Parallel tool calling
A model capability where the LLM returns multiple tool calls in a single response — the agent runtime executes them concurrently rather than serially, cutting latency on independent operations.
On simple tool use, the agent asks "what tool next?" the model picks one, the tool runs, the result returns, repeat. Parallel tool calling collapses this when the next-best tools do not depend on each other. The model returns "call A, B, and C," the runtime fires all three at once, then feeds the joined result back.
OpenAI, Anthropic, and Google all support parallel tool calling in 2026. The latency win is biggest when tools have per-call overhead (network round-trips, slow APIs) — agents that fetch from 5 sources in parallel feel dramatically faster.
The catch is the model has to correctly predict independence. Frontier models do this well; older models will sometimes call dependent tools in parallel and get wrong results. Eval the behavior before relying on it for irreversible operations.
Frequently asked
Does every model support parallel tool calling?+
In 2026, all frontier models do. Smaller models often advertise support but in practice serialize the calls or get the dependencies wrong. Test on your task.
When should I disable parallel tool calling?+
When tools have side effects (sending email, writing files) and the order matters. Force serial execution to keep the audit trail clear.