I'm comparing AutoGen (Microsoft Agent Framework) and LangGraph for a supply chain project. We need a framework that can handle thousands of concurrent state transitions. Which one offers better stability for long-running agents that might need to "wake up" after a week to process an invoice?
3 answers
For long-running, state-heavy processes, the AutoGen (Microsoft Agent Framework) v0.4 release is a strong contender because of its actor-model foundation. This model is inherently designed for concurrency and distributed state. While LangGraph is excellent for defining rigid, graph-based workflows, AutoGen's event-driven architecture allows agents to persist their state in a database and resume exactly where they left off when a new event (like an invoice arrival) occurs. This makes it very resilient for enterprise applications that don't follow a linear time path and require agents to stay "dormant" without consuming active compute resources.
Does AutoGen have a built-in "checkpointer" equivalent to LangGraph for saving the state to a Postgres DB?
If your workflow is mostly conversational, AutoGen is much easier to set up and manage than a complex graph.
True, and the ability to have agents "debate" a solution before finalizing an invoice helps catch errors that a linear graph might miss.
Yes, it uses a session-based persistence layer. You can configure it to save the message history and agent internal states to a Redis or SQL backend, which is critical for those "week-long" gaps in your workflow.