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AI / Apache Burr Interview questions

When would you choose a RunnableGraph over a single action for a parallel branch?

Choose a RunnableGraph when the thing you want to fan out isn’t a single computation but a small multi-step subflow — for example, a "process the prompt, then call the LLM" pair of actions that should run together as one parallel branch.

graph = (
    GraphBuilder()
    .with_action(process_prompt=process_prompt, query_llm=query_llm)
    .with_transitions(("process_prompt", "query_llm"))
    .build()
)

runnable_graph = RunnableGraph(
    graph=graph,
    entrypoint="process_prompt",
    halt_after=["query_llm"],
)

class TestMultiplePromptsWithSubgraph(MapStates):
    def action(self, state, inputs):
        return runnable_graph
    def states(self, state, context, inputs):
        for prompt in [...]:
            yield state.update(prompt=prompt)
    ...

You return the RunnableGraph from .action()/.actions() exactly where you would otherwise return a plain Action — Burr treats it the same way under the hood, as a sub-application driven by recursion. Use a single action when one function is genuinely sufficient; reach for RunnableGraph as soon as that branch needs more than one step of its own logic.

A RunnableGraph is the right tool when a parallel branch needs to:
A RunnableGraph requires you to specify which two things beyond the Graph itself?

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