parallel_chat: Submit multiple chats in parallel

In ellmer: Chat with Large Language Models

Submit multiple chats in parallel

Description

If you have multiple prompts, you can submit them in parallel. This is typically considerably faster than submitting them in sequence, especially with Gemini and OpenAI.

If you're using chat_openai() or chat_anthropic() and you're willing to wait longer, you might want to use batch_chat() instead, as it comes with a 50% discount in return for taking up to 24 hours.

Usage

parallel_chat(
  chat,
  prompts,
  max_active = 10,
  rpm = 500,
  on_error = c("return", "continue", "stop")
)

parallel_chat_text(
  chat,
  prompts,
  max_active = 10,
  rpm = 500,
  on_error = c("return", "continue", "stop")
)

parallel_chat_structured(
  chat,
  prompts,
  type,
  convert = TRUE,
  include_tokens = FALSE,
  include_cost = FALSE,
  max_active = 10,
  rpm = 500,
  on_error = c("return", "continue", "stop")
)

Arguments

chat	A chat object created by a chat_ function, or a string passed to chat().
prompts	A vector created by interpolate() or a list of character vectors.
max_active	The maximum number of simultaneous requests to send. For chat_anthropic(), note that the number of active connections is limited primarily by the output tokens per minute limit (OTPM) which is estimated from the max_tokens parameter, which defaults to 4096. That means if your usage tier limits you to 16,000 OTPM, you should either set max_active = 4 (16,000 / 4096) to decrease the number of active connections or use params() in chat_anthropic() to decrease max_tokens.
rpm	Maximum number of requests per minute.
on_error	What to do when a request fails. One of: "return" (the default): stop processing new requests, wait for in flight requests to finish, then return. "continue": keep going, performing every request. "stop": stop processing and throw an error.
type	A type specification for the extracted data. Should be created with a type_() function.
convert	If TRUE, automatically convert from JSON lists to R data types using the schema. This typically works best when type is type_object() as this will give you a data frame with one column for each property. If FALSE, returns a list.
include_tokens	If TRUE, and the result is a data frame, will add input_tokens and output_tokens columns giving the total input and output tokens for each prompt.
include_cost	If TRUE, and the result is a data frame, will add cost column giving the cost of each prompt.

Value

For parallel_chat(), a list with one element for each prompt. Each element is either a Chat object (if successful), a NULL (if the request wasn't performed) or an error object (if it failed).

For parallel_chat_text(), a character vector with one element for each prompt. Requests that weren't succesful get an NA.

For parallel_chat_structured(), a single structured data object with one element for each prompt. Typically, when type is an object, this will be a tibble with one row for each prompt, and one column for each property. If the output is a data frame, and some requests error, an .error column will be added with the error objects.

Examples

chat <- chat_openai()

# Chat ----------------------------------------------------------------------
country <- c("Canada", "New Zealand", "Jamaica", "United States")
prompts <- interpolate("What's the capital of {{country}}?")
parallel_chat(chat, prompts)

# Structured data -----------------------------------------------------------
prompts <- list(
  "I go by Alex. 42 years on this planet and counting.",
  "Pleased to meet you! I'm Jamal, age 27.",
  "They call me Li Wei. Nineteen years young.",
  "Fatima here. Just celebrated my 35th birthday last week.",
  "The name's Robert - 51 years old and proud of it.",
  "Kwame here - just hit the big 5-0 this year."
)
type_person <- type_object(name = type_string(), age = type_number())
parallel_chat_structured(chat, prompts, type_person)

ellmer documentation built on July 14, 2026, 1:07 a.m.