Keycloak custom auth flows let you ditch the pre-baked login journeys and craft entirely bespoke authentication experiences, but the real magic is how they transform authentication from a rigid gatekeeper into a dynamic, stateful conversation with your users.
Let’s see this in action. Imagine we want to implement a "two-step registration" flow where a user first provides an email, we check if it’s already in use, and only then prompt for a password.
Here’s a simplified representation of a custom flow definition in Keycloak’s admin console (you’d typically interact with this via the Admin REST API or by building a custom SPI, but this illustrates the concept):
Flow: "Custom Registration"
1. "Email Check" (Authentication SPI)
* Condition: "Always True"
* Action: "Check Email Exists" (Custom Authenticator)
* If email exists: Go to "Browser - Login" flow
* If email does not exist: Proceed to next step
2. "Password" (Authentication SPI)
* Condition: "New User"
* Action: "Create User" (Built-in Authenticator)
* If successful: Proceed to next step
3. "Terms Acceptance" (Authentication SPI)
* Condition: "Always True"
* Action: "Terms Acceptance Form" (Custom Authenticator)
* If accepted: Proceed to "Success"
* If not accepted: Go to "Terms Rejected" page
This isn’t just a sequence of checks; it’s a state machine. Keycloak’s authentication SPI (Service Provider Interface) allows you to plug in custom Java code that can inspect the current authentication context, interact with user data, and crucially, decide the next step in the flow based on dynamic conditions.
The problem this solves is the inflexibility of standard login/registration. Need to integrate with an external identity provider conditionally? Want to enforce a specific password policy only for certain user groups? Need a multi-factor authentication step that varies based on the user’s location? Custom flows are your answer.
Internally, Keycloak manages this using an AuthenticationSessionModel. This object tracks the state of a user’s login attempt across multiple steps. When your custom authenticator executes, it can read from and write to this session. For example, the "Email Check" authenticator might store the validated email in the AuthenticationSessionModel so the "Create User" step doesn’t need to ask for it again. The AuthenticationSessionModel is what allows the flow to be stateful and for different authenticators to share information.
The exact levers you control are the authenticators and the conditions that link them. An authenticator is a piece of code that performs an authentication action (like checking a password, sending an OTP, or rendering a custom form). A condition determines whether an authenticator is executed. Keycloak provides many built-in conditions (e.g., New User, User is authenticated, Client is in list) and you can write custom ones. By chaining these with specific logic, you build the desired authentication journey.
When you define a flow, you’re essentially building a directed graph where nodes are authenticators and edges are transitions based on conditions. Keycloak traverses this graph. If an authenticator succeeds, it might follow a "success" edge; if it fails or needs more input, it might follow an "alternative" edge or jump to a different part of the graph. You can create entirely new "sub-flows" that are invoked from within other flows, allowing for complex, modular authentication logic.
The most surprising thing about custom flows is how their state management is exposed. You’re not just writing code that does something; you’re writing code that decides what happens next by manipulating the AuthenticationSessionModel and returning specific AuthenticationFlowError codes or executing nextStep() methods. For instance, to force a user back to a previous step or to a completely different flow, you don’t just return from your Java method; you use the context.challenge() or context.attempted() methods to re-render a specific form or trigger another authenticator, effectively controlling the flow’s execution pointer dynamically.
The next hurdle you’ll likely face is debugging these custom flows, especially when dealing with complex conditional logic or state persistence.