🚀 Release v3.1.0: The Spatial AI Engine (Zero-OOM, Lorentz Geometry, & MRL Cascade)

Vector databases are hitting a wall. Flat Euclidean spaces fail to model complex reasoning hierarchies, and storing bloated 1536D dense vectors alongside JSON metadata in RAM leads to catastrophic Out-Of-Memory (OOM) errors at scale.

We are thrilled to announce HyperspaceDB v3.1.0. This release officially transitions HyperspaceDB from a semantic search index into a full-fledged Spatial AI Engine built for agentic swarms, massive enterprise RAG, and edge devices.

Here is what makes this release a game-changer.

🤯 1. The RAM Diet: 50x Memory Reduction via Schema-Driven MRL

We have entirely solved the RAM bloat problem. HyperspaceDB is the only engine natively built for Matryoshka Representation Learning (MRL).

• Instead of holding massive vectors in memory, our engine automatically slices your embeddings. The lightweight navigation core (e.g., 64d or 129d) stays in ultra-fast RAM, while the heavy semantic tail streams dynamically from NVMe SSDs or S3 during the final re-ranking stage.

• The Result: Under concurrent workloads with 100,000 vectors, HyperspaceDB consumes only ~72.0 MB of RAM, compared to >3,000 MB required by legacy databases like Chroma or Milvus. This slashes your cloud infrastructure bills by up to 95%.

🌌 2. First Hybrid Hyperbolic Database (Spatial Reasoning)

Stop forcing LLMs to guess hierarchical relationships. HyperspaceDB introduces the 801D Hybrid Vector architecture (33D Lorentz + 768D Euclidean MRL).

• We natively support Poincaré disks and Lorentz hyperboloids directly inside the index.

• Agentic systems can now map strict taxonomies (like legal codes or medical trees) into negative curvature geometry.

• With our new MathSDK, developers get access to Riemannian math primitives: Geodesic path tracing, Parallel Transport, and Fréchet Mean calculations for continuous agent memory consolidation.

🗄️ 3. The End of the "Two-Database Problem" (Sidecar Payload)

You no longer need to glue Pinecone and MongoDB together with duct tape.

• HyperspaceDB introduces Sidecar Document Storage. You can store massive raw texts, JSON objects, and chat histories directly in the index.
• The engine automatically compresses this data (Zstd) and pushes it to fractal chunks (.hyp) on the disk. You get the microsecond speed of a vector engine and the convenience of a Document DB in one single binary.

🛡️ 4. Zero-Latency Pre-Filtering (Typed Metadata)

Parsing bloated JSON metadata during an HNSW graph traversal kills query performance. We fixed this with Typed Metadata.

• Strict scalar types (int, bool, enum) are compiled directly into the HNSW graph nodes in RAM.
• This provides a hardware-level Single-Stage Pre-Filter before mathematical distances are even calculated, ensuring 100% recall and strict B2B multitenancy isolation with zero QPS drop.

🐝 5. Surviving the Swarm (1000+ Concurrent Clients)

Designed for multi-agent architectures, our Rust-native, lock-free HNSW implementation refuses to bottleneck.

• L0/L2 Cache Accelerator: A dual-tier caching system combines a ~1µs L1 DashMap with an automatic background HNSW graph cache.

• Under extreme stress tests of 1,000 simultaneous active clients, HyperspaceDB maintains over 9,400 QPS with a p99 latency of just 11.83 ms. Competitors under the same load face lock contention, with latencies skyrocketing over 2,000 ms.

🌍 6. Edge-First & WASM Ready

HyperspaceDB is completely hardware-agnostic. You can run the entire spatial index locally on a $35 Raspberry Pi or ARM64 IoT hardware. It boots in under 10ms, operates stably on 512MB RAM with zero external dependencies, and features 256-bucket Merkle Tree XOR Sync for low-bandwidth edge-to-cloud updates.

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📦 Getting Started

Ready to experience spatial AI?

• Deploy instantly: Spin up a cluster on the [YAR Console](https://yar.ink/hyperspace).

• SDKs: We offer native, zero-friction clients for Python, Rust, and Go, with official integrations for LangChain, LlamaIndex, and the Model Context Protocol (MCP).

Upgrade your agent's memory today.
👉 Read the full [Documentation](https://yar.ink/docs)