feat(live): Support live mode of sequential agent

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
This commit is contained in:
Hangfei Lin 2025-05-13 11:55:50 -07:00 committed by Copybara-Service
parent 39f78dc28f
commit 4188673b0f
7 changed files with 180 additions and 19 deletions

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@ -0,0 +1,15 @@
# Copyright 2025 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 . import agent

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@ -0,0 +1,94 @@
# Copyright 2025 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.
import random
from google.adk.agents.llm_agent import LlmAgent
from google.adk.agents.sequential_agent import SequentialAgent
from google.genai import types
# --- Roll Die Sub-Agent ---
def roll_die(sides: int) -> int:
"""Roll a die and return the rolled result."""
return random.randint(1, sides)
roll_agent = LlmAgent(
name="roll_agent",
description="Handles rolling dice of different sizes.",
model="gemini-2.0-flash-exp",
instruction="""
You are responsible for rolling dice based on the user's request.
When asked to roll a die, you must call the roll_die tool with the number of sides as an integer.
""",
tools=[roll_die],
generate_content_config=types.GenerateContentConfig(
safety_settings=[
types.SafetySetting( # avoid false alarm about rolling dice.
category=types.HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT,
threshold=types.HarmBlockThreshold.OFF,
),
]
),
)
def check_prime(nums: list[int]) -> str:
"""Check if a given list of numbers are prime."""
primes = set()
for number in nums:
number = int(number)
if number <= 1:
continue
is_prime = True
for i in range(2, int(number**0.5) + 1):
if number % i == 0:
is_prime = False
break
if is_prime:
primes.add(number)
return (
"No prime numbers found."
if not primes
else f"{', '.join(str(num) for num in primes)} are prime numbers."
)
prime_agent = LlmAgent(
name="prime_agent",
description="Handles checking if numbers are prime.",
model="gemini-2.0-flash-exp",
instruction="""
You are responsible for checking whether numbers are prime.
When asked to check primes, you must call the check_prime tool with a list of integers.
Never attempt to determine prime numbers manually.
Return the prime number results to the root agent.
""",
tools=[check_prime],
generate_content_config=types.GenerateContentConfig(
safety_settings=[
types.SafetySetting( # avoid false alarm about rolling dice.
category=types.HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT,
threshold=types.HarmBlockThreshold.OFF,
),
]
),
)
root_agent = SequentialAgent(
name="code_pipeline_agent",
sub_agents=[roll_agent, prime_agent],
# The agents will run in the order provided: roll_agent -> prime_agent
)

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@ -58,5 +58,5 @@ class LoopAgent(BaseAgent):
async def _run_live_impl(
self, ctx: InvocationContext
) -> AsyncGenerator[Event, None]:
raise NotImplementedError('The behavior for run_live is not defined yet.')
raise NotImplementedError('This is not supported yet for LoopAgent.')
yield # AsyncGenerator requires having at least one yield statement

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@ -94,3 +94,10 @@ class ParallelAgent(BaseAgent):
agent_runs = [agent.run_async(ctx) for agent in self.sub_agents]
async for event in _merge_agent_run(agent_runs):
yield event
@override
async def _run_live_impl(
self, ctx: InvocationContext
) -> AsyncGenerator[Event, None]:
raise NotImplementedError("This is not supported yet for ParallelAgent.")
yield # AsyncGenerator requires having at least one yield statement

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@ -23,6 +23,7 @@ from typing_extensions import override
from ..agents.invocation_context import InvocationContext
from ..events.event import Event
from .base_agent import BaseAgent
from .llm_agent import LlmAgent
class SequentialAgent(BaseAgent):
@ -40,6 +41,36 @@ class SequentialAgent(BaseAgent):
async def _run_live_impl(
self, ctx: InvocationContext
) -> AsyncGenerator[Event, None]:
"""Implementation for live SequentialAgent.
Compared to non-live case, live agents process a continous streams of audio
or video, so it doesn't have a 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.
Args:
ctx: The invocation context of the agent.
"""
# There is no way to know if it's using live during init phase so we have to init it here
for sub_agent in self.sub_agents:
# add tool
def task_completed():
"""
Signals that the model has successfully completed the user's question
or task.
"""
return "Task completion signaled."
if isinstance(sub_agent, LlmAgent):
# Use function name to dedupe.
if task_completed.__name__ not in sub_agent.tools:
sub_agent.tools.append(task_completed)
sub_agent.instruction += f"""If you finished the user' request
according to its description, call {task_completed.__name__} function
to exit so the next agents can take over. When calling this function,
do not generate any text other than the function call.'"""
for sub_agent in self.sub_agents:
async for event in sub_agent.run_live(ctx):
yield event

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@ -135,6 +135,18 @@ class BaseLlmFlow(ABC):
# cancel the tasks that belongs to the closed connection.
send_task.cancel()
await llm_connection.close()
if (
event.content
and event.content.parts
and event.content.parts[0].function_response
and event.content.parts[0].function_response.name
== 'task_completed'
):
# this is used for sequential agent to signal the end of the agent.
await asyncio.sleep(1)
# cancel the tasks that belongs to the closed connection.
send_task.cancel()
return
finally:
# Clean up
if not send_task.done():
@ -237,7 +249,7 @@ class BaseLlmFlow(ABC):
if (
event.content
and event.content.parts
and event.content.parts[0].text
and event.content.parts[0].inline_data is None
and not event.partial
):
# This can be either user data or transcription data.

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@ -254,13 +254,13 @@ class Runner:
"""Runs the agent in live mode (experimental feature).
Args:
session: The session to use. This parameter is deprecated, please use
`user_id` and `session_id` instead.
user_id: The user ID for the session. Required if `session` is None.
session_id: The session ID for the session. Required if `session` is
None.
live_request_queue: The queue for live requests.
run_config: The run config for the agent.
session: The session to use. This parameter is deprecated, please use
`user_id` and `session_id` instead.
Yields:
AsyncGenerator[Event, None]: An asynchronous generator that yields
@ -302,6 +302,8 @@ class Runner:
invocation_context.active_streaming_tools = {}
# TODO(hangfei): switch to use canonical_tools.
# for shell agents, there is no tools associated with it so we should skip.
if hasattr(invocation_context.agent, 'tools'):
for tool in invocation_context.agent.tools:
# replicate a LiveRequestQueue for streaming tools that relis on
# LiveRequestQueue