Responses API | Parasail

Base URL

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https://api.parasail.io/v1

Use your existing Parasail API key for authentication. The API key is the same as for all other Parasail endpoints.

Requirements

Requirement
Details

Base URL

https://api.parasail.io/v1

store parameter
Must be set to false on every request. The gateway does not support server-side state storage. Omitting this will return an error.

Authentication
Authorization: Bearer <PARASAIL_API_KEY> (same key as the standard API)

Compatibility

The table below shows which Responses API features are supported.

Quick Start

Python (OpenAI SDK)

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from openai import OpenAI

client = OpenAI(
    base_url="https://api.parasail.io/v1",
    api_key="<PARASAIL_API_KEY>"
)

response = client.responses.create(
    model="parasail-kimi-k25-elicit",
    store=False,
    input="Explain the difference between TCP and UDP in two sentences."
)

print(response.output_text)

cURL

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curl -s https://api.parasail.io/v1/responses \
  -H "Authorization: Bearer $PARASAIL_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "parasail-kimi-k25-elicit",
    "store": false,
    "input": "Explain the difference between TCP and UDP in two sentences."
  }'

Multi-Turn Conversations

Since previous_response_id is not supported, you maintain conversation history by passing the full message array in input. Each turn appends the assistant's reply and the next user message.

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from openai import OpenAI

client = OpenAI(
    base_url="https://api.parasail.io/v1",
    api_key="<PARASAIL_API_KEY>"
)

# Turn 1
response = client.responses.create(
    model="parasail-kimi-k25-elicit",
    store=False,
    input=[\
        {"role": "user", "content": "I'm planning a trip to Tokyo. What should I see on day 1?"}\
    ]
)
turn1_text = response.output_text
print("Turn 1:", turn1_text)

# Turn 2 — pass the full history
response = client.responses.create(
    model="parasail-kimi-k25-elicit",
    store=False,
    input=[\
        {"role": "user", "content": "I'm planning a trip to Tokyo. What should I see on day 1?"},\
        {"role": "assistant", "content": turn1_text},\
        {"role": "user", "content": "What about day 2? Suggest different areas."}\
    ]
)
print("Turn 2:", response.output_text)

Function Calling (Agentic)

The Responses API supports tool definitions and multi-step agentic loops where the model calls functions and you return results.

Define Tools

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tools = [\
    {\
        "type": "function",\
        "name": "get_weather",\
        "description": "Get the current weather for a city.",\
        "parameters": {\
            "type": "object",\
            "properties": {\
                "city": {"type": "string", "description": "City name"},\
                "units": {\
                    "type": "string",\
                    "enum": ["celsius", "fahrenheit"],\
                    "default": "celsius"\
                }\
            },\
            "required": ["city"]\
        }\
    }\
]

Agentic Loop

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from openai import OpenAI
import json

client = OpenAI(
    base_url="https://api.parasail.io/v1",
    api_key="<PARASAIL_API_KEY>"
)

tools = [...]  # as defined above

def handle_tool_call(name, arguments):
    """Your application logic — call real APIs, databases, etc."""
    args = json.loads(arguments)
    if name == "get_weather":
        return json.dumps({"city": args["city"], "temperature": 22, "condition": "sunny"})
    return json.dumps({"error": "unknown function"})

# Initial request
response = client.responses.create(
    model="parasail-kimi-k25-elicit",
    store=False,
    tools=tools,
    input=[{"role": "user", "content": "What's the weather in Tokyo?"}]
)

# Agentic loop — keep going until the model stops calling tools
while response.output:
    tool_calls = [item for item in response.output if item.type == "function_call"]
    if not tool_calls:
        break  # Model produced a final text response

# Build input for next turn: previous output + tool results
    next_input = list(response.output)
    for tool_call in tool_calls:
        result = handle_tool_call(tool_call.name, tool_call.arguments)
        next_input.append({
            "type": "function_call_output",
            "call_id": tool_call.call_id,
            "output": result
        })

response = client.responses.create(
        model="parasail-kimi-k25-elicit",
        store=False,
        tools=tools,
        input=next_input
    )

# Print final response
print(response.output_text)

The model may call multiple tools in parallel within a single turn. Always provide results for every function_call before sending the next request.

Important Notes

• Always setstore: false . Every request must include "store": false. Server-side response storage is not supported, and omitting this parameter will result in an error.

• No stateful chaining. The previous_response_id field is not supported. Manage conversation history client-side by passing the full input array each turn.

• Same API key. Your existing Parasail API key works across all endpoints. No separate key is needed.

• Model-specific controls. For DeepSeek, Qwen3.5, and GPT-OSS thinking and reasoning parameters, see model-specific notes.

• Model availability. Not all models are available for the Responses API. Use the /v1/models endpoint to check:

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curl -s https://api.parasail.io/v1/models \
  -H "Authorization: Bearer $PARASAIL_API_KEY"