diff --git a/examples/basic_tutorial_ex.py b/examples/basic_tutorial_ex.py
index f103646e..976685bb 100644
--- a/examples/basic_tutorial_ex.py
+++ b/examples/basic_tutorial_ex.py
@@ -50,7 +50,7 @@
 pr.save_rule_trace(interpretation)
 
 interpretations_dict = interpretation.get_dict()
-print("stra")
+print("start")
 pprint(interpretations_dict)
 print("end")
 #Display the changes in the interpretation for each timestep
diff --git a/examples/minimal_example.py b/examples/minimal_example.py
new file mode 100644
index 00000000..455fb57a
--- /dev/null
+++ b/examples/minimal_example.py
@@ -0,0 +1,66 @@
+# Test if the simple hello world program works
+import pyreason as pr
+import faulthandler
+import networkx as nx
+from typing import Tuple
+from pprint import pprint
+
+
+
+# Reset PyReason
+pr.reset()
+pr.reset_rules()
+pr.reset_settings()
+
+
+# ================================ CREATE GRAPH====================================
+# Create a Directed graph
+g = nx.DiGraph()
+
+# Add the nodes
+g.add_nodes_from(['John', 'Mary', 'Justin'])
+g.add_nodes_from(['Dog', 'Cat'])
+
+# Add the edges and their attributes. When an attribute = x which is <= 1, the annotation
+# associated with it will be [x,1]. NOTE: These attributes are immutable
+# Friend edges
+g.add_edge('Justin', 'Mary', Friends=1)
+g.add_edge('John', 'Mary', Friends=1)
+g.add_edge('John', 'Justin', Friends=1)
+
+# Pet edges
+g.add_edge('Mary', 'Cat', owns=1)
+g.add_edge('Justin', 'Cat', owns=1)
+g.add_edge('Justin', 'Dog', owns=1)
+g.add_edge('John', 'Dog', owns=1)
+
+
+# Modify pyreason settings to make verbose
+pr.settings.verbose = True     # Print info to screen
+# pr.settings.optimize_rules = False  # Disable rule optimization for debugging
+
+# Load all the files into pyreason
+pr.load_graph(g)
+# Immediate rule (delta_t=0): popularity spreads to friends who share a pet,
+# and the whole cascade resolves within a single timestep via the fixpoint.
+pr.add_rule(pr.Rule('popular(x) <- popular(y), Friends(x,y), owns(y,z), owns(x,z)', 'popular_rule'))
+pr.add_fact(pr.Fact('popular(Mary)', 'popular_fact', 0, 0))
+
+# Run the program for a single timestep (t=0) to see the full diffusion take place
+faulthandler.enable()
+interpretation = pr.reason(timesteps=0)
+pr.save_rule_trace(interpretation)
+
+interpretations_dict = interpretation.get_dict()
+print("start")
+pprint(interpretations_dict)
+print("end")
+
+# Display the interpretation at timestep 0
+dataframes = pr.filter_and_sort_nodes(interpretation, ['popular'])
+df = dataframes[0]
+print('TIMESTEP - 0')
+print(df)
+print()
+
+assert len(dataframes) == 1, 'There should only be one timestep (t=0)'