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Enable Agno agents to perform petabyte-scale data analysis, execute complex SQL, and even run machine learning models directly within Google Cloud’s data warehouse.

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Prerequisites

  • Set the following environment variables for your Google Cloud project: 
    export GOOGLE_CLOUD_PROJECT="your-project-id"
export GOOGLE_CLOUD_LOCATION="your-location"
  • Instruct the agent to prepend the table name with the project name and dataset name.
  • Describe the table schemas in instructions and use thinking tools for better responses. 

from agno.agent import Agent
from agno.models.google import Gemini
from agno.tools.google_bigquery import GoogleBigQueryTools

# ---------------------------------------------------------------------------
# Create Agent
# ---------------------------------------------------------------------------


agent = Agent(
    instructions=[
        "You are an expert Big query Writer",
        "Always prepend the table name with your_project_id.your_dataset_name when run_sql tool is invoked",
    ],
    tools=[GoogleBigQueryTools(dataset="test_dataset")],
    model=Gemini(id="gemini-3-flash-preview", vertexai=True),
)

# ---------------------------------------------------------------------------
# Run Agent
# ---------------------------------------------------------------------------
if __name__ == "__main__":
    agent.print_response(
        "List the tables in the dataset. Tell me about contents of one of the tables",
        markdown=True,
    )

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Run the Example

# Clone and setup repo
git clone https://github.com/agno-agi/agno.git
cd agno/cookbook/91_tools

# Create and activate virtual environment
./scripts/demo_setup.sh
source .venvs/demo/bin/activate

python google_bigquery_tools.py
For details, see Google BigQuery cookbook.