diff --git a/docs/source/en/guided_tour.md b/docs/source/en/guided_tour.md
index b446245..b44d1a8 100644
--- a/docs/source/en/guided_tour.md
+++ b/docs/source/en/guided_tour.md
@@ -160,7 +160,7 @@ When the agent is initialized, the tool attributes are used to generate a tool d
 Transformers comes with a default toolbox for empowering agents, that you can add to your agent upon initialization with argument `add_base_tools = True`:
 
 - **DuckDuckGo web search***: performs a web search using DuckDuckGo browser.
-- **Python code interpreter**: runs your the LLM generated Python code in a secure environment. This tool will only be added to [`ToolCallingAgent`] if you initialize it with `add_base_tools=True`, since code-based agent can already natively execute Python code
+- **Python code interpreter**: runs your LLM generated Python code in a secure environment. This tool will only be added to [`ToolCallingAgent`] if you initialize it with `add_base_tools=True`, since code-based agent can already natively execute Python code
 - **Transcriber**: a speech-to-text pipeline built on Whisper-Turbo that transcribes an audio to text.
 
 You can manually use a tool by calling it with its arguments.
diff --git a/docs/source/en/index.md b/docs/source/en/index.md
index 7392cfc..fbcfba0 100644
--- a/docs/source/en/index.md
+++ b/docs/source/en/index.md
@@ -15,7 +15,7 @@ rendered properly in your Markdown viewer.
 
 # `smolagents`
 
-This library is the simplest framework out there to build powerful agents! By the way, wtf are "agents"? We provide our definition [in this page](conceptual_guides/intro_agents), whe're you'll also find tips for when to use them or not (spoilers: you'll often be better off without agents).
+This library is the simplest framework out there to build powerful agents! By the way, wtf are "agents"? We provide our definition [in this page](conceptual_guides/intro_agents), where you'll also find tips for when to use them or not (spoilers: you'll often be better off without agents).
 
 This library offers:
 
diff --git a/docs/source/en/tutorials/secure_code_execution.md b/docs/source/en/tutorials/secure_code_execution.md
index d8a6109..60887f6 100644
--- a/docs/source/en/tutorials/secure_code_execution.md
+++ b/docs/source/en/tutorials/secure_code_execution.md
@@ -30,7 +30,7 @@ Code is just a better way to express actions on a computer. It has better:
 - **Composability:** could you nest JSON actions within each other, or define a set of JSON actions to re-use later, the same way you could just define a python function?
 - **Object management:** how do you store the output of an action like `generate_image` in JSON?
 - **Generality:** code is built to express simply anything you can do have a computer do.
-- **Representation in LLM training corpuses:** why not leverage this benediction of the sky that plenty of quality actions have already been included in LLM training corpuses?
+- **Representation in LLM training corpus:** why not leverage this benediction of the sky that plenty of quality actions have already been included in LLM training corpus?
 
 This is illustrated on the figure below, taken from [Executable Code Actions Elicit Better LLM Agents](https://huggingface.co/papers/2402.01030).
 
diff --git a/src/smolagents/agents.py b/src/smolagents/agents.py
index 4d7ddf6..a81b80d 100644
--- a/src/smolagents/agents.py
+++ b/src/smolagents/agents.py
@@ -453,9 +453,9 @@ class MultiStepAgent:
 
         Args:
             task (`str`): The task to perform.
-            stream (`bool`): Wether to run in a streaming way.
-            reset (`bool`): Wether to reset the conversation or keep it going from previous run.
-            single_step (`bool`): Should the agent run in one shot or multi-step fashion?
+            stream (`bool`): Whether to run in a streaming way.
+            reset (`bool`): Whether to reset the conversation or keep it going from previous run.
+            single_step (`bool`): Whether to run the agent in one-shot fashion.
             additional_args (`dict`): Any other variables that you want to pass to the agent run, for instance images or dataframes. Give them clear names!
 
         Example: