# Introducing the Common AI Provider: LLM and AI Agent Support for Apache Airflow

DevFeed: [Introducing the Common AI Provider: LLM and AI Agent Support for Apache Airflow](<https://devfeed.tech/articles/introducing-the-common-ai-provider-llm-and-ai-agent-support-for-apache-airflow-32558.md>)

Original publisher: [Read original article](<https://airflow.apache.org/blog/common-ai-provider/>)

Author: Apache Airflow

Published: 2026-04-14T00:00:00Z

Content type: release

Language: en

Sources: [Apache Airflow Blog](<https://devfeed.tech/sources/apache-airflow-blog.md>)

Topics: [airflow](<https://devfeed.tech/topics/airflow.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [AI Agent](<https://devfeed.tech/topics/ai-agent.md>), [Pydantic](<https://devfeed.tech/topics/pydantic.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>)

Tags: [2025](<https://devfeed.tech/tags/2025.md>), [ai](<https://devfeed.tech/tags/ai.md>), [ai-agent](<https://devfeed.tech/tags/ai-agent.md>), [airflow](<https://devfeed.tech/tags/airflow.md>), [apache](<https://devfeed.tech/tags/apache.md>), [apache-airflow](<https://devfeed.tech/tags/apache-airflow.md>), [community](<https://devfeed.tech/tags/community.md>), [llm](<https://devfeed.tech/tags/llm.md>), [release](<https://devfeed.tech/tags/release.md>)

## AI overview

Apache Airflow is releasing the Common AI Provider 0.1.0, a provider package that adds LLM and AI agent capabilities directly to Airflow. Built on Pydantic AI, it supports more than 20 model providers through a single install and requires Apache Airflow 3.0 or newer.

## Source excerpt

At Airflow Summit 2025, we previewed what native AI integration in Apache Airflow could look like. Today we're shipping it. apache-airflow-providers-common-ai 0.1.0 adds LLM and agent capabilities directly to Airflow. Not a wrapper around another framework, but a provider package that plugs into the orchestrator you already run. It's built on Pydantic AI and supports 20+ model providers (OpenAI, Anthropic, Google, Azure, Bedrock, Ollama, and more) through a single install. pip install 'apache-airflow-providers-common-ai' Requires Apache Airflow 3.0+. Note: This is a 0.x release. We're actively looking for feedback and iterating fast, so breaking changes are possible between minor versions. Try it, tell us what works and what doesn't. Your input directly shapes the API. By the Numbers 6 Operators 6 TaskFlow decorators 5 Toolsets 4 Connection types 20+ Supported model providers via Pydantic AI The Decorator Suite Every operator has a matching TaskFlow decorator. @task.llm: Single LLM Call Send a prompt, get text or structured output back. from pydantic import BaseModel from airflow.providers.common.compat.sdk import dag, task @dag def my_pipeline(): class Entities(BaseModel): names: list[str] locations: list[str] @task.llm( llm_conn_id="my_openai_conn", system_prompt="Extract named entities.", output_type=Entities, ) def extract(text: str): return f"Extract entities from: {text}" extract("Alice visited Paris and met Bob in London.") my_pipeline() The LLM returns a typed Entities object, not a string you have to parse. Downstream tasks get structured data through XCom. @task.agent: Multi-Step Agent with Tools When the LLM needs to query databases, call APIs, or read files across multiple steps, use @task.agent. The agent picks which tools to call and loops until it has an answer. from airflow.providers.common.ai.toolsets.sql import SQLToolset from airflow.providers.common.compat.sdk import dag, task @dag def sql_analyst(): @task.agent( llm_conn_id="my_openai_conn", sys