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

LangGraph is the orchestration layer. Langfuse is the observability and evaluation layer. If you’re building a multi-agent system, start with LangGraph for control flow and add Langfuse for tracing, debugging, and evals.

Quick Comparison

When LangGraph Wins

• •

  You need real orchestration between agents

  If one agent routes tasks to others, validates outputs, retries failures, or escalates to a human, LangGraph is the right tool. Its StateGraph model makes the control flow explicit instead of hiding it inside prompt spaghetti.

• •

  You need stateful multi-step workflows

  Multi-agent systems are rarely just “agent A calls agent B.” They usually involve shared state, partial results, branching paths, and conditional loops. LangGraph handles this cleanly with typed state objects, node transitions, and reducers.

• •

  You need interrupt/resume behavior

  For banking or insurance workflows, human approval matters. LangGraph’s interrupt() pattern and checkpointing let you pause execution for review and resume later without rebuilding context from scratch.

• •

  You want deterministic execution paths

  Agent swarms sound nice until debugging starts. With LangGraph you can design a supervisor-worker architecture where every transition is visible: START -> planner -> executor -> verifier -> END. That matters when failure costs money.

A simple example looks like this:

from langgraph.graph import StateGraph, START, END
from typing import TypedDict

class AgentState(TypedDict):
    task: str
    result: str

def planner(state: AgentState):
    return {"result": f"Plan for {state['task']}"}

def executor(state: AgentState):
    return {"result": f"Executed: {state['result']}"}

graph = StateGraph(AgentState)
graph.add_node("planner", planner)
graph.add_node("executor", executor)

graph.add_edge(START, "planner")
graph.add_edge("planner", "executor")
graph.add_edge("executor", END)

app = graph.compile()

That is boring in the best way possible. Boring is what you want when multiple agents are coordinating customer-impacting actions.

When Langfuse Wins

• •

  You already have agents running and need visibility now

  Langfuse gives you traces across prompts, tool calls, tokens, latency, errors, and user sessions. If your current problem is “I don’t know why this agent failed,” Langfuse solves that faster than rewriting orchestration.

• •

  You care about evals more than orchestration

  Multi-agent systems fail quietly: one agent degrades output quality while another still returns a valid-looking response. Langfuse’s datasets and eval tooling help you score outputs against golden data and track regressions over time.

• •

  You need vendor-neutral observability

  Langfuse works well whether your stack is built on OpenAI SDKs, Anthropic SDKs, LangChain callbacks, or custom Python services. That makes it a strong default when your agent architecture is mixed or still changing.

• •

  You want production debugging across the whole request path

  In multi-agent systems the bug is often not inside one agent; it’s in the interaction between them. Langfuse lets you inspect spans across the entire chain so you can see where context was lost or where token usage exploded.

A typical tracing setup is straightforward:

from langfuse import observe

@observe()
def run_agent(task: str):
    # call model / tools / sub-agents here
    return {"answer": "done"}

That’s not orchestration. It’s visibility. And visibility is what keeps multi-agent systems from becoming unmaintainable after week two.

For multi-agent systems Specifically

Use LangGraph first if your problem is coordination: routing tasks between agents, managing shared state, handling retries, or pausing for approval. Use Langfuse alongside it if your problem is understanding what happened: tracing each node execution, comparing outputs across versions, and catching regressions before users do.

My recommendation is blunt: build the multi-agent workflow in LangGraph and instrument it with Langfuse. If you try to use only Langfuse for orchestration you’ll end up hand-rolling control flow; if you use only LangGraph you’ll be flying blind in production.

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.