Responses API - Build Agentic Workflows

from openai import OpenAI

client = OpenAI(
    base_url="https://api.infercom.ai/v1",
    api_key="your-infercom-api-key"
)

response = client.responses.create(
    model="MiniMax-M2.5",
    input="Explain the difference between supervised and unsupervised learning."
)

# Access the text output
print(response.output_text)

response = client.responses.create(
    model="MiniMax-M2.5",
    instructions="You are a helpful assistant that speaks like a pirate.",
    input="How are you today?"
)

# Turn 1
response_1 = client.responses.create(
    model="MiniMax-M2.5",
    input=[{"role": "user", "content": "My name is Thomas."}]
)

# Turn 2 - include prior messages
response_2 = client.responses.create(
    model="MiniMax-M2.5",
    input=[
        {"role": "user", "content": "My name is Thomas."},
        response_1.output[0],  # Include assistant's response
        {"role": "user", "content": "What is my name?"}
    ]
)

print(response_2.output_text)  # "Your name is Thomas..."

import json

tools = [{
    "type": "function",
    "name": "get_weather",
    "description": "Get current weather for a city.",
    "parameters": {
        "type": "object",
        "properties": {
            "city": {"type": "string", "description": "City name"}
        },
        "required": ["city"]
    }
}]

response = client.responses.create(
    model="MiniMax-M2.5",
    input=[{"role": "user", "content": "What's the weather in Berlin?"}],
    tools=tools
)

# Check if model wants to call a function
for item in response.output:
    if item.type == "function_call":
        print(f"Function: {item.name}")
        print(f"Arguments: {item.arguments}")

# Execute the function locally
def get_weather(city: str) -> dict:
    # Your actual weather API call here
    return {"city": city, "temperature": "18°C", "condition": "Cloudy"}

# Find the function call in the response
tool_call = next(item for item in response.output if item.type == "function_call")
args = json.loads(tool_call.arguments)
result = get_weather(args["city"])

# Send result back to the model
follow_up = client.responses.create(
    model="MiniMax-M2.5",
    input=[
        {"role": "user", "content": "What's the weather in Berlin?"},
        tool_call,  # Include the function call
        {
            "type": "function_call_output",
            "call_id": tool_call.call_id,
            "output": json.dumps(result)
        }
    ],
    tools=tools
)

print(follow_up.output_text)  # "The weather in Berlin is 18°C and cloudy."

response = client.responses.create(
    model="MiniMax-M2.5",
    input="List 3 European capitals",
    text={"format": {"type": "json_object"}}
)

import json
data = json.loads(response.output_text)

response = client.responses.create(
    model="MiniMax-M2.5",
    input="Extract event details: SambaNova launch May 1, 2026 at 10am in San Francisco.",
    text={
        "format": {
            "type": "json_schema",
            "name": "event_extraction",
            "schema": {
                "type": "object",
                "properties": {
                    "title": {"type": "string"},
                    "date": {"type": "string"},
                    "time": {"type": "string"},
                    "location": {"type": "string"}
                },
                "required": ["title", "date", "time", "location"]
            }
        }
    }
)

import json
event = json.loads(response.output_text)
print(event)
# {"title": "SambaNova launch", "date": "May 1, 2026", "time": "10am", "location": "San Francisco"}

response = client.responses.create(
    model="MiniMax-M2.5",
    input="What is 15 * 23?",
    reasoning={"effort": "high"}
)

# Access reasoning separately from the answer
for item in response.output:
    if item.type == "reasoning":
        print("Reasoning:", item.content[0].text)
    elif item.type == "message":
        print("Answer:", item.content[0].text)

stream = client.responses.create(
    model="MiniMax-M2.5",
    input="Write a short poem about speed.",
    stream=True
)

for event in stream:
    if event.type == "response.output_text.delta":
        print(event.delta, end="", flush=True)