Agent Relay logo

Agent Relay

Organization
AgentWorkforce
agent-relay

Use when you need Codex to coordinate multiple agents through Agent Relay for peer-to-peer messaging, lead/worker handoffs, or shared status tracking across sub-agents and terminals.

Overview

PublisherAgentWorkforce
Repositoryrelay
Skill nameagent-relay
Stars
875
Forks
73
Bundled files
8
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 8 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by AgentWorkforce on GitHub. Read the source before you install it.

Installation

Install the Agent Relay AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/AgentWorkforce/relay/tree/main/plugins/codex-relay-skill
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/AgentWorkforce/relay.git /tmp/relay
mkdir -p .claude/skills
cp -r /tmp/relay/plugins/codex-relay-skill .claude/skills/agent-relay
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Agent Relay in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Agent Relay on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Agent Relay is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Agent Relay

Use this skill when Codex needs real-time coordination across multiple agents. It gives Codex a repeatable workflow for:

  • connecting to an Agent Relay workspace
  • spawning relay-aware workers
  • sending direct messages, channel updates, and thread replies
  • keeping lead and worker state synchronized through ACK, STATUS, BLOCKED, and DONE signals

Relay fills the peer-to-peer gap in Codex sub-agent workflows. Codex can spawn and collect worker results, but Agent Relay gives those workers a shared message bus so they can talk to the lead and to each other.

Auto-setup

On first activation, this skill auto-configures Codex by running scripts/setup.sh. This adds the Agent Relay MCP server to .codex/config.toml, enables hooks, installs hooks.json, and copies the relay-worker.toml agent definition. No manual setup is required after installing the skill.

Startup protocol

Every relay-connected Codex agent must complete these steps IN ORDER before substantive work:

  1. Set up a workspace.

    • If RELAY_WORKSPACE_KEY is set in the environment, call set_workspace_key with that key.
    • If only the legacy RELAY_API_KEY alias is set, treat it as the same workspace key.
    • If no key is available, call create_workspace to auto-create one. This returns a workspace key — save it for workers.
  2. Register as an agent. Call register_agent with your agent name and type: "agent". Use RELAY_AGENT_NAME from the environment if set, otherwise derive a name from the task context (e.g., lead, auth-worker).

  3. Keep workspace credentials out of output. Never print the workspace key or construct an observer URL from it — it is an administrative credential. When the user asks to follow the conversation, run agent-relay observer and print the URL it returns. That mints a scoped, read-only token (ot_live_...) that expires in 24 hours and excludes agent DMs, so the link is safe to share. Narrow it further with --channels, widen it with --include-dms or --expires, and revoke it with agent-relay observer revoke <id>.

  4. Check the relay inbox. Call check_inbox to see if there are any pending messages or task assignments.

  5. Send an ACK. If you received a task assignment, send ACK: <one-sentence understanding> to your lead via send_dm. If the assignment is unclear, send BLOCKED: <question> instead of guessing.

  6. When the task is complete, send DONE: <summary with evidence> before stopping.

If workspace creation or registration fails, retry once, then report the failure to the user — do not proceed without a relay connection.

Critical rule

Do not assume the current MCP session already has an active Agent Relay workspace. Always call set_workspace_key or create_workspace before registering.

Working rules

  • Include as: "<agent-name>" on relay calls that support explicit attribution.
  • Keep the relay identity stable for the whole task. Do not switch names mid-task.
  • Check the inbox again after meaningful milestones, before long-running work, and before stopping.
  • Prefer direct messages for lead/worker coordination. Use channels only when multiple agents need the same update.
  • Keep status messages short, factual, and scoped to the assigned work.
  • Do not spawn additional relay workers unless the lead explicitly asks for more delegation.
  • If the lead updates the task, follow the newest explicit instruction.

Message templates

  • ACK: I understand the assignment and I am starting work on <scope>.
  • STATUS: Finished <milestone>; next I am doing <next-step>.
  • BLOCKED: I cannot continue because <blocker>.
  • DONE: Completed <scope>. Evidence: <files changed, commands run, tests, or decisions>.

Worker patterns

There are two current ways to involve more agents. Use the right one for the job.

Registered workspace identities

Use register_agent for an agent process that is already running and only needs a Relay identity. Registration does not start a new model runtime.

Lead steps:

  1. Ensure workspace exists (set_workspace_key or create_workspace).
  2. Register the lead (register_agent).
  3. Give the other running process the workspace key and tell it to call register_agent with a stable name.
  4. Send the assignment via send_dm(to: "worker-name", text: "...").
  5. Poll lead inbox for ACK (check_inbox).

Worker steps:

  1. Call set_workspace_key with the shared key.
  2. Register with register_agent.
  3. Check inbox (check_inbox).
  4. Send ACK to lead via send_dm.
  5. Perform the assigned scope.
  6. Send DONE to lead via send_dm.

Relay-spawned workers

Use add_agent when the lead should ask Relay to start a provider-backed worker. The current tool requires name, cli, and task; optional fields include channel, persona, and model.

Lead steps:

  1. Ensure workspace exists and lead is registered.
  2. Spawn the worker with add_agent(name: "worker-name", cli: "codex", task: "...").
  3. Include https://agentrelay.com/skill, the lead name, exact scope, and completion criteria in the task prompt.
  4. Poll lead inbox for ACK (check_inbox).
  5. Release the worker with remove_agent after the work is accepted.

Worker steps:

  1. Follow the using-agent-relay role from https://agentrelay.com/skill.
  2. Check inbox, send ACK, do the assigned work, and send DONE.

Codex sub-agents

If your Codex surface has a sub-agent spawn capability, use the bundled relay-worker agent definition for code-heavy work that needs a separate Codex runtime with file access and tools. Include the workspace key, relay name, lead name, exact scope, and completion criteria in the sub-agent prompt. If that spawn capability is not available, use add_agent instead.

Worker ACK fallback

If a worker does not ACK within 30 seconds:

  1. Check whether the worker appears in list_agents.
  2. If this is a running process, have it call register_agent.
  3. If this should be a spawned worker, call add_agent with name, cli, and task.
  4. Send (or re-send) the assignment via send_dm.
  5. Poll the lead inbox again for ACK.
  6. If still no ACK after a second attempt, report the exact failed step to the user.

Handoff template

text
Worker: api-worker
Type: relay-spawned worker (use add_agent with name, cli, and task)
Lead: lead
Scope: check the Agent Relay inbox and confirm connectivity
Protocol:
  1. Check inbox
  2. DM lead with ACK
  3. Perform scope
  4. DM lead with DONE

For code-heavy tasks, change the type line to:

text
Type: Codex sub-agent (use relay-worker if your Codex surface provides sub-agent spawning)

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Agent Relay AI skill do?

Use when you need Codex to coordinate multiple agents through Agent Relay for peer-to-peer messaging, lead/worker handoffs, or shared status tracking across sub-agents and terminals.

Why use Agent Relay on TypingMind?

Because you install it once and use it with any model. Agent Relay is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Agent Relay in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/AgentWorkforce/relay/tree/main/plugins/codex-relay-skill. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Agent Relay?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Agent Relay?

As many as you like. As long as a model supports skills, you can use Agent Relay with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Agent Relay AI skill free?

Yes. It is published on GitHub by AgentWorkforce under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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