mirror of
https://github.com/EvolutionAPI/adk-python.git
synced 2025-07-13 23:17:35 -06:00
4188673b0f
Add a `task_completed` function to the agent so when a model finished the task, it can send a signal and the program knows it can go to next agent. This cl include: * Implements the `_run_live_impl` in `sequential_agent` so it can handle live case. * Add an example for sequential agent. * Improve error message for unimplemented _run_live_impl in other agents. Note: 1. Compared to non-live case, live agents process a continuous streams of audio or video, so it doesn't have a native way to tell if it's finished and should pass to next agent or not. So we introduce a task_compelted() function so the model can call this function to signal that it's finished the task and we can move on to next agent. 2. live agents doesn't seems to be very useful or natural in parallel or loop agents so we don't implement it for now. If there is user demand, we can implement it easily using similar approach. PiperOrigin-RevId: 758315430
104 lines
3.2 KiB
Python
104 lines
3.2 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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"""Parallel agent implementation."""
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from __future__ import annotations
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import asyncio
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from typing import AsyncGenerator
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from typing_extensions import override
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from ..agents.invocation_context import InvocationContext
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from ..events.event import Event
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from .base_agent import BaseAgent
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def _set_branch_for_current_agent(
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current_agent: BaseAgent, invocation_context: InvocationContext
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):
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invocation_context.branch = (
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f"{invocation_context.branch}.{current_agent.name}"
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if invocation_context.branch
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else current_agent.name
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)
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async def _merge_agent_run(
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agent_runs: list[AsyncGenerator[Event, None]],
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) -> AsyncGenerator[Event, None]:
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"""Merges the agent run event generator.
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This implementation guarantees for each agent, it won't move on until the
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generated event is processed by upstream runner.
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Args:
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agent_runs: A list of async generators that yield events from each agent.
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Yields:
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Event: The next event from the merged generator.
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"""
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tasks = [
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asyncio.create_task(events_for_one_agent.__anext__())
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for events_for_one_agent in agent_runs
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]
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pending_tasks = set(tasks)
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while pending_tasks:
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done, pending_tasks = await asyncio.wait(
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pending_tasks, return_when=asyncio.FIRST_COMPLETED
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)
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for task in done:
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try:
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yield task.result()
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# Find the generator that produced this event and move it on.
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for i, original_task in enumerate(tasks):
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if task == original_task:
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new_task = asyncio.create_task(agent_runs[i].__anext__())
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tasks[i] = new_task
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pending_tasks.add(new_task)
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break # stop iterating once found
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except StopAsyncIteration:
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continue
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class ParallelAgent(BaseAgent):
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"""A shell agent that run its sub-agents in parallel in isolated manner.
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This approach is beneficial for scenarios requiring multiple perspectives or
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attempts on a single task, such as:
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- Running different algorithms simultaneously.
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- Generating multiple responses for review by a subsequent evaluation agent.
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"""
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@override
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async def _run_async_impl(
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self, ctx: InvocationContext
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) -> AsyncGenerator[Event, None]:
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_set_branch_for_current_agent(self, ctx)
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agent_runs = [agent.run_async(ctx) for agent in self.sub_agents]
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async for event in _merge_agent_run(agent_runs):
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yield event
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@override
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async def _run_live_impl(
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self, ctx: InvocationContext
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) -> AsyncGenerator[Event, None]:
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raise NotImplementedError("This is not supported yet for ParallelAgent.")
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yield # AsyncGenerator requires having at least one yield statement
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