diff --git a/src/optimagic/optimization/history_tools.py b/src/optimagic/optimization/history_tools.py
deleted file mode 100644
index 770f1930e..000000000
--- a/src/optimagic/optimization/history_tools.py
+++ /dev/null
@@ -1,88 +0,0 @@
-from dataclasses import dataclass
-from functools import partial
-
-import numpy as np
-from numpy.typing import NDArray
-from pybaum import tree_just_flatten
-
-from optimagic.optimization.history import History
-from optimagic.parameters.tree_registry import get_registry
-from optimagic.typing import Direction
-
-
-@dataclass(frozen=True)
-class HistoryArrays:
-    fun: NDArray[np.float64]
-    params: NDArray[np.float64]
-    time: NDArray[np.float64]
-    monotone_fun: NDArray[np.float64]
-    is_accepted: NDArray[np.bool_]
-
-    @property
-    def criterion(self) -> NDArray[np.float64]:
-        return self.fun
-
-    @property
-    def monotone_criterion(self) -> NDArray[np.float64]:
-        return self.monotone_fun
-
-    def __getitem__(self, key: str) -> NDArray[np.float64] | NDArray[np.bool_]:
-        return getattr(self, key)
-
-
-def get_history_arrays(history: History, direction: Direction) -> HistoryArrays:
-    # ==================================================================================
-    # Handle deprecations for now
-    # ==================================================================================
-    assert direction in [Direction.MINIMIZE, Direction.MAXIMIZE]
-
-    if isinstance(history, dict):
-        parhist = history["params"]
-        funhist = history["criterion"]
-        timehist = history["runtime"]
-
-    else:
-        parhist = history.params
-        funhist = history.fun
-        timehist = history.time
-
-    # ==================================================================================
-    # Filter out evaluations that do not have a `fun` value
-    # ==================================================================================
-
-    parhist = [p for p, f in zip(parhist, funhist, strict=False) if f is not None]
-    timehist = [t for t, f in zip(timehist, funhist, strict=False) if f is not None]
-    funhist = [f for f in funhist if f is not None]
-
-    # ==================================================================================
-
-    is_flat = (
-        len(parhist) > 0 and isinstance(parhist[0], np.ndarray) and parhist[0].ndim == 1
-    )
-    if is_flat:
-        to_internal = lambda x: x.tolist()
-    else:
-        registry = get_registry(extended=True)
-        to_internal = partial(tree_just_flatten, registry=registry)
-
-    critvals = np.array(funhist)
-
-    params = np.array([to_internal(p) for p in parhist])
-
-    runtimes = np.array(timehist)
-
-    if direction == Direction.MINIMIZE:
-        monotone = np.minimum.accumulate(critvals)
-        is_accepted = critvals <= monotone
-    elif direction == Direction.MAXIMIZE:
-        monotone = np.maximum.accumulate(critvals)
-        is_accepted = critvals >= monotone
-
-    out = HistoryArrays(
-        fun=critvals,
-        params=params,
-        time=runtimes,
-        monotone_fun=monotone,
-        is_accepted=is_accepted,
-    )
-    return out
diff --git a/tests/optimagic/optimization/test_history_tools.py b/tests/optimagic/optimization/test_history_tools.py
deleted file mode 100644
index 4b4f4d100..000000000
--- a/tests/optimagic/optimization/test_history_tools.py
+++ /dev/null
@@ -1,27 +0,0 @@
-import numpy as np
-import pytest
-from numpy.testing import assert_array_almost_equal as aaae
-
-from optimagic.optimization.history_tools import get_history_arrays
-from optimagic.typing import Direction
-
-
-@pytest.fixture()
-def history():
-    hist = {
-        "criterion": [5, 4, 5.5, 4.2],
-        "params": [{"a": 0}, {"a": 1}, {"a": 2}, {"a": 3}],
-        "runtime": [0, 1, 2, 3],
-    }
-    return hist
-
-
-def test_get_history_arrays_minimize(history):
-    calculated = get_history_arrays(history, Direction.MINIMIZE)
-
-    aaae(calculated.is_accepted, np.array([True, True, False, False]))
-
-
-def test_get_history_arrays_maximize(history):
-    calculated = get_history_arrays(history, Direction.MAXIMIZE)
-    aaae(calculated.is_accepted, np.array([True, False, True, False]))