ADK Workflow Multi-Triggers Sample

Overview

This sample demonstrates how a single node can fan out to execute multiple downstream nodes concurrently, and how multiple upstream nodes can trigger a single downstream node independently in ADK Workflows.

In this example, the START node fans out to three different processing functions (make_uppercase, count_characters, and reverse_string). Each of these functions receives the initial user input string, processes it, and then independently outputs its result.

Because the subsequent send_message node receives a continuous flow of outputs and does not use an aggregation mechanism (like JoinNode), it is triggered multiple times—once for every upstream event.

Sample Inputs

Hello World
ADK workflows
testing concurrent nodes

Graph

text

                  [ START ]
                /     |      \
               /      |       \
              v       v        v
[make_uppercase] [count_characters] [reverse_string]
              \       |        /
               \      |       /
                v     v      v
              [ send_message ]
             (Triggered 3 times)

How To

You can specify a tuple of nodes within an edge to create a parallel fan-out segment where the same input is provided to multiple nodes:
python
```
(
    "START",
    (make_uppercase, count_characters, reverse_string),
    # ...
)
```
By continuing the sequence to another node after the tuple, the outputs of all nodes in the tuple will independently trigger that target node:
python
```
(
    "START",
    (make_uppercase, count_characters, reverse_string),
    send_message,
)
```
In this case, send_message will be executed once for make_uppercase's output, once for count_characters's output, and once for reverse_string's output.