fix(embed): stop worker pool from dispatching new slices after a sub-batch failure

Review finding on #3130: when one sub-batch rejected, the surviving pool
workers kept draining ALL remaining slices in the background after
embedBatch had already rejected — real provider spend post-failure,
onBatchComplete firing after the caller handled the error, and stacked
429 pressure when embedBatchWithBackoff retried while the failed run was
still draining. A shared failed flag now stops further dispatch (in-flight
sibling calls still settle, bounded by concurrency-1) and suppresses
post-failure progress callbacks. Pinned by a new test: 10 slices /
concurrency 2 / first call fails → no calls after rejection, no
completions reported.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

src/commands/import.ts

@@ -170,10 +170,14 @@ export async function runImport(
   // v0.22.13 (PR #490 Q2): shared parseWorkers helper rejects bad input
   // (--workers 0, -3, "foo") with a loud error instead of silently falling
   // through to 1. Mirrors sync.ts's flag handling.
-  const { parseWorkers } = await import('../core/sync-concurrency.ts');
-  let workerCount: number;
+  const { parseWorkers, autoConcurrency } = await import('../core/sync-concurrency.ts');
+  // #1207: undefined (no --workers flag) defers to autoConcurrency below —
+  // the shared sync/import policy (PGLite → 1, >100 files → 4) — instead of
+  // hardcoding serial. Large Postgres imports stop paying one embedding
+  // round-trip per file in sequence.
+  let workerCount: number | undefined;
   try {
-    workerCount = parseWorkers(workersArg ?? undefined) ?? 1;
+    workerCount = parseWorkers(workersArg ?? undefined);
   } catch (e) {
     console.error(e instanceof Error ? e.message : String(e));
     process.exit(1);
@@ -252,8 +256,9 @@ export async function runImport(
   }
   const files = resumeFilter(allFiles, dir, completed);
 
-  // Determine actual worker count
-  const actualWorkers = workerCount > 1 ? workerCount : 1;
+  // Determine actual worker count. Explicit --workers wins; otherwise the
+  // shared autoConcurrency policy decides from engine kind + file count.
+  const actualWorkers = autoConcurrency(engine, files.length, workerCount);
   if (actualWorkers > 1) {
     console.log(`Using ${actualWorkers} parallel workers`);
   }

src/commands/models.ts

@@ -50,10 +50,6 @@ const PER_TASK_KEYS: Array<{ key: string; tier: ModelTier; description: string }
   { key: 'models.eval.contradictions_judge', tier: 'utility',  description: 'Contradiction probe judge (v0.34 temporal-aware)' },
   { key: 'models.expansion',                tier: 'utility',   description: 'Query expansion for hybrid search' },
   { key: 'models.chat',                     tier: 'reasoning', description: 'Default `gateway.chat()` model' },
-  { key: 'models.propose_takes',            tier: 'reasoning', description: 'propose_takes claim extractor' },
-  { key: 'models.grade_takes',              tier: 'reasoning', description: 'grade_takes verdict judge' },
-  { key: 'models.calibration_profile',      tier: 'reasoning', description: 'Calibration profile generator' },
-  { key: 'models.brainstorm',               tier: 'reasoning', description: '`gbrain brainstorm` orchestrator' },
 ];
 
 interface ModelEntry {

src/core/ai/gateway.ts

@@ -1513,12 +1513,21 @@ export async function embed(texts: string[], opts?: EmbedOpts): Promise<Float32A
 
   const embedding = recipe.touchpoints?.embedding;
   const maxBatchTokens = embedding?.max_batch_tokens;
+  const maxBatchCount = embedding?.max_batch_count;
   const charsPerToken = embedding?.chars_per_token ?? DEFAULT_CHARS_PER_TOKEN;
 
-  // Pre-split is gated on max_batch_tokens. Recipes without it (e.g. OpenAI)
-  // ride the fast path: one embedMany call, no recursion safety net.
-  const batches = maxBatchTokens
-    ? splitByTokenBudget(truncated, Math.floor(maxBatchTokens * effectiveSafetyFactor(recipe)), charsPerToken)
+  // Pre-split is gated on max_batch_tokens / max_batch_count. Recipes with
+  // neither (e.g. OpenAI) ride the fast path: one embedMany call, no
+  // recursion safety net.
+  const batches = (maxBatchTokens || maxBatchCount)
+    ? splitByTokenBudget(
+        truncated,
+        maxBatchTokens
+          ? Math.floor(maxBatchTokens * effectiveSafetyFactor(recipe))
+          : Number.MAX_SAFE_INTEGER,
+        charsPerToken,
+        maxBatchCount,
+      )
     : [truncated];
 
   const allEmbeddings: Float32Array[] = [];
@@ -1568,6 +1577,9 @@ export async function embed(texts: string[], opts?: EmbedOpts): Promise<Float32A
  *   responsible for applying any safety-factor shrink before passing in.
  * @param charsPerToken - Provider-specific character density. Defaults to
  *   `DEFAULT_CHARS_PER_TOKEN` (4) when omitted, matching OpenAI tiktoken.
+ * @param maxBatchCount - #1199: optional cap on INPUTS per sub-batch, for
+ *   providers that reject batches by count (DashScope: 10). When omitted,
+ *   only the token budget governs.
  *
  * @internal exported for tests; not part of the public gateway API.
  */
@@ -1575,15 +1587,17 @@ export function splitByTokenBudget(
   texts: string[],
   budgetTokens: number,
   charsPerToken: number = DEFAULT_CHARS_PER_TOKEN,
+  maxBatchCount?: number,
 ): string[][] {
   const ratio = charsPerToken > 0 ? charsPerToken : DEFAULT_CHARS_PER_TOKEN;
+  const maxCount = maxBatchCount !== undefined && maxBatchCount > 0 ? maxBatchCount : Infinity;
   const batches: string[][] = [];
   let current: string[] = [];
   let currentTokens = 0;
 
   for (const text of texts) {
     const estTokens = Math.ceil(text.length / ratio);
-    if (current.length > 0 && currentTokens + estTokens > budgetTokens) {
+    if (current.length > 0 && (currentTokens + estTokens > budgetTokens || current.length >= maxCount)) {
       batches.push(current);
       current = [];
       currentTokens = 0;
@@ -1609,7 +1623,11 @@ export function isTokenLimitError(err: unknown): boolean {
     /token.*limit.*exceeded/i.test(msg) ||
     // OpenAI embeddings: "Invalid 'input': maximum request size is 300000 tokens per request."
     /maximum request size.*tokens/i.test(msg) ||
-    /max.*tokens.*per.*request/i.test(msg)
+    /max.*tokens.*per.*request/i.test(msg) ||
+    // DashScope: "batch size is invalid, it should not be larger than 10." (#1199)
+    // Count-cap error, but recursive halving shrinks count too, so the same
+    // safety net converges.
+    /batch size is invalid/i.test(msg)
   );
 }
 

src/core/ai/recipes/dashscope.ts

@@ -31,6 +31,10 @@ export const dashscope: Recipe = {
       // path. Conservative declaration so the gateway pre-splits before
       // hitting whatever undocumented server-side limit exists.
       max_batch_tokens: 8192,
+      // #1199: DashScope hard-caps embeddings at 10 inputs per request
+      // ("batch size is invalid, it should not be larger than 10"). The
+      // token budget alone admits far more than 10 short chunks per batch.
+      max_batch_count: 10,
       // text-embedding-v3 mixes English + CJK heavily; the tokenizer is
       // closer to Voyage density than OpenAI tiktoken for CJK-dominant
       // content. Conservative chars_per_token=2 leaves headroom.

src/core/ai/recipes/google.ts

@@ -16,6 +16,15 @@ export const google: Recipe = {
       dims_options: [768, 1536, 3072],
       cost_per_1m_tokens_usd: 0.15,
       price_last_verified: '2026-04-20',
+      // #970: Gemini's documented limits are per-INPUT (2048 tokens,
+      // silently truncated beyond) and per-REQUEST count (batchEmbedContents
+      // caps at 100 inputs). There is no separate per-request token cap, so
+      // the token budget is derived: 100 inputs × 2048 tokens. The count cap
+      // binds first for typical chunk sizes. Do NOT copy the 2048 per-input
+      // limit into max_batch_tokens — that would over-split 50×.
+      max_batch_tokens: 204_800,
+      chars_per_token: 4,
+      max_batch_count: 100,
     },
     expansion: {
       models: ['gemini-2.0-flash', 'gemini-2.0-flash-lite'],

src/core/ai/recipes/index.ts

@@ -58,5 +58,8 @@ export function getRecipe(id: string): Recipe | undefined {
 }
 
 export function listRecipes(): Recipe[] {
-  return [...ALL];
+  // Read the map (not ALL) so there is one source of truth — getRecipe,
+  // model-resolver, and listRecipes all see the same registry, and tests
+  // can inject a synthetic recipe via RECIPES to exercise registry walks.
+  return [...RECIPES.values()];
 }

src/core/ai/types.ts

@@ -46,6 +46,16 @@ export interface EmbeddingTouchpoint {
    * Only consulted when `max_batch_tokens` is also set.
    */
   chars_per_token?: number;
+  /**
+   * #1199: maximum number of INPUTS per embedding request, for providers
+   * that hard-cap batch size by count rather than (or in addition to)
+   * tokens — DashScope text-embedding-v3 rejects batches > 10 with
+   * `InvalidParameter`, Gemini batchEmbedContents caps at 100 requests.
+   * When set, the gateway's pre-split flushes a sub-batch at this count
+   * even if the token budget still has room. Independent of
+   * `max_batch_tokens`; either alone triggers the pre-split.
+   */
+  max_batch_count?: number;
   /**
    * Budget-utilization ceiling in (0, 1]. The gateway pre-splits at
    * `safety_factor × max_batch_tokens` to leave headroom for tokenizer

src/core/brainstorm/judges.ts

@@ -61,10 +61,7 @@ export const MAX_OUTPUT_TOKENS_CEIL = 32_000;
  * (with a readable error) instead of the provider's opaque HTTP 400.
  */
 export const ANTHROPIC_OUTPUT_CAPS: Record<string, number> = {
-  'claude-fable-5': 64_000,
-  'claude-opus-4-8': 32_000,
   'claude-opus-4-7': 32_000,
-  'claude-sonnet-5': 64_000,
   'claude-sonnet-4-6': 64_000,
   'claude-haiku-4-5': 64_000,
   'claude-haiku-4-5-20251001': 64_000,

src/core/brainstorm/orchestrator.ts

@@ -32,7 +32,6 @@
  */
 
 import type { BrainEngine } from '../engine.ts';
-import { resolveModel } from '../model-config.ts';
 import { chat as defaultChat, embedQuery, type ChatResult, type ChatOpts } from '../ai/gateway.ts';
 import { hybridSearch, hybridSearchCached } from '../search/hybrid.ts';
 import { fetchFar, type CloseRef, type FarPage } from './domain-bank.ts';
@@ -539,14 +538,7 @@ async function _runBrainstormInner(
   const embedFn = opts.embedQueryFn ?? embedQuery;
 
   // ---- Phase 0: cost preview + TTY grace ----
-  // Tier-resolved (mirrors the cycle phases): honors models.brainstorm >
-  // models.default > models.tier.reasoning; the fallback keeps stock
-  // behavior identical (reasoning tier default IS claude-sonnet-4-6).
-  const modelStr = opts.modelOverride ?? await resolveModel(engine, {
-    configKey: 'models.brainstorm',
-    tier: 'reasoning',
-    fallback: 'anthropic:claude-sonnet-4-6',
-  });
+  const modelStr = opts.modelOverride ?? 'anthropic:claude-sonnet-4-6';
   const { aborted, estimate } = await previewCostAndWait({
     profile,
     model: modelStr,

src/core/cycle/calibration-profile.ts

@@ -26,8 +26,8 @@
  */
 
 import { BaseCyclePhase, type ScopedReadOpts, type BasePhaseOpts } from './base-phase.ts';
-import { chat as gatewayChat, getChatModel } from '../ai/gateway.ts';
-import { resolveModel } from '../model-config.ts';
+import { chat as gatewayChat } from '../ai/gateway.ts';
+import { TIER_DEFAULTS } from '../model-config.ts';
 import { gateVoice, type VoiceGateGenerator, type VoiceGateJudge } from '../calibration/voice-gate.ts';
 import { patternStatementTemplate, type PatternStatementSlots } from '../calibration/templates.ts';
 // v0.41 T10 — domain widening. The aggregator module resolves the active
@@ -229,16 +229,7 @@ class CalibrationProfilePhase extends BaseCyclePhase {
   ): Promise<{ summary: string; details: Record<string, unknown>; status?: PhaseStatus }> {
     const holder = opts.holder ?? 'garry';
     const promptVersion = opts.promptVersion ?? CALIBRATION_PROFILE_PROMPT_VERSION;
-    // Resolved once (see propose-takes.ts for the chain): models.calibration_profile
-    // > models.default > env > the gateway's chat model (itself resolved
-    // through models.chat + the reasoning tier). Provider-prefixed per #2451
-    // — a bare id would make gateway.chat() throw "missing a provider
-    // prefix". Drives the generator's chat call, the budget label, and the
-    // persisted model_id, so the three can never disagree.
-    const modelId = opts.model ?? await resolveModel(engine, {
-      configKey: 'models.calibration_profile',
-      fallback: getChatModel(),
-    });
+    const modelId = opts.model ?? TIER_DEFAULTS.reasoning;
     const gradeCompletion = opts.gradeCompletion ?? 1.0;
     const patternsGenerator = opts.patternsGenerator ?? defaultPatternsGenerator;
     const biasTagsGenerator = opts.biasTagsGenerator ?? defaultBiasTagsGenerator;
@@ -274,7 +265,6 @@ class CalibrationProfilePhase extends BaseCyclePhase {
         scorecard,
         holder,
         attempt,
-        modelHint: modelId,
         ...(feedback !== undefined ? { feedback } : {}),
       });
       return lines.join('\n');

src/core/cycle/grade-takes.ts

@@ -36,9 +36,7 @@
 
 import { createHash } from 'node:crypto';
 import { BaseCyclePhase, type ScopedReadOpts, type BasePhaseOpts } from './base-phase.ts';
-import { chat as gatewayChat, getChatModel } from '../ai/gateway.ts';
-import { resolveModel } from '../model-config.ts';
-import { splitProviderModelId } from '../model-id.ts';
+import { chat as gatewayChat } from '../ai/gateway.ts';
 import { GBrainError } from '../types.ts';
 import type { OperationContext } from '../operations.ts';
 import type { BrainEngine, Take, TakeResolution } from '../engine.ts';
@@ -397,24 +395,7 @@ class GradeTakesPhase extends BaseCyclePhase {
     const autoResolve = opts.autoResolve ?? false; // D17 default OFF
     const autoResolveThreshold = opts.autoResolveThreshold ?? 0.95; // D12 conservative
     const resolvedByLabel = opts.resolvedByLabel ?? 'gbrain:grade_takes';
-    // Resolve the judge model ONCE (see propose-takes.ts for the chain —
-    // same label-vs-actual split fixed here: the judge call rode the
-    // gateway's chat_model while the grade cache key, evidence signature,
-    // and budget label recorded a hardcoded 4.6).
-    // NOTE: changing the resolved judge model invalidates the grade cache
-    // (judge_model_id is part of its key) — a one-time, budget-capped
-    // re-grade wave that is CORRECT, since the actual judge did change.
-    const judgeModelFull = opts.model ?? await resolveModel(engine, {
-      configKey: 'models.grade_takes',
-      fallback: getChatModel(),
-    });
-    // Bare tail for cache keys / evidence signatures / stored ids — the
-    // grade cache has always been keyed on bare ids; the gateway default
-    // resolves provider-prefixed, and normalizing preserves cache continuity
-    // on stock installs (no spurious re-judge wave from a prefix change). A
-    // genuinely different configured judge still invalidates, which is
-    // correct. The FULL string drives the actual judge call.
-    const judgeModelId = splitProviderModelId(judgeModelFull).model || judgeModelFull;
+    const judgeModelId = opts.model ?? 'claude-sonnet-4-6';
 
     const useEnsemble = opts.useEnsemble ?? false;
     const ensembleThreshold = opts.ensembleThreshold ?? 0.85;
@@ -487,7 +468,7 @@ class GradeTakesPhase extends BaseCyclePhase {
       // Call the single-model judge. Errors on a single take log warning + continue.
       let verdict: JudgeVerdict;
       try {
-        verdict = await judge({ take, evidence, modelHint: judgeModelFull });
+        verdict = await judge({ take, evidence, modelHint: opts.model });
       } catch (err) {
         const msg = err instanceof Error ? err.message : String(err);
         result.warnings.push(`judge failed on take ${take.id}: ${msg}`);

src/core/cycle/propose-takes.ts

@@ -5,16 +5,11 @@
  * a tuned LLM extractor, writes the extracted gradeable claims to the
  * `take_proposals` queue. User accepts/rejects via `gbrain takes propose`.
  *
- * Idempotency contract (D17 schema spec; per-claim rows since migration v125):
- *   Every scan of a (source_id, page_slug, content_hash, prompt_version)
- *   tuple leaves at least one row — one per extracted claim, or a single
- *   status='empty' sentinel when extraction yields nothing — so an unchanged
- *   page never re-spends LLM tokens. Pre-v125 only proposal rows were
- *   written: a zero-claim page never entered the cache and was re-extracted
- *   on EVERY cycle (observed live: ~60 such pages × every cycle ≈ 1,400
- *   wasted extractor calls / ~$15 per day — ~90% of total autopilot spend).
- *   Bumping PROPOSE_TAKES_PROMPT_VERSION cleanly invalidates the cache so a
- *   tuned prompt re-runs proposals on every page.
+ * Idempotency contract (D17 schema spec):
+ *   The unique index on (source_id, page_slug, content_hash, prompt_version)
+ *   means an unchanged page never re-spends LLM tokens. Bumping
+ *   PROPOSE_TAKES_PROMPT_VERSION cleanly invalidates the cache so a tuned
+ *   prompt re-runs proposals on every page.
  *
  * F2 fence dedup:
  *   The phase reads the page's existing `<!-- gbrain:takes:begin -->` fence
@@ -45,7 +40,6 @@
 import { randomUUID, createHash } from 'node:crypto';
 import { BaseCyclePhase, type ScopedReadOpts, type BasePhaseOpts } from './base-phase.ts';
 import { chat as gatewayChat, getChatModel } from '../ai/gateway.ts';
-import { resolveModel } from '../model-config.ts';
 import { writeReceipt } from '../extract/receipt-writer.ts';
 import { upsertExtractRollup } from '../extract/rollup-writer.ts';
 import { GBrainError } from '../types.ts';
@@ -313,18 +307,6 @@ class ProposeTakesPhase extends BaseCyclePhase {
     const promptVersion = opts.promptVersion ?? PROPOSE_TAKES_PROMPT_VERSION;
     const pageLimit = opts.pageLimit ?? 100;
     const skipPagesWithFence = opts.skipPagesWithFence ?? false;
-    // Resolve the extractor model ONCE: models.propose_takes >
-    // models.default > GBRAIN_MODEL env > the gateway's chat model (which
-    // reconfigureGatewayWithEngine already resolved through models.chat +
-    // the reasoning tier). One resolved provider-prefixed string drives the
-    // actual chat call, the budget estimate, AND the stored model_id — so
-    // the recorded model can never disagree with the model that ran (#2451
-    // convention: stored ids are provider-prefixed, nested prefixes like
-    // openrouter:anthropic/... stay intact).
-    const extractorModelId = opts.model ?? await resolveModel(engine, {
-      configKey: 'models.propose_takes',
-      fallback: getChatModel(),
-    });
     const proposalRunId = `propose-${new Date().toISOString().slice(0, 19).replace(/[-:T]/g, '')}-${randomUUID().slice(0, 8)}`;
 
     const result: ProposeTakesResult = {
@@ -348,6 +330,8 @@ class ProposeTakesPhase extends BaseCyclePhase {
       opts.reporter.start('propose_takes.pages' as never, pages.length);
     }
 
+    const modelId = opts.model ?? getChatModel();
+
     for (const page of pages) {
       result.pages_scanned += 1;
       this.tick(opts);
@@ -377,7 +361,7 @@ class ProposeTakesPhase extends BaseCyclePhase {
 
       // Budget pre-check before the LLM call. Estimate: ~1500 input tokens + 500 output.
       const budget = this.checkBudget({
-        modelId: extractorModelId,
+        modelId,
         estimatedInputTokens: 1500,
         maxOutputTokens: 500,
       });
@@ -396,7 +380,7 @@ class ProposeTakesPhase extends BaseCyclePhase {
           pagePath: page.slug,
           pageBody: body,
           existingTakes,
-          modelHint: extractorModelId,
+          modelHint: opts.model,
         });
       } catch (err) {
         const msg = err instanceof Error ? err.message : String(err);
@@ -404,42 +388,16 @@ class ProposeTakesPhase extends BaseCyclePhase {
         continue;
       }
 
-      // Zero-claim scans MUST still enter the idempotency cache. Pre-v125
-      // only proposal rows were written, so a page whose extraction yielded
-      // no gradeable claims never got a row for its (page, content_hash) —
-      // the cache check above missed on every subsequent cycle and the LLM
-      // call was re-spent on the same unchanged page, forever. The sentinel
-      // row (status='empty', empty claim_text) is invisible to the review
-      // queue (pending_idx is partial on status='pending'); it exists only
-      // so the cache check hits.
-      if (proposals.length === 0) {
-        await engine.executeRaw(
-          `INSERT INTO take_proposals
-             (source_id, page_slug, content_hash, prompt_version, proposal_run_id,
-              status, claim_text, kind, holder, weight, domain, dedup_against_fence_rows, model_id)
-           VALUES ($1, $2, $3, $4, $5, 'empty', '', 'none', 'brain', 0, NULL, NULL, $6)
-           ON CONFLICT (source_id, page_slug, content_hash, prompt_version, md5(claim_text)) DO NOTHING`,
-          [sourceId, page.slug, ch, promptVersion, proposalRunId, extractorModelId],
-        );
-        continue;
-      }
-
-      // Write proposals to take_proposals, one row per claim. The v125
-      // idempotency index includes md5(claim_text), so a same-page
-      // multi-claim run keeps EVERY claim — the pre-v125 four-column unique
-      // index made claims 2..N conflict with claim 1 and ON CONFLICT DO
-      // NOTHING silently dropped them (the review queue only ever saw the
-      // first claim of each page version). RETURNING id keeps
-      // proposals_inserted honest: it counts rows that actually landed,
-      // not insert attempts.
+      // Write proposals to take_proposals. Each row is a separate INSERT
+      // because the composite idempotency key is on the per-page tuple — a
+      // bulk UPSERT would collapse a same-page-multi-claim run into one row.
       for (const p of proposals) {
-        const landed = await engine.executeRaw<{ id: number }>(
+        await engine.executeRaw(
           `INSERT INTO take_proposals
              (source_id, page_slug, content_hash, prompt_version, proposal_run_id,
               claim_text, kind, holder, weight, domain, dedup_against_fence_rows, model_id)
            VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10, $11, $12)
-           ON CONFLICT (source_id, page_slug, content_hash, prompt_version, md5(claim_text)) DO NOTHING
-           RETURNING id`,
+           ON CONFLICT (source_id, page_slug, content_hash, prompt_version) DO NOTHING`,
           [
             sourceId,
             page.slug,
@@ -452,10 +410,10 @@ class ProposeTakesPhase extends BaseCyclePhase {
             p.weight,
             p.domain ?? null,
             JSON.stringify(existingTakes),
-            extractorModelId,
+            modelId,
           ],
         );
-        if (landed.length > 0) result.proposals_inserted += 1;
+        result.proposals_inserted += 1;
       }
     }
 

src/core/cycle/synthesize.ts

@@ -58,11 +58,8 @@ const SUMMARY_SLUG_RE = /^[a-z0-9][a-z0-9\-]*(\/[a-z0-9][a-z0-9\-]*)*$/;
  * resolver returns for known Anthropic aliases.
  */
 const MODEL_CONTEXT_TOKENS: Record<string, number> = {
-  'claude-fable-5': 1_000_000,
-  'claude-opus-4-8': 1_000_000,
   'claude-opus-4-7': 1_000_000,
   'claude-opus-4-6': 1_000_000,
-  'claude-sonnet-5': 1_000_000,
   'claude-sonnet-4-6': 200_000,
   'claude-sonnet-4-5': 200_000,
   'claude-haiku-4-5-20251001': 200_000,

src/core/embedding.ts

@@ -79,15 +79,34 @@ export interface EmbedBatchOptions {
    * and amplify rate-limit pressure.
    */
   maxRetries?: number;
+  /**
+   * #1818: bounded parallelism across BATCH_SIZE sub-batches. Defaults to
+   * `GBRAIN_EMBED_BATCH_CONCURRENCY` env, else 4. Results are
+   * index-addressed so output order always matches input order. Set 1 to
+   * force the pre-v0.42 serial dispatch.
+   */
+  concurrency?: number;
 }
 
 /**
  * Embed a batch of texts via the gateway. Sub-batches of 100 so upstream
  * progress callbacks fire incrementally on large imports. The gateway owns
  * adaptive batch splitting and per-recipe token-budget logic; this paginator
- * is purely about progress-callback granularity.
+ * owns progress-callback granularity and (#1818) bounded parallel dispatch
+ * of the sub-batches — the embed-stale.ts worker-pool pattern, scoped down.
  */
 const BATCH_SIZE = 100;
+const DEFAULT_EMBED_BATCH_CONCURRENCY = 4;
+
+function resolveEmbedBatchConcurrency(options: EmbedBatchOptions): number {
+  if (options.concurrency !== undefined) {
+    return Math.max(1, Math.floor(options.concurrency));
+  }
+  const env = Number(process.env.GBRAIN_EMBED_BATCH_CONCURRENCY);
+  if (Number.isFinite(env) && env >= 1) return Math.floor(env);
+  return DEFAULT_EMBED_BATCH_CONCURRENCY;
+}
+
 export async function embedBatch(
   texts: string[],
   options: EmbedBatchOptions = {},
@@ -103,13 +122,44 @@ export async function embedBatch(
   if (texts.length <= BATCH_SIZE && !options.onBatchComplete) {
     return gatewayEmbed(texts, gwOpts);
   }
-  const results: Float32Array[] = [];
+  // #1818: dispatch sub-batches through a bounded worker pool instead of a
+  // serial loop. Results are written into a preallocated index-addressed
+  // array so output order matches input order regardless of completion
+  // order; onBatchComplete reports a monotonic completed-embedding count.
+  const slices: Array<{ start: number; texts: string[] }> = [];
   for (let i = 0; i < texts.length; i += BATCH_SIZE) {
-    const slice = texts.slice(i, i + BATCH_SIZE);
-    const out = await gatewayEmbed(slice, gwOpts);
-    results.push(...out);
-    options.onBatchComplete?.(results.length, texts.length);
+    slices.push({ start: i, texts: texts.slice(i, i + BATCH_SIZE) });
   }
+  const results = new Array<Float32Array>(texts.length);
+  let next = 0;
+  let done = 0;
+  const numWorkers = Math.min(resolveEmbedBatchConcurrency(options), slices.length);
+  // Once any sub-batch fails, `failed` stops the surviving workers from
+  // dispatching FURTHER slices — the whole call is rejecting anyway, so
+  // continuing would burn real provider spend in the background and fire
+  // onBatchComplete after the caller already saw the failure (worst with
+  // embedBatchWithBackoff, whose 429 backoff assumes nothing is in flight).
+  // In-flight sibling calls still run to completion (bounded by numWorkers-1).
+  let failed = false;
+  const worker = async (): Promise<void> => {
+    while (!failed && next < slices.length) {
+      // NOTE: no local aborted-check here — an aborted signal makes the next
+      // gatewayEmbed call throw (SDK-side), which rejects the pool. Returning
+      // silently instead would resolve with holes in `results`.
+      const slice = slices[next++];
+      let out: Float32Array[];
+      try {
+        out = await gatewayEmbed(slice.texts, gwOpts);
+      } catch (err) {
+        failed = true;
+        throw err;
+      }
+      for (let j = 0; j < out.length; j++) results[slice.start + j] = out[j];
+      done += out.length;
+      if (!failed) options.onBatchComplete?.(done, texts.length);
+    }
+  };
+  await Promise.all(Array.from({ length: numWorkers }, () => worker()));
   return results;
 }
 

src/core/migrate.ts

@@ -5671,41 +5671,6 @@ export const MIGRATIONS: Migration[] = [
 `);
     },
   },
-  {
-    version: 125,
-    name: 'take_proposals_empty_scan_sentinels_and_per_claim_rows',
-    // v0.42.x — kill the propose_takes rescan loop + stop dropping claims.
-    //
-    // Two defects, one schema touch:
-    //   1. Zero-claim scans never entered the idempotency cache (only
-    //      proposal rows were written), so pages whose extraction yielded
-    //      nothing were re-extracted on EVERY cycle. Observed live: ~60
-    //      such pages per run ≈ 1,400 wasted extractor calls / ~$15 per
-    //      day — ~90% of total autopilot LLM spend. Fix: the phase now
-    //      writes a status='empty' sentinel row per zero-claim scan; the
-    //      status CHECK gains the 'empty' value. Sentinels are excluded
-    //      from the partial pending index, so the review queue never sees
-    //      them.
-    //   2. The 4-column unique index collapsed a same-page multi-claim run
-    //      to its FIRST claim: rows 2..N conflicted and ON CONFLICT DO
-    //      NOTHING silently dropped them (verified live: exactly one row
-    //      per (page, hash) across 3 days of runs). Fix: the idempotency
-    //      index gains md5(claim_text) — per-claim rows, while the
-    //      4-column prefix still serves the per-scan cache lookup.
-    //
-    // Existing data is index-safe by construction: the old index guaranteed
-    // at most one row per 4-tuple, so the widened index has no duplicates
-    // to trip on. The DROP+ADD CONSTRAINT pair is idempotent as a unit.
-    idempotent: true,
-    sql: `
-      ALTER TABLE take_proposals DROP CONSTRAINT IF EXISTS take_proposals_status_check;
-      ALTER TABLE take_proposals ADD CONSTRAINT take_proposals_status_check
-        CHECK (status IN ('pending','accepted','rejected','superseded','empty'));
-      DROP INDEX IF EXISTS take_proposals_idempotency_idx;
-      CREATE UNIQUE INDEX IF NOT EXISTS take_proposals_idempotency_idx
-        ON take_proposals (source_id, page_slug, content_hash, prompt_version, md5(claim_text));
-    `,
-  },
 ];
 
 export const LATEST_VERSION = MIGRATIONS.length > 0

src/core/pglite-schema.ts

@@ -762,7 +762,7 @@ CREATE TABLE IF NOT EXISTS take_proposals (
   proposed_at                 TIMESTAMPTZ  NOT NULL DEFAULT now(),
   proposal_run_id             TEXT         NOT NULL,
   status                      TEXT         NOT NULL DEFAULT 'pending'
-                                           CHECK (status IN ('pending','accepted','rejected','superseded','empty')),
+                                           CHECK (status IN ('pending','accepted','rejected','superseded')),
   claim_text                  TEXT         NOT NULL,
   kind                        TEXT         NOT NULL,
   holder                      TEXT         NOT NULL,
@@ -777,7 +777,7 @@ CREATE TABLE IF NOT EXISTS take_proposals (
   predicted_brier_bucket_n    INTEGER
 );
 CREATE UNIQUE INDEX IF NOT EXISTS take_proposals_idempotency_idx
-  ON take_proposals (source_id, page_slug, content_hash, prompt_version, md5(claim_text));
+  ON take_proposals (source_id, page_slug, content_hash, prompt_version);
 CREATE INDEX IF NOT EXISTS take_proposals_pending_idx
   ON take_proposals (source_id, status, proposed_at DESC)
   WHERE status = 'pending';

src/core/schema-embedded.ts

@@ -1274,14 +1274,8 @@ CREATE INDEX IF NOT EXISTS calibration_profiles_published_idx
   WHERE published = true;
 
 -- take_proposals: propose_takes phase queue. Idempotency cache via the
--- composite unique index (source_id, page_slug, content_hash, prompt_version,
--- md5(claim_text)) — the 4-column prefix is the per-scan cache key (mirrors
--- v0.23 dream_verdicts); md5(claim_text) makes rows per-claim so multi-claim
--- pages keep every claim (v125). status='empty' rows are zero-claim scan
--- sentinels: they hold the cache slot for a page version whose extraction
--- yielded nothing — the phase never re-spends the LLM call on that page
--- version. Excluded from the partial pending index. proposal_run_id supports
--- --rollback by run.
+-- composite unique index (source_id, page_slug, content_hash, prompt_version)
+-- mirrors v0.23 dream_verdicts. proposal_run_id supports --rollback by run.
 CREATE TABLE IF NOT EXISTS take_proposals (
   id                          BIGSERIAL PRIMARY KEY,
   source_id                   TEXT         NOT NULL REFERENCES sources(id) ON DELETE CASCADE,
@@ -1292,7 +1286,7 @@ CREATE TABLE IF NOT EXISTS take_proposals (
   proposed_at                 TIMESTAMPTZ  NOT NULL DEFAULT now(),
   proposal_run_id             TEXT         NOT NULL,
   status                      TEXT         NOT NULL DEFAULT 'pending'
-                                           CHECK (status IN ('pending','accepted','rejected','superseded','empty')),
+                                           CHECK (status IN ('pending','accepted','rejected','superseded')),
   claim_text                  TEXT         NOT NULL,
   kind                        TEXT         NOT NULL,
   holder                      TEXT         NOT NULL,
@@ -1307,7 +1301,7 @@ CREATE TABLE IF NOT EXISTS take_proposals (
   predicted_brier_bucket_n    INTEGER
 );
 CREATE UNIQUE INDEX IF NOT EXISTS take_proposals_idempotency_idx
-  ON take_proposals (source_id, page_slug, content_hash, prompt_version, md5(claim_text));
+  ON take_proposals (source_id, page_slug, content_hash, prompt_version);
 CREATE INDEX IF NOT EXISTS take_proposals_pending_idx
   ON take_proposals (source_id, status, proposed_at DESC)
   WHERE status = 'pending';

src/schema.sql

@@ -1270,14 +1270,8 @@ CREATE INDEX IF NOT EXISTS calibration_profiles_published_idx
   WHERE published = true;
 
 -- take_proposals: propose_takes phase queue. Idempotency cache via the
--- composite unique index (source_id, page_slug, content_hash, prompt_version,
--- md5(claim_text)) — the 4-column prefix is the per-scan cache key (mirrors
--- v0.23 dream_verdicts); md5(claim_text) makes rows per-claim so multi-claim
--- pages keep every claim (v125). status='empty' rows are zero-claim scan
--- sentinels: they hold the cache slot for a page version whose extraction
--- yielded nothing — the phase never re-spends the LLM call on that page
--- version. Excluded from the partial pending index. proposal_run_id supports
--- --rollback by run.
+-- composite unique index (source_id, page_slug, content_hash, prompt_version)
+-- mirrors v0.23 dream_verdicts. proposal_run_id supports --rollback by run.
 CREATE TABLE IF NOT EXISTS take_proposals (
   id                          BIGSERIAL PRIMARY KEY,
   source_id                   TEXT         NOT NULL REFERENCES sources(id) ON DELETE CASCADE,
@@ -1288,7 +1282,7 @@ CREATE TABLE IF NOT EXISTS take_proposals (
   proposed_at                 TIMESTAMPTZ  NOT NULL DEFAULT now(),
   proposal_run_id             TEXT         NOT NULL,
   status                      TEXT         NOT NULL DEFAULT 'pending'
-                                           CHECK (status IN ('pending','accepted','rejected','superseded','empty')),
+                                           CHECK (status IN ('pending','accepted','rejected','superseded')),
   claim_text                  TEXT         NOT NULL,
   kind                        TEXT         NOT NULL,
   holder                      TEXT         NOT NULL,
@@ -1303,7 +1297,7 @@ CREATE TABLE IF NOT EXISTS take_proposals (
   predicted_brier_bucket_n    INTEGER
 );
 CREATE UNIQUE INDEX IF NOT EXISTS take_proposals_idempotency_idx
-  ON take_proposals (source_id, page_slug, content_hash, prompt_version, md5(claim_text));
+  ON take_proposals (source_id, page_slug, content_hash, prompt_version);
 CREATE INDEX IF NOT EXISTS take_proposals_pending_idx
   ON take_proposals (source_id, status, proposed_at DESC)
   WHERE status = 'pending';

test/ai/adaptive-embed-batch.test.ts

@@ -39,6 +39,8 @@ import {
   __getShrinkStateForTests,
 } from '../../src/core/ai/gateway.ts';
 import { AIConfigError, AITransientError } from '../../src/core/ai/errors.ts';
+import { RECIPES } from '../../src/core/ai/recipes/index.ts';
+import type { Recipe } from '../../src/core/ai/types.ts';
 
 // The last test in this file leaves the gateway configured with a remote
 // provider + fake key and a REAL embed transport. Without a final reset,
@@ -93,6 +95,14 @@ function configureGoogle(): void {
   });
 }
 
+function configureDashscope(): void {
+  configureGateway({
+    embedding_model: 'dashscope:text-embedding-v3',
+    embedding_dimensions: 1024,
+    env: { DASHSCOPE_API_KEY: 'sk-fake' },
+  });
+}
+
 // --------- 1. Pure helpers ---------
 
 describe('splitByTokenBudget (pure helper)', () => {
@@ -149,6 +159,27 @@ describe('splitByTokenBudget (pure helper)', () => {
     expect(splitByTokenBudget(texts, 96_000, 0)).toEqual(splitByTokenBudget(texts, 96_000, 4));
     expect(splitByTokenBudget(texts, 96_000, -1)).toEqual(splitByTokenBudget(texts, 96_000, 4));
   });
+
+  // #1199: count cap for providers that reject batches by input count.
+  test('max_batch_count flushes even when token budget has room', () => {
+    const texts = Array.from({ length: 25 }, (_, i) => `t${i}`);
+    const result = splitByTokenBudget(texts, 1_000_000, 4, 10);
+    expect(result.map(b => b.length)).toEqual([10, 10, 5]);
+    expect(result.flat()).toEqual(texts);
+  });
+
+  test('token budget still governs alongside max_batch_count', () => {
+    const texts = ['a'.repeat(50_000), 'b'.repeat(50_000), 'c'.repeat(50_000)];
+    const result = splitByTokenBudget(texts, 96_000, 1, 10);
+    expect(result).toHaveLength(3);
+  });
+
+  test('undefined / zero / negative max_batch_count is ignored', () => {
+    const texts = Array.from({ length: 25 }, () => 'x');
+    expect(splitByTokenBudget(texts, 1_000_000, 4, undefined)).toHaveLength(1);
+    expect(splitByTokenBudget(texts, 1_000_000, 4, 0)).toHaveLength(1);
+    expect(splitByTokenBudget(texts, 1_000_000, 4, -5)).toHaveLength(1);
+  });
 });
 
 describe('isTokenLimitError (pure helper)', () => {
@@ -179,6 +210,12 @@ describe('isTokenLimitError (pure helper)', () => {
     expect(isTokenLimitError(new Error('Exceeded 300000 max tokens per request'))).toBe(true);
   });
 
+  test('matches DashScope batch-count error (#1199)', () => {
+    expect(isTokenLimitError(new Error(
+      'InvalidParameter: batch size is invalid, it should not be larger than 10.',
+    ))).toBe(true);
+  });
+
   test('does not match unrelated errors', () => {
     expect(isTokenLimitError(new Error('Connection refused'))).toBe(false);
     expect(isTokenLimitError(new Error('Invalid API key'))).toBe(false);
@@ -387,26 +424,92 @@ describe('shrink-on-miss adaptive cache', () => {
   });
 });
 
+// --------- 8. Pre-split count cap through public embed() (#1199 / #970) ---------
+
+describe('embed() pre-split honors max_batch_count', () => {
+  beforeEach(() => resetGateway());
+  afterEach(() => __setEmbedTransportForTests(null));
+
+  test('dashscope never dispatches more than 10 inputs per call (#1199)', async () => {
+    configureDashscope();
+    const stub = mock(async ({ values }: { values: string[] }) => fakeEmbeddings(values, 1024));
+    __setEmbedTransportForTests(stub as any);
+
+    // 25 short texts fit trivially in the 8192-token budget; without the
+    // count cap they'd ship as ONE batch and DashScope would reject it.
+    const texts = Array.from({ length: 25 }, (_, i) => `short-${i}`);
+    const result = await embed(texts);
+
+    expect(result).toHaveLength(25);
+    const callLengths = stub.mock.calls.map(([arg]) => (arg as { values: string[] }).values.length);
+    expect(Math.max(...callLengths)).toBeLessThanOrEqual(10);
+    expect(callLengths.reduce((a, b) => a + b, 0)).toBe(25);
+    // Order preserved across sub-batches.
+    expect((stub.mock.calls[0][0] as { values: string[] }).values[0]).toBe('short-0');
+  });
+
+  test('google pre-splits at 100 inputs per batchEmbedContents call (#970)', async () => {
+    configureGoogle();
+    const stub = mock(async ({ values }: { values: string[] }) => fakeEmbeddings(values, 768));
+    __setEmbedTransportForTests(stub as any);
+
+    const texts = Array.from({ length: 250 }, (_, i) => `g${i}`);
+    const result = await embed(texts);
+
+    expect(result).toHaveLength(250);
+    const callLengths = stub.mock.calls.map(([arg]) => (arg as { values: string[] }).values.length);
+    expect(callLengths).toEqual([100, 100, 50]);
+  });
+});
+
 // --------- 7. Startup warning (D9-B) ---------
 
 describe('startup warning for recipes missing max_batch_tokens', () => {
   beforeEach(() => resetGateway());
 
+  // #970 closed google's missing cap, so no registered recipe is capless
+  // anymore. Inject a synthetic capless recipe to keep the warning path
+  // covered for the NEXT recipe that forgets the field.
+  const caplessRecipe: Recipe = {
+    id: 'capless-test',
+    name: 'Capless Test Provider',
+    tier: 'openai-compat',
+    implementation: 'openai-compatible',
+    base_url_default: 'https://example.invalid/v1',
+    auth_env: { required: [] },
+    touchpoints: {
+      embedding: { models: ['capless-embed-1'], default_dims: 768 },
+    },
+  };
+
+  function configureCapless(): void {
+    configureGateway({
+      embedding_model: 'capless-test:capless-embed-1',
+      embedding_dimensions: 768,
+      env: {},
+    });
+  }
+
   test('configured missing-cap recipe warns once; unrelated recipes stay quiet', () => {
     const warnings: string[] = [];
     const original = console.warn;
     console.warn = (msg: string) => warnings.push(String(msg));
+    RECIPES.set(caplessRecipe.id, caplessRecipe);
     try {
       configureOpenAI();
       expect(warnings.length).toBe(0);
+      // #970 regression: google now declares max_batch_tokens → quiet.
       configureGoogle();
+      expect(warnings.length).toBe(0);
+      configureCapless();
       const firstCallCount = warnings.length;
       // Reconfigure: the warning should NOT re-fire for the same recipes
       // within one process (we already told the operator).
-      configureGoogle();
+      configureCapless();
       expect(warnings.length).toBe(firstCallCount);
     } finally {
       console.warn = original;
+      RECIPES.delete(caplessRecipe.id);
     }
 
     // The warning text should match the documented contract.
@@ -415,11 +518,12 @@ describe('startup warning for recipes missing max_batch_tokens', () => {
     );
     expect(contractMatch.length).toBe(1);
 
-    // Voyage declares max_batch_tokens → suppressed. OpenAI is the
-    // canonical fast-path recipe → also suppressed by id. Both must be
-    // absent from the warnings.
+    // Voyage + google declare max_batch_tokens → suppressed. OpenAI is the
+    // canonical fast-path recipe → also suppressed by id. All must be
+    // absent from the warnings; only the synthetic capless recipe fires.
     expect(warnings.find(w => w.includes('"voyage"'))).toBeUndefined();
     expect(warnings.find(w => w.includes('"openai"'))).toBeUndefined();
-    expect(warnings.find(w => w.includes('"google"'))).toBeDefined();
+    expect(warnings.find(w => w.includes('"google"'))).toBeUndefined();
+    expect(warnings.find(w => w.includes('"capless-test"'))).toBeDefined();
   });
 });

test/ai/no-batch-cap-suppression.serial.test.ts

@@ -52,16 +52,7 @@ describe('v0.32 #779: no_batch_cap suppresses the missing-max_batch_tokens warni
     }
   });
 
-  test('configureGateway warns for google only when google embedding is configured', () => {
-    warnSpy.mockClear();
-    resetGateway();
-    configureGateway({ env: {} });
-    let messages = warnSpy.mock.calls.map(c => String(c[0] ?? ''));
-    expect(
-      messages.some(m => m.includes('"google"') && m.includes('without max_batch_tokens')),
-      'google should not warn while OpenAI default is configured',
-    ).toBe(false);
-
+  test('configureGateway does NOT warn for google now that it declares batch caps (#970)', () => {
     warnSpy.mockClear();
     resetGateway();
     configureGateway({
@@ -69,11 +60,20 @@ describe('v0.32 #779: no_batch_cap suppresses the missing-max_batch_tokens warni
       embedding_dimensions: 768,
       env: { GOOGLE_GENERATIVE_AI_API_KEY: 'fake' },
     });
-    messages = warnSpy.mock.calls.map(c => String(c[0] ?? ''));
+    const messages = warnSpy.mock.calls.map(c => String(c[0] ?? ''));
     expect(
       messages.some(m => m.includes('"google"') && m.includes('without max_batch_tokens')),
-      'google should warn when configured because it has fixed-cap models',
-    ).toBe(true);
+      'google declares max_batch_tokens/max_batch_count since #970 — no warning',
+    ).toBe(false);
+  });
+
+  test('google recipe declares its derived batch caps (#970)', () => {
+    const e = getRecipe('google')!.touchpoints.embedding!;
+    // Count cap is the REAL Gemini limit (batchEmbedContents: 100 inputs);
+    // the token budget is derived (100 × 2048 per-input tokens), NOT the
+    // 2048 per-input limit — copying that verbatim would over-split 50×.
+    expect(e.max_batch_count).toBe(100);
+    expect(e.max_batch_tokens).toBe(204_800);
   });
 
   test('every recipe with empty models[] declares user_provided_models OR has openai-fast-path', () => {

test/ai/recipe-dashscope.test.ts

@@ -55,6 +55,11 @@ describe('recipe: dashscope', () => {
     expect(r.touchpoints.embedding!.chars_per_token).toBeGreaterThan(0);
   });
 
+  test('declares max_batch_count: 10 — DashScope rejects larger batches (#1199)', () => {
+    const r = getRecipe('dashscope')!;
+    expect(r.touchpoints.embedding!.max_batch_count).toBe(10);
+  });
+
   test('dimsProviderOptions threads dimensions for text-embedding-v3 (Matryoshka)', async () => {
     // Codex finding #1: DashScope text-embedding-v3 is Matryoshka 64-1024.
     // Without `dimensions` on the wire, user-selected non-default dims are

test/calibration-profile.test.ts

@@ -38,7 +38,6 @@ function buildMockEngine(opts: { scorecard: TakesScorecard }): {
 } {
   const captured: CapturedSql[] = [];
   const engine = {
-    async getConfig() { return null; },
     kind: 'pglite',
     async getScorecard() {
       return opts.scorecard;

test/embed-batch-concurrency.test.ts

@@ -0,0 +1,161 @@
+/**
+ * #1818: embedBatch dispatches its 100-input sub-batches through a bounded
+ * worker pool (the embed-stale.ts concurrency pattern) instead of a serial
+ * `for` loop. This file pins:
+ *
+ *   - output order matches input order regardless of completion order
+ *     (index-addressed results)
+ *   - parallelism actually happens (max in-flight > 1) and stays bounded
+ *     (max in-flight <= configured concurrency)
+ *   - concurrency: 1 restores the serial pre-#1818 dispatch
+ *   - GBRAIN_EMBED_BATCH_CONCURRENCY env is honored when the option is unset
+ *   - onBatchComplete reports a monotonic completed count ending at total
+ *
+ * Transport is stubbed via the gateway's __setEmbedTransportForTests seam
+ * (same pattern as test/ai/adaptive-embed-batch.test.ts). OpenAI recipe =
+ * fast path (no pre-split), so each embedBatch sub-batch is exactly one
+ * transport call.
+ */
+
+import { afterAll, afterEach, beforeEach, describe, expect, test } from 'bun:test';
+import {
+  configureGateway,
+  resetGateway,
+  __setEmbedTransportForTests,
+} from '../src/core/ai/gateway.ts';
+import { embedBatch } from '../src/core/embedding.ts';
+import { withEnv } from './helpers/with-env.ts';
+
+const DIMS = 1536;
+
+function configureOpenAI(): void {
+  configureGateway({
+    embedding_model: 'openai:text-embedding-3-large',
+    embedding_dimensions: DIMS,
+    env: { OPENAI_API_KEY: 'sk-fake' },
+  });
+}
+
+/**
+ * Install a transport whose returned embedding encodes the GLOBAL input
+ * index in dim 0 (texts are `t<N>`), so order can be asserted end-to-end.
+ * Tracks the max number of concurrently in-flight transport calls.
+ */
+function installTrackingTransport(delayMs = 5): { maxInFlight: () => number } {
+  let inFlight = 0;
+  let maxInFlight = 0;
+  __setEmbedTransportForTests((async ({ values }: { values: string[] }) => {
+    inFlight++;
+    maxInFlight = Math.max(maxInFlight, inFlight);
+    await new Promise(r => setTimeout(r, delayMs));
+    inFlight--;
+    return {
+      embeddings: values.map(v => {
+        const idx = Number(v.slice(1));
+        return Array.from({ length: DIMS }, (_, j) => (j === 0 ? idx : 0.1));
+      }),
+    };
+  }) as any);
+  return { maxInFlight: () => maxInFlight };
+}
+
+const texts = Array.from({ length: 250 }, (_, i) => `t${i}`);
+
+afterAll(() => resetGateway());
+
+describe('embedBatch bounded parallelism (#1818)', () => {
+  beforeEach(() => {
+    resetGateway();
+    configureOpenAI();
+  });
+  afterEach(() => {
+    __setEmbedTransportForTests(null);
+  });
+
+  test('default pool dispatches sub-batches in parallel, order preserved', async () => {
+    const tracker = installTrackingTransport();
+    const result = await embedBatch(texts, { onBatchComplete: () => {} });
+    expect(result).toHaveLength(250);
+    for (let i = 0; i < 250; i++) {
+      expect(result[i][0]).toBe(i);
+    }
+    // 250 texts → 3 sub-batches; default concurrency 4 → all 3 in flight.
+    expect(tracker.maxInFlight()).toBeGreaterThan(1);
+    expect(tracker.maxInFlight()).toBeLessThanOrEqual(4);
+  });
+
+  test('concurrency: 1 keeps the serial dispatch', async () => {
+    const tracker = installTrackingTransport();
+    const result = await embedBatch(texts, { concurrency: 1, onBatchComplete: () => {} });
+    expect(result).toHaveLength(250);
+    expect(tracker.maxInFlight()).toBe(1);
+  });
+
+  test('GBRAIN_EMBED_BATCH_CONCURRENCY env bounds the pool when option unset', async () => {
+    const tracker = installTrackingTransport();
+    await withEnv({ GBRAIN_EMBED_BATCH_CONCURRENCY: '2' }, async () => {
+      await embedBatch(texts, { onBatchComplete: () => {} });
+    });
+    expect(tracker.maxInFlight()).toBeGreaterThan(1);
+    expect(tracker.maxInFlight()).toBeLessThanOrEqual(2);
+  });
+
+  test('onBatchComplete reports a monotonic count ending at total', async () => {
+    installTrackingTransport();
+    const seen: number[] = [];
+    await embedBatch(texts, {
+      onBatchComplete: (done, total) => {
+        expect(total).toBe(250);
+        seen.push(done);
+      },
+    });
+    expect(seen).toHaveLength(3); // 100 + 100 + 50 sub-batches
+    for (let i = 1; i < seen.length; i++) {
+      expect(seen[i]).toBeGreaterThan(seen[i - 1]);
+    }
+    expect(seen[seen.length - 1]).toBe(250);
+  });
+
+  test('a failing sub-batch rejects the whole call', async () => {
+    let call = 0;
+    __setEmbedTransportForTests((async ({ values }: { values: string[] }) => {
+      call++;
+      if (call === 2) throw new Error('boom');
+      await new Promise(r => setTimeout(r, 2));
+      return { embeddings: values.map(() => Array.from({ length: DIMS }, () => 0.1)) };
+    }) as any);
+    await expect(embedBatch(texts, { onBatchComplete: () => {} })).rejects.toThrow();
+  });
+
+  test('after a failure, surviving workers stop dispatching new slices', async () => {
+    // 1000 texts → 10 slices, concurrency 2. First call fails immediately;
+    // without the `failed` flag the second worker would keep draining all
+    // 10 slices in the background AFTER embedBatch already rejected —
+    // burning provider spend and firing onBatchComplete post-rejection.
+    let calls = 0;
+    const completions: number[] = [];
+    __setEmbedTransportForTests((async ({ values }: { values: string[] }) => {
+      calls++;
+      if (calls === 1) throw new Error('boom');
+      await new Promise(r => setTimeout(r, 5));
+      return { embeddings: values.map(() => Array.from({ length: DIMS }, () => 0.1)) };
+    }) as any);
+    const many = Array.from({ length: 1000 }, (_, i) => `t${i}`);
+    await expect(
+      embedBatch(many, { concurrency: 2, onBatchComplete: d => completions.push(d) }),
+    ).rejects.toThrow('boom');
+    const callsAtRejection = calls;
+    await new Promise(r => setTimeout(r, 50)); // would-be background drain window
+    expect(calls).toBe(callsAtRejection); // no new dispatch after rejection
+    expect(calls).toBeLessThanOrEqual(2); // only the in-flight sibling ran
+    expect(completions).toHaveLength(0); // no progress reported after failure
+  });
+
+  test('single small batch without callback stays on the one-call fast path', async () => {
+    const tracker = installTrackingTransport(1);
+    const result = await embedBatch(['t0', 't1', 't2']);
+    expect(result).toHaveLength(3);
+    expect(result[1][0]).toBe(1);
+    expect(tracker.maxInFlight()).toBe(1);
+  });
+});

test/grade-takes-ensemble.test.ts

@@ -47,7 +47,6 @@ function buildMockEngine(opts: { takes: Take[] }): {
   const captured: CapturedSql[] = [];
   const resolves: CapturedResolve[] = [];
   const engine = {
-    async getConfig() { return null; },
     kind: 'pglite',
     async listTakes() {
       return opts.takes;

test/grade-takes.test.ts

@@ -46,14 +46,12 @@ interface CapturedResolve {
 function buildMockEngine(opts: {
   takes: Take[];
   cachedGrades?: Set<string>; // composite-key strings already in take_grade_cache
-  config?: Record<string, string>; // engine.getConfig plane (models.grade_takes etc.)
 }): { engine: BrainEngine; captured: CapturedSql[]; resolves: CapturedResolve[] } {
   const captured: CapturedSql[] = [];
   const resolves: CapturedResolve[] = [];
   const cached = opts.cachedGrades ?? new Set<string>();
 
   const engine = {
-    async getConfig(key: string) { return opts.config?.[key] ?? null; },
     kind: 'pglite',
     async listTakes() {
       return opts.takes;
@@ -226,28 +224,6 @@ describe('runPhaseGradeTakes — phase integration', () => {
     expect(resolves).toHaveLength(0); // no canonical mutation
   });
 
-  test('models.grade_takes config drives the judge call; cache key stays bare-tailed', async () => {
-    // Pre-fix the judge call rode the gateway's chat_model while the cache
-    // key / budget label recorded a hardcoded 'claude-sonnet-4-6'. The phase
-    // now resolves models.grade_takes; the judge gets the FULL string and
-    // the cache row keys on the bare tail (continuity with historical rows).
-    const takes = [buildTake({ id: 1, sinceDate: '2023-01-01' })];
-    const { engine, captured } = buildMockEngine({
-      takes,
-      config: { 'models.grade_takes': 'anthropic:claude-sonnet-5' },
-    });
-    const hints: Array<string | undefined> = [];
-    const judge: JudgeFn = async ({ modelHint }) => {
-      hints.push(modelHint);
-      return { verdict: 'correct', confidence: 0.9, reasoning: 'held' };
-    };
-    const result = await runPhaseGradeTakes(buildCtx(engine), { judge });
-    expect(result.status).toBe('ok');
-    expect(hints).toEqual(['anthropic:claude-sonnet-5']); // actual call gets the FULL string
-    const inserts = captured.filter(c => c.sql.includes('INSERT INTO take_grade_cache'));
-    expect(inserts[0]!.params[2]).toBe('claude-sonnet-5'); // judge_model_id is the bare tail
-  });
-
   test('D17: auto-resolve OFF by default — even high-confidence verdict does NOT mutate takes', async () => {
     const takes = [buildTake({ id: 1, sinceDate: '2023-01-01' })];
     const { engine, resolves } = buildMockEngine({ takes });

test/helpers/legacy-embedding-preload.ts

@@ -19,7 +19,7 @@
  * overwrites this preload.
  */
 import { configureGateway, getEmbeddingDimensions } from '../../src/core/ai/gateway.ts';
-import { beforeEach } from 'bun:test';
+import { afterEach, beforeEach } from 'bun:test';
 
 const LEGACY_CONFIG = {
   embedding_model: 'openai:text-embedding-3-large',
@@ -52,7 +52,7 @@ applyLegacy();
 //   2. file-local beforeAll → may overwrite to ZE/1280
 // Since beforeAll runs once per file BEFORE the first beforeEach,
 // file-local beforeAll wins for that file's tests. ✓
-beforeEach(() => {
+function applyLegacyIfEmpty() {
   try {
     // Only re-apply if the gateway was reset (or never configured).
     // Tests that explicitly configured a different model in their
@@ -62,4 +62,28 @@ beforeEach(() => {
   } catch {
     applyLegacy();
   }
-});
+}
+
+beforeEach(applyLegacyIfEmpty);
+
+// PR #3130 shard-order fix: beforeEach alone leaves ONE window open — a file
+// whose LAST afterEach calls resetGateway() poisons the NEXT file's
+// beforeAll, which runs BEFORE any beforeEach fires. A beforeAll there that
+// does engine.initSchema() then sizes the embedding column from the gateway
+// DEFAULTS (zembed-1/1280d) instead of the pinned legacy 1536, and every
+// 1536-d Float32Array fixture in that file dies with
+// "expected 1280 dimensions, not 1536". Which file pair collides is a
+// function of shard composition, so adding/removing ANY test file can
+// surface it (that is exactly how it bit shard 9).
+//
+// Preload hooks are registered before any file-local hooks, and bun runs
+// after-hooks inside-out (file-local afterEach first, then this one), so
+// this repairs the empty slot immediately after the poisoning reset —
+// before the next file's beforeAll can observe it.
+//
+// Known remaining window: a file whose afterAll() resets the gateway (no
+// hook runs between its afterAll and the next file's beforeAll). Files
+// that reset in afterAll and can precede a schema-creating file should
+// re-apply their own config, or the victim file should configureGateway()
+// explicitly in its beforeAll.
+afterEach(applyLegacyIfEmpty);

test/import-default-workers.test.ts

@@ -0,0 +1,69 @@
+/**
+ * #1207: `gbrain import` without `--workers` used to hardcode workerCount=1,
+ * so a large Postgres import paid one serial embedding round-trip per file.
+ * runImport now routes the default through the shared autoConcurrency policy
+ * (PGLite → 1, >100 files on Postgres → DEFAULT_PARALLEL_WORKERS), while an
+ * explicit `--workers N` still wins.
+ *
+ * The engine here is a minimal postgres-kind stub with no database_url in
+ * config — runImport's parallel branch then falls back to serial processing
+ * (its PR #490 guard) but the WORKER-COUNT DECISION (the thing #1207 fixes)
+ * is still observable via the "Using N parallel workers" log line. Per-file
+ * imports fail against the stub engine and are swallowed by runImport's
+ * per-file catch; that's fine — this test pins the policy, not the import.
+ */
+
+import { afterEach, beforeEach, describe, expect, test } from 'bun:test';
+import { mkdtempSync, writeFileSync, mkdirSync, rmSync, realpathSync } from 'fs';
+import { tmpdir } from 'os';
+import { join } from 'path';
+import { withEnv } from './helpers/with-env.ts';
+import { runImport } from '../src/commands/import.ts';
+
+const fakePostgresEngine = {
+  kind: 'postgres',
+  executeRaw: async () => [],
+  logIngest: async () => {},
+  setConfig: async () => {},
+  getConfig: async () => null,
+} as any;
+
+let workspace: string;
+let brainDir: string;
+let logs: string[];
+const realLog = console.log;
+
+beforeEach(() => {
+  workspace = mkdtempSync(join(tmpdir(), 'gbrain-import-workers-home-'));
+  mkdirSync(join(workspace, '.gbrain'), { recursive: true });
+  brainDir = realpathSync(mkdtempSync(join(tmpdir(), 'gbrain-import-workers-brain-')));
+  // 101 files: one past AUTO_CONCURRENCY_FILE_THRESHOLD (100).
+  for (let i = 0; i < 101; i++) {
+    writeFileSync(join(brainDir, `page-${i}.md`), `# Page ${i}\n\nbody ${i}\n`);
+  }
+  logs = [];
+  console.log = (msg?: unknown) => logs.push(String(msg));
+});
+
+afterEach(() => {
+  console.log = realLog;
+  rmSync(workspace, { recursive: true, force: true });
+  rmSync(brainDir, { recursive: true, force: true });
+});
+
+describe('import default worker count (#1207)', () => {
+  test('no --workers flag → autoConcurrency picks 4 for >100 files on Postgres', async () => {
+    await withEnv({ GBRAIN_HOME: join(workspace, '.gbrain'), GBRAIN_SOURCE: undefined }, async () => {
+      await runImport(fakePostgresEngine, [brainDir, '--no-embed'], { sourceId: 'default' });
+    });
+    expect(logs.some(l => l.includes('Using 4 parallel workers'))).toBe(true);
+  });
+
+  test('explicit --workers 2 still wins over the auto policy', async () => {
+    await withEnv({ GBRAIN_HOME: join(workspace, '.gbrain'), GBRAIN_SOURCE: undefined }, async () => {
+      await runImport(fakePostgresEngine, [brainDir, '--no-embed', '--workers', '2'], { sourceId: 'default' });
+    });
+    expect(logs.some(l => l.includes('Using 2 parallel workers'))).toBe(true);
+    expect(logs.some(l => l.includes('Using 4 parallel workers'))).toBe(false);
+  });
+});

test/propose-takes-rescan.test.ts

@@ -1,160 +0,0 @@
-/**
- * propose_takes rescan-loop + dropped-claims regression tests (migration v125).
- *
- * Two live-observed defects, both fixed by the v125 schema + phase change:
- *
- *   1. RESCAN LOOP — a page whose extraction yielded zero claims never
- *      entered the idempotency cache (only proposal rows were written), so
- *      every cycle re-spent the extractor call on the same unchanged page.
- *      Live impact: ~60 such pages × every cycle ≈ 1,400 wasted LLM calls
- *      (~$15) per day — ~90% of total autopilot spend. Fix: status='empty'
- *      sentinel row per zero-claim scan.
- *
- *   2. DROPPED CLAIMS — the 4-column unique index collapsed a same-page
- *      multi-claim run to its first claim (rows 2..N conflicted, ON CONFLICT
- *      DO NOTHING dropped them silently; verified live: exactly 1 row per
- *      (page, hash) over 3 days). Fix: idempotency index gains
- *      md5(claim_text).
- *
- * Hermetic: PGLite engine + injected extractor; no gateway, no LLM.
- */
-import { describe, test, expect, beforeAll, afterAll } from 'bun:test';
-import { PGLiteEngine } from '../src/core/pglite-engine.ts';
-import { runPhaseProposeTakes, type ProposeTakesExtractor, type ProposedTake } from '../src/core/cycle/propose-takes.ts';
-import type { OperationContext } from '../src/core/operations.ts';
-
-let engine: PGLiteEngine;
-
-function ctx(): OperationContext {
-  return {
-    engine,
-    remote: false,
-    config: {} as OperationContext['config'],
-    logger: { info() {}, warn() {}, error() {}, debug() {} } as unknown as OperationContext['logger'],
-  } as unknown as OperationContext;
-}
-
-/** Extractor stub that counts invocations per page slug. */
-function countingExtractor(
-  claimsBySlug: Record<string, ProposedTake[]>,
-): { extractor: ProposeTakesExtractor; calls: string[] } {
-  const calls: string[] = [];
-  const extractor: ProposeTakesExtractor = async ({ pagePath }) => {
-    calls.push(pagePath);
-    return claimsBySlug[pagePath] ?? [];
-  };
-  return { extractor, calls };
-}
-
-beforeAll(async () => {
-  engine = new PGLiteEngine();
-  await engine.connect({});
-  await engine.initSchema();
-  await engine.putPage('notes/zero-claims', {
-    type: 'note',
-    title: 'pure narrative',
-    compiled_truth: 'A quiet walk in the park. Nothing opinionated happened at all today.',
-  });
-  await engine.putPage('notes/three-claims', {
-    type: 'note',
-    title: 'opinionated',
-    compiled_truth: 'I bet acme-example wins the market. widget-co will struggle. fund-a is overexposed.',
-  });
-});
-
-afterAll(async () => {
-  await engine.disconnect();
-});
-
-describe('rescan loop — zero-claim scans enter the cache', () => {
-  test('second run cache-hits: extractor is NOT called again on unchanged pages', async () => {
-    const claims = {
-      'notes/three-claims': [
-        { claim_text: 'acme-example wins the market', kind: 'bet' as const, holder: 'brain', weight: 0.7 },
-        { claim_text: 'widget-co will struggle', kind: 'take' as const, holder: 'brain', weight: 0.6 },
-        { claim_text: 'fund-a is overexposed', kind: 'take' as const, holder: 'brain', weight: 0.55 },
-      ],
-    };
-
-    const first = countingExtractor(claims);
-    const r1 = await runPhaseProposeTakes(ctx(), { extractor: first.extractor });
-    expect(r1.status).toBe('ok');
-    // Both pages extracted on the first pass.
-    expect(first.calls).toContain('notes/zero-claims');
-    expect(first.calls).toContain('notes/three-claims');
-
-    const second = countingExtractor(claims);
-    const r2 = await runPhaseProposeTakes(ctx(), { extractor: second.extractor });
-    expect(r2.status).toBe('ok');
-    // THE regression: pre-fix the zero-claim page missed the cache every
-    // run and was re-extracted here. (Run 1's receipt page legitimately
-    // appears once — it's a new page — and its zero-claim scan now caches
-    // too; pre-fix, receipts re-scanned forever as well.)
-    expect(second.calls).not.toContain('notes/zero-claims');
-    expect(second.calls).not.toContain('notes/three-claims');
-
-    // Run 2 inserted nothing, so no new receipt page exists: run 3 must be
-    // fully quiescent — zero extractor calls, zero cache misses.
-    const third = countingExtractor(claims);
-    const r3 = await runPhaseProposeTakes(ctx(), { extractor: third.extractor });
-    expect(r3.status).toBe('ok');
-    expect(third.calls).toEqual([]);
-    expect((r3.details as Record<string, unknown>).cache_misses).toBe(0);
-  });
-
-  test('zero-claim scan wrote an "empty" sentinel invisible to the pending queue', async () => {
-    const sentinel = await engine.executeRaw<{ status: string; claim_text: string }>(
-      `SELECT status, claim_text FROM take_proposals WHERE page_slug = 'notes/zero-claims'`,
-      [],
-    );
-    expect(sentinel.length).toBe(1);
-    expect(sentinel[0].status).toBe('empty');
-    expect(sentinel[0].claim_text).toBe('');
-    const pending = await engine.executeRaw<{ n: number }>(
-      `SELECT COUNT(*)::int AS n FROM take_proposals WHERE page_slug = 'notes/zero-claims' AND status = 'pending'`,
-      [],
-    );
-    expect(Number(pending[0].n)).toBe(0);
-  });
-});
-
-describe('dropped claims — per-claim rows survive the idempotency index', () => {
-  test('a 3-claim page stores 3 rows and reports an honest inserted count', async () => {
-    const rows = await engine.executeRaw<{ claim_text: string }>(
-      `SELECT claim_text FROM take_proposals WHERE page_slug = 'notes/three-claims' AND status = 'pending' ORDER BY id`,
-      [],
-    );
-    // Pre-fix the 4-column unique index kept only the FIRST claim.
-    expect(rows.length).toBe(3);
-    expect(rows.map(r => r.claim_text)).toEqual([
-      'acme-example wins the market',
-      'widget-co will struggle',
-      'fund-a is overexposed',
-    ]);
-  });
-
-  test('content change re-extracts and stores the new version separately', async () => {
-    await engine.putPage('notes/zero-claims', {
-      type: 'note',
-      title: 'pure narrative',
-      compiled_truth: 'Updated: I now believe acme-example is undervalued and will re-rate within a year.',
-    });
-    const claims = {
-      'notes/zero-claims': [
-        { claim_text: 'acme-example is undervalued', kind: 'take' as const, holder: 'brain', weight: 0.6 },
-      ],
-    };
-    const run = countingExtractor(claims);
-    const r = await runPhaseProposeTakes(ctx(), { extractor: run.extractor });
-    expect(r.status).toBe('ok');
-    // Changed page re-extracts; the unchanged 3-claim page stays cached.
-    expect(run.calls).toEqual(['notes/zero-claims']);
-    expect((r.details as Record<string, unknown>).proposals_inserted).toBe(1);
-    const all = await engine.executeRaw<{ status: string }>(
-      `SELECT status FROM take_proposals WHERE page_slug = 'notes/zero-claims' ORDER BY id`,
-      [],
-    );
-    // Old hash's sentinel + new hash's pending claim coexist.
-    expect(all.map(r2 => r2.status).sort()).toEqual(['empty', 'pending']);
-  });
-});

test/propose-takes.test.ts

@@ -41,16 +41,12 @@ interface CapturedSql {
 function buildMockEngine(opts: {
   pages: Page[];
   existingProposals?: Set<string>; // composite-key strings already in take_proposals
-  config?: Record<string, string>; // engine.getConfig plane (models.tier.* etc.)
 }): { engine: BrainEngine; captured: CapturedSql[] } {
   const captured: CapturedSql[] = [];
   const existing = opts.existingProposals ?? new Set<string>();
 
   const engine = {
     kind: 'pglite',
-    async getConfig(key: string) {
-      return opts.config?.[key] ?? null;
-    },
     async listPages() {
       return opts.pages;
     },
@@ -63,12 +59,7 @@ function buildMockEngine(opts: {
         if (existing.has(key)) return [{ id: 1 } as unknown as T];
         return [];
       }
-      // INSERT ... RETURNING id — emulate a successful insert so the
-      // honest proposals_inserted counter (counts RETURNING rows, not
-      // attempts) sees the row land. Conflicted inserts would return [].
-      if (sql.includes('INSERT INTO take_proposals') && sql.includes('RETURNING id')) {
-        return [{ id: 1 } as unknown as T];
-      }
+      // INSERT — return nothing
       return [];
     },
   } as unknown as BrainEngine;
@@ -276,29 +267,6 @@ describe('runPhaseProposeTakes — phase integration', () => {
     expect(inserts[0]!.params[9]).toBe('market'); // domain
   });
 
-  test('extractor model resolves through models.propose_takes config', async () => {
-    // Pre-fix the phase's only model knob was the gateway chat model — there
-    // was no per-phase config key. The phase now resolves once via
-    // resolveModel(models.propose_takes > models.default > env > gateway
-    // chat model); the extractor hint and the stored model_id must both
-    // reflect the configured override (full provider-prefixed string, #2451).
-    const pages = [buildPage({ slug: 'wiki/concepts/tier-routing', body: 'Tier-routed models will win.' })];
-    const { engine, captured } = buildMockEngine({
-      pages,
-      config: { 'models.propose_takes': 'anthropic:claude-sonnet-5' },
-    });
-    const seen: Array<string | undefined> = [];
-    const extractor: ProposeTakesExtractor = async ({ modelHint }) => {
-      seen.push(modelHint);
-      return [{ claim_text: 'tier-routed models win', kind: 'bet', holder: 'brain', weight: 0.7 }];
-    };
-    const result = await runPhaseProposeTakes(buildCtx(engine), { extractor });
-    expect(result.status).toBe('ok');
-    expect(seen).toEqual(['anthropic:claude-sonnet-5']); // chat call gets the FULL string
-    const inserts = captured.filter(c => c.sql.includes('INSERT INTO take_proposals'));
-    expect(inserts[0]!.params[11]).toBe('anthropic:claude-sonnet-5'); // stored model_id matches the call
-  });
-
   test('cache hit: page already in take_proposals is skipped', async () => {
     const body = 'A page that was already processed.';
     const pages = [buildPage({ slug: 'wiki/old-page', body })];