diff --git a/flax/nnx/summary.py b/flax/nnx/summary.py
new file mode 100644
index 0000000000..9348079e20
--- /dev/null
+++ b/flax/nnx/summary.py
@@ -0,0 +1,395 @@
+# Copyright 2024 The Flax Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+import functools
+import inspect
+import io
+import typing as tp
+from itertools import groupby
+from types import MappingProxyType
+
+import jax
+import rich.console
+import rich.table
+import rich.text
+import yaml
+import jax.numpy as jnp
+
+from flax import nnx
+from flax import typing
+from flax.nnx import graph, rnglib, statelib, variablelib
+
+try:
+  from IPython import get_ipython
+
+  in_ipython = get_ipython() is not None
+except ImportError:
+  in_ipython = False
+
+
+class ObjectInfo(tp.NamedTuple):
+  path: statelib.PathParts
+  stats: dict[type[variablelib.Variable], typing.SizeBytes]
+
+
+def _collect_stats(
+  path: statelib.PathParts,
+  node: tp.Any,
+  node_stats: dict[int, ObjectInfo],
+  object_types: set[type],
+):
+  if not graph.is_node(node) and not isinstance(node, variablelib.Variable):
+    raise ValueError(f'Expected a graph node or Variable, got {type(node)!r}.')
+
+  if id(node) in node_stats:
+    return
+
+  stats: dict[type[variablelib.Variable], typing.SizeBytes] = {}
+  node_stats[id(node)] = ObjectInfo(path, stats)
+
+  if isinstance(node, nnx.Object):
+    node._nnx_tabulate_id = id(node)  # type: ignore
+    object_types.add(type(node))
+
+  if isinstance(node, variablelib.Variable):
+    var_type = type(node)
+    if issubclass(var_type, nnx.RngState):
+      var_type = nnx.RngState
+    size_bytes = typing.value_stats(node.value)
+    if size_bytes:
+      stats[var_type] = size_bytes
+
+  else:
+    node_dict = graph.get_node_impl(node).node_dict(node)
+    for key, value in node_dict.items():
+      if id(value) in node_stats:
+        continue
+      if graph.is_node(value) or isinstance(value, variablelib.Variable):
+        _collect_stats((*path, key), value, node_stats, object_types)
+        child_info = node_stats[id(value)]
+        for var_type, size_bytes in child_info.stats.items():
+          if var_type in stats:
+            stats[var_type] += size_bytes
+          else:
+            stats[var_type] = size_bytes
+
+
+class CallInfo(tp.NamedTuple):
+  object_id: int
+  inputs: tp.Any
+  outputs: tp.Any
+
+
+def get_method_wrapper(method: tp.Callable) -> tp.Callable:
+  @functools.wraps(method)
+  def method_wrapper(obj, *args, **kwargs):
+    return method(obj, *args, **kwargs)
+
+  return method_wrapper
+
+
+def _call_obj(object_types: set[type], obj, *args, **kwargs):
+  original_methods: dict[type, dict[str, tp.Callable]] = {}
+  for obj_type in object_types:
+    methods: dict[str, tp.Callable] = {}
+    for name, method in inspect.getmembers(obj_type, inspect.isfunction):
+      if not name.startswith('_') or name == '__call__':
+        methods[name] = method
+        method_wrapper = get_method_wrapper(method)
+        setattr(obj_type, name, method_wrapper)
+
+    original_methods[obj_type] = methods
+
+
+def tabulate(
+  obj,
+  depth: int | None = None,
+  table_kwargs: tp.Mapping[str, tp.Any] = MappingProxyType({}),
+  column_kwargs: tp.Mapping[str, tp.Any] = MappingProxyType({}),
+  console_kwargs: tp.Mapping[str, tp.Any] = MappingProxyType({}),
+) -> str:
+  """Creates a summary of the graph object represented as a table.
+
+  The table summarizes the object's state and metadata. The table is
+  structured as follows:
+
+  - The first column represents the path of the object in the graph.
+  - The second column represents the type of the object.
+  - The following columns provide information about the object's state,
+    grouped by Variable types.
+
+  Example:
+
+    >>> from flax import nnx
+    ...
+    >>> class Block(nnx.Module):
+    ...   def __init__(self, din, dout, rngs: nnx.Rngs):
+    ...     self.linear = nnx.Linear(din, dout, rngs=rngs)
+    ...     self.bn = nnx.BatchNorm(dout, rngs=rngs)
+    ...     self.dropout = nnx.Dropout(0.2, rngs=rngs)
+    ...
+    ...   def __call__(self, x):
+    ...     return nnx.relu(self.dropout(self.bn(self.linear(x))))
+    ...
+    >>> class Foo(nnx.Module):
+    ...   def __init__(self, rngs: nnx.Rngs):
+    ...     self.block1 = Block(32, 128, rngs=rngs)
+    ...     self.block2 = Block(128, 10, rngs=rngs)
+    ...
+    ...   def __call__(self, x):
+    ...     return self.block2(self.block1(x))
+    ...
+    >>> foo = Foo(nnx.Rngs(0))
+    >>> # print(nnx.tabulate(foo))
+
+                                                     Foo Summary
+    ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━┓
+    ┃ path                        ┃ type      ┃ BatchStat          ┃ Param                   ┃ RngState            ┃
+    ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━┩
+    │ block1/bn                   │ BatchNorm │ mean: float32[128] │ bias: float32[128]      │                     │
+    │                             │           │ var: float32[128]  │ scale: float32[128]     │                     │
+    │                             │           │                    │                         │                     │
+    │                             │           │ 256 (1.0 KB)       │ 256 (1.0 KB)            │                     │
+    ├─────────────────────────────┼───────────┼────────────────────┼─────────────────────────┼─────────────────────┤
+    │ block1/dropout/rngs/default │ RngStream │                    │                         │ count:              │
+    │                             │           │                    │                         │   value: uint32[]   │
+    │                             │           │                    │                         │   tag: default      │
+    │                             │           │                    │                         │ key:                │
+    │                             │           │                    │                         │   value: key<fry>[] │
+    │                             │           │                    │                         │   tag: default      │
+    │                             │           │                    │                         │                     │
+    │                             │           │                    │                         │ 2 (12 B)            │
+    ├─────────────────────────────┼───────────┼────────────────────┼─────────────────────────┼─────────────────────┤
+    │ block1/linear               │ Linear    │                    │ bias: float32[128]      │                     │
+    │                             │           │                    │ kernel: float32[32,128] │                     │
+    │                             │           │                    │                         │                     │
+    │                             │           │                    │ 4,224 (16.9 KB)         │                     │
+    ├─────────────────────────────┼───────────┼────────────────────┼─────────────────────────┼─────────────────────┤
+    │ block2/bn                   │ BatchNorm │ mean: float32[10]  │ bias: float32[10]       │                     │
+    │                             │           │ var: float32[10]   │ scale: float32[10]      │                     │
+    │                             │           │                    │                         │                     │
+    │                             │           │ 20 (80 B)          │ 20 (80 B)               │                     │
+    ├─────────────────────────────┼───────────┼────────────────────┼─────────────────────────┼─────────────────────┤
+    │ block2/linear               │ Linear    │                    │ bias: float32[10]       │                     │
+    │                             │           │                    │ kernel: float32[128,10] │                     │
+    │                             │           │                    │                         │                     │
+    │                             │           │                    │ 1,290 (5.2 KB)          │                     │
+    ├─────────────────────────────┼───────────┼────────────────────┼─────────────────────────┼─────────────────────┤
+    │                             │     Total │ 276 (1.1 KB)       │ 5,790 (23.2 KB)         │ 2 (12 B)            │
+    └─────────────────────────────┴───────────┴────────────────────┴─────────────────────────┴─────────────────────┘
+
+                                           Total Parameters: 6,068 (24.3 KB)
+
+
+  Note that ``block2/dropout`` is not shown in the table because it shares the
+  same ``RngState`` with ``block1/dropout``.
+
+  Args:
+    obj: A object to summarize. It can a pytree or a graph objects
+      such as nnx.Module or nnx.Optimizer.
+    depth: The depth of the table.
+    table_kwargs: An optional dictionary with additional keyword arguments
+      that are passed to ``rich.table.Table`` constructor.
+    column_kwargs: An optional dictionary with additional keyword arguments
+      that are passed to ``rich.table.Table.add_column`` when adding columns to
+      the table.
+    console_kwargs: An optional dictionary with additional keyword arguments
+      that are passed to `rich.console.Console` when rendering the table.
+      Default arguments are  ``'force_terminal': True``, and ``'force_jupyter'``
+      is set to ``True`` if the code is running in a Jupyter notebook, otherwise
+      it is set to ``False``.
+
+  Returns:
+    A string summarizing the object.
+  """
+  _console_kwargs = {'force_terminal': True, 'force_jupyter': in_ipython}
+  _console_kwargs.update(console_kwargs)
+  state = graph.state(obj)
+  graph_map = dict(graph.iter_graph(obj))
+  flat_state = sorted(state.flat_state())
+
+  def key_fn(
+    path_state: tuple[graph.PathParts, variablelib.VariableState[tp.Any]],
+  ):
+    path, _ = path_state
+    if depth is None or len(path) <= depth:
+      return path[:-1]
+    else:
+      return path[:depth]
+
+  rows = groupby(flat_state, key_fn)
+  table = sorted((path, list(flat_states)) for path, flat_states in rows)
+
+  state_types_set = {variable_state.type for _, variable_state in flat_state}
+  # replace RngKey and RngCount with RngState
+  if rnglib.RngKey in state_types_set:
+    state_types_set.remove(rnglib.RngKey)
+    state_types_set.add(rnglib.RngState)
+  if rnglib.RngCount in state_types_set:
+    state_types_set.remove(rnglib.RngCount)
+    state_types_set.add(rnglib.RngState)
+  # sort based on MRO
+  state_types = _sort_variable_types(state_types_set)
+
+  rich_table = rich.table.Table(
+    show_header=True,
+    show_lines=True,
+    show_footer=True,
+    title=f'{type(obj).__name__} Summary',
+    **table_kwargs,
+  )
+
+  rich_table.add_column('path', **column_kwargs)
+  rich_table.add_column('type', **column_kwargs)
+
+  for state_type in state_types:
+    rich_table.add_column(state_type.__name__, **column_kwargs)
+
+  for key_path, row_states in table:
+    row: list[str] = []
+    node = graph_map[key_path]
+    type_state_groups = variablelib.split_flat_state(row_states, state_types)
+    path_str = '/'.join(map(str, key_path))
+    node_type = type(node).__name__
+    row.extend([path_str, node_type])
+
+    for state_type, type_path_and_states in zip(state_types, type_state_groups):
+      attributes = {}
+      for state_path, variable_state in type_path_and_states:
+        if len(state_path) == len(key_path) + 1:
+          name = str(state_path[-1])
+          value = variable_state.value
+          value_repr = _render_array(value) if _has_shape_dtype(value) else ''
+          metadata = variable_state.get_metadata()
+
+          if metadata:
+            attributes[name] = {
+              'value': value_repr,
+              **metadata,
+            }
+          elif value_repr:
+            attributes[name] = value_repr
+
+      if attributes:
+        col_repr = _as_yaml_str(attributes) + '\n\n'
+      else:
+        col_repr = ''
+
+      type_states = [state for _, state in type_path_and_states]
+      size_, bytes_ = _size_and_bytes(type_states)
+      col_repr += f'[bold]{_size_and_bytes_repr(size_, bytes_)}[/bold]'
+      row.append(col_repr)
+
+    rich_table.add_row(*row)
+
+  rich_table.columns[1].footer = rich.text.Text.from_markup(
+    'Total', justify='right'
+  )
+  flat_states = variablelib.split_flat_state(flat_state, state_types)
+
+  for i, (state_type, type_path_and_states) in enumerate(
+    zip(state_types, flat_states)
+  ):
+    type_states = [state for _, state in type_path_and_states]
+    size_, bytes_ = _size_and_bytes(type_states)
+    size_repr = _size_and_bytes_repr(size_, bytes_)
+    rich_table.columns[i + 2].footer = size_repr
+
+  rich_table.caption_style = 'bold'
+  rich_table.caption = (
+    f'\nTotal Parameters: {_size_and_bytes_repr(*_size_and_bytes(state))}'
+  )
+
+  return _get_rich_repr(rich_table, _console_kwargs)
+
+
+def _get_rich_repr(obj, console_kwargs):
+  f = io.StringIO()
+  console = rich.console.Console(file=f, **console_kwargs)
+  console.print(obj)
+  return f.getvalue()
+
+
+def _size_and_bytes(pytree: tp.Any) -> tuple[int, int]:
+  leaves = jax.tree.leaves(pytree)
+  size = sum(x.size for x in leaves if hasattr(x, 'size'))
+  num_bytes = sum(
+    x.size * x.dtype.itemsize for x in leaves if hasattr(x, 'size')
+  )
+  return size, num_bytes
+
+
+def _size_and_bytes_repr(size: int, num_bytes: int) -> str:
+  if not size:
+    return ''
+  bytes_repr = _bytes_repr(num_bytes)
+  return f'{size:,} [dim]({bytes_repr})[/dim]'
+
+
+def _bytes_repr(num_bytes):
+  count, units = (
+    (f'{num_bytes / 1e9:,.1f}', 'GB')
+    if num_bytes > 1e9
+    else (f'{num_bytes / 1e6:,.1f}', 'MB')
+    if num_bytes > 1e6
+    else (f'{num_bytes / 1e3:,.1f}', 'KB')
+    if num_bytes > 1e3
+    else (f'{num_bytes:,}', 'B')
+  )
+
+  return f'{count} {units}'
+
+
+def _has_shape_dtype(value):
+  return hasattr(value, 'shape') and hasattr(value, 'dtype')
+
+
+def _normalize_values(x):
+  if isinstance(x, type):
+    return f'type[{x.__name__}]'
+  else:
+    return x
+
+
+def _as_yaml_str(value) -> str:
+  if (hasattr(value, '__len__') and len(value) == 0) or value is None:
+    return ''
+
+  value = jax.tree.map(_normalize_values, value)
+
+  file = io.StringIO()
+  yaml.safe_dump(
+    value,
+    file,
+    default_flow_style=False,
+    indent=2,
+    sort_keys=False,
+    explicit_end=False,
+  )
+  return file.getvalue().replace('\n...', '').replace("'", '').strip()
+
+
+def _render_array(x):
+  shape, dtype = jnp.shape(x), jnp.result_type(x)
+  shape_repr = ','.join(str(x) for x in shape)
+  return f'[dim]{dtype}[/dim][{shape_repr}]'
+
+
+def _sort_variable_types(types: tp.Iterable[type]) -> list[type]:
+  def _variable_parents_count(t: type):
+    return sum(1 for p in t.mro() if issubclass(p, variablelib.Variable))
+
+  type_sort_key = {t: (-_variable_parents_count(t), t.__name__) for t in types}
+  return sorted(types, key=lambda t: type_sort_key[t])