Can I trigger a handoff from inside a tool function?

Yes, by returning a ReplyResult object from a tool, you can simultaneously update shared context variables and specify exactly which agent should take over next.

How do agents share state without bloating the conversation history?

This skill uses ContextVariables, a shared memory layer that persists across handoffs but remains separate from the LLM prompt until explicitly injected, ensuring token efficiency.

What is the primary benefit of using DefaultPattern over AutoPattern?

DefaultPattern provides deterministic control over agent transitions through explicit handoff rules, whereas AutoPattern relies on the LLM to autonomously decide the next speaker, making DefaultPattern better for structured business logic.

What types of handoff conditions are supported?

It supports context-based conditions (deterministic), LLM-based conditions (reasoning), and after-work behaviors that define fallback actions when no specific rule is met.

AG2 Group Chat Orchestrator

Name: AG2 Group Chat Orchestrator
Author: ag2ai

byag2ai

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Developer Tools

Orchestrates complex multi-agent conversations using explicit handoff logic and shared context state for deterministic workflow control.

The AG2 Group Chat skill enables developers to build sophisticated multi-agent systems using the DefaultPattern within the AG2 framework. Unlike autonomous patterns, this skill focuses on explicit handoffs, providing fine-grained control over how tasks move between specialized agents. It supports deterministic routing via context variables, LLM-based reasoning for flexible transitions, and shared memory through ContextVariables. This makes it an ideal solution for building pipelines with validation gates, hierarchical delegation, and mission-critical workflows where predictability and state management are paramount.

Key Features

01Flexible transition targets including specific agents, users, or termination

022 GitHub stars

03Tool-driven handoffs using the ReplyResult pattern

04Deterministic agent routing using context-based conditions

05Shared state management across agents with ContextVariables

06Dynamic system message updates based on session history

Use Cases

01Iterative content creation workflows where an editor agent provides feedback loops for a writer agent

02Hierarchical customer support systems that route queries from triage to specialized technical agents

03Multi-stage software development pipelines with explicit validation gates between agents

Key Features

01Flexible transition targets including specific agents, users, or termination

022 GitHub stars

03Tool-driven handoffs using the ReplyResult pattern

04Deterministic agent routing using context-based conditions

05Shared state management across agents with ContextVariables

06Dynamic system message updates based on session history

Use Cases

01Iterative content creation workflows where an editor agent provides feedback loops for a writer agent

02Hierarchical customer support systems that route queries from triage to specialized technical agents

03Multi-stage software development pipelines with explicit validation gates between agents