RAGentX
What it is
RAGentX is a multi-agent Retrieval-Augmented Generation (RAG) system built with LangGraph that uses a stateful Directed Acyclic Graph (DAG) to orchestrate specialized AI agents for query rewriting, hybrid retrieval, and self-correction. It combines FAISS and BM25 for search and uses a Groq-hosted Llama model for generation, running on a FastAPI backend and Streamlit frontend.
The problem it solves
Traditional RAG systems often produce incorrect answers when the initial context retrieval is irrelevant or insufficient, lacking a mechanism to detect and repair poor retrieval results.
What you could use it for
- Enterprise document Q&A with self-correcting retrieval to reduce hallucinations
- Hybrid search applications requiring both semantic and keyword matching precision
- Building autonomous agents that can evaluate their own context and re-query when initial results are poor
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Summary written by an AI model from the source; check the source before you rely on it.