Integrations

import zeroeval as ze
ze.init()

import openai
client = openai.OpenAI()

# This call is automatically traced
response = client.chat.completions.create(
    model="gpt-4",
    messages=[{"role": "user", "content": "Hello!"}]
)

# Streaming is also automatically traced
stream = client.chat.completions.create(
    model="gpt-4",
    messages=[{"role": "user", "content": "Tell me a story"}],
    stream=True
)

for chunk in stream:
    if chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end="")

import zeroeval as ze
ze.init()

from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate

# All components are automatically traced
model = ChatOpenAI()
prompt = ChatPromptTemplate.from_template("Tell me about {topic}")
chain = prompt | model

response = chain.invoke({"topic": "AI"})

import zeroeval as ze
ze.init()

from langgraph.graph import StateGraph, START, END
from langchain_core.messages import HumanMessage

# Define a multi-node graph
workflow = StateGraph(AgentState)
workflow.add_node("reasoning", reasoning_node)
workflow.add_node("agent", agent_node)
workflow.add_node("tools", tool_node)

workflow.add_conditional_edges(
    "agent",
    should_continue,
    {"tools": "tools", "end": END}
)

app = workflow.compile()

# Full graph execution is automatically traced
result = app.invoke({"messages": [HumanMessage(content="Help me plan a trip")]})

# Streaming is also supported
for chunk in app.stream({"messages": [HumanMessage(content="Hello")]}):
    print(chunk)

import zeroeval as ze
ze.init()

from pydantic_ai import Agent
from pydantic import BaseModel

class Response(BaseModel):
    message: str
    sentiment: str

# Create an agent with structured output
agent = Agent(
    model="openai:gpt-4o-mini",
    output_type=Response,
    system_prompt="You are a helpful assistant."
)

# Single execution - automatically traced
result = await agent.run("Hello!")

# Multi-turn conversation - all turns share the same trace
message_history = []

async with agent.iter("First message", message_history=message_history) as run:
    async for node in run:
        pass
    message_history = run.result.all_messages()

# Second turn reuses the same trace_id
async with agent.iter("Follow-up message", message_history=message_history) as run:
    async for node in run:
        pass
    message_history = run.result.all_messages()

import zeroeval as ze
ze.init()

from livekit import agents
from livekit.agents import AgentSession, Agent
from livekit.plugins import openai

async def entrypoint(ctx: agents.JobContext):
    await ctx.connect()

    # All agent sessions are automatically traced
    session = AgentSession(
        llm=openai.realtime.RealtimeModel(voice="coral")
    )

    await session.start(
        room=ctx.room,
        agent=Agent(instructions="You are a helpful voice AI assistant.")
    )

    # Agent interactions are automatically captured
    await session.generate_reply(
        instructions="Greet the user and offer your assistance."
    )

if __name__ == "__main__":
    agents.cli.run_app(agents.WorkerOptions(entrypoint_fnc=entrypoint))