IBM's MTRAG benchmark (ClapNQ domain, pinned repo SHA): retrieval with
Recall@k/nDCG@k against binary qrels, gold-prefix QA replaying reference
conversation prefixes as message history, and live-session replay
carrying the model's own answers and tool history across turns.
Corpus population gains a bounded, resumable batched ingest path.
ConversationInput case type with transcript rendering for the judge,
eligibility-aware citation scoring, refusal precision/recall via a
label-aware RefusalJudge, per-turn verdicts with judged-turn coverage,
and per-turn tool-traffic attributes counted from each turn's new
messages so the arrays survive prior-turn compaction.
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.
