Weaviate 1.38 Release

Weaviate v1.38 is now available open-source and on Weaviate Cloud.

Two capabilities reach general availability in this release: the HFresh disk-based vector index and the built-in MCP Server. Async replication has been rebuilt to run cluster-wide from a single scheduler, and it now runs by default on every replicated collection. Two new previews join them: the Boost API for query-time rescoring and Nested Object Filtering.

Here are the release highlights!

• HFresh Vector Index - General Availability
• MCP Server - General Availability
• Async Replication, Everywhere
• Boost API (Preview)
• Nested Object Filtering (Preview)
• Performance Improvements and Fixes
• Community Contributions
• Summary

HFresh Vector Index - General Availability​

HFresh, the disk-based vector index we introduced as a technical preview in v1.36, is now generally available. It's inspired by the SPFresh algorithm: instead of keeping every vector in memory like HNSW, HFresh groups vectors into on-disk regions called postings and keeps a small in-memory HNSW index over their centroids to decide which regions to read. Memory stays low and latency stays predictable as a collection grows into the billions, which makes it a good fit for streaming workloads where data changes continuously rather than being loaded once.

How it works​

HFresh is selected per (named) vector, the same way as any other index. In v1.38 it's no longer behind a preview flag — you enable it by configuring a vector to use it:

from weaviate.classes.config import Configure, VectorDistances

client.collections.create(
    "Article",
    vector_config=Configure.Vectors.text2vec_weaviate(
        name="default",
        source_properties=["title", "body"],
        vector_index_config=Configure.VectorIndex.hfresh(
            distance_metric=VectorDistances.COSINE,
        ),
    ),
)

HFresh has RQ-1 quantization built in: postings are stored compressed on disk, and final ranking rescores against the uncompressed vectors for accuracy. It supports the cosine and l2-squared distance metrics.

Because the index rebalances incrementally — splitting oversized postings, merging undersized ones, and reassigning vectors as the boundaries shift — it keeps up with continuous updates without the periodic full rebuilds that other on-disk indexes depend on.

MCP Server - General Availability​

The built-in Model Context Protocol (MCP) server, introduced as a preview in v1.37, is now generally available. It lets LLMs, IDEs, and AI agents work with Weaviate directly — inspecting schemas, running hybrid searches, and writing objects back — with no glue code. The server is a Streamable HTTP endpoint at /v1/mcp on the same port as the REST API, authenticates with a Bearer / API-key token, and respects Weaviate's standard RBAC permissions.

The server exposes four tools:

• weaviate-collections-get-config — inspect a collection's schema and configuration
• weaviate-tenants-list — list the tenants of a multi-tenant collection
• weaviate-query-hybrid — run a hybrid (vector + keyword) search
• weaviate-objects-upsert — insert or update objects (only when write access is enabled)

How it works​

You enable the server with MCP_SERVER_ENABLED, and optionally expose its write tools with MCP_SERVER_WRITE_ACCESS_ENABLED. What's new in v1.38 is that both flags are runtime-configurable: rather than only being read at startup, Weaviate now picks up changes to them from its runtime-overrides file while the cluster is running.

# Runtime-overrides file — applied without a restart
mcp_server_enabled: true
mcp_server_write_access_enabled: true

So you can grant or revoke an agent's write access on a live cluster, with no rolling restart.

Async Replication, Everywhere​

Async replication is the background repair process that keeps replicas in sync on collections with a replication factor greater than 1. In v1.38 it has been re-architected to run cluster-wide from a single scheduler, rather than being configured and run separately per collection. It also now runs by default on every RF > 1 collection, where previously it was opt-in per collection.

How it works​

One scheduler coordinates async repair across all replicated collections, drawing from a single shared worker pool instead of a separate pool per collection. That makes repair behavior consistent across the cluster and simpler to operate at scale.

With the move to a central scheduler, the per-collection maxWorkers and enabled settings are gone. Two cluster-level controls replace them:

# Size of the shared async-replication worker pool
ASYNC_REPLICATION_SCHEDULER_WORKERS: '<n>'
# Kill-switch — pause all async replication, no restart needed
ASYNC_REPLICATION_DISABLED: 'false'

ASYNC_REPLICATION_SCHEDULER_WORKERS sizes the shared pool, and ASYNC_REPLICATION_DISABLED is a cluster-wide kill-switch you can flip at runtime.

Boost API (Preview)​

Sometimes you want to nudge results without removing any. A filter is too blunt for that — it drops everything that doesn't match — when what you really want is to rank fresh articles a little higher, or favor in-stock products, while keeping the full result set. The new Boost API does exactly that.

How it works​

Boost runs after the primary search. It re-scores the candidates by blending their original score with one or more boost conditions, then re-sorts them — promoting or demoting results without dropping any. Conditions can be based on:

• Filter matches — promote results that satisfy a filter
• Property values — rank by a numeric property's value
• Time decay — favor more recent (or near-a-date) objects
• Numeric decay — favor objects closer to a target number

In the Python client (v4.22.0+), you build a boost and pass it to any query via boost=:

from weaviate.classes.query import Boost, Filter

# Softly promote in-stock products without dropping the rest
response = collection.query.hybrid(
    query="wireless headphones",
    limit=10,
    boost=Boost.filter(
        Filter.by_property("in_stock").equal(True),
        weight=0.3,
    ),
)

The weight (0–1) sets how much the boost shifts the final score: the result is (1 - weight) of the original score plus weight of the boost score. Boost is gRPC-only — there's no REST or GraphQL equivalent — and a single query can apply at most 20 conditions.

Nested Object Filtering (Preview)​

Weaviate v1.38 adds a preview for filtering on nested object properties. Until now, object and object[] properties were stored but couldn't be filtered on directly.

How it works​

You filter on a nested field by referencing it with a dotted path — for example, cars.make to filter on the make field inside a cars object property. The feature is off by default and gated behind a preview environment variable:

WEAVIATE_PREVIEW_NESTED_FILTERING: 'true'

Once enabled, the dotted path goes wherever you'd normally supply a property name, including from the clients:

from weaviate.classes.query import Filter

response = collection.query.fetch_objects(
    filters=Filter.by_property("cars.make").equal("Toyota"),
)

This works for data nested inside both object and object[] properties.

Performance Improvements and Fixes​

Beyond the headline features, v1.38 ships a long list of improvements. A few worth calling out:

• Production-ready replica movement: Moving a shard's replicas between nodes — for rebalancing and scaling — graduates to production-ready, backed by a change-capture log that keeps writes flowing during the move.
• Default vector index type: A new cluster-level setting picks the default vector index for new collections (including named vectors), instead of always defaulting to HNSW.
• Usage guardrails: Operators can set server-side limits on the number of objects, collections, tenants, and shards, plus allow-lists for vector-index and compression types.
• New module — text2vec-digitalocean: Generate embeddings through DigitalOcean's inference platform.
• Backup reliability: Backups no longer pause compactions, and object-storage listing is faster — both help large collections back up more reliably.
• Fractional BM25 property boosts: Keyword-search property boosts now accept fractional values (e.g. title^2.5), not just integers.
• Deterministic tie-breaking: Vector searches break ties between equal-distance results deterministically, for stable, repeatable ordering.
• Faster startup and an improved cache for compressed vector indexes.

Community Contributions​

Weaviate is open source, and this release includes work from several first-time contributors. Thank you to:

• @dillonledoux — the new text2vec-digitalocean module (#11298)
• @anishesg — inverted-index and HFresh fixes, including correct handling of negative zero and pre-1970 dates (#11120)
• @msnandhis — fractional BM25 property boosts (#11471)
• @3em0 — reject duplicate static API keys (#11393)
• @kedar49 — collision check for DB user identifiers (#11381)
• @SAY-5 — HFresh stability fix during async init (#11087)

If you'd like to contribute, check out the contributor guide and the good-first-issue label on GitHub.

Summary​

Weaviate v1.38 brings two capabilities to general availability — HFresh and the MCP Server — alongside a rebuilt async replication path and two new previews.

Key highlights:

• HFresh (GA) — The disk-based, SPFresh-inspired vector index for streaming workloads, selected per named vector with vectorIndexType: "hfresh" and built-in RQ-1 quantization
• MCP Server (GA) — The built-in Model Context Protocol server at /v1/mcp, with its enable flags now runtime-configurable
• Async Replication, Everywhere — Cluster-wide async repair from one scheduler and a shared worker pool, on by default for replicated collections, with a runtime kill-switch
• Boost API (Preview) — Query-time rescoring that promotes or demotes results without dropping any
• Nested Object Filtering (Preview) — Filter on object / object[] properties using a dotted path

Ready to get started?

The release is available open-source on GitHub and on Weaviate Cloud, where you can spin up a cluster on the free tier.

For those upgrading a self-hosted version, please check the migration guide for version-specific notes.

Thanks for reading, and happy vector searching!

Ready to start building?​

Check out the Quickstart tutorial, or sign up for a free Weaviate Cloud account.