Spellcaster typos

docs/v1/concepts/events.mdx

@@ -65,7 +65,7 @@ The ToolEvent class is for recording calls to tools e.g. searchWeb, fetchFromDB.
 | name      | str         | None    | "Parsing Tool"                                          | Name of the tool             |
 | logs      | str or dict | None    | "Tool executed successfully" or `{"status": "success"}` | Logs from the tool execution |
 
-The tool event should be created previous to running the tool in order to track the execution time.
+The tool event should be created before running the tool in order to track the execution time.
 
 <CodeGroup>
 ```python python

docs/v1/examples/multi_agent.mdx

@@ -103,7 +103,7 @@ engineer = EngineerAgent()
 
 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.
 
-Lets use these agents!
+Let's use these agents!
 
 
 ```python
@@ -129,7 +129,7 @@ display(Markdown("```python\n" + generated_test + "\n```"))
 
 Perfect! It generated the code as expected, and in the DEBUG logs, you can see that the calls were made by agents named "engineer" and "qa"!
 
-Lets verify one more thing! If we make an LLM call outside of the context of a tracked agent, we want to make sure it gets assigned to the Default Agent.
+Let's verify one more thing! If we make an LLM call outside of the context of a tracked agent, we want to make sure it gets assigned to the Default Agent.
 
 
 ```python

docs/v1/examples/recording_events.mdx

@@ -9,7 +9,7 @@ _View Notebook on <a href={'https://github.com/AgentOps-AI/agentops/blob/main/ex
 {/*  SOURCE_FILE: examples/recording-events.ipynb  */}
 
 # Recording Events
-AgentOps has a number of different [Event Types](https://docs.agentops.ai/v1/details/events)
+AgentOps has a number of different [Event Types](https://docs.agentops.ai/v1/details/events).
 
 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
 
@@ -68,7 +68,7 @@ Click the AgentOps link above to see your session!
 
 ## Action Event
 
-AgentOps allows you to record other actions. The easiest way to record actions is through the use of AgentOp's decorators
+AgentOps allows you to record other actions. The easiest way to record actions is through the use of AgentOps' decorators
 
 
 ```python

docs/v1/examples/simple_agent.mdx

@@ -9,7 +9,7 @@ _View Notebook on <a href={'https://github.com/AgentOps-AI/agentops/blob/main/ex
 {/*  SOURCE_FILE: examples/openai-gpt.ipynb  */}
 
 # AgentOps Basic Monitoring 
-This is an example of how to use the AgentOps library for basic Agent monitoring with OpenAI's GPT
+This is an example of how to use the AgentOps library for basic Agent monitoring with OpenAI's GPT.
 
 First let's install the required packages
 
@@ -85,7 +85,7 @@ Let's start a new session and record some events
 agentops.start_session(tags=["openai-gpt-notebook-events"])
 ```
 
-The easiest way to record actions is through the use of AgentOp's decorators
+The easiest way to record actions is through the use of AgentOps' decorators
 
 
 ```python

examples/multi_agent_example.ipynb

@@ -200,7 +200,7 @@
    "source": [
     "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.\n",
     "\n",
-    "Lets use these agents!"
+    "Let's use these agents!"
    ]
   },
   {
@@ -283,7 +283,7 @@
     }
    },
    "source": [
-    "Lets verify one more thing! If we make an LLM call outside of the context of a tracked agent, we want to make sure it gets assigned to the Default Agent."
+    "Let's verify one more thing! If we make an LLM call outside of the context of a tracked agent, we want to make sure it gets assigned to the Default Agent."
    ]
   },
   {

examples/multi_agent_groq_example.ipynb

@@ -201,7 +201,7 @@
    "source": [
     "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.\n",
     "\n",
-    "Lets use these agents!"
+    "Let's use these agents!"
    ]
   },
   {
@@ -284,7 +284,7 @@
     }
    },
    "source": [
-    "Lets verify one more thing! If we make an LLM call outside of the context of a tracked agent, we want to make sure it gets assigned to the Default Agent."
+    "Let's verify one more thing! If we make an LLM call outside of the context of a tracked agent, we want to make sure it gets assigned to the Default Agent."
    ]
   },
   {

examples/openai-gpt.ipynb

@@ -205,7 +205,7 @@
    "id": "e94f1b56",
    "metadata": {},
    "source": [
-    "The easiest way to record actions is through the use of AgentOp's decorators"
+    "The easiest way to record actions is through the use of AgentOps' decorators"
    ]
   },
   {

examples/recording-events.ipynb

@@ -121,7 +121,7 @@
    "source": [
     "## Action Event\n",
     "\n",
-    "AgentOps allows you to record other actions. The easiest way to record actions is through the use of AgentOp's decorators"
+    "AgentOps allows you to record other actions. The easiest way to record actions is through the use of AgentOps' decorators"
    ]
   },
   {
@@ -260,7 +260,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.5"
+   "version": "3.11.5"
   }
  },
  "nbformat": 4,