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https://github.com/langchain-ai/langgraph.git
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docs: remove langgraph up references (#2847)
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@@ -5,7 +5,7 @@ LangGraph Cloud is available within <a href="https://www.langchain.com/langsmith
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## Prerequisites
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1. 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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1. [Verify that the LangGraph API runs locally](test_locally.md). If the API does not build and run successfully (i.e. `langgraph up`), deploying to LangGraph Cloud will fail as well.
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1. [Verify that the LangGraph API runs locally](test_locally.md). If the API does not run successfully (i.e. `langgraph dev`), deploying to LangGraph Cloud will fail as well.
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## Create New Deployment
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@@ -6,17 +6,11 @@ Testing locally ensures that there are no errors or conflicts with Python depend
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## Setup
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Install the proper packages:
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Install the LangGraph CLI package:
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=== "pip"
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```bash
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pip install -U langgraph-cli
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```
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=== "Homebrew (macOS only)"
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```bash
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brew install langgraph-cli
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```
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```bash
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pip install -U "langgraph-cli[inmem]"
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```
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Ensure you have an API key, which you can create from the [LangSmith UI](https://smith.langchain.com) (Settings > API Keys). This is required to authenticate that you have LangGraph Cloud access. After you have saved the key to a safe place, place the following line in your `.env` file:
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@@ -29,16 +23,26 @@ LANGSMITH_API_KEY = *********
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Once you have installed the CLI, you can run the following command to start the API server for local testing:
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```shell
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langgraph up
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langgraph dev
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```
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This will start up the LangGraph API server locally. If this runs successfully, you should see something like:
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```shell
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Ready!
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- API: http://localhost:8123
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2024-06-26 19:20:41,056:INFO:uvicorn.access 127.0.0.1:44138 - "GET /ok HTTP/1.1" 200
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```
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> Ready!
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>
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> - API: [http://localhost:2024](http://localhost:2024/)
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>
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> - Docs: http://localhost:2024/docs
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>
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> - LangGraph Studio Web UI: https://smith.langchain.com/studio/?baseUrl=http://127.0.0.1:2024
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!!! note "In-Memory Mode"
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The `langgraph dev` command starts LangGraph Server in an in-memory mode. This mode is suitable for development and testing purposes. For production use, you should deploy LangGraph Server with access to a persistent storage backend.
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If you want to test your application with a persistent storage backend, you can use the `langgraph up` command instead of `langgraph dev`. You will
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need to have `docker` installed on your machine to use this command.
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### Interact with the server
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@@ -53,7 +57,7 @@ You can either initialize by passing authentication or by setting an environment
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```python
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from langgraph_sdk import get_client
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# only pass the url argument to get_client() if you changed the default port when calling langgraph up
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# only pass the url argument to get_client() if you changed the default port when calling langgraph dev
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client = get_client(url=<DEPLOYMENT_URL>,api_key=<LANGSMITH_API_KEY>)
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# Using the graph deployed with the name "agent"
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assistant_id = "agent"
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@@ -65,7 +69,7 @@ You can either initialize by passing authentication or by setting an environment
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```js
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import { Client } from "@langchain/langgraph-sdk";
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// only set the apiUrl if you changed the default port when calling langgraph up
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// only set the apiUrl if you changed the default port when calling langgraph dev
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const client = new Client({ apiUrl: <DEPLOYMENT_URL>, apiKey: <LANGSMITH_API_KEY> });
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// Using the graph deployed with the name "agent"
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const assistantId = "agent";
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@@ -91,7 +95,7 @@ If you have a `LANGSMITH_API_KEY` set in your environment, you do not need to ex
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```python
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from langgraph_sdk import get_client
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# only pass the url argument to get_client() if you changed the default port when calling langgraph up
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# only pass the url argument to get_client() if you changed the default port when calling langgraph dev
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client = get_client()
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# Using the graph deployed with the name "agent"
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assistant_id = "agent"
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@@ -103,7 +107,7 @@ If you have a `LANGSMITH_API_KEY` set in your environment, you do not need to ex
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```js
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import { Client } from "@langchain/langgraph-sdk";
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// only set the apiUrl if you changed the default port when calling langgraph up
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// only set the apiUrl if you changed the default port when calling langgraph dev
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const client = new Client();
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// Using the graph deployed with the name "agent"
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const assistantId = "agent";
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