OpenAI Responses API

A OpenAI Responses API is a agentic API that is a OpenAI API that enables developers to generate AI-powered responses through request-response interactions (that support multi-modal input and text output tasks).

• AKA: OpenAI Response Generation API, OpenAI Model Interaction API.
• Context:
  • It can typically process Input Content through text input, image input, and file input mechanisms.
  • It can typically generate AI Response using model selection and response format specifications.
  • It can typically support Conversation State through previous response references and stateful interactions.
  • It can typically extend Model Capability with built-in tools and function calling mechanisms.
  • It can typically customize Response Generation through temperature parameters, top_p settings, and token limits.
  • It can typically manage Response Lifecycle through creation endpoints, retrieval endpoints, and deletion endpoints.
  • ...
  • It can often control Response Behavior through instruction parameters and tool choice specifications.
  • It can often ensure Response Security through user identifiers and abuse monitoring systems.
  • It can often optimize Token Usage through truncation strategy and token tracking mechanisms.
  • It can often enhance Data Organization through metadata attachment and structured response formats.
  • ...
  • It can range from being a Simple OpenAI Responses API Implementation to being a Complex OpenAI Responses API Integration, depending on its tool utilization and response customization.
  • It can range from being a Basic OpenAI Responses API Usage to being an Advanced OpenAI Responses API Application, depending on its model selection and conversation management.
  • ...
  • It can have API Endpoint for response creation, response retrieval, response deletion, and input item listing.
  • It can have Parameter Option for model specification, input definition, instruction provision, and output control.
  • It can have Response Field including status information, token usage, error details, and output content.
  • ...
• Examples:
  • OpenAI Responses API Implementations, such as:
    • OpenAI Responses API Endpoints, such as:
      • OpenAI Responses API Creation Endpoint for model response generation.
      • OpenAI Responses API Retrieval Endpoint for response data access.
      • OpenAI Responses API Deletion Endpoint for response removal.
      • OpenAI Responses API Input Items Endpoint for input data listing.
    • OpenAI Responses API Parameters, such as:
      • OpenAI Responses API Model Parameter for model selection.
      • OpenAI Responses API Input Parameter for input content specification.
      • OpenAI Responses API Instructions Parameter for system message definition.
      • OpenAI Responses API Tools Parameter for tool availability configuration.
  • OpenAI Responses API Use Cases, such as:
    • OpenAI Responses API Text Generations, such as:
      • OpenAI Responses API Story Creation for narrative content.
      • OpenAI Responses API Code Writing for programming solution.
    • OpenAI Responses API Multi-turn Conversations, such as:
      • OpenAI Responses API Chat Application for conversational interface.
      • OpenAI Responses API Customer Support Bot for automated assistance.
    • OpenAI Responses API Tool Integrations, such as:
      • OpenAI Responses API Web Search Tool for external information retrieval.
      • OpenAI Responses API File Search Tool for document content access.
      • OpenAI Responses API Function Calling for custom code execution.
  • ...
• Counter-Examples:
  • OpenAI Chat Completions API, which uses a different conversation format and lacks some advanced tool integration capabilities.
  • OpenAI Images API, which focuses on image generation rather than text response.
  • OpenAI Embeddings API, which generates vector representations of text rather than human-readable responses.
  • OpenAI Fine-tuning API, which customizes underlying models rather than generating immediate responses.
• See: API Endpoint, AI Model, Function Calling, Response Object, HTTP Request, API Parameter, Multi-modal Input, OpenAI API, Cloud Service.

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References

2025-03-11

• Perplexity.ai
  • Question: What is the OpenAI Responses API released in March 2025? Please explain its design, capabilities, and how it relates to existing APIs. Include information about its tool integrations like web search, file search, and computer use. Also explain how the Responses API works with the Agents SDK for building AI agents.
  • Answer: On March 11, 2025, OpenAI released the Responses API, representing a significant evolution from the company's previous APIs. The Responses API combines the simplicity of the Chat Completions API with the tool-use capabilities of the Assistants API, creating a more flexible foundation for developers building agentic applications.
    • API Design Features:
      • Core Architecture:
        • The Responses API enables developers to solve increasingly complex tasks using multiple tools within a single API call.
        • Functions as the fundamental unit for employing models and tools to accomplish specific tasks.
        • Works alongside the Agents SDK to tackle more complex challenges through agent collaboration.
      • Design Improvements:
        • Unified item-based design for consistent data handling and simpler integration.
        • Improved polymorphism for more flexible and intuitive data structures.
        • Intuitive streaming events for real-time interaction and progressive responses.
        • SDK helpers like `response.output_text` for easily accessing model outputs.
      • Evolution Path:
        • Based on substantial updates from developer feedback during the Assistants API beta.
        • OpenAI plans to phase out the Assistants API by mid-2026.
        • Provides a migration path to preserve data and functionality.
    • Built-in Tool Integrations:
      • Web Search Capability:
        • Allows AI models to retrieve up-to-date information from the internet.
        • Uses the same technology that powers ChatGPT's search.
        • Implemented by configuring it in the `tools` array of a Responses API request.
        • Provides properly cited answers with inline citations to source materials.
        • Features deep links to exactly where information was located within search results.
        • Available with gpt-4o and gpt-4o-mini models.
        • Priced at $30 and $25 per 1,000 queries respectively for dedicated search models.
      • File Search Capability:
        • Provides an easily accessible retrieval system for searching through documents.
        • Supports multiple file types, metadata, filtering, and custom reranking.
        • Valuable for use cases such as customer support agents accessing FAQs.
        • Useful for legal assistants referencing past cases.
        • Priced at $2.50 per thousand queries with file storage at $0.10/GB/day.
        • Represents OpenAI's move to bring search and RAG capabilities under a unified API umbrella.
      • Computer Use Capability:
        • Gives AI agents the ability to control computers or virtual machines.
        • Powered by the Computer-Using Agent (CUA) model, the same technology behind OpenAI's Operator agent.
        • Combines GPT-4o's vision capabilities with advanced reasoning through reinforcement learning.
        • Interprets screenshots and generates appropriate mouse and keyboard actions.
        • Achieves impressive performance on browser-based task benchmarks.
        • Priced at $3 per million input tokens and $12 per million output tokens.
        • Currently available only to users in higher API usage tiers (3-5).
    • API Implementation:
      • Sample Code:
        • Basic web search implementation through the Responses API:

```python response = client.responses.create(

 model="gpt-4o",
 tools=[{"type": "web_search_preview"}],
 input="What was a positive news story from today?"

) ```

• • • • Integration Methods:
        • The Responses API can be integrated with various programming languages through OpenAI SDKs.
        • Supports both synchronous and asynchronous request patterns.
        • Enables streaming of partial responses for improved user experience.
      • Response Structure:
        • Returns structured responses with clearly defined items.
        • Supports streaming with intuitive events.
        • Provides direct access to tool outputs and model generations.
    • Integration with Agents SDK:
      • Relationship:
        • The Responses API functions as the fundamental unit for employing models and tools.
        • The Agents SDK allows multiple of these fundamental units to collaborate in tackling more complex challenges.
        • Agents SDK is an open-source framework designed to simplify the creation and orchestration of agent workflows.
        • Represents a production-ready upgrade of OpenAI's previous experimental framework called Swarm.
      • Agent Primitives:
        • Agents: LLMs equipped with instructions and tools for specific tasks.
        • Handoffs: Mechanisms for agents to delegate tasks to other specialized agents.
        • Guardrails: Input validation systems to ensure safety and reliability.
      • Tracing Capabilities:
        • Built-in visualization and debugging for agent execution flows.
        • Accessible through the OpenAI platform dashboard under "traces".
        • Provides insight into what each agent is doing during execution.
    • API Pricing Model:
      • Token-based Pricing:
        • Standard token-based pricing model with no additional charges for the Responses API itself.
        • Developers pay for tokens used by underlying models and for specific tool calls.
      • Model Pricing:
        • GPT-4o: $2.50 per million input tokens, $10.00 per million output tokens.
        • GPT-4o mini: $0.15 per million input tokens, $0.60 per million output tokens.
        • o1: $15.00 per million input tokens, $60.00 per million output tokens.
        • o3-mini: $1.10 per million input tokens, $4.40 per million output tokens.
      • Tool Pricing:
        • Web Search: Varies by model and context size, charged per query.
        • File Search: $2.50 per thousand queries plus storage costs.
        • Computer Use: $3 per million input tokens and $12 per million output tokens.
    • Strategic Significance:
      • Technology Evolution:
        • Represents a significant advancement in making AI agents more accessible to developers and enterprises.
        • Builds upon OpenAI's earlier agent innovations like Deep Research and Operator.
        • Effectively democratizes the technology that powers OpenAI's specialized first-party agents.
      • Future Development:
        • OpenAI will continue investing in deeper integrations across its APIs.
        • New tools to help deploy, evaluate, and optimize agents in production.
        • Goal to provide a seamless platform experience for building agents across industries.
  • Citations:

[1] https://openai.com/index/new-tools-for-building-agents/
[2] https://www.ciodive.com/news/openai-ai-agents-development-tools/742215/
[3] https://platform.openai.com/docs/guides/tools-web-search?api-mode=responses
[5] https://www.theverge.com/news/627556/openai-ai-agents-responses-api-agents-sdk
[6] https://venturebeat.com/programming-development/openai-unveils-responses-api-open-source-agents-sdk-letting-developers-build-their-own-deep-research-and-operator/
[9] https://www.latent.space/p/openai-agents-platform
[21] https://platform.openai.com/docs/pricing
[24] https://openai.github.io/openai-agents-python/
[27] https://platform.openai.com/docs/guides/responses-vs-chat-completions
[31] https://community.openai.com/t/introducing-the-responses-api/1140929
[37] https://cookbook.openai.com/examples/responses_api/responses_example

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