diff --git a/nirtorch/__init__.py b/nirtorch/__init__.py
index a896416..fd5e954 100644
--- a/nirtorch/__init__.py
+++ b/nirtorch/__init__.py
@@ -1,5 +1,5 @@
 from .from_nir import load  # noqa F401
 from .graph import extract_torch_graph  # noqa F401
-from .to_nir import extract_nir_graph  # noqa F401
+from .to_nir import extract_nir_graph, trace_nir_graph  # noqa F401
 
 __version__ = version = "1.0"
diff --git a/nirtorch/graph_fx.py b/nirtorch/graph_fx.py
new file mode 100644
index 0000000..2e76c07
--- /dev/null
+++ b/nirtorch/graph_fx.py
@@ -0,0 +1,207 @@
+from typing import Any, Callable, Dict, Set, Tuple, TypeAlias, Optional
+import operator
+
+import numpy as np
+from torch.nn.modules import Module
+
+import nir
+import torch
+from torch.fx import Graph, GraphModule, Node, Tracer, Transformer
+from torch.fx.passes import shape_prop
+
+
+class NIRTorchTracer(Tracer):
+
+    def __init__(self, custom_leaf_modules: Tuple[torch.nn.Module] = None, **kwargs):
+        """Extends PyTorch's default symbolic tracing with a set of custom leaf nodes"""
+        super().__init__(**kwargs)
+        if custom_leaf_modules is not None and not isinstance(
+            custom_leaf_modules, tuple
+        ):
+            custom_leaf_modules = tuple(custom_leaf_modules)
+        self.custom_leaf_modules = custom_leaf_modules
+
+    def is_leaf_module(self, m: torch.nn.Module, module_qualified_name: str) -> bool:
+        """
+        A method to specify whether a given ``nn.Module`` is a "leaf" module.
+        Leaf modules are the atomic units that appear in
+        the IR, referenced by ``call_module`` calls. By default,
+        Modules in the PyTorch standard library namespace (torch.nn)
+        are leaf modules. All other modules are traced through and
+        their constituent ops are recorded, unless specified otherwise
+        via this parameter.
+        Args:
+            m (Module): The module being queried about
+            module_qualified_name (str): The path to root of this module. For example,
+                if you have a module hierarchy where submodule ``foo`` contains
+                submodule ``bar``, which contains submodule ``baz``, that module will
+                appear with the qualified name ``foo.bar.baz`` here.
+        """
+        # Tests that the module is in the list of custom leaves
+        if self.custom_leaf_modules and isinstance(m, self.custom_leaf_modules):
+            return True
+
+        if hasattr(m, "_is_leaf_module") and m._is_leaf_module:
+            return True
+
+        return super().is_leaf_module(m, module_qualified_name)
+
+
+class NIRTorchTransformer(Transformer):
+    def call_function(self, target: str, args: Tuple, kwargs: Dict) -> Any:
+        print("sup", target)
+        return super().call_function(target, args, kwargs)
+
+    def call_method(self, target: str, args: Tuple, kwargs: Dict) -> Any:
+        return super().call_method(target, args, kwargs)
+
+    def call_module(self, target, args, kwargs):
+        print("mod", target)
+        return super().call_module(target, args, kwargs)
+
+
+def trace_torch_graph(
+    module: torch.nn.Module,
+    module_map: Dict[torch.nn.Module, Callable[[torch.nn.Module], nir.NIRNode]],
+    default_dict: Optional[
+        Dict[torch.nn.Module, Callable[[torch.nn.Module], nir.NIRNode]]
+    ] = None,
+) -> nir.NIRGraph:
+    """
+    Traces a PyTorch module and converts it to a NIR graph using the specified module map.
+
+    Args:
+        module (torch.nn.Module): The module of interest
+        module_map (Dict[torch.nn.Module, Callable[[torch.nn.Module], nir.NIRNode]]): A dictionary that maps
+            a given module type to a function that can convert the model to an NIRNode type
+        default_dict (Optional[Dict[torch.nn.Module, Callable[[torch.nn.Module], nir.NIRNode]]]): An optional dictionary that maps
+            a given module type to a function that can convert the model to an NIRNode type. This dictionary is merged
+            with the module_map dictionary.
+    """
+    # Merge the default dictionary, if it exists
+    if default_dict is not None:
+        module_map = module_map | default_dict
+
+    # Cover the edge case that the incoming module is a leaf node
+    if module.__class__ in module_map:
+        return module_map[module.__class__](module)
+
+    # Trace the graph
+    tracer = NIRTorchTracer(module_map.keys())
+    traced = tracer.trace(module)
+
+    if len(traced.nodes) == 2 and len(list(tracer.root.children())) == 0:
+        raise ValueError(
+            "The module is a leaf node, but does not appear in the module map. We cannot trace it further"
+        )
+
+    graph_module = GraphModule(tracer.root, traced)
+
+    # Create NIR nodes
+    nodes = {}
+    edges = []
+    ignored_nodes = set()
+    skipped_nodes = set()
+
+    def _find_users(node: Node) -> Set[Node]:
+        """
+        Finds all the users (outputs) of a given node, recursively if the node is registered as a skipped node
+        """
+        nodes = set()
+        for user in node.users:
+            if user in ignored_nodes:
+                continue
+            elif user in skipped_nodes:
+                nodes |= _find_users(user)
+            else:
+                nodes.add(user)
+        return nodes
+
+    def _find_inputs(node: Node) -> Set[Node]:
+        """
+        Finds all the inputs (inputs) of a given node, recursively if the node is registered as a skipped node
+        """
+        nodes = set()
+        for in_node in node.all_input_nodes:
+            if in_node in ignored_nodes:
+                continue
+            elif in_node in skipped_nodes:
+                nodes |= _find_inputs(in_node)
+            else:
+                nodes.add(in_node)
+        return nodes
+
+    for node in traced.nodes:
+        # Add Node
+        if node.op == "placeholder":
+            if node.target == "input" or node.prev.op == "root":
+                nodes[str(node.name)] = nir.Input(np.array([1]))
+            else:
+                ignored_nodes.add(node)
+                continue
+        elif node.op == "output":
+            nodes[str(node.name)] = nir.Output(np.array([1]))
+        elif node.op == "call_function":
+            # Ensure that we skip add nodes
+            # TODO: Consider using transformations for this
+            #       https://pytorch.org/docs/stable/fx.html#torch.fx.Transformer
+            if node.target == operator.add:
+                skipped_nodes.add(node)
+            # Raise a warning if we encounter other methods than addition
+            else:
+                raise ValueError(
+                    "The only supported function is addition. Please modify your model or raise an issue on GitHub"
+                )
+        elif node.op == "call_method":
+            # Skip add methods
+            if node.target == "add":
+                skipped_nodes.add(node)
+            else:
+                raise ValueError(
+                    "The only supported method is addition. Please modify your model or raise an issue on GitHub"
+                )
+        elif node.op == "call_module":
+            torch_module = graph_module.get_submodule(node.target)
+            nir_module = module_map[torch_module.__class__](torch_module)
+            nodes[str(node.name)] = nir_module
+        elif node.op == "get_attr":
+            # Skip attribute
+            skipped_nodes.add(node)
+        else:
+            raise ValueError(
+                f"Unsupported operation {node.op}. Please modify your model or raise an issue on GitHub"
+            )
+
+    # Create edges
+    # - This is done in a separate loop to ensure that we correctly ignore the edges in case the nodes
+    #   are ignored out-of-order
+    for node in traced.nodes:
+        if node in ignored_nodes:
+            continue
+
+        # Add edges
+        for in_node in node.all_input_nodes:
+            if in_node in ignored_nodes or in_node in skipped_nodes:
+                continue
+            # If the function is set to be skipped, we simply forward the input to all the outputs
+            if node in skipped_nodes:
+                for next_node in _find_users(node):
+                    edges.append((in_node.name, next_node.name))
+            # Ignore additions as incoming edges
+            elif in_node.op == "call_function" and in_node.target == operator.add:
+                break
+            # Otherwise, add an edge
+            elif in_node not in ignored_nodes:
+                edges.append((in_node.name, node.name))
+    graph = nir.NIRGraph(nodes=nodes, edges=edges)
+    graph.infer_types()
+    return graph
+
+
+if __name__ == "__main__":
+    module = torch.nn.Sequential(torch.nn.Linear(2, 1), torch.nn.Linear(1, 1))
+    graph = trace_torch_graph(module)
+
+    import pprint
+
+    pprint.pprint(graph)
diff --git a/nirtorch/to_nir.py b/nirtorch/to_nir.py
index 6a6de28..e37edb9 100644
--- a/nirtorch/to_nir.py
+++ b/nirtorch/to_nir.py
@@ -1,11 +1,22 @@
 import logging
-from typing import Any, Callable, Optional, Sequence
+from typing import Any, Callable, Dict, Optional, Sequence
+import warnings
 
 import nir
 import numpy as np
 import torch.nn as nn
 
 from .graph import extract_torch_graph
+from .graph_fx import trace_torch_graph
+
+
+DEFAULT_MAPS: Dict[nn.Module, Callable[[nn.Module], nir.NIRNode]] = {
+    nn.Linear: (
+        lambda module: nir.Affine(
+            module.weight.detach().numpy(), module.bias.detach().numpy()
+        )
+    )
+}
 
 
 def extract_nir_graph(
@@ -39,6 +50,11 @@ def extract_nir_graph(
     Returns:
         nir.NIR: Returns the generated NIR graph representation.
     """
+    warnings.warn(
+        "extract_nir_graph is deprecated, use trace_nir_graph instead",
+        DeprecationWarning,
+        stacklevel=2,
+    )
 
     if len(list(model.children())):
         # If the model has submodules, ignore the top level module
@@ -152,3 +168,29 @@ def extract_nir_graph(
             nir_edges.remove(edge)
 
     return nir.NIRGraph(nir_nodes, nir_edges)
+
+
+def trace_nir_graph(
+    model: nn.Module,
+    model_map: Dict[nn.Module, Callable[[nn.Module], nir.NIRNode]],
+    default_map: Optional[Dict[nn.Module, Callable[[nn.Module], nir.NIRNode]]] = DEFAULT_MAPS,
+    model_name: Optional[str] = "model",
+    ignore_submodules_of=None,
+    model_fwd_args=[],
+    ignore_dims: Optional[Sequence[int]] = None,
+) -> nir.NIRNode:
+    """
+    Given a PyTorch `model`, we trace it and recreate a NIR graph using the specified `model_map`.
+
+    Args:
+        model (nn.Module): The model of interest
+        model_map (Dict[nn.Module, Callable[[nn.Module], nir.NIRNode]]): A dictionary that maps
+            a given module type to a function that can convert the model to an NIRNode type
+        model_name (Optional[str], optional): The name of the top level module.
+            Defaults to "model".
+        ignore_submodules_of (Optional[Sequence[nn.Module]]): If specified,
+            the corresponding module's children will not be traversed for graph.
+        ignore_dims (Optional[Sequence[int]]): Dimensions of data to be ignored for
+            type/shape inference. Typically the dimensions that you will want to ignore
+            are for batch and time.
+    """
diff --git a/tests/test_graph_fx.py b/tests/test_graph_fx.py
new file mode 100644
index 0000000..c30f831
--- /dev/null
+++ b/tests/test_graph_fx.py
@@ -0,0 +1,190 @@
+import pytest
+
+import nir
+import numpy as np
+import torch
+
+from nirtorch.to_nir import DEFAULT_MAPS
+from nirtorch.graph_fx import trace_torch_graph
+
+
+def _filter_edges(graph, t1, t2):
+    return [
+        e
+        for e in graph.edges
+        if graph.nodes[e[0]].__class__ == t1 and graph.nodes[e[1]].__class__ == t2
+    ]
+
+
+def _filter_nodes(graph, t):
+    return {k: v for k, v in graph.nodes.items() if v.__class__ == t}
+
+
+def test_trace_unknown_leaf():
+    class MyModule(torch.nn.Module):
+        def forward(self, x):
+            return x
+
+    model = MyModule()
+    with pytest.raises(ValueError):
+        trace_torch_graph(model, {})
+
+
+def test_trace_default_linear():
+    model = torch.nn.Linear(1, 1)
+    graph = trace_torch_graph(model, DEFAULT_MAPS)
+    assert graph.__class__ == nir.Affine
+
+
+def test_trace_mapped_module():
+    class MyModule(torch.nn.Module):
+        def forward(self, x):
+            return x
+
+    def map_my_module(module):
+        return nir.Linear(np.array([[1]]))
+
+    model = MyModule()
+    graph = trace_torch_graph(model, {MyModule: map_my_module})
+    assert graph.__class__ == nir.Linear
+
+
+def test_trace_mapped_module_stateless():
+    class MyModule(torch.nn.Module):
+        def __init__(self):
+            super().__init__()
+            self.lin = torch.nn.Linear(1, 1)
+
+        def forward(self, x, state):
+            return self.lin(x) + state
+
+    model = MyModule()
+    graph = trace_torch_graph(model, DEFAULT_MAPS)
+    assert len(graph.nodes) == 3
+    assert len(graph.edges) == 2
+    assert len(_filter_edges(graph, nir.Input, nir.Affine)) == 1
+    assert len(_filter_edges(graph, nir.Affine, nir.Output)) == 1
+
+
+def test_trace_mapped_module_stateful():
+    class MyModule(torch.nn.Module):
+        def __init__(self):
+            super().__init__()
+            self.lin = torch.nn.Linear(1, 1)
+            self.state = torch.tensor([1.0])
+
+        def forward(self, x):
+            return self.lin(x) + self.state
+
+    model = MyModule()
+    graph = trace_torch_graph(model, DEFAULT_MAPS)
+    assert len(graph.nodes) == 3
+    assert len(graph.edges) == 2
+    assert len(_filter_edges(graph, nir.Input, nir.Affine)) == 1
+    assert len(_filter_edges(graph, nir.Affine, nir.Output)) == 1
+
+
+def test_trace_addition():
+    class MyModule(torch.nn.Module):
+        def __init__(self):
+            super().__init__()
+            self.lin = torch.nn.Linear(1, 1)
+
+        def forward(self, x):
+            return self.lin(x) + self.lin(x)
+
+    model = MyModule()
+    graph = trace_torch_graph(model, DEFAULT_MAPS)
+    assert len(graph.nodes) == 4
+    assert len(graph.edges) == 4
+    assert len(_filter_edges(graph, nir.Input, nir.Affine)) == 2
+    assert len(_filter_edges(graph, nir.Affine, nir.Output)) == 2
+
+
+def test_trace_sequential():
+    model = torch.nn.Sequential(torch.nn.Linear(2, 1), torch.nn.Linear(1, 1))
+    graph = trace_torch_graph(model, DEFAULT_MAPS)
+    assert graph.__class__ == nir.NIRGraph
+    assert len(graph.nodes) == 4
+    assert len(graph.edges) == 3
+    nodes = list(graph.nodes.items())
+    assert len(_filter_nodes(graph, nir.Input)) == 1, "We require one input node"
+    assert len(_filter_nodes(graph, nir.Output)) == 1, "We require one output node"
+    assert len(_filter_nodes(graph, nir.Affine)) == 2, "We require two affine nodes"
+
+    assert len(_filter_edges(graph, nir.Input, nir.Affine)) == 1
+    assert len(_filter_edges(graph, nir.Affine, nir.Affine)) == 1
+    assert len(_filter_edges(graph, nir.Affine, nir.Output)) == 1
+
+
+def test_trace_submodule():
+    class MyModule(torch.nn.Module):
+        def __init__(self):
+            super(MyModule, self).__init__()
+            self.linear = torch.nn.Linear(1, 1)
+
+        def forward(self, x):
+            return self.linear(x)
+
+    model = MyModule()
+    graph = trace_torch_graph(model, DEFAULT_MAPS)
+    assert graph.__class__ == nir.NIRGraph
+    assert len(graph.nodes) == 3
+    assert len(graph.edges) == 2
+    nodes = list(graph.nodes.items())
+    assert len(_filter_nodes(graph, nir.Input)) == 1, "We require one input node"
+    assert len(_filter_nodes(graph, nir.Output)) == 1, "We require one output node"
+    assert len(_filter_nodes(graph, nir.Affine)) == 1, "We require one affine node"
+
+    assert len(_filter_edges(graph, nir.Input, nir.Affine)) == 1
+    assert len(_filter_edges(graph, nir.Affine, nir.Output)) == 1
+
+
+def test_trace_nested_submodule():
+    class MyModule(torch.nn.Module):
+        def __init__(self):
+            super(MyModule, self).__init__()
+            self.linear = torch.nn.Linear(1, 1)
+
+        def forward(self, x):
+            return self.linear(x)
+
+    class MyModule2(torch.nn.Module):
+        def __init__(self):
+            super(MyModule2, self).__init__()
+            self.module = MyModule()
+            self.linear = torch.nn.Linear(1, 1)
+
+        def forward(self, x):
+            return self.linear(self.module(x))
+
+    model = MyModule2()
+    graph = trace_torch_graph(model, DEFAULT_MAPS)
+    assert graph.__class__ == nir.NIRGraph
+    assert len(graph.nodes) == 4
+    assert len(graph.edges) == 3
+
+    assert len(_filter_nodes(graph, nir.Input)) == 1, "We require one input node"
+    assert len(_filter_nodes(graph, nir.Output)) == 1, "We require one output node"
+    assert len(_filter_nodes(graph, nir.Affine)) == 2, "We require two affine nodes"
+
+    assert len(_filter_edges(graph, nir.Input, nir.Affine)) == 1
+    assert len(_filter_edges(graph, nir.Affine, nir.Affine)) == 1
+    assert len(_filter_edges(graph, nir.Affine, nir.Output)) == 1
+
+
+def test_recursive_stateful():
+    class MyModule(torch.nn.Module):
+        def __init__(self):
+            super().__init__()
+            self.linear = torch.nn.Linear(1, 1)
+            self.state = torch.tensor([1.0])
+
+        def forward(self, x):
+            out = self.linear(x + self.state)
+            self.state = out
+            return out
+
+    model = MyModule()
+    graph = trace_torch_graph(model, DEFAULT_MAPS)
+    assert graph.__class__ == nir.NIRGraph