agentic-rag and Awesome-RAG-Reasoning
The first project, a collection of resources, complements the second project, an implementation of Agentic RAG, by providing foundational knowledge and research to support its development and understanding.
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.
About Awesome-RAG-Reasoning
DavidZWZ/Awesome-RAG-Reasoning
[EMNLP 2025] Awesome RAG Reasoning Resources
This collection helps AI researchers and practitioners develop advanced AI systems that can accurately answer complex questions and solve problems. It brings together resources on combining external knowledge retrieval with sophisticated logical thinking, providing a roadmap for building more capable AI agents. Researchers, AI developers, and system architects working on advanced AI applications would use this.
Related comparisons
Scores updated daily from GitHub, PyPI, and npm data. How scores work