AIエージェント
weaviate
Vector search plus structured filtering in one database.
★ 16.9k スターNOASSERTION
ガイドを見る →milvusは、milvus-ioが公開しているオープンソースのAIエージェントプロジェクトです（GitHub: milvus-io/milvus）。GitHubスターは46,337、フォークは4,282で、Apache-2.0ライセンスの下で公開されています。GitHubでの最終更新は2026年10月8日です。
The cloud-native vector database for planet-scale similarity search.
Write down the job you will use the project for before touching any code.
Download the source and read the README's setup section first.
Run the install steps and add any required API keys or config.
Follow the example in the docs to confirm everything works.
Built to scale horizontally across clusters.
Approximate nearest neighbor search over huge vector sets.
Designed for containerized, elastic deployments.
Purpose-built for embedding similarity workloads.
Efficient, concurrency-friendly core.
Permissive open-source licensing.
Download the source and move into the project folder.
git clone https://github.com/milvus-io/milvus.git
cd milvusCompile the Go services.
go build ./...Milvus is typically run via Docker or Kubernetes — follow the official deployment guide.
# see the deployment docs for the supported install pathsIt is maintained by the milvus-io organization at milvus-io/milvus, with 4,282 forks. Check the release notes for the current stable version.
Yes. It is released under the Apache-2.0 license, which permits commercial use, modification, and redistribution. Read the LICENSE file for the exact terms.
Self-hosted Milvus is free; a managed cloud offering also exists if you prefer not to operate it yourself.
Compare with Qdrant, Weaviate, or Chroma on scale, operational burden, and filtering needs.
Read the deployment docs, start with the Docker-based quickstart, and load a small embedding set first.
Vector search plus structured filtering in one database.
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LibreAIカタログに掲載されたAIエージェントプロジェクトの中で最もスターの多いもの — milvusが合わなかった場合に検討したい、現実的な候補リストです。
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