---
id: apache-airflow-providers-neo4j
version: "3.12.1"
license: Apache-2.0
license_treatment: permissive
maintenance: active
---

# apache-airflow-providers-neo4j — Provider package apache-airflow-providers-neo4j for Apache Airflow

License: permissive · Maintenance: active · Downloads: 366.8K/mo

## What it is and what it does

This is an Apache Airflow provider package that bridges Airflow's workflow orchestration engine with Neo4j graph databases. It supplies operators and hooks that allow you to define Airflow DAG tasks that execute Cypher queries, manage graph data, and integrate Neo4j operations into larger data pipelines. The package is maintained as part of the official Apache Airflow ecosystem and follows Airflow's provider plugin architecture.

You install it alongside an existing Airflow instance (version 2.11.0 or later) and a Neo4j driver (5.20.0 or later). Once installed, you can use its operators to construct tasks that read from, write to, or query your Neo4j instance as part of scheduled or triggered workflows. It is production-stable and actively maintained, with support for Python 3.10 through 3.14.

Use it for:

- Schedule regular Cypher queries to extract or analyze graph data and feed results into downstream Airflow tasks.
- Build ETL pipelines that load data from external sources into Neo4j as part of a coordinated workflow.
- Orchestrate graph analytics jobs that depend on other data processing steps in a single DAG.
- Monitor Neo4j database health or query performance as part of a larger Airflow monitoring workflow.
- Synchronize graph data between Neo4j and other data systems (data lakes, warehouses) in a scheduled pipeline.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

Integrates Neo4j graph database operations into Apache Airflow workflows, providing operators and hooks to query, write, and manage graph data as part of DAG tasks.

Yes, if you run Apache Airflow and need to integrate Neo4j operations into your workflows. The package is production-stable, actively maintained, has no known vulnerabilities, and carries a permissive license. Install friction is low. The only prerequisite is an existing Airflow instance and a Neo4j connection configured in Airflow's connection store.

## Install

pip install apache-airflow-providers-neo4j
uv add apache-airflow-providers-neo4j
poetry add apache-airflow-providers-neo4j

## Installing apache-airflow-providers-neo4j

Before you install: Low friction install as a pure-Python wheel. Active maintenance with a release 6 days old; the underlying Apache Airflow project has strong community backing (46490 stars). Requires Airflow >=2.11.0, neo4j >=5.20.0, and apache-airflow-providers-common-compat >=1.10.1.

License in practice: Apache-2.0 permissive license allows use in commercial and private projects with minimal restrictions; you must include a copy of the license and note any modifications.

Quickstart:

pip install apache-airflow-providers-neo4j

from airflow.providers.neo4j.operators.neo4j import Neo4jOperator

query_task = Neo4jOperator(
    task_id='run_query',
    neo4j_conn_id='neo4j_default',
    cypher_query='MATCH (n) RETURN n LIMIT 10'
)

Requires an existing Apache Airflow installation (>=2.11.0) and a configured Neo4j connection in Airflow's connection store.

Verify before relying:

- Specific operators and hooks available beyond the base Neo4jOperator mentioned in the excerpt.
- Whether the provider supports Neo4j authentication methods (basic auth, Kerberos, LDAP, etc.).
- Performance characteristics or limitations for large graph queries in Airflow DAGs.

## Package facts

- License: Apache-2.0 (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: active
- Downloads: 366.8K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

airflow neo4j integration, graph database airflow operator, neo4j airflow provider, airflow neo4j connector, graph workflow orchestration, airflow database provider, neo4j dag tasks, airflow-provider, graph-database, workflow-orchestration

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