LangGraph → OpenObserve

pip install openobserve-python-sdk opentelemetry-instrumentation-langchain langchain-openai langgraph python-dotenv

OPENOBSERVE_URL=https://api.openobserve.ai/
OPENOBSERVE_ORG=your_org_id
OPENOBSERVE_AUTH_TOKEN=Basic <your_base64_token>
OPENAI_API_KEY=your-openai-api-key

from dotenv import load_dotenv
load_dotenv()

from opentelemetry.instrumentation.langchain import LangchainInstrumentor
from openobserve import openobserve_init

LangchainInstrumentor().instrument()
openobserve_init()

from typing import TypedDict
from langchain_openai import ChatOpenAI
from langgraph.graph import StateGraph, END

llm = ChatOpenAI(model="gpt-4o-mini")


class State(TypedDict):
    question: str
    answer: str


def answer_node(state: State):
    response = llm.invoke([{"role": "user", "content": state["question"]}])
    return {"answer": response.content}


graph = StateGraph(State)
graph.add_node("answer", answer_node)
graph.set_entry_point("answer")
graph.add_edge("answer", END)
app = graph.compile()

result = app.invoke({"question": "What is OpenTelemetry?", "answer": ""})
print(result["answer"])

def summarise_node(state: State):
    response = llm.invoke([
        {"role": "user", "content": f"Summarise in five words: {state['answer']}"}
    ])
    return {"answer": response.content}

graph = StateGraph(State)
graph.add_node("answer", answer_node)
graph.add_node("summarise", summarise_node)
graph.set_entry_point("answer")
graph.add_edge("answer", "summarise")
graph.add_edge("summarise", END)
app = graph.compile()

result = app.invoke({"question": "What is distributed tracing?", "answer": ""})
print(result["answer"])