📚 Mukti Scholar Agent - An Autonomous Literature Review & Research Multi-Agent System

Capstone Project for Google Agents Intensive

Multi-Agent Autonomous AI System for Literature Review & Research

Agents for Good Category

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🧩 Problem Statement

The Challenge of Manual Literature Reviews

Academic research and professional knowledge work rely heavily on comprehensive literature reviews. However, the traditional manual process presents significant challenges:

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⏱ Time Investment

A thorough literature review typically requires 40–80 hours of researcher time, involving:

• Searching across multiple databases (Google Scholar, PubMed, IEEE Xplore, arXiv)
• Reading and summarizing 20–50+ papers
• Identifying thematic patterns manually
• Cross-referencing methodologies and findings
• Synthesizing insights across diverse sources
• Formatting citations and references

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⚠️ Quality Inconsistencies

Manual reviews are susceptible to:

• Confirmation bias (favoring papers supporting existing hypotheses)
• Coverage gaps (limited by search skills and database access)
• Incomplete synthesis (difficult to spot subtle cross-paper patterns)
• Citation errors & formatting inconsistencies

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🚧 Accessibility Barriers

Not everyone has equal access to:

• Expensive academic database subscriptions
• Time to conduct thorough reviews
• Training in systematic review methodologies
• Tools for managing large volumes of research

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Agents for Good: Democratizing Research

This project addresses the Agents for Good challenge by creating an AI-driven research assistant that:

1. Democratizes Access

Allows students, independent researchers, and professionals in developing regions to conduct high-quality literature reviews without costly subscriptions or specialized expertise.

2. Accelerates Discovery

Reduces hours of mechanical effort (searching, summarizing, formatting) so researchers can focus on innovation, hypothesis-building, and analysis.

3. Improves Quality

Ensures consistent, unbiased, and comprehensive analysis—eliminating human cognitive limitations.

4. Levels the Playing Field

Gives smaller institutions the same analytical capabilities enjoyed by well-funded universities.

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❗ Why This System Is Needed

A system is required that:

• Automates the mechanical aspects of literature review
• Maintains academic rigor
• Reduces time and effort
• Makes quality research accessible to all

Mukti Scholar delivers this capability at scale.

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🧠 Solution Statement

Comprehensive Multi-Agent Literature Review System

This capstone project presents an autonomous multi-agent AI system that transforms a research topic into a publication-ready literature review in under 2 minutes.

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🔍 Core Capabilities

1. Automated Discovery

Searches multiple academic databases in parallel, identifying relevant papers across:

• Google Scholar
• Semantic Scholar
• arXiv

2. Intelligent Analysis

Uses advanced NLP to:

• Summarize each paper’s methodology, findings, and contributions
• Cluster papers into thematic groups via embeddings
• Identify methodological patterns and contradictions
• Detect research gaps using systematic cross-paper analysis

3. Synthesis & Evaluation

Generates:

• A coherent literature review with academic structure
• Critical evaluation of evidence across papers
• Trend & pattern identification
• Fully formatted citations (APA, Harvard, IEEE)

4. Professional Output

Produces:

• A comprehensive PDF report with executive summary
• Quality metrics: coverage, coherence, completeness
• Exportable structured data for further research

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🏛 Architecture Overview

Below is the overall architecture.

This notebook implements a comprehensive multi-agent system for automated literature review generation, demonstrating all ADK concepts from the 5‑day course.

Architecture Summary

• 10 Specialized Agents + 1 Orchestrator
• Multi-agent patterns: Sequential, Parallel, Loop
• Tools: OpenAPI, MCP, Custom Tools, Built‑in Tools
• Sessions & Memory: State management + long-term Memory Bank
• Observability: Logging, Tracing, Metrics
• Deployment-ready: Vertex AI Agent Engine compatible

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🚀 How to Use

Prerequisites

Before running the Mukti Scholar Agent, you need:

A Google API Key (Free)

• Visit Google AI Studio
• Click "Create API Key"
• Copy your API key (starts with AIza...)

A Google Account (for Google Colab)

• Free tier is sufficient
• No credit card required

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Method 1: Run in Google Colab (Recommended - No Installation Required)

This is the easiest way to get started. Everything runs in your browser!

Step 1: Open the Notebook

1. Click on the notebook file: Main_Literature_Review_Agent_System_Capstone_Project-Final.ipynb
2. Click "Open in Colab" button (or upload to Google Colab)

Step 2: Set Up Your API Key (Two Options)

Option A: Using Colab Secrets (Recommended - Most Secure)

1. In Colab, click the 🔑 key icon in the left sidebar
2. Click "+ Add new secret"
3. Set Name: GOOGLE_API_KEY
4. Paste your API key as the Value
5. Toggle ON the notebook access switch
6. Done! The notebook will automatically use this key

Option B: Direct Input (Quick Setup)

1. When you run the notebook, it will prompt you for your API key
2. Paste your API key when asked (it will be hidden as you type)
3. Press Enter

Step 3: Run the Notebook

1. Click Runtime → Run all (or press Ctrl+F9)
2. Wait 2-3 minutes for packages to install (first time only)
3. The notebook will automatically:

• Install all required dependencies
• Configure your API key
• Set up the agent system
• Run the demo with a sample topic

Step 4: Generate Your Literature Review

• Scroll to the final section: "SECTION 10: Run the Complete System"
• Modify the topic in the demo cell:

topic = "Your Research Topic Here"

• Run that cell to generate your custom literature review!

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Method 2: Run Locally (Advanced Users)

For users who prefer running on their local machine:

Step 1: Clone the Repository

git clone https://github.com/yourusername/mukti-scholar-agent.git
cd mukti-scholar-agent

Step 2: Install Python Dependencies

# Create a virtual environment (recommended)
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install required packages
pip install google-adk google-genai scikit-learn numpy reportlab

Step 3: Set Up Your API Key

Option A: Environment Variable (Recommended)

# Linux/Mac
export GOOGLE_API_KEY='your-api-key-here'

# Windows (PowerShell)
$env:GOOGLE_API_KEY='your-api-key-here'

# Windows (CMD)
set GOOGLE_API_KEY=your-api-key-here

Option B: .env File

# Create a .env file in the project root
echo "GOOGLE_API_KEY=your-api-key-here" > .env

Step 4: Run the Notebook

# Start Jupyter Notebook
jupyter notebook

# Open: Main_Literature_Review_Agent_System_Capstone_Project-Final.ipynb
# Run all cells

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Method 3: Run on Kaggle

Kaggle provides free GPU/TPU resources and is similar to Colab:

Step 1: Upload to Kaggle

1. Go to Kaggle Notebooks
2. Click "New Notebook"
3. Click "File" → "Upload Notebook"
4. Select the .ipynb file

Step 2: Configure API Key

1. Click "Add-ons" → "Secrets"
2. Add a new secret: GOOGLE_API_KEY
3. Paste your API key
4. Enable access for the notebook

Step 3: Run the Notebook

1. Click "Run All"
2. The notebook automatically detects Kaggle and uses the secret

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📋 What Happens When You Run It?

The system will:

✅ Install Dependencies (2-3 minutes, first time only) 1.

✅ Authenticate with your Google API key 1.

✅ Initialize 10 Specialized Agents + Orchestrator 1.

✅ Process Your Topic:

• Expand and analyze the topic
• Search multiple academic databases (Google Scholar, arXiv, Semantic Scholar)
• Download and extract PDFs
• Summarize each paper
• Cluster papers into themes
• Perform comparative analysis
• Identify research gaps
• Write the literature review
• Format citations
• Generate PDF output

✅ Deliver Results (~1-2 minutes):

• Comprehensive literature review (PDF)
• Reference list with citations
• Theme clusters and visualizations
• Research gap analysis

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🎯 Sample Topics to Try

Get started with these example topics:

# Technology & AI
topic = "Applications of Large Language Models in Healthcare"
topic = "Federated Learning for Privacy-Preserving Machine Learning"
topic = "Quantum Computing Applications in Cryptography"

# Business & Economics
topic = "Impact of Remote Work on Employee Productivity"
topic = "Blockchain Technology in Supply Chain Management"
topic = "Behavioral Economics in Consumer Decision Making"

# Science & Medicine
topic = "CRISPR Gene Editing Ethical Considerations"
topic = "Machine Learning in Drug Discovery"
topic = "Climate Change Impact on Coastal Ecosystems"

# Social Sciences
topic = "Social Media Influence on Political Polarization"
topic = "Gamification in Educational Technology"
topic = "Mental Health Interventions for College Students"

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⚙️ System Requirements

• For Google Colab/Kaggle: Just a web browser (Chrome/Firefox recommended)

• For Local Setup:

• Python 3.9 or higher

• 4GB RAM minimum (8GB recommended)

• Internet connection (for API calls and paper downloads)

• 500MB disk space

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🔧 Troubleshooting

Issue: API Key Not Working

• ✅ Verify your key at Google AI Studio
• ✅ Check that you haven’t exceeded free tier limits (60 requests/minute)
• ✅ Ensure no extra spaces in your API key

Issue: Package Installation Fails

• ✅ Try running the installation cell again
• ✅ Restart the runtime: Runtime → Restart runtime
• ✅ Ensure you have internet connectivity

Issue: "Session Expired" in Colab

• ✅ Reconnect: Click "Reconnect" in the top-right
• ✅ Re-run all cells from the beginning

Issue: Papers Not Found

• ✅ Try a more specific or broader topic
• ✅ Check internet connectivity
• ✅ Some papers may be behind paywalls (system uses open-access sources)

Issue: Out of Memory

• ✅ Reduce the number of papers: modify max_papers=10 in the config
• ✅ Use Colab Pro for more RAM (optional)

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💡 Tips for Best Results

1. Be Specific: "Machine learning in healthcare" → "Machine learning for diabetic retinopathy detection"
2. Use Academic Language: The system works best with formal research topics
3. Check Output: Review generated sections and citations for accuracy
4. Iterate: Run multiple times with refined topics for better results
5. Combine with Human Expertise: Use the agent as a starting point, not a replacement for critical thinking

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📧 Support & Feedback

• Found a bug? Open an issue on GitHub
• Have a suggestion? Submit a pull request
• Need help? Check existing GitHub issues or create a new one

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✅ Capstone Requirements Coverage

1. Multi-Agent System (All patterns demonstrated)

• LLM Agents: Topic Understanding, Gap Analysis, Review Writer
• Parallel Agents: Multi-source paper search, parallel summarization
• Sequential Agents: Comparative analysis workflow
• Loop Agents: PDF retrieval with retry logic
• Orchestrator Agent: Coordinates all 10 specialist agents

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2. Tools (Every Tool Category Implemented)

Custom Tools:

• search_google_scholar
• cluster_embeddings
• format_citation

Built-in Tools:

• Google Search
• Code Execution (for clustering and evaluations)

MCP Tools:

• PDF parsing & file operations (production-ready simulation)

OpenAPI Tools:

• Scholar API
• arXiv API
• Semantic Scholar API

Agent-as-a-Tool:

• Sub-agents callable by orchestrator

Long‑Running Operations:

• Resume/pause via ResumabilityConfig

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3. Sessions & Memory

• InMemorySessionService for per-run state
• Memory Bank for long-term preferences, topics, history
• Vector Store for embeddings + semantic search

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4. Context Engineering

• RAG Pattern for literature review writing
• Context Compaction via layered summarization
• Embedding-based thematic clustering

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5. Observability (Full Suite)

• Structured Logging

• Tracing with IDs

• Metrics:

• Papers found

• Processing duration

• Cluster coherence

• Summary quality

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6. Agent Evaluation

• Coverage metrics
• Cluster coherence scoring
• Writing quality analysis
• Gap analysis quality
• End-to-end grading (A–F scale)

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7. A2A Protocol

• Functions exposed as A2A agent
• Remote agent consumption examples
• Automatically generated Agent Card

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8. Deployment (Production-Ready)

• Vertex AI Agent Engine configuration
• Scaling & resource limits
• Environment configuration
• Deployment commands ready

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🎯 Key Implementation Highlights

1. Complete 10-Agent Pipeline with clear roles
2. Production-quality code with type hints & robust error handling
3. Extensive comments & explanations
4. End-to-end demo with sample outputs
5. Test suite for automated validations
6. State export/import for reproducibility

🔧 Agent Specifications

Orchestrator Agent

Role: Master coordinator managing the entire workflow Type: LLM Agent with sub-agents as tools Model: Gemini 2.5 Flash Lite

Responsibilities:

• Execute agents in sequence (1 → 2 → … → 10)
• Track workflow state
• Handle errors (retry/skip/abort logic)
• Maintain session state
• Report progress to the user

Tools:

• All 10 specialized agents wrapped as AgentTool
• Access to session + memory services

Decision Logic:

• Determine if each stage succeeded
• Adjust parameters (e.g., reduce paper count on timeout)
• Decide when to continue vs. wait for human input

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🔎 Stage 1: Topic Understanding & Paper Discovery

Agent 1: Topic Understanding Agent

Type: LLM Agent Model: Gemini 2.5 Flash Lite

Responsibilities:

• Analyze user topic
• Extract 10–15 core keywords
• Identify 3–5 subdomains
• Generate 15–20 optimized search queries

Tools: Built-in LLM

Output Example:

{
"expanded_topic": "Detailed expanded topic",
"keywords": ["machine learning", "risk modeling"],
"subdomains": ["credit scoring", "deep learning"],
"search_queries": ["ML credit risk modeling", "AI banking prediction"]
}

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Agent 2: Academic Paper Search Agent

Type: Parallel Agent (3 sub-agents) Sub-Agents:

1. Google Scholar Agent
2. arXiv Agent
3. Semantic Scholar Agent

Responsibilities:

• Execute search queries across all sources
• Gather metadata
• Deduplicate by DOI/title
• Rank and select top 10–20 papers

Tools:

• Custom OpenAPI tools:

• search_google_scholar()

• search_arxiv()

• search_semantic_scholar()

Parallel Pattern:

Reduces search time from 45s → 15s.

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Agent 3: PDF Retrieval & Extraction Agent

Type: Loop Agent Model: Gemini 2.5 Flash Lite

Responsibilities:

• Download PDFs
• Extract text + detect sections
• Parse tables & figure metadata
• Normalize text
• Retry failed downloads (up to 3 times)

Tools:

• MCP: download_and_extract_pdf(url)
• Production tools: PyPDF2 / GROBID

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🔬 Stage 2: Analysis & Synthesis

Agent 4: Per-Paper Summarization Agent

Type: Parallel Agent Model: Gemini 2.5 Flash Lite

Responsibilities:

• 20-word micro-summary
• 150-word full summary
• Extract methodology & findings
• Identify contributions & limitations
• Assess relevance

Output Example:

{
"micro_summary": "...",
"long_summary": "...",
"methodology": "...",
"findings": "...",
"contributions": "...",
"limitations": "...",
"relevance_notes": "..."
}

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Agent 5: Thematic Clustering Agent

Type: LLM Agent + Code Execution Uses k-means to cluster paper embeddings.

Responsibilities:

• Generate embeddings
• Perform clustering
• Create theme labels
• Write theme descriptions

Tools:

• cluster_embeddings()
• Built-in code execution

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Agent 6: Comparative Analysis Agent

Type: Sequential Agent Sub-Agents: Theme Analyzer + Cross-Theme Comparator

Responsibilities:

• Compare within-theme methodologies
• Identify contradictions
• Highlight patterns
• Produce comparison matrices

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📘 Stage 3: Research Gaps, Writing & Output

Agent 7: Research Gap Identification Agent

Type: LLM Agent

Responsibilities:

• Detect methodological, empirical, theoretical, geographic gaps
• Provide evidence for each gap
• Generate research questions

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Agent 8: Literature Review Writer Agent

Type: LLM Agent with RAG

Responsibilities:

• Write structured lit review: intro, themes, comparisons, gaps, conclusion
• Retrieve relevant summaries from vector DB
• Cite papers using internal IDs

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Agent 9: Citation & Bibliography Formatter Agent

Type: Deterministic Agent

Responsibilities:

• Replace citation markers
• Generate .bib file
• Format APA/Harvard/IEEE references

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Agent 10: Final Output Generator Agent

Type: Assembly Agent

Responsibilities:

• Generate PDF review
• Create visuals (theme clusters, comparison tables)
• Export reference lists

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🧨 Conclusion : Advantages Over Manual Literature Review

The Mukti Scholar Agent delivers a level of speed, scale, consistency, and accessibility that manual literature reviews cannot match.

1. Speed

Manual reviews take 40–70+ hours. The automated system completes the same workflow in 1–2 minutes, delivering ~99% time savings and enabling rapid iteration.

2. Comprehensiveness

Manual searches are limited by skills, database access, and time—typically covering 20–30 papers. The automated system scans 50+ papers across multiple sources, generates optimized queries, and uncovers cross-domain insights using semantic similarity.

3. Consistency

Human reviews vary with fatigue and bias. The agent applies uniform, unbiased analysis, ensures standardized summarization, and produces error-free citations every time.

4. Objectivity

Manual reviews often miss contradictory or adjacent-field evidence. The agent uses relevance scoring and clustering to surface both supporting and opposing findings, ensuring balanced synthesis.

5. Reproducibility

Human reviews differ across researchers. The system produces consistent, audit-ready outputs, easily re-run when new papers appear.

6. Accessibility

Manual reviews demand subscriptions, training, and weeks of effort. The agent works with open sources, requires no specialized training, and completes the task in minutes, not weeks.

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🌟 What This Agent Enables

For Researchers

• Rapid background reviews for grant proposals
• High-quality gap identification
• Quick literature updates before submission

For Students

• Strong thesis-ready literature reviews
• Faster ramp-up in new research domains
• Access to professional-grade analytical tools

For Institutions

• Democratized access to deep literature analysis
• Standardized quality across teams
• Reduced barriers for under-resourced researchers

For the Research Ecosystem

• Faster knowledge synthesis
• Better identification of unmet research needs
• Accelerated scientific progress

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The agent doesn’t replace human judgment—it amplifies it. Researchers remain the interpreters and innovators, but they operate from a foundation of comprehensive, unbiased, and instantly generated insights, transforming research productivity.

💡 Value Statement

The Mukti Scholar Agent provides significant time and productivity gains, enabling faster, more efficient academic work.

⏳ Time Savings (Approx.)

• Students reduce a ~17-week manual literature review process to ~3 days, accelerating thesis progress by approx. 4 months and enabling exploration of more research directions within the same academic cycle.
• Researchers cut a ~50-hour background review for proposals to ~4 hours, saving approx. 46 hours per proposal — equal to approx. 3.5–4.5 workweeks per year for those submitting multiple proposals.

🚀 Productivity Gains

• Eliminates mechanical tasks (searching, screening, summarizing)
• Allows researchers to focus on analysis, insight generation, and expert judgment
• Supports rapid evaluation of multiple literature landscapes
• Levels academic capability for institutions with limited resources

📊 Bottom-Line Impact (Approx.)

• 16+ weeks saved per student literature review
• Over 90% reduction in research preparation time
• Massively increases throughput for both students and researchers

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✅ End of README