LangChain

from pandaprobe.integrations.langchain import LangChainCallbackHandler

handler = LangChainCallbackHandler(
    session_id="conversation-123",
    user_id="user-abc",
    tags=["production"],
)

from langchain.agents import create_agent

agent = create_agent(
    model="openai:gpt-5.4-nano",
    tools=[...],
)

result = agent.invoke(
    {"messages": [{"role": "user", "content": "Hello!"}]},
    config={"callbacks": [handler]},
)

from langchain.agents import create_agent
from langchain.tools import tool

import pandaprobe
from pandaprobe.integrations.langchain import LangChainCallbackHandler


@tool
def get_weather(city: str) -> str:
    """Get the current weather for a given city."""
    weather_data = {
        "london": "Cloudy, 15°C, 70% humidity",
        "tokyo": "Sunny, 28°C, 45% humidity",
        "new york": "Partly cloudy, 22°C, 55% humidity",
        "paris": "Rainy, 12°C, 85% humidity",
    }
    return weather_data.get(city.lower(), f"Weather data not available for {city}")


@tool
def get_population(city: str) -> str:
    """Get the approximate population of a city."""
    populations = {
        "london": "8.8 million",
        "tokyo": "13.9 million",
        "new york": "8.3 million",
        "paris": "2.2 million",
    }
    return populations.get(city.lower(), f"Population data not available for {city}")


agent = create_agent(
    model="openai:gpt-5.4-nano",
    tools=[get_weather, get_population],
    system_prompt="You are a helpful assistant with access to weather and population tools.",
)

handler = LangChainCallbackHandler(tags=["tool-agent", "example"])

result = agent.invoke(
    {"messages": [{"role": "user", "content": "What's the weather like in London and what's its population?"}]},
    config={"callbacks": [handler]},
)

final_message = result["messages"][-1]
print(f"Agent: {final_message.content}")

pandaprobe.flush()
pandaprobe.shutdown()

from langchain_core.prompts import ChatPromptTemplate
from langchain_openai import ChatOpenAI

from pandaprobe.integrations.langchain import LangChainCallbackHandler

prompt = ChatPromptTemplate.from_template("Tell me a joke about {topic}")
llm = ChatOpenAI(model="gpt-5.4-nano")
chain = prompt | llm

handler = LangChainCallbackHandler()
result = chain.invoke(
    {"topic": "pandas"},
    config={"callbacks": [handler]},
)