diff --git a/judo/utils/mujoco_cpp.py b/judo/utils/mujoco_cpp.py
new file mode 100644
index 00000000..629e6507
--- /dev/null
+++ b/judo/utils/mujoco_cpp.py
@@ -0,0 +1,85 @@
+# Copyright (c) 2025 Robotics and AI Institute LLC. All rights reserved.
+
+from typing import Callable, Literal
+
+import numpy as np
+from mujoco import MjData, MjModel
+
+from judo_cpp import rollout, sim
+
+
+class RolloutBackend:
+    """The backend for conducting multithreaded rollouts."""
+
+    def __init__(self, num_threads: int, backend: Literal["mujoco"], task_to_sim_ctrl: Callable) -> None:
+        """Initialize the backend with a number of threads."""
+        self.backend = backend
+        if self.backend == "mujoco":
+            self.setup_mujoco_backend()
+        else:
+            raise ValueError(f"Unknown backend: {self.backend}")
+        self.task_to_sim_ctrl = task_to_sim_ctrl
+
+    def setup_mujoco_backend(self) -> None:
+        """Setup the mujoco backend."""
+        if self.backend == "mujoco":
+            self.rollout_func = rollout
+        else:
+            raise ValueError(f"Unknown backend: {self.backend}")
+
+    def rollout(
+        self,
+        model_data_pairs: list[tuple[MjModel, MjData]],
+        x0: np.ndarray,
+        controls: np.ndarray,
+    ) -> tuple[np.ndarray, np.ndarray]:
+        """Conduct a rollout depending on the backend."""
+        # unpack models into a list of models and data
+        ms, ds = zip(*model_data_pairs, strict=True)
+        ms = list(ms)
+        ds = list(ds)
+
+        # getting shapes
+        nq = ms[0].nq
+        nv = ms[0].nv
+        nu = ms[0].nu
+
+        # the state passed into mujoco's rollout function includes the time
+        # shape = (num_rollouts, num_states + 1)
+        x0_batched = np.tile(x0, (len(ms), 1))
+        processed_controls = self.task_to_sim_ctrl(controls)
+        assert x0_batched.shape[-1] == nq + nv
+        assert x0_batched.ndim == 2
+        assert processed_controls.ndim == 3
+        assert processed_controls.shape[-1] == nu
+        assert processed_controls.shape[0] == x0_batched.shape[0]
+
+        # rollout
+        if self.backend == "mujoco":
+            _states, _out_sensors = self.rollout_func(ms, ds, x0_batched, processed_controls)
+        else:
+            raise ValueError(f"Unknown backend: {self.backend}")
+        out_states = np.array(_states)
+        out_sensors = np.array(_out_sensors)
+        return out_states, out_sensors
+
+    def update(self, num_threads: int) -> None:
+        """Update the backend with a new number of threads."""
+        if self.backend == "mujoco":
+            self.setup_mujoco_backend()
+        else:
+            raise ValueError(f"Unknown backend: {self.backend}")
+
+
+class SimBackend:
+    """The backend for conducting simulation."""
+
+    def __init__(self, task_to_sim_ctrl: Callable) -> None:
+        """Initialize the backend."""
+        self.task_to_sim_ctrl = task_to_sim_ctrl
+
+    def sim(self, sim_model: MjModel, sim_data: MjData, sim_controls: np.ndarray) -> None:
+        """Conduct a simulation step using cpp sim."""
+        processed_ctrl = self.task_to_sim_ctrl(sim_controls)
+        x0 = np.concatenate([sim_data.qpos, sim_data.qvel])
+        sim(sim_model, sim_data, x0, processed_ctrl)
diff --git a/judo_cpp/CMakeLists.txt b/judo_cpp/CMakeLists.txt
new file mode 100644
index 00000000..c97da4e1
--- /dev/null
+++ b/judo_cpp/CMakeLists.txt
@@ -0,0 +1,86 @@
+cmake_minimum_required(VERSION 3.15)
+project(judo_cpp)
+
+set(CMAKE_CXX_STANDARD 17)
+find_package(OpenMP REQUIRED)
+find_package(Eigen3 REQUIRED)
+
+# 1. Locate Python, which is used to find the MuJoCo package
+find_package(Python
+  COMPONENTS
+    Interpreter
+    Development.Module
+  REQUIRED
+)
+execute_process(
+  COMMAND ${Python_EXECUTABLE} -c
+          "import mujoco, os; print(os.path.dirname(mujoco.__file__))"
+  OUTPUT_VARIABLE MJ_PKG_ROOT
+  OUTPUT_STRIP_TRAILING_WHITESPACE
+)
+message(STATUS "MuJoCo package root: ${MJ_PKG_ROOT}")
+
+# 2. Glob for the actual libmujoco.so file(s) there
+file(GLOB MUJOCO_LIB_FILES
+     "${MJ_PKG_ROOT}/libmujoco.so*"  # Linux
+     "${MJ_PKG_ROOT}/libmujoco.dylib"  # macOS default
+     "${MJ_PKG_ROOT}/libmujoco.*.dylib"  # macOS versioned dylib
+)
+if(NOT MUJOCO_LIB_FILES)
+  message(FATAL_ERROR "Could not find any libmujoco.so* in ${MJ_PKG_ROOT}")
+endif()
+list(GET MUJOCO_LIB_FILES 0 MUJOCO_LIB)
+message(STATUS "Linking against MuJoCo library: ${MUJOCO_LIB}")
+
+# 3. Pull in mujoco headers - they're located under the include directory
+set(MJINC_DIR "${MJ_PKG_ROOT}/include")
+message(STATUS "MuJoCo include dir: ${MJINC_DIR}")
+include_directories(${MJINC_DIR})
+
+# 4. Build the pybind11 module
+find_package(pybind11 REQUIRED)
+pybind11_add_module(_judo_cpp
+  bindings.cpp
+  rollout.cpp
+)
+execute_process(
+  COMMAND ${Python_EXECUTABLE} -c "import site; print(site.getsitepackages()[0])"
+  OUTPUT_VARIABLE SITE_PACKAGES_DIR
+  OUTPUT_STRIP_TRAILING_WHITESPACE
+)  # find the site-packages directory
+set_target_properties(_judo_cpp PROPERTIES
+  LIBRARY_OUTPUT_DIRECTORY "${SITE_PACKAGES_DIR}/judo_cpp"
+)  # rebuild the bound module to the site-packages directory
+
+# Configure RPATHs correctly for each platform
+if(APPLE)
+  set_target_properties(_judo_cpp PROPERTIES
+    MACOSX_RPATH ON
+    BUILD_WITH_INSTALL_RPATH TRUE
+    BUILD_RPATH "@loader_path/../mujoco"
+    INSTALL_RPATH "@loader_path/../mujoco"
+  )
+  # MuJoCo pip wheel on macOS ships a flat lib, but exposes a framework-style install_name.
+  # Rewrite the dependency on the built extension to the flat @rpath/libmujoco.*.dylib.
+  get_filename_component(MUJOCO_LIB_NAME "${MUJOCO_LIB}" NAME)
+  add_custom_command(TARGET _judo_cpp POST_BUILD
+    COMMAND install_name_tool -change
+      "@rpath/mujoco.framework/Versions/A/${MUJOCO_LIB_NAME}"
+      "@rpath/${MUJOCO_LIB_NAME}"
+      "$<TARGET_FILE:_judo_cpp>"
+    VERBATIM)
+else()
+  set_target_properties(_judo_cpp PROPERTIES
+    BUILD_RPATH "$ORIGIN/../mujoco"
+    INSTALL_RPATH "$ORIGIN/../mujoco"
+    BUILD_RPATH_USE_ORIGIN TRUE
+  )
+endif()
+target_link_libraries(_judo_cpp PRIVATE
+  Eigen3::Eigen
+  OpenMP::OpenMP_CXX
+  ${MUJOCO_LIB}
+)
+
+# 5. Install the module
+install(TARGETS _judo_cpp DESTINATION judo_cpp)
diff --git a/judo_cpp/__init__.py b/judo_cpp/__init__.py
new file mode 100644
index 00000000..54882598
--- /dev/null
+++ b/judo_cpp/__init__.py
@@ -0,0 +1,11 @@
+# Copyright (c) 2025 Robotics and AI Institute LLC. All rights reserved.
+
+from judo_cpp._judo_cpp import (
+    rollout,
+    sim,
+)
+
+__all__ = [
+    "rollout",
+    "sim",
+]
diff --git a/judo_cpp/bindings.cpp b/judo_cpp/bindings.cpp
new file mode 100644
index 00000000..42cfdf51
--- /dev/null
+++ b/judo_cpp/bindings.cpp
@@ -0,0 +1,103 @@
+#include <pybind11/pybind11.h>
+#include <pybind11/stl.h>
+#include <pybind11/numpy.h>
+#include <mujoco/mujoco.h>
+
+#include "rollout.h"
+
+namespace py = pybind11;
+
+static std::vector<const mjModel*> getModelVector(const py::list& python_models) {
+    std::vector<const mjModel*> model_vector;
+    model_vector.reserve(python_models.size());
+    for (auto&& item : python_models) {
+        auto ptr = item.attr("_address").cast<std::uintptr_t>();
+        model_vector.push_back(reinterpret_cast<const mjModel*>(ptr));
+    }
+    return model_vector;
+}
+
+static std::vector<mjData*> getDataVector(const py::list& python_data) {
+    std::vector<mjData*> data_vector;
+    data_vector.reserve(python_data.size());
+    for (auto&& item : python_data) {
+        auto ptr = item.attr("_address").cast<std::uintptr_t>();
+        data_vector.push_back(reinterpret_cast<mjData*>(ptr));
+    }
+    return data_vector;
+}
+
+PYBIND11_MODULE(_judo_cpp, m) {
+    // Function to shutdown persistent thread pool
+    m.def("shutdown_thread_pool",
+          []() {
+              ThreadPoolManager::instance().shutdown();
+          },
+          R"doc(
+Shutdown the persistent thread pool.
+
+Call this function to clean up the persistent thread pool when done with rollouts.
+The pool will be automatically recreated on the next call to persistent_cpp_rollout.
+)doc");
+
+    // Rollout
+    m.def("rollout",
+          [](const py::list& models,
+             const py::list& data,
+             const py::array_t<double>& x0,
+             const py::array_t<double>& controls)
+          {
+              // turn Python lists into vectors of mjModel*/mjData*
+              auto models_cpp = getModelVector(models);
+              auto data_cpp   = getDataVector(data);
+
+              // call into your C++ implementation
+              return Rollout(models_cpp, data_cpp, x0, controls);
+          },
+          py::arg("models"),
+          py::arg("data"),
+          py::arg("x0"),
+          py::arg("controls"),
+          R"doc(
+Run parallel MuJoCo rollouts.
+
+    Args:
+    models:                 length-B list of mujoco._structs.MjModel
+    data:                   length-B list of mujoco._structs.MjData
+    x0:                     2D array of shape (B, nq+nv), batched initial [qpos;qvel]
+    controls:               3D array of shape (B, horizon, nu), batched control inputs
+
+Returns:
+    tuple of three np.ndarray:
+    states  -> shape (B, horizon+1, nq+nv) - MuJoCo states (includes initial state)
+    sensors -> shape (B, horizon, nsensordata) - sensor data
+)doc");
+
+    // Sim
+    m.def("sim",
+          [](py::object model,
+             py::object data,
+             const py::array_t<double>& x0,
+             const py::array_t<double>& controls)
+          {
+              auto model_ptr = reinterpret_cast<const mjModel*>(model.attr("_address").cast<std::uintptr_t>());
+              auto data_ptr  = reinterpret_cast<mjData*>(data.attr("_address").cast<std::uintptr_t>());
+              Sim(model_ptr, data_ptr, x0, controls);
+          },
+          py::arg("model"),
+          py::arg("data"),
+          py::arg("x0"),
+          py::arg("controls"),
+          R"doc(
+Run a single MuJoCo simulation step.
+
+    Args:
+    model:      mujoco._structs.MjModel
+    data:       mujoco._structs.MjData
+    x0:         1D array of shape (nq+nv), initial [qpos;qvel]
+    controls:   1D array of shape (nu), control input
+
+Returns:
+    None
+)doc");
+}
diff --git a/judo_cpp/pyproject.toml b/judo_cpp/pyproject.toml
new file mode 100644
index 00000000..13b3d298
--- /dev/null
+++ b/judo_cpp/pyproject.toml
@@ -0,0 +1,20 @@
+[build-system]
+requires = ["scikit-build-core[pyproject]", "pybind11", "mujoco"]
+build-backend = "scikit_build_core.build"
+
+[project]
+name = "judo-cpp"
+version = "0.0.1"
+
+[project.optional-dependencies]
+dev = [
+    "ninja",
+    "pybind11",
+]
+
+[tool.scikit-build]
+cmake.source-dir = "."
+wheel.packages = ["judo_cpp"]
+cmake.build-type = "Release"
+build-dir = "build"  # for manual rebuilding during dev
+cmake.args = ["-DEigen3_DIR=$ENV{CONDA_PREFIX}/lib/cmake/eigen3"]
diff --git a/judo_cpp/rollout.cpp b/judo_cpp/rollout.cpp
new file mode 100644
index 00000000..059ab098
--- /dev/null
+++ b/judo_cpp/rollout.cpp
@@ -0,0 +1,271 @@
+#include "rollout.h"
+#include <pybind11/pybind11.h>
+#include <pybind11/numpy.h>
+#include <mujoco/mujoco.h>
+#include <vector>
+#include <iostream>
+#include <omp.h>
+#include <string>
+
+namespace py = pybind11;
+
+// ========== Thread Pool Implementation ==========
+
+PersistentThreadPool::PersistentThreadPool(int num_threads)
+    : num_threads_(num_threads), stop_(false), active_workers_(0), total_tasks_(0), completed_tasks_(0) {
+    threads_.reserve(num_threads);
+    for (int i = 0; i < num_threads; ++i) {
+        threads_.emplace_back(&PersistentThreadPool::worker_thread, this);
+    }
+}
+
+PersistentThreadPool::~PersistentThreadPool() {
+    {
+        std::unique_lock<std::mutex> lock(queue_mutex_);
+        stop_ = true;
+    }
+    condition_.notify_all();
+    for (std::thread& thread : threads_) {
+        if (thread.joinable()) {
+            thread.join();
+        }
+    }
+}
+
+void PersistentThreadPool::worker_thread() {
+    while (true) {
+        std::function<void()> task;
+        {
+            std::unique_lock<std::mutex> lock(queue_mutex_);
+            condition_.wait(lock, [this] { return stop_ || !tasks_.empty(); });
+
+            if (stop_ && tasks_.empty()) {
+                return;
+            }
+
+            if (!tasks_.empty()) {
+                task = std::move(tasks_.front());
+                tasks_.pop();
+                active_workers_++;
+            }
+        }
+
+        if (task) {
+            task();
+
+            std::unique_lock<std::mutex> lock(queue_mutex_);
+            active_workers_--;
+            completed_tasks_++;
+            if (completed_tasks_ == total_tasks_) {
+                finished_.notify_one();
+            }
+        }
+    }
+}
+
+void PersistentThreadPool::execute_parallel(std::function<void(int)> func, int total_work) {
+    {
+        std::unique_lock<std::mutex> lock(queue_mutex_);
+        total_tasks_ = total_work;
+        completed_tasks_ = 0;
+
+        for (int i = 0; i < total_work; ++i) {
+            tasks_.emplace([func, i] { func(i); });
+        }
+    }
+
+    condition_.notify_all();
+
+    // Wait for all tasks to complete
+    std::unique_lock<std::mutex> lock(queue_mutex_);
+    finished_.wait(lock, [this] { return completed_tasks_ == total_tasks_; });
+}
+
+// Thread Pool Manager Implementation
+ThreadPoolManager& ThreadPoolManager::instance() {
+    static ThreadPoolManager instance;
+    return instance;
+}
+
+PersistentThreadPool* ThreadPoolManager::get_pool(int num_threads) {
+    std::lock_guard<std::mutex> lock(pool_mutex_);
+
+    if (!current_pool_ || current_num_threads_ != num_threads) {
+        current_pool_.reset();  // Destroy old pool
+        current_pool_ = std::make_unique<PersistentThreadPool>(num_threads);
+        current_num_threads_ = num_threads;
+    }
+
+    return current_pool_.get();
+}
+
+void ThreadPoolManager::shutdown() {
+    std::lock_guard<std::mutex> lock(pool_mutex_);
+    current_pool_.reset();
+    current_num_threads_ = 0;
+}
+
+// ========== End Thread Pool Implementation ==========
+
+py::array_t<double> make_array(std::vector<double>& buf,
+                              int B, int T, int D) {
+    std::vector<ssize_t> shape   = { B, T, D };
+    std::vector<ssize_t> strides = {
+        static_cast<ssize_t>(sizeof(double) * T * D),
+        static_cast<ssize_t>(sizeof(double) *     D),
+        static_cast<ssize_t>(sizeof(double))
+    };
+
+    // Move buf onto the heap so we can own it in the capsule:
+    auto heap_buf = new std::vector<double>(std::move(buf));
+    // Create a capsule that will delete the vector when the array is gone:
+    py::capsule free_when_done(heap_buf, [](void *p) {
+        delete reinterpret_cast<std::vector<double>*>(p);
+    });
+
+    // Build the array pointing into heap_buf->data() and owning the capsule:
+    return py::array_t<double>(
+        shape, strides,
+        heap_buf->data(),
+        free_when_done
+    );
+}
+
+
+py::tuple Rollout(
+    const std::vector<const mjModel*>& models,
+    const std::vector<mjData*>&        data,
+    const py::array_t<double>&         x0,
+    const py::array_t<double>&         controls
+) {
+    int B = (int)models.size();
+    if (B == 0 || B != (int)data.size()) {
+        throw std::runtime_error("models/data must have same non-zero length");
+    }
+
+    int horizon = (int)controls.shape(1);  // controls: 3D of shape (B, horizon, nu)
+
+    // dims from first model
+    const mjModel* m0 = models[0];
+    int nq     = m0->nq;
+    int nv     = m0->nv;
+    int nu     = m0->nu;
+    int nsens  = m0->nsensordata;
+    int nstate = nq + nv;
+
+    // x0: 2D of shape (B, nstate) for batched initial states
+    if (x0.ndim() != 2 || x0.shape(0) != B || x0.shape(1) != nstate) {
+        throw std::runtime_error("x0 must be a 2D array of shape (B, nq+nv)");
+    }
+    const double* x0_ptr = x0.data();
+
+    // allocate outputs
+    std::vector<double> states_buf(B * (horizon + 1) * nstate);  // +1 for initial state
+    std::vector<double> sens_buf(B * horizon * nsens);
+
+    {
+        py::gil_scoped_release release;
+
+        auto controls_unchecked = controls.unchecked<3>();
+
+        #pragma omp parallel for
+        for (int i = 0; i < B; i++) {
+            try {
+                mjData* d = data[i];
+
+                // set initial qpos+qvel for this batch
+                d->time = 0.0;
+                const double* x0_i = x0_ptr + i * nstate;
+                mj_setState(models[i], d, x0_i, mjSTATE_QPOS | mjSTATE_QVEL);
+                mj_forward(models[i], d);
+                mju_zero(d->qacc_warmstart, m0->nv);
+
+                double* st_ptr = &states_buf[i * (horizon + 1) * nstate];
+                double* se_ptr = &sens_buf[i * horizon * nsens];
+
+                // Store initial state
+                for (int j = 0; j < nq; j++) st_ptr[j] = d->qpos[j];
+                for (int j = 0; j < nv; j++) st_ptr[nq + j] = d->qvel[j];
+                std::vector<double> current_state(nstate);
+                for (int j = 0; j < nq; j++) current_state[j] = d->qpos[j];
+                for (int j = 0; j < nv; j++) current_state[nq + j] = d->qvel[j];
+
+                for (int t = 0; t < horizon; t++) {
+                    for (int j = 0; j < nu; j++) d->ctrl[j] = controls_unchecked(i, t, j);
+
+                    // Step simulation
+                    mj_step(models[i], d);
+
+                    // Record new state
+                    for (int j = 0; j < nq; j++) current_state[j] = d->qpos[j];
+                    for (int j = 0; j < nv; j++) current_state[nq + j] = d->qvel[j];
+
+                    // Store in output buffers
+                    for (int j = 0; j < nstate; j++) {
+                        st_ptr[(t + 1) * nstate + j] = current_state[j];
+                    }
+                    for (int j = 0; j < nsens; j++) {
+                        se_ptr[t * nsens + j] = d->sensordata[j];
+                    }
+                }
+
+            } catch (const std::exception& e) {
+                std::cerr << "Error in rollout thread " << i << ": " << e.what() << std::endl;
+                // Fill with zeros as fallback
+                double* st_ptr = &states_buf[i * (horizon + 1) * nstate];
+                double* se_ptr = &sens_buf[i * horizon * nsens];
+
+                for (int t = 0; t <= horizon; t++) {
+                    for (int j = 0; j < nstate; j++) {
+                        st_ptr[t * nstate + j] = 0.0;
+                    }
+                }
+                for (int t = 0; t < horizon; t++) {
+                    for (int j = 0; j < nsens; j++) {
+                        se_ptr[t * nsens + j] = 0.0;
+                    }
+                }
+            }
+        }
+    }
+
+    auto states_arr = make_array(states_buf, B, horizon + 1, nstate);
+    auto sens_arr = make_array(sens_buf, B, horizon, nsens);
+
+    return py::make_tuple(states_arr, sens_arr);
+}
+
+void Sim(
+    const mjModel* model,
+    mjData*        data,
+    const py::array_t<double>& x0,
+    const py::array_t<double>& controls
+) {
+    // dims from model
+    int nq = model->nq;
+    int nv = model->nv;
+    int nu = model->nu;
+    int nstate = nq + nv;
+
+    // x0: 1D of shape (nstate)
+    if (x0.ndim() != 1 || x0.shape(0) != nstate) {
+        throw std::runtime_error("x0 must be a 1D array of shape (nq+nv)");
+    }
+    if (controls.ndim() != 1 || controls.shape(0) != nu) {
+        throw std::runtime_error("controls must be a 1D array of shape (nu)");
+    }
+    const double* x0_ptr = x0.data();
+    const double* ctrl_ptr = controls.data();
+
+    // set initial qpos+qvel
+    data->time = 0.0;
+    mj_setState(model, data, x0_ptr, mjSTATE_QPOS | mjSTATE_QVEL);
+    mj_forward(model, data);
+    mju_zero(data->qacc_warmstart, nv);
+
+    // set controls
+    for (int j = 0; j < nu; j++) data->ctrl[j] = ctrl_ptr[j];
+
+    // step simulation
+    mj_step(model, data);
+}
diff --git a/judo_cpp/rollout.h b/judo_cpp/rollout.h
new file mode 100644
index 00000000..29441134
--- /dev/null
+++ b/judo_cpp/rollout.h
@@ -0,0 +1,73 @@
+#pragma once
+
+#include <pybind11/pybind11.h>
+#include <pybind11/numpy.h>
+#include <mujoco/mujoco.h>
+#include <vector>
+#include <string>
+#include <memory>
+#include <thread>
+#include <mutex>
+#include <condition_variable>
+#include <queue>
+#include <functional>
+#include <deque>
+#include <unordered_map>
+
+namespace py = pybind11;
+
+// Utility function to create numpy arrays from C++ vectors
+py::array_t<double> make_array(std::vector<double>& buf, int B, int T, int D);
+
+// Thread pool for persistent thread management
+class PersistentThreadPool {
+public:
+    PersistentThreadPool(int num_threads);
+    ~PersistentThreadPool();
+
+    // Execute a function across the thread pool
+    void execute_parallel(std::function<void(int)> func, int total_work);
+
+    int get_num_threads() const { return num_threads_; }
+
+private:
+    int num_threads_;
+    std::vector<std::thread> threads_;
+    std::queue<std::function<void()>> tasks_;
+    std::mutex queue_mutex_;
+    std::condition_variable condition_;
+    std::condition_variable finished_;
+    bool stop_;
+    int active_workers_;
+    int total_tasks_;
+    int completed_tasks_;
+
+    void worker_thread();
+};
+
+// Global persistent thread pool manager
+class ThreadPoolManager {
+public:
+    static ThreadPoolManager& instance();
+    PersistentThreadPool* get_pool(int num_threads);
+    void shutdown();
+
+private:
+    std::mutex pool_mutex_;
+    std::unique_ptr<PersistentThreadPool> current_pool_;
+    int current_num_threads_ = 0;
+};
+
+py::tuple Rollout(
+    const std::vector<const mjModel*>& models,
+    const std::vector<mjData*>&        data,
+    const py::array_t<double>&         x0,
+    const py::array_t<double>&         controls
+);
+
+void Sim(
+    const mjModel* model,
+    mjData*        data,
+    const pybind11::array_t<double>& x0,
+    const pybind11::array_t<double>& controls
+);
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+  sha256: 4a2f488f8df9b19c14f7b66b55431146d18f8070bd722f1760713e28a0c3e9ec
+  md5: b35c9d56c92c2d3846908b87e4d40ede
+  depends:
+  - cffi >=1.11
+  - python >=3.13,<3.14.0a0
+  - python_abi 3.13.* *_cp313
+  - ucrt >=10.0.20348.0
+  - vc >=14.3,<15
+  - vc14_runtime >=14.44.35208
+  - zstd >=1.5.7,<1.5.8.0a0
+  - zstd >=1.5.7,<1.6.0a0
+  license: BSD-3-Clause
+  purls:
+  - pkg:pypi/zstandard?source=hash-mapping
+  size: 351525
+  timestamp: 1756841550515
+- conda: https://conda.anaconda.org/conda-forge/linux-64/zstd-1.5.7-hb8e6e7a_2.conda
+  sha256: a4166e3d8ff4e35932510aaff7aa90772f84b4d07e9f6f83c614cba7ceefe0eb
+  md5: 6432cb5d4ac0046c3ac0a8a0f95842f9
+  depends:
+  - __glibc >=2.17,<3.0.a0
+  - libgcc >=13
+  - libstdcxx >=13
+  - libzlib >=1.3.1,<2.0a0
+  license: BSD-3-Clause
+  license_family: BSD
+  purls: []
+  size: 567578
+  timestamp: 1742433379869
+- conda: https://conda.anaconda.org/conda-forge/osx-64/zstd-1.5.7-h8210216_2.conda
+  sha256: c171c43d0c47eed45085112cb00c8c7d4f0caa5a32d47f2daca727e45fb98dca
+  md5: cd60a4a5a8d6a476b30d8aa4bb49251a
+  depends:
+  - __osx >=10.13
+  - libzlib >=1.3.1,<2.0a0
+  license: BSD-3-Clause
+  license_family: BSD
+  purls: []
+  size: 485754
+  timestamp: 1742433356230
+- conda: https://conda.anaconda.org/conda-forge/win-64/zstd-1.5.7-hbeecb71_2.conda
+  sha256: bc64864377d809b904e877a98d0584f43836c9f2ef27d3d2a1421fa6eae7ca04
+  md5: 21f56217d6125fb30c3c3f10c786d751
+  depends:
+  - libzlib >=1.3.1,<2.0a0
+  - ucrt >=10.0.20348.0
+  - vc >=14.2,<15
+  - vc14_runtime >=14.29.30139
+  license: BSD-3-Clause
+  license_family: BSD
+  purls: []
+  size: 354697
+  timestamp: 1742433568506
diff --git a/pyproject.toml b/pyproject.toml
index adfcdaa8..4d0ce7d1 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -175,7 +175,55 @@ platforms = ["linux-64", "win-64", "osx-64"]
 [tool.pixi.pypi-dependencies]
 judo-rai = { path = ".", editable = true }
 
+[tool.pixi.dependencies]
+python = ">=3.10"
+
 [tool.pixi.environments]
 default = { solve-group = "default" }
 docs = { features = ["docs"], solve-group = "default" }
-dev = { features = ["dev", "docs"], solve-group = "default" }
+dev = { features = ["dev", "docs", "cpp"], solve-group = "default" }
+
+[tool.pixi.feature.dev.dependencies]
+pre-commit = "*"
+pybind11-stubgen = "*"
+pyright = "*"
+pytest = "*"
+pytest-cov = "*"
+
+[tool.pixi.feature.docs.dependencies]
+furo = "*"
+m2r2 = "*"
+sphinx = "*"
+sphinx-autoapi = "*"
+
+[tool.pixi.feature.cpp.dependencies]
+cmake = "*"
+ninja = "*"
+pybind11 = "*"
+cxx-compiler = "*"
+c-compiler = "*"
+eigen = "*"
+
+[tool.pixi.feature.cpp.pypi-dependencies]
+judo-cpp = { path = "./judo_cpp", editable = true }
+
+[tool.pixi.tasks]
+
+[tool.pixi.tasks.configure]
+cmd = """
+sh -c 'mkdir -p judo_cpp/build && \
+cmake -S judo_cpp -B judo_cpp/build \
+  -G Ninja \
+  -DPython_ROOT_DIR=$CONDA_PREFIX \
+  -DPython_EXECUTABLE=$CONDA_PREFIX/bin/python \
+  -Dpybind11_DIR=$(python -m pybind11 --cmakedir)'
+"""
+
+[tool.pixi.tasks.build]
+depends-on = ["configure"]
+cwd = "judo_cpp/build"
+cmd = "ninja _judo_cpp && pybind11-stubgen judo_cpp --numpy-array-use-type-var -o ../../typings/"
+
+[tool.pixi.tasks.clean]
+cwd = "judo_cpp"
+cmd = "rm -rf build/"