LangGraph vs DeepEval for RAG: Which Should You Use?

LangGraph and DeepEval solve different problems, and that’s the first thing to get straight.

LangGraph is for building and orchestrating agent workflows with state, branching, retries, and tool calls. DeepEval is for evaluating LLM outputs and RAG pipelines with metrics like AnswerRelevancy, Faithfulness, and ContextualRecall. For RAG, use LangGraph to build the pipeline and DeepEval to measure whether it actually works.

Quick Comparison

When LangGraph Wins

Use LangGraph when the RAG system is not just “retrieve then answer,” but a real workflow with decision points.

• •

  You need conditional retrieval

  If some queries should hit a vector store, others should call a SQL tool, and some should trigger a web search fallback, LangGraph handles that cleanly with nodes and conditional edges.

  A typical pattern is:

  • •classify query
  • •route to retriever A or B
  • •validate retrieved context
  • •generate answer
  • •retry if confidence is low
• •

  You need durable state across steps

  In production RAG systems, you often need to keep track of conversation state, retrieved chunks, intermediate reasoning artifacts, or user approvals.

  LangGraph gives you StateGraph, MessagesState, reducers for merging state updates, and checkpointing through a checkpointer so the flow can resume after failure.

• •

  You need human-in-the-loop controls

  In regulated environments like banking or insurance, some answers need review before they go out.

  LangGraph makes it practical to insert approval nodes into the graph instead of hacking that logic into prompts or app code.

• •

  You need retries and branching logic

  If retrieval fails or the answer fails validation, you want deterministic fallback paths.

  LangGraph is built for this kind of control flow. That matters when your RAG system has SLAs and cannot just “try again later.”

When DeepEval Wins

Use DeepEval when your main problem is proving that the RAG system is good enough to ship.

• •

  You need repeatable RAG evaluation

  DeepEval gives you metrics that map directly to RAG quality:

  • •AnswerRelevancy
  • •Faithfulness
  • •ContextualPrecision
  • •ContextualRecall

  That is exactly what you want when comparing retrievers, chunking strategies, or prompt variants.

• •

  You need regression tests in CI

  If a prompt tweak or retriever change breaks answer quality, DeepEval catches it before production.

  You can write test cases around expected outputs and run them as part of your release pipeline instead of relying on manual review.

• •

  You need fast iteration on prompt/RAG experiments

  Most RAG teams spend too much time guessing whether an improvement helped.

  DeepEval makes it obvious by scoring outputs against grounded metrics instead of subjective eyeballing.

• •

  You want to benchmark multiple configurations

  If you are comparing chunk sizes, embedding models, rerankers, or prompt templates, DeepEval is the right tool.

  It turns “this feels better” into measurable deltas across test sets.

For RAG Specifically

My recommendation is simple: build the pipeline in LangGraph and evaluate it with DeepEval.

If you are only choosing one tool first because time is tight:

• •choose LangGraph if your biggest risk is workflow complexity
• •choose DeepEval if your biggest risk is shipping a bad RAG system without knowing it

For most serious RAG systems in production at banks or insurers, you need both. LangGraph gives you control over retrieval and generation flow; DeepEval tells you whether that flow is actually producing faithful answers grounded in retrieved context.

Keep learning

• •The complete AI Agents Roadmap — my full 8-step breakdown
• •Free: The AI Agent Starter Kit — PDF checklist + starter code
• •Work with me — I build AI for banks and insurance companies

By Cyprian Aarons, AI Consultant at Topiax.