OpenAI

pip install agentops openai

poetry add agentops openai

uv pip install agentops openai

export OPENAI_API_KEY="your_openai_api_key_here"
export AGENTOPS_API_KEY="your_agentops_api_key_here"

OPENAI_API_KEY="your_openai_api_key_here"
AGENTOPS_API_KEY="your_agentops_api_key_here"

import agentops
from openai import OpenAI

# Initialize AgentOps
agentops.init()

# Create OpenAI client
client = OpenAI()

# Make a streaming API call
stream = client.chat.completions.create(
    model="gpt-4o-mini",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Write a short poem about AI."}
    ],
    stream=True
)

# Process the streaming response
for chunk in stream:
    if chunk.choices[0].delta.content is not None:
        print(chunk.choices[0].delta.content, end="")

import json
import agentops
from openai import OpenAI

# Initialize AgentOps
agentops.init()

# Create OpenAI client
client = OpenAI()

# Define tools
tools = [
    {
        "type": "function",
        "function": {
            "name": "get_weather",
            "description": "Get the current weather in a given location",
            "parameters": {
                "type": "object",
                "properties": {
                    "location": {
                        "type": "string",
                        "description": "The city and state, e.g. San Francisco, CA",
                    }
                },
                "required": ["location"],
            },
        },
    }
]

# Function implementation
def get_weather(location):
    return json.dumps({"location": location, "temperature": "72", "unit": "fahrenheit", "forecast": ["sunny", "windy"]})

# Make a function call API request
messages = [
    {"role": "system", "content": "You are a helpful weather assistant."},
    {"role": "user", "content": "What's the weather like in Boston?"}
]

response = client.chat.completions.create(
    model="gpt-4",
    messages=messages,
    tools=tools,
    tool_choice="auto",
)

# Process response
response_message = response.choices[0].message
messages.append({"role": "assistant", "content": response_message.content, "tool_calls": response_message.tool_calls})

if response_message.tool_calls:
    # Process each tool call
    for tool_call in response_message.tool_calls:
        function_name = tool_call.function.name
        function_args = json.loads(tool_call.function.arguments)
        
        if function_name == "get_weather":
            function_response = get_weather(function_args.get("location"))
            
            # Add tool response to messages
            messages.append(
                {
                    "role": "tool",
                    "tool_call_id": tool_call.id,
                    "name": function_name,
                    "content": function_response,
                }
            )
    
    # Get a new response from the model
    second_response = client.chat.completions.create(
        model="gpt-4",
        messages=messages,
    )
    
    print(second_response.choices[0].message.content)
else:
    print(response_message.content)