Google ADK Python 2.10.0: experimental skill lifecycle, MongoDB vector/hybrid search, eval duration/token metrics

Core Background & Industry Pain Points Teams need one Agent runtime to govern Skill resources, call vector DBs, and quantify eval duration/tokens. Always-on skills bloat tools/context; DB search needs glue; evals lacked first-class cost/latency counters; OpenAI reasoning params and BigQuery protected writes needed stricter defaults. ### Architecture Highlights & Internals v2.10.0 adds experimental skill lifecycle (ADK_ENABLE_SKILL_LIFECYCLE=1), a MongoDB toolset for vector/hybrid search, and eval metrics for duration/tokens/model calls. OpenAI reasoning adapts via effort (not thinking_config); BigQuery protected writes tighten around anonymous-dataset dry-runs. ### Authoritative Benchmarks & Measured Scores No public leaderboard ships—use the new duration/token/model-call metrics on your eval sets and compare skill-lifecycle resource use. Do not invent arena scores. ### Developer Hands-on Guide pip install -U google-adk==2.10.0 (PyPI); docs at adk.dev / Skills / Evaluate. See the release.