---
id: voyager
version: "2.1.0"
license: unclear
license_treatment: permissive
maintenance: active
---

# voyager — Easy-to-use, fast, simple multi-platform approximate nearest-neighbor search library.

License: permissive · Maintenance: active · Downloads: 118.0K/mo

## What it is and what it does

Voyager is a production-grade library for approximate nearest-neighbor search on vector embeddings, built on the HNSW algorithm and used at Spotify to handle hundreds of millions of queries daily. It wraps the open-source hnswlib with additional convenience features and maintains feature parity between Python and Java implementations, making it suitable for both single-machine and distributed systems that need fast similarity lookups on embedding data.

The library is designed for in-memory operation, meaning vectors are loaded into RAM for query speed. It supports multiple distance metrics (e.g., cosine, Euclidean) and is optimized for recall-accuracy tradeoffs typical of approximate search. Installation is straightforward via pip on supported platforms (Linux, macOS, Windows), with pre-built wheels for Python 3.7–3.12, though it does depend on numpy and compiled C++ bindings.

Use it for:

- Powering recommendation systems by finding similar user embeddings or item embeddings in real time.
- Semantic search over document embeddings to retrieve relevant results faster than exact similarity.
- Duplicate detection by indexing embeddings and querying for near-neighbors to identify similar content.
- Machine learning feature retrieval, retrieving nearest neighbors in embedding space for classification or clustering tasks.
- Real-time personalization by querying user preference embeddings against product or content embeddings.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

Voyager performs fast approximate nearest-neighbor searches on in-memory vector collections using the HNSW algorithm, enabling efficient similarity queries on embedding data.

Yes. Voyager is production-ready, actively maintained, permissively licensed, and has no known vulnerabilities. It offers a clean API for a common and performance-critical task (approximate nearest-neighbor search). Install it if you need fast vector similarity queries on embeddings; the medium install friction is offset by broad platform coverage and proven scale at Spotify.

## Install

pip install voyager
uv add voyager
poetry add voyager

## Installing voyager

Before you install: Medium install friction due to compiled C++ bindings, but pre-built wheels cover Python 3.7–3.12 across Linux (x86_64, ARM), macOS (Intel, ARM), and Windows (x86_64). Actively maintained with recent commits and production use at scale.

License in practice: Licensed under Apache 2, a permissive open-source license allowing commercial and private use with minimal restrictions—suitable for most projects without legal concern.

Quickstart:

pip install voyager
import voyager
import numpy

index = voyager.Index(space='cosine')
index.add(vectors)
nearest = index.query(query_vector, k=10)

Requires numpy; compiled wheels are available for common platforms, but installation may require a C++ compiler on unsupported architectures.

Verify before relying:

- Whether the package supports dynamic index updates (add/remove vectors after initial build) or requires rebuild.
- Memory overhead and scalability limits for very large vector collections.
- Tuning guidance for HNSW parameters (M, ef_construction, ef) for different use cases.
- Performance comparison specifics with other approximate nearest-neighbor libraries beyond the ann-benchmarks reference.

## Package facts

- License: not declared (permissive)
- Python support: unspecified
- Install friction: medium
- Maintenance: active
- Downloads: 118.0K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

approximate nearest neighbor search, vector similarity search, embedding search library, HNSW vector index, fast vector retrieval, in-memory vector database, nearest neighbor lookup, vector-search, embeddings, similarity-search

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