Autonomous Literature Review Agent for Academic Paper Analysis

Added Jun 2025 3 design docs

A literature review is the most repetitive stage of research: find candidate papers, read abstracts, discard the irrelevant, extract methods and findings from the rest, and synthesize the field's state. Researchers and graduate students spend weeks on this cycle, and the manual approach does not scale as publication volume grows. An agent that runs the cycle autonomously, while keeping the human in control of direction, is genuinely valuable. The intern builds that agent as a Python system served through FastAPI. LangChain orchestrates the research loop, planning searches, deciding which papers merit deeper reading, and chaining analysis steps, while LlamaIndex indexes the gathered papers so extraction and synthesis are grounded in actual text. The system works with both OpenAI APIs and Llama models, letting the intern compare hosted and open-weight performance on the same tasks. A user profile layer personalizes output depth and focus areas for each researcher, and an experimentation playground lets users adjust the agent's strategy and observe how its behavior changes. Finishing the project demonstrates autonomous agent design, dual-framework retrieval-augmented generation, multi-model integration, and the evaluation discipline needed to verify that an agent's summaries faithfully reflect the papers they cite.

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