diff --git a/examples/python/simple_example.py b/examples/python/simple_example.py
new file mode 100644
index 000000000..9b64142ed
--- /dev/null
+++ b/examples/python/simple_example.py
@@ -0,0 +1,39 @@
+import cognee
+import asyncio
+from cognee.api.v1.search import SearchType
+
+# Prerequisites:
+# 1. Copy `.env.template` and rename it to `.env`.
+# 2. Add your OpenAI API key to the `.env` file in the `LLM_API_KEY` field:
+#    LLM_API_KEY = "your_key_here"
+# 3. (Optional) To minimize setup effort, set `VECTOR_DB_PROVIDER="lancedb"` in `.env".
+
+async def main():
+    # Create a clean slate for cognee -- reset data and system state
+    await cognee.prune.prune_data()
+    await cognee.prune.prune_system(metadata=True)
+
+    # cognee knowledge graph will be created based on this text
+    text = """
+    Natural language processing (NLP) is an interdisciplinary
+    subfield of computer science and information retrieval.
+    """
+
+    # Add the text, and make it available for cognify
+    await cognee.add(text)
+
+    # Use LLMs and cognee to create knowledge graph
+    await cognee.cognify()
+
+    # Query cognee for insights on the added text
+    search_results = await cognee.search(
+        SearchType.INSIGHTS,
+        {'query': 'Tell me about NLP'}
+    )
+
+    # Display search results
+    for result_text in search_results:
+        print(result_text)
+
+if __name__ == '__main__':
+    asyncio.run(main())