Lauren Hirata SinghandGitHub
a926450601
This adds redirects to new Mintlify site and should be merged when old LangGraph docs are deprecated (for v1)
7.8 KiB
Run a local server
This guide shows you how to run a LangGraph application locally.
Prerequisites
Before you begin, ensure you have the following:
- An API key for LangSmith - free to sign up
1. Install the LangGraph CLI
:::python
# Python >= 3.11 is required.
pip install --upgrade "langgraph-cli[inmem]"
:::
:::js
npx @langchain/langgraph-cli
:::
2. Create a LangGraph app 🌱
:::python
Create a new app from the new-langgraph-project-python template. This template demonstrates a single-node application you can extend with your own logic.
langgraph new path/to/your/app --template new-langgraph-project-python
!!! tip "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.
:::
:::js
Create a new app from the new-langgraph-project-js template. This template demonstrates a single-node application you can extend with your own logic.
npm create langgraph
:::
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
cd path/to/your/app
pip install -e .
:::
:::js
cd path/to/your/app
npm 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
langgraph dev
:::
:::js
npx @langchain/langgraph-cli dev
:::
Sample output:
> Ready!
>
> - API: [http://localhost:2024](http://localhost:2024/)
>
> - Docs: http://localhost:2024/docs
>
> - LangGraph 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 access to a persistent storage backend. For more information, see Deployment options.
6. Test your application in LangGraph Studio
LangGraph Studio is a specialized UI that you can connect to LangGraph API server to visualize, interact with, and debug your application locally. Test your graph in LangGraph Studio by visiting the URL provided in the output of the langgraph dev command:
> - LangGraph Studio Web UI: https://smith.langchain.com/studio/?baseUrl=http://127.0.0.1:2024
For a LangGraph Server running on a custom host/port, update the baseURL parameter.
??? info "Safari compatibility"
Use the `--tunnel` flag with your command to create a secure tunnel, as Safari has limitations when connecting to localhost servers:
```shell
langgraph dev --tunnel
```
7. Test the API
:::python === "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
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:
```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="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")
```
=== "Rest API"
```bash
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\"
}"
```
:::
:::js === "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");
// 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"
```bash
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\"
}"
```
:::
Next steps
Now that you have a LangGraph app running locally, take your journey further by exploring deployment and advanced features:
- Deployment quickstart: Deploy your LangGraph app using LangGraph Platform.
- LangGraph Platform overview: Learn about foundational LangGraph Platform concepts.
- LangGraph Server API Reference: Explore the LangGraph Server API documentation.
:::python
- Python SDK Reference: Explore the Python SDK API Reference. :::
:::js
- JS/TS SDK Reference: Explore the JS/TS SDK API Reference. :::