LangGraph

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from langchain_openai import ChatOpenAI
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from langgraph.prebuilt import create_react_agent
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from deepeval.integrations.langchain import CallbackHandler
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def get_weather(city: str) -> str:
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    """Returns the weather in a city"""
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    return f"It's always sunny in {city}!"
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llm = ChatOpenAI(model="gpt-4o-mini")
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agent = create_react_agent(
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    model=llm,
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    tools=[get_weather],
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    prompt="You are a helpful assistant",
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)
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result = agent.invoke(
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    input={"messages": [{"role": "user", "content": "what is the weather in sf"}]},
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    config={
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        "callbacks": [
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            CallbackHandler(metric_collection="task_completion")
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        ],
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    },
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)

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from langgraph.prebuilt import create_react_agent
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from deepeval.metrics import TaskCompletionMetric
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from deepeval.integrations.langchain import CallbackHandler
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def get_weather(city: str) -> str:
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    """Returns the weather in a city"""
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    return f"It's always sunny in {city}!"
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agent = create_react_agent(model="openai:gpt-4o-mini",tools=[get_weather],prompt="You are a helpful assistant",)
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task_completion = TaskCompletionMetric(threshold=0.7, model="gpt-4o-mini", include_reason=True)
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from deepeval.dataset import Golden, EvaluationDataset
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goldens = [
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    Golden(input="What is the weather in Bogotá, Colombia?"),
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    Golden(input="What is the weather in Paris, France?"),
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]
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dataset = EvaluationDataset(goldens=goldens)
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for golden in dataset.evals_iterator():
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    agent.invoke(
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        input={"messages": [{"role": "user", "content": golden.input}]},
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        config={"callbacks": [CallbackHandler(metrics=[task_completion])]},
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    )