diff --git a/examples/CMakeLists.txt b/examples/CMakeLists.txt
index c66df80..70230fb 100644
--- a/examples/CMakeLists.txt
+++ b/examples/CMakeLists.txt
@@ -72,9 +72,13 @@ if(BUILD_CUDA AND BUILD_TBB)
   if(BUILD_STDEXEC)
     add_executable(exec_reco_stdexec exec_reco.cpp)
     target_link_libraries(exec_reco_stdexec PRIVATE CoroutineTests CUDA::cudart TBB::tbb STDEXEC::stdexec)
+    add_executable(exec_delegate_stdexec exec_delegate.cpp)
+    target_link_libraries(exec_delegate_stdexec PRIVATE CoroutineTests CUDA::cudart TBB::tbb STDEXEC::stdexec)
   endif()
   if(BUILD_CAPY)
     add_executable(capy_reco capy_reco.cpp)
     target_link_libraries(capy_reco PRIVATE CoroutineTests CUDA::cudart TBB::tbb Boost::capy)
+    add_executable(capy_delegate capy_delegate.cpp)
+    target_link_libraries(capy_delegate PRIVATE CoroutineTests CUDA::cudart TBB::tbb Boost::capy)
   endif()
 endif()
diff --git a/examples/README.md b/examples/README.md
index 1d9ce3e..a8c3d90 100644
--- a/examples/README.md
+++ b/examples/README.md
@@ -161,3 +161,9 @@ This is a variant of ["Exec tbb" example](#exec-tbb) using Boost.Capy and IoAwai
 Link: [alien_reco.cpp](alien_reco.cpp), [exec_reco.cpp](exec_reco.cpp), [capy_reco.cpp](capy_reco.cpp)
 
 These examples show a setup and coroutine chain loosely inspired by track reconstruction on GPU in high energy physics experiments. The `reconstruct` coroutine prepares a mockup input data on a CUDA device, then `co_await`s the `clustering` coroutine, then receives output from it and `co_await`s the `seeding` coroutine. Both `clustering` and `seeding` coroutines receive a device buffer, copy it asynchronously back to host, suspend until the copy is done, then count non-zero elements and allocate new buffer of that size for their results. In `main` the `reconstruct` coroutines are executed in a TBB task arena either synchronously waiting for the result from the submitting or dynamically starting a few `reconstruct` coroutines and waiting until all the work is finished. In the `alien_reco` the application is implemented with "alien" coroutines (as in [alien examples](#alien)), in `exec_reco_stdexec` the application is implemented with C++26 execution, in `capy_reco` the implementation uses Boost.Capy and IoAwaitables protocol ([p4003](https://www.open-std.org/JTC1/SC22/WG21/docs/papers/2026/p4003r0.pdf)).
+
+## Delegation
+
+Link: [exec_delegation.cpp](exec_delegation.cpp), [capy_delegation.cpp](capy_delegation.cpp)
+
+These examples are a modification of [reconstuction examples](#reconstruction) to delegate all the calls to CUDA API that are happening inside the task to execute on a designated thread by changing scheduler/executor. This represents a specific optimization: invoking CUDA APIs from multiple threads can incur performance penalties due to contention on internal CUDA locks. By delegating these calls to a single thread, this contention can be reduced. The examples demonstrate how this optimization can be implemented using coroutines  schedulers/executors.
diff --git a/examples/capy_delegate.cpp b/examples/capy_delegate.cpp
new file mode 100644
index 0000000..b8236ca
--- /dev/null
+++ b/examples/capy_delegate.cpp
@@ -0,0 +1,256 @@
+#include <cuda_runtime_api.h>
+#include <tbb/task_arena.h>
+
+#include <boost/capy.hpp>
+#include <boost/capy/ex/thread_pool.hpp>
+#include <cstddef>
+#include <iostream>
+#include <latch>
+#include <string_view>
+#include <utility>
+
+#include "capy_stream_await.hpp"         // StreamIoAwaitable
+#include "capy_task_arena_executor.hpp"  // TaskArenaExecutor
+#include "logging_utils.hpp"             // log, format_name
+#include "statuscode.hpp"                // StatusCodeImpl
+
+namespace tools {
+struct Tag {
+    static constexpr const char* name = "tools";
+};
+using StatusCode = StatusCodeImpl<Tag>;
+}  // namespace tools
+
+#define ERROR_CHECK_CUDA(EXP)                                              \
+    do {                                                                   \
+        cudaError_t errorCode = EXP;                                       \
+        if (errorCode != cudaSuccess) {                                    \
+            throw std::runtime_error(                                      \
+                std::format("CUDA error at {}:{}: {}", __FILE__, __LINE__, \
+                            cudaGetErrorString(errorCode)));               \
+        }                                                                  \
+    } while (false)
+
+template <typename T>
+struct DeviceBuffer {
+    T* ptr = nullptr;
+    std::size_t size = 0;
+};
+
+template <typename F>
+boost::capy::task<void> coro_wrapper(F&& f) {
+    std::forward<F>(f)();
+    co_return;
+}
+
+boost::capy::task<DeviceBuffer<int>> clusterization(
+    DeviceBuffer<int> cells, cudaStream_t stream,
+    boost::capy::thread_pool& delegation_thread, std::string_view parent) {
+
+    const auto self = format_name(parent, "clusterization");
+    log(self) << "Starting clusterization" << std::endl;
+
+    const auto nCells = static_cast<int>(cells.size);
+
+    // Copy cells back to host to count non-zero entries
+    auto h_cells = std::vector<int>(nCells);
+
+    co_await boost::capy::run(
+        delegation_thread.get_executor())(coro_wrapper([&]() {
+        log(self) << "Delegated copy of cells from device to host" << std::endl;
+        ERROR_CHECK_CUDA(cudaMemcpyAsync(h_cells.data(), cells.ptr,
+                                         nCells * sizeof(int),
+                                         cudaMemcpyDeviceToHost, stream));
+    }));
+
+    ERROR_CHECK_CUDA(co_await StreamIoAwaitable{stream});
+
+    auto nClusters = 0;
+    for (auto v : h_cells)
+        if (v != 0)
+            ++nClusters;
+
+    log(self) << "Found " << nClusters << " clusters" << std::endl;
+
+    // Allocate clusters of appropiate size on device
+    int* d_clusters = nullptr;
+
+    co_await boost::capy::run(
+        delegation_thread.get_executor())(coro_wrapper([&]() {
+        log(self) << "Delegated allocation of clusters on device" << std::endl;
+        ERROR_CHECK_CUDA(cudaMallocAsync(reinterpret_cast<void**>(&d_clusters),
+                                         nClusters * sizeof(int), stream));
+
+        // Write some dummy data to the clusters buffer to simulate work
+        ERROR_CHECK_CUDA(
+            cudaMemsetAsync(d_clusters, 0, nClusters * sizeof(int), stream));
+        ERROR_CHECK_CUDA(cudaMemsetAsync(d_clusters, 1,
+                                         nClusters / 2 * sizeof(int), stream));
+    }));
+
+    co_return DeviceBuffer<int>{d_clusters,
+                                static_cast<std::size_t>(nClusters)};
+}
+
+boost::capy::task<DeviceBuffer<int>> seeding(
+    DeviceBuffer<int> clusters, cudaStream_t stream,
+    boost::capy::thread_pool& delegation_thread, std::string_view parent) {
+
+    const auto self = format_name(parent, "seeding");
+    log(self) << "Starting seeding" << std::endl;
+
+    const auto nClusters = static_cast<int>(clusters.size);
+
+    // Copy clusters to host to count non-zero entries
+    auto h_clusters = std::vector<int>(nClusters);
+
+    co_await boost::capy::run(delegation_thread.get_executor())(
+        coro_wrapper([&]() {
+            log(self) << "Delegated copy of clusters to host" << std::endl;
+            ERROR_CHECK_CUDA(cudaMemcpyAsync(h_clusters.data(), clusters.ptr,
+                                             nClusters * sizeof(int),
+                                             cudaMemcpyDeviceToHost, stream));
+        }));
+
+    ERROR_CHECK_CUDA(co_await StreamIoAwaitable{stream});
+
+    int nSeeds = 0;
+    for (auto v : h_clusters)
+        if (v != 0)
+            ++nSeeds;
+
+    log(self) << "Found " << nSeeds << " seeds" << std::endl;
+
+    // Allocate seeds of appropiate size on device
+    int* d_seeds = nullptr;
+
+    co_await boost::capy::run(delegation_thread.get_executor())(
+        coro_wrapper([&]() {
+            log(self) << "Delegated allocation of seeds on device" << std::endl;
+            ERROR_CHECK_CUDA(cudaMallocAsync(reinterpret_cast<void**>(&d_seeds),
+                                             nSeeds * sizeof(int), stream));
+
+            // Write some dummy data to the seeds buffer to simulate work
+            ERROR_CHECK_CUDA(
+                cudaMemsetAsync(d_seeds, 0, nSeeds * sizeof(int), stream));
+            ERROR_CHECK_CUDA(
+                cudaMemsetAsync(d_seeds, 1, nSeeds / 2 * sizeof(int), stream));
+        }));
+
+    co_return DeviceBuffer<int>{d_seeds, static_cast<std::size_t>(nSeeds)};
+}
+
+boost::capy::task<tools::StatusCode> reconstruct(
+    cudaStream_t stream, boost::capy::thread_pool& delegation_thread,
+    std::string parent) {
+    const auto self = format_name(parent, "reconstruction");
+    log(self) << "Starting reconstruction" << std::endl;
+
+    // Allocate some dummy input data on the device
+    auto cells = DeviceBuffer<int>{nullptr, 1000};
+    co_await boost::capy::run(
+        delegation_thread.get_executor())(coro_wrapper([&]() {
+        log(self) << "Delegated allocation of input data on device"
+                  << std::endl;
+        ERROR_CHECK_CUDA(cudaMallocAsync(reinterpret_cast<void**>(&cells.ptr),
+                                         cells.size * sizeof(int), stream));
+        ERROR_CHECK_CUDA(
+            cudaMemsetAsync(cells.ptr, 1, cells.size * sizeof(int), stream));
+    }));
+
+    // Run the clusterization and seeding steps
+    auto clusters =
+        co_await clusterization(cells, stream, delegation_thread, self);
+    auto seeds = co_await seeding(clusters, stream, delegation_thread, self);
+
+    // Cleanup
+    co_await boost::capy::run(delegation_thread.get_executor())(
+        coro_wrapper([&]() {
+            log(self) << "Delegated cleanup of device memory" << std::endl;
+            ERROR_CHECK_CUDA(cudaFreeAsync(cells.ptr, stream));
+            ERROR_CHECK_CUDA(cudaFreeAsync(clusters.ptr, stream));
+            ERROR_CHECK_CUDA(cudaFreeAsync(seeds.ptr, stream));
+        }));
+
+    ERROR_CHECK_CUDA(co_await StreamIoAwaitable{stream});
+
+    log(self) << "Finishing reconstruction" << std::endl;
+    co_return tools::StatusCode::SUCCESS;
+}
+
+int main() {
+    int deviceCount = 0;
+    auto error_id = cudaGetDeviceCount(&deviceCount);
+
+    if (error_id != cudaSuccess) {
+        std::cout << "cudaGetDeviceCount returned "
+                  << static_cast<int>(error_id) << "\n"
+                  << cudaGetErrorString(error_id) << "\n";
+        return EXIT_FAILURE;
+    }
+
+    if (deviceCount == 0) {
+        std::cout << "No CUDA devices found.\n";
+        return EXIT_FAILURE;
+    }
+
+    log() << "main Starting" << std::endl;
+
+    auto task_arena = tbb::task_arena{2, 0};
+    auto context = TaskArenaContext(task_arena);
+    auto executor = TaskArenaExecutor(context);
+    auto delegation_thread = boost::capy::thread_pool(1);
+
+    {
+        std::cout << "--- Single event, synchronous wait for completion ---\n";
+        cudaStream_t stream;
+        ERROR_CHECK_CUDA(cudaStreamCreate(&stream));
+        auto final_result = tools::StatusCode{};
+        auto done = std::latch{1};
+        auto result_handler = [&done, &final_result](tools::StatusCode code) {
+            final_result = code;
+            done.count_down();
+        };
+
+        log() << "main Launching algorithm..." << std::endl;
+        boost::capy::run_async(executor, result_handler)(
+            reconstruct(stream, delegation_thread, "main"));
+        done.wait();
+        log() << "main Final status of algorithm " << final_result << ""
+              << std::endl;
+        ERROR_CHECK_CUDA(cudaStreamDestroy(stream));
+    }
+
+    {
+        std::cout << "--- Multiple events, wait for all to complete ---\n ";
+
+        auto streams = std::vector<cudaStream_t>(2);
+        auto status = std::vector<tools::StatusCode>(streams.size());
+        for (auto& stream : streams) {
+            ERROR_CHECK_CUDA(cudaStreamCreate(&stream));
+        }
+
+        auto done = std::latch{static_cast<std::ptrdiff_t>(streams.size())};
+        log() << "main Launching algorithms..." << std::endl;
+
+        for (std::size_t i = 0; i < streams.size(); ++i) {
+            auto result_handler = [&done, &status, i](tools::StatusCode code) {
+                status.at(i) = code;
+                done.count_down();
+            };
+            boost::capy::run_async(executor, result_handler)(reconstruct(
+                streams.at(i), delegation_thread, "event" + std::to_string(i)));
+        }
+        done.wait();
+        for (auto& stream : streams) {
+            ERROR_CHECK_CUDA(cudaStreamDestroy(stream));
+        }
+
+        log() << "main All algorithms completed. Final statuses: ";
+        for (auto s : status) {
+            std::cout << s << ' ';
+        }
+        std::cout << std::endl;
+    }
+    return 0;
+}
diff --git a/examples/exec_delegate.cpp b/examples/exec_delegate.cpp
new file mode 100644
index 0000000..8c5a40a
--- /dev/null
+++ b/examples/exec_delegate.cpp
@@ -0,0 +1,263 @@
+#include <cuda_runtime_api.h>
+#include <tbb/task_arena.h>
+
+#include <cstddef>
+#include <exec/async_scope.hpp>
+#include <exec/single_thread_context.hpp>
+#include <exec/task.hpp>
+#include <iostream>
+#include <stdexec/execution.hpp>
+
+#include "exec_stream_await_sender.hpp"   // stream_await_sender
+#include "exec_task_arena_scheduler.hpp"  // TaskArenaScheduler
+#include "logging_utils.hpp"              // log, format_name
+#include "statuscode.hpp"                 // StatusCodeImpl
+
+namespace tools {
+struct Tag {
+    static constexpr const char* name = "tools";
+};
+using StatusCode = StatusCodeImpl<Tag>;
+}  // namespace tools
+
+#define ERROR_CHECK_CUDA(EXP)                                              \
+    do {                                                                   \
+        cudaError_t errorCode = EXP;                                       \
+        if (errorCode != cudaSuccess) {                                    \
+            throw std::runtime_error(                                      \
+                std::format("CUDA error at {}:{}: {}", __FILE__, __LINE__, \
+                            cudaGetErrorString(errorCode)));               \
+        }                                                                  \
+    } while (false)
+
+template <typename T>
+struct DeviceBuffer {
+    T* ptr = nullptr;
+    std::size_t size = 0;
+};
+
+exec::task<DeviceBuffer<int>> clusterization(
+    DeviceBuffer<int> cells, cudaStream_t stream,
+    exec::single_thread_context& delegation_ctx, std::string_view parent) {
+
+    const auto self = format_name(parent, "clusterization");
+    log(self) << "Starting clusterization" << std::endl;
+
+    const auto nCells = static_cast<int>(cells.size);
+
+    // Copy cells back to host to count non-zero entries
+    auto h_cells = std::vector<int>(nCells);
+
+    stdexec::sender auto copy_cells =
+        stdexec::just() | stdexec::then([&]() {
+            log(self) << "Delegated copy of cells from device to host"
+                      << std::endl;
+            ERROR_CHECK_CUDA(cudaMemcpyAsync(h_cells.data(), cells.ptr,
+                                             nCells * sizeof(int),
+                                             cudaMemcpyDeviceToHost, stream));
+        });
+    co_await stdexec::on(delegation_ctx.get_scheduler(), std::move(copy_cells));
+
+    ERROR_CHECK_CUDA(co_await stream_await_sender{stream});
+
+    auto nClusters = 0;
+    for (auto v : h_cells)
+        if (v != 0)
+            ++nClusters;
+
+    log(self) << "Found " << nClusters << " clusters" << std::endl;
+
+    // Allocate clusters of appropiate size on device
+    int* d_clusters = nullptr;
+
+    stdexec::sender auto allocate_clusters =
+        stdexec::just() | stdexec::then([&]() {
+            log(self) << "Delegated allocation of clusters on device"
+                      << std::endl;
+            ERROR_CHECK_CUDA(
+                cudaMallocAsync(reinterpret_cast<void**>(&d_clusters),
+                                nClusters * sizeof(int), stream));
+
+            // Write some dummy data to the clusters buffer to simulate work
+            ERROR_CHECK_CUDA(cudaMemsetAsync(d_clusters, 0,
+                                             nClusters * sizeof(int), stream));
+            ERROR_CHECK_CUDA(cudaMemsetAsync(
+                d_clusters, 1, nClusters / 2 * sizeof(int), stream));
+        });
+    co_await stdexec::on(delegation_ctx.get_scheduler(),
+                         std::move(allocate_clusters));
+
+    co_return DeviceBuffer<int>{d_clusters,
+                                static_cast<std::size_t>(nClusters)};
+}
+
+exec::task<DeviceBuffer<int>> seeding(
+    DeviceBuffer<int> clusters, cudaStream_t stream,
+    exec::single_thread_context& delegation_ctx, std::string_view parent) {
+
+    const auto self = format_name(parent, "seeding");
+    log(self) << "Starting seeding" << std::endl;
+
+    const auto nClusters = static_cast<int>(clusters.size);
+
+    // Copy clusters to host to count non-zero entries
+    auto h_clusters = std::vector<int>(nClusters);
+
+    stdexec::sender auto copy_clusters =
+        stdexec::just() | stdexec::then([&]() {
+            log(self) << "Delegated copy of clusters from device to host"
+                      << std::endl;
+            ERROR_CHECK_CUDA(cudaMemcpyAsync(h_clusters.data(), clusters.ptr,
+                                             nClusters * sizeof(int),
+                                             cudaMemcpyDeviceToHost, stream));
+        });
+    co_await stdexec::on(delegation_ctx.get_scheduler(),
+                         std::move(copy_clusters));
+
+    ERROR_CHECK_CUDA(co_await stream_await_sender{stream});
+
+    int nSeeds = 0;
+    for (auto v : h_clusters)
+        if (v != 0)
+            ++nSeeds;
+
+    log(self) << "Found " << nSeeds << " seeds" << std::endl;
+
+    // Allocate seeds of appropiate size on device
+    int* d_seeds = nullptr;
+
+    stdexec::sender auto allocate_seeds =
+        stdexec::just() | stdexec::then([&]() {
+            log(self) << "Delegated allocation of seeds on device" << std::endl;
+            ERROR_CHECK_CUDA(cudaMallocAsync(reinterpret_cast<void**>(&d_seeds),
+                                             nSeeds * sizeof(int), stream));
+
+            // Write some dummy data to the clusters buffer to simulate work
+            ERROR_CHECK_CUDA(
+                cudaMemsetAsync(d_seeds, 0, nSeeds * sizeof(int), stream));
+            ERROR_CHECK_CUDA(
+                cudaMemsetAsync(d_seeds, 1, nSeeds / 2 * sizeof(int), stream));
+        });
+    co_await stdexec::on(delegation_ctx.get_scheduler(),
+                         std::move(allocate_seeds));
+
+    co_return DeviceBuffer<int>{d_seeds, static_cast<std::size_t>(nSeeds)};
+}
+
+exec::task<tools::StatusCode> reconstruct(
+    cudaStream_t stream, exec::single_thread_context& delegation_ctx,
+    std::string_view parent) {
+    const auto self = format_name(parent, "reconstruction");
+    log(self) << "Starting reconstruction" << std::endl;
+
+    // Allocate some dummy input data on the device
+    auto cells = DeviceBuffer<int>{nullptr, 1000};
+    stdexec::sender auto allocate_cells =
+        stdexec::just() | stdexec::then([&]() {
+            log(self) << "Delegated allocation of input data on device"
+                      << std::endl;
+            ERROR_CHECK_CUDA(
+                cudaMallocAsync(reinterpret_cast<void**>(&cells.ptr),
+                                cells.size * sizeof(int), stream));
+            ERROR_CHECK_CUDA(cudaMemsetAsync(cells.ptr, 1,
+                                             cells.size * sizeof(int), stream));
+        });
+    co_await stdexec::on(delegation_ctx.get_scheduler(),
+                         std::move(allocate_cells));
+
+    // Run the clusterization and seeding steps
+    auto clusters =
+        co_await clusterization(cells, stream, delegation_ctx, self);
+    auto seeds = co_await seeding(clusters, stream, delegation_ctx, self);
+
+    // Cleanup
+    stdexec::sender auto cleanup =
+        stdexec::just() | stdexec::then([&]() {
+            log(self) << "Delegated cleanup of device memory" << std::endl;
+            ERROR_CHECK_CUDA(cudaFreeAsync(cells.ptr, stream));
+            ERROR_CHECK_CUDA(cudaFreeAsync(clusters.ptr, stream));
+            ERROR_CHECK_CUDA(cudaFreeAsync(seeds.ptr, stream));
+        });
+    co_await stdexec::on(delegation_ctx.get_scheduler(), std::move(cleanup));
+
+    ERROR_CHECK_CUDA(co_await stream_await_sender{stream});
+
+    log(self) << "Finishing reconstruction" << std::endl;
+    co_return tools::StatusCode::SUCCESS;
+}
+
+int main() {
+    int deviceCount = 0;
+    auto error_id = cudaGetDeviceCount(&deviceCount);
+
+    if (error_id != cudaSuccess) {
+        std::cout << "cudaGetDeviceCount returned "
+                  << static_cast<int>(error_id) << "\n"
+                  << cudaGetErrorString(error_id) << "\n";
+        return EXIT_FAILURE;
+    }
+
+    if (deviceCount == 0) {
+        std::cout << "No CUDA devices found.\n";
+        return EXIT_FAILURE;
+    }
+
+    log() << "main Starting" << std::endl;
+
+    tbb::task_arena task_arena{2, 0};
+    execution::scheduler auto scheduler = get_scheduler(task_arena, false);
+    exec::single_thread_context delegation_context;
+
+    {
+        cudaStream_t stream;
+        ERROR_CHECK_CUDA(cudaStreamCreate(&stream));
+        std::cout << "--- Single event, synchronous wait for completion ---\n";
+        log() << "main Launching algorithm..." << std::endl;
+        auto [status] =
+            stdexec::sync_wait(
+                stdexec::starts_on(
+                    scheduler, reconstruct(stream, delegation_context, "main")))
+                .value();
+        log() << "main Final status of algorithm " << status << "" << std::endl;
+        ERROR_CHECK_CUDA(cudaStreamDestroy(stream));
+    }
+
+    {
+        std::cout << "--- Multiple events, wait for all to complete ---\n";
+
+        auto streams = std::vector<cudaStream_t>(2);
+        auto status = std::vector<tools::StatusCode>(streams.size());
+        for (auto& stream : streams) {
+            ERROR_CHECK_CUDA(cudaStreamCreate(&stream));
+        }
+
+        auto scope = exec::async_scope{};
+        log() << "main Launching algorithms..." << std::endl;
+
+        auto payload = [](std::vector<cudaStream_t> streams,
+                          std::vector<tools::StatusCode>& statuses, int i,
+                          exec::single_thread_context& delegation_ctx)
+            -> exec::task<void> {
+            const auto name = std::format("event{}:", i);
+            auto& stream = streams.at(i);
+            auto& status = statuses.at(i);
+            status = co_await reconstruct(stream, delegation_ctx, name);
+        };
+        for (std::size_t i = 0; i < streams.size(); ++i) {
+            scope.spawn(stdexec::starts_on(
+                scheduler, payload(streams, status, i, delegation_context)));
+        }
+        stdexec::sync_wait(scope.on_empty());
+
+        for (auto& stream : streams) {
+            ERROR_CHECK_CUDA(cudaStreamDestroy(stream));
+        }
+
+        log() << "main All algorithms completed. Final statuses: ";
+        for (auto s : status) {
+            std::cout << s << ' ';
+        }
+        std::cout << std::endl;
+    }
+    return 0;
+}
diff --git a/extern/capy/CMakeLists.txt b/extern/capy/CMakeLists.txt
index 7761a5a..e0e324d 100644
--- a/extern/capy/CMakeLists.txt
+++ b/extern/capy/CMakeLists.txt
@@ -6,7 +6,7 @@ message(STATUS "Building capy as part of the CoroutineTests project")
 
 # Declare where to get stdexec from.
 set(COROUTINETESTS_CAPY_SOURCE
-    "URL;https://github.com/cppalliance/capy/archive/df92ffbb0670c06ebf0f201da73f06d1385d5d9b.zip;URL_MD5;d6b6e1e25684986d17ae92c13f992cfc"
+    "URL;https://github.com/cppalliance/capy/archive/e2de31c559b60df471e2d80c2b3e8373e2fc14e8.zip;URL_MD5;a5d719eb6b234daaab69a62c9ff23a01"
     CACHE STRING "Source for capy, when built as part of this project")
 
 mark_as_advanced(COROUTINETESTS_CAPY_SOURCE)