Build a small verified Gemini API call before adding complexity.
The Gemini API is available through official Python and JavaScript SDKs as well as REST. Start with the current Google quickstart, keep credentials outside client code, and confirm model names and API versions in official documentation.
Core building blocks
Interactions and generation
Send text and supported multimodal inputs, receive model output, and choose stateful or application-managed conversation history.
Structured output
Constrain supported model responses to a JSON schema when downstream code needs predictable fields and types.
Function calling
Let the model request approved functions with structured arguments; your application remains responsible for validation and execution.
Tools and grounding
Use supported built-in or custom tools when a workflow needs external information or actions, with explicit trust and safety boundaries.
Quickstart checklist
Create an API key in Google AI Studio, store it as a server-side environment variable, install the official SDK, and make one bounded request before adding tools or persistent state.
- Use GEMINI_API_KEY in the environment and never commit the secret.
- Select a current model for the task instead of assuming an old example name remains available.
- Log request identifiers, latency, usage, and failures without logging sensitive prompt content.
Design the application boundary
Treat model output as untrusted input. Define schemas, validate tool arguments, limit permissions, add timeouts and retries, and decide whether conversation state belongs with Google or in your application.
- Use structured output for final response shape and function calling for application actions.
- Execute requested tools only after server-side validation and authorization.
- Preserve the full required interaction history when using stateless API flows.
Prepare for production
Production quality depends on evaluation, observability, cost controls, privacy review, and graceful failure handling as much as it depends on prompt wording.
- Create task-specific evaluation cases, including adversarial and failure examples.
- Set rate, token, cost, and timeout budgets and surface useful fallback messages.
- Review data handling, safety settings, regional availability, and provider terms before launch.
Python
Store the API key in GEMINI_API_KEY, install the official SDK, and verify the current model identifier before running the request.
pip install -U google-genai
from google import genai
client = genai.Client()
interaction = client.interactions.create(
model="gemini-3.6-flash",
input="Explain this feature in three concise points",
)
print(interaction.output_text)