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agentic-rag

Automate agentic RAG development with adaptive retrieval, multi-step reasoning, and query expansion.

Updated Jan 20, 2026
Search Tags:#query-expansion#semantic-chunking#agentic-rag#adaptive-retrieval#multi-step-reasoning#source-selection
One-click install
npx skills add https://github.com/abhishekmmgn/skills --skill agentic-rag-abhishekmmgn
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: agentic-rag
Source: https://github.com/abhishekmmgn/skills/tree/main/agents/agentic-rag
Command: npx skills add https://github.com/abhishekmmgn/skills --skill agentic-rag-abhishekmmgn

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Strategy for building Agentic RAG systems to move beyond static retrieval by using autonomous agents for adaptive source selection, query expansion, and multi-step reasoning.

Core Features & Use Cases

  • Adaptive Retrieval: Dynamically select the best knowledge source (PDFs, Web, Structured DB) based on context.
  • Multi-Step Reasoning: Break complex queries into sub-queries, retrieve results sequentially, and synthesize the final answer.
  • Context-Aware Query Expansion: Generate refined search terms to improve recall and surface related concepts.
  • Optimization Techniques: Include a Self-Correction mechanism via an Evaluator Agent, semantic chunking, and re-ranking to boost relevance and quality.

Quick Start

Instruct your AI to configure an agentic RAG workflow that dynamically selects sources, reasons through steps, and expands queries for a given task.

Frequently Asked Questions about agentic-rag

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
What is agentic RAG and how does it differ from static retrieval?▼

Agentic RAG uses autonomous agents for adaptive source selection, query expansion, and multi-step reasoning, whereas static retrieval relies on a single knowledge source without dynamic adaptation or multi-step question decomposition.

How do I build an agentic RAG system that queries multiple data sources?▼

To build an agentic RAG system, instruct your AI to configure a workflow that dynamically selects sources across PDFs, web results, and structured databases, then applies multi-step reasoning and query expansion to synthesize answers.

How does multi-step reasoning improve retrieval augmented generation?▼

Multi-step reasoning improves retrieval augmented generation by breaking complex queries into sub-queries, retrieving results sequentially from selected sources, and synthesizing the final answer to ensure contextual relevance.

Can I use adaptive retrieval to search both PDFs and structured databases?▼

Yes, adaptive retrieval dynamically selects the best knowledge source based on context, allowing the agentic RAG system to autonomously query across PDFs, web results, and structured databases.

What is the best way to improve RAG relevance and answer quality?▼

The best way to improve RAG relevance is implementing an Evaluator Agent for self-correction, applying semantic chunking, and using re-ranking techniques to optimize retrieval quality and context accuracy.

When should I not use agentic RAG for my retrieval workflow?▼

Agentic RAG may be unnecessary for simple, direct queries that do not require multi-step reasoning, dynamic source selection across formats, or query expansion, where static retrieval workflows are sufficient.

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