Agentic-RAG-R1 and agentic-rag
These are **competitors** — both implement agentic RAG systems with reinforcement learning approaches to improve reasoning quality, targeting the same use case of enhancing retrieval-augmented generation with agent-like decision-making.
About Agentic-RAG-R1
jiangxinke/Agentic-RAG-R1
Agentic RAG R1 Framework via Reinforcement Learning
This framework helps AI/ML researchers and developers enhance the reasoning and search capabilities of their large language models (LLMs). By training a base LLM with reinforcement learning, you can feed in complex questions and external knowledge bases to get back more accurate and contextually rich answers. It's designed for those building advanced AI applications that require autonomous decision-making and deep information retrieval.
About agentic-rag
FareedKhan-dev/agentic-rag
Agentic RAG to achieve human like reasoning
This project helps financial analysts and researchers to deeply understand complex financial documents like SEC filings. It takes unstructured documents (10-K, 10-Q, 8-K reports) and processes them to generate structured insights, summaries, and trend analyses, mimicking how a human expert would reason and connect information. The output is a comprehensive, validated understanding of the data, going beyond simple fact retrieval.
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