Running a single AI agent is straightforward; running a team of specialized agents that delegate tasks, share memory, and coordinate through structured workflows requires an orchestration layer, and that is exactly what SwarmClaw provides. Define a hierarchy of agents in an org chart where a Coordinator (your CEO agent) delegates research tasks to a Researcher, coding tasks to a Developer, and design tasks to a Designer, each configured with its own LLM provider, tool permissions, and skill set. The Task Board presents a Kanban view of all work items across Backlog, Queued, Running, and Completed columns, with each task card showing its assigned agent, tags, due dates, and approval gates that pause execution until a human reviews and approves. Agents execute work using built-in tools for file operations, shell commands, browser automation, and persistent memory, plus any MCP server you connect via stdio, SSE, or streamable HTTP transport. Durable structured sessions support branching logic, repeat loops, parallel branches with explicit joins, and restart-safe run state that survives crashes without losing progress. Over 23 LLM providers ship built-in: Claude Code CLI, OpenAI, Anthropic, Google Gemini, DeepSeek, Groq, Mistral, xAI Grok, Fireworks, Ollama, and more. Connectors push messages to Discord, Slack, and Telegram, while cron schedules and webhooks trigger agent runs automatically. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. MIT licensed.

Prototype multi-agent AI systems without writing orchestration code: AutoGen Studio is Microsoft's low-code interface over the AutoGen AgentChat framework. You compose teams of LLM-powered agents in a visual Team Builder, either by drag-and-drop from a component library or by editing the declarative JSON specification directly. Each agent gets a model, a prompt, tools (Python functions), and the team gets termination conditions and an orchestration pattern, sequential or LLM-driven. The Playground runs teams interactively with live message streaming between agents, a visual control-transition graph, tool-call and code-execution tracking, and pause/stop controls, which makes it a practical debugger for agent behavior. Finished teams export as JSON for use in any Python application via the TeamManager class, or serve as an API endpoint. Any OpenAI-compatible model endpoint works, including local servers like Ollama or vLLM. Microsoft labels it a research prototype: use it for prototyping and evaluation, and build production systems on the underlying AutoGen framework.