# endee-io/endee

Endee.io – A high-performance vector database, designed to handle up to 1B vectors on a single node, delivering significant performance gains through optimized indexing and execution. Also available in cloud https://endee.io/

Repository: https://github.com/endee-io/endee
Canonical: https://ross.abutalabs.com/products/endee
Homepage: https://endee.io
Language: C++
License: AGPL-3.0
License Family: copyleft
Topics: ai-search, ai-search-engine, ann, endee, hnsw, hybrid-search, image-search, vector, vector-database, vector-search-engine
Last push: 2026-07-29T09:56:00+00:00

## Health v2 (maintenance only)
Score: 76/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 95, release rhythm 85, longevity 15
- inputs: {"age_days": 221, "days_push": 35, "days_rel": 103, "gap_med": 10, "n_releases_24m": 8}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1303, forks 1659 (observed 2026-08-28T04:04:18.333043+00:00)

## What it is
Endee is a high-performance C++ vector database designed to handle up to 1B vectors on a single node, supporting dense, sparse (BM25), and multi-vector retrieval with HNSW-based ANN search, payload filtering, and quantization. It can be self-hosted via Docker or used as a fully-managed serverless cloud, with client integrations for LangChain, LlamaIndex, and CrewAI.

## Use cases
- build a RAG pipeline with semantic search over embeddings
- run hybrid search combining dense vectors and BM25 keyword retrieval
- store and query a billion vectors on a single node
- add filtered vector search with metadata to my app
- power a recommendation engine with fast nearest-neighbor lookup
- deploy vector search on edge devices for visual navigation
- fuse results from multiple vector fields with reciprocal rank fusion

## When to choose
- you need very high vector throughput and low latency at billion-scale on a single node
- you want hybrid dense + sparse retrieval with filtering in one platform
- you need both a managed cloud and a self-hosted Docker option
- you're building RAG, semantic search, or recommendation systems and want LangChain/LlamaIndex integrations

## When to avoid
- you need a mature ecosystem with broad community support and permissive licensing (AGPL-3.0, relatively new project)
- you only need simple full-text search without vectors
- you require multi-node distributed clustering on a budget (cluster tier is enterprise-priced)
- your stack depends on a vector database with wide third-party tooling and long production track record

## Facets
- artifact type: service
- maturity: active
- function: vector-database, search-engine, rag, caching
- domain: databases, artificial-intelligence, machine-learning, developer-tools
- platform: self-hosted, cloud, cpp
- tags: ann, hnsw, hybrid-search, semantic-search, sparse-vectors, bm25, quantization, edge-ai, similarity-search, embeddings, search, retrieval-augmented-generation, docker, linux

## Member repositories
- endee-io/endee (main) score 76

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:18.333043+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T04:51:11.851102+00:00, confidence not recorded.
  - readme: https://github.com/endee-io/endee (fetched 2026-08-28T04:04:18.333043+00:00, sha 3e492c03d6a0)
  - homepage: https://endee.io (fetched 2026-08-29T12:09:19.520362+00:00, sha d3e9d3096a04)
  - site_page: https://docs.endee.io/ (fetched 2026-08-29T12:09:19.531137+00:00, sha 26571ccddd7b)
  - site_page: https://endee.io/about (fetched 2026-08-29T12:09:19.532930+00:00, sha 60676d7355ef)
  - site_page: https://endee.io/pricing (fetched 2026-08-29T12:09:19.529225+00:00, sha 34d0d4b9b9c5)
- Data as of 2026-08-30T08:39:29.467469+00:00.