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This guide demonstrates a unified approach to tool calling across multiple AI providers by standardizing the implementation loop, allowing developers to swap models via a single configuration string. By treating function definitions as portable JSON schemas, it eliminates the need for code rewrites when transitioning between different model APIs. This portability is critical for building robust applications that leverage diverse large language models without vendor lock-in.
Tool Calling Across Any Model: Write the Loop Once, Swap the Model String

Most tool-calling tutorials cover one provider, so you rewrite the loop when you switch. This guide shows the full loop in Python, JavaScript, and cURL, then runs it against three providers by changing one string.
Tool calling, also called function calling, lets a model request a function in structured JSON. Your code runs the function and returns the result, and the model uses it to finish its answer. Every major provider supports a version of this, but most tutorials cover one provider, so code written against OpenAI’s guide needs a rewrite when you move to Claude. With our API you don’t rewrite anything. You write the loop once, change the model string, and keep the tool code unchanged. This guide covers the full loop: defining a tool, sending a request, reading the tool_calls response, running the function, returning its result, and getting the final answer. You’ll then run that same code against three providers by changing one string. Our tool calling docs list every field. This guide shows the whole process from start to finish. One tool, get_weather(location, unit), defined once as an OpenAI-compatible JSON schema. One loop, shown in cURL, Python, and JavaScript/TypeScript. Send tools, read tool_calls, execute locally, return the result, get the final answer. One test across three models. The same loop runs against Claude, GPT, and an open-weight model, with only the model string…
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