Research Paper Indexing And Agentic Rag Paper Collection

via “agentic rag integration with openai agents sdk and tool-use orchestration”

📑 PageIndex: Document Index for Vectorless, Reasoning-based RAG

Unique: Exposes PageIndex retrieval as a first-class tool in agentic frameworks, allowing agents to autonomously invoke retrieval during reasoning loops rather than requiring manual orchestration. Supports iterative refinement where agents can compose multi-step queries based on intermediate results.

vs others: Enables more sophisticated agentic workflows than static RAG because agents can reason about what to retrieve and iterate based on results, rather than executing a single retrieval step before answer generation.

via “research paper aggregation and synthesis by topic domain”

A one stop repository for generative AI research updates, interview resources, notebooks and much more!

Unique: Bridges the gap between academic research and practical implementation by organizing papers within a learning curriculum context, linking each research domain to corresponding hands-on tutorials and project templates. Most research aggregators present papers in isolation; this integrates them into a learning progression.

vs others: More contextually integrated than generic paper repositories like Papers with Code; explicitly maps research to practical learning resources and implementation patterns, whereas academic databases focus on discovery without pedagogical structure.

https://adongwanai.github.io/AgentGuide | AI Agent开发指南 | LangGraph实战 | 高级RAG | 转行大模型 | 大模型面试 | 算法工程师 | 面试题库 | 强化学习｜数据合成

Unique: Separates agentic RAG papers from general agent papers, reflecting the emergence of agentic RAG as a distinct research area; provides context on paper relevance to practical development

vs others: Curated for agent development relevance rather than comprehensive; includes emerging agentic RAG research that general paper collections may not prioritize

via “rag-based private document indexing and retrieval”

Local Deep Research achieves ~95% on SimpleQA benchmark (tested with Qwen 3.6). Supports local and cloud LLMs (Ollama, Google, Anthropic, ...). Searches 10+ sources - arXiv, PubMed, web, and your private documents. Everything Local & Encrypted.

Unique: Implements RAG system with per-user encrypted storage of documents and embeddings, enabling private document search without external vector databases. Document indexing is integrated into research workflow, allowing seamless combination of public source results with private document retrieval in single research execution.

vs others: Simpler deployment than external vector databases (Pinecone, Weaviate) by storing embeddings in encrypted SQLCipher, while maintaining semantic search capability through local or cloud embedding models.

via “research papers and findings collection on prompt engineering, rag, and agents”

🐙 Guides, papers, lessons, notebooks and resources for prompt engineering, context engineering, RAG, and AI Agents.

Unique: Integrates research papers within a practical guide, bridging the gap between academic research and practitioner knowledge by providing both theoretical foundations and practical applications

vs others: More curated than raw paper databases because papers are selected and summarized; more accessible than academic conferences because summaries distill key findings; more current than textbooks because it includes recent research

via “paper search and discovery within collection”

via “research paper collection and citation management for prompt engineering”