fix: Sync with the latest example notebooks (#1089)

* sync with the latest example notebooks

* set correct google genai example doc

* thanks Copilot

Author: dot-agi

docs/v2/examples/autogen.mdx

@@ -1,6 +1,6 @@
 ---
-title: 'Autogen Example'
-description: 'Using Autogen with AgentOps for agent chat monitoring'
+title: 'AutoGen'
+description: 'Microsoft Autogen Chat Example'
 ---
 {/*  SOURCE_FILE: examples/autogen/AgentChat.ipynb  */}
 
@@ -10,25 +10,26 @@ _View Notebook on <a href={'https://github.com/AgentOps-AI/agentops/blob/main/ex
 
 AgentOps automatically configures itself when it's initialized meaning your agent run data will be tracked and logged to your AgentOps dashboard right away.
 
-First let's install the required packages.
+First let's install the required packages
+
+
 
 ## Installation
 <CodeGroup>
   ```bash pip
-  pip install -U autogen-agentchat "autogen-ext[openai]" agentops python-dotenv
+  pip install "autogen-ext[openai]" -U agentops autogen-agentchat python-dotenv
   ```
   ```bash poetry
-  poetry add autogen-agentchat autogen-ext agentops python-dotenv
-  # For autogen-ext[openai] with poetry, you might need to specify openai as an extra:
-  # poetry add autogen-ext -E openai
+  poetry add "autogen-ext[openai]" -U agentops autogen-agentchat python-dotenv
   ```
   ```bash uv
-  uv add autogen-agentchat "autogen-ext[openai]" agentops python-dotenv
+  uv add "autogen-ext[openai]" -U agentops autogen-agentchat python-dotenv
   ```
 </CodeGroup>
 
-## Setup
-Then import them.
+Then import them
+
+
 ```python
 import os
 from dotenv import load_dotenv
@@ -51,23 +52,27 @@ Next, we'll set our API keys. There are several ways to do this, the code below
 
 [Get an AgentOps API key](https://agentops.ai/settings/projects)
 
-1. Create an environment variable in a `.env` file or other method. By default, the AgentOps `init()` function will look for an environment variable named `AGENTOPS_API_KEY`. Or...
+1. Create an environment variable in a .env file or other method. By default, the AgentOps `init()` function will look for an environment variable named `AGENTOPS_API_KEY`. Or...
 
 2. Replace `<your_agentops_key>` below and pass in the optional `api_key` parameter to the AgentOps `init(api_key=...)` function. Remember not to commit your API key to a public repo!
 
+
 ```python
 load_dotenv()
-os.environ["AGENTOPS_API_KEY"] = os.getenv("AGENTOPS_API_KEY", "your_agentops_api_key_here")
+os.environ["AGENTOPS_API_KEY"] = os.getenv("AGENTOPS_API_KEY", "your_api_key_here")
 os.environ["OPENAI_API_KEY"] = os.getenv("OPENAI_API_KEY", "your_openai_api_key_here")
 ```
 
+
 ```python
 # When initializing AgentOps, you can pass in optional tags to help filter sessions
 agentops.init(auto_start_session=False)
-tracer = agentops.start_trace(trace_name="Microsoft Agent Chat Example", tags=["autogen-chat", "microsoft-autogen", "agentops-example"])
+tracer = agentops.start_trace(
+    trace_name="Microsoft Agent Chat Example", tags=["autogen-chat", "microsoft-autogen", "agentops-example"]
+)
 ```
 
-AutoGen will now start automatically tracking:
+AutoGen will now start automatically tracking
 
 * LLM prompts and completions
 * Token usage and costs
@@ -77,9 +82,11 @@ AutoGen will now start automatically tracking:
 * Errors
 
 # Simple Chat Example
+
+
 ```python
 # Define model and API key
-model_name = "gpt-4-turbo" # Or "gpt-4o" / "gpt-4o-mini" as per migration guide examples
+model_name = "gpt-4-turbo"  # Or "gpt-4o" / "gpt-4o-mini" as per migration guide examples
 api_key = os.getenv("OPENAI_API_KEY")
 
 # Create the model client
@@ -88,26 +95,27 @@ model_client = OpenAIChatCompletionClient(model=model_name, api_key=api_key)
 # Create the agent that uses the LLM.
 assistant = AssistantAgent(
     name="assistant",
-    system_message="You are a helpful assistant.", # Added system message for clarity
-    model_client=model_client
+    system_message="You are a helpful assistant.",  # Added system message for clarity
+    model_client=model_client,
 )
 
 user_proxy_initiator = UserProxyAgent("user_initiator")
 
+
 async def main():
-    termination = MaxMessageTermination(max_messages=2) 
+    termination = MaxMessageTermination(max_messages=2)
 
     group_chat = RoundRobinGroupChat(
-        [user_proxy_initiator, assistant], # Corrected: agents as positional argument
-        termination_condition=termination
+        [user_proxy_initiator, assistant],  # Corrected: agents as positional argument
+        termination_condition=termination,
     )
-    
+
     chat_task = "How can I help you today?"
     print(f"User Initiator: {chat_task}")
 
     try:
         stream = group_chat.run_stream(task=chat_task)
-        await Console().run(stream) 
+        await Console().run(stream)
         agentops.end_trace(tracer, end_state="Success")
 
     except StdinNotImplementedError:
@@ -120,14 +128,16 @@ async def main():
     finally:
         await model_client.close()
 
-if __name__ == '__main__':
+
+if __name__ == "__main__":
     try:
         loop = asyncio.get_running_loop()
     except RuntimeError:
         loop = None
 
     if loop and loop.is_running():
         import nest_asyncio
+
         nest_asyncio.apply()
         asyncio.run(main())
     else:
@@ -138,7 +148,8 @@ You can view data on this run at [app.agentops.ai](https://app.agentops.ai).
 
 The dashboard will display LLM events for each message sent by each agent, including those made by the human user.
 
+
 <script type="module" src="/scripts/github_stars.js"></script>
 <script type="module" src="/scripts/scroll-img-fadein-animation.js"></script>
 <script type="module" src="/scripts/button_heartbeat_animation.js"></script>
-<script type="module" src="/scripts/adjust_api_dynamically.js"></script>
+<script type="module" src="/scripts/adjust_api_dynamically.js"></script>

docs/v2/examples/examples.mdx

@@ -13,7 +13,7 @@ description: 'Examples of AgentOps with various integrations'
     Claude integration with tool usage and advanced features
   </Card>
 
-  <Card title="Google Generative AI" icon={<img src="https://www.github.com/agentops-ai/agentops/blob/main/docs/images/external/deepmind/gemini-logo.png?raw=true" alt="Gemini" />} iconType="image" href="/v2/examples/google_generative_ai">
+  <Card title="Google Generative AI" icon={<img src="https://www.github.com/agentops-ai/agentops/blob/main/docs/images/external/deepmind/gemini-logo.png?raw=true" alt="Gemini" />} iconType="image" href="/v2/examples/google_genai">
     Google Gemini models and their examples
   </Card>
 

docs/v2/examples/google_adk.mdx

@@ -1,5 +1,5 @@
 ---
-title: 'Google Adk'
+title: 'Google ADK'
 description: 'Google ADK Example: Human Approval Workflow with AgentOps'
 ---
 {/*  SOURCE_FILE: examples/google_adk/human_approval.ipynb  */}
@@ -74,7 +74,7 @@ APP_NAME = "human_approval_app_notebook"
 USER_ID = "test_user_notebook_123"
 SESSION_ID = "approval_session_notebook_456"
 MODEL_NAME = "gemini-1.5-flash"
-agentops.start_trace(trace_name=APP_NAME, tags=["google_adk","notebook"])
+agentops.start_trace(trace_name=APP_NAME, tags=["google_adk", "notebook"])
 ```
 
 ## 3. Define Schemas
@@ -86,6 +86,8 @@ Pydantic models are used to define the structure of data for approval requests a
 class ApprovalRequest(BaseModel):
     amount: float = Field(description="The amount requiring approval")
     reason: str = Field(description="The reason for the request")
+
+
 class ApprovalDecision(BaseModel):
     decision: str = Field(description="The approval decision: 'approved' or 'rejected'")
     comments: str = Field(description="Additional comments from the approver")
@@ -98,10 +100,10 @@ This tool now directly prompts the user for an approval decision. In a real-worl
 
 ```python
 async def external_approval_tool(amount: float, reason: str) -> str:
-    """ 
+    """
     Prompts for human approval and returns the decision as a JSON string.
     """
-    print(f"🔔 HUMAN APPROVAL REQUIRED:")
+    print("🔔 HUMAN APPROVAL REQUIRED:")
     print(f"   Amount: ${amount:,.2f}")
     print(f"   Reason: {reason}")
     decision = ""
@@ -112,12 +114,8 @@ async def external_approval_tool(amount: float, reason: str) -> str:
     comments = input("   Enter comments (optional): ").strip()
     print(f"   Decision: {decision.upper()}")
     print(f"   Comments: {comments if comments else 'N/A'}")
-    return json.dumps({
-        "decision": decision,
-        "comments": comments,
-        "amount": amount,
-        "reason": reason
-    })
+    return json.dumps({"decision": decision, "comments": comments, "amount": amount, "reason": reason})
+
 
 # Create the approval tool instance
 approval_tool = FunctionTool(func=external_approval_tool)
@@ -145,13 +143,13 @@ prepare_request = LlmAgent(
         4. Respond with a summary of what will be submitted for approval
     If the user input is missing amount or reason, ask for clarification.
     """,
-    output_key="request_prepared"
+    output_key="request_prepared",
 )
 
 # Agent 2: Request human approval using the tool
 request_approval = LlmAgent(
     model=MODEL_NAME,
-    name="RequestHumanApprovalAgent", 
+    name="RequestHumanApprovalAgent",
     description="Calls the external approval system with prepared request details",
     instruction="""You are a human approval request agent.
         Your task:
@@ -162,7 +160,7 @@ request_approval = LlmAgent(
     Always use the exact values from the session state for the tool call.
     """,
     tools=[approval_tool],
-    output_key="approval_requested"
+    output_key="approval_requested",
 )
 
 # Agent 3: Process the approval decision
@@ -180,7 +178,7 @@ process_decision = LlmAgent(
 
     Be professional and helpful in your response.
     """,
-    output_key="final_decision"
+    output_key="final_decision",
 )
 ```
 
@@ -193,7 +191,7 @@ Combine the agents into a sequential workflow. The `SequentialAgent` ensures tha
 approval_workflow = SequentialAgent(
     name="HumanApprovalWorkflowNotebook",
     description="Complete workflow for processing approval requests with human oversight",
-    sub_agents=[prepare_request, request_approval, process_decision]
+    sub_agents=[prepare_request, request_approval, process_decision],
 )
 ```
 
@@ -205,11 +203,7 @@ Set up an in-memory session service and the workflow runner.
 ```python
 session_service = InMemorySessionService()
 # Create runner
-workflow_runner = Runner(
-    agent=approval_workflow,
-    app_name=APP_NAME,
-    session_service=session_service
-)
+workflow_runner = Runner(agent=approval_workflow, app_name=APP_NAME, session_service=session_service)
 ```
 
 ## 8. Helper Function to Run Workflow
@@ -220,15 +214,12 @@ This function encapsulates the logic to run the workflow for a given user reques
 ```python
 async def run_approval_workflow_notebook(user_request: str, session_id: str):
     """Run the complete approval workflow with a user request in the notebook environment"""
-    print(f"{'='*60}")
+    print(f"{'=' * 60}")
     print(f" Starting Approval Workflow for Session: {session_id}")
-    print(f"{'='*60}")
+    print(f"{'=' * 60}")
     print(f"User Request: {user_request}")
     # Create user message
-    user_content = types.Content(
-        role='user', 
-        parts=[types.Part(text=user_request)]
-    )
+    user_content = types.Content(role="user", parts=[types.Part(text=user_request)])
     step_count = 0
     final_response = "No response received"
     # Run the workflow
@@ -247,12 +238,12 @@ async def run_approval_workflow_notebook(user_request: str, session_id: str):
                     final_response = response_text
     session = await session_service.get_session(
         app_name=APP_NAME,
-        user_id=USER_ID, 
+        user_id=USER_ID,
         session_id=session_id,
     )
-    print(f"{'='*60}")
+    print(f"{'=' * 60}")
     print(f"📊 Workflow Complete - Session State ({session_id}):")
-    print(f"{'='*60}")
+    print(f"{'=' * 60}")
     for key, value in session.state.items():
         print(f"   {key}: {value}")
     print(f"🎯 Final Response: {final_response}")
@@ -269,18 +260,16 @@ async def main_notebook():
     test_requests = [
         "I need approval for $750 for team lunch and celebrations",
         "Please approve $3,000 for a conference ticket and travel expenses",
-        "I need $12,000 approved for critical software licenses renewal"
+        "I need $12,000 approved for critical software licenses renewal",
     ]
     for i, request in enumerate(test_requests, 1):
-        current_session_id = f"approval_session_notebook_{456 + i -1}"
+        current_session_id = f"approval_session_notebook_{456 + i - 1}"
         # Create the session before running the workflow
-        await session_service.create_session(
-            app_name=APP_NAME,
-            user_id=USER_ID,
-            session_id=current_session_id
-        )
+        await session_service.create_session(app_name=APP_NAME, user_id=USER_ID, session_id=current_session_id)
         print(f"Created session: {current_session_id}")
         await run_approval_workflow_notebook(request, current_session_id)
+
+
 try:
     asyncio.run(main_notebook())
     agentops.end_trace(end_state="Success")