apache-airflow-providers-pinecone
Provider package apache-airflow-providers-pinecone for Apache Airflow
Decision gist · record as of 2026-08-14
Yes, if you run Apache Airflow and need to integrate Pinecone vector operations into your DAGs. The package is actively maintained, has no known vulnerabilities, carries a permissive Apache-2.0 license, and installs with low friction. Install it only if you have an existing Airflow deployment >=2.11.0 and a Pinecone account; it is not a standalone tool.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires an existing Apache Airflow >=2.11.0 installation and a Pinecone account with API credentials configured.
- Low install friction with a pure-Python wheel.
- Actively maintained as part of the Apache Airflow ecosystem; last commit 2026-08-14.
License · maintenance · safety
Apache-2.0 (permissive) — Apache-2.0 permissive license. You may use, modify, and distribute this package freely in commercial and private projects, provided you include a copy of the license and notice of modifications.
last release 2026-05-23 (83 days) · last repo commit 2026-08-14 · 46,491 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 128,397 downloads/mo, #11,711 on PyPI
Alternatives
Verify before relying
pip install apache-airflow-providers-pinecone
from airflow import DAG
from airflow.providers.pinecone.operators.pinecone import PineconeOperator
with DAG('my_dag') as dag:
task = PineconeOperator(task_id='pinecone_task')- What specific Pinecone operations (upsert, query, delete) are exposed as operators or hooks.
- Whether the provider includes sensors for waiting on Pinecone index readiness or query completion.
- How authentication to Pinecone is configured (API key management, connection types).
What it is and what it does
This is an Apache Airflow provider package that bridges Airflow's workflow orchestration with Pinecone's vector database. It supplies operators, hooks, and connection types that let you build Airflow DAGs to manage vector embeddings, perform similarity searches, and automate vector data pipelines. The package depends on Apache Airflow >=2.11.0, the Pinecone Python client >=7.0.0, and a compatibility layer for common Airflow provider patterns.
The provider is part of the official Apache Airflow ecosystem and is actively maintained. It supports Python 3.10 through 3.14 and is classified as Production/Stable. Use it when you need to schedule and monitor Pinecone operations as part of a larger data workflow, such as ingesting embeddings into an index on a schedule or querying the index as a step in a multi-stage pipeline.
Use it for
- Schedule periodic ingestion of vector embeddings into Pinecone indexes as part of a data pipeline DAG.
- Orchestrate multi-step workflows where Pinecone similarity search is one task among many data transformations.
- Automate vector index maintenance tasks (upserts, deletes) on a defined schedule using Airflow's scheduling engine.
- Monitor and log Pinecone operations within Airflow's centralized task tracking and alerting framework.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you run Apache Airflow and need to integrate Pinecone vector operations into your DAGs.
The package is actively maintained, has no known vulnerabilities, carries a permissive Apache-2.0 license, and installs with low friction. Install it only if you have an existing Airflow deployment >=2.11.0 and a Pinecone account; it is not a standalone tool.
Install
apache-airflow-providers-pinecone on PyPI
Before you install
Low install friction with a pure-Python wheel. Actively maintained as part of the Apache Airflow ecosystem; last commit 2026-08-14. Requires Apache Airflow >=2.11.0, pinecone >=7.0.0, and Python >=3.10.
Requires an existing Apache Airflow >=2.11.0 installation and a Pinecone account with API credentials configured.
License in practice
Apache-2.0 permissive license. You may use, modify, and distribute this package freely in commercial and private projects, provided you include a copy of the license and notice of modifications.
Quickstart
pip install apache-airflow-providers-pinecone
from airflow import DAG
from airflow.providers.pinecone.operators.pinecone import PineconeOperator
with DAG('my_dag') as dag:
task = PineconeOperator(task_id='pinecone_task')Verify before relying
- What specific Pinecone operations (upsert, query, delete) are exposed as operators or hooks.
- Whether the provider includes sensors for waiting on Pinecone index readiness or query completion.
- How authentication to Pinecone is configured (API key management, connection types).
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-compatpinecone |
| Maintenance | Actively maintained 83 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 128,397 / month, #11,711 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_pinecone-2.4.5-py3-none-any.whl
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See also apache-airflow-providers-apache-beam · apache-airflow-providers-pgvector · apache-airflow-providers-qdrant · apache-airflow-providers-zendesk · apache-airflow-providers-neo4j · pinecone-client · apache-airflow-providers-apache-tinkerpop · apache-airflow-core · apache-airflow-providers-alibaba · apache-airflow-providers-asana