diff --git a/src/vertex_components.cpp b/src/vertex_components.cpp
index 850aae9b..c48f8742 100644
--- a/src/vertex_components.cpp
+++ b/src/vertex_components.cpp
@@ -2,6 +2,8 @@
 #include <igl/vertex_components.h>
 #include <nanobind/nanobind.h>
 #include <nanobind/eigen/dense.h>
+#include <nanobind/eigen/sparse.h>
+#include <nanobind/stl/tuple.h>
 
 namespace nb = nanobind;
 using namespace nb::literals;
@@ -16,6 +18,16 @@ namespace pyigl
     igl::vertex_components(F, C);
     return C;
   }
+
+  // Wrapper for vertex_components with adjacency matrix
+  auto vertex_components_from_adjacency_matrix(
+    const Eigen::SparseMatrixI &adjacency)
+  {
+    Eigen::VectorXI c;
+    Eigen::VectorXI counts;
+    igl::vertex_components(adjacency, c, counts);
+    return std::make_tuple(c, counts);
+  }
 }
 
 // Bind the wrappers to the Python module
@@ -30,4 +42,13 @@ void bind_vertex_components(nb::module_ &m)
 
 @param[in] F  #F by 3 matrix of triangle (face) indices
 @return       Vector C of per-vertex connected-component ids)");
+
+  m.def(
+    "vertex_components_from_adjacency_matrix",
+    &pyigl::vertex_components_from_adjacency_matrix,
+    "adjacency"_a,
+    R"(Compute the connected components of a graph using an adjacency matrix, returning component IDs and counts.
+
+@param[in] adjacency  n by n sparse adjacency matrix
+@return A tuple (c, counts) where c is an array of component ids (starting with 0) and counts is a #components array of counts for each component)");
 }
diff --git a/tests/test_all.py b/tests/test_all.py
index 370c7431..27c70ca1 100644
--- a/tests/test_all.py
+++ b/tests/test_all.py
@@ -1791,3 +1791,24 @@ def test_resolve_duplicated_faces():
     F = np.array([[0, 1, 2], [0, 1, 2], [0, 1, 2], [3, 4, 5]], dtype=np.int64)
     F2, J = igl.resolve_duplicated_faces(F)
     assert set(map(tuple, F2.tolist())) == {(3, 4, 5)}
+
+
+def test_vertex_components_from_adjacency_matrix():
+    # Two disconnected components: a triangle {0,1,2} and an edge {3,4}.
+    edges = [(0, 1), (1, 2), (0, 2), (3, 4)]
+    n = 5
+    rows, cols = [], []
+    for i, j in edges:
+        rows += [i, j]
+        cols += [j, i]
+    A = scipy.sparse.csr_matrix(
+        (np.ones(len(rows)), (rows, cols)), shape=(n, n)).astype(np.int64)
+    c, counts = igl.vertex_components_from_adjacency_matrix(A)
+    assert c.shape[0] == n
+    # vertices in the same component share an id; different components differ
+    assert c[0] == c[1] == c[2]
+    assert c[3] == c[4]
+    assert c[0] != c[3]
+    # counts is per-component and sums to the number of vertices
+    assert counts.sum() == n
+    assert sorted(counts.ravel().tolist()) == [2, 3]