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method
argument is gone, and ten non-core helpers are now internal.enable_structured_output()’s local bookkeeping fields no
longer enter the identity.llm_batch_submit(state_path = ) refuses to persist a
job whose config carries a literal API key; environment-variable handles
round-trip as before.json_output argument of
call_llm_par() and the inert .fields argument
of the chat session’s $send_structured() and
$send_tags(). llm_methods_text() is deprecated
in favor of report() and will be removed once the current
‘LLMRagent’ release no longer calls it."openrouter": chat, streaming, and
structured output through OpenRouter’s OpenAI-compatible aggregator
(model ids like "openai/gpt-4o-mini"; key from
OPENROUTER_API_KEY). OpenRouter has no embeddings or batch
API; its optional attribution headers can be set through the
req_builder hook.A bug-fix release; all changes are backward compatible.
llm_hash() no longer depends on the collation locale,
so the same object hashes identically on every machine; hashes recorded
under the C locale are unchanged.{column} reference) now fill every
row; previously rows 2..n were sent with NA content and
rejected by providers.llm_mutate() appends its generated columns again
(instead of moving them to the front), and
.before/.after now work on the embedding,
structured, tag, and row-batched paths.llm_usage() and llm_failures() work on
llm_mutate_structured() and llm_mutate_tags()
results; config-side and log-side request hashes agree after provider
parameter renames.req_builder hook and the request
timeout. Assorted smaller fixes; see the commit log.call_llm_par(.request_hash =),
llm_add_request_hash(), llm_log_active(),
llm_tool_signature(), and llm_uuid().llm_agreement() gains ordinal and interval
Krippendorff’s alpha (metric =); the nominal default is
unchanged.anes_2024_personas: 100
participant profiles derived from the ANES 2024 public release, the
shared persona dataset of the LLMR family.llm_persona_split(),
llm_persona_overview(),
llm_persona_dictionary(),
llm_persona_demographic_fields(),
llm_validate_persona_frame().transcript_as_messages() and
ensure_alternating_messages(): build a provider-safe,
role-flipped message array from a multi-speaker transcript (own turns
become assistant, others become labeled user
turns)..batch_size ->
.rows_per_prompt, and kin); the word “batch” is now
reserved for the asynchronous provider Batch API.llm_log_read(): parse a JSONL call log into records
plus a per-record manifest with record and request hashes.llm_request_hash() canonicalizes message shape and keys
on all generation parameters, so a logged call and its config hash
identically.reset() generic.llm_response_record() (one-row
response record; a failed call is a row, never a dropped call) and
llm_request_hash() (stable identity for a call).diagnostics() and
report(), implemented by the LLMR method packages.llm_batch_submit(), llm_batch_status(),
llm_batch_fetch(), llm_batch_cancel() (OpenAI,
Groq, Anthropic, Gemini).llm_log_enable()),
a draft methods paragraph (llm_methods_text()), and
replication with agreement statistics (llm_replicate(),
llm_agreement()).llm_tool(),
call_llm_tools()) and streaming
(call_llm_stream()).llm_logprobs()), canonical
seed, model_version and thinking
on every response, prompt caching, and cost estimates from a
user-supplied price_table.llm_preview(): render exactly what a call would send,
and flag problems, without any API call.llm_usage() and llm_failures(): outcome
counts, token totals, and per-row failure listings for any result
frame.NA, not
0; a bare environment-variable name passed as
api_key is always treated as a reference.llm_par_resume() re-runs only the failed rows; JSON
array recovery and embedding-dimension fixes..tags on
llm_mutate(), plus llm_mutate_tags(),
llm_fn_tags(), and the tag parsers.llm_mutate() shorthand
(answer = "{question}") and
.structured = TRUE.call_llm() returns an llmr_response
object; as.character(x) extracts the text.llm_mutate().These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.