# asinghcsu/AgenticRAG-Survey

Agentic-RAG explores advanced Retrieval-Augmented Generation systems enhanced with AI LLM agents.

Repository: https://github.com/asinghcsu/AgenticRAG-Survey
Canonical: https://ross.abutalabs.com/products/agenticrag-survey
Homepage: https://arxiv.org/abs/2501.09136
License Family: other
Topics: agentic, agentic-ai, agentic-framework, agentic-rag, agentic-workflow, rag, agentic-pattern, llm-agent, multi-agent-systems, multiagent, reflection, tools
Last push: 2025-10-20T00:30:31+00:00

## Health v2 (maintenance only)
Score: 42/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 47, release rhythm 35, longevity 42
- inputs: {"age_days": 601, "days_push": 318, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1723, forks 185 (observed 2026-08-28T04:05:27.927416+00:00)

## What it is
A survey repository accompanying the arXiv paper 'Agentic Retrieval-Augmented Generation: A Survey on Agentic RAG', providing a taxonomy, visualizations, and analysis of Agentic RAG systems. It covers agentic patterns like reflection, planning, tool use, and multi-agent collaboration, plus comparisons of RAG frameworks and real-world applications.

## Use cases
- learn how agentic RAG differs from traditional RAG
- find a taxonomy of agentic RAG architectures
- understand multi-agent RAG patterns like reflection and planning
- research corrective and adaptive RAG systems
- find real-world applications of agentic RAG in healthcare and finance
- cite a survey on agentic retrieval-augmented generation

## When to choose
- you need a conceptual overview or literature review of Agentic RAG
- you are researching agent design patterns for retrieval pipelines
- you want diagrams and taxonomy references for a paper or presentation

## When to avoid
- you need production-ready RAG code or a runnable framework
- you want a maintained software library with tests and releases
- you need a licensed codebase to build on

## Facets
- artifact type: learning-resource
- maturity: active
- function: rag, agent-framework, llm-inference, nlp, documentation
- domain: artificial-intelligence, large-language-models, tutorials
- platform: cross-platform
- tags: survey-paper, agentic-rag, llm-agents, multi-agent-systems, academic-research, taxonomy, retrieval-augmented-generation, ai-agents, natural-language-processing

## Member repositories
- asinghcsu/AgenticRAG-Survey (main) score 42

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:27.927416+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T03:33:56.855167+00:00, confidence not recorded.
  - readme: https://github.com/asinghcsu/AgenticRAG-Survey (fetched 2026-08-28T04:05:27.927416+00:00, sha 780fdaf6707b)
  - homepage: https://arxiv.org/abs/2501.09136 (fetched 2026-08-29T11:09:53.158513+00:00, sha ba4ef12a45c2)
  - site_page: https://info.arxiv.org/about/donate.html (fetched 2026-08-29T11:09:53.161138+00:00, sha cca9c3a11c56)
  - site_page: https://info.arxiv.org/about/ourmembers.html (fetched 2026-08-29T11:09:53.164915+00:00, sha 47cbc55ff1de)
  - site_page: https://info.arxiv.org/about (fetched 2026-08-29T11:09:53.167038+00:00, sha a1f16f915a9a)
  - site_page: https://info.arxiv.org/labs/index.html (fetched 2026-08-29T11:09:53.162948+00:00, sha b14a8d05a0ec)
- Data as of 2026-08-30T08:39:29.467469+00:00.