
Academia.AI is a resilient, multi-agent RAG-powered system designed to ingest, analyze, and synthesize academic research papers. Built as the deliverable for Agentic AI Developer Certification โ Module 3, this system demonstrates the transition from prototype to production-grade AI tooling.
Academia.AI enhances standard RAG pipelines with engineering-grade reliability:
HealthCheck agent validates connectivity before workflow execution.Academia.AI operates through a structured LangGraph workflow coordinated across three agents:
git clone https://github.com/maddiravi/academia-ai-production.git cd academia-ai-production
pip install -r requirements.txt
cp .env_example .env
Edit .env with your API key:
OPENROUTER_API_KEY=sk-or-v1-xxxx OPENROUTER_API_BASE=https://openrouter.ai/api/v1
Launch the Streamlit interface:
streamlit run app.py
Run the automated unittest suite:
python -m unittest tests.test_academia
DocumentIngestor workflow validationacademia-ai-production/
โโโ agents/
โ โโโ document_ingestor.py
โ โโโ thesis_extractor.py
โ โโโ insight_synthesizer.py
โโโ tools/
โ โโโ health_check.py
โ โโโ file_processor.py
โโโ tests/
โ โโโ test_academia.py
โโโ app.py
โโโ requirements.txt
โโโ README.md
Distributed under the MIT License.
This project is open-source and free for academic and development use.


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