diff --git a/docs/swarms/structs/agent.md b/docs/swarms/structs/agent.md
index 12dd099b..39076a5e 100644
--- a/docs/swarms/structs/agent.md
+++ b/docs/swarms/structs/agent.md
@@ -2,9 +2,6 @@
 
 Swarm Agent is a powerful autonomous agent framework designed to connect Language Models (LLMs) with various tools and long-term memory. This framework provides the ability to ingest and process various types of documents such as PDFs, text files, Markdown files, JSON files, and more. The Swarm Agent offers a wide range of features to enhance the capabilities of LLMs and facilitate efficient task execution.
 
-
-The `Agent` class serves several key purposes:
-
 1. **Conversational Loop**: It establishes a conversational loop with a language model. This means it allows you to interact with the model in a back-and-forth manner, taking turns in the conversation.
 
 2. **Feedback Collection**: The class allows users to provide feedback on the responses generated by the model. This feedback can be valuable for training and improving the model's responses over time.
@@ -128,15 +125,13 @@ The `Agent` class serves several key purposes:
 
 ## Getting Started
 
-To get started with the Swarm Agent, follow these steps:
+First run the following:
 
-1. Install the required dependencies by running `pip install swarms`.
-2. Import the necessary modules and classes from the `swarms` package.
-3. Initialize an instance of the `Agent` class, providing the necessary configuration parameters such as the language model, system prompt, tools, and memory components.
-4. Define the tasks you want the agent to perform and pass them to the `run()` method.
-5. Optionally, customize the agent's behavior by adjusting parameters such as the maximum number of loops, stopping conditions, and tool integration.
+```bash
+pip3 install swarms
+```
 
-Here's a basic example to illustrate the usage of the Swarm Agent:
+And, then now you can get started with the following:
 
 ```python
 from swarms.models import OpenAIChat
@@ -274,5 +269,3 @@ tasks = [
 responses = agent.bulk_run(tasks)
 print(responses)
 ```
-
-These examples demonstrate just a few of the many features and capabilities offered by the Swarm Agent framework. For more detailed information and additional examples, please refer to the comprehensive documentation provided within the codebase.