Cloud quickstart

This page walks you end-to-end through Multica Cloud — sign up → install the CLI → start the daemon → create an agent → assign your first task. Takes about 5 minutes.

One prerequisite: you already have at least one AI coding tool installed locally (Claude Code, Codex, Cursor, Copilot, Gemini, Hermes, Kimi, OpenCode, OpenClaw, or Pi). The daemon auto-detects them on startup and refuses to start if none are present.

1. Create an account

Sign up at multica.ai. You can log in with email (6-digit verification code) or Google.

After sign-up you're automatically placed in a default workspace (generated from your account name). You can rename it later, or create new workspaces.

2. Install the Multica CLI

macOS / Linux (Homebrew recommended):

brew install multica-ai/tap/multica

macOS / Linux (no Homebrew):

curl -fsSL https://raw.githubusercontent.com/multica-ai/multica/main/scripts/install.sh | bash

Windows (PowerShell):

irm https://raw.githubusercontent.com/multica-ai/multica/main/scripts/install.ps1 | iex

Verify the install:

multica version

3. Log in + start the daemon

A single command handles login and starts the daemon:

multica setup

multica setup will:

1. Configure the CLI to connect to Multica Cloud
2. Open your browser for login (same email verification code / Google OAuth as the web)
3. Store the generated PAT in ~/.multica/config.json
4. Start the daemon automatically — it begins polling for tasks every 3 seconds and sending heartbeats every 15 seconds

Verify the daemon is running:

multica daemon status

online means it has registered with the server.

4. Verify the runtime is online

In the web UI, go to Settings → Runtimes. The daemon you just started should appear as one or more active runtimes — one per AI coding tool installed locally.

If it shows as offline, don't panic — see Troubleshooting → Daemon can't reach the server.

5. Create an agent

In the web UI, go to Settings → Agents and click New Agent:

• Name — the name shown for this agent on boards and in comments. Pick something you like
• Provider — choose an AI coding tool you have installed locally (the dropdown only lists tools detected by your runtimes)
• Model (optional) — the model selection inside that tool (a static list or dynamic discovery, depending on the provider)
• Instructions (optional) — system prompt for this agent

Once created, the agent shows up in your workspace member list and can be assigned work like a human member.

6. Assign your first task

Create an issue in the web UI, or from the CLI:

multica issue create --title "Add an ASCII architecture diagram to the README"

Assign the issue to the agent you just created — click its avatar in the web UI, or use the CLI:

multica issue assign MUL-1 --to my-agent-name

--to takes the name of an agent or member. A substring match works — if the agent is called my-code-reviewer, reviewer resolves to it.

What happens next from the daemon:

1. It picks up the task within 3 seconds (status goes from queued to dispatched)
2. It invokes the matching AI coding tool to start work (status becomes running)
3. The AI works locally — it may read your code directory, run commands, edit files
4. When done, it reports the result back to Multica (status becomes completed or failed, depending on whether auto-retry kicks in)

The web UI updates in real time (via WebSocket) — no refresh needed.

Next steps

• Daemon and runtimes — how the daemon operates and what runtimes mean
• Tasks — task lifecycle and retry rules
• AI coding tools compared — capability differences across the 10 tools
• Desktop app — if you'd rather not run the daemon yourself
• Self-host quickstart — run your own backend