This repository contains a portfolio of specialized AI workflow automation agents built using n8n and robust Large Language Model (LLM) and Retrieval-Augmented Generation (RAG) architectures.
The core objective is to demonstrate proficiency in creating robust, autonomous systems that integrate external APIs with local/cloud-based generative models for complex decision-making.
We leverage a hybrid architecture combining local open-source models with cloud-based intelligence to achieve stability and high-quality results.
- n8n (Self-Hosted): Serves as the central nervous system (orchestration layer). We use Docker Compose for reliable, self-hosted deployment.
- Demonstrated Skill: Managing complex, multi-step agent logic, handling data flow reliability, and creating robust control flows (e.g., using the
Switchnode).
- LLMs Used: Google Gemini 2.5 Flash (for high-precision, instruction-following reasoning) and Ollama (Mistral, Gemma, Nomic) for local processing.
- RAG: Implemented using Ollama's Nomic-Embed-Text and the built-in Simple Vector Store to ground AI responses in project-specific documentation, effectively eliminating hallucination.
- Agent Tool Use: Agents are designed to utilize Tool Use capability (calling other systems).
- Custom API Integration: We use the
HTTP Requestnode to call and interact with custom APIs (like GitHub's REST API), transforming passive reasoning into autonomous action.
An autonomous agent that monitors a designated repository for new issues, automatically classifies them, and provides immediate, context-aware action.
- Primary Goal: Automated issue classification and initial response, ensuring rapid and accurate triage without human intervention.
- Key LLM Function: Employs the Gemini 2.5 Flash model for superior classification logic (
question,bug, orfeature), utilizing a dedicated multi-step workflow to ensure model reliability. - RAG Implementation: A dedicated Answer Agent is instantiated only on the
questionpath, using a RAG pipeline to retrieve relevant installation or usage instructions from project documentation before drafting a final comment. - Actionable Integration: Executes real-world actions via the GitHub API for two distinct purposes: posting the RAG-generated answer and applying the corresponding triage label.
An efficient, autonomous pipeline designed to monitor the academic landscape and deliver curated intelligence directly to the user's inbox on a fixed schedule.
- Primary Goal: Automated data acquisition, complex data transformation, LLM synthesis, and report delivery in a resource-efficient manner.
- Key LLM Function: Employs Gemini 2.5 Flash for advanced synthesis of complex abstracts, distilling multiple papers into a single, high-level digest.
- RAG Implementation: Uses a dynamic RAG pipeline that ingests the latest research abstracts upon every scheduled run, guaranteeing the summary is grounded in the current day's findings.
- Actionable Integration: Demonstrates multi-stage custom API integration including fetching complex Atom XML data from the ArXiv API and utilizing SMTP to send the final report.
An advanced, scheduled agent that performs live data aggregation from the Hacker News API and analyzes unstructured, real-time user-generated content.
- Primary Goal: To demonstrate a dynamic RAG pipeline on live user comments, moving beyond static document analysis to perform real-time sentiment synthesis.
- Key LLM Function: Employs Gemini 2.5 Flash to synthesize sentiment and key discussion points from dozens of unstructured user comments into a concise, executive-level summary.
- RAG Implementation: This is the project's core complexity. It creates a "just-in-time" vector store for each individual news story, using the story's title as a dynamic
Memory Key. This perfectly isolates the AI's context for each summary. - Actionable Integration: Fetches data via a complex, nested-loop API call (story -> comments) and delivers a single, aggregated report via SMTP.
A high-value business automation agent that acts as an automated data-entry specialist, demonstrating a "unstructured-to-structured" pipeline.
- Primary Goal: To parse unstructured text (like an email), extract key entities, and write them to a Google Sheet, proving efficiency in data integration.
- Key LLM Function: Employs Gemini 2.5 Flash for structured data extraction (text-to-JSON).
- RAG Implementation: Uses a "RAG-Zero" or "In-Context Learning" approach by providing "few-shot" examples in the system prompt, which is a highly effective and low-overhead RAG pattern.
- Actionable Integration: Uses the Google Sheets API (via Service Account) to authenticate and append the structured data as a new row in a spreadsheet.