Recommender Multi-Agent for Complex Suggestions

This challenge tasks you with building a cutting-edge multi-agent recommendation system using CrewAI. Your system will leverage GPT-5 for highly creative and nuanced recommendation generation, supported by Claude Sonnet 4 for robust information retrieval and synthesis. Agents will collaborate to understand complex user intent, retrieve obscure related data via RAG, and generate highly personalized, multi-faceted recommendations for music or other domains. A critical component is the integration of MCP-enabled tools to access external data sources (e.g., music catalogs, user preference databases, external reviews) to enrich the recommendation process and provide context beyond the LLM's initial training data. The system must demonstrate advanced extended thinking capabilities, adapting its reasoning budget based on recommendation complexity.

• Master CrewAI for defining roles, tasks, and process flows for collaborative agent teams.

• Implement advanced RAG techniques using vector databases (e.g., Qdrant, ChromaDB) to retrieve obscure information relevant to recommendation queries.

• Design and build MCP-enabled agents with tool definitions to interact with simulated external APIs (e.g., music streaming services, product databases).

• Leverage GPT-5's advanced reasoning capabilities for generating creative, context-aware, and highly personalized recommendations.

• Utilize Claude Sonnet 4 for robust information synthesis, fact-checking, and ensuring factual accuracy in recommendations.

• Develop extended thinking patterns within agents, allowing for iterative refinement and adaptive reasoning budgets based on query complexity.

• Orchestrate agent communication and task handoffs to achieve complex recommendation generation.