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Academic Literature Review Automation With Multi-Agent Pipelines

Added Jun 2025 3 design docs

Researchers and graduate students drown in publications: keeping up with a single field can mean scanning hundreds of papers a month, and literature reviews consume weeks of focused effort. This project builds a platform where autonomous agents shoulder that reading load.

The intern implements a multi-agent pipeline with CrewAI and Langchain in which specialized agents collect academic literature, analyze it, and produce structured summaries, with LlamaIndex indexing papers for retrieval and OpenAI models powering advanced summarization. A FastAPI backend coordinates agent runs and stores results, while a Streamlit dashboard lets researchers define topics, monitor collection progress, and read synthesized findings, with personalization features tuning the platform to each user's research interests over time, all built in Python.

The project demands real engineering judgment, including orchestrating agents that hand work to one another, grounding summaries in source documents to limit fabrication, and presenting machine-read literature in a form scholars trust. Completing it demonstrates the intern can apply agentic AI to knowledge work at research grade, a capability valued in both academia and industry R&D.

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