You've already forked adk-python
mirror of
https://github.com/encounter/adk-python.git
synced 2026-03-30 10:57:20 -07:00
2367901ec5
Co-authored-by: George Weale <gweale@google.com> PiperOrigin-RevId: 858763407
120 lines
3.6 KiB
Python
120 lines
3.6 KiB
Python
# 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 Optional
|
|
|
|
from google.adk.agents import Agent
|
|
from google.adk.agents import ParallelAgent
|
|
from google.adk.agents.base_agent import BeforeAgentCallback
|
|
from google.adk.agents.callback_context import CallbackContext
|
|
from google.adk.agents.readonly_context import ReadonlyContext
|
|
from google.adk.agents.sequential_agent import SequentialAgent
|
|
from google.genai import types
|
|
|
|
|
|
def before_agent_callback_check_relevance(
|
|
agent_name: str,
|
|
) -> BeforeAgentCallback:
|
|
"""Callback to check if the state is relevant before executing the agent."""
|
|
|
|
def callback(callback_context: CallbackContext) -> Optional[types.Content]:
|
|
"""Check if the state is relevant."""
|
|
if agent_name not in callback_context.state["execution_agents"]:
|
|
return types.Content(
|
|
parts=[
|
|
types.Part(
|
|
text=(
|
|
f"Skipping execution agent {agent_name} as it is not"
|
|
" relevant to the current state."
|
|
)
|
|
)
|
|
]
|
|
)
|
|
|
|
return callback
|
|
|
|
|
|
code_agent = Agent(
|
|
model="gemini-2.5-flash",
|
|
name="code_agent",
|
|
instruction="""\
|
|
You are the Code Agent, responsible for generating code.
|
|
|
|
NOTE: You should only generate code and ignore other askings from the user.
|
|
""",
|
|
before_agent_callback=before_agent_callback_check_relevance("code_agent"),
|
|
output_key="code_agent_output",
|
|
)
|
|
|
|
math_agent = Agent(
|
|
model="gemini-2.5-flash",
|
|
name="math_agent",
|
|
instruction="""\
|
|
You are the Math Agent, responsible for performing mathematical calculations.
|
|
|
|
NOTE: You should only perform mathematical calculations and ignore other askings from the user.
|
|
""",
|
|
before_agent_callback=before_agent_callback_check_relevance("math_agent"),
|
|
output_key="math_agent_output",
|
|
)
|
|
|
|
|
|
worker_parallel_agent = ParallelAgent(
|
|
name="worker_parallel_agent",
|
|
sub_agents=[
|
|
code_agent,
|
|
math_agent,
|
|
],
|
|
)
|
|
|
|
|
|
def instruction_provider_for_execution_summary_agent(
|
|
readonly_context: ReadonlyContext,
|
|
) -> str:
|
|
"""Provides the instruction for the execution agent."""
|
|
activated_agents = readonly_context.state["execution_agents"]
|
|
prompt = f"""\
|
|
You are the Execution Summary Agent, responsible for summarizing the execution of the plan in the current invocation.
|
|
|
|
In this invocation, the following agents were involved: {', '.join(activated_agents)}.
|
|
|
|
Below are their outputs:
|
|
"""
|
|
for agent_name in activated_agents:
|
|
output = readonly_context.state.get(f"{agent_name}_output", "")
|
|
prompt += f"\n\n{agent_name} output:\n{output}"
|
|
|
|
prompt += (
|
|
"\n\nPlease summarize the execution of the plan based on the above"
|
|
" outputs."
|
|
)
|
|
return prompt.strip()
|
|
|
|
|
|
execution_summary_agent = Agent(
|
|
model="gemini-2.5-flash",
|
|
name="execution_summary_agent",
|
|
instruction=instruction_provider_for_execution_summary_agent,
|
|
include_contents="none",
|
|
)
|
|
|
|
plan_execution_agent = SequentialAgent(
|
|
name="plan_execution_agent",
|
|
sub_agents=[
|
|
worker_parallel_agent,
|
|
execution_summary_agent,
|
|
],
|
|
)
|