Build AI Agents on Graph API
Build intelligent AI Agents on top of Graph API with Peliqan. Query your data in plain English, automate workflows with AI, and write changes back – all through a single platform with 300+ connectors and a built-in data warehouse.
- No credit card required
- 5-minute setup
- SOC 2 Type II certified
Build AI Agents on top of Graph API
Build intelligent AI Agents on top of Graph API, with Peliqan’s AI data foundation:
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- Graph API MCP Server – let Claude, ChatGPT, or any AI client query and update your Graph API data
- Graph API in n8n AI Agents – build automated workflows that reason on your data
- Graph API in Make – trigger AI-powered actions from your Graph API events
- Text-to-SQL on Graph API – ask questions in plain English, get SQL-powered answers
- Graph API RAG – semantic search with out-of-the-box embeddings (vectors)
- Graph API Graph RAG – traverse relationships across your data entities
- Unified 360° data – combine Graph API with 250+ other sources in one warehouse
Graph API MCP Server
Publish a Graph API MCP Server with full read and write capabilities.
Peliqan syncs a read-only copy of your Graph API data into its built-in warehouse – your AI agents query the cache, your live Graph API is never touched. Connect Claude, ChatGPT, or any MCP client and start querying Graph API in plain English.
Graph API in n8n
Build AI Chatbots and AI Agents in n8n that query your Graph API data using Text-to-SQL and perform RAG on Graph API documents and records.
Peliqan acts as the data layer – syncing your Graph API data, converting natural language to SQL, and returning results your n8n agent can act on.
Implement a “Text to SQL” chatbot on Graph API
Implement an AI Chatbot that answers analytical questions on Graph API 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 Graph API warehouse, and returns structured results – no SQL knowledge required.
Implement a chatbot with RAG on Graph API
Implement RAG (retrieval-augmented generation) on Graph API data with Peliqan’s out-of-the-box vector store.
Peliqan automatically creates embeddings of your Graph API 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 Graph API data with 300+ sources
Combine data from Graph API with 300+ other connectors in Peliqan’s built-in warehouse.
Build unified 360° views of customers, deals, products, or employees – pulling from Graph API, 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 Graph API data for AI
Access, combine, and report on data from Graph API 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 Graph API data directly. Peliqan solves this by syncing Graph API 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 Graph API 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 Graph API data in a single context.
How to build an AI agent for Graph API ?
There are different ways to build an AI agent that can query data in Graph API and take actions in Graph API. For example you can build an AI agent in n8n and use Peliqan as the data foundation. Peliqan will sync your Graph API 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 Graph API data, combined with data from 250+ other sources.
How can I use Graph API in n8n ?
First sign up for a free trial on Peliqan.io, next connect Graph API 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 Graph API data using Text to SQL.
How to use RAG with Graph API ?
There are different options to use RAG (retrieval augmented generation) in your AI Agent with Graph API data. One option is to create a workflow in n8n that fetches all Graph API 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 Graph API ?
Connect Graph API in Peliqan, then create an API handler using the built-in MCP template. Expose the Graph API 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 Graph API is never touched. The full setup takes under 10 minutes.
How do I build an AI chatbot with “Text to SQL” for Graph API ?
n8n is a great tool to build AI chatbots that use Text to SQL, to answer any analytical question on your Graph API data. Any question will be converted by the AI agent into an SQL query, which is executed by Peliqan on the Graph API data in the data warehouse.
How to create embeddings in a vector store for Graph API data ?
In order to prepare your Graph API 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 Graph API data from Peliqan and stores it in Supabase as a vector store, with embeddings created using e.g. OpenAI.
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Built-in data warehouse, superior data activation capabilities, and AI-powered development assistance.