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docs: Create how-to for setting up LangGraph app and how-to for deploying to LangGraph Cloud (#801)
* Create how-to for setting up LangGraph app and how-to for deploying to LangGraph Cloud. * Fix spelling errors.
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Andrew Nguonly
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# How to Deploy to LangGraph Cloud
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LangGraph Cloud is available within <a href="https://www.langchain.com/langsmith" target="_blank">LangSmith</a>. To deploy a LangGraph Cloud API, navigate to the <a href="https://smith.langchain.com/" target="_blank">LangSmith UI</a>.
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## Setup GitHub Repository
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LangGraph Cloud applications are deployed from GitHub repositories. Configure and upload a LangGraph Cloud application to a GitHub repository in order to deploy it to LangGraph Cloud.
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## Create New Deployment
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Starting from the <a href="https://smith.langchain.com/" target="_blank">LangSmith UI</a>...
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1. In the left-hand navigation panel, select `Deployments`. The `Deployments` view contains a list of existing LangGraph Cloud deployments.
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1. In the top-right corner, select `+ New Deployment` to create a new deployment.
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1. In the `Create New Deployment` panel, fill out the required fields.
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1. `Deployment details`
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1. Select `Import from GitHub` and follow the GitHub OAuth workflow to install and authorize LangChain's `hosted-langserve` GitHub app to access the selected repositories. After installation is complete, return to the `Create New Deployment` panel and select the GitHub repository to deploy from the dropdown menu.
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1. Specify a name for the deployment.
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1. Specify the full path to the [LangGraph API config file](../reference/cli.md#configuration-file) including the file name. For example, if the file `langgraph.json` is in the root of the repository, simply specify `langgraph.json`.
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1. Specify the desired `git` reference (e.g. branch name). For example, different branches of the repository can be deployed.
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1. Select the desired `Deployment Type`.
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1. `Development` deployments are meant for non-production use cases and are provisioned with minimal resources.
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1. `Production` deployments can serve up to 500 requests/second and are provisioned with highly available storage with automatic backups.
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1. Specify `Environment Variables` and secrets.
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1. Sensitive values such as API keys (e.g. `OPENAI_API_KEY`) should be specified as secrets.
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1. Additional non-secret environment variables can be specified as well.
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1. A new LangSmith `Tracing Project` is automatically created with the same name as the deployment.
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1. In the top-right corner, select `Submit`. After a few seconds, the `Deployment` view appears and the new deployment will be queued for provisioning.
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## Create New Revision
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When [creating a new deployment](#create-a-new-deployment), a new revision is created by default. Subsequent revisions can be created to deploy new code changes.
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Starting from the <a href="https://smith.langchain.com/" target="_blank">LangSmith UI</a>...
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1. In the left-hand navigation panel, select `Deployments`. The `Deployments` view contains a list of existing LangGraph Cloud deployments.
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1. Select an existing deployment to create a new revision for.
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1. In the `Deployment` view, in the top-right corner, select `+ New Revision`.
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1. In the `New Revision` modal, fill out the required fields.
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1. Specify the full path to the [LangGraph API config file](../reference/cli.md#configuration-file) including the file name. For example, if the file `langgraph.json` is in the root of the repository, simply specify `langgraph.json`.
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1. Specify the desired `git` reference (e.g. branch name). For example, different branches of the repository can be deployed.
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1. Specify `Environment Variables` and secrets. Existing secrets and environment variables are prepopulated.
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1. Add new secrets or environment variables.
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1. Remove existing secrets or environment variables.
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1. Update the value of existing secrets or environment variables.
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1. Select `Submit`. After a few seconds, the `New Revision` modal will close and the new revision will be queued for deployment.
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## Asynchronous Deployment
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New [deployments](#create-new-deployment) and [revisions](#create-new-revision) are provisioned and deployed asynchronously. They are not deployed immediately after submission. Currently, deployment can take up to several minutes.
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The `Deployment` view continually updates the status of pending revisions.
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### Run your server locally
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# How to Self-Host LangGraph Cloud API
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First, make sure that Docker is up and running. Test that your server works by running:
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```python
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langgraph up -c langgraph.json
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```
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This will bring up a local server with your graph! Access the auto-generated server for your playground to confirm everything works as planned at [http://localhost:8124](http://localhost:8124) .
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Coming soon...
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# How to Set Up a LangGraph Application for Deployment
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A LangGraph application must be configured with a [LangGraph API configuration file](../reference/cli.md#configuration-file) in order to be deployed to LangGraph Cloud (or to be self-hosted). This how-to guide discusses the basic steps to setup a LangGraph application for deployment.
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After each step, an example file directory is provided to demonstrate how code can be organized.
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## Specify Dependencies
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Dependencies can optionally be specified in one of the following files: `pyproject.toml`, `setup.py`, or `requirements.txt`. If neither of these files is created, then dependencies can be specified later in the [LangGraph API configuration file](#create-langgraph-api-config).
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Example `requirements.txt` file:
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```
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langgraph
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langchain_openai
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```
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Example file directory:
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```
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my-app/
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|-- requirements.txt # Python packages required for your graph
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```
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## Specify Environment Variables
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Environment variables can optionally be specified in a file (e.g. `.env`).
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Example `.env` file:
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```
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MY_ENV_VAR_1=foo
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MY_ENV_VAR_2=bar
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```
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Example file directory:
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```
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my-app/
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|-- requirements.txt
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|-- .env # file with environment variables
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```
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## Define Graphs
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Implement your graphs! Graphs can be defined in a single file or multiple files. Make note of the variable names of each [CompiledGraph](../../../reference/graphs/#compiledgraph) to be included in the LangGraph application. The variable names will be used later when creating the [LangGraph API configuration file](../reference/cli.md#configuration-file).
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Example `openai_agent.py` file:
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```python
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from langchain_openai import ChatOpenAI
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from langgraph.graph import END, MessageGraph
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model = ChatOpenAI(temperature=0)
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graph_workflow = MessageGraph()
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graph_workflow.add_node("agent", model)
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graph_workflow.add_edge("agent", END)
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graph_workflow.set_entry_point("agent")
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agent = graph_workflow.compile()
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```
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Example file directory:
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```
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my-app/
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|-- requirements.txt
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|-- .env
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|-- openai_agent.py # code for your graph
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|-- anthropic_agent.py # code for your graph
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```
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## Create LangGraph API Config
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Create a [LangGraph API configuration file](../reference/cli.md#configuration-file) called `langgraph.json`. See the [LangGraph CLI reference](../reference/cli.md#configuration-file) for detailed explanations of each key in the JSON object of the configuration file.
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Example `langgraph.json` file:
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```json
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{
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"dependencies": [
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"./my-app"
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],
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"graphs": {
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"openai_agent": "./openai_agent.py:agent",
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"anthropic_agent": "./anthropic_agent.py:agent"
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},
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"env": ".env"
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}
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```
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Example file directory:
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```
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my-app/
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|-- requirements.txt
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|-- .env
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|-- openai_agent.py
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|-- anthropic_agent.py
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|-- langgraph.json # configuration file for LangGraph
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```
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## Upload to GitHub
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To deploy the LangGraph application to LangGraph Cloud, the code must be uploaded to a GitHub repository.
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+4
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@@ -171,10 +171,11 @@ nav:
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- 'cloud/index.md'
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- Tutorials:
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- Quick Start: 'cloud/quick_start.md'
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- Deployment:
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- Self-Hosted: 'cloud/deployment/self_hosted.md'
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- Managed: 'cloud/deployment/managed.md'
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- How-to Guides:
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- Deployment:
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- Setup App: 'cloud/deployment/setup.md'
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- Deploy to Cloud: 'cloud/deployment/cloud.md'
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- Self-Host: 'cloud/deployment/self_hosted.md'
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- Streaming:
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- Stream Values: 'cloud/how-tos/cloud_examples/stream_values.ipynb'
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- Stream Updates: 'cloud/how-tos/cloud_examples/stream_updates.ipynb'
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