Molecule AI vs AutoGen — when each one fits

What each tool does

AutoGen is an open-source framework built by Microsoft for orchestrating conversations between agents. The core abstraction is the ConversableAgent — an agent that can send and receive messages — and the GroupChat / GroupChatManager that coordinates several ConversableAgents around a shared conversation. It is documented at microsoft.github.io/autogen; we link out rather than paraphrase so anything we say here can be checked against the canonical source.

Molecule AI is the open-core control plane for heterogeneous AI agent teams. It introduces a few platform-level primitives that no agent SDK ships on its own: every agent runs in a workspace with private and shared hierarchical memory, workspaces delegate to each other over A2A across processes and machines, and the topology is editable on the canvas. The agent inside a workspace can be backed by any SDK the control plane has an adapter for — AutoGen is one of those adapters.

Side-by-side

Layer-by-layer. Where AutoGen ships a capability we note it explicitly; where the capability belongs to the layer above, we point to that layer instead of asserting AutoGen lacks it.

When AutoGen alone is the right answer

If your multi-agent system is a conversational team that fits inside one Python (or .NET) application — a researcher agent talking to a coder agent talking to a critic agent, all sharing context through a single GroupChat — AutoGen by itself is a great fit. The conversation is the centerpiece of the framework; that is exactly what you want. Adding a control plane on top would not earn its weight while everything fits in one process.

When you'd want Molecule AI on top

If you have multiple AutoGen GroupChats (each its own service) that need to coordinate, or an AutoGen GroupChat that needs to talk to non-AutoGen agents (a Claude Code engineer, an OpenClaw web-automation agent, a LangGraph workflow), or you need delegation that is audited and gateable by approval, or you want non-engineers to read or steer the topology from a visual canvas — that is what Molecule AI adds. You keep using AutoGen for the conversational teams whose internals you've already designed; Molecule AI wraps each GroupChat in a workspace and lets workspaces find, delegate to, and share memory with each other.

Can I use both? Yes — that's the point.

Molecule AI ships an AutoGen runtime adapter so a workspace can be backed by your existing GroupChat without rewriting any of it. The adapter speaks the platform's standard lifecycle (start, message, stop, status, capabilities) on one side and your AutoGen agents on the other. From the canvas perspective the workspace looks the same as any other; from AutoGen's perspective the workspace is just where the platform calls in. Production Molecule AI teams routinely have AutoGen-backed workspaces alongside workspaces backed by other SDKs for the agents whose value is in tool use or model choice.

References

We cite every cross-project claim against the project's own canonical source rather than paraphrasing. If a claim on this page disagrees with the linked source, the linked source is right — open an issue and we'll fix the page.

• AutoGen documentation: microsoft.github.io/autogen
• AutoGen repository (MIT license): github.com/microsoft/autogen
• Molecule AI architecture: moleculesai.app/architecture
• Molecule AI glossary: moleculesai.app/glossary
• Molecule AI pricing: moleculesai.app/pricing