Multi-Agent Systems

AutoGen separates multi-agent systems into Core, AgentChat, and Extensions, with AgentTool, MCP Workbench, and AutoGen Studio supporting orchestration from prototype to tool integration. This article examines the architecture, permissions, observability, maintenance-mode trade-offs, and adoption and migration strategies for existing and new projects.

Google ADK 2.0 separates Agent reasoning and tool use from Workflow execution order and state. This article examines its workflow runtime, Task API, multi-agent composition, MCP tools, evaluation, deployment, and the practical checks required before production adoption.

CrewAI separates multi-agent applications into complementary layers: Crews for role-based agent collaboration and Flows for events, state, branching, and side effects. This article starts with a minimal Flow, embeds a Crew, and explains the validation, observability, and authorization boundaries required for production.

HKUDS/nanobot is a self-buildable personal AI Agent framework developed in Python, integrating WebUI, CLI, chat channel, tools, long-term memory, MCP, sub-agent, scheduling and OpenAI-compatible API. This article summarizes how it moves from a chat robot to a permanent workbench from the perspective of Agent loop, quick start, self-established boundaries and engineering checkpoints.

QwenPaw 2.0 is more than a chat interface: it combines models, memory, Skills, MCP, multiple channels, and security policies into a self-hosted Agent OS. This article starts with Docker and explains its adoption path, permission boundaries, and practical limitations.