Group Chat and Speaker Selection
Shared-conversation orchestration puts N agents in one conversation; a selector function (LLM, round-robin, or custom) picks who speaks next. This is the archetype of emergent multi-agent conversation — agents do not know their role in a static graph, they just react to the shared pool. AutoGen GroupChat and AG2 GroupChat are the reference implementations: AutoGen v0.2's GroupChat semantics were preserved in the AG2 fork; AutoGen v0.4 rewrote it as an event-driven actor model. Microsoft put AutoGen into maintenance mode in February 2026 and merged it with Semantic Kernel into Microsoft Agent Framework (RC February 2026). The GroupChat primitive survives in both AG2 and Microsoft Agent Framework — learn it once, use it everywhere. Static graphs (LangGraph) are great when the workflow is known. Real conversations are not static: sometimes the coder asks the reviewer, sometimes the researcher, sometimes the writer. Hardcoding every possible handoff produces an edge explosion. You want agents reacting to a shared pool, with some function deciding who talks next. That is exactly what AutoGen GroupChat does. Every agent sees every message. A selector function is invoked at each turn to pick who speaks next. Round-robin. Fixed cycle. Deterministic. Scales linearly in N but ignores context — a coder gets the turn even when the topic is legal review. LLM-selected. A call to an LLM that reads…
Group Chat and Speaker Selection: Shared-conversation orchestration puts N agents in one conversation; a selector function (LLM, round-robin, or custom) picks…
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