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Changelog

Source: NEWS.md

ellmer 0.4.0

CRAN release: 2025-11-15

Lifecycle changes

New features

  • batch_*() no longer hashes properties of the provider besides the name, model, and base_url. This should provide some protection from accidentally reusing the same .json file with different providers, while still allowing you to use the same batch file across ellmer versions. It also has a new ignore_hash argument that allows you to opt out of the check if you’re confident the difference only arises because ellmer itself has changed.
  • chat_claude() gains new cache parameter to control caching. By default it is set to “5m”. This should (on average) reduce the cost of your chats (#584).
  • chat_openai() now uses OpenAI’s responses endpoint (#365, #801). This is their recommended endpoint and gives more access to built-in tools.
  • chat_openai_compatible() replaces chat_openai() as the interface to use for OpenAI-compatible APIs, and chat_openai() is reserved for the official OpenAI API. Unlike previous versions of chat_openai(), the base_url parameter is now required (#801).
  • chat_*() functions now use a credentials function instead of an api_key (#613). This means that API keys are never stored in the chat object (which might be saved to disk), but are instead retrieved on demand as needed. You generally shouldn’t need to use the credentials argument, but when you do, you should use it to dynamically retrieve the API key from some other source (i.e. never inline a secret directly into a function call).
  • New set of claude_file_() functions for managing file uploads with Claude (@dcomputing, #761).
  • ellmer now supports a variety of built-in web search and fetch tools (#578):
  • parallel_chat() and friends now have a more permissive attitude to errors. By default, they will now return when hitting the first error (rather than erroring), and you can control this behaviour with the on_error argument. Or if you interrupt the job, it will finish up current requests and then return all the work done so far. The main downside of this work is that the output of parallel_chat() is more complex: it is now a mix of Chat objects, error objects, and NULL (#628).
  • parallel_chat_structured() no longer errors if some results fail to parse. Instead it warns, and the corresponding rows will be filled in with the appropriate missing values (#628).
  • New schema_df() to describe the schema of a data frame to an LLM (#744).
  • tool()s can now return image or PDF content types, with content_image_file() or content_image_pdf() (#735).
  • params() gains new reasoning_effort and reasoning_tokens so you can control the amount of effort a model spends on thinking. Initial support is provided for chat_claude(), chat_google_gemini(), and chat_openai() (#720).
  • New type_ignore() allows you to specify that a tool argument should not be provided by the LLM when the R function has a suitable default value (#764).

Minor improvements and bug fixes

  • Updated pricing data (#790).
  • AssistantTurns now have a @duration slot, containing the total time to complete the request (@simonpcouch, #798).
  • batch_chat() logs tokens once, on retrieval (#743).
  • batch_chat() now retrieves failed results for chat_openai() (#830) and gracefully handles invalid JSON (#845).
  • batch_chat() now works once more for chat_anthropic() (#835).
  • batch_chat_*() and parallel_chat_*() now accept a string as the chat object, following the same rules as chat() (#677).
  • chat_claude() and chat_aws_bedrock() now default to Claude Sonnet 4.5 (#800).
  • chat_databricks() lifts many of its restrictions now that Databricks’ API is more OpenAI compatible (#757).
  • chat_google_gemini() and chat_openai() support image generation (#368).
  • chat_google_gemini() has an experimental fallback interactive OAuth flow, if you’re in an interactive session and no other authentication options can be found (#680).
  • chat_groq() now defaults to llama-3.1-8b-instant.
  • chat_openai() gains a service_tier argument (#712).
  • chat_portkey() now requires you to supply a model (#786).
  • chat_portkey(virtual_key) no longer needs to be supplied; instead Portkey recommends including the virtual key/provider in the model (#786).
  • Chat$chat(), Chat$stream(), and similar methods now add empty tool results when a the chat is interrupted during a tool call loop, allowing the conversation to be resumed without causing an API error (#840).
  • Chat$chat_structured() and friends now only warn if multiple JSON payloads found (instead of erroring) (@kbenoit, #732).
  • Chat$get_tokens() gives a brief description of the turn contents to make it easier to see which turn tokens are spent on (#618) and also returns the cost (#824). It now returns one row for each assistant turn, better representing the underlying data received from LLM APIs. Similarly, the print() method now reports costs on each assistant turn, rather than trying to parse out individual costs.
  • interpolate_package() now provides an informative error if the requested prompt file is not found in the package’s prompts/ directory (#763) and now works with in-development packages loaded with devtools (#766).
  • models_mistral() lists available models (@rplsmn, #750).
  • models_ollama() was fixed to correctly query model capabilities from remote Ollama servers (#746).
  • chat_ollama() now uses credentials when checking if Ollama is available and models_ollama() now has a credentials argument. This is useful when accessing Ollama servers that require authentication (@AdaemmerP, #863).
  • parallel_chat_structured() now returns a tibble, since this does a better job of printing more complex data frames (#787).

ellmer 0.3.2

CRAN release: 2025-09-03

ellmer 0.3.1

CRAN release: 2025-08-24

ellmer 0.3.0

CRAN release: 2025-07-24

New features

  • New chat() allows you to chat with any provider using a string like chat("anthropic") or chat("openai/gpt-4.1-nano") (#361).

  • tool() has a simpler specification: you now specify the name, description, and arguments. I have done my best to deprecate old usage and give clear errors, but I have likely missed a few edge cases. I apologize for any pain that this causes, but I’m convinced that it is going to make tool usage easier and clearer in the long run. If you have many calls to convert, ?tool contains a prompt that will help you use an LLM to convert them (#603). It also now returns a function so that you can call it (and/or export it from your package) (#602).

  • type_array() and type_enum() now have the description as the second argument and items/values as the first. This makes them easier to use in the common case where the description isn’t necessary (#610).

  • ellmer now retries requests up to 3 times, controllable with option(ellmer_max_tries), and will retry if the connection fails (rather than just if the request itself returns a transient error). The default timeout, controlled by option(ellmer_timeout_s), now applies to the initial connection phase. Together, these changes should make it much more likely for ellmer requests to succeed.

  • New parallel_chat_text() and batch_chat_text() make it easier to just get the text response from multiple prompts (#510).

  • ellmer’s cost estimates are considerably improved. chat_openai(), chat_google_gemini(), and chat_anthropic() capture the number of cached input tokens. This is primarily useful for OpenAI and Gemini since both offer automatic caching, yielding improved cost estimates (#466). We also have a better source of pricing data, LiteLLM. This considerably expands the number of providers and models that include cost information (#659).

Bug fixes and minor improvements

ellmer 0.2.1

CRAN release: 2025-06-03

  • When you save a Chat object to disk, API keys are This means that you can no longer easily resume a chat you’ve saved on disk (we’ll figure this out in a future release) but ensures that you never accidentally save your secret key in an RDS file (#534).

  • chat_anthropic() now defaults to Claude Sonnet 4, and I’ve added pricing information for the latest generation of Claude models.

  • chat_databricks() now picks up on Databricks workspace URLs set in the configuration file, which should improve compatibility with the Databricks CLI (#521, @atheriel). It now also supports tool calling (#548, @atheriel).

  • chat_snowflake() no longer streams answers that include a mysterious list(type = "text", text = "") trailer (#533, @atheriel). It now parses streaming outputs correctly into turns (#542), supports structured ouputs (#544), and standard model parameters (#545, @atheriel).

  • chat_snowflake() and chat_databricks() now default to Claude Sonnet 3.7, the same default as chat_anthropic() (#539 and #546, @atheriel).

  • type_from_schema() lets you to use pre-existing JSON schemas in structured chats (#133, @hafen)

ellmer 0.2.0

CRAN release: 2025-05-17

Breaking changes

  • We have made a number of refinements to the way ellmer converts JSON to R data structures. These are breaking changes, although we don’t expect them to affect much code in the wild. Most importantly, tools are now invoked with their inputs coerced to standard R data structures (#461); opt-out by setting convert = FALSE in tool().

    Additionally ellmer now converts NULL to NA for type_boolean(), type_integer(), type_number(), and type_string() (#445), and does a better job with arrays when required = FALSE (#384).

  • chat_ functions no longer have a turn argument. If you need to set the turns, you can now use Chat$set_turns() (#427). Additionally, Chat$tokens() has been renamed to Chat$get_tokens() and returns a data frame of tokens, correctly aligned to the individual turn. The print method now uses this to show how many input/output tokens were used by each turn (#354).

New features

  • Two new interfaces help you do multiple chats with a single function call:

    • batch_chat() and batch_chat_structured() allow you to submit multiple chats to OpenAI and Anthropic’s batched interfaces. These only guarantee a response within 24 hours, but are 50% of the price of regular requests (#143).

    • parallel_chat() and parallel_chat_structured() work with any provider and allow you to submit multiple chats in parallel (#143). This doesn’t give you any cost savings, but it’s can be much, much faster.

    This new family of functions is experimental because I’m not 100% sure that the shape of the user interface is correct, particularly as it pertains to handling errors.

  • google_upload() lets you upload files to Google Gemini or Vertex AI (#310). This allows you to work with videos, PDFs, and other large files with Gemini.

  • models_google_gemini(), models_anthropic(), models_openai(), models_aws_bedrock(), models_ollama() and models_vllm(), list available models for Google Gemini, Anthropic, OpenAI, AWS Bedrock, Ollama, and VLLM respectively. Different providers return different metadata so they are only guaranteed to return a data frame with at least an id column (#296). Where possible (currently for Gemini, Anthropic, and OpenAI) we include known token prices (per million tokens).

  • interpolate() and friends are now vectorised so you can generate multiple prompts for (e.g.) a data frame of inputs. They also now return a specially classed object with a custom print method (#445). New interpolate_package() makes it easier to interpolate from prompts stored in the inst/prompts directory inside a package (#164).

  • chat_anthropic(), chat_azure(), chat_openai(), and chat_gemini() now take a params argument, that coupled with the params() helper, makes it easy to specify common model parameters (like seed and temperature) across providers. Support for other providers will grow as you request it (#280).

  • ellmer now tracks the cost of input and output tokens. The cost is displayed when you print a Chat object, in tokens_usage(), and with Chat$get_cost(). You can also request costs in parallel_chat_structured(). We do our best to accurately compute the cost, but you should treat it as an estimate rather than the exact price. Unfortunately LLM providers currently make it very difficult to figure out exactly how much your queries cost (#203).

Provider updates

Developer tooling

  • New Chat$get_provider() lets you access the underlying provider object (#202).

  • Chat$chat_async() and Chat$stream_async() gain a tool_mode argument to decide between "sequential" and "concurrent" tool calling. This is an advanced feature that primarily affects asynchronous tools (#488, @gadenbuie).

  • Chat$stream() and Chat$stream_async() gain support for streaming the additional content types generated during a tool call with a new stream argument. When stream = "content" is set, the streaming response yields Content objects, including the ContentToolRequest and ContentToolResult objects used to request and return tool calls (#400, @gadenbuie).

  • New Chat$on_tool_request() and $on_tool_result() methods allow you to register callbacks to run on a tool request or tool result. These callbacks can be used to implement custom logging or other actions when tools are called, without modifying the tool function (#493, @gadenbuie).

  • Chat$chat(echo = "output") replaces the now-deprecated echo = "text" option. When using echo = "output", additional output, such as tool requests and results, are shown as they occur. When echo = "none", tool call failures are emitted as warnings (#366, @gadenbuie).

  • ContentToolResult objects can now be returned directly from the tool() function and now includes additional information (#398 #399, @gadenbuie):

    • extra: A list of additional data associated with the tool result that is not shown to the chatbot.
    • request: The ContentToolRequest that triggered the tool call. ContentToolResult no longer has an id property, instead the tool call ID can be retrieved from request@id.

    They also include the error condition in the error property when a tool call fails (#421, @gadenbuie).

  • ContentToolRequest gains a tool property that includes the tool() definition when a request is matched to a tool by ellmer (#423, @gadenbuie).

  • tool() gains an .annotations argument that can be created with the tool_annotations() helper. Tool annotations are described in the Model Context Protocol and can be used to describe the tool to clients. (#402, @gadenbuie)

  • New tool_reject() function can be used to reject a tool request with an explanation for the rejection reason. tool_reject() can be called within a tool function or in a Chat$on_tool_request() callback. In the latter case, rejecting a tool call will ensure that the tool function is not evaluated (#490, #493, @gadenbuie).

Minor improvements and bug fixes

  • All requests now set a custom User-Agent that identifies that the requests come from ellmer (#341). The default timeout has been increased to 5 minutes (#451, #321).

  • chat_anthropic() now supports the thinking content type (#396), and content_image_url() (#347). It gains a beta_header argument to opt-in to beta features (#339). It (along with chat_bedrock()) no longer chokes after receiving an output that consists only of whitespace (#376). Finally, chat_anthropic(max_tokens =) is now deprecated in favour of chat_anthropic(params = ) (#280).

  • chat_google_gemini() and chat_google_vertex() gain more ways to authenticate. They can use GEMINI_API_KEY if set (@t-kalinowski, #513), authenticate with Google default application credentials (including service accounts, etc) (#317, @atheriel) and use viewer-based credentials when running on Posit Connect (#320, @atheriel). Authentication with default application credentials requires the {gargle} package. They now also can now handle responses that include citation metadata (#358).

  • chat_ollama() now works with tool() definitions with optional arguments or empty properties (#342, #348, @gadenbuie), and now accepts api_key and consults the OLLAMA_API_KEY environment variable. This is not needed for local usage, but enables bearer-token authentication when Ollama is running behind a reverse proxy (#501, @gadenbuie).

  • chat_openai(seed =) is now deprecated in favour of chat_openai(params = ) (#280).

  • create_tool_def() can now use any Chat instance (#118, @pedrobtz).

  • live_browser() now requires {shinychat} v0.2.0 or later which provides access to the app that powers live_browser() via shinychat::chat_app(), as well as a Shiny module for easily including a chat interface for an ellmer Chat object in your Shiny apps (#397, @gadenbuie). It now initializes the UI with the messages from the chat turns, rather than replaying the turns server-side (#381).

  • Provider gains name and model fields (#406). These are now reported when you print a chat object and are used in token_usage().

ellmer 0.1.1

CRAN release: 2025-02-06

Lifecycle changes

  • option(ellmer_verbosity) is no longer supported; instead use the standard httr2 verbosity functions, such as httr2::with_verbosity(); these now support streaming data.

  • chat_cortex() has been renamed chat_cortex_analyst() to better disambiguate it from chat_snowflake() (which also uses “Cortex”) (#275, @atheriel).

New features

Bug fixes and minor improvements

  • Chat$get_model() returns the model name (#299).

  • chat_azure() has greatly improved support for Azure Entra ID. API keys are now optional and we can pick up on ambient credentials from Azure service principals or attempt to use interactive Entra ID authentication when possible. The broken-by-design token argument has been deprecated (it could not handle refreshing tokens properly), but a new credentials argument can be used for custom Entra ID support when needed instead (for instance, if you’re trying to use tokens generated by the AzureAuth package) (#248, #263, #273, #257, @atheriel).

  • chat_azure() now reports better error messages when the underlying HTTP requests fail (#269, @atheriel). It now also defaults to api_version = "2024-10-21" which includes data for structured data extraction (#271).

  • chat_bedrock() now handles temporary IAM credentials better (#261, @atheriel) and chat_bedrock() gains api_args argument (@billsanto, #295).

  • chat_databricks() now handles the DATABRICKS_HOST environment variable correctly whether it includes an HTTPS prefix or not (#252, @atheriel). It also respects the SPARK_CONNECT_USER_AGENT environment variable when making requests (#254, @atheriel).

  • chat_gemini() now defaults to using the gemini-2.0-flash model.

  • print(Chat) no longer wraps long lines, making it easier to read code and bulleted lists (#246).

ellmer 0.1.0

CRAN release: 2025-01-09

  • New chat_vllm() to chat with models served by vLLM (#140).

  • The default chat_openai() model is now GPT-4o.

  • New Chat$set_turns() to set turns. Chat$turns() is now Chat$get_turns(). Chat$system_prompt() is replaced with Chat$set_system_prompt() and Chat$get_system_prompt().

  • Async and streaming async chat are now event-driven and use later::later_fd() to wait efficiently on curl socket activity (#157).

  • New chat_bedrock() to chat with AWS bedrock models (#50).

  • New chat$extract_data() uses the structured data API where available (and tool calling otherwise) to extract data structured according to a known type specification. You can create specs with functions type_boolean(), type_integer(), type_number(), type_string(), type_enum(), type_array(), and type_object() (#31).

  • The general ToolArg() has been replaced by the more specific type_*() functions. ToolDef() has been renamed to tool.

  • content_image_url() will now create inline images when given a data url (#110).

  • Streaming ollama results works once again (#117).

  • Streaming OpenAI results now capture more results, including logprobs (#115).

  • New interpolate() and prompt_file() make it easier to create prompts that are a mix of static text and dynamic values.

  • You can find how many tokens you’ve used in the current session by calling token_usage().

  • chat_browser() and chat_console() are now live_browser() and live_console().

  • The echo can now be one of three values: “none”, “text”, or “all”. If “all”, you’ll now see both user and assistant turns, and all content types will be printed, not just text. When running in the global environment, echo defaults to “text”, and when running inside a function it defaults to “none”.

  • You can now log low-level JSON request/response info by setting options(ellmer_verbosity = 2).

  • chat$register_tool() now takes an object created by Tool(). This makes it a little easier to reuse tool definitions (#32).

  • new_chat_openai() is now chat_openai().

  • Claude and Gemini are now supported via chat_claude() and chat_gemini().

  • The Snowflake Cortex Analyst is now supported via chat_cortex() (#56).

  • Databricks is now supported via chat_databricks() (#152).