Mastering LoopAgent: Orchestrating Multi‑Agent Workflows with ADK

LoopAgent Overview

LoopAgent is a component of the Agent Development Kit (ADK) that orchestrates a sequence of sub‑agents and repeats the sequence for a configurable number of iterations. This enables multi‑agent collaboration and iterative refinement for complex AI workflows.

Key Parameters

name – Identifier for the LoopAgent instance.

max_iterations – Upper bound on how many times the sub‑agent chain is executed. Example: max_iterations=3 limits the loop to three rounds.

sub_agents – Ordered list of instantiated sub‑agents that will be invoked each iteration. In the YouTube‑shorts example the list contains scriptwriter_agent, visualizer_agent, and formatter_agent.

Execution Model

During each iteration LoopAgent walks through sub_agents in the defined order, calling each agent’s run(state) (or equivalent) method. After the last sub‑agent finishes, LoopAgent checks whether the number of completed iterations has reached max_iterations or whether a sub‑agent has set a termination flag in the shared state. If neither condition is met, the loop restarts from the first sub‑agent.

Multi‑Agent Collaboration

Specialized division of labor – Each sub‑agent focuses on a distinct stage: scriptwriter_agent generates a script, visualizer_agent creates visual concepts, and formatter_agent assembles and formats the final output.

Shared state – Agents communicate through a mutable state dictionary. For example, scriptwriter_agent writes state['generated_script'], which visualizer_agent reads to produce state['visual_concepts']. The formatter_agent then accesses both entries to produce the final artifact.

Iterative Refinement

Because the sequence can be executed multiple times, LoopAgent supports progressive improvement:

Step‑by‑step enhancement – An early iteration may produce a draft; subsequent iterations can review, edit, and re‑format the draft.

Complex logic decomposition – Multi‑round execution breaks down intricate workflows into manageable sub‑steps, converging on the desired result.

Parameter tuning – Each loop can adjust hyper‑parameters or try alternative strategies based on feedback stored in state, stopping when a quality threshold is met.

Concrete Example

The following Python‑like snippet illustrates a typical LoopAgent configuration:

from adk import LoopAgent, ScriptWriter, Visualizer, Formatter

scriptwriter_agent = ScriptWriter(name="scriptwriter")
visualizer_agent   = Visualizer(name="visualizer")
formatter_agent    = Formatter(name="formatter")

youtube_shorts_agent = LoopAgent(
    name="youtube_shorts",
    max_iterations=3,
    sub_agents=[scriptwriter_agent, visualizer_agent, formatter_agent]
)

# Execute the orchestrated workflow
final_state = youtube_shorts_agent.run(initial_state={})
print(final_state["output_video_path"])

With max_iterations=3, the workflow “script → visual → format” can repeat up to three times, allowing the system to refine the script, improve visual concepts, and adjust formatting before producing the final short video.

Summary

LoopAgent provides a deterministic orchestration pattern for AI pipelines: define an ordered list of sub‑agents, set a maximum iteration count, and rely on a shared mutable state for information flow. This pattern makes it straightforward to build applications that require multi‑stage processing, collaborative agent behavior, and iterative refinement.