The Orchestration War
We spent a month prototyping a complex software-engineering workflow (ticket creation -> code generation -> testing -> review) using both Microsoft's AutoGen and LangChain's LangGraph.
The Verdict
AutoGen is fantastic for quick, conversation-driven multi-agent setups. If you just want a "Coder" agent and a "Reviewer" agent to freely chat with each other until a problem is solved, it works out of the box with very little boilerplate. However, it acts like a black box. Debugging why an agent got stuck in a conversational loop is a nightmare.
LangGraph, however, treats agentic workflows as explicit state machines (Directed Acyclic Graphs). This provides significantly more control over the flow of execution, error handling, and state persistence.
# LangGraph forces you to define state transitions explicitly
workflow.add_conditional_edges(
"coder_agent",
check_tests, # A function that decides the next node
{
"tests_passed": "reviewer_agent",
"tests_failed": "coder_agent" # Loop back
}
)For enterprise-grade, deterministic agent loops where you need strict guardrails, LangGraph is the clear winner.