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llm.api 0.1.4

CRAN release consolidating the 0.1.3.1–0.1.3.5 development cycle. Highlights since the on-CRAN 0.1.3:

• Cache-aware cost estimates. New exported usage_cost() prices a usage object (Anthropic cache writes/reads via published multipliers; ‘OpenAI’ / ‘Moonshot’ cache hits from the bundled per-model rate), and chat() / agent() carry it as usage$cost. New prices_snapshot_stale() for staleness alerts. (0.1.3.4)
• Refreshed default models per provider: ‘OpenAI’ gpt-5.4-mini, ‘Anthropic’ claude-sonnet-4-6, ‘Moonshot’ kimi-k2.5, ‘Ollama’ qwen3.5:9b. (0.1.3.5)
• agent() gains a history_callback for snapshotting intermediate state across an interrupt. (0.1.3.1)
• chat() / agent() gain cache (Anthropic prompt caching) and thinking_budget_tokens (extended thinking); ‘OpenAI’ max_tokens is mapped to max_completion_tokens. (0.1.3.2)

The per-cycle detail follows.

llm.api 0.1.3.5

Refreshed default models

When no model is given, each provider now defaults to a recent, cost-appropriate, snapshot-priceable model, replacing dated defaults:

• OpenAI: gpt-5.4-mini (was gpt-4o-mini / gpt-4o)
• Anthropic: claude-sonnet-4-6 everywhere, including agent() and chat_session_anthropic() (which still defaulted to the dated claude-sonnet-4-20250514)
• Moonshot: kimi-k2.5 (was kimi-k2, which wasn’t in the price snapshot, so cost estimates came back NA)
• Ollama: qwen3.5:9b (was llama3.2)

This affects chat(), agent(), and the chat_*() / chat_session_*() wrappers. Pass model = explicitly to use any other model.

llm.api 0.1.3.4

Cache-aware cost estimates

usage$cost (from chat() and agent()) now accounts for prompt caching instead of billing every input token at the full rate. Anthropic cache writes/reads are priced from Anthropic’s published multipliers (5-minute write 1.25x, 1-hour write 2x, read 0.1x of the base input rate), and OpenAI / Moonshot cache hits are priced from each model’s cached-input rate in the bundled snapshot.

New exported helpers:

• usage_cost(model, provider, usage) returns the USD estimate for a usage object (the same value attached as usage$cost), so callers can price usage objects directly. Scalar return; cache-aware.
• prices_snapshot_stale(max_age_days = 90) reports whether the bundled price snapshot is older than a threshold, for staleness alerts.

agent()$usage now also carries cumulative cache_read_input_tokens and cache_creation_input_tokens so callers can inspect cache activity after a multi-turn run.

The bundled price snapshot was refreshed (2026-05-24) to carry per-model cached-input rates; base input/output rates for existing models are unchanged. Cost estimates remain offline and approximate; prices_snapshot_date() docs now spell that out, with source URLs.

llm.api 0.1.3.3

Fix: cache / thinking_budget_tokens silently disabled under the default provider

The Anthropic-only guards in chat() ran before provider auto-detection, comparing against the literal "auto" default. So chat(prompt, model = "claude-...", cache = "5m") tripped a spurious “Anthropic-only” warning, downgraded the opt-in, and fell through to the default provider. Detection now runs first, so the guards see the resolved provider. .validate_thinking_budget() still runs up front as provider-independent input validation. Network-free regression coverage added.

llm.api 0.1.3.2

Three additions, all backward-compatible (new parameters default to no-op behaviour) and zero new dependencies.

Anthropic prompt caching (cache parameter)

chat(cache = c("none", "5m", "1h")) and agent(cache = c("none", "5m", "1h")). Default "none" preserves current behaviour; opting in wraps the system message in an ephemeral cache_control block. "5m" uses Anthropic’s default TTL; "1h" requests the longer cache window. Worth turning on when the system prompt is long-lived across calls — cache reads cost ~10% of normal input tokens but cache writes cost ~25% more, so opt-in is the right default. Anthropic-only; warns and degrades to no-op for other providers.

Anthropic extended thinking budget (thinking_budget_tokens)

chat(thinking_budget_tokens = N) and agent(thinking_budget_tokens = N). When set, sends thinking = {type: "enabled", budget_tokens: N} to the Anthropic Messages API. Validates inputs early: must be a single integer >= 1024, and (when max_tokens is set) must be strictly less than it since the budget is counted against max_tokens. Anthropic-only; warns and degrades for other providers.

OpenAI max_tokens → max_completion_tokens mapping

OpenAI deprecated max_tokens in favour of max_completion_tokens, and o-series reasoning models reject max_tokens entirely. chat() and agent() now rename for OpenAI requests only; Moonshot and Ollama (which share the OpenAI-compatible code path) continue to receive max_tokens since their endpoints still expect it. The rename is gated on the caller not already passing max_completion_tokens, so explicit-set values win.

llm.api 0.1.3.1

• agent() gains a history_callback parameter. The callback is invoked with the current full history after each assistant message is appended and after each tool result is appended. Callers (e.g. corteza) use it to snapshot intermediate state so an interrupt mid-turn doesn’t lose tool calls that already completed in this batch. Callback errors are swallowed so telemetry can’t break a turn. Tool results are now appended incrementally to history (still as a single batched user message on Anthropic, per the API contract); the old .add_tool_results() internal helper remains for backwards compatibility.

llm.api 0.1.3

• chat() and agent() now return $usage$cost, a USD scalar derived from a bundled snapshot of BerriAI/litellm’s model_prices_and_context_window.json (the same upstream ellmer uses). Ollama is treated as free (cost = 0); models absent from the snapshot leave cost = NA_real_. A new exported helper prices_snapshot_date() returns the snapshot date so callers can decide when to refresh. Refresh by re-running data-raw/prices.R.
• New exported helpers history_tool_calls(history) and history_count_tool_calls(history, completed_only = FALSE) for walking the message history agent() returns. Provider history must stay native (it’s the input format on the next API call), but consumers now get a single canonical record list instead of having to know that Anthropic uses content blocks (tool_use / tool_result) while OpenAI / moonshot / ollama use a separate tool_calls field plus role = "tool" result messages. Each record carries id, name, arguments, result, completed, call_message_index, result_message_index, and provider_shape.
• agent() now writes the synthesized tool-call id back into the Ollama assistant message when the upstream response omits one. Previously assistant.tool_calls[i].id and the corresponding role = "tool" message’s tool_call_id could disagree, breaking history walks that paired calls with results.
• New exported helper provider_default_model(provider). Returns the model id chat() falls back to when no model is specified, so client code can display the resolved model upfront without duplicating the lookup table or reaching into internals.
• chat() now returns $thinking and $finish_reason. Reasoning models (DeepSeek-R1, Moonshot Kimi, Anthropic extended thinking, OpenRouter) put their chain-of-thought in a separate field and previously had it silently dropped. $thinking is normalized across providers (reasoning_content, reasoning, Anthropic thinking blocks). $finish_reason is normalized to OpenAI vocabulary; Anthropic’s max_tokens becomes "length" and end_turn becomes "stop".
• chat() now warns when a reasoning model truncates mid-thought (finish_reason == "length" with empty content but populated thinking). Previously this returned content == "" with no indication; the actionable signal is “raise max_tokens”.

llm.api 0.1.1

• Initial CRAN submission.
• Add Moonshot (Kimi) provider alongside OpenAI, Anthropic, and Ollama. Detected by base URL or model name; key resolution falls back to OPENAI_API_KEY since the API is OpenAI-compatible.
• Fix conversation history bug in agent() where the final assistant message was not appended to the returned history when the agent loop exited without further tool calls. Affected all providers but was most visible with non-Claude models.
• Drop the "local" provider and chat_local() / list_local_models() exports. Direct llama.cpp inference via the localLLM package is no longer supported; use provider = "ollama" instead.

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