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docs: General language cleanup (#4649)

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- README updates based on feedback
- Auth tutorial cleanup
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Lauren Hirata Singh
2025-05-12 06:11:08 +00:00
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docs/docs/tutorials/auth/getting_started.md
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# Setting up Custom Authentication (Part 1/3)
!!! note "This is part 1 of our authentication series:"
1. Basic Authentication (you are here) - Control who can access your bot
2. [Resource Authorization](resource_auth.md) - Let users have private conversations
3. [Production Auth](add_auth_server.md) - Add real user accounts and validate using OAuth2
!!! tip "Prerequisites"
This guide assumes basic familiarity with the following concepts:
* [**Authentication & Access Control**](../../concepts/auth.md)
* [**LangGraph Platform**](../../concepts/langgraph_platform.md)
!!! note "Python only"
We currently only support custom authentication and authorization in Python deployments with `langgraph-api>=0.0.11`. Support for LangGraph.JS will be added soon.
???+ note "Support by deployment type"
Custom auth is supported for all deployments in the **managed LangGraph Cloud**, as well as **Enterprise** self-hosted plans. It is not supported for **Lite** self-hosted plans.
# Set up custom authentication
In this tutorial, we will build a chatbot that only lets specific users access it. We'll start with the LangGraph template and add token-based security step by step. By the end, you'll have a working chatbot that checks for valid tokens before allowing access.
## Setting up our project
This is part 1 of our authentication series:
First, let's create a new chatbot using the LangGraph starter template:
1. Set up custom authentication (you are here) - Control who can access your bot
2. [Make conversations private](resource_auth.md) - Let users have private conversations
3. [Connect an authentication provider](add_auth_server.md) - Add real user accounts and validate using OAuth2 for production
This guide assumes basic familiarity with the following concepts:
* [**Authentication & Access Control**](../../concepts/auth.md)
* [**LangGraph Platform**](../../concepts/langgraph_platform.md)
!!! note
Custom auth is only available for LangGraph Cloud SaaS deployments or Enterprise Self-Hosted deployments.
## 1. Create your app
Create a new chatbot using the LangGraph starter template:
```bash
pip install -U "langgraph-cli[inmem]"
@@ -34,37 +27,40 @@ langgraph new --template=new-langgraph-project-python custom-auth
cd custom-auth
```
The template gives us a placeholder LangGraph app. Let's try it out by installing the local dependencies and running the development server.
The template gives us a placeholder LangGraph app. Try it out by installing the local dependencies and running the development server:
```shell
pip install -e .
langgraph dev
```
If everything works, the server should start and open the studio in your browser.
The server will start and open the studio in your browser:
```
> - 🚀 API: http://127.0.0.1:2024
> - 🎨 Studio UI: https://smith.langchain.com/studio/?baseUrl=http://127.0.0.1:2024
> - 📚 API Docs: http://127.0.0.1:2024/docs
>
> This in-memory server is designed for development and testing.
> For production use, please use LangGraph Cloud.
```
The graph should run, and if you were to self-host this on the public internet, anyone could access it!
If you were to self-host this on the public internet, anyone could access it!
![No auth](./img/no_auth.png)
Now that we've seen the base LangGraph app, let's add authentication to it!
???+ tip "Placeholder token"
In part 1, we will start with a hard-coded token for illustration purposes.
We will get to a "production-ready" authentication scheme in part 3, after mastering the basics.
## 2. Add authentication
Now that you have a base LangGraph app, add authentication to it.
## Adding Authentication
!!! note
In this tutorial, you will start with a hard-coded token for example purposes. You will get to a "production-ready" authentication scheme in the third tutorial.
The [`Auth`](../../cloud/reference/sdk/python_sdk_ref.md#langgraph_sdk.auth.Auth) object lets you register an authentication function that the LangGraph platform will run on every request. This function receives each request and decides whether to accept or reject.
Create a new file `src/security/auth.py`. This is where our code will live to check if users are allowed to access our bot:
Create a new file `src/security/auth.py`. This is where your code will live to check if users are allowed to access your bot:
```python hl_lines="10 15-16" title="src/security/auth.py"
from langgraph_sdk import Auth
@@ -98,12 +94,12 @@ async def get_current_user(authorization: str | None) -> Auth.types.MinimalUserD
}
```
Notice that our [authentication](../../cloud/reference/sdk/python_sdk_ref.md#langgraph_sdk.auth.Auth.authenticate) handler does two important things:
Notice that your [authentication](../../cloud/reference/sdk/python_sdk_ref.md#langgraph_sdk.auth.Auth.authenticate) handler does two important things:
1. Checks if a valid token is provided in the request's [Authorization header](https://developer.mozilla.org/en-US/docs/Web/HTTP/Headers/Authorization)
2. Returns the user's [identity](../../cloud/reference/sdk/python_sdk_ref.md#langgraph_sdk.auth.types.MinimalUserDict)
Now tell LangGraph to use our authentication by adding the following to the [`langgraph.json`](../../cloud/reference/cli.md#configuration-file) configuration:
Now tell LangGraph to use authentication by adding the following to the [`langgraph.json`](../../cloud/reference/cli.md#configuration-file) configuration:
```json hl_lines="7-9" title="langgraph.json"
{
@@ -118,28 +114,28 @@ Now tell LangGraph to use our authentication by adding the following to the [`la
}
```
## Testing Our "Secure" Bot
## 3. Test your bot
Let's start the server again to test everything out!
Start the server again to test everything out:
```bash
langgraph dev --no-browser
```
??? note "Custom auth in the studio"
If you didn't add the `--no-browser`, the studio UI will open in the browser. You may wonder, how is the studio able to still connect to our server? By default, we also permit access from the LangGraph studio, even when using custom auth. This makes it easier to develop and test your bot in the studio. You can remove this alternative authentication option by setting `disable_studio_auth: "true"` in your auth configuration:
If you didn't add the `--no-browser`, the studio UI will open in the browser. You may wonder, how is the studio able to still connect to our server? By default, we also permit access from the LangGraph studio, even when using custom auth. This makes it easier to develop and test your bot in the studio. You can remove this alternative authentication option by
setting `disable_studio_auth: "true"` in your auth configuration:
```json
{
"auth": {
"path": "src/security/auth.py:auth",
"disable_studio_auth": "true"
}
```json
{
"auth": {
"path": "src/security/auth.py:auth",
"disable_studio_auth": "true"
}
```
}
```
Now let's try to chat with our bot. If we've implemented authentication correctly, we should only be able to access the bot if we provide a valid token in the request header. Users will still, however, be able to access each other's resources until we add [resource authorization handlers](../../concepts/auth.md#resource-specific-handlers) in the next section of our tutorial.
## 4. Chat with your bot
You should now only be able to access the bot if you provide a valid token in the request header. Users will still, however, be able to access each other's resources until you add [resource authorization handlers](../../concepts/auth.md#resource-specific-handlers) in the next section of the tutorial.
![Authentication, no authorization handlers](./img/authentication.png)
@@ -181,10 +177,10 @@ You should see that:
Congratulations! You've built a chatbot that only lets "authenticated" users access it. While this system doesn't (yet) implement a production-ready security scheme, we've learned the basic mechanics of how to control access to our bot. In the next tutorial, we'll learn how to give each user their own private conversations.
## What's Next?
## Next steps
Now that you can control who accesses your bot, you might want to:
1. Continue the tutorial by going to [Making Conversations Private (Part 2/3)](resource_auth.md) to learn about resource authorization.
1. Continue the tutorial by going to [Make cnversations private](resource_auth.md) to learn about resource authorization.
2. Read more about [authentication concepts](../../concepts/auth.md).
3. Check out the [API reference](../../cloud/reference/sdk/python_sdk_ref.md) for more authentication details.