Relay: Open-Source Control Plane for OpenClaw AI Agents

What Relay Is

Relay is an open-source Electron desktop application that serves as a control plane for OpenClaw AI agents. If OpenClaw is the runtime, Relay functions as your local command center, providing the same workflow patterns as Claude Cowork but running on your own infrastructure with your choice of models.

Architecture and Components

The system follows a clear separation between control and execution planes:

• Relay (Control Plane): Desktop app where you define tasks, review deliverables, and approve actions
• OpenClaw Gateway (Execution Plane): Runtime that runs on your infrastructure (local, VPS, or custom URL) and executes tasks

Communication between Relay and OpenClaw happens via WebSocket/API connections.

Key Features from Source

• Dispatch & Chat: Give tasks in natural language, agent plans steps, review polished deliverables
• Governance: Approval gates for file operations, shell commands, and data sends; exportable audit trail; cost tracking per task
• Configuration: Schedule recurring tasks, browse and edit agent memory, manage connectors (Slack, Notion, GitHub, Jira, etc.), set project working folders
• Model Flexibility: Routes tasks to any LLM backend including Claude, GPT-4, Llama, Gemini, Mixtral, or custom endpoints
• Data Sovereignty: Processes files on your infrastructure rather than Anthropic's servers

Example Workflow

The source provides a concrete example of a scheduled daily briefing:

• In Relay: Create schedule for "Daily 8am", set connectors (Slack + Notion)
• OpenClaw executes: Cron fires at 8am, agent reads project files, pulls Slack threads and Notion pages, calls your chosen LLM, writes briefing to memory
• Briefing appears in Relay for review, approval, or redirection

Target Use Cases

Relay addresses three structural limitations of Claude Cowork: data sovereignty concerns for regulated industries, model lock-in (only Claude), and compliance gaps where activities aren't captured in standard audit logs. This makes it particularly relevant for organizations with strict data policies or those needing to use multiple LLM providers.