Qdrant Clients SDK Agent Skill
Qdrant provides client SDKs for various programming languages, allowing easy integration with Qdrant deployments.
Install
one command, takes just this skill from the repositorynpx skills add https://github.com/qdrant/skills --skill qdrant-clients-sdkThe instruction itself
3 sections, as written by the authorQdrant Clients SDK
Qdrant has the following officially supported client SDKs:
- Python — qdrant-client · Installation:
pip install qdrant-client[fastembed] - JavaScript / TypeScript — qdrant-js · Installation:
npm install @qdrant/js-client-rest - Rust — rust-client · Installation:
cargo add qdrant-client - Go — go-client · Installation:
go get github.com/qdrant/go-client - .NET — qdrant-dotnet · Installation:
dotnet add package Qdrant.Client - Java — java-client · Available on Maven Central: https://central.sonatype.com/artifact/io.qdrant/client
API Reference
All interaction with Qdrant can happen through the REST API or gRPC API. We recommend using the REST API if you are using Qdrant for the first time or working on a prototype.
- REST API - OpenAPI Reference - GitHub
- gRPC API - gRPC protobuf definitions
Code examples
To obtain code examples for a specific client and use case, you can send a search request to the library of curated code snippets for the Qdrant client.
curl -X GET "https://skills.qdrant.tech/snippets/search?language=python&query=how+to+upload+points"
Available languages: python, typescript, rust, java, go, csharp
Response example:
## Snippet 1
*qdrant-client* (vlatest) — https://skills.qdrant.tech/md/documentation/manage-data/points/
Uploads multiple vector-embedded points to a Qdrant collection using the Python qdrant_client (PointStruct) with id, payload (e.g., color), and a 3D-like vector for similarity search. It supports parallel uploads (parallel=4) and a retry policy (max_retries=3) for robust indexing. The operation is idempotent: re-uploading with the same id overwrites existing points; if ids aren’t provided, Qdrant auto-generates UUIDs.
client.upload_points(
collection_name="{collection_name}",
points=[
models.PointStruct(
id=1,
payload={
"color": "red",
},
vector=[0.9, 0.1, 0.1],
),
models.PointStruct(
id=2,
payload={
"color": "green",
},
vector=[0.1, 0.9, 0.1],
),
],
parallel=4,
max_retries=3,
)
Default response format is markdown, if snippet output is required in JSON format, you can add &format=json to the query string.
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How to use it
Take qdrant/qdrant-clients-sdk from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.
The instructions reference pip, npm.
Without those the skill loads but fails at the first command.