Gemini Function Calling

Google’s Gemini API implements function calling through function declarations: you describe each function with a name, description, and an OpenAPI-flavored schema for its parameters, then bundle declarations into a tools list on the request. When Gemini wants to act, it returns a functionCall part containing the function name and arguments — delivered as a structured object, not a JSON string — and you reply with a functionResponse part carrying your result so the model can finish its answer.

If you’re building on Google’s stack — the Gemini API directly or through Vertex AI — this is the tool interface you’ll live in, and its differences from OpenAI’s shape are exactly what trips up developers porting code. Gemini’s distinctive control is the function calling mode: AUTO lets the model decide, ANY forces it to call some function (optionally restricted to an allowed list), and NONE disables calling — a clean lever for building predictable pipelines. Gemini also supports parallel calls and compositional calling, chaining multiple functions across turns to satisfy one request.

In practice you’ll use the google-genai SDK, where the fast path is passing plain Python functions as tools — the SDK generates declarations from your signatures and docstrings and can run the whole call-execute-respond loop automatically. For finer control you’ll declare schemas explicitly, inspect function_call parts yourself, and manage the conversation history manually, the same agent loop pattern you’d use with any provider.