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Documentation Index

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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.
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LangGraph Tracing Tutorial (Google Colab)

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Prerequisites

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Launch Arize AX

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

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Install

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

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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>"

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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.")

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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"),
    tools=[get_weather],
)

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

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

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Expected output

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

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Verify in Arize AX

  1. Open your Arize AX space and select project langgraph-tracing-example.
  2. 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.
  3. If no traces appear, see Troubleshooting.

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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. 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.

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Resources

LangGraph Documentation

OpenInference LangChain Instrumentor (used for LangGraph)

LangChain Tracing Guide