agentic-rag-for-dummies and Awesome-RAG-Reasoning
A practical implementation framework for building agentic RAG systems complements B's curated collection of RAG reasoning research and techniques, as one provides executable code while the other provides the theoretical foundations and reasoning strategies to inform that implementation.
About agentic-rag-for-dummies
GiovanniPasq/agentic-rag-for-dummies
A modular Agentic RAG built with LangGraph — learn Retrieval-Augmented Generation Agents in minutes.
Built on LangGraph's agentic framework, this system implements hierarchical parent-child chunk indexing for precision search paired with context-rich retrieval, conversation memory across turns, and human-in-the-loop query clarification. Multi-agent map-reduce parallelizes sub-query resolution with self-correction and context compression, while supporting pluggable LLM providers (Ollama, OpenAI, Anthropic, Google) and Qdrant vector storage—all orchestrated through observable graph execution with Langfuse integration.
About Awesome-RAG-Reasoning
DavidZWZ/Awesome-RAG-Reasoning
[EMNLP 2025] Awesome RAG Reasoning Resources
Curates papers, benchmarks, and implementations across three integration patterns: reasoning-enhanced RAG (optimizing retrieval and generation), RAG-enhanced reasoning (grounding with external knowledge), and synergized systems using iterative retrieval-reasoning loops. Organizes taxonomy covering chain/tree/graph-based workflows, single/multi-agent orchestration, and tool-using approaches within agentic AI frameworks. Provides evaluation resources spanning single/multi-hop QA, fact-checking, summarization, and domain-specific tasks alongside code implementations.
Related comparisons
Scores updated daily from GitHub, PyPI, and npm data. How scores work