diff --git a/graphblas/io/_networkx.py b/graphblas/io/_networkx.py
index 8cf84e576..c4a31e6db 100644
--- a/graphblas/io/_networkx.py
+++ b/graphblas/io/_networkx.py
@@ -27,6 +27,121 @@ def from_networkx(G, nodelist=None, dtype=None, weight="weight", name=None):
 
     if dtype is not None:
         dtype = lookup_dtype(dtype).np_type
+
+    # The node selection below mirrors nx.to_scipy_sparse_array so that empty
+    # graphs, nodelist subsets, missing nodes, and duplicate nodes raise the
+    # same errors and produce the same ordering. Building the coo arrays here
+    # (instead of via a scipy sparse round-trip) skips the extra coo -> csr
+    # materialization and the scipy.sparse import, which alone costs over 100ms.
+    import numpy as np
+
+    from ..binary import plus
+    from ..core.matrix import Matrix
+
+    if len(G) == 0:
+        raise nx.NetworkXError("Graph has no nodes or edges")
+
+    if nodelist is None:
+        nodelist = list(G)
+        nlen = len(G)
+    else:
+        nlen = len(nodelist)
+        if nlen == 0:
+            raise nx.NetworkXError("nodelist has no nodes")
+        nodeset = set(G.nbunch_iter(nodelist))
+        if nlen != len(nodeset):
+            for n in nodelist:
+                if n not in G:
+                    raise nx.NetworkXError(f"Node {n} in nodelist is not in G")
+            raise nx.NetworkXError("nodelist contains duplicates.")
+        if nlen < len(G):
+            G = G.subgraph(nodelist)
+
+    index = dict(zip(nodelist, range(nlen), strict=True))
+    coefficients = zip(
+        *((index[u], index[v], wt) for u, v, wt in G.edges(data=weight, default=1)),
+        strict=True,
+    )
+    try:
+        row, col, data = coefficients
+    except ValueError:
+        # there is no edge in the (sub)graph
+        row, col, data = (), (), ()
+
+    if G.is_directed():
+        rows, cols, vals = row, col, data
+        # A multigraph can have parallel edges (duplicate ``(u, v)``); summing
+        # them with ``plus`` matches scipy's coo -> csr accumulation. A simple
+        # graph has no duplicate coordinates, so ``dup_op=None`` is exact and
+        # skips the accumulator.
+        dup_op = plus if G.is_multigraph() else None
+    else:
+        # Symmetrize: mirror off-diagonal entries. Self-loops would be double
+        # counted, so subtract the diagonal contribution once, matching
+        # nx.to_scipy_sparse_array. dup_op=plus then sums the diagonal triple
+        # (wt + wt - wt) back to wt. For a multigraph, plus also sums parallel
+        # edges (duplicate coordinates) the same way scipy's coo -> csr does;
+        # for a simple graph off-diagonal entries are unique so plus is a no-op
+        # there.
+        d = data + data
+        r = row + col
+        c = col + row
+        selfloops = list(nx.selfloop_edges(G, data=weight, default=1))
+        if selfloops:
+            diag_index, diag_data = zip(*((index[u], -wt) for u, v, wt in selfloops), strict=True)
+            d += diag_data
+            r += diag_index
+            c += diag_index
+        rows, cols, vals = r, c, d
+        dup_op = plus
+
+    values = np.array(vals, dtype=dtype)
+    if dtype is None and values.dtype == np.int32:  # pragma: no cover (win64 numpy < 2)
+        # numpy < 2 infers the platform C long for a sequence of Python ints, which
+        # is 32-bit on Windows. values_to_numpy_buffer widens the same way for
+        # non-numpy input, so this keeps from_networkx agreeing with
+        # Matrix.from_coo on INT64 for an unweighted graph on every platform.
+        values = values.astype(np.int64)
+    if values.ndim != 1 or values.dtype.kind not in "biufc":
+        # Defer to scipy so the error matches the previous behavior exactly.
+        # Two kinds of weight land here: non-numeric attributes (object arrays,
+        # but also e.g. all-string weights, which infer a <U dtype rather than
+        # object), and sequence-valued attributes of uniform length, which infer
+        # a 2-D numeric array that from_coo would otherwise accept as a UDT.
+        try:
+            return _from_networkx_via_scipy(G, nodelist, dtype, weight, name)
+        except (nx.NetworkXError, TypeError) as err:
+            # Since scipy 1.15 an unsupported dtype is rejected while the coo array
+            # is built, and that ValueError reaches us unchanged. Older scipy builds
+            # the array and fails in the coo -> csr conversion instead: scipy raises
+            # TypeError ("no supported conversion for types"), which networkx 3.4+
+            # wraps in a NetworkXError that blames the sparse format while
+            # networkx <= 3.3 lets the TypeError propagate.
+            # Restate it so every supported stack reports the same error for the
+            # same graph. The graph and nodelist checks above are the ones
+            # nx.to_scipy_sparse_array makes, so a NetworkXError or TypeError from
+            # the fallback can only be that dtype complaint.
+            raise ValueError(
+                f"scipy.sparse does not support dtype {values.dtype}; "
+                "edge weights must be numeric scalars"
+            ) from err
+    if values.size == 0:
+        # An empty graph has no data to infer a dtype from; scipy defaults an
+        # empty coo array to float64, so match that when dtype is unset.
+        return Matrix(lookup_dtype(values.dtype), nrows=nlen, ncols=nlen, name=name)
+
+    rows = np.array(rows, dtype=np.uint64)
+    cols = np.array(cols, dtype=np.uint64)
+    return Matrix.from_coo(rows, cols, values, nrows=nlen, ncols=nlen, dup_op=dup_op, name=name)
+
+
+def _from_networkx_via_scipy(G, nodelist, dtype, weight, name):
+    """Fallback path: convert through a scipy sparse array.
+
+    ``dtype`` is already normalized to a numpy type (or None) by the caller.
+    """
+    import networkx as nx
+
     A = nx.to_scipy_sparse_array(G, nodelist=nodelist, dtype=dtype, weight=weight)
     return from_scipy_sparse(A, name=name)
 
diff --git a/graphblas/tests/test_io.py b/graphblas/tests/test_io.py
index a8d80e9ca..d2e57cebe 100644
--- a/graphblas/tests/test_io.py
+++ b/graphblas/tests/test_io.py
@@ -160,6 +160,78 @@ def test_matrix_to_from_networkx():
     assert M.shape == (1, 1)
 
 
+@pytest.mark.skipif("not nx")
+def test_from_networkx_undirected():
+    # Undirected graphs go through from_networkx's direct COO path (no scipy),
+    # which symmetrizes off-diagonal entries and keeps self-loops single-counted.
+    G = nx.Graph()
+    G.add_weighted_edges_from([(0, 1, 2.0), (0, 0, 7.0), (1, 2, 3.0)])
+    M = gb.io.from_networkx(G)
+    expected = gb.Matrix.from_coo([0, 0, 1, 1, 2], [0, 1, 0, 2, 1], [7.0, 2.0, 2.0, 3.0, 3.0])
+    assert M.isequal(expected, check_dtype=True)
+
+    # weight=None ignores edge weights (all entries 1) and yields an int Matrix
+    M_none = gb.io.from_networkx(G, weight=None)
+    expected_none = gb.Matrix.from_coo([0, 0, 1, 1, 2], [0, 1, 0, 2, 1], 1)
+    assert M_none.isequal(expected_none, check_dtype=True)
+
+
+@pytest.mark.skipif("not nx or not ss")
+@pytest.mark.parametrize(
+    "graph_cls", [nx.Graph, nx.DiGraph, nx.MultiGraph, nx.MultiDiGraph] if nx else []
+)
+def test_from_networkx_rejects_array_weights(graph_cls):
+    # Sequence weights of uniform length infer a 2-D numeric array, which passes
+    # a dtype-kind test but which from_coo would happily read as a UDT. scipy has
+    # always rejected these, so the direct path must defer rather than quietly
+    # widen what from_networkx accepts.
+    G = graph_cls()
+    G.add_edge(0, 1, weight=[1, 2])
+    G.add_edge(1, 0, weight=[3, 4])
+    with pytest.raises(ValueError, match="must be 1-D"):
+        gb.io.from_networkx(G)
+
+
+@pytest.mark.skipif("not nx or not ss")
+@pytest.mark.parametrize(
+    "graph_cls", [nx.Graph, nx.DiGraph, nx.MultiGraph, nx.MultiDiGraph] if nx else []
+)
+def test_from_networkx_matches_scipy(graph_cls):
+    # Both simple graphs and multigraphs now build the coo directly (no scipy
+    # round-trip); the result must reproduce nx.to_scipy_sparse_array exactly,
+    # including parallel-edge and parallel-self-loop weight sums.
+    G = graph_cls()
+    G.add_weighted_edges_from([(0, 1, 2.0), (1, 2, 3.0), (2, 0, 4.0), (0, 0, 5.0)])
+    if G.is_multigraph():
+        G.add_edge(0, 1, weight=1.5)  # parallel edge -> weights summed
+        G.add_edge(0, 0, weight=2.5)  # parallel self-loop -> diagonal summed
+    G.add_edge(3, 4)  # missing weight attr -> default 1
+    G.add_node(9)  # isolated node
+
+    A = nx.to_scipy_sparse_array(G, weight="weight")
+    reference = gb.io.from_scipy_sparse(A)
+    M = gb.io.from_networkx(G, weight="weight")
+    assert M.isequal(reference, check_dtype=True)
+    assert M.shape == reference.shape
+
+
+@pytest.mark.skipif("not nx")
+def test_from_networkx_multigraph_is_scipy_free(monkeypatch):
+    # A numeric-weight multigraph must ingest via the direct coo path, not the
+    # scipy fallback, so networkx ingest no longer requires scipy for this case.
+    import graphblas.io._networkx as _gnx
+
+    def _boom(*args, **kwargs):  # pragma: no cover (only runs if the direct path regresses)
+        raise AssertionError("scipy fallback should not be used for a numeric multigraph")
+
+    monkeypatch.setattr(_gnx, "_from_networkx_via_scipy", _boom)
+    G = nx.MultiDiGraph()
+    G.add_weighted_edges_from([(0, 1, 2.0), (0, 1, 3.0), (2, 0, 1.0), (1, 1, 4.0), (1, 1, 0.5)])
+    M = gb.io.from_networkx(G)
+    assert M[0, 1].new().value == 5.0  # parallel edges summed
+    assert M[1, 1].new().value == 4.5  # parallel self-loops summed
+
+
 @pytest.mark.skipif("not ss")
 @pytest.mark.parametrize("engine", ["auto", "scipy", "fmm"])
 def test_mmread_mmwrite(engine):