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Lauren Hirata Singh
2025-05-13 11:40:30 -07:00
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docs/docs/cloud/quick_start.md
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@@ -13,7 +13,7 @@ Before you begin, ensure you have the following:
To deploy an 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 [`new-langgraph-project` repository](https://github.com/langchain-ai/new-langgraph-project).
1. Go to the [`new-langgraph-project` repository](https://github.com/langchain-ai/new-langgraph-project) or [`new-langgraphjs-project` template](https://github.com/langchain-ai/new-langgraphjs-project).
1. Click the `Fork` button in the top right corner to fork the repository to your GitHub account.
1. Click **Create fork**.
docs/docs/tutorials/langgraph-platform/local-server.md
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## 2. Create a LangGraph app 🌱
Create a new app from the [`new-langgraph-project` template](https://github.com/langchain-ai/new-langgraph-project) or [`new-langgraphjs-projec` template](https://github.com/langchain-ai/new-langgraphjs-project). This template demonstrates a simple chatbot that maintains chat memory, which allows for coherent conversations across multiple interactions.
Create a new app from the [`new-langgraph-project` template](https://github.com/langchain-ai/new-langgraph-project) or [`new-langgraphjs-project` template](https://github.com/langchain-ai/new-langgraphjs-project). This template demonstrates a simple chatbot that maintains chat memory, which allows for coherent conversations across multiple interactions.
=== "Python server"