ADK Workflow Fan-Out / Fan-In Sample
Overview
This sample demonstrates how to run multiple nodes in parallel and aggregate their results using a Fan-Out / Fan-In pattern in ADK Workflows.
It takes an input string and fans out to three different processing functions concurrently: make_uppercase, count_characters, and reverse_string. Instead of independently triggering the downstream node (as seen in the multi_triggers sample), this workflow uses a JoinNode to wait for all the parallel processes to complete. Once all results are ready, the JoinNode packages them into a single dictionary and passes it to an aggregate node, which formats the final combined response.
In ADK Workflows, the JoinNode is a critical component for synchronizing parallel execution paths, ensuring that a downstream node only executes once all of its required upstream dependencies have furnished their outputs.
Sample Inputs
-
Hello World -
ADK workflows -
testing concurrent nodes
Graph
graph TD
START --> make_uppercase
START --> count_characters
START --> reverse_string
make_uppercase --> join_node[join_node <br/>Waits for all 3]
count_characters --> join_node
reverse_string --> join_node
join_node --> aggregate
How To
-
Define a
JoinNodein your code:from google.adk.workflow import JoinNode join_node = JoinNode(name="join_for_results") -
In the
Workflowedges definition, specify a tuple of nodes to fan out execution, followed by yourjoin_nodeto fan in the results, and finally the node that processes the aggregated output:( "START", (make_uppercase, count_characters, reverse_string), join_node, aggregate, ) -
The node following the
JoinNode(in this case,aggregate) will receive adictas its input. The keys of this dictionary are the names of the upstream nodes, and the values are their respective outputs:async def aggregate(node_input: dict[str, Any]): uppercase_result = node_input['make_uppercase'] # ...