Research Agent — A Lightweight Assistant for Academic Search and Rapid Paper Reading

At-a-glance summary

Research Agent is a lightweight research assistant built with Streamlit. It integrates three practical capabilities into one interactive interface:

quick literature lookup (arXiv-oriented search),
webpage and abstract scraping,
PDF text extraction (via PyMuPDF) and LLM-based summarization or hypothesis suggestion.

The tool is intended to chain these steps into a single workflow so you can find papers, extract the useful sections, and generate concise summaries or draft hypotheses — all from a small local application.

Who this is for

Research Agent is designed for people who need a practical, low-friction process to handle academic literature:

research assistants and lab members who must triage many papers;
graduate students who want a single UI to fetch, read, and summarize PDFs;
solo researchers and small teams who prefer a lightweight Streamlit app instead of a heavy, multi-tool workflow.

It emphasizes fast iteration: search → extract → summarize → human review. The application is an assistant, not a substitute for careful reading and verification.

What it does (feature list)

ArXiv-focused literature search: quick keyword-based lookup of arXiv entries (the README shows an implementation that performs arXiv queries via a lightweight search flow).
Webpage and abstract scraping: fetch and extract targeted paragraphs or abstracts from webpages for downstream processing.
PDF extraction: open and extract text from uploaded or downloaded PDF files using PyMuPDF, with a basic ability to parse document text for summary generation.
LLM-driven generation: submit extracted text or uploaded PDFs to a specified model to produce summaries, highlight main results, or propose hypotheses. The README provides a default model selection and version ID that the app can use.
Streamlit-based UI: all modules work inside a Streamlit app that lets you interactively run searches, upload files, extract text, and call the model.

Each of these features is implemented as a module inside the same Streamlit application so you can move from searching to summarizing without leaving the app.

Project layout

The repository is intentionally small and focused. The main files referenced in the README are:

research-agent/
├── app.py              # Main Streamlit application
├── requirements.txt    # Required Python libraries
└── README.md           # Project documentation (source)

app.py contains the Streamlit UI and wiring between search, scraping, PDF parsing, and model calls. requirements.txt lists the libraries required to run the app.

Installation and first run — step-by-step

Follow these steps to run the Research Agent locally. The commands are taken verbatim from the README.

1. Install Python dependencies

From the project root directory run:

# Install all dependencies from the provided requirements file
pip install -r requirements.txt

If you prefer to install only the main libraries shown in the README, you can run:

pip install streamlit replicate requests beautifulsoup4 PyMuPDF

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Run these commands in an environment with internet access and in a Python environment that you control (virtualenv, venv, Conda, etc.).

2. Configure the Replicate API token (optional but required for model inference)

If you plan to use the built-in model integration (Replicate) described in the README, obtain an API token on the Replicate platform and set it as an environment variable:

export REPLICATE_API_TOKEN=your_token_here

The README also shows an example of directly setting the token in code (not recommended for production):

REPLICATE_API_TOKEN = "your_token_here"
client = replicate.Client(api_token=REPLICATE_API_TOKEN)

Storing the token as an environment variable is the recommended practice to avoid committing credentials to a repository.

3. Start the Streamlit app

From the project root execute:

streamlit run app.py

Streamlit will start a local server and print an address (LocalHost) to open in a browser. Open that address to use the Research Agent UI.

Default model and model configuration

The README includes a default model configuration used by the example app. The model referenced in the source documentation is:

Model name: ibm-granite/granite-3.3-8b-instruct
Version ID: 3ff9e6e20ff1f31263bf4f36c242bd9be1acb2025122daeefe2b06e883df0996

These values are configurable within app.py. The README shows how the model is used through the Replicate client. If you want to use a different model, update the model identifier inside the application code.

Interface and interaction overview

The app presents a small set of modules in a Streamlit UI:

Search module
- Enter keywords or topics; the app runs a lightweight search for arXiv entries (the README demonstrates a flow that locates arXiv papers via a simple web search).
- The module returns a list of results that you can inspect or open.
Web scraping module
- Provide a URL to fetch page content. The module extracts the abstract or targeted paragraphs for model consumption.
PDF upload and extraction module
- Upload a PDF or point the app to a downloaded PDF. The module uses PyMuPDF to extract text and organize it into blocks that are easier to feed to the model.
LLM-based generation module
- Submit the extracted text or uploaded PDF to the configured model to produce: concise summaries, main-result highlights, methodology outlines, or suggested hypotheses.

The app is meant to minimize friction: you typically search, fetch the target PDF or web text, extract the content, and then ask the model for the specific output you need.

Example prompts (ready to use in the app)

The README provides sample prompts that demonstrate how to interact with the model. You can paste these directly into the app’s text input to observe the kinds of outputs the model will generate:

Find three recent research papers on the ethical implications of using CRISPR technology in humans.
Summarize the uploaded paper and highlight the main results and methodology.
Suggest a hypothesis based on the uploaded PDF.
Draft an abstract for a paper on AI in climate modeling.

These examples are included verbatim as use cases in the source documentation.

Typical workflow — concise steps

A common, practical workflow using Research Agent looks like this:

Search: Enter topic keywords into the search module to find candidate papers (arXiv results).
Acquire: Download or upload the PDF of interest into the PDF module.
Extract: Use the PDF extraction functionality (PyMuPDF) to pull text segments such as abstract, introduction, method, and results.
Generate: Send extracted text to the model to produce a summary, a plain-language description of the methods and results, or suggested research hypotheses.
Verify: Manually review and edit the generated output; the tool is an aid, not a final editorial step.

This linear flow is intentionally simple so the assistant sits inside your existing workflow with minimal overhead.

Output types the app can produce (as shown in README)

Concise summaries of uploaded papers.
Methodology highlights that extract and rephrase technical steps in clearer language.
Result overviews that identify and summarize the main findings.
Suggested hypotheses based on the paper content — useful as a brainstorming aid for follow-up experiments or literature review notes.

The README emphasizes that LLM outputs should be verified before any publication or formal use.

Limitations and cautionary notes

The README includes explicit cautions which are important for responsible use:

Third-party models: The application uses third-party hosted models and APIs. Outputs come from those services and must be treated as draft content.
Human verification required: Always manually check model-generated summaries, claims, or hypotheses before using them in formal writing, citations, or publication.
Not a final editor: The tool is designed to accelerate initial reading and drafting; it does not replace careful critical reading and rigorous peer review.

These points are retained exactly as guidance from the original documentation.

Future directions listed in the README

The README names several practical improvements that are candidates for next steps (these are suggestions present in the source file):

Export capabilities: add options to export outputs as PDF, BibTeX, or CSV.
Follow-up Q&A: enable interactive question-and-answer sessions based on a single paper’s content for deeper exploration.
Semantic search: integrate vector embeddings and a vector store to enable better semantic retrieval across documents.

These ideas are given as a roadmap in the source documentation and are presented here as faithful reproductions of that list.

Practical recommendations (based on the README content)

The README implies a few practical best practices for working with the app. Follow these to reduce friction:

prefer environment variables for storing API tokens rather than hard-coding them;
use the provided requirements.txt for dependency consistency;
treat the model outputs as a first draft and apply human editing and verification.

These recommendations are simple operational practices that the source documentation highlights.

Author and contact details

The README lists the author identity and contact pointers:

Author: Samarth Pujari
The README also references the author’s LinkedIn and Kaggle profiles as points of contact or further background.

Use these details if you want to reach out to the author or review related work.

Frequently Asked Questions (FAQ)

Below are common questions and direct answers extracted from the README content.

Q: What libraries do I need?
A: The README suggests installing the dependencies from requirements.txt or installing the main libraries individually: streamlit replicate requests beautifulsoup4 PyMuPDF.

Q: How do I set up the model token?
A: Generate a token on the Replicate platform and set it in your environment, for example: export REPLICATE_API_TOKEN=your_token_here. The README shows both the environment-variable approach and a code snippet that hard-codes the token (the latter is not recommended for production).

Q: Which model does the app use by default?
A: The README references ibm-granite/granite-3.3-8b-instruct together with a specific version ID. This model reference can be changed inside app.py.

Q: How do I run the app?
A: From the root of the project execute streamlit run app.py and open the LocalHost address Streamlit prints.

Q: Can I export results to BibTeX or CSV?
A: Export features are listed under future improvements in the README but are not implemented in the present state of the project.

Structured HowTo (JSON-LD)

To help integration with structured-data-aware systems or for easier embedding on a webpage, the README includes a HowTo schema example. Below is that JSON-LD, presented verbatim from the source documentation:

{
  "@context": "https://schema.org",
  "@type": "HowTo",
  "name": "Run Research Agent locally",
  "step": [
    {
      "@type": "HowToStep",
      "name": "Install dependencies",
      "text": "Run `pip install -r requirements.txt` in the project root, or install the required libraries individually."
    },
    {
      "@type": "HowToStep",
      "name": "Set Replicate API Token (optional)",
      "text": "Generate an API token on the Replicate platform and export it as an environment variable: `export REPLICATE_API_TOKEN=your_token_here`."
    },
    {
      "@type": "HowToStep",
      "name": "Start the Streamlit app",
      "text": "Execute `streamlit run app.py` and open LocalHost in a browser."
    }
  ]
}

Embedding this JSON-LD on a site allows structured-data systems to recognize the installation steps in a machine-readable way.

Structured FAQ (JSON-LD)

The README also provides an FAQPage JSON-LD. The content below is taken directly from the source and is suitable for embedding into documentation pages:

{
  "@context": "https://schema.org",
  "@type": "FAQPage",
  "mainEntity": [
    {
      "@type": "Question",
      "name": "Which dependencies should I install?",
      "acceptedAnswer": {
        "@type": "Answer",
        "text": "Install from requirements.txt or install streamlit replicate requests beautifulsoup4 PyMuPDF."
      }
    },
    {
      "@type": "Question",
      "name": "How to configure the Replicate API token?",
      "acceptedAnswer": {
        "@type": "Answer",
        "text": "Generate a token on Replicate and set it as an environment variable: export REPLICATE_API_TOKEN=your_token_here."
      }
    },
    {
      "@type": "Question",
      "name": "How do I run the Streamlit app?",
      "acceptedAnswer": {
        "@type": "Answer",
        "text": "From the project root run: streamlit run app.py and open the LocalHost URL in your browser."
      }
    }
  ]
}

Visual reference

The README includes a demonstration screenshot to show what the Streamlit interface looks like. The image is referenced in the source file; the demonstration image is intended only as an example of the app’s UI.

A practical closing note

Research Agent is intentionally minimal: it places common tasks — search, extraction, and summarization — into a single, easy-to-run tool. The README documents core setup steps and contains example prompts, a default model configuration, and a short roadmap for future improvements. Use it to accelerate initial reading and drafting work, and always treat model outputs as a draft that requires human verification.

Appendix: Ready-to-copy command summary

# Install all requirements
pip install -r requirements.txt

# (or) install the key libraries individually
pip install streamlit replicate requests beautifulsoup4 PyMuPDF

# Set Replicate environment variable (if using model features)
export REPLICATE_API_TOKEN=your_token_here

# Run the Streamlit app
streamlit run app.py

Appendix: Ready-to-copy example prompts for the app

Find three recent research papers on the ethical implications of using CRISPR technology in humans.
Summarize the uploaded paper and highlight the main results and methodology.
Suggest a hypothesis based on the uploaded PDF.
Draft an abstract for a paper on AI in climate modeling.

Research Agent Unveiled: Your Lightweight Secret Weapon for Academic Paper Mastery