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...
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, VectorDistancesclient.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. Related resources Concepts: Vector Index - HFresh SPFresh: Incremental In-Place Update for Billion-Scale Vector Search 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 restartmcp_server_enabled: truemcp_server_write_access_enabled: true So you can grant or revoke an agent's write access on a live cluster, with no rolling restart. Related resources Docs: Weaviate MCP server Model Context Protocol 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 poolASYNC_REPLICATION_SCHEDULER_WORKERS: ''# Kill-switch — pause all async replication, no restart neededASYNC_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. Related resources Concepts: Replication architecture - Async replication Configuration: Replication 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 restresponse = 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. PreviewThe Boost API is a preview feature, available over gRPC. The API and behavior may change in future releases. Related resources How-to: Search - Boost results 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 Filterresponse = collection.query.fetch_objects( filters=Filter.by_property("cars.make").equal("Toyota"),) This works for data nested inside both object and object[] properties. PreviewNested Object Filtering is a preview feature, off by default behind WEAVIATE_PREVIEW_NESTED_FILTERING. The API and behavior may change in future releases. Related resources How-to: Filters Config references: Data types - object 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. Related resources Weaviate 1.38: GitHub Release Notes 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. noteNot all features may be available on Weaviate Cloud. Some capabilities — preview features in particular, and those that require specific environment configuration — may not be enabled on managed clusters, or may become available on a different schedule. 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. GitHubForumX (Twitter)Don't want to miss another blog post?Sign up for our bi-weekly newsletter to stay updated! By submitting, I agree to the Terms of Service and Privacy Policy.