mirror of
https://github.com/google/adk-docs.git
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Gururaj Deshpande
ff454ac1c9
* fix: change call_agent to call_agent_async * added await in storyflow where missing * updated callbacks and storyflow with await and user comment * fixed more * added licencse header --------- Co-authored-by: Lavi Nigam <98014943+lavinigam-gcp@users.noreply.github.com> Co-authored-by: Lavi Nigam <lavinigam@google.com>
99 lines
3.5 KiB
Python
99 lines
3.5 KiB
Python
# Copyright 2025 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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# Shows how to call all the sub-agents in a loop iteratively. Run this as you would run a standard python file.
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from google.adk.agents import LlmAgent, LoopAgent
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from google.adk.tools import google_search
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from .util import load_instruction_from_file
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# --- Sub Agent 1: Scriptwriter ---
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scriptwriter_agent = LlmAgent(
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name="ShortsScriptwriter",
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model="gemini-2.0-flash-001",
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instruction=load_instruction_from_file("scriptwriter_instruction.txt"),
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tools=[google_search],
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output_key="generated_script", # Save result to state
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)
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# --- Sub Agent 2: Visualizer ---
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visualizer_agent = LlmAgent(
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name="ShortsVisualizer",
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model="gemini-2.0-flash-001",
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instruction=load_instruction_from_file("visualizer_instruction.txt"),
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description="Generates visual concepts based on a provided script.",
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output_key="visual_concepts", # Save result to state
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)
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# --- Sub Agent 3: Formatter ---
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# This agent would read both state keys and combine into the final Markdown
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formatter_agent = LlmAgent(
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name="ConceptFormatter",
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model="gemini-2.0-flash-001",
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instruction="""Combine the script from state['generated_script'] and the visual concepts from state['visual_concepts'] into the final Markdown format requested previously (Hook, Script & Visuals table, Visual Notes, CTA).""",
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description="Formats the final Short concept.",
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output_key="final_short_concept",
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)
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# --- Loop Agent Workflow ---
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youtube_shorts_agent = LoopAgent(
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name="youtube_shorts_agent",
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sub_agents=[scriptwriter_agent, visualizer_agent, formatter_agent],
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)
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# --- Root Agent for the Runner ---
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# The runner will now execute the workflow
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root_agent = youtube_shorts_agent
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# Code required to make the agent programmatically runnable.
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from google.genai import types
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from google.adk.sessions import InMemorySessionService
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from google.adk.runners import Runner
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from util import load_instruction_from_file
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# Load .env
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# Replace the API_KEY in .env file.
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from dotenv import load_dotenv
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load_dotenv()
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# Instantiate constants
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APP_NAME = "youtube_shorts_app"
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USER_ID = "12345"
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SESSION_ID = "123344"
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# Session and Runner
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async def setup_session_and_runner():
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session_service = InMemorySessionService()
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session = await session_service.create_session(app_name=APP_NAME, user_id=USER_ID, session_id=SESSION_ID)
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runner = Runner(agent=youtube_shorts_agent, app_name=APP_NAME, session_service=session_service)
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return session, runner
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# Agent Interaction
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async def call_agent_async(query):
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content = types.Content(role='user', parts=[types.Part(text=query)])
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session, runner = await setup_session_and_runner()
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events = runner.run_async(user_id=USER_ID, session_id=SESSION_ID, new_message=content)
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async for event in events:
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if event.is_final_response():
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final_response = event.content.parts[0].text
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print("Agent Response: ", final_response)
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call_agent_async("I want to write a short on how to build AI Agents")
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