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Add custom authentication

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. It is not supported for Lite 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.

  1. 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
    }
    1. This handler receives the request (headers, etc.), validates the user, and returns a dictionary with at least an identity field.
    2. You can add any custom fields you want (e.g., OAuth tokens, roles, org IDs, etc.).

  2. 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"
}
}

  3. 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:

    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()

    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(...)

    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();

    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 -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.

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