Address the PR comment

Author: shashi-stackone

README.md

@@ -179,7 +179,7 @@ tools = toolset.fetch_tools(actions=["workday_*"], account_ids=[account_id])
 langchain_tools = tools.to_langchain()
 
 # Use with LangChain models
-model = ChatOpenAI(model="gpt-4o-mini")
+model = ChatOpenAI(model="gpt-5.4")
 model_with_tools = model.bind_tools(langchain_tools)
 
 # Execute AI-driven tool calls
@@ -233,7 +233,7 @@ graph = StateGraph(State)
 graph.add_node("tools", to_tool_node(langchain_tools))
 
 def call_llm(state: dict):
-    llm = ChatOpenAI(model="gpt-4o-mini")
+    llm = ChatOpenAI(model="gpt-5.4")
     llm = bind_model_with_tools(llm, langchain_tools)
     resp = llm.invoke(state["messages"])  # returns AIMessage with optional tool_calls
     return {"messages": state["messages"] + [resp]}
@@ -271,7 +271,7 @@ agent = Agent(
     goal="Analyze employee data and generate insights",
     backstory="Expert in HR analytics and employee management",
     tools=langchain_tools,
-    llm="gpt-4o-mini"
+    llm="gpt-5.4"
 )
 
 # Define task and execute

examples/auth_management.py

@@ -86,7 +86,10 @@ def main() -> None:
         print("STACKONE_API_KEY is not set. Please export it or add it to .env")
         return
 
-    account_id = os.getenv("STACKONE_ACCOUNT_ID", "test-account-id")
+    account_id = os.getenv("STACKONE_ACCOUNT_ID")
+    if not account_id:
+        print("Set STACKONE_ACCOUNT_ID to run this example.")
+        return
 
     api_key_setup()
     single_account(account_id)

examples/crewai_integration.py

@@ -48,15 +48,15 @@ def crewai_integration():
         goal="List the first 5 employees in the company",
         backstory="With over 10 years of experience in HR and employee management, "
         "you excel at finding patterns in complex datasets.",
-        llm="gpt-5.1",
+        llm="gpt-5.4",
         tools=langchain_tools,
         max_iter=2,
     )
 
     task = Task(
         description="List the first 5 employees in the company",
         agent=agent,
-        expected_output="A JSON object containing the employee's information",
+        expected_output="A list of the first 5 employees with their details",
     )
 
     crew = Crew(agents=[agent], tasks=[task])

examples/langchain_integration.py

@@ -30,6 +30,9 @@ def langchain_integration() -> None:
     if not account_id:
         print("Set STACKONE_ACCOUNT_ID to run this example.")
         return
+    if not os.getenv("OPENAI_API_KEY"):
+        print("Set OPENAI_API_KEY to run this example.")
+        return
 
     toolset = StackOneToolSet()
     tools = toolset.fetch_tools(
@@ -44,7 +47,7 @@ def langchain_integration() -> None:
         print(f"  - {tool.name}")
 
     # Create model with tools
-    model = ChatOpenAI(model="gpt-5.1")
+    model = ChatOpenAI(model="gpt-5.4")
     model_with_tools = model.bind_tools(langchain_tools)
 
     result = model_with_tools.invoke("List the first 5 employees")

examples/openai_integration.py

@@ -11,6 +11,7 @@
 
 from __future__ import annotations
 
+import json
 import os
 
 try:
@@ -67,7 +68,7 @@ def openai_integration() -> None:
     ]
 
     response = client.chat.completions.create(
-        model="gpt-5.1",
+        model="gpt-5.4",
         messages=messages,
         tools=openai_tools,
         tool_choice="auto",
@@ -88,20 +89,19 @@ def openai_integration() -> None:
     for i, result in enumerate(results):
         print(f"  Result {i + 1}: {str(result)[:200]}...")
 
-    # Continue the conversation with the results
-    messages.extend(
-        [
-            {"role": "assistant", "content": None, "tool_calls": tool_calls},
+    # Continue the conversation with all tool call results
+    messages.append(response.choices[0].message.model_dump(exclude_none=True))
+    for tc, result in zip(tool_calls, results):
+        messages.append(
             {
                 "role": "tool",
-                "tool_call_id": tool_calls[0].id,
-                "content": str(results[0]),
-            },
-        ]
-    )
+                "tool_call_id": tc.id,
+                "content": json.dumps(result),
+            }
+        )
 
     final_response = client.chat.completions.create(
-        model="gpt-5.1",
+        model="gpt-5.4",
         messages=messages,
         tools=openai_tools,
         tool_choice="auto",

examples/search_tools.py

@@ -119,7 +119,7 @@ def search_and_execute(account_id: str) -> None:
 
     for step in range(5):
         response = client.chat.completions.create(
-            model="gpt-5.1",
+            model="gpt-5.4",
             messages=messages,
             tools=openai_tools,
             tool_choice="auto",
@@ -131,7 +131,7 @@ def search_and_execute(account_id: str) -> None:
             break
 
         # Append the assistant message (with tool calls) to history
-        messages.append({"role": "assistant", "content": message.content, "tool_calls": message.tool_calls})
+        messages.append(message.model_dump(exclude_none=True))
 
         for tool_call in message.tool_calls:
             name = tool_call.function.name