apache-airflow-providers-common-ai
Provider package apache-airflow-providers-common-ai for Apache Airflow
Decision gist · record as of 2026-08-14
Yes, if you run Apache Airflow >=3.0.0 and need to add LLM capabilities to your data pipelines. The package is actively maintained, has no known vulnerabilities, installs with low friction, and is licensed permissively. Start with the base install; add optional extras only for the LLM providers you actually use.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Apache Airflow >=3.0.0 and Python >=3.10; pydantic-ai-slim >=2.0.0 must be installed.
- Low friction installation as a pure Python wheel.
- Actively maintained with a release 6 days old.
License · maintenance · safety
Apache-2.0 (permissive) — Apache-2.0 permissive license allows commercial use, modification, and redistribution with minimal restrictions—standard for Apache Foundation projects.
last release 2026-08-08 (6 days) · last repo commit 2026-08-14 · 46,490 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 354,678 downloads/mo, #7,295 on PyPI
Alternatives
Verify before relying
pip install apache-airflow-providers-common-ai
from airflow.providers.common.ai import ...
# Use AI operators/hooks in your Airflow DAG definitions- Which specific AI operators and hooks are available in the common.ai provider package.
- Whether the package supports streaming or batch LLM inference modes.
- How error handling and retries are configured for LLM calls in Airflow tasks.
- Whether optional extras (anthropic, bedrock, google, openai, mcp) require separate API credentials or configuration.
What it is and what it does
This is an Apache Airflow provider package that adds AI and LLM capabilities to Airflow pipelines by wrapping pydantic-ai. It supplies hooks and operators designed to let you orchestrate LLM-driven tasks as part of Airflow DAGs—allowing you to call language models, process their outputs, and integrate AI into data workflows alongside traditional data pipeline steps.
The package depends on Apache Airflow >=3.0.0, pydantic-ai-slim >=2.0.0, and Airflow's common-compat and standard providers. It supports Python 3.10 through 3.14. Optional extras unlock integrations with specific LLM providers (OpenAI, Anthropic, Bedrock, Google) and add-ons like code execution, safety shields, and data format support (Avro, Parquet, PDF, DOCX).
Use it for
- Build Airflow DAGs that call LLMs to generate summaries, classifications, or transformations of data flowing through your pipeline.
- Orchestrate multi-step workflows where LLM outputs feed into downstream tasks, combining AI inference with traditional ETL logic.
- Integrate with multiple LLM providers (OpenAI, Anthropic, Bedrock, Google) through a unified Airflow operator interface.
- Add AI-powered data quality checks or anomaly detection as Airflow tasks using LLM reasoning.
- Process documents (PDF, DOCX) or query databases with AI assistance as part of scheduled Airflow jobs.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you run Apache Airflow >=3.0.0 and need to add LLM capabilities to your data pipelines.
The package is actively maintained, has no known vulnerabilities, installs with low friction, and is licensed permissively. Start with the base install; add optional extras only for the LLM providers you actually use.
Install
apache-airflow-providers-common-ai on PyPI
Before you install
Low friction installation as a pure Python wheel. Actively maintained with a release 6 days old. Requires Airflow >=3.0.0 and pydantic-ai-slim >=2.0.0; integrates cleanly into existing Airflow deployments.
Requires Apache Airflow >=3.0.0 and Python >=3.10; pydantic-ai-slim >=2.0.0 must be installed.
License in practice
Apache-2.0 permissive license allows commercial use, modification, and redistribution with minimal restrictions—standard for Apache Foundation projects.
Quickstart
pip install apache-airflow-providers-common-ai
from airflow.providers.common.ai import ...
# Use AI operators/hooks in your Airflow DAG definitionsVerify before relying
- Which specific AI operators and hooks are available in the common.ai provider package.
- Whether the package supports streaming or batch LLM inference modes.
- How error handling and retries are configured for LLM calls in Airflow tasks.
- Whether optional extras (anthropic, bedrock, google, openai, mcp) require separate API credentials or configuration.
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-airflow-providers-common-compatapache-airflow-providers-standardapache-airflowpydantic-ai-slim |
| Maintenance | Actively maintained 6 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 354,678 / month, #7,295 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_common_ai-0.7.0-py3-none-any.whl
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See also apache-airflow-providers-cohere · apache-airflow-providers-informatica · dify-plugin · apache-airflow-providers-apache-drill · apache-airflow-providers-standard · apache-airflow-providers-git · apache-airflow-providers-databricks · apache-airflow-providers-apache-cassandra · apache-airflow-providers-github · apache-airflow-providers-openai