docs: Simplify LGP quickstarts

docs/docs/cloud/quick_start.md

@@ -9,20 +9,11 @@ Before you begin, ensure you have the following:
 - A [GitHub account](https://github.com/)
 - A [LangSmith account](https://smith.langchain.com/) – free to sign up
 
-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 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:
+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 [`new-langgraph-project` template](https://github.com/langchain-ai/react-agent) for your application:
 
-1. Go to the [ReAct Agent](https://github.com/langchain-ai/react-agent) repository.
+1. Go to the [`new-langgraph-project` repository](https://github.com/langchain-ai/new-langgraph-project).
 1. Click the `Fork` button in the top right corner to fork the repository to your GitHub account.
 1. Click **Create fork**. 
 
@@ -32,12 +23,7 @@ To deploy a LangGraph application to **LangGraph Platform**, your application co
 1. In the left sidebar, select **LangGraph Platform**.
 1. Click the **+ New Deployment** button. A modal will open where you can fill in the required fields.
 1. If you are a first time user or adding a private repository that has not been previously connected, click the **Import from GitHub** button and follow the instructions to connect your GitHub account.
-1. Select your ReAct Agent repository.
-1. In the **Environment Variables** section, set the following secrets:
-    
-    - **ANTHROPIC_API_KEY**: Get an API key from [Anthropic](https://console.anthropic.com/).
-    - **TAVILY_API_KEY**: Get an API key on the [Tavily website](https://app.tavily.com/).
-
+1. Select your New LangGraph Project repository.
 1. Click **Submit** to deploy.
 
     This may take about 15 minutes to complete. You can check the status in the **Deployment details** view.
@@ -52,7 +38,7 @@ Once your application is deployed:
     LangGraph Studio will open to display your graph.
 
     <figure markdown="1">
-    [![image](deployment/img/09_langgraph_studio.png){: style="max-height:400px"}](deployment/img/09_langgraph_studio.png)
+    [![image](deployment/img/langgraph_studio.png){: style="max-height:400px"}](deployment/img/langgraph_studio.png)
     <figcaption>
         Sample graph run in LangGraph Studio.
     </figcaption>
@@ -129,41 +115,6 @@ You can now test the API:
             print("\n\n")
         ```
 
-=== "Javascript SDK"
-
-    1. Install the LangGraph JS SDK
-
-        ```shell
-        npm install @langchain/langgraph-sdk
-        ```
-
-    1. Send a message to the assistant (threadless run):
-
-        ```js
-        const { Client } = await import("@langchain/langgraph-sdk");
-
-        const client = new Client({ apiUrl: "your-deployment-url", apiKey: "your-langsmith-api-key" });
-
-        const streamResponse = client.runs.stream(
-            null, // Threadless run
-            "agent", // Assistant ID
-            {
-                input: {
-                    "messages": [
-                        { "role": "user", "content": "What is LangGraph?"}
-                    ]
-                },
-                streamMode: "messages",
-            }
-        );
-
-        for await (const chunk of streamResponse) {
-            console.log(`Receiving new event of type: ${chunk.event}...`);
-            console.log(JSON.stringify(chunk.data));
-            console.log("\n\n");
-        }
-        ```
-
 === "Rest API"
 
     ```bash

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

@@ -8,15 +8,6 @@ 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 [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 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
 
 ```bash
@@ -27,19 +18,11 @@ pip install --upgrade "langgraph-cli[inmem]"
 
 ## 2. Create a LangGraph app 🌱 
 
-Create a new app from the `react-agent` template. This template is a simple agent that can be flexibly extended to many tools.
+Create a new app from the [`new-langgraph-project` template](https://github.com/langchain-ai/new-langgraph-project). This template demonstrates a simple chatbot that maintains chat memory, which allows for coherent conversations across multiple interactions.
 
-=== "Python Server"
-
-    ```shell
-    langgraph new path/to/your/app --template react-agent-python 
-    ```
-
-=== "Node Server"
-
-    ```shell
-    langgraph new path/to/your/app --template react-agent-js
-    ```
+```shell
+langgraph new path/to/your/app --template new-langgraph-project 
+```
 
 !!! tip "Additional templates"
 
@@ -49,19 +32,10 @@ Create a new app from the `react-agent` template. This template is a simple agen
 
 In the root of your new LangGraph app, install the dependencies in `edit` mode so your local changes are used by the server:
 
-=== "Python server"
-
-    ```shell
-    cd path/to/your/app
-    pip install -e .
-    ```
-
-=== "Node server"
-
-    ```shell
-    cd path/to/your/app
-    yarn install
-    ```
+```shell
+cd path/to/your/app
+pip install -e .
+```
 
 ## 4. Create a `.env` file
 
@@ -69,25 +43,15 @@ You will find a `.env.example` in the root of your new LangGraph app. Create a `
 
 ```bash
 LANGSMITH_API_KEY=lsv2...
-TAVILY_API_KEY=tvly-...
-ANTHROPIC_API_KEY=sk-
 ```
 
 ## 5. Launch LangGraph Server 🚀 
 
 Start the LangGraph API server locally:
 
-=== "Python Server"
-
-    ```shell
-    langgraph dev
-    ```
-
-=== "Node Server"
-
-    ```shell
-    npx @langchain/langgraph-cli dev
-    ```
+```shell
+langgraph dev
+```
 
 Sample output:
 
@@ -188,42 +152,6 @@ For a LangGraph Server running on a custom host/port, update the baseURL paramet
             print("\n\n")
         ```
 
-=== "Javascript SDK"
-
-    1. Install the LangGraph JS SDK:
-
-        ```shell
-        npm install @langchain/langgraph-sdk
-        ```
-
-    1. Send a message to the assistant (threadless run):
-
-        ```js
-        const { Client } = await import("@langchain/langgraph-sdk");
-
-        // only set the apiUrl if you changed the default port when calling langgraph dev
-        const client = new Client({ apiUrl: "http://localhost:2024"});
-
-        const streamResponse = client.runs.stream(
-            null, // Threadless run
-            "agent", // Assistant ID
-            {
-                input: {
-                    "messages": [
-                        { "role": "user", "content": "What is LangGraph?"}
-                    ]
-                },
-                streamMode: "messages-tuple",
-            }
-        );
-
-        for await (const chunk of streamResponse) {
-            console.log(`Receiving new event of type: ${chunk.event}...`);
-            console.log(JSON.stringify(chunk.data));
-            console.log("\n\n");
-        }
-        ```
-
 === "Rest API"
 
     ```bash
@@ -244,7 +172,7 @@ For a LangGraph Server running on a custom host/port, update the baseURL paramet
         }" 
     ```
 
-## Next Steps
+## Next steps
 
 Now that you have a LangGraph app running locally, take your journey further by exploring deployment and advanced features: