I have recently been developing in a graph-based domain-specific language at work that powers most of the user onboarding flows. It was created in-house and has evolved over the years. I wanted to share some interesting learnings about the language itself as well as improvements that we made to enhance developer efficiency and language safety.

Language and Framework

The domain-specific language was designed to facilitate building workflows in a visual manner. In a user onboarding flow, there are various sequential steps that the user must go through. For example, we may first show the user a screen to enter their phone number. Once they do that, we run some logic behind the scenes to generate and send a one-time password. Then, we present the user with a screen to input the one-time password that they received.

flowchart LR Source([Source]) --> Pane[/Pane/] Pane -->|Action 1| Processor[Processor] Processor --> Switch{{Switch}} Switch -->|Case 1| Pane Switch -->|Case 2| Sink([Sink]) Pane -->|Action 2| Sink

The diagram above shows a very simple workflow that contains the main node types in the language. A workflow is a directed but not necessarily acyclic graph starting from a single source node and ending at one or more sink nodes. The framework is responsible for interpreting the workflow and maintaining state for sessions. Each traversal of the graph represents a unique session, and state between workflow sessions are kept separate. However, nodes are free to interact with other services to manipulate global system state outside of the workflow.

Some nodes can read or write to session state through predefined primitives, which are just protocol buffer object types. Each type effectively operates as a unique variable name. The framework handles populating input primitives before invoking a node and updating the workflow state based on a node’s output primitives. This does require that nodes specify input and output primitives, but this is similar to defining the signature for a function.

We describe each of the node types in more detail.

  • Pane: This node type is used to display information to users or retrieve user input. It allows zero or more input and output primitives. Generally, a pane can read global system state but not mutate it. All outputs should be written to the session state. A pane can have different output types depending on the action that is taken (e.g., the submit and exit buttons may have different outputs).
  • Processor: This node type is used to run arbitrary business logic. It allows zero or more input and output primitives. A processor can mutate global system state (e.g., performing database writes) in addition to the session state.
  • Sink: This is a special node type that indicates to the framework that a workflow is complete. There may be more than one sink node with different designations to indicate to the framework what type of exit was taken.
  • Source: This is a special node type that is used to mark the start of the workflow. The framework will always begin executing a workflow from the source node.
  • Switch: This node type is used to handle conditional behavior in a workflow. It accepts exactly one input primitive and does not output anything. The framework will match the primitive’s value against each of the switch’s case values and select an edge to traverse. Each switch is required to have a default case.

Workflows are stored in JSON format. An example with some fields omitted is shown below. While this representation is easy for the framework to load and interpret, it is fairly difficult for developers to edit directly. Instead, we rely on a visual workflow editor that converts the JSON to a graph representation and allows developers to work through the visual representation instead.

    "edges": [
            "id": "edge_1",
            "from_node_id": "node_1",
            "to_node_id": "node_2",
    "processor_nodes": [
            "id": "node_1",
            "description": "Node 1",
            "outgoing_edge_id": "edge_1",
            "configuration": {},
    "switch_nodes": [
            "id": "node_2",
            "description": "Node 2",
            "condition_primitive": {},
            "cases": [
                    "value": 1,
                    "outgoing_edge_id": "edge_2",

At a high level, the language draws on inspirations from the node graph architecture1. However, it is not designed to be a low-code development workflow since many changes involve altering the underlying implementations of processors and panes. The graph representation enforces abstraction in the form of nodes, but also has various drawbacks that we will discuss in later sections.

Static Analysis

Static analysis can be performed on a workflow before it is published to minimize errors and unintended behavior. This is similar to what a compiler would do after transforming a program into a control-flow graph2. However, a workflow in this language is effectively already a control-flow graph where nodes are basic blocks. We do not introspect the implementation of processors and panes since they can be arbitrarily complex. Not all of the analysis types described below are implemented in production, but they are still interesting to explore.

Unset Inputs

One runtime error that can occur is when a node specifies an input primitive that has never been set in the session. Due to branching behavior introduced by switches, it is also possible that only some paths to a node will leave an input primitive unset. To avoid these issues, we want to warn developers when a node has an input that is not guaranteed to have been set.

flowchart LR Source([Source]) --> A["A():(p1)"] A --> B{{"B(p1):()"}} B --> C["C(p1):(p2)"] B --> D["D(p1):(p3)"] D --> E["E(p2):()"] C --> E E --> Sink([Sink])

In the example above, we show the input and output primitives for each node. E has a potentially unset input p2 since there is a path going through node D from the source to E where p2 is never set by any node. Using this observation, we can define a node n as having a potentially unset input p if there exists at least one path from the source to n where p is not the output of any nodes along that path.

For a given node n and input p, we can determine if that input is potentially unset by using depth-first search to identify a path from n back to the source in the transpose graph3 considering only nodes that do not output p. If such a path exists, then p is a potentially unset input for n, and the path is one traversal where p will not be set. This approach is not efficient if done for every node and input in the workflow.

Another approach is to use data-flow analysis4 to determine the set of primitive that are guaranteed to be assigned before the entry point of every node. We can perform a forward analysis starting with the empty set at the source. We define a transfer function for each node where the exit set is the union of the entry set and the node’s output primitives. We also define a meet operation that combines the exit sets of predecessors by computing their intersection. Potentially unset inputs can be determined by computing the set difference between a node’s input primitives and the guaranteed-assigned primitives before the entry point of that node.

Unused Outputs

A node can set an output primitive that will never be used by another node. This does not cause any runtime errors, but could indicate that the developer forgot to add new nodes or additional inputs to existing nodes. It is also possible for the output primitive of a node to be ineffective in that it will be overwritten by the output of another node prior to its first use. We want to avoid both of these cases because polluting the session state increases the likelihood of bugs.

flowchart LR Source([Source]) --> A["A():(p1,p2)"] A --> B{{"B(p1):()"}} B --> C["C():(p2,p3)"] B --> D["D():(p2)"] C --> E["E(p2):()"] D --> E E --> Sink([Sink])

In the example above, p3 is unused since there are no nodes after C which has p3 as an input. p2 is also unused from A because it is overwritten by C and D prior to its first use in E. We define an output p as potentially used from node n if there is a path from n to the sink (assuming that the sink is always reachable from every node) where p is used as an input prior to reassignment.

We can use depth-first search starting from n. We ignore the successors of any nodes where p is set as an output since it would indicate that p is reassigned. If we encounter any node where p is used as an input, exit immediately because we know that p is potentially used. After the search terminates and has visited all reachable nodes starting from n without finding a node that uses p, we can assert that p is unused from n.

Data-flow analysis can also be used to determine the set of primitives that are potentially used after the exit point of each node. This is the same problem as live variable analysis5 with use/def of variables corresponding to inputs/outputs of nodes. Once the live variables are obtained for each node, unused outputs can be determined by computing the set difference between a node’s output primitives and the potentially-used primitives after the exit point of that node.

Unreachable Nodes

If a node is not reachable from the source, then it will never be executed, and we should not include it in the workflow. This usually happens when new nodes are added to a workflow but are not connected to the remainder of the workflow correctly.

flowchart LR Source([Source]) --> A A --> B C --> D D --> E{{E}} E --> B E --> C B --> Sink([Sink])

Unreachable nodes can be found by performing depth-first search starting from the source. All reachable nodes will be marked by the search. Any remaining nodes are guaranteed to be unreachable. In the example above, nodes C, D, E are unreachable. Note that it is not enough to just validate that each node has an input edge since cycles are permitted.

We can do slightly better if we are able to prove that certain switch branches are never taken. However, due to the lack of node introspection, we can only assert basic properties such as whether a primitive is definitely not set. To do this, we can examine the transpose graph and use depth-first search to verify that no visited nodes output the given primitive.

Infinite Loops

There are cases where an infinite loop can be introduced in a workflow. In general, we cannot determine whether a cycle in the graph will terminate, but we can determine if a cycle will never be able to reach the sink. There are runtime limits on the total number of visits to a node in a given session, but we would like to detect some infinite loops statically.

flowchart LR Source([Source]) --> A A --> B{{B}} B --> C C --> Sink([Sink]) B --> D{{D}} D --> A D --> E{{E}} E --> F F --> G G --> E

In the example above, the cycle formed by A, B, D could be an infinite loop, but we cannot be sure because it is possible for the edge from B to C to be taken at some point. However, the cycle formed by E, F, G is a provable infinite loop because there is no way to reach the sink once the traversal hits one of these nodes.

We can perform depth-first search from every node to see if there is a path to the sink, but it is more efficient to perform a single search starting from the sink in the transpose graph. Any nodes that are not marked are either unreachable from the source or are participating in cycles that cannot reach the sink.

Workflow Transpilation

Over time, the largest workflows have grown to hundreds of nodes and thousands of edges. While we do have a visual workflow editor, we still found it increasingly difficult to understand workflows. Developer efficiency was negatively impacted, and engineers avoided editing the workflow whenever possible. We think there were a variety of causes that contributed to this.

  • There was no integration with the rest of the codebase. For example, navigating from a processor to its underlying implementation was a multi-step process that involved searching for the name of the processor in the IDE.
  • The inputs and outputs of a node were not clearly specified. It was difficult to see how data flowed through a workflow.
  • Due to limitations in the workflow editor, switch cases on enums used numbers instead of named representations, which made it very difficult to determine what the cases actually were.
  • The speed of the workflow editor on large graphs made editing and navigation cumbersome. It could take more than a second for some changes to be displayed. Redesigning the editor to have better performance would take substantial engineering investment.
  • Reviewing workflow changes was difficult due to the JSON representation. PRs often had images attached to show relevant routing changes, but it was easy for those images to be out of date or not comprehensive.

To address these issues, we created a tool to transpile workflows into compilable (but not necessarily executable) Go code. By choosing a good representation, we can leverage existing IDE capabilities for navigation and analysis. We model each node as a function, and edges are encoded as function calls. This inverts how we perceive a workflow. Instead of the framework invoking nodes, the nodes will invoke virtual framework functions and handle their own control flow explicitly.

// Framework represents the workflow framework and contains various helper functions
// that nodes can use to interact with the state or run node logic.
var Framework struct {
	State     State
	Pane      func(input any, renderFn any) (action string, output any)
	Processor func(input any, processorFn any) (output any)

// State contains all primitives that are used as inputs or outputs of any node.
type State struct {
    Authenticated *primitives.Authenticated
    Credentials   *primitives.Credentials

// id: get_credentials_pane
func Pane_GetCredentials() {
    input := &panes.CredentialsPane_Input{}
    renderFn := (*panes.CredentialsPane).Render
    action, output := Framework.Pane(input, renderFn)
    switch action {
    case "Exit":
        // Call function to go to the exit sink node. 
    case "Submit":
        output := output.(*panes.CredentialsPane_Actions_Submit_Output)
        Framework.State.Credentials = output.Credentials

// id: check_credentials_processor
func Processor_CheckCredentials() {
    input := &processors.CheckCredentialsProcessor_Input{
        Credentials: Framework.State.Credentials,
    processFn := (*processors.CheckCredentialsProcessor).Process
    output := Framework.Processor(input, processFn).
    Framework.State.Authenticated = output.Authenticated

// id: is_authenticated_switch
func Switch_IsAuthenticated() {
    switch Framework.State.Authenticated.IsAuthenticated {
    case true:
        // Call function to go to the next node.

The code above shows an example output of workflow transpilation where the user is asked to input their credentials for validation. The IDs of nodes are annotated above each function to allow for easy lookup. The function names are derived from the description of nodes. Processors and panes can invoke the corresponding framework functions to run logic associated with the nodes. Input primitives are explicitly defined in each function, and output primitives are explicitly written into the state one field at a time after the node’s logic runs. Switches directly access the framework state variables. We ensure that the transpiled code is stable to allow diffing (i.e., switch cases and functions are always ordered in a consistent manner).

We currently expose a command line tool for transpiling all workflows, but hope to integrate it as part of the build and lint process so that transpiled code is kept up to date in the repository. There are various benefits of this representation, especially if transpilation is automated.

  • It is possible to quickly navigate to the next node or find all callers of a given node since each node is just a function.
  • Data-flow analysis (including nested primitive fields) is now easy to perform with the IDE. Reads and writes for any field can be shown for the entire repository.
  • Navigating to the underlying implementation for processors or panes can be done with one click while exploring a workflow.
  • Processor and pane usages can be tracked across workflows. Previously, this was difficult to do since there was no repository integration.
  • Workflow changes are now easier to diff since the transpiled output is easier to understand and explore in the IDE compared to the JSON representation.

Note that the resulting Go code is not designed to be transpiled back into the JSON format, although we are considering moving away from the JSON format entirely in favor of a builder pattern in Go. However, there are tradeoffs between representations that are easy for humans to understand versus easy for machines to parse and compile. We think that there will still be merits to the transpiled representation even if the underlying language representation is changed.

Refactoring with Subgraphs

Recently, we realized that there was no way to compose nodes into reusable components. As a result, two undesirable properties emerged. First, groups of nodes were duplicated in workflows. This meant that subtle bugs could emerge if changes were introduced in one part of the workflow that were not reflected in duplicated node groups. Second, in an effort to not duplicate nodes, processors became increasingly overloaded with branching behavior. This made it hard to statically validate routing since switches were scattered everywhere, each with many cases to consider.

We introduced the concept of a subgraph in the language. A subgraph is a collection of nodes similar to a workflow with one source and one or more sinks. At runtime, the subgraph effectively behaves as if the nodes inside it were copied over to every usage. The source and sink nodes of a subgraph only exist for routing and logging purposes. Subgraphs allowed teams to build out reusable components and be confident that changes would be propagated to all usages in different workflows.

flowchart LR subgraph Subgraph B --> C{{C}} C --> D end Source([Source]) --> A A --> B C --> Sink([Sink]) D --> E{{E}} E --> Sink E --> C

However, refactoring existing workflows to pull out reusable subgraphs is still challenging due to the sheer complexity of these graphs. Our team was tasked with pulling out user authentication into a subgraph since it is shared across multiple workflows and would allow the existing workflows to be wired up in a more understandable way. The example above shows a very simple workflow with a subgraph containing multiple exit edges (corresponding to sinks in the subgraph). If we are able to build a subgraph, then B, C, D would be collapsed to a single node. In our case, there were around 60 nodes in a workflow containing 450 nodes that we wanted to collapse.

There is a problematic edge in the example subgraph from E to C because subgraphs can only have one entry point, and E points to a node in the middle of the subgraph. This is easy to spot in a small example, but much harder to do in a complex workflow. Given the set of nodes that should be in a subgraph, we need to identify all edges that go from a node outside of the subgraph to a node inside the subgraph which is not connected to the subgraph source node. We then need to reason about these edges individually to remove them or route them to the start of the subgraph.

Another analysis that we want to perform is identifying all nodes that cannot reach any node in the subgraph. We generally do not need to consider these nodes at all while refactoring since they have no impact on the subgraph. This can be done by performing depth-first search in the transpose graph starting from an additional super node that can reach all nodes in the subgraph. Once the search is complete, all unmarked nodes are ones which cannot reach the subgraph.

To perform the actual migration once the subgraph is built, we can place a switch right before the entry point of the subgraph that routes to either the subgraph or the existing expanded nodes in the workflow. After validating that no traffic is flowing through the nodes duplicated by the subgraph, the existing nodes in the workflow can be deleted.


  1. Wikipedia (2024). Node graph architecture

  2. University of Toronto (2017). 17.8 Application: Control Flow Graphs

  3. Wikipedia (2024). Transpose graph

  4. Carnegie Mellon University (2011). Introduction to Data Flow Analysis

  5. Cornell University (2022). Live Variable Analysis