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Responses is the core MKA1 API for building model interactions. Send input to a model, give it access to the tools or knowledge it needs, and receive a response. The same API supports text, image, document, and audio inputs, with output capabilities determined by the selected model. Start with Text generation for a complete request, or follow the Quickstart if you have not configured a client yet.

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Build a response

  1. Provide input. Send text, or include images, documents, or audio.
  2. Add context and tools. Retrieve context from vector stores or graph stores. Use function calling, MCP tools, or code execution when the model needs to act.
  3. Choose the output. Receive text, enable image generation, or request audio output. Check the feature guide for its model requirements and request fields.
  4. Manage the interaction. Stream output, continue a conversation, or run a long request in the background.

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Inputs, resources, and tools

Start with Files to upload content and obtain file IDs for document inputs and vector stores. Inputs belong to a request. Resources such as files, vector stores, and sandbox sessions have their own creation and management APIs and can be reused across requests. For retrieval, upload and index your files before searching the store. The retrieval guides show how to obtain context that you can pass to a response. Graph retrieval changes how a vector store finds connected evidence. Tools let the model request actions as part of generating a response. Function calling lets your application execute those actions; MCP connects tool servers. Shell handles broader workspace tasks, while code interpreter focuses on writing and running code. Both use the same sandbox platform; see Code execution.

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Choose a standalone API when appropriate

Realtime voice is a standalone API with its own session workflow. Start there when building a live voice interaction. For document-to-JSON extraction with reusable schemas, use the standalone Extract API.

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Build reusable workflows

Agents build on Responses by saving instructions, model selection, and tool configuration for repeated runs. Structured extraction provides a dedicated workflow for turning documents and images into JSON using an inline or saved schema. Use prompt templates to reuse prompts, history and memory to retain context, and evals to measure behavior.