NeMo Flow LangGraph Integration

Use the nemo_flow.integrations.langgraph package to add NeMo Flow observability to LangGraph workflows through public LangGraph APIs.

Setup

Install the LangGraph integration extra in your application environment.

uv

uv add "nemo-flow[langgraph]"

pip

pip install "nemo-flow[langgraph]"

Installing the langgraph extra also installs the LangChain integration dependencies.

Usage Example

from typing_extensions import TypedDict

import nemo_flow
from langgraph.graph import END, START, StateGraph
from nemo_flow.integrations.langgraph import NemoFlowCallbackHandler


class State(TypedDict):
    value: int


def increment(state: State) -> State:
    return {"value": state["value"] + 1}


builder = StateGraph(State)
builder.add_node("increment", increment)
builder.add_edge(START, "increment")
builder.add_edge("increment", END)

graph = builder.compile()

with nemo_flow.scope.scope("langgraph-request", nemo_flow.ScopeType.Agent):
    result = graph.invoke(
        {"value": 1},
        config={"callbacks": [NemoFlowCallbackHandler()]},
    )

print(result)

For LangChain agents inside a LangGraph workflow, use NemoFlowMiddleware from this package the same way as the LangChain integration and pass the LangGraph config into the nested agent call:

from langchain.agents import create_agent
from langchain_core.runnables import RunnableConfig
from nemo_flow.integrations.langgraph import NemoFlowMiddleware

agent = create_agent(
    model="nvidia:nvidia/nemotron-3-nano-30b-a3b",
    tools=[],
    middleware=[NemoFlowMiddleware()],
)


def agent_node(state: dict, config: RunnableConfig) -> dict:
    return agent.invoke({"messages": state["messages"]}, config=config)

Install the NVIDIA LangChain provider if you want to run the nested agent example as written:

uv

uv add "nemo-flow[langgraph,langchain-nvidia]"

pip

pip install "nemo-flow[langgraph,langchain-nvidia]"

Observability

Refer to Observability for details on exporting NeMo Flow observability data to third-party systems.