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Showing content from https://langchain-ai.github.io/langgraphjs/cloud/quick_start/ below:

Cloud Deploy

This guide shows you how to set up and use LangGraph Platform for a cloud deployment.

Before you begin, ensure you have the following:

• A GitHub account
• A LangSmith account – free to sign up

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 for your application:

1. Go to the new-langgraph-project repository or new-langgraphjs-project template.
2. Click the Fork button in the top right corner to fork the repository to your GitHub account.
3. Click Create fork.

1. Log in to LangSmith.
2. In the left sidebar, select Deployments.
3. Click the + New Deployment button. A pane will open where you can fill in the required fields.
4. 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.
5. Select your New LangGraph Project repository.

6. Click Submit to deploy.

   This may take about 15 minutes to complete. You can check the status in the Deployment details view.

Once your application is deployed:

1. Select the deployment you just created to view more details.

2. Click the LangGraph Studio button in the top right corner.

   LangGraph Studio will open to display your graph.

   Sample graph run in LangGraph Studio.

1. In the Deployment details view in LangGraph, click the API URL to copy it to your clipboard.
2. Click the URL to copy it to the clipboard.

You can now test the API:

Python SDK (Async)Python SDK (Sync)JavaScript SDKRest API

1. Install the LangGraph Python SDK:

   pip install langgraph-sdk
   

2. Send a message to the assistant (threadless run):

   from langgraph_sdk import get_client
   
   client = get_client(url="your-deployment-url", api_key="your-langsmith-api-key")
   
   async for chunk in client.runs.stream(
       None,  # Threadless run
       "agent", # Name of assistant. Defined in langgraph.json.
       input={
           "messages": [{
               "role": "human",
               "content": "What is LangGraph?",
           }],
       },
       stream_mode="updates",
   ):
       print(f"Receiving new event of type: {chunk.event}...")
       print(chunk.data)
       print("\n\n")
   

1. Install the LangGraph Python SDK:

   pip install langgraph-sdk
   

2. Send a message to the assistant (threadless run):

   from langgraph_sdk import get_sync_client
   
   client = get_sync_client(url="your-deployment-url", api_key="your-langsmith-api-key")
   
   for chunk in client.runs.stream(
       None,  # Threadless run
       "agent", # Name of assistant. Defined in langgraph.json.
       input={
           "messages": [{
               "role": "human",
               "content": "What is LangGraph?",
           }],
       },
       stream_mode="updates",
   ):
       print(f"Receiving new event of type: {chunk.event}...")
       print(chunk.data)
       print("\n\n")
   

1. Install the LangGraph JS SDK

   npm install @langchain/langgraph-sdk
   

2. Send a message to the assistant (threadless run):

   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");
   }
   

curl -s --request POST \
    --url <DEPLOYMENT_URL>/runs/stream \
    --header 'Content-Type: application/json' \
    --header "X-Api-Key: <LANGSMITH API KEY> \
    --data "{
        \"assistant_id\": \"agent\",
        \"input\": {
            \"messages\": [
                {
                    \"role\": \"human\",
                    \"content\": \"What is LangGraph?\"
                }
            ]
        },
        \"stream_mode\": \"updates\"
    }" 

Congratulations! You have deployed an application using LangGraph Platform.

Here are some other resources to check out:

• LangGraph Platform overview
• Deployment options

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