High-performance open-source vector database for AI search, RAG, semantic search, and hybrid retrieval.
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.
- 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.
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.shThe server listens on port 8080. For detailed setup paths, supported operating systems, CPU optimization flags, Docker usage, and authentication examples, use:
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.
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-latency filtered vector search, so your autonomous agents get the right context without stalling their reasoning loop.
Build semantic search experiences for documents, products, support content, and knowledge bases using vector similarity search instead of exact keyword-only matching.
Combine dense retrieval, sparse vectors, and filtering to improve relevance for search workflows where both semantic understanding and term-level precision matter.
Support recommendation, similarity matching, and nearest-neighbor retrieval workflows across text, embeddings, and other high-dimensional representations.
- Vector search for AI retrieval and semantic similarity workloads.
- Hybrid retrieval support with sparse vector capabilities documented in docs/sparse.md.
- Payload filtering for structured retrieval logic documented in docs/filter.md.
- Backup APIs and flows documented in docs/backup-system.md.
- Operational logging and instrumentation documented in docs/logs.md and docs/mdbx-instrumentation.md.
- CPU-targeted builds for AVX2, AVX512, NEON, and SVE2 deployments.
- Docker deployment options for local and server environments.
Endee exposes an HTTP API for managing indexes and serving retrieval workloads. The current repo documentation and examples focus on running the server directly and calling its API endpoints.
Current developer entry points:
- Getting Started for local build and run flows
- Hosted Docs for product documentation
- Release Notes 1.0.0 for recent platform changes
- Join the community on Discord
- Visit the website at endee.io
- For trademark or branding permissions, contact enterprise@endee.io
We welcome contributions from the community to help make vector search faster and more accessible for everyone.
- Submit pull requests for fixes, features, and improvements
- Report bugs or performance issues through GitHub issues
- Propose enhancements for search quality, performance, and deployment workflows
Endee is open source software licensed under the Apache License 2.0. See the LICENSE file for full terms.
“Endee” and the Endee logo are trademarks of Endee Labs.
The Apache License 2.0 does not grant permission to use the Endee name, logos, or branding in a way that suggests endorsement or affiliation.
If you offer a hosted or managed service based on this software, you must use your own branding and avoid implying it is an official Endee service.
This project includes or depends on third-party software components licensed under their respective open-source licenses. Use of those components is governed by their own license terms.