Summary

This project offers an enterprise-grade, multi-agent e-commerce recommendation and marketing system in Python, Java, and Go. It solves common e-commerce issues like disconnected inventory/recommendations, generic copy, and siloed operations by orchestrating specialized AI agents. It targets engineers interested in Multi-Agent architectures and serves as a job-seeking resource with resume and interview prep materials.

How It Works

A Supervisor pattern orchestrates four core agents: User Profile, Product Recommendation, Marketing Copy, and Inventory Decision. The Supervisor manages parallel and serial execution phases using frameworks like LangGraph (Python), Spring AI (Java), and LangChainGo (Go). This design enables efficient, low-latency processing through concurrent agent execution, ensuring real-time validation across recommendation, inventory, and marketing functions. Key technologies include Redis for feature stores, LLMs for personalization, and A/B testing for optimization.

Quick Start & Requirements

• Prerequisites: Python 3.11+, Java 17+, or Go 1.22+. An LLM API Key (e.g., MiniMax, Alibaba Tongyi) is required.
• Installation (Python): Clone, cd python, set up virtual environment, pip install -r requirements.txt, configure .env with LLM API key, run python main.py.
• Docker: docker-compose up -d from the root directory provides one-click deployment.
• Running: Execute language-specific entry points (python main.py, mvn spring-boot:run, go run cmd/main.go).
• API: Core recommendation endpoint at /api/v1/recommend (Python: 8000, Java/Go: 8080).
• Links: Project GitHub, Docker Compose.

Highlighted Details

• Multi-Language Support: Python (LangGraph), Java (Spring AI Alibaba), and Go (goroutine concurrency) implementations cater to diverse needs.
• Agent Specialization: Dedicated agents for user profiling (RFM, Redis), product recommendation (multi-stage recall, LLM re-ranking), marketing copy (template-based, compliance), and inventory management (real-time stock).
• Reliability: BaseAgent includes configurable timeouts, exponential backoff retries, and fallback strategies for system stability.
• A/B Testing: Integrated engine with traffic bucketing and Thompson Sampling for dynamic optimization.
• Job-Seeking Resources: Comprehensive interview guides, resume templates, and code walkthroughs are included.

Maintenance & Community

The README focuses on technical implementation and job-seeking utility, lacking details on specific maintenance schedules, contributor statistics, or community channels (e.g., Slack, Discord).

Licensing & Compatibility

Released under the permissive MIT License, allowing free use, modification, and commercial distribution with license notice retention.

Limitations & Caveats

System performance depends on LLM API availability and latency. Comparative benchmarks across languages are not detailed. The inherent complexity of Multi-Agent systems requires significant understanding for effective deployment.