High-performance open-source vector database for AI search, RAG, semantic search, and hybrid retrieval.

Quick Start • Why Endee • Use Cases • Features • API and Clients • Docs • Contact

Endee: Open-Source Vector Database for AI Search

Endee is a high-performance open-source vector database built for AI search and retrieval workloads. It is designed for teams building RAG pipelines, semantic search, hybrid search, recommendation systems, and filtered vector retrieval APIs that need production-oriented performance and control.

Endee combines vector search with filtering, sparse retrieval support, backup workflows, and deployment flexibility across local builds and Docker-based environments. The project is implemented in C++ and optimized for modern CPU targets, including AVX2, AVX512, NEON, and SVE2.

If you want the fastest path to evaluate Endee locally, start with the Getting Started guide or the hosted docs at docs.endee.io.

Why Endee

• Built as a dedicated vector database for AI applications, search systems, and retrieval-heavy workloads.
• Supports dense vector retrieval plus sparse search capabilities for hybrid search use cases.
• Includes payload filtering for metadata-aware retrieval and application-specific query logic.
• Ships with operational features already documented in this repo, including backup flows and runtime observability.
• Offers flexible deployment paths: local scripts, manual builds, Docker images, and prebuilt registry images.

Getting Started

The full installation, build, Docker, runtime, and authentication instructions are in docs/getting-started.md.

Fastest local path:

chmod +x ./install.sh ./run.sh
./install.sh --release --avx2
./run.sh

The server listens on port 8080. For detailed setup paths, supported operating systems, CPU optimization flags, Docker usage, and authentication examples, use:

• Getting Started
• Hosted Quick Start Docs

Use Cases

RAG and AI Retrieval

Use Endee as the retrieval layer for question answering, chat assistants, copilots, and other RAG applications that need fast vector search with metadata-aware filtering.

Agentic AI and AI Agent Memory

Use Endee as the long-term memory and context retrieval layer for AI agents built with frameworks like LangChain, CrewAI, AutoGen, and LlamaIndex. Store and retrieve past observations, tool outputs, conversation history, and domain knowledge mid-execution with low-l