Turn an AI use case into an architecture, privacy/security review, governance controls, adversarial evaluation, and an implementation recommendation—with every handoff visible.
This lab uses BourgeoisAI real inference. Select a framework to require framework-native architecture and terminology; repository reference implementations exercise the corresponding orchestration libraries.
Do not enter PHI, secrets, or production credentials. This reference lab produces architecture guidance, not clinical decisions.
The same use case can require very different orchestration. The lab makes those differences explicit while keeping privacy, security, evaluation and human control in the workflow.
LangChain supplies model and tool abstractions; LangGraph adds stateful graph execution with nodes, edges, conditional routing, persistence, checkpoints and human interrupts. It suits workflows where state and control need to be explicit.
CrewAI organizes specialist Agents around Tasks and Crews, with Flows for stateful process control. It suits role-oriented collaboration when responsibilities are clear and the surrounding workflow still needs controlled execution.
AutoGen AgentChat supports conversational teams, selector-based coordination, handoffs, tools and bounded termination. It suits dynamic collaboration where shared context determines which specialist should act next.
What this demonstrates: real model inference, RAG-ready architecture, tool boundaries, state and memory, routing and handoffs, structured outputs, human-in-the-loop approval, security and privacy review, AI governance, adversarial evaluation, observability, failure handling and bounded execution.
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