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Changelog

Source: NEWS.md

tidyprompt (development version)

  • limit_requests() and send_prompt(max_requests = ...) bound model requests across initial responses, tool follow-ups and feedback rounds for both regular and ‘ellmer’ providers. The counter resets for each send_prompt() evaluation. If both limits are supplied, the smaller applies; max_interactions separately controls the outer evaluation loop.

  • Prompt composition and nested evaluation fixes:

    • llm_verify() reviews complete answers and isolates the verifier from answer-specific wraps, schemas and handlers. Verification and rejection summaries share the outer request budget without removing its request-limit hooks. Rejection summaries now use the available chat history.
    • Multiple native ‘ellmer’ tool wraps accumulate tools and reject conflicting names. Multiple native structured-output wraps fail before requesting a reply.
    • Native schema validation handles list columns and nested data-frame columns. answer_as_dataframe() retains array constraints from the supplied schema.
    • send_prompt() evaluates every permitted response, including when max_interactions = 1, and respects feedback from checks after a break.

  • ‘ellmer’ compatability fixes & improvements:

    • Chat inputs: llm_verify(), persistent_chat-class$new() and prompt rendering now accept raw Chats, as send_prompt() does.

    • Streaming: structured output now streams with ‘ellmer’ 0.5.0 when the provider supports it, with blocking extraction as a fallback. Streams expose rich content events, support cancellation and retain partial results after errors.

    • Attachments: use add_content() to attach native ‘ellmer’ content, including documents and file references. It requires an ‘ellmer’ provider and retains attachments across feedback turns.

    • Answers and history: send_prompt() now returns complete assistant replies and includes $citations when return_mode = "full". It preserves native tool and reasoning turns and usage metadata, respects history compaction, assigns messages to the correct roles, and isolates callbacks between evaluations. Returned cleaned history also preserves tool requests and results together for safe replay.

    • Schemas: conversion now preserves JSON Schema constraints and singleton arrays without deprecated schema calls. Validation retains R classes and correctly handles optional fields and empty objects. answer_as_dataframe() preserves nested cells without expanding rows and checks final row counts.

    • Tools: renaming ‘ellmer’ tools or converting them for other providers now preserves argument schemas and defaults. Tools with no arguments register correctly, list and data-frame results serialize as JSON, and native content results stay intact. Conversion errors identify the affected tool. Structured-output diagnostics also cover tools registered on the Chat.

    • Documentation: new interoperability vignette explains how to configure callbacks, limit requests, stream responses and use tools before structured extraction.

  • Built-in OpenAI-compatible and Ollama request failures now signal tidyprompt_request_error with the original condition in parent, plus status_code and request_id fields when available. This preserves HTTP diagnostics through send_prompt() for both streaming connection setup and non-streaming requests. Error messages include a provider’s explicit error message instead of appending the entire JSON response body.

  • Fixed a connection leak in streaming requests: req_llm_stream() (used internally when stream = TRUE) now closes the underlying httr2 streaming connection once a response has been read. Previously, the connection was never closed, so each streamed LLM call permanently used up one of R’s limited (128) connection slots; after enough streamed calls, this would cause unrelated code (e.g. textConnection()/capture.output()) to fail with “all connections are in use”.

tidyprompt 0.4.0

CRAN release: 2026-04-21

  • New prompt wrap answer_as_dataframe() for extracting tabular results via structured output, with support for row schemas, array-of-row schemas, and optional row-count validation.

  • New prompt wrap answer_as_numeric() for extracting numeric responses, including optional minimum and maximum value validation.

  • send_prompt() can now directly use an ‘ellmer’ chat object as the llm_provider to evaluate the prompt with (will build an llm_provider_ellmer() under the hood)

  • llm_provider_ellmer() was improved to better synchronize with the native ‘ellmer’ state, for instance for streaming, multimodal/image content, and persistent chats, with clearer warnings when settings need to be configured on the underlying ‘ellmer’ chat object.

  • answer_as_json() and answer_using_tools() have broader ‘ellmer’ compatibility, including ellmer::type_from_schema(), ‘ellmer’ built-in tools, and better handling of optional or ignored tool arguments.

  • Chat history handling is more robust for tool and native ‘ellmer’ workflows: tool rows are supported, non-replayable native rows (tool call and thinking rows) are kept for inspection but not re-sent to the LLM provider, and related metadata is normalized more reliably.

  • Update e-mail address of maintainer in DESCRIPTION file (change to a personal e-mail address due to leaving the organization).

tidyprompt 0.3.0

CRAN release: 2025-11-30

  • llm_provider-class: can now take a stream_callback function, which can be used to intercept streamed tokens as they arrive from the LLM provider. This may be used to build custom streaming behavior, for instance to show a live response in a Shiny app (see new vignette("streaming_shiny_ipc") for an example)

  • ’llm_provider_ellmer()`: now supports streaming responses

  • add_image(): new prompt wrap to add an image to a prompt, for use with multimodal LLMs

  • answer_using_r(): fixed error with unsafe conversion of resulting object to character

tidyprompt 0.2.0

CRAN release: 2025-08-25

  • Add provider-level prompt wraps (provider_prompt_wrap()) these are prompt wraps which can be attached to a LLM provider object. They can be applied to any prompt which is sent through this LLM provider, either before or after prompt-specific prompt wraps. This is useful when you want to achieve certain behavior for various prompts, without having to re-apply the same prompt wrap to each prompt

  • answer_as_json(): support ‘ellmer’ definitions of structured output (e.g., ellmer::type_object()). answer_as_json() can convert between ‘ellmer’ definitions and the previous R list objects which represent JSON schemas; thus, ‘ellmer’ and R list object definitions work with both regular and ‘ellmer’ LLM providers. When using an llm_provider_ellmer(), answer_as_json() will ensure the native ‘ellmer’ functions for obtaining structured output are used

  • answer_using_tools(): support ‘ellmer’ definitions of tools (from ellmer::tool()). answer_using_tools() can convert between ‘ellmer’ tool definitions and the previous R function objects with documentation from tools_add_docs(); thus, ‘ellmer’ and tools_add_docs() definitions work with both regular and ‘ellmer’ LLM providers. When using an llm_provider_ellmer(), answer_using_tools() will ensure the native ‘ellmer’ functions for registering tools are used.

  • answer_using_tools(): because of the above, and the fact that package ‘mcptools’ returns ‘ellmer’ tool definitions with mcptools::mcp_tools(), answer_using_tools() can now also be used with tools from Model Context Protocol (MCP) servers

  • send_prompt() can now return an updated ‘ellmer’ chat object when using an llm_provider_ellmer() (containing for instance the history of ‘ellmer’ turns and tool calls). Additionally fixed issues with how turn history is handled in ‘ellmer’ chat objects

  • send_prompt()’s clean_chat_history argument is now defaulted to FALSE, as it may be confusing for users to see cleaned chat histories without having actively requested this. If return_mode = "full", $clean_chat_history is also no longer included when clean_chat_history = FALSE

  • llm_provider_openai() now supports (as default) the OpenAI responses API, which allows setting parameters like ‘reasoning_effort’ and ‘verbosity’ (relevant for gpt-5). The OpenAI chat completions API is also still supported

  • llm_provider_google_gemini() has been superseded by llm_provider_ellmer(ellmer::chat_google_gemini())

  • Add a json_type & tool_type field to LLM provider objects; when automatically determining the route towards structured output (in answer_as_json()) and tool use (in answer_using_tools()), this can override the type decided by the api_type field (e.g., user can use this field to force the text-based type, for instance when using an OpenAI type LLM provider but with a model which does not support the typical OpenAI API parameters for structured output)

  • Update how responses are streamed (with httr2::req_perform_connection(), since httr2::req_perform_stream() is being deprecated)

  • Fix bug where the LLM provider object was not properly passed on to modify_fn in prompt_wrap(), which could lead to errors when dynamically constructing prompt text based on the LLM provider type

tidyprompt 0.1.0

CRAN release: 2025-08-18

  • New prompt wraps answer_as_category() and answer_as_multi_category()

  • New llm_break_soft() interrupts prompt evaluation without error

  • New experimental provider llm_provider_ellmer() for ‘ellmer’ chat objects

  • Ollama provider gains num_ctx parameter to control context window size

  • set_option() and set_options() are now available for the Ollama provider to configure options

  • Error messages are more informative when an LLM provider cannot be reached

  • Google Gemini provider now works without errors in affected cases

  • Chat history handling is safer; rows with NA values no longer cause errors in specific cases

  • Final-answer extraction in chain-of-thought prompts is more flexible

  • Printed LLM responses now use message() instead of cat()

  • Moved repository to https://github.com/KennispuntTwente/tidyprompt

tidyprompt 0.0.1

CRAN release: 2025-01-08

  • Initial CRAN release

tidyprompt 0.0.0.9000

  • Initial development version available on GitHub