping=scoping, server=server_type
+        )
+        minmax_op = core.operators.min_max.min_max_fc_inc(res_op, server=server_type)
+
+        return (res_op, minmax_op)
+
+    # incremental
+    (start_op, end_op) = create_wf()
+    inc_op = core.split_workflow_in_chunks(start_op, end_op, scoping, chunk_size=5)
+
+    # reference
+    (_, ref_op) = create_wf()
+
+    # assert
+    inc_field_min = inc_op.get_output(0, core.types.field)
+    inc_field_max = inc_op.get_output(1, core.types.field)
+    ref_field_min = ref_op.get_output(0, core.types.field)
+    ref_field_max = ref_op.get_output(1, core.types.field)
+
+    assert np.isclose(ref_field_min.data, inc_field_min.data).all()
+    assert np.isclose(ref_field_max.data, inc_field_max.data).all()
+
+
+@pytest.mark.skipif(
+    not conftest.SERVERS_VERSION_GREATER_THAN_OR_EQUAL_TO_6_0,
+    reason="for_each not implemented below 6.0",
+)
+def test_incremental_accumulate(server_type, plate_msup):
+    ds = core.DataSources(plate_msup, server=server_type)
+    scoping = core.time_freq_scoping_factory.scoping_on_all_time_freqs(ds)
+
+    def create_wf():
+        res_op = core.operators.result.displacement(
+            data_sources=ds, time_scoping=scoping, server=server_type
+        )
+        acc_op = core.operators.math.accumulate_over_label_fc(res_op, server=server_type)
+        acc_op.connect(1, common.DefinitionLabels.time)
+
+        return (res_op, acc_op)
+
+    # incremental
+    (start_op, end_op) = create_wf()
+    inc_op = core.split_workflow_in_chunks(start_op, end_op, scoping, chunk_size=5)
+
+    # reference
+    (_, ref_op) = create_wf()
+
+    # assert
+    inc_field = inc_op.get_output(0, core.types.field)
+    ref_field = ref_op.get_output(0, core.types.field)
+
+    assert np.isclose(ref_field.data, inc_field.data).all()
+
+
+@pytest.mark.skipif(
+    not conftest.SERVERS_VERSION_GREATER_THAN_OR_EQUAL_TO_6_0,
+    reason="for_each not implemented below 6.0",
+)
+def test_incremental_average(server_type, plate_msup):
+    ds = core.DataSources(plate_msup, server=server_type)
+    scoping = core.time_freq_scoping_factory.scoping_on_all_time_freqs(ds)
+
+    def create_wf():
+        res_op = core.operators.result.displacement(
+            data_sources=ds, time_scoping=scoping, server=server_type
+        )
+        avg_op = core.operators.math.average_over_label_fc(res_op, server=server_type)
+        avg_op.connect(1, common.DefinitionLabels.time)
+        return (res_op, avg_op)
+
+    # incremental
+    (start_op, end_op) = create_wf()
+    inc_op = core.split_workflow_in_chunks(start_op, end_op, scoping, chunk_size=5)
+
+    # reference
+    (_, ref_op) = create_wf()
+
+    # assert
+    inc_field = inc_op.get_output(0, core.types.field)
+    ref_field = ref_op.get_output(0, core.types.field)
+
+    assert np.isclose(ref_field.data, inc_field.data).all()
+
+
+@pytest.mark.skipif(
+    not conftest.SERVERS_VERSION_GREATER_THAN_OR_EQUAL_TO_6_0,
+    reason="for_each not implemented below 6.0",
+)
+def test_incremental_estimation(server_type, plate_msup):
+    ds = core.DataSources(plate_msup, server=server_type)
+    scoping = core.time_freq_scoping_factory.scoping_on_all_time_freqs(ds)
+
+    def create_wf():
+        res_op = core.operators.result.displacement(
+            data_sources=ds, time_scoping=scoping, server=server_type
+        )
+        concat_op = core.Operator("incremental::merge::fields_container", server=server_type)
+        concat_op.connect(0, res_op, 0)
+
+        return (res_op, concat_op)
+
+    # incremental
+    (start_op, end_op) = create_wf()
+
+    helper = core.IncrementalHelper(start_op, end_op, scoping, scoping_pin=0)
+    # 9432 bytes per output according to the heuristic for this plate_msup example
+    chunk_size = helper.estimate_size(max_bytes=50 * 1024)
+
+    assert chunk_size == 5