Home / AI Agent Frameworks / Best AI Agent Frameworks for RAG (Retrieval-Augmented Generation)
Best AI Agent Frameworks for RAG (Retrieval-Augmented Generation)
Building a RAG pipeline? These frameworks excel at document retrieval, vector search integration, and knowledge-grounded AI responses.
#1
LangChain
Python
MIT
Intermediate
Framework for building context-aware LLM applications
Ease of Use4/5
Scalability5/5
Documentation5/5
Community5/5
Performance4/5
#2
LlamaIndex Agents
Python
MIT
Intermediate
Data framework for LLM applications with agent support
Ease of Use4/5
Scalability4/5
Documentation5/5
Community5/5
Performance4/5
#3
Haystack
Python
Apache-2.0
Intermediate
LLM orchestration framework for building NLP applications
Ease of Use4/5
Scalability4/5
Documentation5/5
Community4/5
Performance4/5
#4
Langflow
Python
MIT
Beginner
Low-code platform for building AI agents and RAG workflows
Ease of Use5/5
Scalability4/5
Documentation4/5
Community5/5
Performance4/5
#5
Flowise
TypeScript
Apache-2.0
Beginner
Open-source low-code tool for building customized LLM flows
Ease of Use5/5
Scalability3/5
Documentation4/5
Community4/5
Performance3/5
#6
Dify
Python
Apache-2.0
Intermediate
Production-ready platform for LLMOps and agent development
Ease of Use4/5
Scalability5/5
Documentation5/5
Community5/5
Performance4/5
#7
AWS Bedrock Agents
Python
Proprietary
Intermediate
Fully managed service for building and deploying AI agents
Ease of Use3/5
Scalability5/5
Documentation4/5
Community4/5
Performance5/5
Compare all 40+ frameworks
Interactive comparison table with filters, scores, and head-to-head matchups.
View All Frameworks