Google ADK Quickstart

pip install "visiq>=0.5.0" google-adk

# pip install visiq google-adk
from google.adk.agents import Agent
from google.adk.runners import InMemoryRunner
from google.genai import types

from visiq import govern


def issue_refund(customer_id: str, amount: int) -> str:
    """Issue a refund to a customer."""
    return f"Refunded ${amount} to {customer_id}"


def search_knowledge(query: str) -> list:
    """Search the knowledge base."""
    # Your real RAG store returns the same shape: a list of
    # {"page_content", "metadata"} dicts the retrieval facet can filter.
    return [
        {"page_content": f"Q3 revenue was $4.2M. (matched: {query})",
         "metadata": {"classification": "internal"}},
    ]


agent = Agent(
    name="support",
    model="gemini-2.0-flash",
    instruction="You are a helpful assistant.",
    tools=[issue_refund, search_knowledge],
)

# ── One call: every tool on this agent (and its sub-agents) is now governed ──
govern(agent, agent_id="support-bot", retrieval_tools={"search_knowledge"})

runner = InMemoryRunner(agent=agent, app_name="support")
session = runner.session_service.create_session_sync(app_name="support", user_id="u1")
for event in runner.run(
    user_id="u1", session_id=session.id,
    new_message=types.Content(role="user", parts=[types.Part(text="What was Q3 revenue?")]),
):
    if event.is_final_response() and event.content:
        print(event.content.parts[0].text)

pip install "google-adk[extensions]"

# pip install visiq "google-adk[extensions]"
from google.adk.agents import Agent
from google.adk.models.lite_llm import LiteLlm

from visiq import govern

agent = Agent(
    name="support",
    model=LiteLlm(model="bedrock/amazon.nova-micro-v1:0", temperature=0),
    tools=[issue_refund, search_knowledge],
)
govern(agent, agent_id="support-bot", retrieval_tools={"search_knowledge"})