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
78 lines
1.9 KiB
Python
78 lines
1.9 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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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 google.adk.workflow import node
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from pydantic import BaseModel
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class TopicExplanation(BaseModel):
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topic: str
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explanation: str
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def process_input(node_input: str):
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"""Puts user input in the state."""
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return Event(state={"topic": node_input})
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find_related_topics = Agent(
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name="find_related_topics",
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instruction="""Given the specific topic "{topic}", generate a list of 3 related topics.""",
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output_schema=list[str],
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)
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@node(parallel_worker=True)
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def make_upper_case(node_input: str):
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yield node_input.upper()
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explain_topic = Agent(
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name="explain_topic",
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instruction=(
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"""Explain how the following topic relates the the original topic: "{topic}"."""
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),
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parallel_worker=True,
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output_schema=TopicExplanation,
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)
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def aggregate(node_input: list[TopicExplanation]):
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return Event(
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message="\n\n---\n\n".join(
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f"{explanation.topic}: {explanation.explanation}"
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for explanation in node_input
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),
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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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process_input,
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find_related_topics,
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make_upper_case,
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explain_topic,
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aggregate,
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),
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],
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)
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