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Zvec is an open-source, in-process vector database — lightweight, lightning-fast, and designed to embed directly into applications. Battle-tested within Alibaba Group, it delivers production-grade, low-latency and scalable similarity search with minimal setup.

[!Important] 🚀 v0.3.1 (Apr 17, 2026)

Relaxed collection path restrictions and improved Windows path handling.

🚀 v0.3.0 (April 3, 2026)

New Platforms: Initial Windows (MSVC) and Android support. Published official Windows Python and Node.js packages.

Efficiency: RabitQ quantization and CPU Auto-Dispatch for optimized SIMD execution.

Ecosystem: C-API for custom language bindings and MCP / Skill integration for AI Agents.

👉 Read the Release Notes | View Roadmap 📍

💫 Features

Blazing Fast: Searches billions of vectors in milliseconds.
Simple, Just Works: Install and start searching in seconds. Pure local, no servers, no config, no fuss.
Dense + Sparse Vectors: Work with both dense and sparse embeddings, with native support for multi-vector queries in a single call.
Hybrid Search: Combine semantic similarity with structured filters for precise results.
Durable Storage: Write-ahead logging (WAL) guarantees persistence — data is never lost, even on process crash or power failure.
Concurrent Access: Multiple processes can read the same collection simultaneously; writes are single-process exclusive.
Runs Anywhere: As an in-process library, Zvec runs wherever your code runs — notebooks, servers, CLI tools, or even edge devices.

📦 Installation

Python

Requirements: Python 3.10 - 3.14

pip install zvec

Node.js

npm install @zvec/zvec

✅ Supported Platforms

Linux (x86_64, ARM64)
macOS (ARM64)
Windows (x86_64)

🛠️ Building from Source

If you prefer to build Zvec from source, please check the Building from Source guide.

⚡ One-Minute Example

import zvec

# Define collection schema
schema = zvec.CollectionSchema(
    name="example",
    vectors=zvec.VectorSchema("embedding", zvec.DataType.VECTOR_FP32, 4),
)

# Create collection
collection = zvec.create_and_open(path="./zvec_example", schema=schema)

# Insert documents
collection.insert([
    zvec.Doc(id="doc_1", vectors={"embedding": [0.1, 0.2, 0.3, 0.4]}),
    zvec.Doc(id="doc_2", vectors={"embedding": [0.2, 0.3, 0.4, 0.1]}),
])

# Search by vector similarity
results = collection.query(
    zvec.VectorQuery("embedding", vector=[0.4, 0.3, 0.3, 0.1]),
    topk=10
)

# Results: list of {'id': str, 'score': float, ...}, sorted by relevance
print(results)

📈 Performance at Scale

Zvec delivers exceptional speed and efficiency, making it ideal for demanding production workloads.

For detailed benchmark methodology, configurations, and complete results, please see our Benchmarks documentation.

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❤️ Contributing

We welcome and appreciate contributions from the community! Whether you're fixing a bug, adding a feature, or improving documentation, your help makes Zvec better for everyone.

Check out our Contributing Guide to get started!