本地运行 LangGraph 应用程序

本快速入门教程向您展示如何在本地上设置LangGraph应用程序进行测试和开发。

前提条件

在开始之前，请确保您已注册LangSmith（免费注册）并获取了API密钥。

1. 安装 LangGraph CLI

Python server

# Python >= 3.11 is required.

pip install -U "langgraph-cli[inmem]"

Node server

npx @langchain/langgraph-cli

2. Create a LangGraph app 🌱

Create a new app from the new-langgraph-project-python template or new-langgraph-project-js template. This template demonstrates a single-node application you can extend with your own logic.

Python server

langgraph new path/to/your/app --template new-langgraph-project-python

Node server

langgraph new path/to/your/app --template new-langgraph-project-js

Additional templates
If you use langgraph new without specifying a template, you will be presented with an interactive menu that will allow you to choose from a list of available templates.

3. Install dependencies

In the root of your new LangGraph app, install the dependencies in edit mode so your local changes are used by the server:

Python server

cd path/to/your/app
pip install -e .

Node server

cd path/to/your/app
yarn install

4. Create a .env file

You will find a .env.example in the root of your new LangGraph app. Create a .env file in the root of your new LangGraph app and copy the contents of the .env.example file into it, filling in the necessary API keys:

LANGSMITH_API_KEY=lsv2...

5. Launch LangGraph Server 🚀

Start the LangGraph API server locally:

Python server

langgraph dev

Node server

npx @langchain/langgraph-cli dev

Sample output:

>    Ready!
>
>    - API: [http://localhost:2024](http://localhost:2024/)
>
>    - Docs: http://localhost:2024/docs
>
>    - Studio Web UI: https://smith.langchain.com/studio/?baseUrl=http://127.0.0.1:2024

The langgraph dev command starts LangGraph Server in an in-memory mode. This mode is suitable for development and testing purposes.

For production use, deploy LangGraph Server with a persistent storage backend. For more information, refer to the LangSmith hosting options.

6. Test the API

Python SDK (async)

1. Install the LangGraph Python SDK:

pip install langgraph-sdk

2. Send a message to the assistant (threadless run):

from langgraph_sdk import get_client
import asyncio

client = get_client(url="http://localhost:2024")

async def main():
    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?",
            }],
        },
    ):
        print(f"Receiving new event of type: {chunk.event}...")
        print(chunk.data)
        print("\n\n")

asyncio.run(main())

Python SDK (sync)

1. Install the LangGraph Python SDK:

pip install langgraph-sdk

2. Send a message to the assistant (threadless run):

from langgraph_sdk import get_sync_client

client = get_sync_client(url="http://localhost:2024")

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="messages-tuple",
):
    print(f"Receiving new event of type: {chunk.event}...")
    print(chunk.data)
    print("\n\n")

Javascript SDK

1. Install the LangGraph JS SDK:

npm install @langchain/langgraph-sdk

2. Send a message to the assistant (threadless run):

const { Client } = await import("@langchain/langgraph-sdk");

// only set the apiUrl if you changed the default port when calling langgraph dev
const client = new Client({ apiUrl: "http://localhost:2024"});

const streamResponse = client.runs.stream(
    null, // Threadless run
    "agent", // Assistant ID
    {
        input: {
            "messages": [
                { "role": "user", "content": "What is LangGraph?"}
            ]
        },
        streamMode: "messages-tuple",
    }
);

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

curl -s --request POST \
    --url "http://localhost:2024/runs/stream" \
    --header 'Content-Type: application/json' \
    --data "{
        \"assistant_id\": \"agent\",
        \"input\": {
            \"messages\": [
                {
                    \"role\": \"human\",
                    \"content\": \"What is LangGraph?\"
                }
            ]
        },
        \"stream_mode\": \"messages-tuple\"
    }"

下一步

现在您已经在本地上运行了 LangGraph 应用程序，您就可以准备部署它了：

为LangSmith选择一个托管选项：

• 云：快速设置，全面管理（推荐）。
• 混合：在您的云中运行时环境，您的LangGraph服务器和智能体执行的地方。数据平面，由LangChain管理的控制平面。
• 自托管：在您的基础设施中拥有完全控制权。

有关更多详细信息，请参阅托管比较。

然后部署您的应用：

• 部署到云快速入门：快速设置指南。
• 完整云部署指南：全面的部署文档。

探索功能：

• 工作室: 使用工作室UI可视化、交互和调试您的应用程序。尝试工作室快速入门。
• API参考：LangGraph服务器API，Python SDK，JS/TS SDK

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