Awesome-LLM-RAG
a curated list of advanced retrieval augmented generation (RAG) in Large Language Models
GraphCanon updated today · GitHub synced today
Decision brief
Awesome-LLM-RAG is a curated list specific to advanced retrieval augmented generation (RAG) techniques for Large Language Models.
Good fit when
- When you are focusing on the detailed implementation and utilization of RAG in large language models, as Awesome-LLM-RAG provides a deep dive into advanced RAG approaches.
- For researchers or developers interested in curating their resources around specific RAG advancements and embedding techniques within LLMs.
Avoid when
- If you are looking for introductory material on LLM frameworks broadly; Awesome-LLM-RAG does not cover basics of large language models but rather focuses on advanced topics.
- Not recommended if your interest is in broad categories like general vector databases or data retrieval without a focus on RAG within LLMs, as the content is highly specialized.
Observed Jul 12, 2026 · Source: enrich:decision_facts
Verify the decision
Maintenance and security
Full trust report- Maintenance
- Steady (31d since push)
- As of today
- Provenance
- Not a fork · Personal account
- As of today
- Security (OSV)
- No lockfile
- As of 1mo
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
git clone https://github.com/jxzhangjhu/Awesome-LLM-RAGSimilar tools
Same-category neighbours. No typed graph edges are catalogued for this tool yet.
Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
Awesome-LLM-RAG provides a comprehensive overview of resources related to Retrieval-Augmented Generation techniques used with Large Language Models.
Capability facts
No sourced capability facts yet. Facts appear after ingest scans repo manifests (Dockerfile, package.json, MCP configs).
Categories
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Aug 22, 2026)
- [ChunkTuner](https://github.com/shantanu-deshmukh/chunktuner) - Open-source Python/CLI/MCP tooling to benchmark chunking strategies for RAG and recommend configurSource link
Source: README excerpt (regex_v1, Aug 22, 2026)
eihtisham/omni-skills-forge) - 50,000+ curated AI agent skills for Claude Code, Cursor, Copilot, Windsurf, Cline. Visual dashboard, one-click install, skill doctor, aSource link
Source: README excerpt (regex_v1, Aug 22, 2026)
ills-forge) - 50,000+ curated AI agent skills for Claude Code, Cursor, Copilot, Windsurf, Cline. Visual dashboard, one-click install, skill doctor, auto-update.Source link
Tags
README
Awesome LLM RAG
\
This repo aims to record advanced papers on Retrieval Augmented Generation (RAG) in LLMs.
We strongly encourage the researchers that want to promote their fantastic work to the LLM RAG to make pull request to update their paper's information!
Contents
- Bernstein - Multi-agent orchestrator with RAG-enhanced task planning. Decomposes goals using context from codebase analysis.
- Resources
- Workshops and Tutorials
- Books
- Papers
- Survey and Benchmark
- Retrieval-enhanced LLMs
- RAG Instruction Tuning
- RAG In-Context Learning
- RAG Embeddings
- RAG Simulators
- RAG Search
- RAG Long-text and Memory
- RAG Evaluation
- RAG Optimization
- RAG Application
- RAG for Missing Modalities
Resources
- guardian-agent-prompts - 49 production-tested AI agent system prompts for Claude Code multi-agent orchestration with retrieval-augmented generation patterns. MIT licensed.
- CCHub - A desktop control panel for the Claude Code / Codex / Gemini CLI ecosystem. Manage MCP servers, config profiles, agent skills, CLAUDE.md, hooks, and workflow templates from a single Tauri app (Windows / macOS / Linux).
- ChunkTuner - Open-source Python/CLI/MCP tooling to benchmark chunking strategies for RAG and recommend configurations using retrieval metrics (optional RAGAS).
Workshops and Tutorials
- Agent Shadow Brain - Self-evolving AI coding intelligence with infinite memory (TurboQuant), genetic algorithm self-evolution, predictive bug detection, PageRank knowledge graphs, swarm intelligence, and adversarial defense.
- Omni Skills Forge - 50,000+ curated AI agent skills for Claude Code, Cursor, Copilot, Windsurf, Cline. Visual dashboard, one-click install, skill doctor, auto-update.
- RAG Techniques - 35+ runnable Jupyter-notebook tutorials covering advanced RAG techniques: chunking, query transformation/HyDE, reranking, self-RAG, graph RAG, and evaluation.
Personalized Generative AI
Zheng Chen, Ziyan Jiang, Fan Yang, Zhankui He, Yupeng Hou, Eunah Cho, Julian McAuley, Aram Galstyan, Xiaohua Hu, Jie Yang
CIKM 23 – Oct 2023 [link]
First Workshop on Recommendation with Generative Models
Wenjie Wang, Yong Liu, Yang Zhang, Weiwen Liu, Fuli Feng, Xiangnan He, Aixin Sun
CIKM 23 – Oct 2023 [link]
First Workshop on Generative Information Retrieval
Gabriel Bénédict, Ruqing Zhang, Donald Metzler
SIGIR 23 – Jul 2023 [link]
Retrieval-based Language Models and Applications
Akari Asai, Sewon Min, Zexuan Zhong, Danqi Chen
ACL 23 – Jul 2023 [link]
Become a Generative AI Developer Richie Cotton, Olivier Mertens, Korey Stegared-Pace, James Briggs, Vincent Vankrunkelsven, Alara Dirik, Jacob Marquez, Priyanka Asnani DataCamp [link]
Books
Build a Large Language Model (From Scratch)
Sebastian Raschka
Manning Publications - Sep 2024 [link]
**Build a Reasoning Model (From S
For agents
This page has a .md twin and JSON over the API.