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

LangGraph and Helicone solve different problems. LangGraph is the orchestration layer for building stateful agent workflows with nodes, edges, checkpoints, and human-in-the-loop control; Helicone is an observability and gateway layer for LLM traffic, with request logging, prompt/version tracking, caching, rate limits, and analytics.

For multi-agent systems, use LangGraph to build the system and Helicone to monitor it. If you have to pick one for orchestration, pick LangGraph.

Quick Comparison

When LangGraph Wins

• •

  You need real multi-agent coordination.

  • •Example: a supervisor agent routes work between a research agent, a policy-checking agent, and a response-drafting agent.
  • •LangGraph gives you StateGraph, conditional routing with add_conditional_edges, and explicit state transitions.
• •

  You need durable execution with interruption points.

  • •In banking or insurance flows, agents often need approval before taking action.
  • •LangGraph supports checkpointing through checkpointers like MemorySaver or persistent stores so you can pause at an interrupt and resume later.
• •

  You need deterministic control over branching logic.

  • •Multi-agent systems fail when routing becomes “whatever the model feels like.”
  • •With LangGraph you define the graph topology yourself instead of hoping prompt instructions keep agents in line.
• •

  You need nested workflows.

  • •A parent graph can call subgraphs for underwriting checks, claims triage, fraud review, or KYC validation.
  • •That structure is hard to maintain in a plain chat loop and easy to express in LangGraph.

from typing import TypedDict
from langgraph.graph import StateGraph

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

def router(state: AgentState):
    # decide which agent handles the task
    return {"route": "research"}

def research_agent(state: AgentState):
    return {"result": "researched answer"}

graph = StateGraph(AgentState)
graph.add_node("router", router)
graph.add_node("research", research_agent)
graph.set_entry_point("router")
graph.add_edge("router", "research")
compiled = graph.compile()

When Helicone Wins

• •

  You already have agents running and need visibility fast.

  • •Helicone shows prompts, completions, latency, token usage, cost breakdowns, errors, and model comparisons without rebuilding your stack.
• •

  You want provider-agnostic logging across teams.

  • •If one team uses OpenAI and another uses Anthropic or Azure OpenAI APIs through compatible endpoints, Helicone centralizes traffic analysis.
• •

  You care about cost control more than orchestration.

  • •Features like caching and request-level analytics help reduce spend on repeated tool calls or repetitive prompt patterns.
• •

  You need production monitoring for LLM calls made by multiple services.

  • •In distributed systems where several agents call models independently, Helicone acts as the control tower.
  • •It’s useful when your main problem is “what happened?” not “how do I route work between agents?”

import openai

client = openai.OpenAI(
    api_key="your-key",
    base_url="https://oai.helicone.ai/v1"
)

resp = client.chat.completions.create(
    model="gpt-4o-mini",
    messages=[{"role": "user", "content": "Summarize this claim."}]
)

For multi-agent systems Specifically

Use LangGraph as the orchestration engine and Helicone as the observability layer. Multi-agent systems break down when routing is implicit; LangGraph gives you explicit state transitions with StateGraph, checkpoints, interrupts, and subgraphs so the system stays maintainable under real business rules.

Helicone does not replace that. It tells you what your agents did after the fact; it does not coordinate them. If your goal is to ship a reliable multi-agent system in production — especially in regulated environments — build it in LangGraph first and put Helicone around it for tracing and cost control.

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.