diff --git a/docs/gallery/howto/autogen/factorial_graph_builder.py b/docs/gallery/howto/autogen/factorial_graph_builder.py
new file mode 100644
index 00000000..dbcfd7f2
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+++ b/docs/gallery/howto/autogen/factorial_graph_builder.py
@@ -0,0 +1,27 @@
+from aiida_workgraph import task, WorkGraph
+
+
+@task.calcfunction
+def identity(x):
+    return x.clone()
+
+
+@task.calcfunction
+def multiply(x, y):
+    return x * y
+
+
+@task.graph_builder(outputs=[{"name": "result", "from": "context.task_out"}])
+def factorial(n):
+    wg = WorkGraph()
+    if n == 1:
+        task = wg.add_task(identity, name="identity", x=n)
+    else:
+        factorial_recursion = wg.add_task(factorial, name="factorial", n=n - 1)
+        task = wg.add_task(
+            multiply, name="multiply", x=n, y=factorial_recursion.outputs["result"]
+        )
+
+    # set the identity or multiply task as output
+    task.set_context({"result": "task_out"})
+    return wg
diff --git a/docs/gallery/howto/autogen/recursive.py b/docs/gallery/howto/autogen/recursive.py
new file mode 100644
index 00000000..86ea9365
--- /dev/null
+++ b/docs/gallery/howto/autogen/recursive.py
@@ -0,0 +1,103 @@
+"""
+======================================
+Recursive workflows with Graph Builder
+======================================
+"""
+# %%
+# For this how-to please familiarize yourself before with the concepts:
+# - `Graph Builder <graph_builder.html>`_
+# - `Context <../context.html>`_
+
+# %%
+# Introduction
+# ============
+# In this how-to, you will learn how to create a recursive workflow using
+# the Graph Builder. As recursive functions invoke themself we need to define
+# the recursive function in a separate module to be able execute it.
+# TODO Xing maybe you can add some technical aspects. For me it is a bit unclear
+
+# Load AiiDA profile
+from aiida import load_profile
+
+load_profile()
+
+
+# %%
+# Factorial example
+# =================
+# In this example we will define a factorial function using the Graph Builder
+#
+# .. code-block:: python
+#
+#    def factorial(n):
+#        if n == 1:
+#            return n
+#        else:
+#            return n * factorial(n-1)
+
+from aiida_workgraph import task, WorkGraph
+
+
+@task.calcfunction
+def identity(x):
+    return x.clone()
+
+
+@task.calcfunction
+def multiply(x, y):
+    return x * y
+
+
+@task.graph_builder(outputs=[{"name": "result", "from": "context.task_out"}])
+def factorial(n):
+    wg = WorkGraph()
+    if n == 1:
+        task = wg.add_task(identity, name="identity", x=n)
+    else:
+        factorial_recursion = wg.add_task(factorial, name="factorial", n=n - 1)
+        task = wg.add_task(
+            multiply, name="multiply", x=n, y=factorial_recursion.outputs["result"]
+        )
+
+    # set the identity or multiply task as output
+    task.set_context({"result": "task_out"})
+    return wg
+
+
+# %%
+# However we this alone will not work when we execute we will get an error that
+# factorial is not defined.
+
+try:
+    wg = WorkGraph()
+    wg.add_task(factorial, n=2)
+    wg.run()
+except Exception as err:
+    print(err)
+
+# %%
+# We have to load the definition of the graph builder from a module. We copied the
+# definiton above and put it into this file `factorial_graph_builder.py <factorial_graph_builder.py>`_.
+
+from factorial_graph_builder import factorial
+
+wg = WorkGraph()
+wg.add_task(factorial, n=2)
+wg.to_html()
+
+# %%
+# We run the workgraph
+wg.submit(wait=True)
+
+# %%
+# We can also see the recursive execution of the workgraphs in the provenance graph
+
+from aiida_workgraph.utils import generate_node_graph
+
+generate_node_graph(wg.pk)
+
+# %%
+# Note that by each recursion steep you create a new WorkGraph and the limit of
+# the number of processes (WorkGraph, CalcJobs, etc.) is 200 processes in AiiDA.
+# It can as increased as explaind in
+# `parallel how-to <parallel.html#maximum-number-of-active-workgraphs>.