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Open Responses API Examples

Below are practical examples showing how to use the Julep Open Responses API for various use cases.
  • The Open Responses API requires self-hosting. See the installation guide below.
  • Being in Alpha, the API is subject to change. Check back frequently for updates.
  • For more context, see the OpenAI Responses API documentation.

API Key Configuration

  • RESPONSE_API_KEY is the API key that you set in the .env file.

Model Selection

  • While using models other than OpenAI, one might need to add the provider/ prefix to the model name.
  • For supported providers, see the LiteLLM Providers documentation.

Environment Setup

  • Add the relevant provider keys to the .env file to use their respective models.

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Setup

First, set up your environment and create a client:
from openai import OpenAI

# Create an OpenAI client pointing to Julep's Open Responses API
client = OpenAI(base_url="http://localhost:8080/", api_key="RESPONSE_API_KEY")

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Using Reasoning Features

Enhance your model’s reasoning capabilities for solving complex problems:
# Create a response with explicit reasoning
reasoning_response = client.responses.create(
    model="o1",
    input="If Sarah has 3 apples and John has 5, and they combine their apples, then how many apples do they have in total? Explain your approach.",
    reasoning={
        "effort": "medium"  # Control reasoning depth with "low", "medium", or "high"
    }
)

# Access the final answer
print(reasoning_response.output_text)
# Output: They would have 8 apples in total. The approach is straightforward: you simply add the number of apples Sarah has (3) to the number of apples John has (5), giving 3 + 5 = 8..

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Using Web Search Tool

Python
web_search_response = openai_client.responses.create(
model="gpt-4o-mini",
tools=[{"type": "web_search_preview"}],
    input="What was a positive news story from today?",
)
# The output will include both the text response and any tool calls that were made

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Maintaining Conversation History

Create a continuous conversation by referencing previous responses:
# Reference a previous response to continue a conversation
follow_up_response = client.responses.create(
    model="gpt-4o-mini",
    input="What was the final answer?",
    previous_response_id=reasoning_response.id
)

print(follow_up_response.output_text)

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Retrieving Past Responses

Access previously created responses by their ID:
# Retrieve a response by ID
retrieved_response = client.responses.retrieve(response_id="your-response-id")

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Next Steps

You’ve got Open Responses running – here’s what to explore next: