diff --git a/ann_benchmarks/algorithms/deglib/Dockerfile b/ann_benchmarks/algorithms/deglib/Dockerfile
new file mode 100644
index 000000000..0145240f0
--- /dev/null
+++ b/ann_benchmarks/algorithms/deglib/Dockerfile
@@ -0,0 +1,14 @@
+FROM ann-benchmarks
+
+RUN apt update && apt install -y cmake ninja-build
+
+RUN git clone https://github.com/Visual-Computing/DynamicExplorationGraph.git; cd DynamicExplorationGraph
+
+RUN python -m pip install pybind11 numpy setuptools>=77.0 build
+RUN cd DynamicExplorationGraph/python; python setup.py copy_build_files
+RUN cd DynamicExplorationGraph/python; python -m pip install .
+
+# RUN python -m pip install deglib==0.1.4
+
+RUN python -c 'import deglib'
+
diff --git a/ann_benchmarks/algorithms/deglib/config.yml b/ann_benchmarks/algorithms/deglib/config.yml
new file mode 100644
index 000000000..68d4707f3
--- /dev/null
+++ b/ann_benchmarks/algorithms/deglib/config.yml
@@ -0,0 +1,13 @@
+float:
+  any:
+  - base_args: ['@metric']
+    constructor: DegLib
+    disabled: false
+    docker_tag: ann-benchmarks-deglib
+    module: ann_benchmarks.algorithms.deglib
+    name: deglib
+    run_groups:
+      DEG_30:
+        arg_groups: [{edges_per_vertex: 30, extend_k: 60, extend_eps: 0.1, optimization_target: 0, remove_edges: true}]
+        args: {}
+        query_args: [[{eps: 0.001}, {eps: 0.01}, {eps: 0.05}, {eps: 0.1}, {eps: 0.15}, {eps: 0.2}, {eps: 0.3}]]
diff --git a/ann_benchmarks/algorithms/deglib/module.py b/ann_benchmarks/algorithms/deglib/module.py
new file mode 100644
index 000000000..f71844478
--- /dev/null
+++ b/ann_benchmarks/algorithms/deglib/module.py
@@ -0,0 +1,87 @@
+import sys
+import time
+
+import deglib
+import numpy as np
+
+from ..base.module import BaseANN
+
+
+def build_from_data(
+        data, edges_per_vertex = 30,
+        metric = deglib.Metric.L2, optimization_target = deglib.builder.OptimizationTarget.StreamingData,
+        extend_k = 60, extend_eps = 0.1,
+        improve_k = 30, improve_eps = 0.001, max_path_length = 5,
+        swap_tries = 0, additional_swap_tries = 0, remove_edges = True
+):
+    print('edges_per_vertex', edges_per_vertex)
+    print('metric', metric)
+    print('optimization_target', optimization_target)
+    print('extend_k', extend_k)
+    print('extend_eps', extend_eps)
+    print('improve_k', improve_k)
+    print('improve_eps', improve_eps)
+    print('max_path_length', max_path_length)
+    print('swap_tries', swap_tries)
+    print('additional_swap_tries', additional_swap_tries)
+
+    graph = deglib.graph.SizeBoundedGraph.create_empty(data.shape[0], data.shape[1], edges_per_vertex, metric)
+    builder = deglib.builder.EvenRegularGraphBuilder(
+        graph, optimization_target=optimization_target, extend_k=extend_k, extend_eps=extend_eps, improve_k=improve_k,
+        improve_eps=improve_eps, max_path_length=max_path_length, swap_tries=swap_tries,
+        additional_swap_tries=additional_swap_tries
+    )
+    builder.set_thread_count(1)
+    labels = np.arange(data.shape[0], dtype=np.uint32)
+    builder.add_entry(labels, data)
+    builder.build()
+
+    if remove_edges:
+        graph.remove_non_mrng_edges()
+
+    return graph
+
+
+class DegLib(BaseANN):
+    def __init__(self, metric, method_params):
+        self.metric = {"angular": deglib.Metric.InnerProduct, "euclidean": deglib.Metric.L2}[metric]
+        self.method_params = method_params
+        self.query_params = {}
+        self.name = None
+        self.index = None
+
+    def fit(self, X):
+        # fix optimization target datatype
+        build_params = {}
+        for key, value in self.method_params.items():
+            if key == 'optimization_target':
+                value = deglib.builder.OptimizationTarget(value)
+            build_params[key] = value
+
+        if self.metric == deglib.Metric.InnerProduct:
+            norms = np.linalg.norm(X, axis=1, keepdims=True)
+            norms[norms == 0] = 1.0
+            X = X / norms
+
+        if X.dtype != np.float32:
+            X = X.astype(np.float32)
+
+        self.index = build_from_data(X, **build_params, metric=self.metric)
+
+    def set_query_arguments(self, params):
+        self.query_params = params
+        self.name = "deglib (buildParams: {}, queryParams: {})".format(self.method_params, self.query_params)
+
+    def query(self, v, n):
+        v = np.expand_dims(v, axis=0)
+
+        if self.metric == deglib.Metric.InnerProduct:
+            v /= np.linalg.norm(v)
+
+        if v.dtype != np.float32:
+            v = v.astype(np.float32)
+
+        return self.index.search(v, k=n, **self.query_params)[0][0]
+
+    def freeIndex(self):
+        del self.index