Update Documentation: Corrected Typos and Improved Clarity (#11725)

Docs updates

---------

Co-authored-by: Advaya <126754021+bluevayes@users.noreply.github.com>
Co-authored-by: Erick Friis <erick@langchain.dev>

docs/docs/modules/agents/agent_types/index.mdx

@@ -38,7 +38,7 @@ It uses the ReAct framework to decide which tool to use, and uses memory to reme
 ## [Self-ask with search](/docs/modules/agents/agent_types/self_ask_with_search)
 
 This agent utilizes a single tool that should be named `Intermediate Answer`.
-This tool should be able to lookup factual answers to questions. This agent
+This tool should be able to look up factual answers to questions. This agent
 is equivalent to the original [self-ask with search paper](https://ofir.io/self-ask.pdf),
 where a Google search API was provided as the tool.
 
@@ -46,7 +46,7 @@ where a Google search API was provided as the tool.
 
 This agent uses the ReAct framework to interact with a docstore. Two tools must
 be provided: a `Search` tool and a `Lookup` tool (they must be named exactly as so).
-The `Search` tool should search for a document, while the `Lookup` tool should lookup
+The `Search` tool should search for a document, while the `Lookup` tool should look up
 a term in the most recently found document.
 This agent is equivalent to the
 original [ReAct paper](https://arxiv.org/pdf/2210.03629.pdf), specifically the Wikipedia example.

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

@@ -1,4 +1,4 @@
-# Custom LLM agent
+# Custom LLM Agent
 
 This notebook goes through how to create your own custom LLM agent.
 

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

@@ -1,13 +1,13 @@
-# Custom LLM Agent (with a ChatModel)
+# Custom LLM Chat Agent
 
-This notebook goes through how to create your own custom agent based on a chat model.
+This notebook explains how to create your own custom agent based on a chat model.
 
-An LLM chat agent consists of three parts:
+An LLM chat agent consists of four key components:
 
-- `PromptTemplate`: This is the prompt template that can be used to instruct the language model on what to do
-- `ChatModel`: This is the language model that powers the agent
-- `stop` sequence: Instructs the LLM to stop generating as soon as this string is found
-- `OutputParser`: This determines how to parse the LLM output into an `AgentAction` or `AgentFinish` object
+- `PromptTemplate`: This is the prompt template that instructs the language model on what to do.
+- `ChatModel`: This is the language model that powers the agent.
+- `stop` sequence: Instructs the LLM to stop generating as soon as this string is found.
+- `OutputParser`: This determines how to parse the LLM output into an `AgentAction` or `AgentFinish` object.
 
 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)

docs/docs/modules/agents/how_to/mrkl.mdx

@@ -3,7 +3,7 @@
 This walkthrough demonstrates how to replicate the [MRKL](https://arxiv.org/pdf/2205.00445.pdf) system using agents.
 
 This uses the example Chinook database.
-To set it up follow the instructions on https://database.guide/2-sample-databases-sqlite/, placing the `.db` file in a notebooks folder at the root of this repository.
+To set it up, follow the instructions on https://database.guide/2-sample-databases-sqlite/ and place the `.db` file in a "notebooks" folder at the root of this repository.
 
 ```python
 from langchain.chains import LLMMathChain
@@ -127,7 +127,7 @@ mrkl.run("What is the full name of the artist who recently released an album cal
 
 </CodeOutputBlock>
 
-## With a chat model
+## Using a Chat Model
 
 ```python
 from langchain.chat_models import ChatOpenAI

docs/docs/modules/agents/tools/index.mdx

@@ -4,17 +4,17 @@ sidebar_position: 2
 # Tools
 
 :::info
-Head to [Integrations](/docs/integrations/tools/) for documentation on built-in tool integrations.
+For documentation on built-in tool integrations, visit [Integrations](/docs/integrations/tools/).
 :::
 
 Tools are interfaces that an agent can use to interact with the world.
 
-## Get started
+## Getting Started
 
 Tools are functions that agents can use to interact with the world.
 These tools can be generic utilities (e.g. search), other chains, or even other agents.
 
-Currently, tools can be loaded with the following snippet:
+Currently, tools can be loaded using the following snippet:
 
 ```python
 from langchain.agents import load_tools

docs/docs/modules/agents/tools/toolkits.mdx

@@ -4,7 +4,7 @@ sidebar_position: 3
 # Toolkits
 
 :::info
-Head to [Integrations](/docs/integrations/toolkits/) for documentation on built-in toolkit integrations.
+For documentation on built-in toolkit integrations, visit [Integrations](/docs/integrations/toolkits/).
 :::
 
-Toolkits are collections of tools that are designed to be used together for specific tasks and have convenience loading methods.
+Toolkits are collections of tools that are designed to be used together for specific tasks and have convenient loading methods.