- Added CredentialRankingStrategy scope hooks so providers can rank and block only the limits relevant to the requested model.
- Scoped Antigravity usage reports by model family: Gemini/Gemma use Google counters, Claude uses Anthropic counters, and GPT/OpenAI models use OpenAI counters.
- Added scoped backoff keys so a Gemini quota block no longer suppresses healthy Claude/OpenAI Antigravity sessions on the same OAuth credential.
- Threaded modelId through coding-agent API-key resolvers and usage-limit rotation paths.
- Added regression coverage proving a Google/Gemini exhaustion block still allows Claude selection on the same credential.
Fixes#2198
- Added lazy async module loaders for @babel/parser, linkedom, puppeteer/browsers, @mozilla/readability, @xterm/headless, and mnemopi to avoid loading them during cold startup.
- Added an interactive startup splash before session construction and skipped it for resume/fork/continue, quiet mode, timing mode, or non-TTY runs.
- Updated JS import-rewrite and memory tests to match async parser loading and preloaded mnemopi modules for sync state helpers.
- Introduced memoized dynamic import loaders for Babel parser, mnemopi modules, puppeteer, and HTML-related packages.
- Refactored eval import-rewrite helpers and runtime call sites to use asynchronous wrapping and parsing flows.
- Shifted fetch and web-scraper linkedom usage to on-demand imports so heavy modules load only when needed.
- Routed image-gen, inspect-image, and web search providers through `withAuth`.
- Used `reuseInitialApiKey`/`createAuthStorageResolver` for force-refresh and rotate retries.
- Attached HTTP status to thrown errors so the retry classifier detects retryable failures.
- Added `ApiKeyResolver`/`ApiKey` types and exported auth-retry helpers.
- Changed stream and gateway auth retry handling to use resolver steps.
- Added initial-key, force-refresh, and rotate credential retries for auth failures.
- Updated agent and coding-agent integrations to use context-aware API-key resolvers.
The beam backend never invoked the embedding pipeline during normal
operation: `remember()`/`rememberBatch()`/`updateWorking()` skipped `embed()`
entirely and `recall()`/`recallEnhanced()` never called `embedQuery()` on
the query text. As a result `memory_embeddings` stayed empty in every
deployment and recall silently degraded to FTS-only regardless of the
configured provider (fastembed, OpenAI-compatible API, custom).
- Added `scheduleEmbedding` on `beam.pendingExtractions` (mirroring
`scheduleFactExtraction`) and wired it from `remember`, `rememberBatch`,
`updateWorking`, and `consolidateToEpisodic`. Writes
`INSERT OR REPLACE INTO memory_embeddings(memory_id, embedding_json, model)`
with the active runtime-options model, captured before the AsyncLocalStorage
scope exits and re-entered inside the task.
- Auto-derived `queryEmbedding` inside `recall()` via `embedQuery(query)` when
the caller did not pass one. `queryEmbedding: null` is preserved as the
explicit FTS-only opt-out; `undefined` triggers auto-derive.
- Propagated `queryEmbedding` through `Mnemopi`'s `toRecallOptions` so the
facade no longer strips the override on the way to the beam layer.
- Made `Mnemopi.recall`/`recallEnhanced`/`search`/`query`, the module-level
exports, `BeamMemory.recall`/`recallEnhanced`, the free `recall`/`recallEnhanced`,
and `orchestrateRecall` async. MCP `handleToolCall`/`callToolJson`/`handleJsonRpc`
follow suit so the recall handler can await.
- Fixed `withBeam`/`withSharedBeam` to defer `beam.close()` until the async
handler resolves; otherwise the new async recall hit
`RangeError: Cannot use a closed database`.
- Updated CLI, MCP entrypoints, coding-agent `MnemopiSessionState`, and every
affected test to await the new shapes.
Verified with a new regression suite (`test/issue-1832-embedding-population.test.ts`)
exercising both ends of the bug: empty `memory_embeddings` and zero
`dense_score`.
Fixes#1832
can1357