LangGraph

Google Colab

tutorials/python/llm/tracing/langchain/langgraph-tracing.ipynb at main · Arize-ai/tutorialsGitHub

The same instrumentations for LangChain work for LangGraph.

Install packages

pip install langgraph langchain_openai

pip install arize-phoenix arize-otel

pip install openinference-instrumentation-openai openinference-instrumentation-langchain

Setup tracing

from arize.otel import register

tracer_provider = register(
    space_id="",
    api_key="",
    project_name="langgraph-tracing",
)

from openinference.instrumentation.langchain import LangChainInstrumentor
LangChainInstrumentor().instrument(tracer_provider=tracer_provider)

Run Langgraph

from typing import Literal
from langchain_core.messages import HumanMessage
from langchain_openai import ChatOpenAI
from langgraph.checkpoint.memory import MemorySaver
from langgraph.graph import END, StateGraph, MessagesState
from langgraph.prebuilt import ToolNode
from langchain_core.tools import tool

import os
os.environ["OPENAI_API_KEY"] = "" # Fill in here
     
@tool
def search(query: str):
    if "sf" in query.lower() or "san francisco" in query.lower():
        return "It's 60 degrees and foggy."
    return "It's 90 degrees and sunny."

def should_continue(state: MessagesState) -> Literal["tools", END]:
    messages = state["messages"]
    last_message = messages[-1]
    if last_message.tool_calls:
        return "tools"
    return END

def call_model(state: MessagesState):
    messages = state["messages"]
    response = model.invoke(messages)
    return {"messages": [response]}

tools = [search]
tool_node = ToolNode(tools)
model = ChatOpenAI(temperature=0).bind_tools(tools)

workflow = StateGraph(MessagesState)
workflow.add_node("agent", call_model)
workflow.add_node("tools", tool_node)
workflow.set_entry_point("agent")
workflow.add_conditional_edges(
    "agent",
    should_continue,
)
workflow.add_edge("tools", "agent")

checkpointer = MemorySaver()
app = workflow.compile(checkpointer=checkpointer)

final_state = app.invoke(
    {"messages": [HumanMessage(content="what is the weather in sf")]},
    config={"configurable": {"thread_id": 42}},
)
final_state["messages"][-1].content