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.
- _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withdrawn it; the post-grace state gets the plain no-tools text back.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.
Migrate off two APIs slated for removal in pydantic-ai 2.0:
- Agent(tool_retries=, output_retries=) -> Agent(retries={"tools": ...,
"output": ...}) in the LLM-as-judge evaluator.
- Evaluator.evaluation_name class attribute -> overriding
get_default_evaluation_name() on the citation MRR / MAP evaluators.
haiku.skills 0.17.0 already migrated its internal AGUIAdapter,
MCPToolset and ProcessEventStream usage; no further changes needed on
our side beyond the pin bumps.
Context expansion is now automatic and structure-aware. For structured
documents, expands within the section containing the match. For sections
that exceed the budget or are too small, expands item-by-item outward
skipping noise labels. Unstructured documents use budget-based outward
expansion. Results sorted by relevance score.
