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How do parallel tool calls work?

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Answer

  • Modern APIs (OpenAI, Anthropic, Gemini) allow the model to emit multiple tool calls in a single assistant turn — e.g., 'call searchflights(NYCLA,  12/15)\mathrm{search}_{\mathrm{flights}}(\mathrm{NYC} - \mathrm{LA}, \;12 / 15) AND searchhotels(LA,  12/1517)\mathrm{search}_{\mathrm{hotels}}(\mathrm{LA}, \;12 / 15 - 17)'.
  • Runtime executes them concurrently, waits for both, appends both results, then re-invokes the LLM.
  • Reduces latency for independent tools (network parallelism).
  • Model has to be trained / prompted to emit them together — some models default to sequential.
  • Trade-off: harder to debug + partial failures need explicit handling.
Check yourself — multiple choice
  • Only sequential
  • LLM emits multiple tool calls in one turn → run concurrently → append all results → re-invoke; reduces latency for independent tools
  • Impossible
  • Only for training

Parallel tool calls: multiple in one turn → concurrent execution → latency win.

#function-calling#agents#latency

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