apache-airflow-providers-atlassian-jira
Provider package apache-airflow-providers-atlassian-jira for Apache Airflow
Decision gist · record as of 2026-08-14
Yes. This is a production-stable, actively maintained provider package from the Apache Airflow project with no known vulnerabilities. Install it if you run Airflow and need to integrate Jira into your workflows. The low install friction and permissive Apache-2.0 license remove barriers; the only prerequisite is having Airflow >=2.11.0 and Python >=3.10 already in place.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Apache Airflow >=2.11.0 and Python >=3.10; Jira connection credentials must be configured in Airflow.
- Low friction install as a pure-Python wheel.
- Actively maintained with a release 17 days old; the parent Apache Airflow project has 46490 stars and commits through 2026-08-14.
License · maintenance · safety
Apache-2.0 (permissive) — Licensed under Apache-2.0 (permissive). You may use, modify, and distribute this package freely, including in commercial contexts, provided you retain license notices.
last release 2026-07-28 (17 days) · last repo commit 2026-08-14 · 46,490 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 1,031,026 downloads/mo, #4,468 on PyPI
Alternatives
Verify before relying
pip install apache-airflow-providers-atlassian-jira
from airflow.providers.atlassian.jira import JiraHook
hook = JiraHook(jira_conn_id='jira_default')- What specific Jira operations (issue creation, transitions, searches, etc.) are exposed by this provider.
- Whether the provider supports Jira Cloud, Jira Server, or both deployment models.
- Performance characteristics and rate-limiting behavior when interacting with Jira APIs.
What it is and what it does
This is an Apache Airflow provider package that integrates Jira into Airflow workflows. It wraps atlassian-python-api and exposes Jira operations as Airflow operators and hooks, allowing you to orchestrate Jira tasks within DAGs. The package is production-stable and actively maintained as part of the Apache Airflow ecosystem.
You install it on top of an existing Airflow installation (>=2.11.0) and then import operators and hooks from the airflow.providers.atlassian.jira namespace to create tasks that interact with Jira. It depends on apache-airflow, apache-airflow-providers-http, apache-airflow-providers-common-compat, and atlassian-python-api (>3.41.10) to handle the actual Jira API communication.
Use it for
- Create Jira issues automatically from Airflow DAG tasks when data pipelines complete or fail.
- Query Jira for issues matching criteria and use results to trigger downstream workflow logic.
- Update Jira issue status or add comments as part of multi-system orchestration workflows.
- Synchronize project management data between Jira and other systems in a scheduled Airflow pipeline.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
This is a production-stable, actively maintained provider package from the Apache Airflow project with no known vulnerabilities. Install it if you run Airflow and need to integrate Jira into your workflows. The low install friction and permissive Apache-2.0 license remove barriers; the only prerequisite is having Airflow >=2.11.0 and Python >=3.10 already in place.
Install
apache-airflow-providers-atlassian-jira on PyPI
Before you install
Low friction install as a pure-Python wheel. Actively maintained with a release 17 days old; the parent Apache Airflow project has 46490 stars and commits through 2026-08-14. Requires Apache Airflow >=2.11.0.
Requires Apache Airflow >=2.11.0 and Python >=3.10; Jira connection credentials must be configured in Airflow.
License in practice
Licensed under Apache-2.0 (permissive). You may use, modify, and distribute this package freely, including in commercial contexts, provided you retain license notices.
Quickstart
pip install apache-airflow-providers-atlassian-jira
from airflow.providers.atlassian.jira import JiraHook
hook = JiraHook(jira_conn_id='jira_default')Verify before relying
- What specific Jira operations (issue creation, transitions, searches, etc.) are exposed by this provider.
- Whether the provider supports Jira Cloud, Jira Server, or both deployment models.
- Performance characteristics and rate-limiting behavior when interacting with Jira APIs.
Package facts
| License | Apache-2.0 permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 4 packagesapache-airflowapache-airflow-providers-common-compatapache-airflow-providers-httpatlassian-python-api |
| Maintenance | Actively maintained 17 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 1,031,026 / month, #4,468 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_atlassian_jira-3.3.5-py3-none-any.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “airflow jira integration”
- apache-airflow-providers-atlassian-jiraProvides Apache Airflow operators and hooks to create, query, and…
- apache-airflow-providers-jiraIntegrates Apache Airflow with Atlassian Jira, enabling workflow…
- jiraA Python library that wraps the Jira REST API, letting you…
Give your agent the search over MCP, or paste the wish link into any chat.
More Monitoring packages
Wraps any iterable to display a real-time progress bar in the terminal or Jupyter notebook, showing iteration count, elapsed time, and estimated time remaining.
Provides generated Python code for OpenTelemetry semantic conventions, enabling standardized attribute naming and constant definitions for instrumentation and telemetry collection.
Install it if you are using OpenTelemetry and want to follow semantic conventions correctly.
Provides the reference implementation of the OpenTelemetry API for collecting and exporting traces, metrics, and logs from Python applications.
Provides the abstract API and interfaces for OpenTelemetry instrumentation in Python, defining how to emit traces, metrics, and logs without tying code to a specific SDK implementation.
Exports OpenTelemetry observability data to an OpenTelemetry Collector using Protobuf-encoded messages over HTTP.
Install it if you are using OpenTelemetry in Python and need to send data to a Collector over HTTP.
Provides automatic instrumentation commands and programmatic APIs to inject distributed tracing into Python applications without code changes, detecting and instrumenting packages used by your program.
Install it if you need distributed tracing without code changes and have compatible instrumented packages in your environment.
See also apache-airflow-providers-jira · apache-airflow-providers-apache-livy · apache-airflow-providers-asana · atlassian-python-api · apache-airflow-providers-informatica · apache-airflow-providers-github · apache-airflow-providers-zendesk · apache-airflow-providers-opensearch · jira · apache-airflow-providers-neo4j