diff --git a/Project.toml b/Project.toml
index b7ab00d2..ed028999 100644
--- a/Project.toml
+++ b/Project.toml
@@ -4,7 +4,6 @@ version = "0.8.0"
 
 [deps]
 ADTypes = "47edcb42-4c32-4615-8424-f2b9edc5f35b"
-ColPack = "ffa27691-3a59-46ab-a8d4-551f45b8d401"
 ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 NLPModels = "a4795742-8479-5a88-8948-cc11e1c8c1a6"
@@ -12,13 +11,14 @@ Requires = "ae029012-a4dd-5104-9daa-d747884805df"
 ReverseDiff = "37e2e3b7-166d-5795-8a7a-e32c996b4267"
 SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
 SparseConnectivityTracer = "9f842d2f-2579-4b1d-911e-f412cf18a3f5"
+SparseMatrixColorings = "0a514795-09f3-496d-8182-132a7b665d35"
 
 [compat]
 ADTypes = "1.2.1"
-ColPack = "0.4"
-SparseConnectivityTracer = "0.5"
 ForwardDiff = "0.9.0, 0.10.0"
 NLPModels = "0.18, 0.19, 0.20, 0.21"
 Requires = "1"
 ReverseDiff = "1"
+SparseConnectivityTracer = "0.5"
+SparseMatrixColorings = "0.3.3"
 julia = "^1.6"
diff --git a/src/ADNLPModels.jl b/src/ADNLPModels.jl
index 18650372..875bbb47 100644
--- a/src/ADNLPModels.jl
+++ b/src/ADNLPModels.jl
@@ -4,8 +4,10 @@ module ADNLPModels
 using LinearAlgebra, SparseArrays
 
 # external
-using ADTypes: ADTypes, AbstractSparsityDetector
-using SparseConnectivityTracer, ColPack, ForwardDiff, ReverseDiff
+using ADTypes: ADTypes, AbstractColoringAlgorithm, AbstractSparsityDetector
+using SparseConnectivityTracer: TracerSparsityDetector
+using SparseMatrixColorings: GreedyColoringAlgorithm
+using ForwardDiff, ReverseDiff
 
 # JSO
 using NLPModels
diff --git a/src/ad.jl b/src/ad.jl
index 94ed72a7..d6d1a807 100644
--- a/src/ad.jl
+++ b/src/ad.jl
@@ -499,36 +499,3 @@ function ADModelNLSBackend(
     hessian_residual_backend,
   )
 end
-
-abstract type ColorationAlgorithm end
-
-struct ColPackColoration{F, C, O} <: ColorationAlgorithm
-  partition_choice::F
-  coloring::C
-  ordering::O
-end
-
-function ColPackColoration(;
-  partition_choice = (m, n) -> false, # TODO: (m, n; μ = 0.6) -> n < μ * m ? true : false,
-  coloring::ColPack.ColoringMethod = d1_coloring(),
-  ordering::ColPack.ColoringOrder = incidence_degree_ordering(),
-)
-  return ColPackColoration{typeof(partition_choice), typeof(coloring), typeof(ordering)}(
-    partition_choice,
-    coloring,
-    ordering,
-  )
-end
-
-function sparse_matrix_colors(A, alg::ColPackColoration)
-  m, n = size(A)
-  partition_by_rows = alg.partition_choice(m, n)
-  if !isempty(A.nzval)
-    adjA = ColPack.matrix2adjmatrix(A; partition_by_rows = partition_by_rows)
-    CPC = ColPackColoring(adjA, alg.coloring, alg.ordering)
-    colors = get_colors(CPC)
-  else
-    colors = zeros(Int, n)
-  end
-  return colors
-end
diff --git a/src/sparse_hessian.jl b/src/sparse_hessian.jl
index c28cdb1e..d7755f42 100644
--- a/src/sparse_hessian.jl
+++ b/src/sparse_hessian.jl
@@ -20,14 +20,15 @@ function SparseADHessian(
   ncon,
   c!;
   x0::S = rand(nvar),
-  alg = ColPackColoration(),
+  alg::AbstractColoringAlgorithm = GreedyColoringAlgorithm(),
   detector::AbstractSparsityDetector = TracerSparsityDetector(),
   kwargs...,
 ) where {S}
   T = eltype(S)
   H = compute_hessian_sparsity(f, nvar, c!, ncon, detector = detector)
 
-  colors = sparse_matrix_colors(H, alg)
+  # TODO: use ADTypes.symmetric_coloring instead if you have the right decompression
+  colors = ADTypes.column_coloring(H, alg)
   ncolors = maximum(colors)
 
   d = BitVector(undef, nvar)
@@ -92,13 +93,14 @@ function SparseReverseADHessian(
   ncon,
   c!;
   x0::AbstractVector{T} = rand(nvar),
-  alg = ColPackColoration(),
+  alg::AbstractColoringAlgorithm = GreedyColoringAlgorithm(),
   detector::AbstractSparsityDetector = TracerSparsityDetector(),
   kwargs...,
 ) where {T}
   H = compute_hessian_sparsity(f, nvar, c!, ncon, detector = detector)
 
-  colors = sparse_matrix_colors(H, alg)
+  # TODO: use ADTypes.symmetric_coloring instead if you have the right decompression
+  colors = ADTypes.column_coloring(H, alg)
   ncolors = maximum(colors)
 
   d = BitVector(undef, nvar)
diff --git a/src/sparse_jacobian.jl b/src/sparse_jacobian.jl
index b89317e0..58766230 100644
--- a/src/sparse_jacobian.jl
+++ b/src/sparse_jacobian.jl
@@ -15,14 +15,15 @@ function SparseADJacobian(
   ncon,
   c!;
   x0::AbstractVector{T} = rand(nvar),
-  alg = ColPackColoration(),
+  alg::AbstractColoringAlgorithm = GreedyColoringAlgorithm(),
   detector::AbstractSparsityDetector = TracerSparsityDetector(),
   kwargs...,
 ) where {T}
   output = similar(x0, ncon)
   J = compute_jacobian_sparsity(c!, output, x0, detector = detector)
 
-  colors = sparse_matrix_colors(J, alg)
+  # TODO: use ADTypes.row_coloring instead if you have the right decompression and some heuristic recommends it
+  colors = ADTypes.column_coloring(J, alg)
   ncolors = maximum(colors)
 
   d = BitVector(undef, nvar)