OpenAI Engineer: APIs, Libraries, Models, Agents SDK

Learn to set up the OpenAI project on Mac or Windows, test a Hello World with an LLM, and instantiate the OpenAI client with required and optional parameters.

Instantiate the OpenAI client with a mandatory API key (via environment variables or a parameter), and access the libraries and objects by configuring timeout, max retries, and default headers.

Create and continue multi-turn conversations with the Conversations API by obtaining a Conversation ID and passing it to the Responses API, enabling server-side storage and context across calls.

Explore using deep research with the Responses API, including web search tools and background mode. Learn to poll updates and manage token use with a practical project example.

Compare the responses and images APIs to generate and edit images from image and text inputs, explore multi-turn editing, and examine vision limitations and streaming outputs.

Explore image generation with the images API, including parameters like N, quality, size, streaming, and timeout, and code examples using images.generate to produce and store base64 PNGs.

Use the images API to edit images by supplying input images and an optional mask to apply targeted changes, such as adding a UFO in the upper left corner.

Enable streaming for image generation by setting stream to true and partial_images to up to three, then observe progress through intermediate images and the final image in the Images API.

Identify image generation limitations in the images API, including latency, imperfect text rendering, and consistency across edits. Learn how composition, element counting, and prompt refinements affect outputs.

Explore video generation from prompts to endpoints, including rendering, progress tracking, and downloading. Seed videos from an image or a character, animate movements, and apply edits within system limitations.

Create a 2 to 4 second animation from a character reference by uploading the character to videos/characters, then generate final video in a futuristic charging station, monitoring progress until completion.

Explore audio and speech APIs, including speech-to-speech input/output and transcription-to-speech pipelines, plus end-to-end systems from transcription to speech.

Explore the audio and speech APIs used in voice-enabled AI, including real-time, chat completions, transcriptions, speech, and responses APIs.

Demonstrate speech-to-speech via a browser-based web app using the real-time API, converting spoken input to natural audio responses from an LLM, and moving from Python to a web server demonstration.

Learn to process audio input with the chat completions API by converting a wav file to base64, passing it as input audio with modalities text, and receiving text output.

Shows how to generate audio output with the chat completions API by setting modalities to audio, choosing a voice and format, converting base64 to wav, and coordinating text and audio.

Explore how the transcription API converts audio to text, passes the result through the responses API, and then converts it into speech, with English best and a 25 MB limit.

Convert audio input with the transcription API to text. Process the text with the responses API using GPT 5.5 and output audio with the speech API in an end-to-end flow.

Explore tools for OpenAI models, covering categories of tools, function tools, built-in tools like web search and shell access, skills, MCP protocol, and code interpreters, plus the user–LLM workflow.

Expose custom tools to the model by defining functions with clear names, descriptions, and input parameters via JSON. See a getWeather function invoked and results returned via the responses API.

Learn to integrate the web search tool into models, filter allowed and blocked domains, request complete sources with full URLs, and use user location for contextual results.

Explore how the Shell tool provides a sandboxed environment for the model, with admin-controlled access to an OpenAI hosted container and commands to inspect the environment.

Connect your model to external capabilities via the model context protocol, effectively acting as an api to access data, tools, and functionality on a hosted server.

Explore the code interpreter tool to run Python code in a sandbox, generating code, executing it in an auto container, and returning Fibonacci numbers, factorials, and primes with explanations.

Demonstrates retrieval using a vector store and file search tool, limiting results and including sources, then queries two files for answers on King Sverre of Norway and FIS disciplines.

Explore token counting and cost awareness, manage long-running conversations with state and compaction, trigger model reasoning, and optimize workflows with background tasks, WebSockets, and prompt caching.

Count input and output tokens to estimate costs when sending requests. Track tokens across messages, conversations, images, and tool use to understand total consumption.

Learn how compaction summarizes long conversations to fit the model's context window. See how the responses API compacts dialogue to reduce tokens, lower costs, and speed responses.

Learn to run long reasoning tasks asynchronously in the background, keeping the main thread unblocked while streaming tokens. Manage background tasks by checking progress and canceling when needed.

Explore how evaluations automate testing for the system built with LLMs, outlining the full run from datasets and input data to tasks, grader parameters, and the final generated report.

Explore evaluations by outlining the dataset, prompts, developer role, graders, and run workflow that assess model outputs. See how automated checks and behavior-driven development keep behavior correct as features grow.

Explore datasets as test cases with inputs and expected outputs, evaluate model responses using multiple graders, and create a customer support dataset uploaded as JSON via the OpenAI client.files API.

Graders define testing criteria by evaluating the model's response against the input and expected result using string checks, with two graders and operations like equal, not equal, like, and I like.

Define the data source, instructions, and user prompt, then run and evaluate the responses, grade the results, and generate a run report to verify test outcomes.