LangChain Hyperspace Integration: Spatial AI Memory

Give your agents reflex-level speed and spatial reasoning.

This is the official LangChain integration for HyperspaceDB — the world's first spatial AI engine designed for Autonomous Agents, Robotics, and Continuous Learning.

🧠 Beyond Vector Search: Spatial AI Memory

Traditional vector databases are built to search static text. HyperspaceDB is built to model human cognition and physical world hierarchies:

• Fractal Knowledge Graphs: Euclidean vectors fail at hierarchies. Our native Hyperbolic engine (Poincaré & Lorentz models) compresses complex codebases or taxonomies into low-dimensional spaces, reducing RAM usage by 50x without losing semantic context.
• Continuous Reconsolidation: Transform raw information into episodic memory. Use built-in Flow Matching and Riemannian Math (Fréchet mean, parallel transport) natively within your LangChain chains.
• Edge-to-Cloud Sync: Robots and web-agents can't wait for cloud latency. Use the Merkle Tree Delta Sync protocol to handshakes episodic memory chunks between local devices and the cloud.
• Lock-Free ArcSwap Architecture: Built on Rust. Achieve up to 12,000 Search QPS and 60,000 Ingest QPS with near-zero latency, even under extreme agent concurrency.

📦 Installation

npm install langchain-hyperspace hyperspace-sdk-ts @langchain/core

🛠 Usage

Hyperbolic Memory Initialization (Poincaré Ball)

import { HyperspaceStore } from "langchain-hyperspace";
import { HyperspaceClient } from "hyperspace-sdk-ts";

const client = new HyperspaceClient("localhost:50051", "YOUR_API_KEY");

const vectorStore = new HyperspaceStore(
    embeddings, // Your favorite embeddings or useServerSideEmbedding
    {
        client,
        collectionName: "agent_spatial_memory",
        metric: "lorentz", // Use hyperbolic geometry for hierarchical knowledge
        dimension: 64,      // High semantic compression
    }
);

Advanced Spatial Pruning (Geometric Search)

Go beyond simple similarity. Prune memories using spatial regions:

const results = await vectorStore.similaritySearch("Find drone flight patterns", 5, {
    spatial_region: {
        $in_ball: {
            center: [0.12, -0.45, 0.88, ...],
            radius: 0.15
        }
    }
});

📡 Edge-Cloud Handshake

Synchronize episodic memory between local robot/agent and the fleet:

// Handshake hashes to identify memory drift
const digest = await client.getDigest("agent_memories");
await syncWithCloud(digest.state_hash, digest.buckets);

📖 Documentation

• Why Spatial AI? (YARlabs Manifesto)
• HyperspaceDB Official Docs

📄 License

Apache-2.0. Copyright © 2026 YARlabs.