Best agent frameworks for RAG workflows
RAG workflows need retrieval context, data connectors, evaluation, and deployment paths that keep source provenance visible.
LlamaIndex
Data-oriented agent and workflow framework for building LLM agents over private data, tools, retrieval, and workflows.
Haystack
Open-source AI orchestration framework for pipelines, agents, retrieval, tools, and production RAG applications.
LangChain / LangGraph
Agent and graph runtime ecosystem for building tool-using, stateful, and observable LLM applications.
Hugging Face MCP Server
MCP server entry for Hugging Face Hub model, dataset, and agent integration workflows.
Filesystem MCP Server
MCP server for controlled local filesystem access, including reading and writing files within configured directories.
Ranking signals
| Signal | Weight | Rationale |
|---|---|---|
| Retrieval context | 5 | RAG workflows depend on source retrieval, chunking, and provenance quality. |
| Evaluation | 4 | Retrieval and answer quality need repeatable checks before production use. |
| Deployment fit | 3 | RAG systems usually need server, container, or workflow runtime deployment paths. |
Source boundary
Scenario recommendation is derived from retrieval context, dataset context, evaluation, and deployment metadata in this graph.
When should teams choose LangChain / LangGraph over LlamaIndex?
Choose LangChain / LangGraph when graph-style control flow and broad integrations dominate. Choose LlamaIndex when document context, private data, and retrieval-first workflows are the center of the system.
What is the practical difference between LlamaIndex and Haystack?
LlamaIndex is a strong fit when data-connected agent workflows and document context are central. Haystack is a strong fit when production RAG pipelines and component composition are the dominant shape.
What makes a RAG workflow agent-ready?
A RAG workflow is agent-ready when retrieval context, source provenance, evaluation, deployment shape, and data access risk can be inspected from shared structured metadata.