DocumentationAI commerce operations

Legion

Munx Legion is the Multinex command layer for automated resale workflows, marketplace operations, liquidity farming research, crypto and DeFi trading signals, staking watchlists, and approval-ready MemQ-backed agent execution.

Commerce agents that remember

Legion coordinates resale, marketplace, DeFi research, staking, and operator modules through MemQ-backed agent workflows. The goal is simple: save the decision once, then let the next teammate or agent resume with the same context.

Operators can start with one income workflow, prove the handoff, then add modules as the workflow becomes repeatable.

Customer-visible modules

Legion is organized around practical commerce and income operations.

  • Automated resale workflows for demand, pricing, inventory velocity, listing prep, and fulfillment handoff
  • Marketplace modules for MemQ Brain, OpenClaw skills, operator tools, and commerce packs
  • Liquidity farming research lanes with pool notes, timing windows, and run history
  • Staking operations watchlists with remembered criteria and approval-ready next steps

Evaluate one workflow first

Start with one resale, marketplace, liquidity, or staking workflow. Save the baseline context into MemQ, run the agent team, review the packet, and decide which modules should become part of the repeatable path.

Approval-ready operations

Legion keeps agent output organized as operator packets so the next step is clear before execution.

  • What the agents found
  • What changed since the last run
  • Which module produced the signal
  • What needs approval next

How to evaluate Legion

Use this page to decide whether the product solves the immediate bottleneck: forgotten AI context, sensitive-data risk, or uncoordinated agent work. Then follow the lowest-risk setup path and prove one workflow before expanding.

Buyer checklist

Legion decision path

Legion is a fit when the value is clear to the user who has to do the work, not only to the team buying the platform.

  • Confirm the user problem this product removes first.
  • Run one practical setup path and verify the outcome.
  • Add the next Multinex layer only after the first proof is useful.