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* feat(gateway): cache-aware cost accounting + /api/console observability endpoints - Capture prompt-cache hits (usage_metadata.input_token_details.cache_read) in RunJournal and SubagentTokenCollector as a sparse cache_read_tokens key in token_usage_by_model (JSON field — no schema migration; legacy bucket shapes unchanged) - New read-only /api/console router: GET /stats (headline counters), GET /runs (cross-thread paginated history joined with thread titles), GET /usage (zero-filled daily token series + per-model breakdown); user-scoped, 503 on the memory database backend - Optional models[*].pricing (currency, input_per_million, output_per_million, input_cache_hit_per_million) powers real spend estimation; cache-hit input tokens are billed at the hit price (omitted hit price falls back to the miss price as a conservative upper bound); unpriced models yield cost: null - create_chat_model strips the presentation-only pricing block so it never reaches the provider client (unknown kwargs are forwarded into the completion payload and break live calls) - Tests: console router SQLite round-trips, journal/collector cache capture incl. a DeepSeek raw-usage pin test, factory strip regression Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> * refactor: address review feedback on cost sum and sparse cache_read_tokens - console.py: replace the walrus-in-generator total-cost sum with an explicit loop (review noted the multi-line form reads ambiguously) - token_collector.py: omit cache_read_tokens from usage records when the provider reported no cache hits, matching the journal's sparse per-model bucket shape; absent is treated as 0 downstream - add a regression test pinning the sparse record shape Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> --------- Co-authored-by: coffeeFish <codeingforcoffee@users.noreply.github.com> Co-authored-by: Claude Fable 5 <noreply@anthropic.com>