Build intelligent AI Agents on top of Github with Peliqan, the leading data foundation for the Agentic AI world.
Build intelligent AI Agents on top of Github, with Peliqan’s AI data foundation:
Publish a Github MCP Server to query data from Github and to take actions in Github such as doing updates and adding new data in Github.
Build AI Chatbots and AI Agents in n8n that can perform “Text to SQL” to query Github data and perform RAG and Graph RAG on information from Github.
Implement an AI Chatbot that can answer analytical data questions on Github data using “Text to SQL”.
Implement RAG (retrieval-augmented generation) on Github data with an out-of-the-box vector store (embeddings) of all business entities and other information in Github.
Combine data from Github with data from 250+ other connectors, and build 360° views of business entities such as customers, leads, products, employees etc.
Feed unified 360° data models to your AI Agents with RAG and “Text to SQL”. Allow your AI Agents to access all business data in one uniform data model.
Access, combine, and report on data from Github and all your SaaS apps instantly.
Gain valuable insights by bringing all your business data together in one place within minutes.
SOC 2 Type 2 validates our security controls, ensuring your data is protected by independently audited security measures.
ISO 27001 certification (in progress) ensures enterprise-grade information security, protecting your business with globally recognized standards.
GDPR compliance guarantees EU data protection compliance, keeping PII data secure within EU boundaries.
Peliqan is an all-in-one data platform with 250+ data connectors (ERP, CRM, Accounting, ATS/HRM, cloud storage etc.) – including Github – and a built-in data warehouse. Peliqan allows you to unleash, prepare and combine your Github data for AI, including relational & non-relational data. Peliqan turns your Github data into 360° views that can be used in AI Agents built in n8n, Make, langChain, langGraph or any other framework. Use Peliqan to create embeddings, store them in a vector store so that your AI chatbots can use RAG and Graph RAG, combined with Text-to-SQL for analytical reasoning. Peliqan is the only platform that allows your AI Agents to combine RAG and Text-to-SQL to apply deep reasoning on your Github data. Use Peliqan to expose any Github as an MCP server to query data and to take actions.
There are different ways to build an AI agent that can query data in Github and take actions in Github. For example you can build an AI agent in n8n and use Peliqan as the data foundation. Peliqan will sync your Github 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 Github data, combined with data from 250+ other sources.
First sign up for a free trial on Peliqan.io, next connect Github 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 Github data using Text to SQL.
There are different options to use RAG (retrieval augmented generation) in your AI Agent with Github data. One option is to create a workflow in n8n that fetches all Github data from Peliqan and stores it in Supabase as a vector store, with embeddings created using e.g. OpenAI.
In Peliqan, you can set up API endpoints and expose them as MCP Server. In the API endpoint handler script, you can configure actions to be taken in Github such as querying data, doing lookups, adding new items or performing updates.
n8n is a great tool to build AI chatbots that use Text to SQL, to answer any analytical question on your Github data. Any question will be converted by the AI agent into an SQL query, which is executed by Peliqan on the Github data in the data warehouse.
In order to prepare your Github 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 Github data from Peliqan and stores it in Supabase as a vector store, with embeddings created using e.g. OpenAI.
