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what is endee fr?

endee-io/endee — explained in plain English

Analysis updated 2026-05-18

1,320C++Audience · developerComplexity · 4/5LicenseSetup · moderate

tl;dr

Endee is an open-source vector database for AI search that can handle up to a billion vectors on one machine.

vibe map

mindmap
  root((Endee))
    What it does
      Vector database
      Search by meaning
      Billion scale
    Tech stack
      C++
      Docker
    Use cases
      RAG pipelines
      Semantic search
      Agent memory
    Audience
      Developers
    Features
      Dense and sparse search
      Payload filtering
      Hosted cloud option

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filefunction / class

what do people make with this?

VIBE 1

Build a RAG pipeline where an AI assistant fetches relevant documents before answering.

VIBE 2

Add semantic search over documents or products based on meaning, not exact keywords.

VIBE 3

Store and retrieve AI agent memory using frameworks like LangChain or LlamaIndex.

what's the stack?

C++Docker

how it stacks up fr

endee-io/endeemicrosoft/intelligent-terminalmsnightmare/rogueplanet
Stars1,3201,3491,256
LanguageC++C++C++
Last pushed2026-07-03
MaintenanceActive
Setup difficultymoderateeasy
Complexity4/53/5
Audiencedeveloperdeveloperresearcher

Figures from each repo's GitHub metadata at analysis time.

how do i run it?

Difficulty · moderate time til it works · 30min

Runs locally or in Docker, a hosted cloud version is also available.

in plain english

Endee is an open-source vector database written in C++ and built for AI-powered search and retrieval. A vector database stores data as numerical representations (called vectors or embeddings) instead of plain text, which lets you search by meaning rather than exact keywords, for example, finding documents that are conceptually similar to a query even if they share no words. It is designed to handle up to one billion vectors on a single machine, making it suitable for large-scale AI applications. Endee supports dense vector search (meaning-based similarity), sparse search (term-precision matching), and hybrid search that combines both. You can also layer in payload filtering, which means narrowing results by structured metadata like categories, dates, or tags alongside the semantic search. Common use cases include building RAG pipelines (Retrieval-Augmented Generation, a technique where an AI assistant fetches relevant documents before answering), semantic search over documents or products, AI agent memory stores, and recommendation systems. The README specifically mentions compatibility with AI frameworks like LangChain, CrewAI, AutoGen, and LlamaIndex for agent memory use cases. The server runs locally or in Docker and exposes an HTTP API on port 8080. Builds are optimized for modern CPU instruction sets including AVX2, AVX512, NEON, and SVE2. A hosted cloud version is also available at endee.io. The project is licensed under Apache 2.0.

prompts (copy fr)

prompt 1
Explain the difference between dense, sparse, and hybrid search in Endee.
prompt 2
Walk me through running Endee in Docker and calling its HTTP API on port 8080.
prompt 3
How do I connect Endee to LangChain for an AI agent memory store?
prompt 4
Show me how to combine payload filtering with semantic search in Endee.

Frequently asked questions

what is endee fr?

Endee is an open-source vector database for AI search that can handle up to a billion vectors on one machine.

What language is endee written in?

Mainly C++. The stack also includes C++, Docker.

How hard is endee to set up?

Setup difficulty is rated moderate, with roughly 30min to a first successful run.

Who is endee for?

Mainly developer.

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