Build AI Agents on Apache Airflow
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- 5-minute setup
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Build AI Agents on top of Apache Airflow
Build intelligent AI Agents on top of Apache Airflow, with Peliqan’s AI data foundation:
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- Apache Airflow MCP Server – let Claude, ChatGPT, or any AI client query and update your Apache Airflow data
- Apache Airflow in n8n AI Agents – build automated workflows that reason on your data
- Apache Airflow in Make – trigger AI-powered actions from your Apache Airflow events
- Text-to-SQL on Apache Airflow – ask questions in plain English, get SQL-powered answers
- Apache Airflow RAG – semantic search with out-of-the-box embeddings (vectors)
- Apache Airflow Graph RAG – traverse relationships across your data entities
- Unified 360° data – combine Apache Airflow with 250+ other sources in one warehouse
Apache Airflow MCP Server
Publish a Apache Airflow MCP Server with full read and write capabilities.
Peliqan syncs a read-only copy of your Apache Airflow data into its built-in warehouse – your AI agents query the cache, your live Apache Airflow is never touched. Connect Claude, ChatGPT, or any MCP client and start querying Apache Airflow in plain English.
Apache Airflow in n8n
Build AI Chatbots and AI Agents in n8n that query your Apache Airflow data using Text-to-SQL and perform RAG on Apache Airflow documents and records.
Peliqan acts as the data layer – syncing your Apache Airflow data, converting natural language to SQL, and returning results your n8n agent can act on.
Implement a “Text to SQL” chatbot on Apache Airflow
Implement an AI Chatbot that answers analytical questions on Apache Airflow data using Text-to-SQL.
Ask “what were my top 10 customers last quarter?” or “which deals are stalled?” and get instant, accurate answers.
Peliqan converts your question to SQL, runs it against your Apache Airflow warehouse, and returns structured results – no SQL knowledge required.
Implement a chatbot with RAG on Apache Airflow
Implement RAG (retrieval-augmented generation) on Apache Airflow data with Peliqan’s out-of-the-box vector store.
Peliqan automatically creates embeddings of your Apache Airflow records, notes, and documents – so your AI agents can search semantically, not just by keywords.
Combine RAG with Text-to-SQL for AI that can both reason on numbers and understand context.
Combine Apache Airflow data with 250+ sources
Combine data from Apache Airflow with 250+ other connectors in Peliqan’s built-in warehouse.
Build unified 360° views of customers, deals, products, or employees – pulling from Apache Airflow, your CRM, ERP, HR systems, and more.
Feed these unified data models to your AI Agents so they can answer cross-functional questions in a single query.
Prepare your Apache Airflow data for AI
Access, combine, and report on data from Apache Airflow and all your SaaS apps instantly. Gain valuable insights by bringing all your business data together in one place within minutes.
Unify, Automate & Activate Your Data
Connect all your SaaS apps, databases, and spreadsheets into one workspace. Build automations, analytics pipelines, and data apps — all in one place.
Frequently Asked Questions
Why do I need Peliqan for my AI ?
Most AI tools can’t access your Apache Airflow data directly. Peliqan solves this by syncing Apache Airflow into a built-in data warehouse and exposing it through MCP, Text-to-SQL, and RAG.
Your AI agents get governed, real-time access to Apache Airflow data combined with 250+ other sources – without building custom integrations.
Peliqan is the only platform that lets AI agents combine structured SQL queries with semantic RAG search on Apache Airflow data in a single context.
How to build an AI agent for Apache Airflow ?
There are different ways to build an AI agent that can query data in Apache Airflow and take actions in Apache Airflow. For example you can build an AI agent in n8n and use Peliqan as the data foundation. Peliqan will sync your Apache Airflow data to its built-in data warehouse and allow the AI Agent to perform “Text to SQL” and RAG to answer questions and to perform reasoning on Apache Airflow data, combined with data from 250+ other sources.
How can I use Apache Airflow in n8n ?
First sign up for a free trial on Peliqan.io, next connect Apache Airflow in Peliqan. Once that is done, create an AI agent in n8n and use the Peliqan n8n node in your worflow. Add Peliqan as a “tool” to your AI Agent node, so that the AI agent can query your Apache Airflow data using Text to SQL.
How to use RAG with Apache Airflow ?
There are different options to use RAG (retrieval augmented generation) in your AI Agent with Apache Airflow data. One option is to create a workflow in n8n that fetches all Apache Airflow data from Peliqan and stores it in Supabase as a vector store, with embeddings created using e.g. OpenAI.
How to build an MCP Server for Apache Airflow ?
Connect Apache Airflow in Peliqan, then create an API handler using the built-in MCP template. Expose the Apache Airflow tables and actions your AI needs, set role-based permissions, and run pip install mcp-server-peliqan to connect Claude or ChatGPT. Your AI agents query a cached copy of your data – your live Apache Airflow is never touched. The full setup takes under 10 minutes.
How do I build an AI chatbot with “Text to SQL” for Apache Airflow ?
n8n is a great tool to build AI chatbots that use Text to SQL, to answer any analytical question on your Apache Airflow data. Any question will be converted by the AI agent into an SQL query, which is executed by Peliqan on the Apache Airflow data in the data warehouse.
How to create embeddings in a vector store for Apache Airflow data ?
In order to prepare your Apache Airflow data for RAG, you need to create embeddings and store them in a vector store. This can be done by creating a workflow in n8n that fetches all Apache Airflow data from Peliqan and stores it in Supabase as a vector store, with embeddings created using e.g. OpenAI.
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