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vearch/vearch

Distributed vector search for AI-native applications

GraphCanon updated today · GitHub synced today · 26 views this month

2.3k stars365 forksLast push 3w Python Apache-2.0

Decision brief

Vearch is a distributed vector database for efficient vector search in AI applications.

Good fit when

  • - When your application requires high performance and scalability for vector data operations.
  • - If you are focusing on AI-native applications that heavily rely on similarity search over large embedding datasets.

Avoid when

  • - If you prioritize languages other than Go for your development stack, which might complicate integration into existing architectures.
  • - Your project does not benefit from a highly scalable architecture designed specifically around vector searches and instead requires more general relational data handling capabilities.

Observed Jul 12, 2026 · Source: enrich:decision_facts

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Maintenance and security

Full trust report
Maintenance
Active (25d since push)
As of today
Provenance
Not a fork · Organization account
As of today
Security (OSV)
No lockfile
As of 1mo

Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.

Install

pip install vearch
PyPI

How it fits your stack(11)

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Alternative

Relationship graph

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Similar tools

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Evidence and technical details

Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.

Overview

Vearch is a distributed vector database designed to support vector search in AI-native applications.

Capability facts

Languages
python

Source: github.language · Aug 21, 2026

Categories

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Tags

README

Quick start

Kubernetes Deployment


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## License

Vearch is licensed under the [Apache License, Version 2.0](./LICENSE).

For complete licensing details, please see [LICENSE and NOTICE](https://github.com/vearch/vearch/blob/master/LICENSE) in our repository.

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<div align="center">
  <small>© 2019 Vearch Contributors. All Rights Reserved.</small>
</div>

For agents

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