LLMR 0.8.6

Renames

• The generative row-packing controls on llm_mutate(), llm_fn(), and their _structured/_tags variants are renamed to remove a clash with the word “batch”: .batch_size -> .rows_per_prompt, .batch_payload -> .rowpack_payload, .batch_recovery -> .rowpack_recovery. The diagnostic output columns follow (_batch/_bn/_bi -> _rowpack/_rpn/_rpi), as do the llm_usage() summary fields (batch_calls -> rowpack_calls, rows_per_batch_call -> rows_per_rowpack), the error:batch_* finish reasons (-> error:rowpack_*), and the exported helper llm_parse_batch_tags() -> llm_parse_rowpack_tags(). The word “batch” is now reserved for the asynchronous provider Batch API (llm_batch_submit() and friends); embedding request chunking keeps get_batched_embeddings(batch_size). These row-packing names were added after the last CRAN release and are renamed without aliases. A new shared documentation section, “Batching, chunking, and row packing,” appears on llm_batch_submit(), get_batched_embeddings(), and llm_mutate() to keep the three senses apart.

New features

• Added llm_log_read(), which parses a JSONL audit log (written by llm_log_enable()) into its records and a per-record manifest tibble, with a record hash over each verbatim line and a request hash identifying each call. This is the generic log reader the ecosystem’s archive tooling builds on.
• Added the reset() S3 generic with an erroring default, alongside diagnostics() and report(), so a method package can register a reset for a stateful result object.

Changes

• llm_request_hash() now canonicalizes its messages to a provider-neutral list of role/content turns before hashing, so the message shape (a bare string, a named character vector, or a list of turns) no longer changes the hash; only roles and text do. It keys on all generation parameters (everything in model_params except transport knobs) and gains provider/model arguments so a call read from a log hashes identically to the same call described by a config. This changes the hash values relative to 0.8.5; the hashes are new in this release cycle and nothing depends on the old values.

LLMR 0.8.5

New features

• Added two additive provenance helpers for the method packages. llm_response_record() flattens a single llmr_response (or a caught error) into a one-row tibble with the family’s response-contract columns (response_text, response_id, provider, model, model_version, finish_reason, token counts, success, error_message, duration_s, created_at, request_hash); a failed call is a row, never a dropped call. llm_request_hash() gives a stable identity for a call over provider, model, messages, generation parameters, and any tool/schema signature, excluding API keys and transport-only knobs. The archive layer keys deduplication and replay on it. Both build on the existing llm_hash() convention. No existing behavior changes.

LLMR 0.8.4

New features

• Added the exported S3 generics diagnostics(x, ...) and report(x, ...), with default methods that error when no class-specific method is available. These are additive infrastructure for the LLMR method packages (at the time the then-separate LLMRcoder, LLMRvalid, LLMRarchive, since merged into LLMRcontent, and LLMRpanel), which register methods on them; diagnostics() returns a result object’s machine-readable health numbers and report() drafts its methods-section prose. No existing behavior changes.

LLMR 0.8.3

New features

• Provider batch APIs. llm_batch_submit(), llm_batch_status(), llm_batch_fetch(), and llm_batch_cancel() drive the asynchronous batch endpoints of OpenAI, Groq, Anthropic, and Gemini, which price requests at roughly half the live rate. Jobs are plain R objects without secrets (keys stay environment references), so a job can be saved with state_path=, the session closed, and results fetched later or from another machine.
• llm_hash(): one content-hash convention for the ecosystem. Canonical form (classes stripped, named lists sorted, functions deparsed), canonical JSON, SHA-256 over the UTF-8 bytes – independent of R’s serialization format and of construction order. Downstream packages (locked coding protocols, replication archives) use these hashes as identifiers of record.
• Audit log for reproducible research. Each record carries a schema_version field (currently "1.0") so downstream tools can parse the log against a stable contract. llm_log_enable(path) appends one JSON record per API call (timestamp, provider, model, the served model_version, full request parameters and messages, reply text, token usage including cached tokens, request id, status, timing) to a JSONL file; llm_log_disable() and llm_log_status() manage it. Failed calls are logged too. include_messages = FALSE keeps a metadata-only trail for confidential prompts.
• A draft methods paragraph. llm_methods_text() turns a result frame into the transparency paragraph journals increasingly require: models, providers, call counts, recorded inference settings, token totals, and failure counts, stated only as far as the data supports.
• Replication and reliability. llm_replicate() runs every row .times times through the parallel engine; llm_agreement() reports per-unit majority labels and overall reliability (mean pairwise agreement and Krippendorff’s alpha for nominal data, missing-safe).
• Native tool calling with an execution loop. llm_tool() wraps an R function with a JSON-Schema argument spec; call_llm_tools() sends the definitions, executes the calls the model makes, feeds results back, and returns the final response with a tool_history attribute. tool_calls() extracts requested calls from any response. Supported on OpenAI-compatible providers and Anthropic.
• Streaming. call_llm_stream() delivers the reply chunk by chunk (callback) for OpenAI-compatible providers, Anthropic, and Gemini, and returns a normal llmr_response at the end, with usage when the provider reports it in the stream.
• Token log-probabilities. Pass logprobs = TRUE (and top_logprobs = k) in llm_config(); llm_logprobs() returns them tidily (OpenAI-compatible providers and Gemini) for confidence scoring and calibration.
• Reproducibility knobs. A canonical seed parameter is forwarded where supported; every response now records model_version (the identifier the server reports having served) and, when returned separately, the model’s reasoning text in a thinking field.
• Prompt caching. cache = TRUE marks the system prompt and tools as cacheable for Anthropic; cached prompt tokens are now extracted from all providers that report them (tokens(x)$cached, cached_tokens / *_cached diagnostic columns) and counted by llm_usage().
• Cost estimates on your own prices. llm_usage(price_table = ...) accepts a user-supplied table (per-million-token prices, optional cached rate) and adds a cost_estimate; LLMR still ships no price list, on purpose.
• NA policy for templates. llm_fn()/llm_mutate() gain .na_action = c("send", "skip", "error"); llm_preview() flags rows whose templates reference NA or render empty prompts.
• Quality-of-life. chat_session() gains quiet= and multimodal sends (named-vector shortcut); a one-line summary message after runs with failures or truncations points to llm_failures(); print(llm_config) is masked and informative; options(llmr.quiet = TRUE) silences advisory notes; every request carries a total timeout (timeout= per config or options(llmr.timeout=), default 600 s).

Provider-path overhaul

• The nine OpenAI-compatible providers (groq, together, deepseek, xiaomi, alibaba, zhipu, moonshot, xai, ollama) now share one request builder with the full feature set: multimodal file parts (previously serialized raw, with the local path leaked to the provider), the complete canonical parameter set, structured output, tools, verbatim extras passthrough, and the modifiability hooks. Parameters a provider rejects are dropped with a console note instead of silently.
• The req_builder, response_modifier, and request_modifier hooks now apply on every provider path (previously only OpenAI) and are documented in llm_config().
• Gemini: thinking_budget / include_thoughts are finally sent (generationConfig.thinkingConfig); presence/frequency penalties, seed, and logprobs are translated rather than dropped; enable_structured_output() sends the schema by default via responseJsonSchema (set gemini_enable_response_schema = FALSE for old models); embeddings use batchEmbedContents (one HTTP call per up to 100 texts instead of one per text) and go through the standard error handling; Gemini thought parts are kept out of the answer text and surfaced as thinking.
• Anthropic: top_k is supported again (it was wrongly dropped as “unsupported”); no_change is honored; PDFs are sent as document blocks (previously mislabeled as images); an invalid thinking_budget >= max_tokens combination warns before the call; unknown typed content blocks pass through, enabling the tool loop; the false claim that LLMR “sets the beta header automatically” is gone from the docs (anthropic_beta = ... sends one).
• OpenAI: max_completion_tokens is honored directly (reasoning models no longer pay a guaranteed failed round trip); the Responses-API autodetect recognizes gpt-5-pro and the deep-research models and no longer matches nonexistent ones; the Responses path drops the parameters that API rejects (penalties, logprobs, seed) with a note, maps reasoning_effort to its nested shape, and no longer duplicates system text when all messages are system.
• Embedding routing now follows the documented inference (embedding = NULL
  • “embedding” in the model name) for every provider, and embedding endpoints no longer receive chat-only parameters from a reused config. get_batched_embeddings() gains retry controls and its verbose default is documented correctly.

Reliability fixes

• The retry helper no longer sleeps after the final failed attempt (previously up to ~104 minutes wasted on the default schedule); waits honor Retry-After when a 429 provides one, and add jitter so parallel workers do not retry in lockstep.
• Retry classification is exact for typed errors: rate limits and server errors (now including 403->auth, 408->retryable) retry; parameter, authentication, and quota errors fail fast. The over-broad "exceeded" message pattern (which retried context_length_exceeded for the full schedule) is gone.
• call_llm_robust() defaults are humane (wait_seconds = 2, backoff_factor = 3) and consistent with call_llm_par(); start_jitter in the parallel engine defaults to 0 (it silently added up to 5 s per row).
• Failed rows in call_llm_par() results now carry the provider’s raw error body in raw_response_json.

Bug fixes

• Gemini multi-turn roles: assistant turns are now sent as Gemini’s "model" role. Previously every turn was sent as "user", so the model saw its own prior replies as user messages, corrupting chat sessions and any multi-turn agent memory on Gemini.
• extract_text() (Anthropic): responses with several text blocks (typical around tool use) now concatenate all of them; previously only the last block was returned and earlier content was silently dropped.
• llm_fn_structured() / llm_mutate_structured(): with .schema = NULL these now request JSON-object mode as documented; previously the config was sent unchanged and the model could return arbitrary prose.
• call_llm_tools(): the return value now carries attr(x, "tool_loop") with model_calls, aggregate sent/rec token totals across every internal round, and tool_calls – tokens(x) alone covers only the final call, which undercounted multi-round loops. A new max_tool_calls argument caps tool executions across the loop, raising a typed llmr_tool_limit condition instead of continuing to spend.
• call_llm.openai(): top_k and repetition_penalty are now dropped (with the usual one-time note) instead of being forwarded to an endpoint that rejects them; the streaming and batch paths already did this.
• Anthropic stop_sequence now maps to finish reason "stop" rather than "other".
• Templated .messages with partially named vectors (e.g. c(system = ..., "{x}")) now default unnamed elements to the user role, as documented, instead of erroring.
• call_llm_broadcast() with zero messages returns the full diagnostic column schema (finish reasons, token columns, response), matching non-empty results.
• llm_batch_submit(): a named character vector like c(system = "...", user = "...") is treated as a single multi-role request rather than being split into separate batch requests; unnamed character vectors still expand one element per request.
• llm_batch_fetch() / call_llm_stream(): total token count is NA when both sent and received counts are unknown, consistent with the call_llm() path, instead of a false 0.
• chat_session(): rejects embedding configs inferred from model names (e.g. "gemini-embedding-001") in addition to those with embedding = TRUE set explicitly.
• expand_llm_config(): sweeping provider now updates the S3 class, so the swept config dispatches to the right API (previously every swept provider was called through the original provider’s path).
• call_llm_par_structured() / call_llm_par_tags(): prompts containing literal braces (typical for structured-output instructions) no longer abort the run; strings glue cannot parse pass through verbatim.
• Key handling: an empty-string api_key (what Sys.getenv() returns for an unset variable) falls back to the provider’s default variables with a warning instead of sending an empty Bearer header; NA keys are rejected; a vector api_key works as ordered fallbacks; legacy configs holding a literal key string resolve correctly and error messages never echo a key.
• Chat sessions: the NA-token fix now also covers send_structured() and send_tags() (one usage-less response could previously poison the running totals); multi-part sends are no longer truncated to their first element.
• Column safety: llm_mutate() replaces an existing output column with a notice (mutate semantics) instead of letting bind_cols() mangle both names; hoisted structured/tag fields never silently overwrite existing columns (suffix + warning); the parallel engine’s collision warning fires regardless of verbose; summary.llmr_experiment() follows collision-renamed columns.
• Row batching: user tags shaped like row_2 are rejected in batched tag mode (they would scramble the demultiplexer); assistant turns error instead of being silently dropped; duration is attributed once per batch so llm_usage() stops overcounting wall time; fully-failed batch calls attribute their token spend to the first failed row; the protocol instructions state the actual item count.
• Structured output: JSON recovery is string-aware (braces inside string values no longer corrupt extraction); disable_structured_output() removes a custom-named schema tool; llm_mutate_structured() validates locally like llm_fn_structured() (new .validate_local). enable_structured_output() now knows which providers reject a server-side json_schema (DeepSeek, Alibaba, Zhipu, Moonshot, Xiaomi) and requests JSON-object mode with local validation there, instead of a guaranteed HTTP 400. In strict mode the schema sent to the provider is hardened the way the OpenAI protocol formally requires (additionalProperties: false and all properties required, filled in only where unspecified), so plain schemas work on OpenAI and Groq without boilerplate.
• llm_par_resume() works on call_llm_sweep() / call_llm_broadcast() / call_llm_compare() results (they now keep the config list-column) and refreshes structured_ok / structured_data for re-run rows.
• Responses: OpenAI-style refusals surface their text and map to finish_reason = "filter" (as do Gemini safety verdicts such as RECITATION); llm_judge() gains .output= and refuses to clobber a .target column.
• Assorted: call_llm() troubleshooting output prints real newlines; dead code removed (parse_embeddings() no-op branch, unreachable per-provider default models); embedding example code no longer uses the discouraged Sys.getenv() key pattern.

Documentation

• New vignette “Interactive calls: tools, streaming, and logprobs”: the tool loop end to end (definitions, history, aggregate spend, max_tool_calls), streaming with custom callbacks, and log-probabilities as graded measurements, with honest notes on provider support.
• llm_config() now documents the full canonical parameter set (including seed, logprobs, thinking_budget, timeout, cache) and the three request hooks. The Anthropic thinking example is valid (max_tokens > thinking_budget). build_factorial_experiments() documents that system prompts are crossed, not recycled. Vignettes updated accordingly, plus a new article on reproducibility and cost.
• Live tests and examples run on inexpensive open-weight models (Groq openai/gpt-oss-20b, DeepSeek, Moonshot, Qwen, Gemini Flash-Lite).

LLMR 0.8.0

New features

• Preview a call before spending anything. llm_preview() renders exactly what llm_fn() / llm_mutate() would send (using the same internal renderer, so it can never drift from the real path) without making any API call or reading/encoding files. It returns a row-level tibble with the rendered messages, roles, character counts, file presence/existence, the batch plan (batch_id / batch_size / batch_row), and an issues list-column that flags problems up front: missing files, "file" content combined with .batch_size > 1, an embedding config with row batching, .return = "object" with batching, or a schema supplied without .structured. llm_render_messages() exposes just the rendered message objects.
• Summarize a finished run. llm_usage() reports outcome counts and token totals (sent / received / total / reasoning) plus truncation and filter counts, reading the diagnostic columns that call_llm_par() and llm_mutate() already produce. It works on both result shapes and sums tokens with na.rm = TRUE, which is correct under row batching. It reports tokens, not money: no dollar figures and no built-in price table (which would go stale).
• Find and re-run failures. llm_failures() lists exactly which rows failed or were truncated/filtered, with status_code, error_code, bad_param, and error_message. For a call_llm_par() result, pass the original frame to the existing llm_par_resume() to re-run only those rows.

Internal

• The per-row message rendering used by llm_fn(), llm_mutate(), and llm_preview() is now a single shared internal helper, locked by golden tests so its output stays byte-identical to previous releases.

Bug fixes

• .fields = FALSE now correctly skips field extraction in structured/JSON mode (keeping only the structured_data list-column), matching tag mode. Previously the logical FALSE was treated as a one-element field name.
• Missing token usage is reported as NA, not 0. When a provider returns no usage metadata, chat sessions and responses no longer record the call as having used zero tokens; running chat totals add NA as 0 so one unknown response cannot poison the cumulative count.
• llm_usage() / llm_failures() / llm_par_resume() handle the case where call_llm_par() collision-renamed its output columns (because the input frame already had a column named success, etc.): the summaries follow the renamed columns, and llm_par_resume() raises a clear, actionable error.
• Bare environment-variable API keys. api_key = "OPENAI_API_KEY" is now always treated as an environment-variable reference, even when that variable is not yet set (it then fails with a clear “missing env var” message at call time instead of silently sending the literal name as the key), matching the documented behavior.
• llm_api_key_env(required = FALSE) is now honored: a missing variable yields an empty key instead of an authentication error.
• llm_parse_structured_col() now returns a tibble on every path, including when the structured column is absent.
• llm_usage() gains an n_unknown_tokens count so an all-NA token column (a provider that reports no usage) is no longer indistinguishable from a true zero. The token sums still use na.rm = TRUE, which is correct for batching.

Documentation

• llm_api_key_env() is now exported and documented. The help also notes that the simplest approach is to set the standard <PROVIDER>_API_KEY variable and pass no key at all.
• New “LLMR in 5 minutes” quickstart vignette: install, set a key, a first call_llm(), a generative llm_fn() over a vector, a data-frame llm_mutate(), and tagged + batched extraction, all on the open-weight gpt-oss-20b so the examples are runnable for everyone. The structured-output articles now lead with a concrete example before the provider-by-provider details.
• The troubleshooting help no longer claims the API key is printed (it is masked). The embeddings vignette can now be enabled with LLMR_RUN_VIGNETTES=true (its run flag was previously hard-coded off), and its stale prebuilt HTML was removed.
• Fixed a vignette that referenced a non-existent function and assorted stale version labels; removed non-ASCII look-alike punctuation from R sources.

LLMR 0.7.2

Bug fixes

• Retry/error classification. API error messages containing curly braces (common when providers echo JSON fragments) no longer break error construction: the typed condition class, status_code, and provider message are preserved, so retryable rate-limit/server errors are retried correctly instead of being misclassified. The retry helper also re-raises the original typed error after exhausting attempts, and its wait-time message no longer errors on fractional backoff values.
• llm_par_resume() now re-runs only the failed/NA rows instead of every row (it previously collapsed the per-row success vector with isTRUE()).
• Parallel tag/structured helpers. Unnamed (bare-prompt) messages passed to call_llm_par_tags() / call_llm_par_structured() no longer have their template text used as the message role.
• JSON recovery. A top-level JSON array embedded in prose is now extracted in full (a regex character-class bug previously truncated it to the first object).
• Embeddings. get_batched_embeddings() no longer locks in a wrong vector dimension when the first batch returns empty, preserving the one-row-per-input contract.
• Gemini token counts. Usage is preserved when candidatesTokenCount is absent (e.g. reasoning models with no visible output).
• Printing. print() on an llmr_response with a non-standard finish reason no longer drops the status line.

LLMR 0.7.1

New features

• Row batching for generative calls. llm_fn() and llm_mutate() (and the _tags / _structured variants) gain .batch_size, .batch_payload, and .batch_recovery. With .batch_size > 1, several rows are packed into one request wrapped in numbered <row_1>...</row_1> tags and de-multiplexed back into rows, with fault-tolerant recovery (split-and-retry down to single rows) for dropped, reordered, or truncated rows. Composes with .tags (one level of required nesting) and with structured/JSON output ({"results":[{"row":i, ...}]}). The default .batch_size = 1 reproduces the previous one-call-per-row behaviour exactly. New exported helper llm_parse_batch_tags().

LLMR 0.7.0

New features

• Soft structured output via XML-like tags. llm_mutate() gains .tags, backed by llm_mutate_tags(), llm_fn_tags(), llm_parse_tags(), llm_parse_tags_col(), and call_llm_par_tags().
• Four new providers: Xiaomi MiMo, Alibaba (Qwen), Zhipu (GLM), and Moonshot (Kimi). All use OpenAI-compatible structured output.
• Gemini Vertex AI supported via llm_config("gemini", ..., vertex = TRUE).
• Multi-variable API key fallback. Providers can declare multiple environment variable names (e.g., XIAOMI_KEY or XIAOMI_API_KEY); the first one found is used.

Bug fixes

• Fixed API key resolution for providers with multiple fallback env vars.
• Removed dead requireNamespace("LLMR") guard inside llm_mutate().

LLMR 0.6.3

• Added Ollama provider (local generative and embedding models).
• Stable column names (v1…vN) in get_batched_embeddings().

LLMR 0.6.2

• llm_mutate() shorthand: llm_mutate(answer = "{question}", .config = cfg).
• .structured = TRUE flag in llm_mutate() for inline JSON parsing.
• setup_llm_parallel() accepts a positional numeric workers argument.

LLMR 0.6.1

• Fixed a bug that affected Anthropic calls.

LLMR 0.6.0

• call_llm() now returns an llmr_response object. Use as.character(x) for plain text. Legacy json= arguments are removed.
• Secure API key handling: literal keys are moved to temporary env vars.
• Structured JSON output and schema validation.
• Multi-column injection in llm_mutate().