docs: agents & callbacks fixes (#10066)

Various improvements to the Agents & Callbacks sections of the
documentation including formatting, spelling, and grammar fixes to
improve readability.

docs/snippets/modules/agents/agent_types/openai_functions_agent.mdx

@@ -1,6 +1,8 @@
-Install openai,google-search-results packages which are required as the langchain packages call them internally
+Install `openai`, `google-search-results` packages which are required as the LangChain packages call them internally.
 
->pip install openai google-search-results
+```bash
+pip install openai google-search-results
+```
 
 ```python
 from langchain import LLMMathChain, OpenAI, SerpAPIWrapper, SQLDatabase, SQLDatabaseChain

docs/snippets/modules/agents/agent_types/plan_and_execute.mdx

@@ -53,7 +53,7 @@ executor = load_agent_executor(model, tools, verbose=True)
 agent = PlanAndExecute(planner=planner, executor=executor, verbose=True)
 ```
 
-## Run Example
+## Run example
 
 
 ```python

docs/snippets/modules/agents/agent_types/structured_chat.mdx

@@ -202,7 +202,7 @@ print(response)
 
 ## Adding in memory
 
-Here is how you add in memory to this agent
+Here is how you add in memory to this agent:
 
 
 ```python

docs/snippets/modules/agents/get_started.mdx

@@ -1,12 +1,12 @@
 This will go over how to get started building an agent.
 We will use a LangChain agent class, but show how to customize it to give it specific context.
-We will then define custom tools, and then run it all in the standard LangChain AgentExecutor.
+We will then define custom tools, and then run it all in the standard LangChain `AgentExecutor`.
 
 ### Set up the agent
 
-We will use the OpenAIFunctionsAgent.
+We will use the `OpenAIFunctionsAgent`.
 This is easiest and best agent to get started with.
-It does however require usage of ChatOpenAI models.
+It does however require usage of `ChatOpenAI` models.
 If you want to use a different language model, we would recommend using the [ReAct](/docs/modules/agents/agent_types/react) agent.
 
 For this guide, we will construct a custom agent that has access to a custom tool.
@@ -40,7 +40,7 @@ tools = [get_word_length]
 
 Now let us create the prompt.
 We can use the `OpenAIFunctionsAgent.create_prompt` helper function to create a prompt automatically.
-This allows for a few different ways to customize, including passing in a custom SystemMessage, which we will do.
+This allows for a few different ways to customize, including passing in a custom `SystemMessage`, which we will do.
 
 ```python
 from langchain.schema import SystemMessage
@@ -55,7 +55,7 @@ Putting those pieces together, we can now create the agent.
 agent = OpenAIFunctionsAgent(llm=llm, tools=tools, prompt=prompt)
 ```
 
-Finally, we create the AgentExecutor - the runtime for our agent.
+Finally, we create the `AgentExecutor` - the runtime for our agent.
 
 ```python
 from langchain.agents import AgentExecutor
@@ -97,7 +97,7 @@ Let's fix that by adding in memory.
 In order to do this, we need to do two things:
 
 1. Add a place for memory variables to go in the prompt
-2. Add memory to the AgentExecutor (note that we add it here, and NOT to the agent, as this is the outermost chain)
+2. Add memory to the `AgentExecutor` (note that we add it here, and NOT to the agent, as this is the outermost chain)
 
 First, let's add a place for memory in the prompt.
 We do this by adding a placeholder for messages with the key `"chat_history"`.

docs/snippets/modules/agents/how_to/custom_llm_agent.mdx

@@ -1,5 +1,5 @@
-The LLMAgent is used in an AgentExecutor. This AgentExecutor can largely be thought of as a loop that:
-1. Passes user input and any previous steps to the Agent (in this case, the LLMAgent)
+The LLM Agent is used in an `AgentExecutor`. This `AgentExecutor` can largely be thought of as a loop that:
+1. Passes user input and any previous steps to the Agent (in this case, the LLM Agent)
 2. If the Agent returns an `AgentFinish`, then return that directly to the user
 3. If the Agent returns an `AgentAction`, then use that to call a tool and get an `Observation`
 4. Repeat, passing the `AgentAction` and `Observation` back to the Agent until an `AgentFinish` is emitted.
@@ -43,7 +43,7 @@ tools = [
 ]
 ```
 
-## Prompt Template
+## Prompt template
 
 This instructs the agent on what to do. Generally, the template should incorporate:
 
@@ -112,11 +112,11 @@ prompt = CustomPromptTemplate(
 )
 ```
 
-## Output Parser
+## Output parser
 
 The output parser is responsible for parsing the LLM output into `AgentAction` and `AgentFinish`. This usually depends heavily on the prompt used.
 
-This is where you can change the parsing to do retries, handle whitespace, etc
+This is where you can change the parsing to do retries, handle whitespace, etc.
 
 
 ```python
@@ -164,7 +164,7 @@ This depends heavily on the prompt and model you are using. Generally, you want
 
 ## Set up the Agent
 
-We can now combine everything to set up our agent
+We can now combine everything to set up our agent:
 
 
 ```python
@@ -225,7 +225,7 @@ agent_executor.run("How many people live in canada as of 2023?")
 
 If you want to add memory to the agent, you'll need to:
 
-1. Add a place in the custom prompt for the chat_history
+1. Add a place in the custom prompt for the `chat_history`
 2. Add a memory object to the agent executor.
 
 

docs/snippets/modules/agents/how_to/custom_llm_chat_agent.mdx

@@ -1,5 +1,5 @@
-The LLMAgent is used in an AgentExecutor. This AgentExecutor can largely be thought of as a loop that:
-1. Passes user input and any previous steps to the Agent (in this case, the LLMAgent)
+The LLM Agent is used in an `AgentExecutor`. This `AgentExecutor` can largely be thought of as a loop that:
+1. Passes user input and any previous steps to the Agent (in this case, the LLM Agent)
 2. If the Agent returns an `AgentFinish`, then return that directly to the user
 3. If the Agent returns an `AgentAction`, then use that to call a tool and get an `Observation`
 4. Repeat, passing the `AgentAction` and `Observation` back to the Agent until an `AgentFinish` is emitted.
@@ -35,7 +35,7 @@ import re
 from getpass import getpass
 ```
 
-## Set up tool
+## Set up tools
 
 Set up any tools the agent may want to use. This may be necessary to put in the prompt (so that the agent knows to use these tools).
 
@@ -57,7 +57,7 @@ tools = [
 ]
 ```
 
-## Prompt Template
+## Prompt template
 
 This instructs the agent on what to do. Generally, the template should incorporate:
     
@@ -131,11 +131,11 @@ prompt = CustomPromptTemplate(
 )
 ```
 
-## Output Parser
+## Output parser
 
 The output parser is responsible for parsing the LLM output into `AgentAction` and `AgentFinish`. This usually depends heavily on the prompt used.
 
-This is where you can change the parsing to do retries, handle whitespace, etc
+This is where you can change the parsing to do retries, handle whitespace, etc.
 
 
 ```python
@@ -188,7 +188,7 @@ This depends heavily on the prompt and model you are using. Generally, you want
 
 ## Set up the Agent
 
-We can now combine everything to set up our agent
+We can now combine everything to set up our agent:
 
 
 ```python

docs/snippets/modules/callbacks/get_started.mdx

@@ -72,7 +72,7 @@ class BaseCallbackHandler:
 
 LangChain provides a few built-in handlers that you can use to get started. These are available in the `langchain/callbacks` module. The most basic handler is the `StdOutCallbackHandler`, which simply logs all events to `stdout`.
 
-**Note** when the `verbose` flag on the object is set to true, the `StdOutCallbackHandler` will be invoked even without being explicitly passed in.
+**Note**: when the `verbose` flag on the object is set to true, the `StdOutCallbackHandler` will be invoked even without being explicitly passed in.
 
 ```python
 from langchain.callbacks import StdOutCallbackHandler
@@ -137,6 +137,6 @@ The `verbose` argument is available on most objects throughout the API (Chains,
 
 ### When do you want to use each of these?
 
-- Constructor callbacks are most useful for use cases such as logging, monitoring, etc., which are _not specific to a single request_, but rather to the entire chain. For example, if you want to log all the requests made to an LLMChain, you would pass a handler to the constructor.
+- Constructor callbacks are most useful for use cases such as logging, monitoring, etc., which are _not specific to a single request_, but rather to the entire chain. For example, if you want to log all the requests made to an `LLMChain`, you would pass a handler to the constructor.
 - Request callbacks are most useful for use cases such as streaming, where you want to stream the output of a single request to a specific websocket connection, or other similar use cases. For example, if you want to stream the output of a single request to a websocket, you would pass a handler to the `call()` method