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Readme

relay-mcp

External working memory for multi-agent systems. An MCP server that keeps every AI agent — on any machine — in sync through a shared task board.

Why

When multiple Claude Code agents work across sessions or machines, context resets every time. relay_summary restores full system state in ~300 tokens, so no agent ever asks "what were we working on?"

Install

claude mcp add relay \
  -e RELAY_API_URL=https://agent-task-tracker.onrender.com \
  -e RELAY_API_KEY=your-api-key \
  -e RELAY_AGENT=claude-code \
  -- npx relay-mcp

Replace RELAY_API_URL and RELAY_API_KEY with your Relay backend credentials. Set RELAY_AGENT to a unique name per machine (e.g. windows-agent, macbook, claude-code).

Usage

Start every session with:

Call relay_summary to see current tasks.

The agent gets back something like:

total:12  in_progress:2  pending:5  blocked:1  done:4

ACTION NEEDED:
  [urgent] Deploy to prod — Push to Render after env vars confirmed

That's it. Full context in one call.

Tools

Tool	When to call it
`relay_summary`	Start of every session — orients the agent without burning context
`relay_create`	When starting a significant task
`relay_update`	Progress updates, status changes
`relay_done`	Task complete
`relay_block`	Stuck — surfaces to human immediately
`relay_list`	Full task list when you need details

Environment variables

Variable	Required	Default	Description
`RELAY_API_URL`	Yes	—	Base URL of your Relay backend
`RELAY_API_KEY`	Yes	—	`x-api-key` credential
`RELAY_AGENT`	No	`claude-code`	Name to tag tasks with

Self-hosting

The backend is Agent Task Tracker — deploy it to Render in one click, point RELAY_API_URL at your instance.

License

MIT