apache-airflow-providers-qdrant
Provider package apache-airflow-providers-qdrant for Apache Airflow
Decision gist · record as of 2026-08-14
Yes, if you are already running Apache Airflow and need to incorporate Qdrant vector operations into your workflows. The package is production-stable, actively maintained, has no known vulnerabilities, and installs with low friction. Install only if you have Airflow >=2.11.0 and a use case that requires vector search within your DAGs.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Apache Airflow >=2.11.0 and qdrant_client >=1.17.1; Python 3.10 or later.
- Low friction install as a pure-Python wheel.
- Actively maintained with recent releases; requires Apache Airflow >=2.11.0 and qdrant_client >=1.17.1 as runtime dependencies.
License · maintenance · safety
Apache-2.0 (permissive) — Apache-2.0 permissive license allows commercial and private use without restriction, typical for Apache Airflow ecosystem packages.
last release 2026-06-07 (68 days) · last repo commit 2026-08-14 · 46,490 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 202,120 downloads/mo, #9,651 on PyPI
Alternatives
Verify before relying
pip install apache-airflow-providers-qdrant
from airflow.providers.qdrant.hooks.qdrant import QdrantHook
hook = QdrantHook(conn_id='qdrant_default')
# Use hook to interact with Qdrant in your DAG tasks- What specific Qdrant operations (e.g., insert, search, delete) are exposed as Airflow operators or hooks
- Whether the provider includes pre-built task templates or only low-level client integration
- Performance characteristics or limits when handling large vector collections in Airflow workflows
What it is and what it does
This is an Apache Airflow provider package that bridges Airflow workflows with Qdrant, a vector database. It allows you to build DAGs that interact with Qdrant for vector search, similarity matching, and other vector operations as part of larger data pipelines. The package wraps qdrant_client and integrates it with Airflow's task and hook system, so you can orchestrate vector operations alongside traditional data processing tasks.
The package requires Apache Airflow >=2.11.0, qdrant_client >=1.17.1, and the common-compat provider. It supports Python 3.10 through 3.14 and is actively maintained by the Apache Airflow project. Installation is straightforward via pip, and the package is production-stable (Development Status 5).
Use it for
- Orchestrate vector embeddings generation and storage in Qdrant as part of a scheduled Airflow pipeline
- Build multi-step DAGs that query Qdrant for similarity search results and feed them into downstream tasks
- Manage vector database operations (indexing, updates, deletions) as scheduled or triggered Airflow tasks
- Integrate Qdrant vector operations with other Airflow providers for end-to-end ML data workflows
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are already running Apache Airflow and need to incorporate Qdrant vector operations into your workflows.
The package is production-stable, actively maintained, has no known vulnerabilities, and installs with low friction. Install only if you have Airflow >=2.11.0 and a use case that requires vector search within your DAGs.
Install
apache-airflow-providers-qdrant on PyPI
Before you install
Low friction install as a pure-Python wheel. Actively maintained with recent releases; requires Apache Airflow >=2.11.0 and qdrant_client >=1.17.1 as runtime dependencies.
Requires Apache Airflow >=2.11.0 and qdrant_client >=1.17.1; Python 3.10 or later.
License in practice
Apache-2.0 permissive license allows commercial and private use without restriction, typical for Apache Airflow ecosystem packages.
Quickstart
pip install apache-airflow-providers-qdrant
from airflow.providers.qdrant.hooks.qdrant import QdrantHook
hook = QdrantHook(conn_id='qdrant_default')
# Use hook to interact with Qdrant in your DAG tasksVerify before relying
- What specific Qdrant operations (e.g., insert, search, delete) are exposed as Airflow operators or hooks
- Whether the provider includes pre-built task templates or only low-level client integration
- Performance characteristics or limits when handling large vector collections in Airflow workflows
Package facts
| License | Apache-2.0 permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 3 packagesapache-airflowapache-airflow-providers-common-compatqdrant_client |
| Maintenance | Actively maintained 68 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 202,120 / month, #9,651 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableEnvironment :: ConsoleEnvironment :: Web EnvironmentFramework :: Apache AirflowFramework :: Apache Airflow :: ProviderIntended Audience :: DevelopersIntended Audience :: System AdministratorsProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Topic :: System :: Monitoring |
Evidence: apache_airflow_providers_qdrant-1.5.6-py3-none-any.whl
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See also apache-airflow-providers-pinecone · qdrant-client · apache-airflow-providers-pgvector · apache-airflow-providers-mongo · apache-airflow-providers-asana · apache-airflow-providers-influxdb · apache-airflow-providers-zendesk · apache-airflow-providers-segment · apache-airflow-providers-opensearch · apache-airflow-providers-sendgrid