AutoGen Essentials

Course Content

AutoGen Essentials

7 sections · 28 lessons

When would you choose AutoGen over LangChain/LangGraph or CrewAI?

Four frameworks, three questionsAgents in a chatTeam rule or selectorMaintenance modeTyped state graphYour edgesActiveRoles and tasksProcess: seqor managerActiveAgents plus workflowsWorkflow edges1.0, the successorCore ideaNext step decided byStatus in 2026AutoGen 0.4+LangGraphCrewAIMS Agent FrameworkDurable pause and resume is built into LangGraph and Agent Framework, not AutoGen teams.
The core abstraction decides the fit, and in 2026 AutoGen's status decides whether it should hold a new long-lived system at all.

What you need to know

The core abstraction decides the fit

Core ideaWho decides the next stepStrong at
AutoGen (0.4+)Agents in a conversation (team)Team rule: round robin, LLM selector, handoffs, or a GraphFlow graphCode-write-run-fix loops, research prototypes
LangGraphTyped state passed through a graph of nodesYour edges and conditionsDeterministic, resumable, auditable workflows
CrewAIAgents with roles, given tasks, in a crewSequential or manager-led processStanding up a role-based team quickly
Microsoft Agent FrameworkAgents plus typed graph WorkflowsWorkflow edges; built-in group chat, handoff, Magentic patternsNew production systems on Azure and .NET or Python

Questions I would ask before choosing

  • Is the path known? If the steps are fixed ("classify, look up order, draft reply"), a graph is clearer than a conversation. If the number of steps is unknown (debugging), a conversation loop fits.
  • Do I need durable pause and resume? LangGraph and Microsoft Agent Framework have built-in checkpointing and human-input pauses. AutoGen teams have save_state / load_state, but you build the pause/resume flow yourself.
  • Is it a new project that must live for years? A framework in maintenance mode gets security fixes, not features. That is fine for a prototype, risky for a platform.
  • What does the team already run? An existing AutoGen 0.4 system can stay; the migration guide to Agent Framework maps AssistantAgent to Agent, and teams to workflows.

What AutoGen still does well

  • Code execution: DockerCommandLineCodeExecutor, CodeExecutorAgent, Azure dynamic sessions.
  • Many team patterns out of the box: RoundRobinGroupChat, SelectorGroupChat, Swarm, MagenticOneGroupChat, GraphFlow.
  • autogen-core for event-driven or distributed agents.

A real-life example

A telecom company builds a customer-support triage team: classify the complaint, look up the account, draft a reply, and ask a supervisor before any credit above ₹500.

  • The flow is mostly fixed and must be auditable for the regulator, and the supervisor may answer hours later. That points to a graph with checkpoints: LangGraph, or Microsoft Agent Framework since they run on Azure. They choose Agent Framework.
  • Separately, their data team wants an internal "ask the data" tool where an agent writes SQL and Python, runs it, and fixes errors. The number of steps varies from 2 to 15. They already had an AutoGen 0.4 prototype with a Docker executor that works, so they keep it for the internal tool and plan to migrate it when it needs new features.

Follow-up questions to expect

  • "Would you start a new project on AutoGen today?" — For a prototype or research, maybe. For a long-lived production system, I'd use Microsoft Agent Framework or LangGraph, because AutoGen now only gets maintenance fixes.
  • "Doesn't AutoGen have graphs too?" — Yes, GraphFlow with DiGraphBuilder (added in 0.6, marked experimental) supports sequences, branches, fan-out and loops. It lacks LangGraph's built-in durable checkpointing.
  • "Where does Semantic Kernel fit?" — It was Microsoft's enterprise SDK for plugins and planners. Microsoft Agent Framework merges Semantic Kernel's enterprise features with AutoGen's agent patterns.