LangGraph vs DeepEval for batch processing: Which Should You Use?

LangGraph and DeepEval solve different problems, and that matters a lot for batch processing. LangGraph is an orchestration framework for building stateful agent workflows; DeepEval is an evaluation framework for scoring LLM outputs, test cases, and RAG pipelines.

For batch processing, use LangGraph when you need to coordinate work and DeepEval when you need to measure it. If you’re choosing one tool to run large offline jobs, LangGraph is the default pick.

Quick Comparison

When LangGraph Wins

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  You need batch orchestration with real control flow.
  If your job has steps like ingest → normalize → classify → enrich → summarize → persist, LangGraph fits cleanly. You can model the pipeline with StateGraph, define nodes as functions, and use conditional edges to route records based on intermediate results.

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  You need retries, branching, and state across a long-running job.
  Batch jobs fail in the middle. LangGraph handles this better because the graph is built around explicit state passing and node-level execution. A failed enrichment step can be retried without rebuilding the whole pipeline from scratch.

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  You want parallelism without turning your codebase into spaghetti.
  For high-volume record processing, you often want fan-out/fan-in patterns. LangGraph gives you a structured way to split work across nodes and merge results through reducers instead of hand-rolling async logic everywhere.

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  You are building agentic batch systems.
  If each record needs tool calls, conditional decisions, or human review before final output, LangGraph is the right abstraction. Its compile() model plus checkpointing hooks make it usable for production workflows where state matters.

A practical example: processing insurance claims in bulk where some claims go straight through, some require document extraction with ToolNode, and some get routed to manual review based on confidence thresholds. That is a graph problem, not an evaluation problem.

When DeepEval Wins

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  You need to score outputs at scale.
  DeepEval is built for running evaluations over datasets using LLMTestCase objects and metrics like GEval, AnswerRelevancyMetric, FaithfulnessMetric, and ContextualPrecisionMetric. If your batch job is “evaluate 50k responses,” DeepEval is the correct tool.

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  You care about regression testing after prompt or model changes.
  Batch processing often means rerunning historical samples after a prompt update. DeepEval makes this straightforward with repeatable test cases and assertions like assert_test_case.

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  You are measuring RAG quality instead of orchestrating steps.
  If your batch job pulls retrieval contexts and checks whether answers stayed grounded in those contexts, DeepEval is built for that exact workflow. Its RAG-focused metrics are far more useful than forcing an orchestration framework into evaluation duty.

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  You want a lightweight evaluation layer in CI or scheduled jobs.
  DeepEval slots neatly into nightly runs or release gates. You feed it datasets from CSVs or JSONL files, run metrics in bulk, and get pass/fail signals without inventing your own scoring harness.

A concrete example: you’ve generated 10k customer support replies with an LLM and want to check hallucination rate, answer relevance, and context adherence before shipping. DeepEval does that job directly with less code and less ceremony.

For batch processing Specifically

If the task is orchestrating batch work end-to-end — routing records, retrying failures, merging results, calling tools — pick LangGraph. If the task is evaluating batch outputs — scoring responses against references or contexts — pick DeepEval.

My recommendation is blunt: use LangGraph as the batch engine and DeepEval as the quality gate behind it. That combination gives you production control flow plus measurable output quality; trying to make one tool do both jobs will waste time and produce brittle code.

Keep learning

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By Cyprian Aarons, AI Consultant at Topiax.