OpenAI API docs llms.txt · AI Docs Browser · mdream [llms.txt Tools](https://mdream.dev/llms-txt/platform/llms-txt) # OpenAI API docs llms.txt Browse OpenAI API docs documentation as markdown. Click any page in the sidebar to view it, or copy the full llms.txt for your AI tools. platform.openai.com/llms.txt [Raw llms.txt](https://platform.openai.com/docs/llms.txt) [Raw llms-full.txt](https://platform.openai.com/llms-full.txt) Copy Markdown [](https://platform.openai.com/llms.txt) llms.txt Documentation sets Combined API docs Actions Data retrieval with GPT Actions Getting started with GPT Actions GPT Action authentication GPT Actions GPT Actions library Production notes on GPT Actions Sending and returning files with GPT Actions Assistants Assistants API deep dive Assistants API tools Assistants Code Interpreter Assistants File Search Assistants Function Calling Assistants migration guide Bots Overview of OpenAI Crawlers Concepts Key concepts Deprecations Deprecations Gpts GPT Release Notes Guides Actions in ChatKit Admin APIs Advanced integrations with ChatKit Advanced usage Agent Builder Agent definitions Agents SDK API deployment checklist Apply Patch Audio and speech Background mode Batch API ChatGPT Developer mode ChatKit ChatKit widgets Citation Formatting Code generation Code Interpreter Compaction Completions API Computer use Configuring workload identity federation for AWS Configuring workload identity federation for GitHub Actions Configuring workload identity federation for Google Cloud Configuring workload identity federation for Kubernetes Configuring workload identity federation for Microsoft Azure Configuring workload identity federation for SPIFFE Conversation state Cost optimization Counting tokens Cybersecurity checks Data controls in the OpenAI platform Deep research Direct preference optimization Error codes Evaluate agent workflows Evaluate external models Evaluation best practices File inputs File search Fine-tuning best practices Flex processing Frontend prompt instructions Function calling Getting started with datasets Graders Guardrails and human review Image generation Image generation Images and vision Integrations and observability IP egress ranges Latency optimization Local shell Manage permissions in the OpenAI platform Managing costs MCP and Connectors Migrate from Agent Builder Migrate from prompt objects Migrate to the Responses API Model optimization Model selection Models and providers Moderation Multi-agent Node reference OpenAI models in Amazon Bedrock Optimizing LLM Accuracy Orchestration and handoffs Predicted Outputs Priority processing Private Link Production best practices Programmatic Tool Calling Prompt caching Prompt engineering Prompt generation Prompt optimizer Prompting Prompting guidance for GPT-5.6 Sol Quickstart Rate limits Realtime and audio Realtime API with SIP Realtime API with WebRTC Realtime API with WebSocket Realtime conversations Realtime transcription Realtime translation Realtime with tools Reasoning best practices Reasoning models Red teaming Reinforcement fine-tuning Reinforcement fine-tuning use cases Results and state Retrieval Running agents Safety best practices Safety checks Safety in building agents Sandbox Agents Secure MCP Tunnel Shell Skills Speech to text Streaming API responses Structured model outputs Supervised fine-tuning Text generation Text to speech Theming and customization in ChatKit Tool search Trace grading Under 18 API Guidance Upgrading to GPT-5.4 Upgrading to GPT-5.5 Upgrading to GPT-5.6 Sol Using GPT-4.1 Using GPT-5 Using GPT-5.1 Using GPT-5.2 Using GPT-5.3-Codex Using GPT-5.4 Using GPT-5.5 Using GPT-5.6 Using realtime models Using tools Vector embeddings Video generation with Sora Vision fine-tuning Voice activity detection (VAD) Voice agents Web search Webhooks Webhooks and server-side controls WebSocket Mode Working with evals Workload identity federation Libraries SDKs and CLI OpenAI CLI Mcp Building MCP servers for ChatGPT Apps and API integrations Pricing Pricing Quickstart Developer quickstart Supported Countries Supported countries and territories Tutorials Meeting minutes Web QA with embeddings Ui Kit Demo UI Kit Demo ## What is OpenAI API docs's llms.txt? OpenAI API docs publishes an `/llms.txt` file that provides AI systems with a structured index of its documentation. It follows the [llms.txt specification](https://llmstxt.org), organizing links to guides, API references, and tutorials under section headings so that LLMs like ChatGPT, Claude, and Gemini can quickly understand what OpenAI API docs offers and where to find details. Pass Spec 15 Sections 166 Links 29.8 KB Size ~7.6k Tokens Sections: Documentation sets · Actions · Assistants · Bots · Concepts · Deprecations · and 9 more ## Add OpenAI API docs Docs to Your AI Assistant Select your tool to see how to add OpenAI API docs's llms.txt as a documentation source. Cursor Windsurf Claude Code ChatGPT Zed Copilot 1. 1 Open any chat or composer panel 2. 2 Type @Docs and select "Add new doc" 3. 3 Paste the URL: https://platform.openai.com/llms.txt 4. 4 Wait for the green dot (indexing complete) 5. 5 Reference @Docs in chat when asking about OpenAI API docs Cursor crawls the URL and indexes all subpages. Add a trailing slash to index child pages too. ## Spec Compliance 1 note ## Frequently Asked Questions Where is OpenAI API docs's llms.txt file? OpenAI API docs publishes its llms.txt at https://platform.openai.com/llms.txt. This file provides a structured, markdown-formatted index of OpenAI API docs's documentation that AI systems can consume to understand the project's APIs, guides, and references. How do I use OpenAI API docs's llms.txt with AI coding assistants? Copy the llms.txt content and paste it into your AI assistant (Cursor, Windsurf, Claude, ChatGPT) as context. This gives the AI an accurate map of OpenAI API docs's documentation so it can provide better code suggestions and answers about OpenAI API docs. What does OpenAI API docs's llms.txt contain? OpenAI API docs's llms.txt contains 15 sections and 166 documentation links in 29.8 KB (~7.6k tokens). Key sections include Documentation sets, Actions, Assistants, Bots, Concepts, Deprecations. How many tokens does OpenAI API docs's llms.txt use? The concise llms.txt index is approximately 7.6k tokens (29.8 KB). Most AI assistants can fit this within their context window. For the full expanded documentation, look for an llms-full.txt variant which embeds all content inline. What is OpenAI API docs? Guides and conceptual documentation for building with the OpenAI API.. OpenAI API docs's llms.txt file helps AI systems understand this by providing a structured overview of its documentation, making it easier for developers to get accurate AI-assisted help when working with OpenAI API docs. Can I generate an llms.txt for my own project? Yes. Use the free llms.txt generator at mdream.dev/tools/llms-txt/generator to crawl your site and produce a spec-compliant llms.txt file. You can also validate existing files with the llms.txt validator. ## Browse More llms.txt Files [Egg Born to build](https://mdream.dev/llms-txt/platform/llms-txt/egg) [Easy!Appointments Online Appointment Scheduler](https://mdream.dev/llms-txt/platform/llms-txt/easy-appointments) [Split docs Documentation](https://mdream.dev/llms-txt/platform/llms-txt/split) [FalkorDB Ultra-fast, Multi-tenant Graph Database](https://mdream.dev/llms-txt/platform/llms-txt/falkordb) [Elastic Documentation Elastic provides an open source search, analytics, and AI platform, and out-of-the-box solutions for observability and security. 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