chat_claude() is no longer deprecated and is an alias
for chat_anthropic(), reflecting Anthropic’s recent
rebranding of developer tools under the Claude name (#758).
models_claude() is now an alias for
models_anthropic().parallel_chat() and batch_chat() are no
longer experimental.Chat$extract_data() ->
chat$chat_structured() (0.2.0)Chat$extract_data_async() ->
chat$chat_structured_async() (0.2.0)chat_anthropic(max_tokens) ->
chat_anthropic(params) (0.2.0)chat_azure() -> chat_azure_openai()
(0.2.0)chat_azure_openai(token) (0.1.1)chat_bedrock() -> chat_aws_bedrock()
(0.2.0)chat_claude() -> chat_anthropic()
(0.2.0)chat_cortex() -> chat_snowflake()
(0.2.0)chat_gemini() -> chat_google_gemini()
(0.2.0)chat_openai(seed) ->
chat_openai(params) (0.2.0)create_tool_def(model) ->
create_tool_def(chat) (0.2.0)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).claude_file_() functions for managing file
uploads with Claude (@dcomputing, #761).claude_tool_web_search() and
claude_tool_web_fetch() for Claude.google_tool_web_search() and
google_tool_web_fetch() for Gemini.openai_tool_web_search() for OpenAI. If you want to do
web fetch for other providers, you could use
btw::btw_tool_web_read_url().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).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).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).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).chat() is now compatible with most chat_
functions (#699).
chat_aws_bedrock(), chat_databricks(),
chat_deepseek(), chat_github(),
chat_groq(), chat_ollama(),
chat_openrouter(), chat_perplexity(), and
chat_vllm() now support a params argument that
accepts common model parameters from params().deployment_id argument in
chat_azure_openai() was deprecated and replaced with
model to better align with other providers.chat_openai() now correctly maps
max_tokens and top_k from
params() to the OpenAI API parameters (#699).chat_anthropic() drops empty assistant turns to
avoid API errors (#710).
chat_github() now uses the
https://models.github.ai/inference endpoint and
chat() supports GitHub models in the format
chat("github/openai/gpt-4.1") (#726).
chat_google_vertex() authentication was fixed using
broader scope (#704, @netique)
chat_google_vertex() can now use global
project location (#704, @netique)
chat_openai() now uses OPENAI_BASE_URL,
if set, for the base_url. Similarly,
chat_ollama() also uses OLLAMA_BASE_URL if set
(#713).
contents_record() and contents_replay()
now record and replay custom classes that extend ellmer’s
Turn or Content classes (#689).
contents_replay() now also restores the tool definition in
ContentToolResult objects (in @request@tool)
(#693).
chat_snowflake() now supports Privatelink accounts
(#694, @robert-norberg). and works against
Snowflake’s latest API changes (#692, @robert-norberg).
models_google_vertex() works once again (#704, @netique)
In the value_turn() method for OpenAI providers,
usage is checked if NULL before logging tokens
to avoid errors when streaming with some OpenAI-compatible services
(#706, @stevegbrooks).
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).
ellmer_echo option controls the default value
for echo.batch_chat_structured() provides clear messaging when
prompts/path/provider don’t match (#599).chat_aws_bedrock() allows you to set the
base_url() (#441).chat_aws_bedrock(), chat_google_gemini(),
chat_ollama(), and chat_vllm() use a more
robust method to generate model URLs from the base_url
(#593, @benyake).chat_cortex_analyst() is deprecated; please use
chat_snowflake() instead (#640).chat_github() (and other OpenAI extensions) no longer
warn about seed (#574).chat_google_gemini() and
chat_google_vertex() default to Gemini 2.5 flash
(#576).chat_huggingface() works much better.chat_openai() supports content_pdf_()
(#650).chat_portkey() works once again, and reads the virtual
API key from the PORTKEY_VIRTUAL_KEY env var (#588).chat_snowflake() works with tool calling (#557, @atheriel).Chat$chat_structured() and friends no longer
unnecessarily wrap type_object() for
chat_openai() (#671).Chat$chat_structured() suppresses tool use. If you need
to use tools and structured data together, first use
$chat() for any needed tools, then
$chat_structured() to extract the data you need.Chat$chat_structured() no longer requires a prompt
(since it may be obvious from the context) (#570).Chat$register_tool() shows a message when you replace
an existing tool (#625).contents_record() and contents_replay()
record and replay Turn related information from a
Chat instance (#502). These methods can be used for
bookmarking within {shinychat}.models_github() lists models for
chat_github() (#561).models_ollama() includes a capabilities
column with a comma-separated list of model capabilities (#623).parallel_chat() and friends accept lists of
Content objects in the prompt (#597, @thisisnic).tool() checks that the name is valid
(#625).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)
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).
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$get_cost(). You
can also request costs in parallel_chat_structured(). We do
our best to accurately compute the cost, but you should treat it as an
estimate rather than the exact price. Unfortunately LLM providers
currently make it very difficult to figure out exactly how much your
queries cost (#203).
We have support for three new providers:
chat_huggingface() for models hosted at https://huggingface.co
(#359, @s-spavound).chat_mistral() for models hosted at https://mistral.ai
(#319).chat_portkey() and models_portkey() for
models hosted at https://portkey.ai (#363, @maciekbanas).We also renamed (with deprecation) a few functions to make the naming scheme more consistent (#382, @gadenbuie):
chat_azure_openai() replaces
chat_azure().chat_aws_bedrock() replaces
chat_bedrock().chat_anthropic() replaces
chat_anthropic().chat_google_gemini() replaces
chat_gemini().We have updated the default model for a couple of providers:
chat_anthropic() uses Sonnet 3.7 (which it also now
displays) (#336).chat_openai() uses GPT-4.1 (#512)New Chat$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).
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().
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).
All providers now wait for up to 60s to get the complete
response. You can increase this with, e.g.,
option(ellmer_timeout_s = 120) (#213, #300).
chat_azure(), chat_databricks(),
chat_snowflake(), and chat_cortex_analyst()
now detect viewer-based credentials when running on Posit Connect (#285,
@atheriel).
chat_deepseek() provides support for DeepSeek models
(#242).
chat_openrouter() provides support for models hosted
by OpenRouter (#212).
chat_snowflake() allows chatting with models hosted
through Snowflake’s Cortex
LLM REST API (#258, @atheriel).
content_pdf_file() and
content_pdf_url() allow you to upload PDFs to supported
models. Models that currently support PDFs are Google Gemini and Claude
Anthropic. With help from @walkerke and @andrie (#265).
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).
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).
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