wehub-resource-sync
ec2b666284
Continuous Integration / Pre-commit Linter (push) Waiting to run
Continuous Integration / Mypy Check (Python 3.10) (push) Waiting to run
Continuous Integration / Mypy Check (Python 3.11) (push) Waiting to run
Continuous Integration / Mypy Check (Python 3.12) (push) Waiting to run
Continuous Integration / Mypy Check (Python 3.13) (push) Waiting to run
Continuous Integration / Unit Tests (Python 3.10) (push) Waiting to run
Continuous Integration / Unit Tests (Python 3.11) (push) Waiting to run
Continuous Integration / Unit Tests (Python 3.12) (push) Waiting to run
Continuous Integration / Unit Tests (Python 3.13) (push) Waiting to run
Continuous Integration / Unit Tests (Python 3.14) (push) Waiting to run
Continuous Integration / A2A v0.3 Tests (Python 3.10) (push) Waiting to run
Continuous Integration / A2A v0.3 Tests (Python 3.11) (push) Waiting to run
Continuous Integration / A2A v0.3 Tests (Python 3.12) (push) Waiting to run
Continuous Integration / A2A v0.3 Tests (Python 3.13) (push) Waiting to run
Continuous Integration / A2A v0.3 Tests (Python 3.14) (push) Waiting to run
Copybara PR Handler / close-imported-pr (push) Waiting to run
60 lines
2.6 KiB
Markdown
60 lines
2.6 KiB
Markdown
# ADK Workflow Dynamic Node Execution Sample
|
|
|
|
## Overview
|
|
|
|
This sample demonstrates how to use `ctx.run_node` to execute nodes dynamically during workflow execution in **ADK Workflows**.
|
|
|
|
In standard workflow execution, the execution path is defined statically by the `edges`. However, there are scenarios where the exact nodes, or the number of times a node runs, cannot be determined until runtime.
|
|
|
|
In this sample, we handle the dynamic loop scenario: an `orchestrate` Python node acts as the driver. It uses a `while True:` loop to first execute a `generate_headline` agent to create a headline based on a given topic, and then an `evaluate_headline` agent to grade it. If the grade is `"tech-related"`, the loop returns the headline. If `"unrelated"`, the feedback is passed back into the state, and the loop repeats.
|
|
|
|
This is a rewritten version of the standard `loop` sample, achieved without complex graph edge routing (e.g., without conditional routing functions in `edges`), by instead leveraging native Python control flow (`while` loops) combined with asynchronous `ctx.run_node` calls.
|
|
|
|
## Sample Inputs
|
|
|
|
- `flower`
|
|
|
|
- `quantum mechanics`
|
|
|
|
- `renewable energy`
|
|
|
|
## Graph
|
|
|
|
```mermaid
|
|
graph TD
|
|
START --> orchestrate[orchestrate <br/>PYTHON FUNCTION]
|
|
orchestrate -.->|ctx.run_node| generate_headline
|
|
generate_headline --> evaluate_headline
|
|
evaluate_headline -.-> orchestrate
|
|
```
|
|
|
|
## How To
|
|
|
|
1. **Enable Resumability**: For a python node to use `ctx.run_node`, it must be declared with `@node(rerun_on_resume=True)`. This tells the engine to pause and possibly re-run the orchestrator if any dynamically scheduled node gets interrupted (e.g., waiting for human-in-the-loop).
|
|
|
|
```python
|
|
from google.adk.workflow import node
|
|
|
|
@node(rerun_on_resume=True)
|
|
async def orchestrate(ctx: Context, node_input: str) -> str:
|
|
# ...
|
|
```
|
|
|
|
1. **Run Node from Context**: Inject `ctx: Context` into your python node definition and await `ctx.run_node(node_to_run)`. The return value is the final output of that execution. You can also yield events to update the state within the loop before the next iteration.
|
|
|
|
```python
|
|
@node(rerun_on_resume=True)
|
|
async def orchestrate(ctx: Context, node_input: str) -> str:
|
|
yield Event(state={"topic": node_input})
|
|
|
|
while True:
|
|
headline = await ctx.run_node(generate_headline)
|
|
feedback = Feedback.model_validate(
|
|
await ctx.run_node(evaluate_headline, node_input=headline)
|
|
)
|
|
|
|
if feedback.grade == "tech-related":
|
|
yield headline
|
|
break # or return headline
|
|
```
|