LangChain

pip install agentx-python langchain langchain-openai

from agentx import AgentX
from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser

client = AgentX.from_env()

llm = ChatOpenAI(model="gpt-4o-mini", temperature=0)
prompt = ChatPromptTemplate.from_messages([
    ("system", "You are a helpful support agent."),
    ("human", "{question}"),
])
chain = prompt | llm | StrOutputParser()

def langchain_agent(case):
    output = chain.invoke({"question": case.query})
    return {"output": output, "metadata": {"model": "gpt-4o-mini"}}

run_context = (
    client.evaluations
    .run(dataset_id="...", subject={"kind": "custom_agent", "displayName": "Support Chain", "framework": "langchain"})
    .execute(langchain_agent)
    .finalize()
)
print("Average rating:", run_context.average_rating)

from agentx import AgentX
from agentx.integrations.langchain import AgentXCallbackHandler
from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser

client = AgentX.from_env()
handler = AgentXCallbackHandler(tracer=client.tracer, name="support-chain")

llm = ChatOpenAI(model="gpt-4o-mini", temperature=0)
prompt = ChatPromptTemplate.from_messages([
    ("system", "You are a helpful support agent."),
    ("human", "{question}"),
])
chain = prompt | llm | StrOutputParser()

def langchain_agent(case):
    with client.tracer.trace("support-chain-call", framework="langchain", sync=True) as span:
        output = chain.invoke({"question": case.query}, config={"callbacks": [handler]})
        span.output = output

    return {
        "output": output,
        "metadata": {"model": "gpt-4o-mini"},
        "trace_id": span.trace_id,
    }