Search the API docs Using GPT-6 Astra Conversation state Background mode Mid-turn steering Counting tokens Supported countries OpenAI Crawlers Terms and policies Agent Builder Overview Migration guide Node reference Safety in building agents Migration guide Safety in building agents Evals Getting started Working with evals Prompt optimizer External models Best practices Graders Getting started Working with evals Prompt optimizer External models Fine-tuning Optimization cycle Supervised fine-tuning Vision fine-tuning Direct preference optimization Reinforcement fine-tuning RFT use cases Best practices Optimization cycle Supervised fine-tuning Vision fine-tuning Direct preference optimization Reinforcement fine-tuning Assistants API Migration guide Migration guide Model selection Text generation Code generation Structured output Prompt engineering Citation formatting Migration guide Prompt generation Frontend prompting Reasoning models Reasoning best practices Images and video Images and vision Image input cost calculator Image input cost calculator Image generation Overview Image prompting Image prompting Video generation Realtime and audio Audio and speech Getting started Specialized models Configuring Agents Sessions Run and continue sessions Events and items Manage sessions Webhooks Run and continue sessions Events and items Manage sessions Environments and sandboxes OpenAI-hosted sandboxes Self-hosted sandboxes Sandbox lifecycle Sandbox security Files and artifacts OpenAI-hosted sandboxes Self-hosted sandboxes Sandbox lifecycle Sandbox security Files and artifacts Tools and integrations Web search Functions MCP connections Plugins Vaults MCP connections Observability and usage Agent definitions Models and providers Results and state Integrations and observability Evaluate agent workflows Advanced integrations Function calling Search and retrieval Connect tools and data Secure MCP Tunnel Build tool workflows Programmatic tool calling Async tool calling Computer and code Code interpreter Image generation Getting started Managing sessions Delegation and tools Migrate to GPT-Live Partner integrations Getting started Managing conversations Voice activity detection Build with voice Cost optimization WebRTC with WARP Telephony and SIP Server-side controls Audio processing File transcription Live transcription Live translation Audio in Chat Completions Production best practices Deployment checklist Performance and quality Latency optimization Predicted Outputs Accuracy optimization Cost and throughput Cost optimization Prompt caching Prompt cache diagnostics Prompt cache diagnostics Flex processing Safety and governance Safety best practices Safety checks Safety classifiers Cybersecurity checks Misalignment monitoring Safety classifiers Cybersecurity checks Misalignment monitoring Under-18 guidance Content provenance Infrastructure and access Terraform provider Overview Projects and access Service accounts Rate limits and spend Model, tool, and data controls Import and reconciliation Projects and access Service accounts Rate limits and spend Model, tool, and data controls Import and reconciliation Workload identity federation Federation rules X.509 certificates Kubernetes AWS Microsoft Azure Google Cloud Oracle Cloud Infrastructure GitHub Actions SPIFFE Federation rules X.509 certificates Microsoft Azure Oracle Cloud Infrastructure IP egress ranges Plugin architecture Brainstorm use cases Build an MCP server Add UI to your MCP server (optional) Authenticate users Package your plugin Test and publish Connect and test your plugin Submit and publish Submission error reference Conversion specs Restaurant reservation spec Product checkout spec Optimize Metadata Submit a Claude Code plugin Security & Privacy Troubleshooting Plugin guidelines MCP server review requirements Plugin UI reference Checkout API reference Trigger workspace agent runs Authenticate with Workspace Agent access tokens Measurement Pixel Multiple Pixels (Advanced) Conversions API Supported Events Campaign Management Bidding & Budgets Conversion Tracking Troubleshooting Account Management Conversion Setup Get started with Work Import from another agent Personalize ChatGPT Skills & Plugins ChatGPT desktop app ChatGPT on the web Codex IDE extension

Feature Maturity Projects and chats Scheduled tasks Long-running work Image generation Browser extension Work with files Troubleshooting Computer History Advanced Config Config Reference Environment Variables Agent configuration Extend ChatGPT and Codex Record & Replay Windows sandbox Development workflows Integrated terminal Extend and automate Site tools (WebMCP) Local environments Cloud environment Build with Codex Non-interactive mode Third-party integrations CLI customization Developer commands Developer settings Agent approvals & security Internet access Codex Security plugin Quickstart Run a security scan Run a deep scan Review code changes Use the Security workbench Triage a backlog Fix findings Propose security hardening Write vulnerability reports Export and track findings Changelog Run a security scan Run a deep scan Review code changes Use the Security workbench Triage a backlog Propose security hardening Write vulnerability reports Export and track findings Codex Security CLI Quickstart Run bulk scans Run scans in CI GitLab CI/CD Reference FAQ Run scans in CI Codex Security cloud Setup Security Review Improving the threat model FAQ Security Review Improving the threat model Models & Trusted Access Recommended configuration Getting started Admin rollout guide ChatGPT Work Overview ChatGPT Work cloud security ChatGPT Work local security ChatGPT Work admin FAQ ChatGPT Work: usage and cost Identity and authentication Authentication overview Personal Access Tokens Service accounts Workspace access, policy, and models Groups and provisioning User lifecycle management Roles and workspace permissions GPTs and Sharing Managed configuration HIPAA configuration Workspace model availability Plugin and connector controls Plugin controls Plugin management Usage, governance, and compliance Workspace analytics Compliance API and audit events Deployment and model providers Manage app updates Windows app deployment Remote connections Explore use cases Online trainings Codex Ambassadors Codex for Students Codex for Open Source Explore use cases Online trainings Codex Ambassadors Codex for Students Codex for Open Source Rethinking skills and prompts for GPT-6 Astra Architectural visualization with Astra Building games with Astra Meet Rosalind Workbench: Empowering every scientist to be their own research team Automating repetitive work at OpenAI with Codex Cookbook on GitHub OpenAI Developers plugin Image generation Video generation Codex Ambassadors Codex for Students Codex for Open Source OpenAI for Startups Developer Forum This page covers the main ways to build with the OpenAI API : official SDKs for application code, the OpenAI CLI for shell-native workflows, the Agents SDK for orchestration, or your own preferred HTTP client. Create and export an API key Before you begin, create an API key in the dashboard , which you’ll use to securely access the API . Store the key in a safe location, like a .zshrc file or another text file on your computer. Once you’ve generated an API key, export it as an environment variable in your terminal. OpenAI SDKs are configured to automatically read your API key from the system environment. Install an official SDK To use the OpenAI API in server-side JavaScript environments like Node.js, Deno, or Bun, you can use the official OpenAI SDK for TypeScript and JavaScript . Get started by installing the SDK using npm or your preferred package manager: With the OpenAI SDK installed, create a file called example.mjs and copy the example code into it: Execute the code with node example.mjs (or the equivalent command for Deno or Bun). In a few moments, you should see the output of your API request. Discover more SDK capabilities and options on the library’s GitHub README. To use the OpenAI API in Python, you can use the official OpenAI SDK for Python . Get started by installing the SDK using pip : With the OpenAI SDK installed, create a file called example.py and copy the example code into it: Execute the code with python example.py . In a few moments, you should see the output of your API request. Discover more SDK capabilities and options on the library’s GitHub README. In collaboration with Microsoft, OpenAI provides an officially supported API client for C#. You can install it with the .NET CLI from NuGet . A simple API request to the Responses API would look like this: OpenAI provides an API helper for the Java programming language, currently in beta. You can include the Maven dependency using the following configuration: A simple API request to Responses API would look like this: To learn more about using the OpenAI API in Java, check out the GitHub repo linked below! Discover more SDK capabilities and options on the library’s GitHub README. OpenAI provides an API helper for the Go programming language, currently in beta. You can import the library using the code below: A first API request to the Responses API would look like this: To learn more about using the OpenAI API in Go, check out the GitHub repo linked below! Discover more SDK capabilities and options on the library’s GitHub README. To use the OpenAI API in Ruby, you can use the official OpenAI SDK for Ruby . Get started by adding the gem to your application: With the OpenAI SDK installed, create a file called example.rb and copy the example code into it: Execute the code with ruby example.rb . In a few moments, you should see the output of your API request. Discover more SDK capabilities and options on the library’s GitHub README. To call the OpenAI API directly from your terminal, install the generated openai command-line tool: Then run a basic API request from your shell: Use the CLI for repeatable terminal workflows such as extracting structured data from files, generating images, creating speech, and composing API calls with shell tools like jq . Learn more about CLI workflows and command patterns. Use the Agents SDK Use the official OpenAI SDKs above for direct API requests. Use the Agents SDK when your application needs code-first orchestration for agents, tools, handoffs, guardrails, tracing, or sandbox execution. If you are deciding between direct API requests and code-first orchestration, see how the Responses API compares with the Agents SDK . Build your first agent with the Agents SDK. OpenAI Agents SDK for TypeScript OpenAI Agents SDK for Python Azure OpenAI libraries Microsoft’s Azure team maintains libraries that are compatible with both the OpenAI API and Azure OpenAI services. Read the library documentation below to learn how you can use them with the OpenAI API. Azure OpenAI client library for .NET Azure OpenAI client library for JavaScript Azure OpenAI client library for Java Azure OpenAI client library for Go Community libraries The libraries below are built and maintained by the broader developer community. You can also watch our OpenAPI specification repository on GitHub to get timely updates on when we make changes to our API. Please note that OpenAI does not verify the correctness or security of these projects. Use them at your own risk! openai-clojure by wkok openai by anasfik DelphiOpenAI by HemulGM openai.ex by mgallo openai-kotlin by Mouaad Aallam orhanerday/open-ai by orhanerday openai-php client by openai-php async-openai by 64bit openai-scala-client by cequence-io AIProxySwift by Lou Zell OpenAIKit by dylanshine OpenAI by MacPaw com.openai.unity by RageAgainstThePixel OpenAI-Api-Unreal by KellanM Other OpenAI repositories tiktoken - counting tokens simple-evals - simple evaluation library mle-bench - library to evaluate machine learning engineer agents gym - reinforcement learning library swarm - educational orchestration repository Loading docs agent...