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Serve Google ADK agents through Motus for full session history replay, automatic tracing, and cloud deployment. Import Agent from motus.google_adk.agents.llm_agent instead of the ADK directly. Your model, tools, and instructions stay the same.

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Installation

uv sync --extra google-adk
Requires google-adk>=1.27.2.

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Basic usage

Import Agent from motus.google_adk.agents.llm_agent instead of the ADK package: 
from motus.google_adk.agents.llm_agent import Agent

agent = Agent(
    model="gemini-2.0-flash",
    name="my_agent",
    instruction="You are a helpful assistant.",
)
The Agent class is a direct subclass of the Google ADK Agent. It accepts the same constructor arguments (model, name, instruction, tools, and any other ADK parameters) and adds a run_turn() method that integrates with Motus serving.

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Adding tools

Pass standard ADK tools or plain Python functions directly: 
from motus.google_adk.agents.llm_agent import Agent

def get_weather(city: str) -> str:
    """Get the weather for a city."""
    return f"Sunny in {city}"

agent = Agent(
    model="gemini-2.0-flash",
    name="weather_agent",
    instruction="You are a helpful weather assistant.",
    tools=[get_weather],
)

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Deployment

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Local serving

Pass the agent object directly to motus serve start: 
motus serve start myapp:agent --port 8000
Where agent is an Agent instance defined at module level in myapp.py. 
# myapp.py
from motus.google_adk.agents.llm_agent import Agent

def get_weather(city: str) -> str:
    """Get the weather for a city."""
    return f"Sunny in {city}"

agent = Agent(
    model="gemini-2.0-flash",
    name="weather_agent",
    instruction="You are a helpful weather assistant.",
    tools=[get_weather],
)

motus serve start myapp:agent --port 8000

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Cloud deployment

cd my_project
motus deploy --name my-adk-agent agent:root_agent
When deploying to Motus cloud, include requirements.txt with google-adk>=1.27.2. No API key secrets are needed - the platform routes Gemini API calls through the model proxy. Session state (conversation history) is persisted in DynamoDB and survives backend restarts, failovers, and scaling events.

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Session history replay

Each turn, Motus passes the full prior conversation as a list of ChatMessage objects to run_turn(). The agent replays that history into an ADK InMemoryRunner session before executing the new turn, so the model sees full conversation context on every request. You do not need to manage history yourself. Motus stores the updated state after each turn and provides it to the next one automatically.
A fresh InMemoryRunner and session are created for each turn. History is replayed by appending prior messages as ADK Event objects before the new user message is sent.

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Tracing

Tracing is automatic when the Motus runtime is active (as it is inside motus serve). The MotusSpanProcessor implements the OpenTelemetry SpanProcessor interface and is registered with Google ADK’s OTEL provider once per worker process. Google ADK emits OTEL spans for:
ADK spanMotus span typeContents
invoke_agentagent_callAgent name, invocation duration
generate_contentmodel_callModel name, token usage, response content
execute_tooltool_callTool name, input arguments, output, error type
The processor converts each completed ADK span into Motus task_meta format. It extracts model name, token usage, tool arguments and responses, and error types from ADK’s semantic convention attributes and ingests them into TraceManager. Traces are automatically exported on process exit.
On the Motus cloud platform, the Google ADK client picks up platform-injected environment variables that route requests through the model proxy. You do not need to set GOOGLE_API_KEY at deploy time.