LangGraph vs DeepEval for multi-agent systems: Which Should You Use?

LangGraph and DeepEval solve different problems, and that matters if you’re building multi-agent systems. LangGraph is the orchestration layer: stateful graphs, branching, retries, human-in-the-loop, and durable execution. DeepEval is the evaluation layer: test cases, metrics, regression checks, and LLM-as-judge scoring.

For multi-agent systems, use LangGraph to build and run the agents, then use DeepEval to measure whether they’re any good.

Quick Comparison

When LangGraph Wins

• •

  You need real orchestration between agents

  If your system has a planner agent delegating work to research, compliance, and summarization agents, LangGraph is the right tool. Its StateGraph model makes these handoffs explicit instead of burying them in callback spaghetti.

• •

  You need branching logic based on intermediate state

  Multi-agent systems fail when every step is linear. With LangGraph you can route based on state using conditional edges like add_conditional_edges(), which is exactly what you want when one agent decides whether another agent should be called.

• •

  You need persistence and recovery

  Production agents crash or get interrupted. LangGraph supports checkpointing through its persistence patterns so you can resume a graph from saved state instead of replaying everything from scratch.

• •

  You need human-in-the-loop approvals

  In banking or insurance workflows, a claims agent or underwriting assistant often needs review before acting. LangGraph handles interrupt-and-resume flows cleanly with graph state rather than forcing awkward custom control logic.

A typical pattern looks like this:

from langgraph.graph import StateGraph, START, END

def planner(state):
    return {"next": "research"}

def research(state):
    return {"facts": ["..."]}

def supervisor(state):
    if state["risk"] > 0.8:
        return {"next": "human_review"}
    return {"next": "finalize"}

graph = StateGraph(dict)
graph.add_node("planner", planner)
graph.add_node("research", research)
graph.add_node("supervisor", supervisor)

graph.add_edge(START, "planner")
graph.add_edge("planner", "research")
graph.add_edge("research", "supervisor")
graph.add_edge("supervisor", END)

app = graph.compile()

That’s the core value: explicit control over agent flow.

When DeepEval Wins

• •

  You need to know if your agents are actually improving

  Multi-agent systems get worse in subtle ways: one agent becomes verbose, another starts hallucinating citations, a third stops following policy constraints. DeepEval is built for regression testing these failures with metrics like HallucinationMetric and AnswerRelevancyMetric.

• •

  You want CI-friendly evaluation

  If every prompt tweak or tool change can break your agent swarm, you need automated tests in CI/CD. DeepEval gives you test cases that can be run repeatedly against model outputs so you catch regressions before they hit production.

• •

  You care about LLM-as-judge scoring

  For open-ended multi-agent outputs like case summaries or investigation reports, exact-match metrics are useless. DeepEval’s GEval lets you score against custom criteria such as completeness, policy adherence, or grounded reasoning.

• •

  You already have an agent system and need observability around quality

  If the orchestration is already built — maybe in LangGraph or custom Python — DeepEval slots in as the quality gate without forcing a rewrite of your runtime architecture.

A simple eval pattern looks like this:

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

test_case = LLMTestCase(
    input="Summarize this claim dispute",
    actual_output="The claimant...",
    expected_output="..."
)

metric = AnswerRelevancyMetric(threshold=0.8)
metric.measure(test_case)
print(metric.score)

That’s where DeepEval earns its keep: repeatable quality checks for messy generative behavior.

For multi-agent systems Specifically

Use LangGraph first if you are building the actual multi-agent runtime. It gives you the control plane: routing, shared state via reducers, conditional execution, retries, checkpoints, and human review points.

Use DeepEval alongside it if you care about production quality — which you should — because multi-agent systems fail in ways unit tests won’t catch.

If I had to pick one for a new multi-agent system: LangGraph. It’s the foundation; DeepEval is the gatekeeper that tells you whether the foundation is holding up under real workloads.

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.