diff --git a/src/functions/search.ts b/src/functions/search.ts
index 74af9ff1..b4444b48 100644
--- a/src/functions/search.ts
+++ b/src/functions/search.ts
@@ -86,6 +86,99 @@ export async function vectorIndexAddGuarded(
   }
 }
 
+// Batched variant: calls EmbeddingProvider.embedBatch ONCE for the whole
+// batch, then writes each resulting vector. Use this for bulk paths
+// (rebuildIndex, future bulk-add APIs) where per-item serial awaits
+// dominate wallclock. A batch of N has roughly the latency of a single
+// embed (network + GPU setup amortized), so backfilling a 500k-obs
+// corpus drops from days to hours on a per-batch endpoint like vLLM.
+//
+// Per-item failure shape:
+//   - whole-batch network/provider error → all skipped, single warn line
+//   - per-item dimension mismatch → that item skipped, others continue
+export async function vectorIndexAddBatchGuarded(
+  items: Array<{
+    id: string
+    sessionId: string
+    text: string
+    context: { kind: "memory" | "observation" | "synthetic"; logId: string }
+  }>,
+): Promise<{ ok: number; fail: number }> {
+  const vi = vectorIndex
+  const ep = currentEmbeddingProvider
+  if (!vi || !ep || items.length === 0) return { ok: 0, fail: 0 }
+
+  let embeddings: Float32Array[]
+  try {
+    embeddings = await ep.embedBatch(items.map((i) => clipEmbedInput(i.text)))
+  } catch (err) {
+    logger.warn("vector-index add batch: embed failed — skipping batch", {
+      batchSize: items.length,
+      provider: ep.name,
+      error: err instanceof Error ? err.message : String(err),
+    })
+    return { ok: 0, fail: items.length }
+  }
+
+  if (embeddings.length !== items.length) {
+    logger.warn(
+      "vector-index add batch: provider returned wrong length — skipping batch",
+      {
+        batchSize: items.length,
+        returned: embeddings.length,
+        provider: ep.name,
+      },
+    )
+    return { ok: 0, fail: items.length }
+  }
+
+  let ok = 0
+  let fail = 0
+  for (let i = 0; i < items.length; i++) {
+    const item = items[i]
+    const embedding = embeddings[i]
+    if (embedding.length !== ep.dimensions) {
+      logger.warn("vector-index add batch: dimension mismatch — skipping item", {
+        kind: item.context.kind,
+        id: item.context.logId,
+        provider: ep.name,
+        expected: ep.dimensions,
+        received: embedding.length,
+      })
+      fail++
+      continue
+    }
+    try {
+      vi.add(item.id, item.sessionId, embedding)
+      ok++
+    } catch (err) {
+      logger.warn("vector-index add batch: index write failed — skipping item", {
+        kind: item.context.kind,
+        id: item.context.logId,
+        error: err instanceof Error ? err.message : String(err),
+      })
+      fail++
+    }
+  }
+  return { ok, fail }
+}
+
+// Embed-batch size for rebuild. Each item is one /v1/embeddings call's
+// `input` array element; the provider sees the whole batch as one HTTP
+// round-trip. 32 fits comfortably under typical per-request token budgets
+// (32 × ~110 tok/item ≈ 3.5k tokens) and gets close to per-call
+// throughput for GPU-backed endpoints (vLLM, Triton, etc.). Override via
+// REBUILD_EMBED_BATCH_SIZE for endpoints that prefer smaller/larger
+// batches. Set to 1 to fall back to the legacy per-item path.
+const DEFAULT_REBUILD_EMBED_BATCH = 32
+
+function getRebuildEmbedBatchSize(): number {
+  const raw = process.env.REBUILD_EMBED_BATCH_SIZE
+  if (!raw) return DEFAULT_REBUILD_EMBED_BATCH
+  const n = parseInt(raw, 10)
+  return Number.isFinite(n) && n > 0 ? n : DEFAULT_REBUILD_EMBED_BATCH
+}
+
 export async function rebuildIndex(kv: StateKV): Promise<number> {
   const idx = getSearchIndex()
   idx.clear()
@@ -96,8 +189,28 @@ export async function rebuildIndex(kv: StateKV): Promise<number> {
   // repopulation loops run, so BM25 and vector stay in sync.
   vectorIndex?.clear()
 
+  const batchSize = getRebuildEmbedBatchSize()
+  // Accumulator for the batched embed flush. BM25 add is synchronous and
+  // doesn't need batching — only the vector path benefits.
+  type EmbedJob = {
+    id: string
+    sessionId: string
+    text: string
+    context: { kind: "memory" | "observation" | "synthetic"; logId: string }
+  }
+  const pending: EmbedJob[] = []
   let count = 0
 
+  const flush = async (): Promise<void> => {
+    if (pending.length === 0) return
+    await vectorIndexAddBatchGuarded(pending)
+    pending.length = 0
+  }
+  const enqueue = async (job: EmbedJob): Promise<void> => {
+    pending.push(job)
+    if (pending.length >= batchSize) await flush()
+  }
+
   // Memories live in their own KV scope outside per-session observation
   // scopes, so they need a separate walk. Without this, mem::remember
   // entries vanish from BM25 on every restart even after the live-write
@@ -108,12 +221,12 @@ export async function rebuildIndex(kv: StateKV): Promise<number> {
       if (memory.isLatest === false) continue
       if (!memory.title || !memory.content) continue
       idx.add(memoryToObservation(memory))
-      await vectorIndexAddGuarded(
-        memory.id,
-        memory.sessionIds[0] ?? 'memory',
-        memory.title + ' ' + memory.content,
-        { kind: "memory", logId: memory.id },
-      )
+      await enqueue({
+        id: memory.id,
+        sessionId: memory.sessionIds[0] ?? 'memory',
+        text: memory.title + ' ' + memory.content,
+        context: { kind: "memory", logId: memory.id },
+      })
       count++
     }
   } catch (err) {
@@ -123,7 +236,10 @@ export async function rebuildIndex(kv: StateKV): Promise<number> {
   }
 
   const sessions = await kv.list<Session>(KV.sessions)
-  if (!sessions.length) return count
+  if (!sessions.length) {
+    await flush()
+    return count
+  }
 
   const obsPerSession: CompressedObservation[][] = []
   const failedSessions: string[] = []
@@ -148,16 +264,19 @@ export async function rebuildIndex(kv: StateKV): Promise<number> {
     for (const obs of observations) {
       if (obs.title && obs.narrative) {
         idx.add(obs)
-        await vectorIndexAddGuarded(
-          obs.id,
-          obs.sessionId,
-          obs.title + ' ' + obs.narrative,
-          { kind: "observation", logId: obs.id },
-        )
+        await enqueue({
+          id: obs.id,
+          sessionId: obs.sessionId,
+          text: obs.title + ' ' + obs.narrative,
+          context: { kind: "observation", logId: obs.id },
+        })
         count++
       }
     }
   }
+
+  // Drain the last partial batch.
+  await flush()
   return count
 }