Car Dealership Agent with Redis Agent Memory Server

Car dealership AI agent demonstrates how Redis Agent Memory Server enables long-term memory and conversation context retrieval, allowing the agent to remember customer preferences across sessions and provide personalized car purchase assistance.

Table of Contents

• Demo Objectives
• Setup
• Running the Demo
• Architecture
• Resources
• Maintainers
• License

Demo Objectives

• Demonstrate long-term memory storage using Redis Agent Memory Server
• Demonstrate short-term/working memory storage using LangGraph checkpointers and Redis Agent Memory Server
• Showcase conversation context retrieval for personalized interactions
• Show agentic orchestration with LangGraph workflow stages

Setup

Dependencies

• Python 3.11+
• Node.js 18+
• Docker (for running frontend and backend together)

Configuration

Clone the repository:

git clone <repository-url>
cd dealership-chatbot-agent-memory-demo

Create a .env file in the project root:

OPENAI_API_KEY=your_openai_api_key_here
REDIS_URL=<redis-cloud-url>
MEMORY_SERVER_URL=http://localhost:8000

Running the Demo

1. Agent Memory Server Setup

Get the Pre-built Docker Images from https://hub.docker.com/r/redislabs/agent-memory-server

Quick Start: Run the API Server (Requires Separate Redis)

docker run -p 8000:8000 \
  -e REDIS_URL=<redis-cloud-url> \
  -e OPENAI_API_KEY=your-key \
  redislabs/agent-memory-server:latest

Notes: This command starts only the Agent Memory Server API. You must have a running Redis instance accessible at the URL you provide. For more advanced configuration, see the full documentation: https://github.com/redis-developer/agent-memory-server

2. Docker Setup

# Start all services with Docker
docker-compose up --build

# Access the application
# Frontend: http://localhost:3000
# Backend: http://localhost:8001

Architecture

Workflow Orchestration (LangGraph): Manages conversation state and guides customers through the car purchase journey

Short-Term Memory (Redis Agent Memory Server): Maintains working memory of the ongoing conversation

Long-Term Memory (Redis Agent Memory Server): Semantic memory to stores customer preferences and conversation history across sessions

Modern Frontend (React 18 + TypeScript + Tailwind): Beautiful, responsive dealership UI with full-page chatbot interface

Architecture Flow

User Query
    ↓
[Retrieve Conversation Context] → Load past preferences from long-term memory
    ↓
[Parse Slots] → Extract car preferences using LLM
    ↓
[Ensure Readiness] → Check if all required slots are filled
    ↓
[Decide Next]
    ├→ Missing slots? → Ask follow-up question
    └→ All slots filled? → Advance to next stage
         ↓
    [Workflow Stages]
         ├→ Brand Selected? → Suggest Models
         ├→ Model Selected? → Suggest Test Drive
         ├→ Test Drive Completed? → Suggest Financing
         └→ Financing Discussed? → Prepare for Delivery
         ↓
    [Save to Memory] → Store conversation and preferences to working & long-term memory
         ↓
    Response to User
         ↓
    [UI Updates] → Update workflow visualization

Resources

• Redis Agent Memory Server
• LangGraph Documentation
• LangChain Documentation
• FastAPI Documentation

Maintainers

Bhavana Giri — bhavanagiri

License

This project is licensed under the MIT License.