As a researcher and developer, I found myself spending hours manually searching academic databases, reading abstracts, and trying to synthesize findings across multiple sources. For my work on circula

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A step-by-step guide to building a production-grade research automation pipeline using n8n, Groq (Llama 3.3 70B), and academic APIs (Semantic Scholar, OpenAlex, arXiv, PubMed). The pipeline covers six stages: centralized configuration, parallel API collection with failure isolation, data normalization and DOI-based deduplication, structured LLM extraction with strict JSON prompting, relevance and quality scoring, and delivery to Google Sheets. The tutorial also covers lightweight eval checks to catch silent regressions in AI extraction, and practical error handling patterns like batching, retries, and partial-failure recovery.

How to Build an AI-Powered Research Automation System with n8n, Groq, and Academic APIs

The Project Structure: How to Think About an n8n Workflow Like Software

Stage 2: Parallel API Collection (With Failure Isolation)

Stage 3: Normalisation and Deduplication (DOI-first, Title fallback)

Stage 4: AI-Powered Content Extraction (Strict JSON)

Beginner-Friendly Evals: Retrieval and Extraction QA