feat: Update examples to use create_tracker() factory pattern

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

Author: jsonbailey

examples/bedrock/bedrock_example.py

@@ -58,12 +58,13 @@ def main():
         context,
         variables={'myUserVariable': "Testing Variable"}
     )
-    tracker = config_value.tracker
 
     if not config_value.enabled:
         print("AI Config is disabled")
         return
 
+    tracker = config_value.create_tracker()
+
     # Map the messages to the format expected by Bedrock
     chat_messages = [{'role': msg.role, 'content': [{'text': msg.content}]} for msg in config_value.messages if msg.role != 'system']
     system_messages = [{'text': msg.content} for msg in config_value.messages if msg.role == 'system']

examples/gemini/gemini_example.py

@@ -128,12 +128,13 @@ def main():
         context,
         variables={'myUserVariable': "Testing Variable"}
     )
-    tracker = config_value.tracker
 
     if not config_value.enabled:
         print("AI Config is disabled")
         return
 
+    tracker = config_value.create_tracker()
+
     # Configure Google Generative AI
     client = genai.Client(
         api_key=google_api_key,

examples/langchain/langchain_example.py

@@ -66,12 +66,13 @@ async def async_main():
         context,
         variables={'myUserVariable': "Testing Variable"}
     )
-    tracker = config_value.tracker
 
     if not config_value.enabled:
         print("AI Config is disabled")
         return
 
+    tracker = config_value.create_tracker()
+
     try:
         # Create LangChain model instance using init_chat_model
         # Map the provider from config_value to LangChain format

examples/langgraph_agent/langgraph_agent_example.py

@@ -115,7 +115,7 @@ def main():
 
     try:
         # Track and execute the agent
-        response = track_langgraph_metrics(agent_config.tracker, lambda: agent.invoke({
+        response = track_langgraph_metrics(agent_config.create_tracker(), lambda: agent.invoke({
             "messages": [{"role": "user", "content": "What is the weather in Tokyo?"}]
         }))
 

examples/langgraph_multi_agent/langgraph_multi_agent_example.py

@@ -95,7 +95,7 @@ def create_agent_with_config(aiclient, config_key, context):
     # Create a React agent with the LLM
     agent = create_react_agent(llm, [], prompt=agent_config.instructions)
 
-    return agent, agent_config.tracker, False
+    return agent, agent_config.create_tracker(), False
 
 def ai_node(
     state: CodeReviewState, 

examples/openai/openai_example.py

@@ -60,11 +60,12 @@ def main():
         context,
         variables={'myUserVariable': "Testing Variable"}
     )
-    tracker = config_value.tracker
 
     if not config_value.enabled:
         print("AI Config is disabled")
         return
+
+    tracker = config_value.create_tracker()
 
     messages = [message.to_dict() for message in (config_value.messages or [])]