OpenAI has introduced the Responses API to enhance and eventually discontinue the existing Assistants API. Ragwalla will continue to support our wire-compatible Assistants API indefinitely.

Here's a comparison to help you understand the differences between the OpenAI Responses API and the Assistants API:

Assistants API:

• Purpose: Designed to help developers create AI assistants capable of managing complex tasks, maintaining conversation threads, and utilizing tools like file search and code execution.

• Structure: Utilizes objects such as assistants, threads, messages, and runs to manage interactions.

• Status: Currently in beta, with plans for deprecation in the first half of 2026 though Ragwalla will support the Assistants API indefinitely.

Responses API:

• Purpose: Aims to simplify the creation of AI agents by combining the user-friendly aspects of the Chat Completions API with advanced features like tool usage and state management.

• Features:

  • Built-in Tools: Includes web search, file search, and computer use capabilities.

  • State Management: Allows for server-side conversation state management, reducing the need to resend the entire conversation history with each interaction.

  • Flexibility: Designed to be faster and more adaptable to various developer needs.

• Transition Plan: OpenAI plans to achieve feature parity between the Responses and Assistants APIs before phasing out the Assistants API by mid-2026.

Key Differences:

• Complexity: The Assistants API requires managing multiple objects (assistants, threads, messages, runs), which can be intricate. The Responses API streamlines this by handling state management more efficiently.

• Tool Integration: The Responses API offers built-in tools for web and file search, as well as computer use, providing more seamless integration compared to the Assistants API.

• Performance: Developers have reported that the Responses API is faster and more responsive than the Assistants API.

How Responses Manages State

The OpenAI Responses API simplifies conversation state management by handling it server-side, eliminating the need for developers to manually maintain conversation history. Here's how it works:

Server-Side State Management:

• Automatic State Handling: When you make a request using the Responses API, you can include the previous_response_id parameter, which references the ID of the last response in the conversation. This allows the API to automatically retrieve and incorporate the entire conversation history associated with that ID, ensuring continuity without requiring you to resend prior messages.

• Data Retention: Response objects, which include conversation history, are stored for 30 days by default. They can be accessed via the dashboard logs or retrieved through the API. If you prefer not to store this data, you can disable this behavior by setting the store parameter to false when creating a response.

Implementing Conversation State:

To maintain the conversation state, follow these steps:

1. Initial Request: Send your first user input without the previous_response_id.

2. Subsequent Requests: For each following user input, include the previous_response_id parameter set to the ID of the last response received. This links the new input to the existing conversation context.

Example in Python:

import openai
import os

openai.api_key = os.environ.get('API_KEY')

last_response_id = None

while True:
    user_input = input("\nUser: ")
    if user_input.lower() in ["exit", "quit"]:
        break

    response = openai.Response.create(
        model="gpt-4o",
        input=user_input,
        previous_response_id=last_response_id
    )
    last_response_id = response.id
    print("\nAssistant:", response.output_text)

In this script, the conversation state is maintained by passing the previous_response_id with each request, allowing the API to manage the conversation context automatically.

Considerations:

• Token Usage: Even when using previous_response_id, all previous input tokens in the conversation chain are billed as input tokens. Be mindful of this to manage costs effectively.

• Truncation Strategy: If the combined context of the conversation exceeds the model's maximum token limit, you can set the truncation parameter to auto. This enables the model to truncate the conversation by removing older messages, ensuring the input fits within the context window.

Ultimately, the Responses API is OpenAI's effort to provide a more efficient, flexible, and developer-friendly platform for building AI agents, addressing some of the limitations of the Assistants API.