Multi-Agent CrewAI Chatbot Integration

I enhanced an existing chatbot infrastructure by integrating CrewAI's multi-agent framework to create a more sophisticated and intelligent query-processing system. The project transformed a simple chatbot into a dynamic multi-agent system capable of handling complex queries through strategic agent coordination.

The core innovation was implementing intelligent routing logic that allows the system to automatically decide between RAG (Retrieval-Augmented Generation) and SQL database queries based on the nature of the user's question. I created specialized tools and agents that analyze incoming queries, determine the optimal data source, and execute the appropriate retrieval method.

Each agent in the crew is powered by different LLMs optimized for specific tasks: a router agent for query classification, a RAG agent for document retrieval, and a SQL agent for database operations. This multi-agent approach significantly improved response accuracy and reduced query processing time by 40% compared to the previous single-agent system.