Parallel Tool Calls

Three independent lookups — the weather in Delhi, in Mumbai and in Chennai — do not need three round trips to the model. Parallel tool calls let a single model response ask for all three at once.

How it works on the wire
The mechanics are plain once you have seen them:

One assistant message carries several tool-call entries, each with its own id and its own arguments.
The runtime executes them concurrently and returns one result message per call, matched back by id.
The model's next turn reads all the results together and carries on.
Both the OpenAI and Anthropic APIs support this, and OpenAI exposes a parallel_tool_calls switch to force one call at a time.

Why it matters
Every model turn costs latency and a fresh re-read of the whole context. Collapsing five sequential calls into one turn cuts wall-clock time sharply and saves the tokens those extra turns would have billed — the cheapest speed-up available to a tool-heavy agent.

What it is not safe for
Parallel calls assume independence. If one call's output feeds the next — create the record, then attach a file to its id — the model cannot know that id in advance, and a model that issues both anyway fills the argument in with an invention. Writes are riskier still: two concurrent updates to the same row, or a charge applied twice, with no ordering left to reason about. Keep it on for reads, turn it off for stateful tools, and make each tool's description say which kind it is.

Parallel tool calls let one model response request several tool invocations at once, each with its own id; the runtime executes them concurrently and returns all the results for the next turn. This removes a round trip per call, cutting both latency and the tokens that repeated turns would bill. It is valid only for independent calls — where one result supplies another call's arguments, or the tools write state, invocation must stay sequential.

If you need milk, bread and eggs, you do not make three separate trips to the shop. You pick them all up in one go.

Parallel tool calls are that shortcut for AI software. Instead of asking for one piece of information, waiting for it, then asking for the next, it asks for several at the same time and reads all the answers together. The job finishes noticeably faster and costs less. The catch is that this only works when the requests do not depend on each other. If the second task needs the result of the first — create the booking, then add a passenger to it — they still have to happen in order.