Function Calling / Tool Use
Structured way for an LLM to invoke external functions or APIs with typed arguments.
Function calling lets an LLM decide to invoke a named function with structured arguments — typically defined via JSON schema. The model outputs a tool call; your code executes it; the result is fed back into the conversation.
This is the foundation of agentic AI. Every major model now supports function/tool calling: OpenAI, Anthropic Claude, Google Gemini, and open-source models like Llama 3.1+ and Mistral.
Best practice: keep tool schemas tight and typed, validate arguments before execution, allowlist which tools an agent can call, and log every tool invocation for audit.
Key points
- Model outputs structured tool calls; your code executes them
- JSON schema defines expected arguments
- Every frontier model supports it; open-source is catching up
- Tight schemas + allowlists + audit are non-negotiable in production
Common use cases
Frequently asked
Related terms
An LLM-driven system that plans, calls tools, and executes multi-step tasks autonomously.
A structured pipeline where an LLM makes decisions at defined branch points, but the overall flow is deterministic.
An open standard that lets LLMs discover and call external tools, data, and services in a uniform way.
A neural network trained on massive text corpora to understand and generate human-like language.
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