What Is Function Calling in AI?

Quick Definition#

Function calling is the mechanism that allows a large language model to output structured, machine-readable instructions to invoke an external function or API. Instead of returning free-form text, the model outputs a JSON object containing a function name and its arguments, which an external system then executes. This is the foundational capability that turns language models from text generators into action-taking agents.

For related concepts, see Tool Calling for the broader execution framework and What Are AI Agents? for how function calling fits into agentic workflows. Browse the full AI Agents Glossary for all foundational terms.

Why Function Calling Matters#

Language models have strong reasoning and generation capabilities, but they cannot inherently write to a database, check a live price feed, send an email, or call an enterprise API. Function calling solves this by giving the model a structured way to request external actions.

Without function calling, developers had to parse free-form model output and guess at intent. With function calling, the model returns a validated, typed JSON payload that maps directly to a defined function signature. This predictability is what makes large-scale agent automation reliable.

For a comparison of platforms that support function calling, see Best AI Agent Platforms in 2026.

How Function Calling Works#

The basic flow has four components:

1. Tool Schema Definition#

The developer provides the model with a list of available functions, each described by a JSON schema that specifies the function name, description, parameters, and required fields. This is the "menu" of actions the model can select from.

2. Model Decision#

When processing a user message or agent task, the model decides whether to call a function or return a text response. This decision is based on the conversation context and the function descriptions provided. A well-written function description is critical — vague descriptions lead to incorrect function selection.

3. Argument Construction#

If the model selects a function, it generates a JSON object with the function name and all required arguments. The format is validated against the schema before execution, preventing malformed calls from reaching downstream systems.

4. Execution and Response#

The application layer executes the function with the model-supplied arguments, then returns the result as a new message in the conversation. The model incorporates this result into its next reasoning step, continuing the Agent Loop.

OpenAI Function Calling vs. Anthropic Tool Use#

Both OpenAI and Anthropic support function calling with slightly different naming and syntax, but equivalent capabilities.

OpenAI uses the term "function calling" and more recently "tool calling" in their chat completions API. Functions are defined as JSON objects with name, description, and parameters fields.

Anthropic uses the term "tool use" in their Claude models API. Tools are defined with name, description, and input_schema fields. The conceptual flow is identical: define tools, let the model select and invoke them, handle results.

The practical difference for builders is API syntax, not capability. Teams using frameworks like LangChain or the AI Agent Frameworks ecosystem handle these differences transparently.

Parallel Function Calls#

Modern implementations support parallel function calls, where the model requests multiple tool invocations in a single response rather than waiting for each result sequentially.

For example, an agent checking travel options might simultaneously call:

• A flight search function
• A hotel availability function
• A weather forecast function

All three calls are issued in parallel, results are gathered, and the model resumes reasoning with all three results available at once. This significantly reduces total latency for workflows with independent tool dependencies.

When to use parallel function calls:

• Multiple independent data lookups in a single step
• Aggregation tasks where several sources must be combined
• Dashboard or report generation pulling from multiple systems

When to avoid parallel function calls:

• When one function's output determines another's input (sequential dependency)
• When downstream systems cannot handle concurrent requests reliably
• When idempotency is uncertain for write-capable functions

Function Calling in Agent Workflows#

In a multi-step Agent Loop, function calling is the primary mechanism for taking action. Each loop iteration may include zero, one, or multiple function calls depending on the reasoning output. The pattern from AI Agent Orchestration determines how function results are routed and which agent handles them next.

For frameworks that abstract this behavior, see Build an AI Agent with LangChain and Build an AI Agent with CrewAI.

Common Failure Modes#

Schema mismatch#

The model generates arguments that do not conform to the function schema, causing validation failures. This is most common with complex nested schemas or ambiguous parameter descriptions.

Fix: Use clear, specific parameter descriptions and provide examples in the schema when possible.

Incorrect function selection#

The model calls the wrong function when multiple functions have overlapping descriptions. This produces incorrect results even when argument construction succeeds.

Fix: Make function descriptions distinct and use a routing layer to restrict available functions based on workflow context.

Missing required parameters#

The model omits a required parameter, either because the information was not available in context or the schema did not make the dependency clear.

Fix: Surface required context earlier in the conversation or use a validation step that prompts the model to re-attempt with correct arguments.

Implementation Checklist#

1. Write clear, specific function descriptions that differentiate each tool.
2. Validate model-generated arguments against schema before execution.
3. Limit available functions to those relevant for the current workflow stage.
4. Handle function execution errors and return structured error messages the model can interpret.
5. Log all function calls, arguments, and results for debugging.
6. Test with edge case inputs that could produce ambiguous function selection.
7. Use parallel calls only for provably independent operations.

Related Terms and Further Reading#

• Tool Calling
• Agent Loop
• AI Agents
• AI Agent Orchestration
• Agent Framework
• Build an AI Agent with LangChain
• AI Agent Examples in Business

Frequently Asked Questions#

What is the difference between function calling and tool calling?#

Function calling is the specific mechanism by which a model outputs structured JSON to invoke a defined function. Tool calling refers to the broader execution process including selection, argument construction, execution, and result handling. Function calling is the model's contribution to that full cycle.

Does function calling require special model support?#

Yes. It requires models trained to output structured JSON conforming to a schema. OpenAI, Anthropic, Google, and Mistral all support this in their production models.

What are parallel function calls and when should I use them?#

Parallel function calls let the model invoke multiple tools at once. Use them when tool calls are independent — such as fetching multiple data sources simultaneously. Avoid them when one call's output determines another's input.