Open-Source Vector Databases

XERJ is a local search engine for developers building AI agents that need to retrieve code, documents or stored knowledge without reading whole files into their context. It runs on a laptop or self-hosted server.

FalkorDB is a self-hosted graph database for developers building GraphRAG applications and AI agents that retrieve connected facts. It stores entities, relationships and their attributes, with Cypher queries and vector search for finding relevant information. Docker runs the database on your own hardware; a separate hosted cloud service is also available.

chromem-go is a vector database for Go developers adding semantic search or retrieval augmented generation (RAG) to an application. It runs inside the app, so it doesn't need a separate database server.

Vearch is a self-hosted, distributed vector database for developers building AI applications that search embedding vectors. It combines similarity search with filters on stored fields, so an application can retrieve related content while applying criteria beyond vector similarity. It's open source under Apache 2.0 and can run on your servers through Docker, Docker Compose, or Kubernetes.

sqlite-vec adds vector storage and search to SQLite for developers building local AI apps or other software that uses embeddings. It keeps vectors and related application data in the same database.

Chroma DB is a search database for developers building AI apps and agents that need to retrieve relevant information from their data. You can run it locally or on your own infrastructure.

Typesense is a self-hosted search engine for developers building site search, product catalogs or AI search over their own data. It combines keyword matching with searches based on meaning.

txtai is a Python framework for developers building search, chat with their data, and AI agents on their own hardware. It combines a searchable knowledge store with model workflows, so applications can retrieve context and act on it.

Meilisearch is a search engine for developers adding search and AI retrieval to websites, apps, catalogs or internal data. You can host the engine on your own server or use the separate managed Meilisearch Cloud service.

Vespa is an AI search platform for developers building search, RAG and recommendation applications over large datasets that change continuously. It retrieves candidate results and evaluates their relevance with machine-learned models within the same platform.

pgvector adds vector similarity search to Postgres for developers building applications that need to find related items. It keeps embeddings alongside application records, so vector searches can use the same database as the rest of your data.

ParadeDB adds search to the Postgres database that holds your application data. It's for developers building search or AI retrieval who want to keep queries and data in one database without synchronizing a separate search service.

Qdrant is a vector database for developers building semantic search, retrieval-augmented generation (RAG), recommendations and AI agent memory. It stores embeddings with metadata so applications can find similar items within a relevant subset of their data.

ColGREP helps developers and coding agents find relevant code even when they don't know the exact words to search for. It runs locally, while NextPlaid, the search engine behind it, can also serve other retrieval applications on a self-hosted server.

Weaviate is a vector database for developers building search, recommendation and RAG applications. It stores data alongside embeddings so applications can retrieve related content and apply structured filters.

HelixDB is a database for developers building AI agent memory and retrieval-augmented generation (RAG). It keeps entities, relationships, embeddings, and text together so applications can search both content and its connections.

Infinity is a search database for developers building retrieval-augmented generation (RAG), question answering and recommendation systems. It lets applications combine matches based on meaning with exact text matches and structured filters.

SurrealDB is a database for developers building AI agents, knowledge graphs and applications with several kinds of data. It lets application records and retrieval data share one engine.

Vald is a distributed vector search engine for developers building similarity search into applications. It runs on a self-hosted Kubernetes cluster and finds approximate nearest neighbors in dense vectors using NGT. It's open source under the Apache 2.0 license, with an architecture designed to spread large vector collections across multiple machines.

pgvectorscale extends pgvector for developers who want to search large embedding datasets within PostgreSQL. It adds indexing and compression for AI applications that keep vector data in their existing database.

LEANN is a local vector database for people building AI search over personal data or knowledge collections. It reduces index storage by computing embeddings on demand rather than keeping every embedding on disk.

Marqo Open Source is a self-hosted search engine for developers building document search, product discovery, or retrieval for AI applications. The project is deprecated and no longer receives updates.

LanceDB is a vector database for developers building AI retrieval applications and teams working with multimodal datasets. Its embedded library can run locally or in your own cloud.

OpenSearch is a self-hosted search and analytics engine for developers building AI retrieval and document search, and teams analyzing operational data. Its Vector Engine provides a database for machine learning and generative AI applications. You can run the search server and dashboards on your own hardware through Docker on Linux, macOS or Windows.

Milvus is a vector database for developers building AI retrieval, image search and other applications that find related content. It stores embeddings alongside metadata for searching text, images and multimodal information.