---
title: "qdrant-client"
type: "tool"
slug: "qdrant-qdrant-client"
canonical_url: "https://www.graphcanon.com/tools/qdrant-qdrant-client"
github_url: "https://github.com/qdrant/qdrant-client"
homepage_url: "https://qdrant.tech"
stars: 1320
forks: 245
primary_language: "Python"
license: "Apache-2.0"
categories: ["vector-databases"]
tags: ["vector-search-engine", "qdrant", "vector-database", "vector-search"]
updated_at: "2026-07-07T18:46:58.588545+00:00"
---
# qdrant-client
> Python client for Qdrant vector search engine
Python library with type definitions and sync/async API calls for Qdrant, a vector search engine. It supports local mode without needing to run the server.
## Facts
- Repository: https://github.com/qdrant/qdrant-client
- Homepage: https://qdrant.tech
- Stars: 1,320 · Forks: 245 · Open issues: 165 · Watchers: 7
- Primary language: Python
- License: Apache-2.0
- Last pushed: 2026-06-26T07:45:37+00:00
## Categories
- [Vector Databases](/categories/vector-databases.md)
## Tags
vector-search-engine, qdrant, vector-database, vector-search
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## README (excerpt)
```text
Python Client library for the Qdrant vector search engine.
# Python Qdrant Client
Client library and SDK for the [Qdrant](https://github.com/qdrant/qdrant) vector search engine.
Library contains type definitions for all Qdrant API and allows to make both Sync and Async requests.
Client allows calls for all [Qdrant API methods](https://api.qdrant.tech/) directly.
It also provides some additional helper methods for frequently required operations, e.g. initial collection uploading.
See [QuickStart](https://qdrant.tech/documentation/quick-start/#create-collection) for more details!
## Installation
```
pip install qdrant-client
```
## Features
- Type hints for all API methods
- Local mode - use same API without running server
- REST and gRPC support
- Minimal dependencies
- Extensive Test Coverage
## Local mode
Python client allows you to run same code in local mode without running Qdrant server.
Simply initialize client like this:
```python
from qdrant_client import QdrantClient
client = QdrantClient(":memory:")
# or
client = QdrantClient(path="path/to/db") # Persists changes to disk
```
Local mode is useful for development, prototyping and testing.
- You can use it to run tests in your CI/CD pipeline.
- Run it in Colab or Jupyter Notebook, no extra dependencies required. See an [example](https://colab.research.google.com/drive/1Bz8RSVHwnNDaNtDwotfPj0w7AYzsdXZ-?usp=sharing)
- When you need to scale, simply switch to server mode.
## Connect to Qdrant server
To connect to Qdrant server, simply specify host and port:
```python
from qdrant_client import QdrantClient
client = QdrantClient(host="localhost", port=6333)
# or
client = QdrantClient(url="http://localhost:6333")
```
You can run Qdrant server locally with docker:
```bash
docker run -p 6333:6333 qdrant/qdrant:latest
```
See more launch options in [Qdrant repository](https://github.com/qdrant/qdrant#usage).
## Connect to Qdrant cloud
You can register and use [Qdrant Cloud](https://cloud.qdrant.io/) to get a free tier account with 1GB RAM.
Once you have your cluster and API key, you can connect to it like this:
```python
from qdrant_client import QdrantClient
qdrant_client = QdrantClient(
url="https://xxxxxx-xxxxx-xxxxx-xxxx-xxxxxxxxx.us-east.aws.cloud.qdrant.io:6333",
api_key="",
)
```
## Inference API
Qdrant Client has Inference API that allows to seamlessly create embeddings and use them in Qdrant.
Inference API can be used locally with FastEmbed or remotely with models available in Qdrant Cloud.
### Local Inference with FastEmbed
```
pip install qdrant-client[fastembed
```
---
**Machine-readable endpoints**
- JSON: [`/api/graphcanon/tools/qdrant-qdrant-client`](/api/graphcanon/tools/qdrant-qdrant-client)
- 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/_
