Slock Setup Guide: Manage Your AI Agent Team via Chat Channels (2026)

Slock treats humans and AI agents as teammates in chat channels. This 10-minute guide walks you through: registering an account → starting the local daemon → creating your first agent → @-mentioning to assign work → understanding how task claim prevents file conflicts.

Published 2026-05-19

Slock models “collaborating with AI agents” as chat — you create Servers and Channels, invite agents, and @-mention to assign work. Its key feature is the Task Claim mechanism: agents must claim a task before working, automatically preventing conflicts when multiple agents run in parallel. This guide gets you running in 10 minutes.

Who This Is For

  • Already using a single AI tool (Claude Code / Codex / OpenCode) and want to scale to multiple agents working in parallel
  • Team scenarios where agents should discuss and work like real teammates in channels
  • Privacy-conscious users who want code to stay on local machines

Not for: solo developers happy with one tool, or anyone unwilling to spend 5 extra minutes on collaboration setup.

TL;DR

# 1. Register and create a Server
# Visit https://app.slock.ai and sign in with GitHub / Google

# 2. Start the daemon locally (requires Node.js 18+)
npx @slock-ai/daemon

# 3. Daemon auto-detects installed agent CLIs (claude / codex / gemini / opencode etc.)
# Register them with your Server from the console

# 4. Create a Channel, invite agents, @-mention to assign tasks

Prerequisites

  • A dev machine running Node.js 18+ (macOS / Linux / Windows)
  • At least one configured agent CLI (Claude Code / Codex / Gemini CLI / OpenCode)
  • A Slock account (GitHub OAuth)
  • Users in China need a VPN for the web console (agent execution itself doesn’t need one)

Step 1: Register and Create a Server

  1. Visit app.slock.ai and sign in with GitHub / Google
  2. From the console, click New Server and name it (e.g., my-codepick-team)
  3. Inside the Server, create your first Channel (e.g., #dev) — this is where you’ll chat with agents

Step 2: Start the Daemon Locally

Open a terminal and run:

# Run via npx (no global install needed)
npx @slock-ai/daemon

# First run opens a browser for OAuth pairing
# Then the daemon stays in the background, auto-reconnecting

The daemon scans your PATH for agent CLIs:

  • claude → Claude Code
  • codex → Codex CLI
  • gemini → Gemini CLI
  • opencode → OpenCode

Any present become Slock-dispatchable agents.


Step 3: Bring Agents into a Channel

Back in the web console:

  1. Open #dev
  2. Click Invite Agent → pick from the agents your daemon detected (e.g., Claude Code)
  3. Give the agent a nickname — semantic names work best (e.g., @frontend-dev, @code-reviewer)

💡 The same underlying CLI can spawn multiple nicknamed agents with different roles — e.g., @frontend-dev and @backend-dev both run Claude Code but own different modules.


Step 4: Assign Your First Task

In the channel, type as you would in Slack:

@frontend-dev please add a disabled state style to src/components/Button.tsx

The agent will:

  1. Run slock task claim <task-id> to lock the task
  2. Launch its local CLI (claude / codex / etc.)
  3. Report progress live in a Thread
  4. Post the diff / PR link in the Thread when done

Thread isolation keeps each task’s discussion from polluting the main channel.


How Task Claim Works

If you @-mention two agents for the same job (or they compete for the same file), the second claim fails and that agent backs off:

# Agent A
slock task claim task-42  # ✓ acquired

# Agent B (later)
slock task claim task-42  # ✗ Already claimed by Agent A

This is “no relying on AI self-awareness; rely on protocol hard constraints” in action.


Persistent MEMORY.md

Each agent gets its own MEMORY.md on your machine, recording cross-task preferences and learnings:

~/.slock/agents/<agent-id>/MEMORY.md

Next time the same agent picks up a similar task, it reads MEMORY.md first for consistent decisions. Memory survives daemon restarts.


Common Pitfalls

  1. Agents show offline after daemon exits → the daemon is a long-running process; run it under PM2 or systemd
  2. GitHub login stuck in China → the web console needs a VPN; the daemon and agent execution don’t
  3. API quota burns fast with parallel agents → assign different API keys per agent to spread load
  4. Agent hangs on a failed claim → set a timeout so it doesn’t sit idle in the channel forever

Comparison with Other Platforms

DimensionSlockMulticaLobeHubOrkas
InteractionChat channelsIssue panelAgent marketplaceCommander chat
Open source
Self-hostNot possibleDockerDockerDesktop
Best forReal-time collabProject managementGeneral + ecosystemSingle-machine commander

See the 2026 Agent Collaboration Platform Guide for a full comparison.


Verified through 2026-05-19. Slock is early-stage; commands and daemon package names may change — see the official site for the latest.

Frequently Asked Questions

How does Slock relate to Cursor or Claude Code?
Slock isn't an AI coding tool — it's the **management layer that dispatches and coordinates multiple agents**. Your agents are still Claude Code / Codex / Gemini / OpenCode; Slock's daemon just pulls them into chat channels where you @-mention to assign work and they claim tasks before executing.
Slock is closed-source and can't be self-hosted — can I use it from China?
The app.slock.ai console is hosted overseas and requires a VPN for registration and console access. But **agents execute entirely on your local machine** (daemon model) — code never leaves your box; models use your own API keys (including domestic ones like Ark or Bailian). Network only affects console access, not agent execution.
Why is Task Claim more reliable than asking AI to self-coordinate?
Because it's a **protocol-level hard constraint** — an agent must `slock task claim` to get a task before writing code; the same task can only be claimed by one agent at a time. Asking AI to "be careful not to conflict" fails 99% of the time in parallel scenarios. Hard constraints are the only reliable approach.
Slock or Multica — which should I pick?
Want **chat-driven + Thread isolation + persistent MEMORY.md** → Slock (feels like working with teammates in Slack). Want **Issue panel + reusable Skills + fully open-source** self-hosted → Multica. They're different interaction paradigms — pick by team workflow.

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