# Quickstart: Deploy on LangGraph Cloud
!!! note "Prerequisites"
Before you begin, ensure you have the following:
- [GitHub account](https://github.com/)
- [LangSmith account](https://smith.langchain.com/)
## Create a repository on GitHub
To deploy a LangGraph application to **LangGraph Cloud**, your application code must reside in a GitHub repository. Both public and private repositories are supported.
You can deploy any [LangGraph Application](../concepts/application_structure.md) to LangGraph Cloud.
For this guide, we'll use the pre-built Python [**ReAct Agent**](https://github.com/langchain-ai/react-agent) template.
??? note "Get Required API Keys for the ReAct Agent template"
This **ReAct Agent** application requires an API key from [Anthropic](https://console.anthropic.com/) and [Tavily](https://app.tavily.com/). You can get these API keys by signing up on their respective websites.
**Alternative**: If you'd prefer a scaffold application that doesn't require API keys, use the [**New LangGraph Project**](https://github.com/langchain-ai/new-langgraph-project) template instead of the **ReAct Agent** template.
1. Go to the [ReAct Agent](https://github.com/langchain-ai/react-agent) repository.
2. Fork the repository to your GitHub account by clicking the `Fork` button in the top right corner.
## Deploy to LangGraph Cloud
??? note "1. Log in to [LangSmith](https://smith.langchain.com/)"
[{: style="max-height:300px"}](deployment/img/01_login.png)
Go to [LangSmith](https://smith.langchain.com/) and log in. If you don't have an account, you can sign up for free.
??? note "2. Click on LangGraph Platform (the left sidebar)"
[{: style="max-height:300px"}](deployment/img/02_langgraph_platform.png)
Select **LangGraph Platform** from the left sidebar.
??? note "3. Click on + New Deployment (top right corner)"
[{: style="max-height:300px"}](deployment/img/03_deployments_page.png)
Click on **+ New Deployment** to create a new deployment. This button is located in the top right corner.
It'll open a new modal where you can fill out the required fields.
??? note "4. Click on Import from GitHub (first time users)"
[](deployment/img/04_create_new_deployment.png)
Click on **Import from GitHub** and follow the instructions to connect your GitHub account. This step is needed for **first-time users** or to add private repositories that haven't been connected before.
??? note "5. Select the repository, configure ENV vars etc"
[{: style="max-height:300px"}](deployment/img/05_configure_deployment.png)
Select the repository, add env variables and secrets, and set other configuration options.
- **Repository**: Select the repository you forked earlier (or any other repository you want to deploy).
- Set the secrets and environment variables required by your application. For the **ReAct Agent** template, you need to 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/).
??? note "6. Click Submit to Deploy!"
[{: style="max-height:300px"}](deployment/img/05_configure_deployment.png)
Please note that this step may ~15 minutes to complete. You can check the status of your deployment in the **Deployments** view.
Click the Submit button at the top right corner to deploy your application.
## Lagraph Studio Web UI
Once your application is deployed, you can test it in **LangGraph Studio**.
??? note "1. Click on an existing deployment"
[{: style="max-height:300px"}](deployment/img/07_deployments_page.png)
Click on the deployment you just created to view more details.
??? note "2. Click on LangGraph Studio"
[{: style="max-height:300px"}](deployment/img/08_deployment_view.png)
Click on the LangGraph Studio button to open LangGraph Studio.
[{: style="max-height:400px"}](deployment/img/09_langgraph_studio.png)
Sample graph run in LangGraph Studio.
## Test the API
!!! note
The API calls below are for the **ReAct Agent** template. If you're deploying a different application, you may need to adjust the API calls accordingly.
Before using, you need to get the `URL` of your LangGraph deployment. You can find this in the `Deployment` view. Click the `URL` to copy it to the clipboard.
You also need to make sure you have set up your API key properly, so you can authenticate with LangGraph Cloud.
```shell
export LANGSMITH_API_KEY=...
```
=== "Python SDK (Async)"
**Install the LangGraph Python SDK**
```shell
pip install langgraph-sdk
```
**Send a message to the assistant (threadless run)**
```python
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")
```
=== "Python SDK (Sync)"
**Install the LangGraph Python SDK**
```shell
pip install langgraph-sdk
```
**Send a message to the assistant (threadless run)**
```python
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")
```
=== "Javascript SDK"
**Install the LangGraph JS SDK**
```shell
npm install @langchain/langgraph-sdk
```
**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
curl -s --request POST \
--url \
--header 'Content-Type: application/json' \
--data "{
\"assistant_id\": \"agent\",
\"input\": {
\"messages\": [
{
\"role\": \"human\",
\"content\": \"What is LangGraph?\"
}
]
},
\"stream_mode\": \"updates\"
}"
```
## Next Steps
Congratulations! If you've worked your way through this tutorial you are well on your way to becoming a LangGraph Cloud expert. Here are some other resources to check out to help you out on the path to expertise:
### LangGraph Framework
- **[LangGraph Tutorial](../tutorials/introduction.ipynb)**: Get started with LangGraph framework.
- **[LangGraph Concepts](../concepts/index.md)**: Learn the foundational concepts of LangGraph.
- **[LangGraph How-to Guides](../how-tos/index.md)**: Guides for common tasks with LangGraph.
### 📚 Learn More about LangGraph Platform
Expand your knowledge with these resources:
- **[LangGraph Platform Concepts](../concepts/index.md#langgraph-platform)**: Understand the foundational concepts of the LangGraph Platform.
- **[LangGraph Platform How-to Guides](../how-tos/index.md#langgraph-platform)**: Discover step-by-step guides to build and deploy applications.
- **[Launch Local LangGraph Server](../tutorials/langgraph-platform/local-server.md)**: This quick start guide shows how to start a LangGraph Server locally for the **ReAct Agent** template. The steps are similar for other templates.