diff --git a/src/openhuman/memory_queue/handlers/mod.rs b/src/openhuman/memory_queue/handlers/mod.rs
index 4fabaaaec..e39f6dbc4 100644
--- a/src/openhuman/memory_queue/handlers/mod.rs
+++ b/src/openhuman/memory_queue/handlers/mod.rs
@@ -23,7 +23,7 @@ use crate::openhuman::memory_store::content::{
     self as content_store, read as content_read, tags as content_tags,
 };
 use crate::openhuman::memory_tree::score;
-use crate::openhuman::memory_tree::score::embed::{build_write_embedder, pack_checked};
+use crate::openhuman::memory_tree::score::embed::{build_write_embedder, pack_checked, Embedder};
 use crate::openhuman::memory_tree::score::store as score_store;
 use crate::openhuman::memory_tree::tree::store as summary_store;
 use crate::openhuman::memory_tree::tree::{LeafRef, TreeFactory};
@@ -703,6 +703,89 @@ fn try_mark_summary_reembed_skipped(
     }
 }
 
+/// Read each row's stored source text, embed the readable bodies in **one
+/// batched call**, and classify per position exactly as the legacy per-row
+/// loop did. Generic over the chunk vs summary read/tombstone functions so
+/// both Phase-2 passes share one implementation.
+///
+/// Failure semantics are preserved verbatim from #1574 §6:
+/// - body read failure → log + persistent tombstone (`"body read failed: {e}"`)
+/// - embed wrong dim → log + tombstone (`"embed wrong dim"`)
+/// - embed error → log + tombstone (`"embed failed: {e}"`)
+///
+/// The single difference vs the old code is the embed call shape: one
+/// `embed_batch` (which collapses N provider round-trips into one for batch-
+/// capable providers, falling back to per-text internally) instead of N
+/// sequential `embed` awaits. Ordering is irrelevant — results are zipped
+/// back to their ids by position and folded into order-independent state.
+async fn reembed_collect(
+    config: &Config,
+    embedder: &dyn Embedder,
+    active_sig: &str,
+    ids: &[String],
+    label: &str,
+    read_body: impl Fn(&Config, &str) -> Result<String>,
+    mark_skipped: impl Fn(&Config, &str, &str, &str),
+) -> Result<Vec<(String, Vec<f32>)>> {
+    // Phase A: read bodies; persistently tombstone read failures so an
+    // unreadable row is attempted at most once per signature.
+    let mut readable: Vec<(&String, String)> = Vec::with_capacity(ids.len());
+    for id in ids {
+        match read_body(config, id) {
+            Ok(body) => readable.push((id, body)),
+            Err(e) => {
+                log::warn!(
+                    "[memory::jobs] reembed_backfill: {label} {id} body read failed: {e}; skipping (sig={active_sig})"
+                );
+                mark_skipped(config, id, active_sig, &format!("body read failed: {e}"));
+            }
+        }
+    }
+    if readable.is_empty() {
+        return Ok(Vec::new());
+    }
+
+    // Phase B: one batched embed call. Scope `texts` so its borrow on
+    // `readable` ends before we consume `readable` below.
+    let results = {
+        let texts: Vec<&str> = readable.iter().map(|(_, body)| body.as_str()).collect();
+        embedder.embed_batch(&texts).await
+    };
+    if results.len() != readable.len() {
+        // `embed_batch`'s contract is one result per input position. A
+        // violation means we can't attribute results to ids. Returning an
+        // empty Vec here would make the handler write nothing yet still
+        // `Defer`, re-selecting the same ids forever — a non-converging
+        // chain. Surface it as an error so the chain terminates instead.
+        anyhow::bail!(
+            "reembed_backfill: {label} embed_batch returned {} results for {} texts (sig={active_sig})",
+            results.len(),
+            readable.len()
+        );
+    }
+
+    // Phase C: classify per position exactly as the legacy loop did.
+    let mut out: Vec<(String, Vec<f32>)> = Vec::with_capacity(readable.len());
+    for ((id, _body), result) in readable.into_iter().zip(results) {
+        match result {
+            Ok(v) if pack_checked(&v).is_ok() => out.push((id.clone(), v)),
+            Ok(_) => {
+                log::warn!(
+                    "[memory::jobs] reembed_backfill: {label} {id} embed wrong dim, skipping (sig={active_sig})"
+                );
+                mark_skipped(config, id, active_sig, "embed wrong dim");
+            }
+            Err(e) => {
+                log::warn!(
+                    "[memory::jobs] reembed_backfill: {label} {id} embed failed: {e}; skipping (sig={active_sig})"
+                );
+                mark_skipped(config, id, active_sig, &format!("embed failed: {e}"));
+            }
+        }
+    }
+    Ok(out)
+}
+
 async fn handle_reembed_backfill(config: &Config, job: &Job) -> Result<JobOutcome> {
     let payload: ReembedBackfillPayload =
         serde_json::from_str(&job.payload_json).context("parse ReembedBackfill payload")?;
@@ -820,78 +903,26 @@ async fn handle_reembed_backfill(config: &Config, job: &Job) -> Result<JobOutcom
                 return Ok(JobOutcome::Done);
             }
         };
-    let mut chunk_vecs: Vec<(String, Vec<f32>)> = Vec::new();
-    for id in &chunk_ids {
-        match content_read::read_chunk_body(config, id) {
-            Ok(body) => match embedder.embed(&body).await {
-                Ok(v) if pack_checked(&v).is_ok() => chunk_vecs.push((id.clone(), v)),
-                Ok(_) => {
-                    log::warn!(
-                        "[memory::jobs] reembed_backfill: chunk {id} embed wrong dim, skipping (sig={active_sig})"
-                    );
-                    try_mark_chunk_reembed_skipped(config, id, &active_sig, "embed wrong dim");
-                }
-                Err(e) => {
-                    log::warn!(
-                        "[memory::jobs] reembed_backfill: chunk {id} embed failed: {e}; skipping (sig={active_sig})"
-                    );
-                    try_mark_chunk_reembed_skipped(
-                        config,
-                        id,
-                        &active_sig,
-                        &format!("embed failed: {e}"),
-                    );
-                }
-            },
-            Err(e) => {
-                log::warn!(
-                    "[memory::jobs] reembed_backfill: chunk {id} body read failed: {e}; skipping (sig={active_sig})"
-                );
-                try_mark_chunk_reembed_skipped(
-                    config,
-                    id,
-                    &active_sig,
-                    &format!("body read failed: {e}"),
-                );
-            }
-        }
-    }
-    let mut summary_vecs: Vec<(String, Vec<f32>)> = Vec::new();
-    for id in &summary_ids {
-        match content_read::read_summary_body(config, id) {
-            Ok(body) => match embedder.embed(&body).await {
-                Ok(v) if pack_checked(&v).is_ok() => summary_vecs.push((id.clone(), v)),
-                Ok(_) => {
-                    log::warn!(
-                        "[memory::jobs] reembed_backfill: summary {id} embed wrong dim, skipping (sig={active_sig})"
-                    );
-                    try_mark_summary_reembed_skipped(config, id, &active_sig, "embed wrong dim");
-                }
-                Err(e) => {
-                    log::warn!(
-                        "[memory::jobs] reembed_backfill: summary {id} embed failed: {e}; skipping (sig={active_sig})"
-                    );
-                    try_mark_summary_reembed_skipped(
-                        config,
-                        id,
-                        &active_sig,
-                        &format!("embed failed: {e}"),
-                    );
-                }
-            },
-            Err(e) => {
-                log::warn!(
-                    "[memory::jobs] reembed_backfill: summary {id} body read failed: {e}; skipping (sig={active_sig})"
-                );
-                try_mark_summary_reembed_skipped(
-                    config,
-                    id,
-                    &active_sig,
-                    &format!("body read failed: {e}"),
-                );
-            }
-        }
-    }
+    let chunk_vecs = reembed_collect(
+        config,
+        embedder.as_ref(),
+        &active_sig,
+        &chunk_ids,
+        "chunk",
+        content_read::read_chunk_body,
+        try_mark_chunk_reembed_skipped,
+    )
+    .await?;
+    let summary_vecs = reembed_collect(
+        config,
+        embedder.as_ref(),
+        &active_sig,
+        &summary_ids,
+        "summary",
+        content_read::read_summary_body,
+        try_mark_summary_reembed_skipped,
+    )
+    .await?;
 
     // Phase 3 (one short tx): persist all collected vectors to the sidecar.
     chunk_store::with_connection(config, |conn| {
diff --git a/src/openhuman/memory_tree/score/embed/cloud.rs b/src/openhuman/memory_tree/score/embed/cloud.rs
index 682c1eeed..c0787cb9b 100644
--- a/src/openhuman/memory_tree/score/embed/cloud.rs
+++ b/src/openhuman/memory_tree/score/embed/cloud.rs
@@ -76,6 +76,14 @@ impl Embedder for CloudEmbedder {
         }
         Ok(v)
     }
+
+    /// Collapse N per-text round-trips into a single batched Voyage request by
+    /// delegating to the inner provider's native batch `embed`. Falls back to
+    /// per-text embedding (preserving per-position error attribution) on a
+    /// whole-batch failure or a length mismatch.
+    async fn embed_batch(&self, texts: &[&str]) -> Vec<Result<Vec<f32>>> {
+        super::embed_batch_via_provider(&self.inner, "cloud", texts).await
+    }
 }
 
 #[cfg(test)]
diff --git a/src/openhuman/memory_tree/score/embed/mod.rs b/src/openhuman/memory_tree/score/embed/mod.rs
index 7f678857b..00a783085 100644
--- a/src/openhuman/memory_tree/score/embed/mod.rs
+++ b/src/openhuman/memory_tree/score/embed/mod.rs
@@ -61,6 +61,120 @@ pub trait Embedder: Send + Sync {
     /// [`EMBEDDING_DIM`]. Hard failure — ingest / seal treat `Err` as
     /// "don't persist the row" so retries stay idempotent on `chunk_id`.
     async fn embed(&self, text: &str) -> Result<Vec<f32>>;
+
+    /// Embed many texts, returning **one [`Result`] per input position**
+    /// aligned by index. A single failing text does not strand the rest of
+    /// the batch — its slot carries the `Err` while the others succeed —
+    /// which lets bulk callers (e.g. the re-embed backfill) attribute and
+    /// skip individual rows exactly as a per-text loop would.
+    ///
+    /// The default implementation issues one sequential [`Embedder::embed`]
+    /// call per text: correct for any provider, but with no batching win.
+    /// Providers whose backend accepts many texts in a single request
+    /// (cloud / OpenAI-compatible) override this to collapse N network
+    /// round-trips into one — see [`embed_batch_via_provider`].
+    ///
+    /// The returned vector always has `texts.len()` elements.
+    async fn embed_batch(&self, texts: &[&str]) -> Vec<Result<Vec<f32>>> {
+        log::debug!(
+            "[memory_tree::embed::{}] embed_batch:enter sequential texts={}",
+            self.name(),
+            texts.len()
+        );
+        let mut out = Vec::with_capacity(texts.len());
+        for text in texts {
+            out.push(self.embed(text).await);
+        }
+        out
+    }
+}
+
+/// Validate that a freshly-produced embedding has exactly [`EMBEDDING_DIM`]
+/// floats, returning a labelled error otherwise. Shared by the per-text and
+/// batched provider adapters so the "wrong dims" diagnostic is identical
+/// regardless of path.
+pub(crate) fn check_embed_dim(v: Vec<f32>, label: &str) -> Result<Vec<f32>> {
+    if v.len() != EMBEDDING_DIM {
+        anyhow::bail!(
+            "{label} embedder returned {} dims, expected {}",
+            v.len(),
+            EMBEDDING_DIM
+        );
+    }
+    Ok(v)
+}
+
+/// Batch-embed `texts` through a unified [`EmbeddingProvider`] in a single
+/// request and adapt the result to the [`Embedder::embed_batch`] contract:
+/// one dimension-checked [`Result`] per input position.
+///
+/// On a wholesale batch failure (network / auth) **or** a length-contract
+/// violation from the provider, this falls back to per-text
+/// [`EmbeddingProvider::embed_one`] so a single transient blip cannot fail —
+/// and, in the backfill, *tombstone* — every row in the batch. That preserves
+/// the resilience of the original per-item loop while keeping the happy-path
+/// single-round-trip win.
+pub(crate) async fn embed_batch_via_provider(
+    inner: &dyn crate::openhuman::embeddings::EmbeddingProvider,
+    label: &str,
+    texts: &[&str],
+) -> Vec<Result<Vec<f32>>> {
+    if texts.is_empty() {
+        return Vec::new();
+    }
+    log::debug!(
+        "[memory_tree::embed::{label}] embed_batch:enter texts={}",
+        texts.len()
+    );
+    match inner.embed(texts).await {
+        Ok(vectors) if vectors.len() == texts.len() => {
+            log::debug!(
+                "[memory_tree::embed::{label}] embed_batch:success collapsed {} texts into one provider call",
+                texts.len()
+            );
+            vectors
+                .into_iter()
+                .map(|v| check_embed_dim(v, label))
+                .collect()
+        }
+        Ok(vectors) => {
+            log::warn!(
+                "[memory_tree::embed::{label}] embed_batch:fallback batch returned {} vectors for {} texts; \
+                 falling back to per-text embedding",
+                vectors.len(),
+                texts.len()
+            );
+            embed_each_via_provider(inner, label, texts).await
+        }
+        Err(e) => {
+            log::warn!(
+                "[memory_tree::embed::{label}] embed_batch:fallback batch embed failed ({e:#}); \
+                 falling back to per-text embedding"
+            );
+            embed_each_via_provider(inner, label, texts).await
+        }
+    }
+}
+
+/// Sequential per-text fallback used when a provider's native batch call is
+/// unavailable or fails wholesale. Each slot is dimension-checked so the
+/// result is interchangeable with the happy-path mapping in
+/// [`embed_batch_via_provider`].
+async fn embed_each_via_provider(
+    inner: &dyn crate::openhuman::embeddings::EmbeddingProvider,
+    label: &str,
+    texts: &[&str],
+) -> Vec<Result<Vec<f32>>> {
+    let mut out = Vec::with_capacity(texts.len());
+    for text in texts {
+        let result = inner
+            .embed_one(text)
+            .await
+            .with_context(|| format!("{label} embeddings failed"))
+            .and_then(|v| check_embed_dim(v, label));
+        out.push(result);
+    }
+    out
 }
 
 /// Cosine similarity between two equal-length vectors.
@@ -234,4 +348,193 @@ mod tests {
         let correct = vec![0.0_f32; EMBEDDING_DIM];
         assert!(pack_checked(&correct).is_ok());
     }
+
+    // --- batch-embedding (variant B) scaffolding + tests ---
+
+    use crate::openhuman::embeddings::EmbeddingProvider;
+    use std::sync::atomic::{AtomicUsize, Ordering};
+    use std::sync::Arc;
+
+    fn ok_vec() -> Vec<f32> {
+        vec![0.5_f32; EMBEDDING_DIM]
+    }
+
+    #[derive(Clone)]
+    enum ProviderMode {
+        /// One correct-dim vector per text (single batch call succeeds).
+        Ok,
+        /// Batch (`len > 1`) call errors, per-text (`len == 1`) succeeds —
+        /// exercises the whole-batch-error fallback path.
+        BatchFailsPerTextOk,
+        /// Batch (`len > 1`) returns one extra vector, per-text is fine —
+        /// exercises the length-mismatch fallback path.
+        WrongCount,
+        /// Returns `len` vectors but the one at `idx` has the wrong dim —
+        /// length matches so no fallback; that position must map to `Err`.
+        OneWrongDim(usize),
+    }
+
+    struct FakeProvider {
+        calls: Arc<AtomicUsize>,
+        mode: ProviderMode,
+    }
+
+    #[async_trait::async_trait]
+    impl EmbeddingProvider for FakeProvider {
+        fn name(&self) -> &str {
+            "fake"
+        }
+        fn model_id(&self) -> &str {
+            "fake-model"
+        }
+        fn dimensions(&self) -> usize {
+            EMBEDDING_DIM
+        }
+        async fn embed(&self, texts: &[&str]) -> anyhow::Result<Vec<Vec<f32>>> {
+            self.calls.fetch_add(1, Ordering::SeqCst);
+            match self.mode {
+                ProviderMode::Ok => Ok(texts.iter().map(|_| ok_vec()).collect()),
+                ProviderMode::BatchFailsPerTextOk => {
+                    if texts.len() > 1 {
+                        anyhow::bail!("simulated batch endpoint failure")
+                    } else {
+                        Ok(texts.iter().map(|_| ok_vec()).collect())
+                    }
+                }
+                ProviderMode::WrongCount => {
+                    if texts.len() > 1 {
+                        Ok((0..texts.len() + 1).map(|_| ok_vec()).collect())
+                    } else {
+                        Ok(texts.iter().map(|_| ok_vec()).collect())
+                    }
+                }
+                ProviderMode::OneWrongDim(idx) => Ok(texts
+                    .iter()
+                    .enumerate()
+                    .map(|(i, _)| if i == idx { vec![0.0_f32; 3] } else { ok_vec() })
+                    .collect()),
+            }
+        }
+    }
+
+    #[tokio::test]
+    async fn embed_batch_via_provider_happy_is_single_call() {
+        let calls = Arc::new(AtomicUsize::new(0));
+        let p = FakeProvider {
+            calls: calls.clone(),
+            mode: ProviderMode::Ok,
+        };
+        let out = embed_batch_via_provider(&p, "test", &["a", "b", "c"]).await;
+        assert_eq!(out.len(), 3);
+        assert!(out.iter().all(|r| r.is_ok()));
+        assert_eq!(
+            calls.load(Ordering::SeqCst),
+            1,
+            "happy path must collapse to exactly one batch call"
+        );
+    }
+
+    #[tokio::test]
+    async fn embed_batch_via_provider_empty_makes_no_call() {
+        let calls = Arc::new(AtomicUsize::new(0));
+        let p = FakeProvider {
+            calls: calls.clone(),
+            mode: ProviderMode::Ok,
+        };
+        let texts: [&str; 0] = [];
+        let out = embed_batch_via_provider(&p, "test", &texts).await;
+        assert!(out.is_empty());
+        assert_eq!(calls.load(Ordering::SeqCst), 0);
+    }
+
+    #[tokio::test]
+    async fn embed_batch_via_provider_falls_back_on_batch_error() {
+        let calls = Arc::new(AtomicUsize::new(0));
+        let p = FakeProvider {
+            calls: calls.clone(),
+            mode: ProviderMode::BatchFailsPerTextOk,
+        };
+        let out = embed_batch_via_provider(&p, "test", &["a", "b", "c"]).await;
+        assert_eq!(out.len(), 3);
+        assert!(
+            out.iter().all(|r| r.is_ok()),
+            "per-text fallback should still produce all vectors"
+        );
+        // 1 failed batch call + 3 per-text calls.
+        assert_eq!(calls.load(Ordering::SeqCst), 4);
+    }
+
+    #[tokio::test]
+    async fn embed_batch_via_provider_falls_back_on_length_mismatch() {
+        let calls = Arc::new(AtomicUsize::new(0));
+        let p = FakeProvider {
+            calls: calls.clone(),
+            mode: ProviderMode::WrongCount,
+        };
+        let out = embed_batch_via_provider(&p, "test", &["a", "b"]).await;
+        assert_eq!(out.len(), 2);
+        assert!(out.iter().all(|r| r.is_ok()));
+        // 1 mismatched batch call + 2 per-text calls.
+        assert_eq!(calls.load(Ordering::SeqCst), 3);
+    }
+
+    #[tokio::test]
+    async fn embed_batch_via_provider_maps_wrong_dim_per_position() {
+        let calls = Arc::new(AtomicUsize::new(0));
+        let p = FakeProvider {
+            calls: calls.clone(),
+            mode: ProviderMode::OneWrongDim(1),
+        };
+        let out = embed_batch_via_provider(&p, "test", &["a", "b", "c"]).await;
+        assert_eq!(out.len(), 3);
+        assert!(out[0].is_ok());
+        assert!(out[1].is_err(), "wrong-dim vector maps to Err at its slot");
+        assert!(out[2].is_ok());
+        // Length matched, so no fallback — a single batch call.
+        assert_eq!(calls.load(Ordering::SeqCst), 1);
+    }
+
+    struct SeqEmbedder {
+        calls: Arc<AtomicUsize>,
+    }
+
+    #[async_trait::async_trait]
+    impl Embedder for SeqEmbedder {
+        fn name(&self) -> &'static str {
+            "seq"
+        }
+        async fn embed(&self, text: &str) -> Result<Vec<f32>> {
+            self.calls.fetch_add(1, Ordering::SeqCst);
+            if text == "bad" {
+                anyhow::bail!("simulated per-text failure")
+            }
+            Ok(ok_vec())
+        }
+        // Uses the default `embed_batch`.
+    }
+
+    #[tokio::test]
+    async fn default_embed_batch_calls_embed_per_text() {
+        let calls = Arc::new(AtomicUsize::new(0));
+        let e = SeqEmbedder {
+            calls: calls.clone(),
+        };
+        let out = e.embed_batch(&["a", "b", "c"]).await;
+        assert_eq!(out.len(), 3);
+        assert!(out.iter().all(|r| r.is_ok()));
+        assert_eq!(calls.load(Ordering::SeqCst), 3);
+    }
+
+    #[tokio::test]
+    async fn default_embed_batch_preserves_per_position_errors() {
+        let calls = Arc::new(AtomicUsize::new(0));
+        let e = SeqEmbedder {
+            calls: calls.clone(),
+        };
+        let out = e.embed_batch(&["ok", "bad", "ok"]).await;
+        assert_eq!(out.len(), 3);
+        assert!(out[0].is_ok());
+        assert!(out[1].is_err());
+        assert!(out[2].is_ok());
+    }
 }
diff --git a/src/openhuman/memory_tree/score/embed/openai_compat.rs b/src/openhuman/memory_tree/score/embed/openai_compat.rs
index 9e18cd735..8dd8bfb92 100644
--- a/src/openhuman/memory_tree/score/embed/openai_compat.rs
+++ b/src/openhuman/memory_tree/score/embed/openai_compat.rs
@@ -107,6 +107,14 @@ impl Embedder for OpenAiCompatEmbedder {
         }
         Ok(v)
     }
+
+    /// Collapse N per-text round-trips into a single batched request by
+    /// delegating to the inner provider's native batch `embed`. Falls back to
+    /// per-text embedding (preserving per-position error attribution) on a
+    /// whole-batch failure or a length mismatch.
+    async fn embed_batch(&self, texts: &[&str]) -> Vec<Result<Vec<f32>>> {
+        super::embed_batch_via_provider(self.inner.as_ref(), self.label, texts).await
+    }
 }
 
 #[cfg(test)]