> ## Documentation Index
> Fetch the complete documentation index at: https://langchain-5e9cc07a-preview-docsdy-1782656769-5d7a089.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.
# Deployment
> Deploy LangGraph agents to production with LangSmith Cloud or JavaScript frameworks and hosting platforms.
When you are ready to deploy your LangGraph agent to production, choose a hosting model that fits your stack. **[LangSmith Cloud](/langsmith/deploy-to-cloud)** provides fully managed infrastructure for stateful, long-running agents with persistent state and background execution.
You can also deploy on **JavaScript frameworks and platforms** such as Next.js, SvelteKit, Nuxt, Cloudflare Workers, and Deno Deploy using the same [Agent Streaming Protocol](https://github.com/langchain-ai/agent-protocol/tree/main/streaming).
LangSmith offers multiple deployment options beyond Cloud, including [hybrid](/langsmith/hybrid), [standalone servers](/langsmith/deploy-standalone-server), and [self-hosted with control plane](/langsmith/deploy-with-control-plane). For more information, see the [LangSmith Deployment overview](/langsmith/deployment).
## LangSmith Cloud
This section walks through deploying your agent to LangSmith Cloud from a GitHub repository. LangSmith handles infrastructure, scaling, and operational concerns.
### Prerequisites
Before you begin, ensure you have the following:
* A [GitHub account](https://github.com/)
* A [LangSmith account](https://smith.langchain.com?utm_source=docs\&utm_medium=cta\&utm_campaign=langsmith-signup\&utm_content=oss-langgraph-deploy) (free to sign up)
### Deploy your agent
#### 1. Create a repository on GitHub
Your application's code must reside in a GitHub repository to be deployed on LangSmith. Both public and private repositories are supported. For this quickstart, first make sure your app is LangGraph-compatible by following the [local server setup guide](/oss/javascript/langgraph/studio#set-up-local-agent-server). Then, push your code to the repository.
#### 2. Deploy to LangSmith
Log in to [LangSmith](https://smith.langchain.com?utm_source=docs\&utm_medium=cta\&utm_campaign=langsmith-signup\&utm_content=oss-langgraph-deploy). In the left sidebar, select **Deployments**.
Click the **+ New Deployment** button. A pane will open where you can fill in the required fields.
If you are a first time user or adding a private repository that has not been previously connected, click the **Add new account** button and follow the instructions to connect your GitHub account.
Select your application's repository. 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 Studio
Once your application is deployed:
1. Select the deployment you just created to view more details.
2. Click the **Studio** button in the top right corner. Studio will open to display your graph.
#### 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.
2. Click the `URL` to copy it to the clipboard.
#### 5. Test the API
You can now test the API:
1. Install LangGraph SDK:
```shell theme={null}
npm install @langchain/langgraph-sdk
```
2. Send a message to the agent:
```ts theme={null}
import { Client } from "@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", // Name of agent. Defined in langgraph.json.
{
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");
}
```
```bash theme={null}
curl -s --request POST \
--url /runs/stream \
--header 'Content-Type: application/json' \
--header "X-Api-Key: \
--data "{
\"assistant_id\": \"agent\", `# Name of agent. Defined in langgraph.json.`
\"input\": {
\"messages\": [
{
\"role\": \"human\",
\"content\": \"What is LangGraph?\"
}
]
},
\"stream_mode\": \"updates\"
}"
```
***
[Connect these docs](/use-these-docs) to Claude, VSCode, and more via MCP for real-time answers.
[Edit this page on GitHub](https://github.com/langchain-ai/docs/edit/main/src/oss/langgraph/deploy.mdx) or [file an issue](https://github.com/langchain-ai/docs/issues/new/choose).
