What is OpenAI Academy?

OpenAI Academy is the premier free interactive learning platform by Infinity Tech Stack designed to help developers master OpenAI's models and APIs, including ChatGPT, GPT-4o, GPT-6 Astra, Sol 6.1, the Responses API, and the Agents SDK through 17 modules, 34 lessons, 34 terminal labs, and 105 quizzes with zero sign-up required.

How is Infinity OpenAI Academy different from official OpenAI Academy?

While OpenAI's official initiative (academy.openai.com) invests in developer communities and enterprise incubation, Infinity Tech Stack's OpenAI Academy is an independent, 100% free developer learning platform requiring no sign-up. It provides 17 deep technical modules, 34 lessons, 34 interactive in-browser terminal labs, and 105 quizzes covering the Responses API, Agents SDK, GPT-4o, GPT-6 Astra (Astra 6), Sol 6.1, structured outputs, and fine-tuning with zero barrier to entry.

Is the OpenAI Academy free?

Yes, 100% free. No sign-up, no credit card. All 17 modules, 34 lessons, 34 terminal labs, and 105 quizzes are immediately accessible. Progress saves locally in your browser.

What topics does the OpenAI Academy cover?

The course covers ChatGPT essentials, Custom GPT creation, API integration, the Responses API, the Conversations API, the Agents SDK & Agents API, GPT-6 Astra (Astra 6), GPT-6.1 Sol (Sol 6.1), GPT-6.1 Terra, GPT-6 Luna, GPT-Live-1 voice streaming, GPT Image 2, and frontier reasoning architectures.

Do I need programming experience?

No prerequisites required for the first modules (ChatGPT and Custom GPTs). The later modules on the API and Agents SDK require basic programming knowledge, particularly in JavaScript or Python.

What is the Responses API?

The Responses API (/v1/responses) is OpenAI's unified agentic endpoint that superseded the Assistants API (sunset August 26, 2026) and replaces Chat Completions for new builds, offering built-in tools (web_search, file_search, code_interpreter, computer_use_preview, remote MCP, image_generation) and native conversational state.

What is the difference between GPT-6 Astra and Sol 6.1?

GPT-6 Astra (gpt-6-astra, $10/$50 per 1M tokens) is OpenAI's premier frontier flagship model combining multimodal intelligence, deep reasoning, 1.05M context, 128K max output, and live voice integration. GPT-6.1 Sol (gpt-6.1-sol, $2/$10 per 1M tokens) delivers near-Astra coding and reasoning performance at 1/5 the inference cost, complemented by GPT-6.1 Terra ($1/$5) and GPT-6 Luna ($0.10/$0.50).

Does the OpenAI Academy include hands-on labs?

Yes. 34 hands-on terminal labs are built into the lessons - call the Responses API with curl and calibrate frontier reasoning.effort levels (low, medium, high, xhigh, max) using real, vendor-verified commands. Labs include objectives, hints, solutions, and XP rewards. No sign-up or setup needed.

How do Structured Outputs work in the OpenAI API?

Structured Outputs guarantee that the model response strictly adheres to a developer-supplied JSON Schema (supported natively with Pydantic and Zod) via text.format: { type: 'json_schema', strict: true, schema: ... } in the Responses API or response_format in Chat Completions, eliminating hallucinated keys or invalid types.

What is the OpenAI Agents SDK and how does it handle handoffs?

The OpenAI Agents SDK is a lightweight, production-ready framework for Python (openai-agents) and TypeScript/JavaScript (@openai/agents) that models autonomous workflows as multi-agent systems with first-class primitives for Agents, Runner loops, Guardrails, MCP servers, and declarative Handoffs, complemented by the managed OpenAI Agents API (/v1/agents).

How does Realtime WebRTC audio streaming work with GPT-4o?

The OpenAI Realtime API (/v1/realtime) and full-duplex GPT-Live-1 API stream bidirectional audio over WebRTC or WebSocket with sub-300ms conversational latency using models like gpt-realtime-2.1 and GPT-Live-1, enabling natural voice-to-voice interruptions, emotion recognition, and simultaneous tool calls.

How does the OpenAI Batch API reduce enterprise inference costs?

The Batch API allows developers to submit asynchronous pools of requests with a 24-hour turnaround window in exchange for a 50% discount on both input and output token pricing, ideal for evaluation datasets and nightly summarization jobs.

What are OpenAI reasoning models and how does reasoning_effort work?

OpenAI's GPT-6 family (gpt-6-astra, gpt-6.1-sol, gpt-6-luna) and GPT-5.6 series supersede legacy o1/o3-mini/o4-mini models (retired October 23, 2026) by generating internal chains of thought before answering. The reasoning.effort parameter ('none' on Luna, 'low', 'medium', 'high', 'xhigh', 'max') controls reasoning depth to balance accuracy, latency, and token cost.

How does function calling differ from tool calling in OpenAI APIs?

Tool calling is the modern superset of function calling. It allows models to generate parallel function call requests in a single response turn and integrates natively with built-in tools like file search and code interpreter.

What are best practices for text-embedding-3-large embeddings?

text-embedding-3-large supports the dimensions parameter, allowing developers to truncate embeddings from 3072 down to 1024 or 512 dimensions while maintaining competitive MTEB retrieval performance and outperforming earlier ada-002 baselines, drastically saving vector database RAM and indexing latency.

How does fine-tuning work on modern OpenAI models?

Supervised fine-tuning (/v1/fine_tuning/jobs) customizes model style, domain vocabulary, and task precision using JSONL datasets. It supports automated validation metrics, custom learning rates, and Direct Preference Optimization (DPO).

What is Computer Use (CUA) in the OpenAI ecosystem?

Computer-Using Agent (CUA) capabilities empower models to inspect screen pixels, parse UI accessibility trees, and synthesize mouse and keyboard events to automate legacy desktop and web applications.

Can I earn an official certificate upon completing OpenAI Academy?

Yes! Completing all 17 modules unlocks the official Infinity OpenAI Engineering Mastery Certificate featuring your name, verification ID, and one-click LinkedIn badge sharing.

What major capability was introduced by OpenAI on September 10, 2026?

The OpenAI Agents API in public beta, providing a fully managed cloud harness with sandboxing, session state, and auto-compaction — The OpenAI Agents API launched on September 10, 2026, offering a managed cloud harness for stateful, sandboxed multi-agent workflows.

Under an AOE operational model, when should you choose the Agents API over the client-side Agents SDK?

When you want OpenAI to manage cloud sandboxing, session persistence, and parallel subagent execution without maintaining local server loops — The Agents API handles runtime sandboxing, persistent memory, and subagent orchestration in the cloud, while the Agents SDK gives you direct code control on your own servers.

How do you install the client-side Agents SDK for Python?

pip install openai-agents — In Python, install via pip install openai-agents (while the official TypeScript/JavaScript package is installed via npm install @openai/agents).

Agents SDK Fundamentals — The Agents SDK

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Agents SDK Fundamentals

🤖 The Agents SDK 15 min 300 BASE XP⌨ HANDS-ON LAB

OpenAI's Agent Framework & Agents API

In 2026, OpenAI offers complementary paradigms for building agentic systems: the open-source self-hosted Agents SDK for Python (pip install openai-agents) and TypeScript/JavaScript (npm install @openai/agents), the fully managed OpenAI Agents API (launched in public beta on September 10, 2026 at /v1/agents), and developer coding harnesses like Codex CLI (npm install -g @openai/codex) and Codex Cloud.

September 10, 2026 Update: The OpenAI Agents API

While the Agents SDK runs inside your own application server and requires you to manage execution loops and local memory, the newly released Agents API exposes the core execution harness behind Codex as a managed cloud service:

Managed Sandboxing: Safely executes code, tools, MCP servers, and computer actions in ephemeral micro-containers without exposing host infrastructure.
Persistent Session State: Automatically stores multi-turn thread history and checkpoint states on OpenAI's infrastructure.
Native Context Compaction: Transparently summarizes long-horizon conversations when approaching token limits without breaking tool continuity.
Parallel Subagent Coordination: Spawns and supervises multiple worker subagents in parallel cloud threads.

Core Primitives (Agents SDK & API)

Primitive	Purpose	Example
Agent	An LLM with instructions, tools, and MCP servers	A customer support agent
Handoff	Delegate to another agent	Triage → Billing Agent
Guardrail	Safety validation on input/output	Block PII, reject jailbreaks
Tracing	Observability for debugging	Visualize agent execution flow

from agents import Agent, Runner

# Define a simple agent
support_agent = Agent(
    name="Support Agent",
    instructions="You are a helpful support agent. Answer questions about our product.",
    model="gpt-6.1-sol"
)

# Run it
result = await Runner.run(support_agent, "How do I reset my password?")
print(result.final_output)

SOE & AOE Operational Runbook for OpenAI Agents

When running enterprise agent fleets, implement top Standard Operating Environment (SOE) and AI Operations Engineering (AOE) controls:

SOE Standard: Enforce strict organization-level API keys (OPENAI_API_KEY, OPENAI_ORG_ID), pin SDK dependencies (openai-agents >= 0.4.0 in Python or @openai/agents in TypeScript), enforce default developer messages for reasoning models, and configure zero-retention (ZDR) endpoints for enterprise data compliance.
AOE Architecture: Choose Agents API when you need zero-maintenance infrastructure, auto-scaling subagents, and managed cloud sandboxes; choose Agents SDK when you need strict on-prem/VPC residency, custom vector database stores, or offline tool evaluation. Always route user inputs through the free omni-moderation-latest API before passing to the agent loop.

⌨ HANDS-ON LABBootstrap the Agents SDK

⭐ +150 XP

Provision the OpenAI Agents SDK and launch your first agent script - the two commands every agent project starts with.

1Install the Agents SDK for Python with pip.

2Run your agent entry-point script with Python (file: hello_agent.py).

lab-sandbox — simulated environment

INFINITY LAB SANDBOX v2.6 — simulated shell
Type the command for the current objective. Helpers: "hint", "solution", "clear".
$

OBJECTIVE 1 / 2 — type "hint" if stuck

SYNAPSE VERIFICATION

QUERY 1 // 4

What framework did the Agents SDK replace?

LangChain

The experimental Swarm framework

AutoGPT

CrewAI