The notice told the model to answer from what it had the moment
qa.max_searches ran out, while up to 15 code executions remained and
in-code search() does not count against that budget. It now names the
spent tool and points at whichever evidence tool still has budget,
falling back to answer-and-cite only when none do.
Also count RetryPromptPart in n_failed_tools: _cite rejects with
ModelRetry, so a run whose every cite attempt was refused reported zero
failures. And note that n_requests is the run's request count, which
tracks a capability's own budget only while it stays loaded.
It was n_rejected_searches > 0 recorded next to the integer it derived
from, and the name overclaimed: analysis_execute_code also raises
ToolFailed when execute_count exceeds max_executions, which the flag
never saw. Callers can compare the counters directly.
- _budget_notice no longer names the cite tool after prepare_tools has
withdrawn it; the post-grace state gets the plain no-tools text back.
- Split search-budget rejections from any failed tool call: the code tool
raises ToolFailed for every error in model-written Python, so
budget_spent was true for a ZeroDivisionError.
- docs/capabilities/rag.md described the old single-turn removal.
- Drop the rationale clause from the CHANGELOG entry.
Retrieval and QA only read from the database, but the benchmark opened it
writable, where an embedder identity differing from the stored one aborts
instead of warning. Running a pre-built database against a different
serving stack then needed a `rebuild --set-embedder` first.
Correct the debug-evals skill alongside it: the pydantic-ai span names are
`execute_tool {tool_name}` and `invoke_agent agent`, targets are
`{rag,analysis}-capability`, and no `skill_model` metadata key exists.
