Building a Full-Stack Research Agent with Gemini and LangGraph

Implementing Dynamic Search + Knowledge Iteration for Intelligent Q&A Systems

Have you ever faced this scenario?
When researching complex topics, traditional search engines return fragmented information. You manually sift through sources, verify accuracy, and piece together insights—a time-consuming process. This open-source solution using Google Gemini and LangGraph automates dynamic search → knowledge iteration → trusted answers with full citation support.

This guide explores a full-stack implementation covering:

• ✅ Zero-to-production deployment with React + LangGraph
• ✅ The 7-step workflow of research agents
• ✅ Docker deployment for production environments
• ✅ Troubleshooting common issues (with tested code)

1. Core Capabilities: How AI Achieves “Human-Level Research”

Unlike static chatbots, this research agent simulates human inquiry through a dynamic closed-loop system:

graph LR
A[User Question] --> B[Generate Search Terms]
B --> C[Google Search]
C --> D[Analyze Results]
D --> E{Sufficient Knowledge?}
E -->|No| F[Generate New Queries]
E -->|Yes| G[Generate Cited Answer]
F --> C

Five key innovations:

1. Dynamic Query Generation
   Gemini transforms vague questions into precise search terms (e.g., “quantum computing business applications” → “quantum computers financial modeling case studies 2024”)
2. Knowledge Gap Detection
   After each search, AI self-evaluates: “Does current data cover the core issue? What dimensions are missing?”
3. Iterative Search Refinement
   Conducts up to 5 search cycles (configurable) to overcome single-query limitations
4. Verifiable Answer Generation
   Every conclusion links to source materials
5. Real-Time Streaming
   Redis delivers progressive results during processing

💡 Technical Comparison: Standard ChatGPT plugins perform single searches, while this is the first open-source framework with “refine-iterate” capabilities.

2. 10-Minute Local Deployment Guide (Dev Mode)

Prerequisites

Launch in 3 Steps

# 1. Clone repository (~2 minutes)
git clone https://github.com/langchain-ai/langgraph.git
cd gemini-fullstack-langgraph

# 2. Configure backend (Critical!)
cd backend
cp .env.example .env

Add your Gemini API key to .env:

GEMINI_API_KEY="YOUR_ACTUAL_KEY_HERE"

# 3. Launch full-stack service (auto hot-reload)
make dev

Access at http://localhost:5173/app:

Interface components: Query input, real-time response stream, collapsible citations

3. Research Agent Deep Dive

Core logic in backend/src/agent/graph.py implements seven phases:

Phase 1: Query Decomposition

def generate_queries(question):
    prompt = f"""
    User question: "{question}" 
    Generate 3-5 precise search terms with:
    1. Professional terminology variants (e.g., "AI" and "Artificial Intelligence")
    2. Time constraints (past 3 years unless historical)
    3. Geographic filters (for policies/markets)
    """
    return gemini_pro(prompt).split(";")

Example: “vaccine efficacy” → [“COVID-19 vaccine effectiveness 2024”, “mRNA vaccine long-term side effects”, “inactivated vs mRNA vaccine comparison”]

Phase 2: Parallel Web Research

Using Google Search API, Gemini:

1. Extracts core arguments
2. Filters ads/low-quality sources
3. Flags contentious claims

Phase 3: Knowledge Gap Analysis

AI generates reflection reports:

Current coverage:
✓ Quantum computing in drug discovery  
✗ Financial sector case studies (missing post-2023 data)  
✗ Manufacturing cost-benefit analysis (no Asian cases)  
→ New queries: ["quantum computing supply chain optimization case studies 2023-2024"]

Phase 4: Iteration Control (Max 5 cycles)

MAX_LOOPS = 5  # Configurable
while gap_found and loop_count < MAX_LOOPS:
    new_queries = generate_refined_queries()
    do_web_research(new_queries)
    loop_count += 1

Phase 5: Answer Synthesis

Gemini structures responses:

1. Core findings (≤3 sentences)
2. Evidence chain (≥3 independent sources)
3. Uncertainty disclosure (e.g., "Japanese market data unpublished")

4. Production Deployment Architecture

Scale-ready infrastructure:

Infrastructure Dependencies

Docker Deployment

# Build image (~5 minutes)
docker build -t gemini-fullstack-langgraph -f Dockerfile .

# Launch service (requires 2 keys)
docker-compose up -d \
  -e GEMINI_API_KEY=your_key \
  -e LANGSMITH_API_KEY=your_key

Access at http://your-server-ip:8123/app

Critical Configuration Parameters

📌 Note: Update apiUrl in frontend/src/App.tsx for production environments

5. Tech Stack Analysis: Why These Tools?

💡 Key Advantage: LangGraph’s StateGraph enables cyclical workflows impossible in standard LangChain.

6. Troubleshooting Guide (FAQ)

Q1: “Invalid Gemini API Key” error?

Verification Steps:

1. Confirm correct key in backend/.env (no trailing spaces)
2. Enable API access in Google AI Studio
3. Test connectivity:

curl -X POST https://generativelanguage.googleapis.com/v1beta/models/gemini-pro:generateContent?key=YOUR_KEY

Q2: Frontend can’t connect to backend?

Diagnosis:

# 1. Check backend port (default: 2024)
lsof -i :2024

# 2. Test API endpoint
curl http://localhost:2024/healthcheck

# 3. Update frontend config
// In frontend/src/App.tsx
const apiUrl = import.meta.env.DEV 
  ? "http://localhost:2024" 
  : "https://your-prod-domain.com"

Q3: How to improve answer quality?

Modify backend/src/config.py:

# Deeper reflection (default: medium)
REFLECTION_MODE = "high"  

# Broader search (default: 5 pages)
SEARCH_DEPTH = 8

7. Real-World Use Case

Scenario: Researching “Solar Cell Tech Breakthroughs”

Traditional Search Pain Points:

• Manual separation of “lab research” vs. “commercial products”
• Difficulty tracking multinational patent trends

Agent Output:

1. Key Advances:
   • Perovskite cells: 33.7% efficiency (NREL 2024)
   • LONGi's silicon-perovskite tandem mass production (Source: PV Magazine)
   
2. Risks:
   • Stability: Outdoor degradation exceeds industry standards (Source: Science)
   • EU regulations: 95% recycling rate required from 2025 (Source: EC)

3. Emerging Trends:
   • Korea's SNE solid-state PV roadmap (2026 pilot)

Source links direct to original documents

8. Conclusion: Value Proposition & Evolution

Current Capabilities:

• ✅ Handles open-ended research (market analysis, tech comparisons)
• ✅ Generates academic/commercial draft reports
• ⚠️ Unsuitable for math calculations/code debugging

Future Enhancements:

1. Multi-agent collaboration: Domain-specialist roles
2. Cross-language research: Auto-translate non-English sources
3. Private knowledge integration: Combine with internal documents

Licensed under Apache 2.0 for commercial use. This solution pioneers a new paradigm for professional research through dynamic search, knowledge iteration, and verifiable citations.

Source Code: github.com/langchain-ai/langgraph
Documentation: LangGraph Deployment Guide