Is the AI Agents Academy completely free?

Yes, 100% free. No sign-up, no credit card. All 13 modules, 58 lessons, 58 terminal labs, and 115 quiz questions are accessible immediately. Your progress saves locally in your browser.

How is Infinity AI Agents Academy different from vendor-locked agent courses?

Unlike single-framework platforms (such as LangChain Academy or CrewAI University) that tie you to a single proprietary ecosystem, Infinity Tech Stack's AI Agents Academy is a 100% free, vendor-neutral engineering curriculum. It teaches the complete production stack across ReAct loops, LangGraph v1.x, CrewAI Flows, Google ADK, OpenAI Agents SDK, Anthropic Claude Agent SDK, Microsoft Agent Framework, A2A protocol, and Model Context Protocol (MCP) integration with in-browser terminal labs and zero sign-up required.

What topics does the AI Agents Academy cover?

The course covers agent fundamentals, ReAct loops, Plan-and-Solve, tool use & function calling, MCP integration, Agentic RAG, multi-agent systems (LangGraph v1.x, CrewAI Flows, OpenAI Agents SDK, Claude Agent SDK, Microsoft Agent Framework), A2A protocol, Google ADK, agent memory, safety & guardrails, red teaming, CI/CD for agents, and production observability.

What is the A2A protocol?

A2A (Agent-to-Agent) is an open protocol (originated by Google and governed by the Agentic AI Foundation under the Linux Foundation) for enabling interoperability between AI agents. It allows agents from different vendors and frameworks to discover each other via /.well-known/agent-card.json manifests, exchange capabilities, and delegate tasks securely over JSON-RPC 2.0 and SSE.

Google ADK (Agent Development Kit) is an open-source framework for building, orchestrating, and deploying AI agents. ADK 2.x supports Python, TypeScript, Go, Java, and Kotlin, and integrates natively with MCP for tools and A2A for agent-to-agent communication.

Do I need any prerequisites?

Basic programming knowledge is helpful but not required. The course starts from absolute fundamentals (what is an agent?) and progressively advances to expert-level topics like multi-agent orchestration and production safety.

Does the AI Agents Academy include hands-on labs?

Yes. Hands-on terminal labs are built into the lessons - build the minimal agent loop, scaffold a CrewAI crew, and launch LangGraph dev using real, vendor-verified commands, with objectives, hints, solutions, and XP rewards. No sign-up or setup needed.

What is the recommended approach for beginners learning to build AI agents?

Build a simple while-loop from scratch without frameworks — Building a raw while-loop from scratch helps you understand the fundamental reasoning/acting cycle before abstractions hide the details.

The Agent space — What Are AI Agents?

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The Agent space

🤖 What Are AI Agents?⏱ 6 min⭐ 50 BASE XP⌨ HANDS-ON LAB

Frameworks & Ecosystems

The agent ecosystem is rapidly expanding. Here is a breakdown of the leading frameworks in 2026:

Framework / Protocol	Model & Architecture	Best For
LangGraph v1.2+	Graph-based state machines	Production systems: `StateGraph`, dynamic `interrupt()` / `Command` HITL, `PostgresSaver`, and LangSmith Deployment (formerly LangGraph Platform/Cloud).
OpenAI Agents SDK	Lightweight production SDK (`openai-agents`)	Typed `Agent` + `Runner.run`, `handoffs`, `@function_tool`, `InputGuardrail` / `OutputGuardrail`, `MCPServerStreamableHttp`, and built-in tracing.
Anthropic Claude Agent SDK	Autonomous loop & workflow patterns	`claude-agent-sdk` (Python) & `@anthropic-ai/claude-agent-sdk` (TS, formerly `claude-code-sdk`) exposing `query()`, `ClaudeSDKClient`, and `ClaudeAgentOptions`.
Google ADK & A2A Protocol	Agent Development Kit & Linux Foundation A2A	Cross-vendor interoperability via `/.well-known/agent-card.json`, JSON-RPC 2.0 + SSE streaming, `MCPToolset`, and `RemoteA2aAgent`.
CrewAI (Crews + Flows)	Role-based teams + event-driven Flows	Autonomous `Crew` / `Task` / memory teams orchestrated by deterministic `Flow` decorators (`@start`, `@listen`, `@router`).
Microsoft Agent Framework	Unified Semantic Kernel + AutoGen v0.4+	`microsoft/agent-framework` (`ChatAgent`, `@ai_function`, graph workflows, MCP/A2A) in Python & .NET, plus `autogen-agentchat` & AG2.
Vercel AI SDK / Core	App-first framework tooling	Streaming agent UI, generative UI, client-side tool execution, and Next.js full-stack integration.
Managed Agent Runtimes	Hosted cloud execution (OpenAI / Claude / Azure)	Provider-managed ephemeral microVMs, server-side context compaction, and automatic security gating.

Note: Building a raw agent from scratch (a simple while loop) is strongly recommended for learning before adopting complex abstractions like LangChain or CrewAI.

⌨ HANDS-ON LABBenchmark Multi-Agent Framework Capabilities

⭐ +150 XP

Audit installed agent frameworks in your virtual environment verifying LangGraph, AutoGen, and CrewAI compatibility.

1Check installed agent orchestrator packages via pip list.

lab-sandbox — simulated environment

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

OBJECTIVE 1 / 1 — type "hint" if stuck

SYNAPSE VERIFICATION

QUERY 1 // 2

Which framework is best known for using graph-based state machines for reliable production agents?

CrewAI

LangGraph

AutoGPT

Semantic Kernel