chore: import upstream snapshot with attribution

contributing/samples/workflows/route/README.md

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+# ADK Workflow Routing Sample
+
+## Overview
+
+This sample demonstrates how to use routing in **ADK Workflows**.
+
+It takes user input and uses an LLM node to categorize it as a **question**, a **statement**, or **other**. Based on the classification, it appropriately routes the execution to a specialized agent or function to handle that specific type of input.
+
+In ADK Workflows, **routing** allows conditionally executing different execution paths based on the output of a previous node.
+
+## Sample Inputs
+
+- `What is the capital of France?`
+
+- `The weather is very nice today.`
+
+- `Translate bonjour to english`
+
+## Graph
+
+```mermaid
+graph TD
+    START --> process_input
+    process_input --> classify_input
+    classify_input --> route_on_category
+    route_on_category -->|question| answer_question
+    route_on_category -->|statement| comment_on_statement
+    route_on_category -->|other| handle_other
+```
+
+## How To
+
+1. A node (agent or function) yields an `Event` with a specific route name:
+
+   ```python
+   yield Event(route="your_route_name")
+   ```
+
+1. In the `Workflow` edges definition, conditional edges are constructed using a routing map dict as the second element of the edge tuple:
+
+   ```python
+   (source_node, {"your_route_name": target_node})
+   ```

contributing/samples/workflows/route/agent.py

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+# Copyright 2026 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from typing import Literal
+
+from google.adk import Agent
+from google.adk import Event
+from google.adk import Workflow
+from pydantic import BaseModel
+
+
+class InputCategory(BaseModel):
+  category: Literal["question", "statement", "other"]
+
+
+def process_input(node_input: str):
+  return Event(state={"input": node_input})
+
+
+classify_input = Agent(
+    name="classify_input",
+    instruction=(
+        "Based on this input, decide which category it belongs to: {input}"
+    ),
+    output_schema=InputCategory,
+    output_key="category",
+)
+
+
+def route_on_category(category: InputCategory):
+  """Yields an Event with a specific route based on the classification."""
+  yield Event(route=category.category)
+
+
+answer_question = Agent(
+    name="answer_question",
+    instruction="""Answer the question: {input}""",
+)
+
+
+comment_on_statement = Agent(
+    name="comment_on_statement",
+    instruction="""Comment on the statement: {input}""",
+)
+
+
+def handle_other():
+  yield Event(
+      message="Sorry I can only anwer questions or comment on statements."
+  )
+
+
+root_agent = Workflow(
+    name="root_agent",
+    edges=[
+        ("START", process_input, classify_input, route_on_category),
+        (
+            route_on_category,
+            {
+                "question": answer_question,
+                "statement": comment_on_statement,
+                "other": handle_other,
+            },
+        ),
+    ],
+)

contributing/samples/workflows/route/tests/who_are_you.json

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+{
+  "appName": "route",
+  "events": [
+    {
+      "author": "user",
+      "content": {
+        "parts": [
+          {
+            "text": "who are you"
+          }
+        ],
+        "role": "user"
+      },
+      "id": "e-1",
+      "invocationId": "i-1",
+      "nodeInfo": {
+        "path": ""
+      }
+    },
+    {
+      "actions": {
+        "stateDelta": {
+          "input": "who are you"
+        }
+      },
+      "author": "root_agent",
+      "id": "e-2",
+      "invocationId": "i-1",
+      "nodeInfo": {
+        "path": "root_agent@1/process_input@1"
+      }
+    },
+    {
+      "actions": {
+        "stateDelta": {
+          "category": {
+            "category": "question"
+          }
+        }
+      },
+      "author": "classify_input",
+      "content": {
+        "parts": [
+          {
+            "text": "{\"category\": \"question\"}"
+          }
+        ],
+        "role": "model"
+      },
+      "finishReason": "STOP",
+      "id": "e-3",
+      "invocationId": "i-1",
+      "nodeInfo": {
+        "messageAsOutput": true,
+        "outputFor": [
+          "root_agent@1/classify_input@1"
+        ],
+        "path": "root_agent@1/classify_input@1"
+      }
+    },
+    {
+      "actions": {
+        "route": "question"
+      },
+      "author": "root_agent",
+      "id": "e-4",
+      "invocationId": "i-1",
+      "nodeInfo": {
+        "path": "root_agent@1/route_on_category@1"
+      }
+    },
+    {
+      "author": "answer_question",
+      "content": {
+        "parts": [
+          {
+            "text": "I am a large language model, trained by Google."
+          }
+        ],
+        "role": "model"
+      },
+      "finishReason": "STOP",
+      "id": "e-5",
+      "invocationId": "i-1",
+      "nodeInfo": {
+        "messageAsOutput": true,
+        "outputFor": [
+          "root_agent@1/answer_question@1",
+          "root_agent@1"
+        ],
+        "path": "root_agent@1/answer_question@1"
+      }
+    }
+  ],
+  "id": "f1ec33bf-99ef-49a5-b64a-e4d31f85db85",
+  "state": {
+    "__session_metadata__": {
+      "displayName": "who are you"
+    },
+    "category": {
+      "category": "question"
+    },
+    "input": "who are you"
+  },
+  "userId": "user"
+}