---
title: "qdrant"
type: "tool"
slug: "qdrant-qdrant"
canonical_url: "https://www.graphcanon.com/tools/qdrant-qdrant"
github_url: "https://github.com/qdrant/qdrant"
homepage_url: "https://qdrant.tech"
stars: 33007
forks: 2462
primary_language: "Rust"
license: "Apache-2.0"
categories: ["inference-serving", "ai-agents", "vector-databases"]
tags: ["knn-algorithm", "embeddings-similarity", "machine-learning", "ai-search", "ai-search-engine", "hybrid-search", "hnsw", "image-search"]
updated_at: "2026-07-07T17:43:39.62579+00:00"
---

# qdrant

> Qdrant - High-performance, massive-scale Vector Database and Vector Search Engine for the next generation of AI. Also available in the cloud

Qdrant - High-performance, massive-scale Vector Database and Vector Search Engine for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/

## Facts

- Repository: https://github.com/qdrant/qdrant
- Homepage: https://qdrant.tech
- Stars: 33,007 · Forks: 2,462 · Open issues: 616 · Watchers: 152
- Primary language: Rust
- License: Apache-2.0
- Last pushed: 2026-07-07T17:27:25+00:00

## Categories

- [Inference & Serving](/categories/inference-serving.md)
- [AI Agents](/categories/ai-agents.md)
- [Vector Databases](/categories/vector-databases.md)

## Tags

knn-algorithm, embeddings-similarity, machine-learning, ai-search, ai-search-engine, hybrid-search, hnsw, image-search

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## README (excerpt)

```text
<p align="center">
  <picture>
      <source media="(prefers-color-scheme: dark)" srcset="https://github.com/qdrant/qdrant/raw/master/docs/logo-dark.svg">
      <source media="(prefers-color-scheme: light)" srcset="https://github.com/qdrant/qdrant/raw/master/docs/logo-light.svg">
      <img height="100" alt="Qdrant" src="https://github.com/qdrant/qdrant/raw/master/docs/logo.svg">
  </picture>
</p>

<p align="center">
    <b>Vector Search Engine for the next generation of AI applications</b>
</p>

<p align=center>
    <a href="https://github.com/qdrant/qdrant/actions/workflows/rust.yml"><img src="https://img.shields.io/github/actions/workflow/status/qdrant/qdrant/rust.yml?style=flat-square" alt="Tests status"></a>
    <a href="https://api.qdrant.tech/"><img src="https://img.shields.io/badge/Docs-OpenAPI%203.0-success?style=flat-square" alt="OpenAPI Docs"></a>
    <a href="https://github.com/qdrant/qdrant/blob/master/LICENSE"><img src="https://img.shields.io/github/license/qdrant/qdrant?style=flat-square" alt="Apache 2.0 License"></a>
    <a href="https://qdrant.to/discord"><img src="https://img.shields.io/discord/907569970500743200?logo=Discord&style=flat-square&color=7289da" alt="Discord"></a>
    <a href="https://qdrant.to/roadmap"><img src="https://img.shields.io/badge/Roadmap-2025-bc1439.svg?style=flat-square" alt="Roadmap 2025"></a>
    <a href="https://cloud.qdrant.io/"><img src="https://img.shields.io/badge/Qdrant-Cloud-24386C.svg?logo=cloud&style=flat-square" alt="Qdrant Cloud"></a>
</p>

**Qdrant** (read: _quadrant_) is a vector similarity search engine and vector database.
It provides a production-ready service with a convenient API to store, search, and manage points—vectors with an additional payload.
Qdrant is tailored for extended filtering support, making it useful for all sorts of neural-network or semantic-based matching, faceted search, and other applications.

Qdrant is written in Rust 🦀, which makes it fast and reliable even under high load. See [benchmarks](https://qdrant.tech/benchmarks/).

With Qdrant, embeddings or neural network encoders can be turned into full-fledged applications for matching, searching, recommending, and much more!

Qdrant is also available as a fully managed **[Qdrant Cloud](https://cloud.qdrant.io/)** ⛅ including a **free tier**.

<p align="center">
<strong><a href="https://qdrant.tech/documentation/quickstart/">Quick Start</a> • <a href="#agent-skills">Agent Skills</a> • <a href="#clients">Client Libraries</a> • <a href="#demo-projects">Demo Projects</a> • <a href="#integrations">Integrations</a> • <a href="#contacts">Contact</a>

</strong>
</p>

## Getting Started

### Agent Skills

Qdrant provides a collection of ready-to-use [agent skills](https://github.com/qdrant/skills) that bring Qdrant's vector search capabilities directly into your AI coding assistant. Install these skills to empower your agent in making critical engineering decisions for optimal vector search performance, such as quantization, sharding, tenant isolation, hybrid search, model migration, and more.

### Client-Server

To experience the full power of Qdrant locally, run the container with this command:

```bash
docker run -p 6333:6333 qdrant/qdrant
```

Note that this starts an insecure deployment without authentication, open to all network interfaces. Please refer to [secure your instance](https://qdrant.tech/documentation/security/#secure-your-instance).

Now you can connect to the server with any [client](#clients). For example, using Python:

```python
from qdrant_client import QdrantClient

client = QdrantClient(url="http://localhost:6333")
```

Before deploying Qdrant to production, be sure to read our [installation](https://qdrant.tech/documentation/installation/) and [security](https://qdrant.tech/documentation/security/) guides.

### Clients

Qdrant offers the following client libraries to help you integrate it into your application stack:

- Official:
  - [Go client](https://github.com/qdrant/go-c
```

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/tools/qdrant-qdrant`](/api/graphcanon/tools/qdrant-qdrant)
- LLM index: [/llms.txt](/llms.txt)
- Full corpus: [/llms-full.txt](/llms-full.txt)

_GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_