AutoGen vs Google ADK: Which Agent Framework to Use?

Paradigm

AutoGen models cooperation as conversation. Two agents alternate via initiate_chat(), and N agents share a transcript through GroupChat with a GroupChatManager picking the next speaker — round-robin, random, or LLM-selected. ADK models it as hierarchy: an LlmAgent holds a sub_agents list and delegates downward, with SequentialAgent, ParallelAgent, and LoopAgent as explicit workflow primitives. AutoGen's structure emerges turn by turn from the transcript; ADK's structure is declared statically before the run.

Ecosystem

AutoGen has the older, larger footprint — ~57k stars since August 2023, Microsoft Research backing, and a built-in code execution sandbox for agents that need to write and run code. ADK is newer (April 2025, ~19k stars) but ships with first-class Vertex AI deployment, Gemini-tuned defaults, managed Session/State services, and bidirectional audio/video streaming. If you live on Google Cloud and Gemini, ADK has the cleaner deployment story; otherwise AutoGen has more community examples, a longer track record, and a published research paper.

Use case

Pick AutoGen when the interaction pattern itself is the value — agents debating, critiquing, or iteratively refining each other's work, where you don't know in advance who should speak next. register_nested_chats() is genuinely hard to reproduce well. Pick ADK when you know the topology up front and want it expressed as a tree plus workflow primitives, then containerized to Vertex AI Agent Engine or Cloud Run. AutoGen rewards dynamic, emergent collaboration; ADK rewards structured orchestration backed by a managed cloud runtime.

Pick AutoGen if your project lives or dies on agents talking to each other dynamically.

• Multi-agent debate is the workflow: Code review with author/reviewer cycles, planner/executor pairs, or any setup where agents critique and revise each other. GroupChat with LLM-based speaker selection is the payoff.
• Sub-conversations branch off the main thread: register_nested_chats() lets an agent spin up a focused sub-discussion and inject the result back. Hand-rolling this with proper is_termination_msg handling is tedious.
• Agents need to write and run code: AutoGen's code execution sandbox is built in. If your agents iterate on generated scripts, that's table stakes you don't have to reinvent.

Pick Google ADK if your agents need to ship to Vertex AI with hierarchy and workflows declared up front.

• You're already on Google Cloud: LlmAgent + Gemini + Vertex AI Agent Engine is one stack. Deployment, observability, and managed scaling come without glue code.
• Workflow primitives map to your problem: SequentialAgent, ParallelAgent, and LoopAgent are first-class. If your spec reads like "do A, then B and C in parallel, then loop until done," ADK encodes it directly.
• Hierarchical delegation is real: A root agent routing to specialized sub-agents — research, writing, review — fits the sub_agents model cleanly, with Session/State carrying context between them.

Both frameworks bring substantial surface area: class hierarchies, Runner/GroupChatManager orchestration, session services, callbacks for termination and speaker selection. That's worth the import when you genuinely need multi-agent orchestration — but it's a lot of API to ramp up on for a single-agent system that just calls a few tools.

You also inherit the framework's release cadence. AutoGen v0.4 was a major rewrite that broke the v0.2 API; ADK is barely a year old and still expanding its primitives. Pinning versions and reading changelogs becomes part of the maintenance budget.