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

LangGraph and DeepEval solve different problems. LangGraph is for building agent workflows: stateful graphs, tool calls, branching, retries, and human-in-the-loop control. DeepEval is for measuring whether your agent is actually good: test cases, metrics, hallucination checks, retrieval quality, and regression testing.

If you’re building AI agents, use LangGraph to orchestrate them and DeepEval to verify them.

Quick Comparison

When LangGraph Wins

Use LangGraph when the agent is not just chatting but executing a workflow.

• •

  You need deterministic control over agent flow

  If your agent must decide between tool calls, fallback paths, or human approval steps, LangGraph is the right abstraction. StateGraph gives you explicit nodes and edges instead of a black-box loop.

• •

  You need durable state and recovery

  For insurance claims intake or bank onboarding flows, losing context mid-process is unacceptable. LangGraph’s checkpointing lets you persist graph state and resume execution instead of restarting from scratch.

• •

  You need human-in-the-loop review

  If a fraud review agent should pause before submitting a high-risk action, LangGraph handles interrupts cleanly. That matters more than raw “agent intelligence” in regulated environments.

• •

  You are building multi-agent or tool-heavy systems

  When one node fetches policy data, another validates documents, and another drafts a response, LangGraph keeps the workflow explicit. You can route between tools with add_node, add_edge, conditional routing, and custom reducers.

Example pattern:

from langgraph.graph import StateGraph

graph = StateGraph(MyState)
graph.add_node("classify", classify_intent)
graph.add_node("fetch_docs", fetch_documents)
graph.add_node("draft_reply", draft_reply)

graph.set_entry_point("classify")
graph.add_conditional_edges("classify", route_by_intent)

That is production-grade orchestration. It is not just prompt chaining with a nicer name.

When DeepEval Wins

Use DeepEval when you need proof that the agent works before it reaches users.

• •

  You need repeatable evaluation

  DeepEval gives you test-first validation for LLM outputs using metrics like GEval, FaithfulnessMetric, AnswerRelevancyMetric, and ContextualRecallMetric. If you want CI to catch regressions before deployment, this is the tool.

• •

  You are evaluating RAG-heavy agents

  Most enterprise agents depend on retrieval quality as much as generation quality. DeepEval is strong here because it measures whether answers stay grounded in retrieved context instead of making things up.

• •

  You need model comparisons

  If you are choosing between GPT-4o-mini vs Claude vs an internal model for an agent step, DeepEval makes comparison structured instead of anecdotal. Run the same dataset through multiple models and compare scores.

• •

  You want guardrails around quality gates

  A bank support agent should not ship if faithfulness drops below threshold or hallucination rate spikes. DeepEval fits directly into release pipelines where quality thresholds matter more than subjective demos.

Example pattern:

from deepeval.metrics import FaithfulnessMetric
from deepeval.test_case import LLMTestCase

test_case = LLMTestCase(
    input="What is the refund policy?",
    actual_output="Refunds are available within 30 days.",
    retrieval_context=["Refunds are available within 14 days for eligible purchases."]
)

metric = FaithfulnessMetric()
metric.measure(test_case)
print(metric.score)

That tells you immediately whether your agent is grounded or guessing.

For AI agents Specifically

My recommendation: build with LangGraph first, then wrap it with DeepEval in CI.

LangGraph solves the hard production problem: how to make an agent follow a real workflow with state, branching logic, retries, interruptions, and tool use. DeepEval solves the equally important second problem: how to know that workflow still behaves correctly after prompt changes, model swaps, or retrieval updates.

If you are shipping AI agents into banking or insurance systems without both pieces, you are either under-engineering the runtime or flying blind on quality.

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.