Agentic-RAG-R1

About

Agentic RAG‑R1 is an open‑source initiative to build an Agentic Retrieval‑Augmented Generation (RAG) system by endowing a base language model with autonomous search & reasoning skills through reinforcement learning (currently using the GRPO algorithm).

Our architecture is inspired by TC‑RAG and features an agent memory stack that orchestrates the full deliberation loop, supporting the following actions:

Motivated by DeepSeek-R1, we apply GRPO (Generalized Relevance Policy Optimization) to reinforce the agent's choice of reasoning steps and retrieval actions, effectively boosting both search depth and answer quality.

We use conda to manage the environment. Follow these steps to set up:…