airflow Agent工作流
airflow Agent工作流 是 AI Skill Hub 本期精选Agent工作流之一。在 GitHub 上收获超过 45.4k 颗 Star，综合评分 8.5 分，整体质量较高。我们强烈推荐将其纳入你的 AI 工具库，帮助提升工作效率。
airflow Agent工作流 工作流的设计遵循"最小配置，最大复用"原则：核心逻辑已经封装好，用户只需配置自己的 API Key 和业务参数即可快速上手。工作流内置错误处理和重试机制，在网络波动或 API 限速等情况下仍能稳定运行，适合作为生产环境的自动化基础设施。
在实际部署时，建议先在测试环境中运行 3-5 次，验证各个环节的输出结果符合预期，再部署到生产环境。AI Skill Hub 评分 8.5 分，是同类 Agent 工作流中的精选推荐。
airflow Agent工作流 是一套完整的 AI Agent 自动化工作流方案。通过可视化的节点编排，将复杂的多步骤任务拆解为清晰的自动化流程，实现全程无人值守的智能处理。支持与数百种外部服务和 API 无缝集成，适合构建数据处理管线、业务自动化和 AI 辅助决策系统。
airflow Agent工作流 是一套完整的 AI Agent 自动化工作流方案。通过可视化的节点编排，将复杂的多步骤任务拆解为清晰的自动化流程，实现全程无人值守的智能处理。支持与数百种外部服务和 API 无缝集成，适合构建数据处理管线、业务自动化和 AI 辅助决策系统。
- 可视化 Agent 工作流编排，无需编写复杂代码
- 支持多步骤自动化任务链，实现全流程无人值守
- 与外部 API、数据库和第三方服务无缝集成
- 内置错误处理与自动重试机制，保障稳定运行
- 提供可复用的自动化模板，快速在同类场景部署
- 自动化日常重复性工作，将精力集中于创造性任务
- 构建数据采集 → 处理 → 输出的完整自动化管线
- 实现跨平台、跨系统的数据流转和业务协同
# 方式一：pip 安装（推荐）
pip install airflow
# 方式二：虚拟环境安装（推荐生产环境）
python -m venv .venv
source .venv/bin/activate # Windows: .venv\Scripts\activate
pip install airflow
# 方式三：从源码安装（获取最新功能）
git clone https://github.com/apache/airflow
cd airflow
pip install -e .
# 验证安装
python -c "import airflow; print('安装成功')"
- 访问 GitHub 仓库获取工作流文件
- 在对应平台（Dify / Flowise / Make 等）中找到「导入工作流」功能
- 上传工作流文件
- 按照提示配置必要的环境变量和 API Key
- 运行测试确认流程正常后投入使用
# 命令行使用
airflow --help
# 基本用法
airflow input_file -o output_file
# Python 代码中调用
import airflow
# 示例
result = airflow.process("input")
print(result)
# airflow 配置文件示例（config.yml） app: name: "airflow" debug: false log_level: "INFO" # 运行时指定配置文件 airflow --config config.yml # 或通过环境变量配置 export AIRFLOW_API_KEY="your-key" export AIRFLOW_OUTPUT_DIR="./output"
Apache Airflow
| Category | Badges |
|---|---|
| License | [](https://www.apache.org/licenses/LICENSE-2.0.txt) |
| PyPI | [](https://badge.fury.io/py/apache-airflow) [](https://pypi.org/project/apache-airflow/) [](https://pypi.org/project/apache-airflow/) |
| Containers | [](https://hub.docker.com/r/apache/airflow) [](https://hub.docker.com/r/apache/airflow) [](https://artifacthub.io/packages/search?repo=apache-airflow) |
| Community | [](https://github.com/apache/airflow/graphs/contributors) [](https://s.apache.org/airflow-slack)  [](https://insights.linuxfoundation.org/project/apache-airflow) |
| Dev tools | [](https://github.com/j178/prek) |
| Version | Build Status |
|---|---|
| Main | [](https://github.com/apache/airflow/actions/workflows/ci-amd.yml) [](https://github.com/apache/airflow/actions/workflows/ci-arm.yml) |
| 3.x | [](https://github.com/apache/airflow/actions/workflows/ci-amd.yml) [](https://github.com/apache/airflow/actions/workflows/ci-arm.yml) |
<picture width="500"> <img src="https://github.com/apache/airflow/blob/19ebcac2395ef9a6b6ded3a2faa29dc960c1e635/docs/apache-airflow/img/logos/wordmark_1.png?raw=true" alt="Apache Airflow logo" /> </picture>
Apache Airflow (or simply Airflow) is a platform to programmatically author, schedule, and monitor workflows.
When workflows are defined as code, they become more maintainable, versionable, testable, and collaborative.
Use Airflow to author workflows (Dags) that orchestrate tasks. The Airflow scheduler executes your tasks on an array of workers while following the specified dependencies. Rich command line utilities make performing complex surgeries on Dags a snap. The rich user interface makes it easy to visualize pipelines running in production, monitor progress, and troubleshoot issues when needed.
Table of contents
- Project Focus
- Principles
- Requirements
- Getting started
- Installing from PyPI
- Installation
- Official source code
- Convenience packages
- User Interface
- Semantic versioning
- Version Life Cycle
- Support for Python and Kubernetes versions
- Base OS support for reference Airflow images
- Approach to dependencies of Airflow
- Contributing
- Agent-assisted contribution (apache-steward)
- Voting Policy
- Who uses Apache Airflow?
- Who maintains Apache Airflow?
- What goes into the next release?
- Can I use the Apache Airflow logo in my presentation?
- Links
- Sponsors
Requirements
Apache Airflow is tested with:
| Main version (dev) | Stable version (3.2.0) | Stable version (2.11.2) | |
|---|---|---|---|
| Python | 3.10, 3.11, 3.12, 3.13, 3.14 | 3.10, 3.11, 3.12, 3.13, 3.14 | 3.10, 3.11, 3.12 |
| Platform | AMD64/ARM64 | AMD64/ARM64 | AMD64/ARM64(\*) |
| Kubernetes | 1.30, 1.31, 1.32, 1.33, 1.34, 1.35 | 1.30, 1.31, 1.32, 1.33, 1.34, 1.35 | 1.26, 1.27, 1.28, 1.29, 1.30 |
| PostgreSQL | 14, 15, 16, 17, 18 | 14, 15, 16, 17, 18 | 12, 13, 14, 15, 16 |
| MySQL | 8.0, 8.4, Innovation | 8.0, 8.4, Innovation | 8.0, Innovation |
| SQLite | 3.15.0+ | 3.15.0+ | 3.15.0+ |
\* Experimental
Note: MariaDB is not tested/recommended.
Note: SQLite is used in Airflow tests. Do not use it in production. We recommend using the latest stable version of SQLite for local development.
Note: Airflow currently can be run on POSIX-compliant Operating Systems. For development, it is regularly tested on fairly modern Linux Distros and recent versions of macOS. On Windows you can run it via WSL2 (Windows Subsystem for Linux 2) or via Linux Containers. The work to add Windows support is tracked via #10388, but it is not a high priority. You should only use Linux-based distros as "Production" execution environment as this is the only environment that is supported. The only distro that is used in our CI tests and that is used in the Community managed DockerHub image is Debian Bookworm.
Approach to dependencies of Airflow
Airflow has a lot of dependencies - direct and transitive, also Airflow is both - library and application, therefore our policies to dependencies has to include both - stability of installation of application, but also ability to install newer version of dependencies for those users who develop Dags. We developed the approach where constraints are used to make sure airflow can be installed in a repeatable way, while we do not limit our users to upgrade most of the dependencies. As a result we decided not to upper-bound version of Airflow dependencies by default, unless we have good reasons to believe upper-bounding them is needed because of importance of the dependency as well as risk it involves to upgrade specific dependency. We also upper-bound the dependencies that we know cause problems.
The constraint mechanism of ours takes care about finding and upgrading all the non-upper bound dependencies automatically (providing that all the tests pass). Our main build failures will indicate in case there are versions of dependencies that break our tests - indicating that we should either upper-bind them or that we should fix our code/tests to account for the upstream changes from those dependencies.
Whenever we upper-bound such a dependency, we should always comment why we are doing it - i.e. we should have a good reason why dependency is upper-bound. And we should also mention what is the condition to remove the binding.
Approach for dependencies for Airflow Core
Those dependencies are maintained in `pyproject.toml`.
There are few dependencies that we decided are important enough to upper-bound them by default, as they are known to follow predictable versioning scheme, and we know that new versions of those are very likely to bring breaking changes. We commit to regularly review and attempt to upgrade to the newer versions of the dependencies as they are released, but this is manual process.
The important dependencies are:
SQLAlchemy: upper-bound to specific MINOR version (SQLAlchemy is known to remove deprecations and introduce breaking changes especially that support for different Databases varies and changes at various speed) Alembic: it is important to handle our migrations in predictable and performant way. It is developed together with SQLAlchemy. Our experience with Alembic is that it very stable in MINOR version Flask: We are using Flask as the back-bone of our web UI and API. We know major version of Flask are very likely to introduce breaking changes across those so limiting it to MAJOR version makes sense werkzeug: the library is known to cause problems in new versions. It is tightly coupled with Flask libraries, and we should update them together celery: Celery is a crucial component of Airflow as it used for CeleryExecutor (and similar). Celery follows SemVer, so we should upper-bound it to the next MAJOR version. Also, when we bump the upper version of the library, we should make sure Celery Provider minimum Airflow version is updated. kubernetes: Kubernetes is a crucial component of Airflow as it is used for the KubernetesExecutor (and similar). Kubernetes Python library follows SemVer, so we should upper-bound it to the next MAJOR version. Also, when we bump the upper version of the library, we should make sure Kubernetes Provider minimum Airflow version is updated.
Approach for dependencies in Airflow Providers and extras
The main part of the Airflow is the Airflow Core, but the power of Airflow also comes from a number of providers that extend the core functionality and are released separately, even if we keep them (for now) in the same monorepo for convenience. You can read more about the providers in the Providers documentation. We also have set of policies implemented for maintaining and releasing community-managed providers as well as the approach for community vs. 3rd party providers in the providers document.
Those extras and providers dependencies are maintained in provider.yaml of each provider.
By default, we should not upper-bound dependencies for providers, however each provider's maintainer might decide to add additional limits (and justify them with comment).
Getting started
Visit the official Airflow website documentation (latest stable release) for help with installing Airflow, getting started, or walking through a more complete tutorial.
Note: If you're looking for documentation for the main branch (latest development branch): you can find it on s.apache.org/airflow-docs.
For more information on Airflow Improvement Proposals (AIPs), visit the Airflow Wiki.
Documentation for dependent projects like provider distributions, Docker image, Helm Chart, you'll find it in the documentation index.
Installing from PyPI
We publish Apache Airflow as apache-airflow package in PyPI. Installing it however might be sometimes tricky because Airflow is a bit of both a library and application. Libraries usually keep their dependencies open, and applications usually pin them, but we should do neither and both simultaneously. We decided to keep our dependencies as open as possible (in pyproject.toml) so users can install different versions of libraries if needed. This means that pip install apache-airflow will not work from time to time or will produce unusable Airflow installation.
To have repeatable installation, however, we keep a set of "known-to-be-working" constraint files in the orphan constraints-main and constraints-2-0 branches. We keep those "known-to-be-working" constraints files separately per major/minor Python version. You can use them as constraint files when installing Airflow from PyPI. Note that you have to specify correct Airflow tag/version/branch and Python versions in the URL.
- Installing just Airflow:
Note: Only pip installation is currently officially supported.
While it is possible to install Airflow with tools like Poetry or pip-tools, they do not share the same workflow as pip - especially when it comes to constraint vs. requirements management. Installing via Poetry or pip-tools is not currently supported.
If you wish to install Airflow using those tools, you should use the constraint files and convert them to the appropriate format and workflow that your tool requires.
pip install 'apache-airflow==3.2.0' \
--constraint "https://raw.githubusercontent.com/apache/airflow/constraints-3.2.0/constraints-3.10.txt"- Installing with extras (i.e., postgres, google)
pip install 'apache-airflow[postgres,google]==3.2.0' \
--constraint "https://raw.githubusercontent.com/apache/airflow/constraints-3.2.0/constraints-3.10.txt"For information on installing provider distributions, check providers.
Installation
For comprehensive instructions on setting up your local development environment and installing Apache Airflow, please refer to the INSTALLING.md file.
User Interface
- Dags: Overview of all Dags in your environment.
- Assets: Overview of Assets with dependencies.
- Grid: Grid representation of a Dag that spans across time.
- Graph: Visualization of a Dag's dependencies and their current status for a specific run.
- Home: Summary statistics of your Airflow environment.
- Backfill: Backfilling a Dag for a specific date range.
- Code: Quick way to view source code of a Dag.
Base OS support for reference Airflow images
The Airflow Community provides conveniently packaged container images that are published whenever we publish an Apache Airflow release. Those images contain:
Base OS with necessary packages to install Airflow (stable Debian OS) Base Python installation in versions supported at the time of release for the MINOR version of Airflow released (so there could be different versions for 2.3 and 2.2 line for example) Libraries required to connect to supported Databases (again the set of databases supported depends on the MINOR version of Airflow) Predefined set of popular providers (for details see the Dockerfile). Possibility of building your own, custom image where the user can choose their own set of providers and libraries (see Building the image) In the future Airflow might also support a "slim" version without providers nor database clients installed
The version of the base OS image is the stable version of Debian. Airflow supports using all currently active stable versions - as soon as all Airflow dependencies support building, and we set up the CI pipeline for building and testing the OS version. Approximately 6 months before the end-of-regular support of a previous stable version of the OS, Airflow switches the images released to use the latest supported version of the OS.
For example switch from `Debian Bullseye to Debian Bookworm` has been implemented before 2.8.0 release in October 2023 and `Debian Bookworm` will be the only option supported as of Airflow 2.10.0.
Users will continue to be able to build their images using stable Debian releases until the end of regular support and building and verifying of the images happens in our CI but no unit tests were executed using this image in the main branch.
Convenience packages
There are other ways of installing and using Airflow. Those are "convenience" methods - they are not "official releases" as stated by the ASF Release Policy, but they can be used by the users who do not want to build the software themselves.
Those are - in the order of most common ways people install Airflow:
- PyPI releases to install Airflow using standard pip tool - Docker Images to install airflow via docker tool, use them in Kubernetes, Helm Charts, docker-compose, docker swarm, etc. You can read more about using, customizing, and extending the images in the Latest docs, and learn details on the internals in the images document. - Tags in GitHub to retrieve the git project sources that were used to generate official source packages via git
All those artifacts are not official releases, but they are prepared using officially released sources. Some of those artifacts are "development" or "pre-release" ones, and they are clearly marked as such following the ASF Policy.
业界标杆级工作流编排工具，生态完善、功能强大、文档优秀。适合中大型团队，但学习曲线陡峭，需评估组织能力。
- 构建多智能体协作系统的 Agent 开发者
- 生产部署优先使用 Docker Compose 隔离依赖，并挂载 volume 持久化数据
- Agent 任务先做 dry-run 验证工具调用链，再开启自主执行
- API key 直接提交到 git 仓库（请用 .env 并加入 .gitignore）
- 容器内无法访问宿主机 localhost — 使用 host.docker.internal
- Python 依赖冲突：建议用 venv / uv 隔离环境
- Docker：airflow 提供官方镜像，docker compose up 一键启动
- CLI：直接 npm install -g / pip install，命令行调用
- 云端托管：可放在 Vercel / Railway / Fly.io 等 PaaS 平台
- 可视化 Agent 工作流编排，无需编写复杂代码
- 支持多步骤自动化任务链，实现全流程无人值守
- 与外部 API、数据库和第三方服务无缝集成
- 内置错误处理与自动重试机制，保障稳定运行
- 提供可复用的自动化模板，快速在同类场景部署
- 构建多智能体协作系统的 Agent 开发者
- 生产部署优先使用 Docker Compose 隔离依赖，并挂载 volume 持久化数据
- Agent 任务先做 dry-run 验证工具调用链，再开启自主执行
- API key 直接提交到 git 仓库（请用 .env 并加入 .gitignore）
- 容器内无法访问宿主机 localhost — 使用 host.docker.internal
- Python 依赖冲突：建议用 venv / uv 隔离环境
- 自动化日常重复性工作，将精力集中于创造性任务
- 构建数据采集 → 处理 → 输出的完整自动化管线
- 实现跨平台、跨系统的数据流转和业务协同
- +GitHub 45.4k Star，社区高度认可
- +Apache-2.0 协议，可免费商用
- +大幅减少重复性人工操作
- +可视化流程，清晰直观
- +可扩展性强，支持复杂场景
- −初始配置和调试需投入一定时间
- −强依赖外部服务的稳定性
- −复杂场景需具备一定技术基础
AI Skill Hub 为第三方内容聚合平台，本页面信息基于公开数据整理，不对工具功能和质量作任何法律背书。
建议在沙箱或测试环境中充分验证后，再部署至生产环境，并做好必要的安全评估。
✅ Apache 2.0 — 宽松开源协议，可商用，需保留版权声明和 NOTICE 文件，含专利授权条款。
经综合评估，airflow Agent工作流 在Agent工作流赛道中表现稳健，质量优秀。如果你已有明确的使用需求，可以直接上手体验；如果还在评估阶段，建议对比同类工具后再做决策。
| 原始名称 | airflow |
| 原始描述 | 开源AI工作流：Apache Airflow - A platform to programmatically author, schedule, and monitor wo。⭐45.4k · Python |
| Topics | 工作流编排任务调度DAG数据管道自动化 |
| GitHub | https://github.com/apache/airflow |
| License | Apache-2.0 |
| 语言 | Python |
收录时间：2026-05-14 · 更新时间：2026-05-16 · License：Apache-2.0 · AI Skill Hub 不对第三方内容的准确性作法律背书。