Monitor your LangGraph agents in production.

A LangGraph run can loop through nodes dozens of times before it finishes, or does not finish. AgentPing rolls the whole graph execution into one run record: what it cost, whether it completed, how long it took, and whether the output held up.

Track your first AI workflow Try it live first, no signup Read the Python SDK docs

Production AI in LangGraph fails quietly.

Graphs make agent control flow explicit, and they make runaway cost invisible. A conditional edge that keeps routing back through your model node burns tokens on every pass, and nothing in the provider bill tells you which graph, which customer, or which loop did it. LangSmith traces the steps; the run-level questions, what did this execution cost and did it succeed, still need answering.

Wire it up in a few lines.

Initialise once, then wrap the graph invocation. Every node the graph visits, including loops, retries and tool calls, rolls up into a single run record with cost, status and latency.

Install

install LangGraph 
pip install agentping

Instrument

example · python LangGraph 
from agentping import init, run

init(api_key=os.environ["AGENTPING_API_KEY"], agent="research-graph")

# Wrap the invoke; every node the graph visits rolls up into one run.
with run(agent="research-graph", customer_id="acme-corp"):
    result = graph.invoke({"messages": [("user", question)]})

Full setup, environment variables and options are in the Python SDK docs.

What AgentPing tracks for LangGraph.

  • Cost per graph execution across every model call inside it, priced server side.
  • Cost by agent, customer and feature, tagged when the run starts.
  • Loops and retries made visible: node revisits show up as steps in one run, not scattered calls.
  • Run status and latency, including graphs that never reach an end state.
  • Missed run alerts for scheduled graphs.
  • Output quality checks and rubric scoring on sampled runs.
Read the Python SDK docs Heartbeat webhook docs

What you see in the dashboard.

  • A run list for LangGraph, each row showing cost, status and latency, filterable by agent, customer and feature.
  • Cost by agent, customer and feature, so you can see which accounts and features drive AI spend, not just one provider total.
  • An incident the moment a scheduled run misses its window or a cost spike breaks its baseline, delivered to Slack, email, PagerDuty or a webhook.
  • A quality trend once you turn on scoring, so a regression in your LangGraph output shows up as a falling line, not a support ticket.
How do I monitor a LangGraph agent?
Wrap the graph invocation with the AgentPing run context. Every node execution inside it, model calls, tool calls, and loop iterations, is captured as one run record, with cost priced on our servers from the model and token counts.
Does it handle cycles and conditional edges?
Yes, that is the point. A graph that loops through a node ten times shows up as one run with ten steps and the summed cost, so a routing bug that quietly multiplies your spend is visible the day it ships, not on the invoice.
How is this different from LangSmith?
LangSmith is a debugging and tracing tool: excellent for stepping through why a graph took the path it took. AgentPing answers the operational questions: what does this agent cost per run and per customer, is it still running on schedule, and is output quality drifting. Teams commonly run both.
What does it cost to monitor LangGraph?
There is a free plan with no card and no time limit, enough to wire up your first agents. Paid plans start at $99/month with named limits and no metered billing, so the bill never surprises you. Annual billing is two months free.
Does AgentPing see my prompts and outputs?
Only if you choose to send them. By default AgentPing prices runs from model and token counts and tracks status and timing, which needs no prompt or output content. Payload capture for quality scoring is opt-in and configurable per agent.

Monitor your LangGraph AI workflows.

Send one run from LangGraph and see cost, status and latency. Then add an alert for cost spikes or missed runs.

Track your first AI workflow Try it live first, no signup Read the Python SDK docs

Monitoring plain LangChain chains → AgentPing vs LangSmith → Spend, cost attribution → Verify, quality scoring → Pulse, live monitoring →