4.4 KiB
Loop template workflow agent
The LoopAgent class is a template workflow agent that executes its sub-agents in a loop for a specified number of iterations or until a termination condition is met. Use the LoopAgent when your workflow involves repetition or iterative refinement, such as revising code or a document. As with other templated workflows, the execution of a LoopAgent object is not controlled by an AI model, and is deterministic in how it executes its sub-agents. The sub-agents within the defined loop may or may not utilize AI models, but the overall execution of those sub-agents is ultimately managed by the LoopAgent object you define.
!!! note "Alternative: graph-based workflows"
Starting in ADK 2.0 for Python and Go, templated workflows have been superseded
by more flexible workflow structures, including
[graph-based workflows](/graphs/) and
[dynamic workflows](/graphs/dynamic/).
Example scenario
You want to build an agent that can generate images of food, but sometimes when
you want to generate a specific number of items, such as bananas, the agent
generates a different number of those items in the image, such as an image of 7
bananas. You have two tools: Generate Image, Count Food Items. If your goal
is to keep generating images until it either correctly generates the specified
number of items, or after a certain number of iterations, you can build your
agent using a LoopAgent workflow.
How it Works
When the LoopAgent's Run Async method is called, it performs the following actions:
-
Sub-Agent Execution: It iterates through the Sub Agents list in order. For each sub-agent, it calls the agent's
Run Asyncmethod. -
Termination Check:
Crucially, the
LoopAgentitself does not inherently decide when to stop looping. You must implement a termination mechanism to prevent infinite loops. Common strategies include:- Max Iterations: Set a maximum number of iterations in the
LoopAgent. The loop will terminate after that many iterations. - Escalation from sub-agent: Design one or more sub-agents to evaluate a condition (e.g., "Is the document quality good enough?", "Has a consensus been reached?"). If the condition is met, the sub-agent can signal termination (e.g., by raising a custom event, setting a flag in a shared context, or returning a specific value).
- Max Iterations: Set a maximum number of iterations in the
Full Example: Iterative Document Improvement
Imagine a scenario where you want to iteratively improve a document:
-
Writer Agent: An
LlmAgentthat generates or refines a draft on a topic. -
Critic Agent: An
LlmAgentthat critiques the draft, identifying areas for improvement.LoopAgent(sub_agents=[WriterAgent, CriticAgent], max_iterations=5)
In this setup, the LoopAgent would manage the iterative process. The CriticAgent could be designed to return a "STOP" signal when the document reaches a satisfactory quality level, preventing further iterations. Alternatively, the max iterations parameter could be used to limit the process to a fixed number of cycles, or external logic could be implemented to make stop decisions. The loop would run at most five times, ensuring the iterative refinement doesn't continue indefinitely.
???+ "Full Code"
=== "Python"
```py
--8<-- "examples/python/snippets/agents/workflow-agents/loop_agent_doc_improv_agent.py:init"
```
=== "TypeScript"
```typescript
--8<-- "examples/typescript/snippets/agents/workflow-agents/loop_agent_doc_improv_agent.ts:init"
```
=== "Go"
```go
--8<-- "examples/go/snippets/agents/workflow-agents/loop/main.go:init"
```
=== "Java"
```java
--8<-- "examples/java/snippets/src/main/java/agents/workflow/LoopAgentExample.java:init"
```
!!! tip "Global instructions for all agents"
Apply consistent rules or identity across all agents in your workflow using `GlobalInstructionPlugin` registered on your `App` or `Runner` object. Do not use the `global_instruction` parameter on `Agent` which is deprecated since ADK Python v1.16.0.
