LangGraph vs DeepEval for production AI: Which Should You Use?

LangGraph and DeepEval solve different problems, and that matters in production.

LangGraph is an orchestration framework for building stateful agent workflows with nodes, edges, checkpoints, and human-in-the-loop control. DeepEval is an evaluation framework for measuring LLM behavior with test cases, metrics, and regression checks. If you’re shipping production AI, use LangGraph to run the system and DeepEval to prove it works.

Quick Comparison

When LangGraph Wins

• •

  You need stateful orchestration, not just a chain of prompts.
  LangGraph’s StateGraph is the right abstraction when your system needs branching logic, retries, conditional routing, or shared state across steps.

• •

  You need human-in-the-loop approvals.
  The interrupt() pattern is built for production flows where a claim review, payment action, or policy exception must pause until a human approves it.

• •

  You need durable execution with checkpoints.
  With a checkpointer, LangGraph can persist graph state and resume after failure. That’s non-negotiable when your agent handles long-running workflows or external API calls that fail halfway through.

• •

  You are building an agent runtime.
  If the product is “an AI assistant that uses tools,” LangGraph gives you the control plane: nodes for tool calls, reducers for state updates, conditional edges for routing decisions.

Example pattern:

from langgraph.graph import StateGraph
from langgraph.checkpoint.memory import MemorySaver

# Build a workflow with explicit state transitions
builder = StateGraph(MyState)
builder.add_node("triage", triage_node)
builder.add_node("lookup", lookup_node)
builder.add_edge("triage", "lookup")

graph = builder.compile(checkpointer=MemorySaver())

That kind of structure is what you want when failures have business consequences.

When DeepEval Wins

• •

  You need automated quality gates in CI.
  DeepEval is built for test-driven LLM development. Define test cases with LLMTestCase, run assertions like assert_test(), and fail the pipeline when outputs regress.

• •

  You need to measure RAG quality.
  Metrics like AnswerRelevancyMetric, FaithfulnessMetric, and retrieval-focused checks are exactly what teams need when they’re tuning retrieval pipelines before launch.

• •

  You need repeatable evaluation across prompt versions.
  DeepEval makes it easy to compare prompt changes against a fixed dataset of cases. That’s how you stop shipping “looks fine in demo” prompts into production.

• •

  You want LLM-as-a-judge style scoring without writing custom scoring logic from scratch.
  Metrics like GEval let you define rubric-based evaluations for correctness, tone, completeness, or policy compliance.

Example pattern:

from deepeval import assert_test
from deepeval.test_case import LLMTestCase
from deepeval.metrics import AnswerRelevancyMetric

test_case = LLMTestCase(
    input="What is our refund policy?",
    actual_output="Refunds are available within 30 days."
)

metric = AnswerRelevancyMetric(threshold=0.8)
assert_test(test_case=test_case, metrics=[metric])

That’s the right tool when you care about proving quality before deployment.

For production AI Specifically

Use LangGraph in the application layer and DeepEval in the validation layer. LangGraph should own execution because it gives you deterministic control over agent state, retries, branching, persistence, and approvals. DeepEval should own release confidence because it gives you measurable regressions on outputs that matter to users and risk teams.

If I had to pick one first for a production AI team: pick LangGraph if you’re building the live system; pick DeepEval if you already have a system and need to stop quality drift before it hits customers.

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.