Fix broken-links and typos

docs_new/additional-features/deployment-to-production.mdx

@@ -108,7 +108,7 @@ Find more detailed instructions in the video below:
 
 <Accordion title="Option 2: Deploy Tools as Separate API Endpoints" defaultOpen={false}>
 
-Alternatively, you can deploy your tools as separate API endpoints (e.g., serverless functions) and integrate them into your agents using [OpenAPI schemas](/advanced-features/openapi-schemas). This method is ideal if you plan to reuse tools across different agents or services.
+Alternatively, you can deploy your tools as separate API endpoints (e.g., serverless functions) and integrate them into your agents using [OpenAPI schemas](/additional-features/openapi-schemas). This method is ideal if you plan to reuse tools across different agents or services.
 
 Deploying tools separately provides flexibility and scalability. You can deploy individual tools as serverless functions on platforms like AWS Lambda, Google Cloud Functions, or Azure Functions. This ensures on-demand scaling and cost efficiency. Alternatively, you can host your tools as custom API endpoints using frameworks like FastAPI or Flask and deploy them on platforms such as Heroku, DigitalOcean, Railway, or your own servers.
 

docs_new/core-framework/agencies.mdx

@@ -54,7 +54,7 @@ Utilizing an Agency instead of individual agents offers several advantages:
 
 In Agency Swarm, communication flows are **user-defined** and **highly flexible**. They are set up in the agency chart as a directed graph, allowing permissions to initiate communication to flow from left to right.
 
-Communication flows are defined when creating the agency, specifying which agents can communicate with each other. More information on advanced communication flows can be found on the [Custom Communication Flows](/advanced-features/custom-communication-flows/overview) page.
+Communication flows are defined when creating the agency, specifying which agents can communicate with each other. More information on advanced communication flows can be found on the [Custom Communication Flows](/additional-features/custom-communication-flows/overview) page.
 
 ## Creating an Agency
 

docs_new/core-framework/agents.mdx

@@ -170,7 +170,7 @@ agent = Agent(name='MyAgent', model='gpt-4o-mini-model-name')
 
 <Note>
 
-**Note**: For more information on using OpenAPI schemas to create tools for your agents, see the [OpenAPI Schemas](/advanced-features/openapi-schemas) page.
+**Note**: For more information on using OpenAPI schemas to create tools for your agents, see the [OpenAPI Schemas](/additional-features/openapi-schemas) page.
 
 </Note>
 
@@ -194,7 +194,7 @@ To check available agents, simply run the command without any arguments.
 
 ## Response Validator
 
-See the [Output Validation](/advanced-features/output-validation) page for more information on how to use response validators to validate the response before sending it to the user or another agent.
+See the [Output Validation](/additional-features/output-validation) page for more information on how to use response validators to validate the response before sending it to the user or another agent.
 
 ## Agent Template Using the CLI
 
@@ -230,7 +230,7 @@ Running the command generates the following folder structure:
 **Explanation:**
 
 - **files**: Store files to be uploaded to OpenAI. Acceptable formats are listed in [File Search](https://platform.openai.com/docs/assistants/tools/file-search#supported-files) and [Code Interpreter](https://platform.openai.com/docs/assistants/tools/code-interpreter#supported-files). Uploaded files receive an ID to prevent re-uploading.
-- **schemas**: Store OpenAPI schemas to be automatically converted into tools. Place the schema in this folder and specify it when initializing your agent. [Read more](/advanced-features/openapi-schemas).
+- **schemas**: Store OpenAPI schemas to be automatically converted into tools. Place the schema in this folder and specify it when initializing your agent. [Read more](/additional-features/openapi-schemas).
 - **tools**: Store tools as Python files. Each file must have the same name as the tool class (e.g., `ExampleTool.py` must contain a class called `ExampleTool`). [Read more](./tools).
 
 </Step>

docs_new/core-framework/state-management.mdx

@@ -44,7 +44,7 @@ Example usage:
 - Set shared state within a tool: `self._shared_state.set("key", value)`
 - Retrieve shared state in another tool: `value = self._shared_state.get("key")`
 
-See more in [Shared State](/advanced-features/shared-state).
+See more in [Shared State](/additional-features/shared-state).
 
 ## Persistence Between Restarts
 

docs_new/core-framework/tools/advanced-tool-features.mdx

@@ -11,7 +11,7 @@ Ensuring the reliability of agent outputs is critical, especially in production
 - **Tool Input/Output Validation:** Tools validate data before and after execution to reduce runtime errors through Pydantic integration.
 - **Custom Validators:** Implement custom validation logic within tools using field validators and model validators.
 
-See the [Output Validation](/advanced-features/output-validation) page for more information on how to use validators for tools.
+See the [Output Validation](/additional-features/output-validation) page for more information on how to use validators for tools.
 
 ### Tool Error Handling
 
@@ -99,7 +99,7 @@ self._shared_state.set("key", value)
 value = self._shared_state.get("key")
 ```
 
-See more in [Shared State](advanced-features/shared-state).
+See more in [Shared State](additional-features/shared-state).
 
 ## Method Composition
 

docs_new/tutorials/tutorials.mdx

@@ -111,4 +111,4 @@ icon: "play"
 
 ## Next Steps
 - **[API Reference](/api)**
-- **[Advanced Topics](/advanced-features)**
+- **[Advanced Topics](/additional-features)**

docs_new/welcome/ai-agency-vs-other-frameworks.mdx

@@ -29,9 +29,9 @@ Unlike other frameworks, Agency Swarm takes a unique approach with three key dif
 | **Design & Architecture**          | - Modular, production-focused<br/>- Flexible agent structure via agency chart<br/>- Built for real business needs                                           | - Event-driven, scalable<br/>- Asynchronous message passing<br/>- Supports distributed computing                        | - Flexible agent organization into crews<br/>- Agents with rich attributes                                                  |
 | **Key Entities**                   | - Agents<br/>- Tools<br/>- Agencies                                                                                                                         | - ConversableAgent<br/>- AssistantAgent<br/>- UserProxyAgent                                                            | - Agents<br/>- Tasks<br/>- Crews                                                                                            |
 | **Prompt Flexibility**             | - Full control over prompts<br/>- No predefined prompts<br/>- Does not write prompts for you                                                                | - Flexible prompt engineering<br/>- Supports few-shot learning                                                          | - Extensive prompt customization                                                                                            |
-| **Type Safety**                    | - Input validation with Pydantic<br/>- Custom response validators<br/>- Prevents hallucinations with automatic type checking and error correction           | - Robust error handling<br/>- Code execution validation<br/>- Self-correction by agents                                 | - No built-in type checking or error correction<br/>- Built on Langchain                                                    |
+| **Type Safety**                    | - Input validation with Pydantic<br/>- Custom response validators<br/>- Prevents hallucinations with automatic type checking and error correction           | - Robust error handling<br/>- Code execution validation<br/>- Self-correction by agents                                 | - No built-in type checking or error correction<br/>- Built on LangChain                                                    |
 | **Communication Control**          | - Enables easy definition of communication flows<br/>- Agents determine communication based on their descriptions<br/>- Uses specialized `SendMessage` tool | - Uses extra model call for "role play" to determine next speaker<br/>- Recently added support for hardcoded conditions | - Uses "process" concept for communication flow<br/>- Delegation control via agent attributes                               |
-| **Integration with Modern Models** | - Built for modern function-calling models<br/>- Direct integration with OpenAI Assistants API                                                              | - Supports various interaction patterns<br/>- Event-driven architecture facilitates this                                | - Built on Langchain                                                                                                        |
+| **Integration with Modern Models** | - Built for modern function-calling models<br/>- Direct integration with OpenAI Assistants API                                                              | - Supports various interaction patterns<br/>- Event-driven architecture facilitates this                                | - Built on LangChain                                                                                                        |
 | **State Management**               | - Shared state among agents and tools<br/>- Conversation context via OpenAI API                                                                             | - Conversation history maintenance<br/>- Dynamic context truncation                                                     | - Three-tier memory system<br/>- Context sharing among agents and tasks                                                     |
 | **Production Readiness**           | - Designed for production environments<br/>- Business-oriented design<br/>- Robust validation and error correction                                          | - Scalable and distributed<br/>- Active development towards production features                                         | - Requires additional development for production<br/>- Scalability testing needed                                           |
 | **Task Delegation**                | - Agent autonomy in task delegation<br/>- Hierarchical delegation via agency chart                                                                          | - Automated task coordination<br/>- Function calls for delegation                                                       | - Autonomous delegation based on capabilities<br/>- Hand-off mechanisms with context preservation                           |

docs_new/welcome/getting-started/cursor-ide.mdx

@@ -56,32 +56,32 @@ Follow these steps to create your first Agency using Cursor:
   <Step title="Send Your First Prompt to Composer">
     Spend as much time as possible on your first prompt. This will save you a lot of time later. Make sure to include:
       - All agents that you want to create
-      - All tools and APIs that the agents must use.
-      - Communication flows between your agents.
+      - All tools and APIs that the agents must use
+      - Communication flows between your agents
   </Step>
 
-  <Step title="Varify The Agency Structure">
+  <Step title="Verify The Agency Structure">
     Sometimes Cursor forgets certain files or hallucinates the agency structure. Make sure to verify that:
-    - All import statements are correct.
-    - `requirements.txt` file exists.
-    - `agency.py` file executes properly.   
+    - All import statements are correct
+    - `requirements.txt` file exists
+    - `agency.py` file executes properly
 
     If any of these are missing or not working, instruct composer to create them or fix the code.
   </Step>
 
   <Step title="Adjust the Tools">
-    Run each tool file to make sure they work as expected. You will see the test cases that Cursor created at the bottom in `if __name__ == "__main__"` statements.  
+    Run each tool file to make sure they work as expected. You will see the test cases that Cursor created at the bottom in `if __name__ == "__main__"` statements.
     - If any of the tools are not working, send the error message to composer and ask it to fix the code.
-    - If you want to adjust the functionality of the tool, either use inline editing `CMD+K` (macOS) or `Ctrl+K` (Windows/Linux) or send another message to composer.
-    - If you are stuck, use Chat `CMD+L` (macOS) or `Ctrl+L` (Windows/Linux) to ask questions and brain storm alternative approaches. For example, ask "What other APIs can I use for this tool?"
-  
+    - If you want to adjust the functionality of the tool, either use inline editing `Cmd+K` (macOS) or `Ctrl+K` (Windows/Linux) or send another message to composer.
+    - If you are stuck, use Chat `Cmd+L` (macOS) or `Ctrl+L` (Windows/Linux) to ask questions and brainstorm alternative approaches. For example, ask "What other APIs can I use for this tool?"
+
   </Step>
 
   <Step title="Adjust the Agents">
     Verify the agent instructions and parameters are correct after adjustments. **Actually think about instructions.**
     - Use Chat or Inline Editing to adjust the `instruction.md` files to your needs.
     - Edit instructions manually and remove all boilerplate text.
-    - Make sure each agent has access to all the new tools either by speciying `tools_folder` path or adding them in `tools` parameter.
+    - Make sure each agent has access to all the new tools either by specifying `tools_folder` path or adding them in `tools` parameter.
   </Step>
 
   <Step title="Test Your Agency">
@@ -92,7 +92,7 @@ Follow these steps to create your first Agency using Cursor:
   </Step>
 
   <Step title="Repeat">
-    Iterate on your agency by repeating the process until you are satisfied with the results.  
+    Iterate on your agency by repeating the process until you are satisfied with the results.
 
       <Note>
       **Agent Development is an Iterative Process**
@@ -108,4 +108,3 @@ Follow these steps to create your first Agency using Cursor:
 
 - Learn the core concepts of Tools, Agents, and Agencies.
 - Watch the [Deployment Tutorial](https://www.youtube.com/watch?v=53_e3lmk6Mo).
-

docs_new/welcome/getting-started/genesis-agency.mdx

@@ -40,7 +40,7 @@ Genesis Agency is a special Agency within the framework that helps you to create
     After executing the `genesis` command, specialized agents such as `GenesisCEO` and `ToolCreator` will begin constructing your agency's structure and developing the required tools.
   </Step>
 
-  <Step title="Fine Tune Your Agents and Tools">
+  <Step title="Fine-Tune Your Agents and Tools">
     Once Genesis has created your agents, you'll find all the agent folders in the directory where you ran the `genesis` command. Fine-tune the agents and tools as needed by following these steps:
 
     1. **Adjust Tools**: Modify the tools in each agent's `tools` directory to meet your requirements.
@@ -58,5 +58,5 @@ Genesis Agency is a special Agency within the framework that helps you to create
 
 ## Next Steps
 
-- Learn the core concepts of Tools, Agents, and Agencies.
+- Learn the core concepts of [Tools](/core-framework/tools), [Agents](/core-framework/agents), and [Agencies](/core-framework/agencies).
 - Watch the [Deployment Tutorial](https://www.youtube.com/watch?v=53_e3lmk6Mo).

docs_new/welcome/installation.mdx

@@ -57,7 +57,7 @@ When it comes to building your first AI Agency, you have three options:
     fully customized solution.
   </Card>
   <Card title="Use Genesis Agency" icon="wand-sparkles" iconType="duotone" href="./getting-started/genesis-agency">
-    Let Genesis Agency to create your agents for you. Best for simple use cases or for creating your initial agency
+    Let Genesis Agency create your agents for you. Best for simple use cases or for creating your initial agency
     structure quickly.
   </Card>
   <Card title="Cursor (Recommended)" icon="cube" iconType="duotone" href="./getting-started/cursor-ide">