AI Multi-Agent Research Assistant

A multi-agent research system built with LangGraph and LangChain. You give it a topic, and 4 specialized agents — Researcher, Analyst, Writer, and Reviewer — work through a stateful pipeline to produce a polished, self-reviewed report.

The frontend is React + Vite with a black/white design. The backend is FastAPI streaming real-time agent progress via SSE. LangSmith handles full trace monitoring. Supabase stores every report.

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Architecture

System Overview

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LangGraph Agent State Machine

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How It Works

Each agent reads from and writes to a shared ResearchState. The pipeline is linear — no conditional loops in this version.

User Query → Researcher → Analyst → Writer → Reviewer → Final Report

Agent progress streams live to the frontend via Server-Sent Events (SSE). When complete, the report is saved to Supabase.

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The 4 Agents

Agent	Built with	Does
Researcher	LangChain ReAct Agent + DuckDuckGo	Runs 4–6 web searches, compiles raw findings
Analyst	LCEL chain (prompt → llm → parser)	Extracts key insights and confidence scores
Writer	LCEL chain	Writes a 1000+ word structured Markdown report
Reviewer	LCEL chain	Scores the report and delivers a final edited version

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Stack

Layer	Tech	Why
LLM	OpenAI gpt-4o-mini	Cheap, fast, high quality
Agent Framework	LangChain 0.3	LCEL chains, ReAct agents
Orchestration	LangGraph 0.2	Stateful pipeline
Monitoring	LangSmith	Full trace visibility per run
Web Search	DuckDuckGo	Free, no API key
Backend	FastAPI + SSE	Real-time streaming
Frontend	React + Vite	Clean, fast
Database	Supabase (Postgres)	Stores all reports
Backend Deploy	Render	Free tier
Frontend Deploy	Vercel	Free tier

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Project Structure

ai-research-assistant/
├── api/
│   └── main.py              # FastAPI app with SSE streaming
│
├── agents/
│   ├── researcher.py        # ReAct Agent + DuckDuckGo
│   ├── analyst.py           # LCEL chain
│   ├── writer.py            # LCEL chain
│   └── reviewer.py          # LCEL chain
│
├── tools/
│   ├── web_search.py        # DuckDuckGo wrapper
│   └── text_tools.py
│
├── frontend/                # React + Vite app
│   ├── src/
│   │   ├── App.jsx          # Main app (query, agents, report)
│   │   ├── api.js           # SSE fetch + history API
│   │   └── index.css        # Black/white design system
│   ├── package.json
│   ├── vite.config.js
│   └── vercel.json
│
├── docs/
│   ├── system-architecture.png
│   ├── langgraph-state-machine.png
│   └── supabase_schema.sql  # Run this in Supabase SQL editor
│
├── config.py                # Env vars + LangSmith setup
├── database.py              # Supabase client
├── orchestrator.py          # LangGraph pipeline (for CLI use)
├── main.py                  # CLI runner
├── render.yaml              # Render deployment config
├── requirements.txt
└── .env.example

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Running Locally

1. Clone and install

git clone https://github.com/kushalsai-01/research-agent
cd research-agent
pip install -r requirements.txt

2. Set up environment variables

cp .env.example .env
# Then edit .env and fill in your keys

You need at minimum:

• OPENAI_API_KEY — from platform.openai.com

Optional but recommended:

• SUPABASE_URL + SUPABASE_KEY — from supabase.com (free)
• LANGCHAIN_API_KEY — from smith.langchain.com (free)

3. Start the backend

uvicorn api.main:app --reload --port 8000
# http://localhost:8000
# http://localhost:8000/health  ← check config status

4. Start the frontend

cd frontend
npm install
npm run dev
# http://localhost:5173

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Supabase Setup

1. Create a free project at supabase.com
2. Go to SQL Editor and run the contents of docs/supabase_schema.sql
3. Copy your project URL and anon key into .env

CREATE TABLE IF NOT EXISTS reports (
  id           UUID PRIMARY KEY DEFAULT gen_random_uuid(),
  topic        TEXT NOT NULL,
  final_report TEXT,
  raw_research TEXT,
  analysis     TEXT,
  created_at   TIMESTAMPTZ DEFAULT now()
);

The app works without Supabase — reports just won't be saved to history.

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LangSmith Setup

1. Create a free account at smith.langchain.com
2. Create a project called ai-research-assistant
3. Copy your API key into .env:

LANGCHAIN_TRACING_V2=true
LANGCHAIN_API_KEY=ls__your-key-here
LANGCHAIN_PROJECT=ai-research-assistant

Every agent run will now appear in LangSmith with full input/output traces, latency, and token usage across all 4 agents.

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Deployment

Backend → Render

1. Push this repo to GitHub
2. render.com → New Web Service → connect the repo
3. Render will auto-detect render.yaml — just add your env vars:
   • OPENAI_API_KEY
   • SUPABASE_URL
   • SUPABASE_KEY
   • LANGCHAIN_API_KEY
4. It'll deploy automatically on every push to main

Frontend → Vercel

1. vercel.com → Add New Project → import this repo
2. Set Root Directory to frontend
3. Add environment variable:
   • VITE_API_URL = your Render backend URL (e.g. https://your-app.onrender.com)
4. Deploy — Vercel handles the rest

Verify deployment

GET https://your-app.onrender.com/health

Returns:

{
  "status": "ok",
  "langsmith_tracing": true,
  "supabase_configured": true,
  "model": "gpt-4o-mini"
}

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Cost

Everything runs on free tiers.

Service	Cost
OpenAI gpt-4o-mini	~$0.01–0.05 per report
Render	Free
Vercel	Free
Supabase	Free (500MB)
LangSmith	Free (5K traces/month)

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License

MIT

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Credits

• LangChain
• LangGraph
• LangSmith
• Supabase
• OpenAI