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Instead of just generating text responses, LLMs can understand when to call specific functions and provide the necessary parameters to execute real-world actions.
Illustration of how models call external functions via structured JSON (via Superface.ai Blog
Function calling is a mechanism that lets a language model like GPT or Claude invoke external functions or APIs — in a structured and secure way.
Instead of producing free-form output, the model returns a JSON object specifying which function to call and with what arguments. A developer (or orchestrator) can then execute the function and optionally feed the result back into the model.
This enables agents that can:
- Look up real-time weather
- Query databases
- Fetch news or stock prices
- Write to a file, send an email, or start a workflow
Why is this important?
Function calling makes LLMs interactive, not just informative. It turns the model from a passive predictor into a decision-making orchestrator of tools.
How it works:
- Developer provides a function signature (name, params, types, description)
- Model returns a JSON object like:
{
"name": "getWeather",
"arguments": { "location": "Tokyo" }
}
- The app executes
getWeather("Tokyo")and optionally feeds the result back
It’s the basis for modern agent systems, plugin platforms, and automation flows.
FAQ
What is function calling in LLMs?
It’s a method where the model returns structured JSON to request the execution of specific functions or APIs.
Why do models need function calling?
It allows them to trigger real-world actions like fetching live data, writing to files, or integrating with external systems.
Is function calling safe?
Yes — because the model doesn’t directly run code. Developers define the functions and decide whether to execute them.
What platforms support this?
OpenAI (GPT-4), Anthropic (Claude), Google Gemini, and others have native support for structured function calling.
Related Stuff
- AI Agents: Systems that reason and take action autonomously
- Tool Use in LLMs: How language models interact with external software
- Model Context Protocol: A standardized way for models to access tools and context