Agentes de IA
milvus
The cloud-native vector database for planet-scale similarity search.
★ 46,3k estrellasApache-2.0
Ver la guía →weaviate es un proyecto de agentes de IA de código abierto de weaviate (weaviate/weaviate en GitHub). Tiene 16.874 estrellas en GitHub y 1.423 bifurcaciones, se publica bajo la licencia NOASSERTION y se actualizó por última vez en GitHub el 8 de octubre de 2026.
Vector search plus structured filtering in one database.
Be clear about the problem you are solving before installing.
Clone the repository and skim the docs for hardware or key requirements.
Install dependencies, then add API keys, endpoints, or local-model settings.
Launch the example flow and verify the output matches expectations.
Store data and embeddings together.
Vector plus keyword search in one query.
Built-in generative retrieval capabilities.
Scalable, fault-tolerant architecture.
Efficient and concurrency-friendly.
Extensive developer documentation.
Download the source and move into the project folder.
git clone https://github.com/weaviate/weaviate.git
cd weaviateCompile the Go services.
go build ./...Follow the developer docs — Docker is the fastest way to try it.
# see https://weaviate.io/developers/weaviate/It is maintained by the weaviate organization at weaviate/weaviate, with 1,423 forks.
GitHub does not assert a license for this repo, so treat the code as all rights reserved until the author adds one. Check the repository for any LICENSE file.
Self-hosting is the free path; a managed cloud also exists.
Compare with Qdrant, Milvus, and pgvector on filtering, generative features, and operations.
The quickstart in the developer docs — usually Docker-based — is the fastest route.
The cloud-native vector database for planet-scale similarity search.
A high-performance vector search engine for next-gen AI.
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Gratis y abierto: consigue el código fuente, dale una estrella al repositorio y empieza a crear.