MiniMax M2.7's Best Feature Nobody's Using (Multi-Agent Teams on OpenClaw)

Summary

If you're running MiniMax M2.7 on OpenClaw as a single chatbot, you're missing its most powerful capability: multi-agent teamwork. This video walks you through how to set up a team of specialized AI agents, each with its own role, workspace, memory, and even Discord channel, all running under a single MiniMax coding plan key. The host demonstrates a real trading workflow where a Strategist agent receives a trading strategy, writes a spec, and hands it off to a Pine Coder, which then forwards output to a Backtester, all without manual prompting between steps. The same architecture applies to any workflow, such as content creation with a researcher, writer, editor, and fact-checker team. You have two setup options. The first is the Route Method, which is the fastest way to get started. You create subfolders inside your main agent's workspace, each named after a specialized role, add a markdown file defining that agent's personality and instructions, then add route commands to your main agent's configuration file. It is centralized, quick to spin up, but tends to break around four or more routes and accumulates technical debt. The second is the Terminal Method, which is what the host actually uses. You run the command 'openclaw agents add [agent-name]' for each specialist, giving every agent a fully isolated environment with its own files, tools, and workspace. It is cleaner, more scalable, and better suited for serious pipelines. The host recommends using Claude Code or Kilo Code to help configure each agent's soul and tools markdown files, since those tools ask clarifying questions and help you build configurations that actually work. What makes MiniMax M2.7 a strong fit for this architecture is its self-evolution feature. Unlike other models, M2.7 can analyze its own outputs, refine instructions, and improve the pipeline autonomously over time without you providing constant feedback. This is especially valuable because most agentic workflows degrade after a few weeks when something breaks. The self-evolution loop is designed to address exactly that problem, and at a fraction of the cost of comparable models.