Building a Claude-powered Agentic RAG: Integrating External Sources and Choosing the Right Framework

Image source: How I finally got agentic RAG to work right

Retrieval-Augmented Generation (RAG) systems enhance AI agents by enabling dynamic retrieval of information from various sources to produce accurate and timely responses. In this blog, we'll explore best practices for connecting external resources with an agentic RAG system using Claude models, focusing on integration with:

Image source: What’s in your stack: The state of tech tools in 2025

We’ll also help you decide on the most suitable agent development framework.


1. Microsoft Teams Integration

Best Practices


2. Jira Service Management (JSM)

Best Practices


3. Notion Integration

Best Practices


4. AWS Documentation Integration

Best Practices


5. Internet Search Integration

Best Practices


Choosing the Right Agent Development Framework

LangChain (Recommended)

Semantic Kernel

CrewAI

AutoGen

Haystack


Recommended Framework: LangChain

Image source: Understanding LangChain - A Framework for LLM Applications

Given ease of integration with Claude, robust external resource support, and powerful PDF ingestion, LangChain emerges as the recommended framework for building your Claude-powered agentic RAG system. It balances flexibility, development efficiency, and comprehensive documentation.


References

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