docs: address feedback

docs/docs/cloud/quick_start.md

@@ -14,6 +14,10 @@ This quickstart uses the [pre-built Python ReAct agent template](https://github.
 - An API key for [Anthropic](https://console.anthropic.com/)
 - An API key for [Tavily](https://app.tavily.com/)
 
+!!! note
+
+    If you want to use a scaffold application that doesn't require API keys, use the [New LangGraph Project](https://github.com/langchain-ai/new-langgraph-project) template instead.
+
 ## 1. Create a repository on GitHub
 
 To deploy a LangGraph application to **LangGraph Platform**, your application code must reside in a GitHub repository. Both public and private repositories are supported. For this quickstart, use the [pre-built Python ReAct agent template](https://github.com/langchain-ai/react-agent) for your application:

docs/docs/concepts/langgraph_platform.md

@@ -5,9 +5,7 @@ search:
 
 # LangGraph Platform
 
-**LangGraph Platform** is a solution for deploying agentic applications to production, built on the open-source [LangGraph framework](../index.md).
-
-<div align="center"><iframe width="560" height="315" src="https://www.youtube.com/embed/pfAQxBS5z88?si=XGS6Chydn6lhSO1S" title="What is LangGraph Platform?" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe></div>
+Develop, deploy, scale, and manage agents with **LangGraph Platform** — the purpose-built platform for long-running, agentic workflows.
 
 !!! tip "Get started with LangGraph Platform"
 
@@ -15,7 +13,9 @@ search:
 
 ## Why use LangGraph Platform?
 
-LangGraph Platform handles common issues that arise when deploying LLM applications to production, allowing you to focus on agent logic instead of managing server infrastructure.
+<div align="center"><iframe width="560" height="315" src="https://www.youtube.com/embed/pfAQxBS5z88?si=XGS6Chydn6lhSO1S" title="What is LangGraph Platform?" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe></div>
+
+LangGraph Platform makes it easy to get your agent running in production —  whether it’s built with LangGraph or another framework — so you can focus on your app logic, not infrastructure. Deploy with one click to get a live endpoint, and use our robust APIs and built-in task queues to handle production scale. 
 
 - **[Streaming Support](../cloud/concepts/streaming.md)**: As agents grow more sophisticated, they often benefit from streaming both token outputs and intermediate states back to the user. Without this, users are left waiting for potentially long operations with no feedback. LangGraph Server provides multiple streaming modes optimized for various application needs.
 
@@ -31,6 +31,8 @@ LangGraph Platform handles common issues that arise when deploying LLM applicati
 
 - **[Human-in-the-loop support](../cloud/how-tos/human_in_the_loop_breakpoint.md)**: In many applications, users require a way to intervene in agent processes. LangGraph Server provides specialized endpoints for human-in-the-loop scenarios, simplifying the integration of manual oversight into agent workflows.
 
+- **[LangGraph Studio](./langgraph_studio.md)**: Enables visualization, interaction, and debugging of agentic systems that implement the LangGraph Server API protocol. Studio also integrates with LangSmith to enable tracing, evaluation, and prompt engineering.
+
 By using LangGraph Platform, you gain access to a robust, scalable deployment solution that mitigates these challenges, saving you the effort of implementing and maintaining them manually. This allows you to focus more on building effective agent behavior and less on solving deployment infrastructure issues.
 
 ## Deployment

docs/docs/tutorials/langgraph-platform/local-server.md

@@ -8,10 +8,14 @@ Before you begin, ensure you have the following:
 
 - An API key for [LangSmith](https://smith.langchain.com/settings) - free to sign up
 
-This quickstart uses the `react-agent` template and requires the following:
+This quickstart uses the [pre-built Python ReAct agent template](https://github.com/langchain-ai/react-agent), which requires the following:
 
 - An API key for [Anthropic](https://console.anthropic.com/)
-- An API key [Tavily](https://app.tavily.com/)
+- An API key for [Tavily](https://app.tavily.com/)
+
+!!! note
+
+    If you want to use a scaffold application that doesn't require API keys, use the [New LangGraph Project](https://github.com/langchain-ai/new-langgraph-project) template instead.
 
 ## 1. Install the LangGraph CLI
 

docs/mkdocs.yml

@@ -186,7 +186,7 @@ nav:
         - LangGraph SDK: concepts/sdk.md
       - Data management:
         - Add semantic search: cloud/deployment/semantic_search.md
-        - Add TTLs how-tos/ttl/configure_ttl.md
+        - Add TTLs: how-tos/ttl/configure_ttl.md
       - Authentication & access control:
         - Overview: concepts/auth.md
         - how-tos/auth/custom_auth.md