Wei (Jack) Sun
1c64a41b53
Introduces two major capabilities: - Workflow runtime: graph-based execution engine for composing deterministic execution flows for agentic apps, with support for routing, fan-out/fan-in, loops, retry, state management, dynamic nodes, human-in-the-loop, and nested workflows - Task API: structured agent-to-agent delegation with multi-turn task mode, single-turn controlled output, mixed delegation patterns, human-in-the-loop, and task agents as workflow nodes Co-Authored-By: Bo Yang <ybo@google.com> Co-Authored-By: George Weale <gweale@google.com> Co-Authored-By: Sean Zhou <seanzhougoogle@google.com> Co-Authored-By: Shangjie Chen <deanchen@google.com> Co-Authored-By: Swapnil Agarwal <swapnilag@google.com> Co-Authored-By: Wei Sun <weisun@google.com> Co-Authored-By: Xuan Yang <xygoogle@google.com> Co-Authored-By: Yifan Wang <wanyif@google.com> Change-Id: I35932c50cfe29ff68559e3781713dbb5eb7b3382
73 lines
2.2 KiB
Python
73 lines
2.2 KiB
Python
# Copyright 2026 Google LLC
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# NOT WORKING YET
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# Pending on correct output passing from LLM node
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from google.adk import Agent
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from google.adk import Event
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from google.adk import Workflow
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from pydantic import BaseModel
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from pydantic import Field
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class TopicDetails(BaseModel):
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title: str = Field(description="The title of the generated topic.")
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description: str = Field(description="A short description of the topic.")
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category: str = Field(description="The broad category of the topic.")
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def generate_string_output(node_input: str):
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"""Returns a simple string. The framework automatically wraps it in an Event."""
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return f"Processed input: {node_input}"
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def generate_event_output(node_input: str):
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"""Explicitly returns an Event object for more control."""
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return Event(output=f"Event wrapped output: {node_input}")
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generate_pydantic_output = Agent(
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name="generate_pydantic_output",
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instruction="Generate a creative topic based on the following input.",
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output_schema=TopicDetails,
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)
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def consume_pydantic_output(node_input: TopicDetails):
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"""
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Relying on the FunctionNode's automatic type parsing.
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The framework will coerce the dictionary or JSON into a TopicDetails object automatically.
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"""
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return (
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"Received Pydantic Model!\n"
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f"Title: {node_input.title}\n"
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f"Description: {node_input.description}\n"
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f"Category: {node_input.category}"
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)
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root_agent = Workflow(
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name="root_agent",
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edges=[
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(
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"START",
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generate_string_output,
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generate_event_output,
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generate_pydantic_output,
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consume_pydantic_output,
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),
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],
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)
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