GitHub Action - Update examples in docs from notebooks

docs/v1/examples/camel.mdx

@@ -58,7 +58,7 @@ Now we will initialize our AgentOps client.
 
 
 ```python
-agentops.init(tags=["camel", "multi-agent", "example"])
+agentops.init(default_tags=["camel", "multi-agent", "example"])
 ```
 
 Let's start with setting our task prompt and setting our tools.

docs/v1/examples/langchain.mdx

@@ -65,7 +65,7 @@ Pass in your API key, and optionally any tags to describe this session for easie
 
 ```python
 agentops_handler = AgentOpsLangchainCallbackHandler(
-    api_key=AGENTOPS_API_KEY, tags=["Langchain Example"]
+    api_key=AGENTOPS_API_KEY, default_tags=["Langchain Example"]
 )
 
 llm = ChatOpenAI(

docs/v1/examples/multi_agent.mdx

@@ -9,7 +9,7 @@ _View Notebook on <a href={'https://github.com/AgentOps-AI/agentops/blob/main/ex
 {/*  SOURCE_FILE: examples/multi_agent_example.ipynb  */}
 
 # Multi-Agent Support
-This is an example implementation of tracking operations from two separate agents
+This is an example implementation of tracking events from two separate agents
 
 First let's install the required packages
 
@@ -25,7 +25,7 @@ Then import them
 
 ```python
 import agentops
-from agentops.sdk.decorators import agent, operation
+from agentops import track_agent
 from openai import OpenAI
 import os
 from dotenv import load_dotenv
@@ -53,20 +53,16 @@ logging.basicConfig(
 
 
 ```python
-agentops.init(AGENTOPS_API_KEY, tags=["multi-agent-notebook"])
+agentops.init(AGENTOPS_API_KEY, default_tags=["multi-agent-notebook"])
 openai_client = OpenAI(api_key=OPENAI_API_KEY)
 ```
 
 Now lets create a few agents!
 
 
 ```python
-@agent(name="qa")
+@track_agent(name="qa")
 class QaAgent:
-    def __init__(self):
-        pass
-
-    @operation
     def completion(self, prompt: str):
         res = openai_client.chat.completions.create(
             model="gpt-3.5-turbo",
@@ -82,12 +78,8 @@ class QaAgent:
         return res.choices[0].message.content
 
 
-@agent(name="engineer")
+@track_agent(name="engineer")
 class EngineerAgent:
-    def __init__(self):
-        pass
-
-    @operation
     def completion(self, prompt: str):
         res = openai_client.chat.completions.create(
             model="gpt-3.5-turbo",
@@ -109,7 +101,7 @@ qa = QaAgent()
 engineer = EngineerAgent()
 ```
 
-Now we have our agents and we tagged them with the `@agent` decorator. Any LLM calls that go through this class will now be tagged as agent calls in AgentOps.
+Now we have our agents and we tagged them with the `@track_agent` decorator. Any LLM calls that go through this class will now be tagged as agent calls in AgentOps.
 
 Let's use these agents!
 

docs/v1/examples/multion.mdx

@@ -92,7 +92,7 @@ When running `agentops.init()`, be sure to set `auto_start_session=False`. Multi
 
 ```python
 agentops.init(
-    AGENTOPS_API_KEY, auto_start_session=False, tags=["MultiOn browse example"]
+    AGENTOPS_API_KEY, auto_start_session=False, default_tags=["MultiOn browse example"]
 )
 ```
 

docs/v1/examples/ollama.mdx

@@ -49,7 +49,7 @@ AGENTOPS_API_KEY = os.getenv("AGENTOPS_API_KEY") or "<your_agentops_key>"
 
 ```python
 # Initialize AgentOps with some default tags
-agentops.init(AGENTOPS_API_KEY, tags=["ollama-example"])
+agentops.init(AGENTOPS_API_KEY, default_tags=["ollama-example"])
 ```
 
 Now let's make some basic calls to Ollama. Make sure you have pulled the model first, use the following or replace with whichever model you want to use.

docs/v1/examples/openai_assistants.mdx

@@ -49,6 +49,7 @@ We'll take a look at how these can be used to create powerful, stateful experien
 ```python
 import json
 
+
 def show_json(obj):
     display(json.loads(obj.model_dump_json()))
 ```
@@ -105,7 +106,7 @@ OPENAI_API_KEY = os.getenv("OPENAI_API_KEY") or "<your_openai_key>"
 
 
 ```python
-agentops.init(api_key=AGENTOPS_API_KEY, tags=["openai", "beta-assistants"])
+agentops.init(api_key=AGENTOPS_API_KEY, default_tags=["openai", "beta-assistants"])
 client = OpenAI(api_key=OPENAI_API_KEY)
 ```
 
@@ -186,6 +187,7 @@ To know when the Assistant has completed processing, we can poll the Run in a lo
 ```python
 import time
 
+
 def wait_on_run(run, thread):
     while run.status == "queued" or run.status == "in_progress":
         run = client.beta.threads.runs.retrieve(
@@ -223,9 +225,7 @@ Let's ask our Assistant to explain the result a bit further!
 
 ```python
 # Create a message to append to our thread
-message = client.beta.threads.messages.create(
-    thread_id=thread.id, role="user", content="Could you explain this to me?"
-)
+message = client.beta.threads.messages.create(thread_id=thread.id, role="user", content="Could you explain this to me?")
 
 # Execute our run
 run = client.beta.threads.runs.create(
@@ -237,9 +237,7 @@ run = client.beta.threads.runs.create(
 wait_on_run(run, thread)
 
 # Retrieve all the messages added after our last user message
-messages = client.beta.threads.messages.list(
-    thread_id=thread.id, order="asc", after=message.id
-)
+messages = client.beta.threads.messages.list(thread_id=thread.id, order="asc", after=message.id)
 show_json(messages)
 ```
 
@@ -265,10 +263,9 @@ MATH_ASSISTANT_ID = assistant.id  # or a hard-coded ID like "asst-..."
 
 client = OpenAI(api_key=os.environ.get("OPENAI_API_KEY", "<your OpenAI API key if not set as env var>"))
 
+
 def submit_message(assistant_id, thread, user_message):
-    client.beta.threads.messages.create(
-        thread_id=thread.id, role="user", content=user_message
-    )
+    client.beta.threads.messages.create(thread_id=thread.id, role="user", content=user_message)
     return client.beta.threads.runs.create(
         thread_id=thread.id,
         assistant_id=assistant_id,
@@ -293,9 +290,7 @@ def create_thread_and_run(user_input):
 
 
 # Emulating concurrent user requests
-thread1, run1 = create_thread_and_run(
-    "I need to solve the equation `3x + 11 = 14`. Can you help me?"
-)
+thread1, run1 = create_thread_and_run("I need to solve the equation `3x + 11 = 14`. Can you help me?")
 thread2, run2 = create_thread_and_run("Could you explain linear algebra to me?")
 thread3, run3 = create_thread_and_run("I don't like math. What can I do?")
 
@@ -307,8 +302,6 @@ Once all Runs are going, we can wait on each and get the responses.
 
 
 ```python
-import time
-
 # Pretty printing helper
 def pretty_print(messages):
     print("# Messages")
@@ -380,9 +373,7 @@ Now, let's ask the Assistant to use its new tool.
 
 
 ```python
-thread, run = create_thread_and_run(
-    "Generate the first 20 fibbonaci numbers with code."
-)
+thread, run = create_thread_and_run("Generate the first 20 fibbonaci numbers with code.")
 run = wait_on_run(run, thread)
 pretty_print(get_response(thread))
 ```
@@ -399,9 +390,7 @@ A Run is composed of one or more Steps. Like a Run, each Step has a `status` tha
 
 
 ```python
-run_steps = client.beta.threads.runs.steps.list(
-    thread_id=thread.id, run_id=run.id, order="asc"
-)
+run_steps = client.beta.threads.runs.steps.list(thread_id=thread.id, run_id=run.id, order="asc")
 ```
 
 Let's take a look at each Step's `step_details`.
@@ -653,19 +642,17 @@ tool_calls = run.required_action.submit_tool_outputs.tool_calls
 for tool_call in tool_calls:
     arguments = json.loads(tool_call.function.arguments)
     responses = display_quiz(arguments["title"], arguments["questions"])
-    tool_outputs.append({
-        "tool_call_id": tool_call.id,
-        "output": json.dumps(responses),
-    })
+    tool_outputs.append(
+        {
+            "tool_call_id": tool_call.id,
+            "output": json.dumps(responses),
+        }
+    )
 ```
 
 
 ```python
-run = client.beta.threads.runs.submit_tool_outputs(
-    thread_id=thread.id,
-    run_id=run.id,
-    tool_outputs=tool_outputs
-)
+run = client.beta.threads.runs.submit_tool_outputs(thread_id=thread.id, run_id=run.id, tool_outputs=tool_outputs)
 show_json(run)
 ```
 
@@ -678,15 +665,6 @@ run = wait_on_run(run, thread)
 pretty_print(get_response(thread))
 ```
 
-Now let's end the AgentOps session. By default, AgentOps will end the session in the "Intedeterminate" state. You can also end the session in the "Success" or "Failure" state.
-
-We will end the session in the "Success" state.
-
-
-```python
-agentops.end_session(end_state="Success")
-```
-
 Woohoo 🎉
 
 ## Conclusion

docs/v1/examples/recording_events.mdx

@@ -11,7 +11,7 @@ _View Notebook on <a href={'https://github.com/AgentOps-AI/agentops/blob/main/ex
 # Recording Operations with Spans
 AgentOps v0.4 uses spans to track different types of operations in your agent workflows.
 
-We automatically instrument your LLM Calls from OpenAI, LiteLLM, Cohere, and more. Just make sure their SDKs are imported before initializing AgentOps like we see below.
+We automatically instrument your LLM Calls from OpenAI, Anthropic, OpenAi Agents, and more. Just make sure their SDKs are imported before initializing AgentOps like we see below.
 
 First let's install the required packages
 
@@ -80,9 +80,7 @@ my_session()
 ```
 
 Click the AgentOps link above to see your session!
-
 ## Operations
-
 AgentOps allows you to record operations using the `@operation` decorator:
 
 
@@ -95,7 +93,6 @@ def add(x, y):
 ```
 
 ## Agents
-
 You can create agent spans that contain operations using the `@agent` decorator:
 
 
@@ -111,7 +108,6 @@ class MyAgent:
 ```
 
 ## Error Handling
-
 Errors are automatically captured by the spans. When an exception occurs within a decorated function, it's recorded in the span:
 
 
@@ -140,7 +136,6 @@ error_session()
 ```
 
 ## Custom Span Attributes
-
 You can add custom attributes to spans for additional context:
 
 

docs/v1/examples/simple_agent.mdx

@@ -50,7 +50,7 @@ The AgentOps library is designed to be a plug-and-play replacement for the OpenA
 
 ```python
 openai = OpenAI(api_key=OPENAI_API_KEY)
-agentops.init(AGENTOPS_API_KEY, tags=["openai-gpt-notebook"])
+agentops.init(AGENTOPS_API_KEY, default_tags=["openai-gpt-notebook"])
 ```
 
 Now just use OpenAI as you would normally!