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Eugene YurtsevandGitHub e3cb2dd23b chore(docs): fix more admonitions (#5792 )

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Add custom authentication

!!! tip "Prerequisites"

This guide assumes familiarity with the following concepts:

  *  [**Authentication & Access Control**](../../concepts/auth.md)
  *  [**LangGraph Platform**](../../concepts/langgraph_platform.md)

For a more guided walkthrough, see [**setting up custom authentication**](../../tutorials/auth/getting_started.md) tutorial.

???+ note "Support by deployment type"

Custom auth is supported for all deployments in the **managed LangGraph Platform**, as well as **Enterprise** self-hosted plans.

This guide shows how to add custom authentication to your LangGraph Platform application. This guide applies to both LangGraph Platform and self-hosted deployments. It does not apply to isolated usage of the LangGraph open source library in your own custom server.

!!! note

Custom auth is supported for all **managed LangGraph Platform** deployments, as well as **Enterprise** self-hosted plans.

Add custom authentication to your deployment

To leverage custom authentication and access user-level metadata in your deployments, set up custom authentication to automatically populate the config["configurable"]["langgraph_auth_user"] object through a custom authentication handler. You can then access this object in your graph with the langgraph_auth_user key to allow an agent to perform authenticated actions on behalf of the user.

:::python

Implement authentication:

!!! note

Without a custom `@auth.authenticate` handler, LangGraph sees only the API-key owner (usually the developer), so requests aren’t scoped to individual end-users. To propagate custom tokens, you must implement your own handler.

from langgraph_sdk import Auth
import requests

auth = Auth()

def is_valid_key(api_key: str) -> bool:
    is_valid = # your API key validation logic
    return is_valid

@auth.authenticate # (1)!
async def authenticate(headers: dict) -> Auth.types.MinimalUserDict:
    api_key = headers.get("x-api-key")
    if not api_key or not is_valid_key(api_key):
        raise Auth.exceptions.HTTPException(status_code=401, detail="Invalid API key")

    # Fetch user-specific tokens from your secret store
    user_tokens = await fetch_user_tokens(api_key)

    return { # (2)!
        "identity": api_key,  #  fetch user ID from LangSmith
        "github_token" : user_tokens.github_token
        "jira_token" : user_tokens.jira_token
        # ... custom fields/secrets here
    }

This handler receives the request (headers, etc.), validates the user, and returns a dictionary with at least an identity field.
You can add any custom fields you want (e.g., OAuth tokens, roles, org IDs, etc.).

In your langgraph.json, add the path to your auth file:

{
  "dependencies": ["."],
  "graphs": {
    "agent": "./agent.py:graph"
  },
  "env": ".env",
  "auth": {
    "path": "./auth.py:my_auth"
  }
}

Once you've set up authentication in your server, requests must include the required authorization information based on your chosen scheme. Assuming you are using JWT token authentication, you could access your deployments using any of the following methods:

=== "Python Client"

```python
from langgraph_sdk import get_client

my_token = "your-token" # In practice, you would generate a signed token with your auth provider
client = get_client(
    url="http://localhost:2024",
    headers={"Authorization": f"Bearer {my_token}"}
)
threads = await client.threads.search()
```

=== "Python RemoteGraph"

```python
from langgraph.pregel.remote import RemoteGraph

my_token = "your-token" # In practice, you would generate a signed token with your auth provider
remote_graph = RemoteGraph(
    "agent",
    url="http://localhost:2024",
    headers={"Authorization": f"Bearer {my_token}"}
)
threads = await remote_graph.ainvoke(...)
```
```python
from langgraph.pregel.remote import RemoteGraph

my_token = "your-token" # In practice, you would generate a signed token with your auth provider
remote_graph = RemoteGraph(
    "agent",
    url="http://localhost:2024",
    headers={"Authorization": f"Bearer {my_token}"}
)
threads = await remote_graph.ainvoke(...)
```

=== "CURL"

```bash
curl -H "Authorization: Bearer ${your-token}" http://localhost:2024/threads
```

Enable agent authentication

After authentication, the platform creates a special configuration object (config) that is passed to LangGraph Platform deployment. This object contains information about the current user, including any custom fields you return from your @auth.authenticate handler.

To allow an agent to perform authenticated actions on behalf of the user, access this object in your graph with the langgraph_auth_user key:

def my_node(state, config):
    user_config = config["configurable"].get("langgraph_auth_user")
    # token was resolved during the @auth.authenticate function
    token = user_config.get("github_token","")
    ...

!!! note

Fetch user credentials from a secure secret store. Storing secrets in graph state is not recommended.

Authorizing a Studio user

By default, if you add custom authorization on your resources, this will also apply to interactions made from the Studio. If you want, you can handle logged-in Studio users differently by checking is_studio_user().

!!! note is_studio_user was added in version 0.1.73 of the langgraph-sdk. If you're on an older version, you can still check whether isinstance(ctx.user, StudioUser).

from langgraph_sdk.auth import is_studio_user, Auth
auth = Auth()

# ... Setup authenticate, etc.

@auth.on
async def add_owner(
    ctx: Auth.types.AuthContext,
    value: dict  # The payload being sent to this access method
) -> dict:  # Returns a filter dict that restricts access to resources
    if is_studio_user(ctx.user):
        return {}

    filters = {"owner": ctx.user.identity}
    metadata = value.setdefault("metadata", {})
    metadata.update(filters)
    return filters

Only use this if you want to permit developer access to a graph deployed on the managed LangGraph Platform SaaS.

:::

:::js

Implement authentication:

!!! note

Without a custom `authenticate` handler, LangGraph sees only the API-key owner (usually the developer), so requests aren’t scoped to individual end-users. To propagate custom tokens, you must implement your own handler.

import { Auth, HTTPException } from "@langchain/langgraph-sdk/auth";

const auth = new Auth()
  .authenticate(async (request) => {
    const authorization = request.headers.get("Authorization");
    const token = authorization?.split(" ")[1]; // "Bearer <token>"
    if (!token) {
      throw new HTTPException(401, "No token provided");
    }
    try {
      const user = await verifyToken(token);
      return user;
    } catch (error) {
      throw new HTTPException(401, "Invalid token");
    }
  })
  // Add authorization rules to actually control access to resources
  .on("*", async ({ user, value }) => {
    const filters = { owner: user.identity };
    const metadata = value.metadata ?? {};
    metadata.update(filters);
    return filters;
  })
  // Assumes you organize information in store like (user_id, resource_type, resource_id)
  .on("store", async ({ user, value }) => {
    const namespace = value.namespace;
    if (namespace[0] !== user.identity) {
      throw new HTTPException(403, "Not authorized");
    }
  });

This handler receives the request (headers, etc.), validates the user, and returns an object with at least an identity field.
You can add any custom fields you want (e.g., OAuth tokens, roles, org IDs, etc.).

In your langgraph.json, add the path to your auth file:

{
  "dependencies": ["."],
  "graphs": {
    "agent": "./agent.ts:graph"
  },
  "env": ".env",
  "auth": {
    "path": "./auth.ts:my_auth"
  }
}

=== "SDK Client"

```javascript
import { Client } from "@langchain/langgraph-sdk";

const my_token = "your-token"; // In practice, you would generate a signed token with your auth provider
const client = new Client({
  apiUrl: "http://localhost:2024",
  defaultHeaders: { Authorization: `Bearer ${my_token}` },
});
const threads = await client.threads.search();
```

=== "RemoteGraph"

```javascript
import { RemoteGraph } from "@langchain/langgraph/remote";

const my_token = "your-token"; // In practice, you would generate a signed token with your auth provider
const remoteGraph = new RemoteGraph({
graphId: "agent",
  url: "http://localhost:2024",
  headers: { Authorization: `Bearer ${my_token}` },
});
const threads = await remoteGraph.invoke(...);
```

=== "CURL"

```bash
curl -H "Authorization: Bearer ${your-token}" http://localhost:2024/threads
```

Enable agent authentication

To allow an agent to perform authenticated actions on behalf of the user, access this object in your graph with the langgraph_auth_user key:

async function myNode(state, config) {
  const userConfig = config["configurable"]["langgraph_auth_user"];
  // token was resolved during the authenticate function
  const token = userConfig["github_token"];
  ...
}

!!! note

Fetch user credentials from a secure secret store. Storing secrets in graph state is not recommended.

:::

11 KiB Raw Blame History Unescape Escape

Add custom authentication

Add custom authentication to your deployment

Enable agent authentication

Authorizing a Studio user

Enable agent authentication

Learn more

11 KiB

Raw Blame History