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docs: Delete LGP nav Items from docs (#5743)

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Remove the files and nav for LGP docs in the mkdocs site.

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Co-authored-by: Lauren Hirata Singh <lauren@langchain.dev>
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Kathryn May
2025-07-30 15:49:54 -04:00
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co-authored by Lauren Hirata Singh
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# Deployment quickstart
This guide shows you how to set up and use LangGraph Platform for a cloud deployment.
## Prerequisites
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
## 1. Create a repository on GitHub
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) 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**.
## 2. Deploy to LangGraph Platform
1. Log in to [LangSmith](https://smith.langchain.com/).
1. In the left sidebar, select **Deployments**.
1. Click the **+ New Deployment** button. A pane 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 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.
## 3. Test your application in LangGraph Studio
Once your application is deployed:
1. Select the deployment you just created to view more details.
1. Click the **LangGraph Studio** button in the top right corner.
LangGraph Studio will open to display your graph.
<figure markdown="1">
[![image](deployment/img/langgraph_studio.png){: style="max-height:400px"}](deployment/img/langgraph_studio.png)
<figcaption>
Sample graph run in LangGraph Studio.
</figcaption>
</figure>
## 4. Get the API URL for your deployment
1. In the **Deployment details** view in LangGraph, click the **API URL** to copy it to your clipboard.
1. Click the `URL` to copy it to the clipboard.
## 5. Test the API
You can now test the API:
=== "Python SDK (Async)"
1. Install the LangGraph Python SDK:
```shell
pip install langgraph-sdk
```
1. 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)"
1. Install the LangGraph Python SDK:
```shell
pip install langgraph-sdk
```
1. 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"
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
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\"
}"
```
## Next steps
Congratulations! You have deployed an application using LangGraph Platform.
Here are some other resources to check out:
- [LangGraph Platform overview](../concepts/langgraph_platform.md)
- [Deployment options](../concepts/deployment_options.md)