Open source. Open possibilities.

A lightning-fast search engine API providing AI-powered hybrid search, typo tolerance, and search-as-you-type capabilities for websites and applications.

Qdrant is a high-performance vector similarity search engine and database written in Rust, designed for AI applications requiring semantic search and large-scale vector management.

LlamaIndex is an open-source data framework designed to build agentic applications by augmenting LLMs with private data.

ParadeDB is a Postgres extension that integrates full-text search, vector retrieval, and analytics into a single database.

LEANN is a lightweight vector database designed for personal RAG applications, reducing storage requirements by up to 97% through graph-based selective recomputation.

TalaDB is an embedded vector and document database designed for on-device AI, providing a unified TypeScript API across Browser, Node.js, and React Native.

HelixDB is an OLTP graph-vector database built in Rust on object storage, designed for knowledge graphs and AI memory.

Ready-to-run cloud templates for RAG, AI pipelines, and enterprise search with live data. Docker-friendly, synced with file systems, Google Drive, SharePoint, S3, Kafka, PostgreSQL, and real-time APIs. Includes built-in vector and hybrid search.

An all-in-one AI framework for semantic search, LLM orchestration, and language model workflows.

AksharaMD is a local, parser-agnostic tool that grades how well a chosen document parser converted a source file into AI-friendly Markdown, returning a per-document readiness score, block-level provenance tags, named warnings, and an optional source-grounded ACCEPT/REJECT verdict.

Deep Lake is a serverless multimodal database for AI that combines vector search with storage for embeddings, images, video, audio, and documents. It streams data directly from cloud storage to PyTorch/TensorFlow, supports versioning, and integrates with LangChain, LlamaIndex, and Weights & Biases.

PageIndex is a vectorless, reasoning-based RAG engine that builds hierarchical tree indexes for long documents and lets an LLM reason through the tree to retrieve relevant sections, avoiding vector databases and chunking.