diff --git a/apps/opik-documentation/documentation/docs/tracing/integrations/ollama.md b/apps/opik-documentation/documentation/docs/tracing/integrations/ollama.md
index 7a54b6719c..93d272aa2a 100644
--- a/apps/opik-documentation/documentation/docs/tracing/integrations/ollama.md
+++ b/apps/opik-documentation/documentation/docs/tracing/integrations/ollama.md
@@ -7,9 +7,7 @@ sidebar_label: Ollama
 
 [Ollama](https://ollama.com/) allows users to run, interact with, and deploy AI models locally on their machines without the need for complex infrastructure or cloud dependencies.
 
-There are multiple ways to interact with Ollama from Python including but not limited to the [ollama python package](https://pypi.org/project/ollama/), [LangChain](https://python.langchain.com/docs/integrations/providers/ollama/) or by using the [OpenAI library](https://github.com/ollama/ollama/blob/main/docs/openai.md).
-
-In this guide, we will focus on tracing Ollama calls made using the OpenAI library and LangChain.
+There are multiple ways to interact with Ollama from Python including but not limited to the [ollama python package](https://pypi.org/project/ollama/), [LangChain](https://python.langchain.com/docs/integrations/providers/ollama/) or by using the [OpenAI library](https://github.com/ollama/ollama/blob/main/docs/openai.md). We will cover how to trace your LLM calls for each of these methods.
 
 
 <div style="display: flex; align-items: center; flex-wrap: wrap; margin: 20px 0;">
@@ -41,6 +39,53 @@ opik configure
 Opik is fully open-source and can be run locally or through the Opik Cloud platform. You can learn more about hosting Opik on your own infrastructure in the [self-hosting guide](/docs/self-host/overview.md).
 :::
 
+## Tracking Ollama calls made with Ollama Python Package
+
+To get started you will need to install the Ollama Python package:
+
+```bash
+pip install --quiet --upgrade ollama
+```
+
+We will then utilize the `track` decorator to log all the traces to Opik    :
+
+```python
+import ollama
+from opik import track, opik_context
+
+@track(tags=['ollama', 'python-library'])
+def ollama_llm_call(user_message: str):
+    # Create the Ollama model
+    response = ollama.chat(model='llama3.1', messages=[
+        {
+            'role': 'user',
+            'content': user_message,
+        },
+    ])
+
+    opik_context.update_current_span(
+        metadata={
+            'model': response['model'],
+            'eval_duration': response['eval_duration'],
+            'load_duration': response['load_duration'],
+            'prompt_eval_duration': response['prompt_eval_duration'],
+            'prompt_eval_count': response['prompt_eval_count'],
+            'done': response['done'],
+            'done_reason': response['done_reason'],
+        },
+        usage={
+            'completion_tokens': response['eval_count'],
+            'prompt_tokens': response['prompt_eval_count'],
+            'total_tokens': response['eval_count'] + response['prompt_eval_count']
+        }
+    )
+    return response['message']
+
+ollama_llm_call("Say this is a test")
+```
+
+The trace will now be displayed in the Opik platform.
+
 ## Tracking Ollama calls made with OpenAI
 
 Ollama is compatible with the OpenAI format and can be used with the OpenAI Python library. You can therefore leverage the Opik integration for OpenAI to trace your Ollama calls: