LangGraph

LangGraph is a stateful multi-actor agent framework built on top of LangChain. Arize AX captures every LangGraph run — graph node invocations, tool calls, LLM calls, and the message state passing through them — via the openinference-instrumentation-langchain package, the same instrumentor that covers LangChain.

LangGraph Tracing Tutorial (Google Colab)

Prerequisites

Python 3.10+
An Arize AX account (sign up)
An OPENAI_API_KEY from the OpenAI Platform

Launch Arize AX

Sign in to your Arize AX account.
From Space Settings, copy your Space ID and API Key. You will set them as ARIZE_SPACE_ID and ARIZE_API_KEY below.

Install

pip install arize-otel \
  openinference-instrumentation-langchain \
  langgraph langchain-openai

Configure credentials

export ARIZE_SPACE_ID="<your-space-id>"
export ARIZE_API_KEY="<your-api-key>"
export ARIZE_PROJECT_NAME="langgraph-tracing-example"
export OPENAI_API_KEY="<your-openai-api-key>"

Setup tracing

# instrumentation.py
import os

from arize.otel import register
from openinference.instrumentation.langchain import LangChainInstrumentor

tracer_provider = register(
    space_id=os.environ["ARIZE_SPACE_ID"],
    api_key=os.environ["ARIZE_API_KEY"],
    project_name=os.environ["ARIZE_PROJECT_NAME"],
)

LangChainInstrumentor().instrument(tracer_provider=tracer_provider)
print("Arize AX tracing initialized for LangGraph.")

Run LangGraph

# example.py

# Importing instrumentation first ensures tracing is set up
# before `langgraph` is imported.
from instrumentation import tracer_provider

from langchain_core.tools import tool
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent


@tool
def get_weather(city: str) -> str:
    """Get the current weather for a city. Returns a short string."""
    if city.lower() in ("sf", "san francisco"):
        return "It's 60 degrees and foggy in San Francisco."
    return f"It's 75 degrees and sunny in {city}."


# ChatOpenAI reads OPENAI_API_KEY from the environment.
agent = create_react_agent(
    model=ChatOpenAI(model="gpt-5.5"),
    tools=[get_weather],
)

result = agent.invoke({
    "messages": [("user", "What's the weather in San Francisco?")],
})

print(result["messages"][-1].content)

Expected output

Arize AX tracing initialized for LangGraph.
The current weather in San Francisco is 60 degrees and foggy.

Verify in Arize AX

Open your Arize AX space and select project langgraph-tracing-example.
You should see a new trace within ~30 seconds containing a LangGraph parent span wrapping the agent’s reasoning loop — agent node spans (ChatOpenAI calls), the tools node span (get_weather invocation), and the final answer.
If no traces appear, see Troubleshooting.

Check from the skill, CLI, or SDK

Confirm spans are actually reaching your Arize AX project. Use whichever fits your workflow — the skill and CLI work for any framework; the SDK check is shown for each language.

Arize skill (agent)
AX CLI
SDK

Install the Arize Skills plugin and let your coding agent check for you:

npx skills add Arize-ai/arize-skills

Then prompt your agent:

Use the arize-trace skill to export and analyze recent traces from my project. Confirm spans are arriving, and summarize any errors or latency issues.

Export recent spans for your project — any rows mean traces are landing:

ax spans export "$ARIZE_PROJECT_NAME" --space "$ARIZE_SPACE_ID" \
  --limit 5 --stdout | jq 'length'

A non-zero count confirms spans reached Arize AX. Run ax auth login first if you have not authenticated. See the ax spans reference.

Query the project’s spans and check that at least one came back.

import os
from arize import ArizeClient

client = ArizeClient(api_key=os.environ["ARIZE_API_KEY"])
resp = client.spans.list(
    project=os.environ["ARIZE_PROJECT_NAME"],
    space=os.environ["ARIZE_SPACE_ID"],
    limit=5,
)
count = len(resp.spans)
print(
    f"{count} span(s) found" if count else "No spans yet — recheck setup"
)

// Reads ARIZE_API_KEY from the environment.
import { listSpans } from "@arizeai/ax-client";

const { data: spans } = await listSpans({
  project: process.env.ARIZE_PROJECT_NAME!,
  space: process.env.ARIZE_SPACE_ID!,
  limit: 5,
});
const count = spans.length;
console.log(
  count ? `${count} span(s) found` : "No spans yet — recheck setup",
);

client, err := arize.NewClient(
    arize.Config{APIKey: os.Getenv("ARIZE_API_KEY")},
)
if err != nil {
    log.Fatal(err)
}
resp, err := client.Spans.List(ctx, spans.ListRequest{
    Project: os.Getenv("ARIZE_PROJECT_NAME"),
    Space:   os.Getenv("ARIZE_SPACE_ID"),
    Limit:   5,
})
if err != nil {
    log.Fatal(err)
}
fmt.Printf("%d span(s) found\n", len(resp.Spans))

SDK span references: Python · TypeScript · Go.

Troubleshooting

No traces in Arize AX. Confirm ARIZE_SPACE_ID and ARIZE_API_KEY are set in the same shell that runs example.py. Enable OpenTelemetry debug logs with export OTEL_LOG_LEVEL=debug and re-run.
Graph ran but no spans appear. LangChainInstrumentor().instrument(...) must run before any langgraph or langchain import. Make sure instrumentation.py is the first import in your entry point.
401 from OpenAI. Verify OPENAI_API_KEY is set and has access to gpt-5.5. Swap for a model your key can call.
Other LLM providers. Install the matching langchain-<provider> package (e.g. langchain-anthropic) and pass that chat model to create_react_agent. The same LangChainInstrumentor covers every provider.

OpenTelemetry

LLM Providers

Agent Frameworks

Coding Agents

Platforms

Orchestration

Evaluation

LangGraph Tracing Tutorial (Google Colab)

Prerequisites

Launch Arize AX

Install

Configure credentials

Setup tracing

Run LangGraph

Expected output

Verify in Arize AX

Check from the skill, CLI, or SDK

Troubleshooting

Resources

LangGraph Documentation

OpenInference LangChain Instrumentor (used for LangGraph)

LangChain Tracing Guide

LangGraph Tracing Tutorial (Google Colab)

​Prerequisites

​Launch Arize AX

​Install

​Configure credentials

​Setup tracing

​Run LangGraph

​Expected output

​Verify in Arize AX

​Check from the skill, CLI, or SDK

​Troubleshooting

​Resources

LangGraph Documentation

OpenInference LangChain Instrumentor (used for LangGraph)

LangChain Tracing Guide

Prerequisites

Launch Arize AX

Install

Configure credentials

Setup tracing

Run LangGraph

Expected output

Verify in Arize AX

Check from the skill, CLI, or SDK

Troubleshooting

Resources