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c366cf2ba1
* feat(agent): add kiro cli acp runtime * fix(agent): align kiro acp prompt and notifications * chore(agent): clarify kiro acp args compatibility
120 lines
4.7 KiB
Plaintext
120 lines
4.7 KiB
Plaintext
---
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title: Cloud quickstart
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description: From sign-up to assigning your first task to an agent in 5 minutes.
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---
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import { Callout } from "fumadocs-ui/components/callout";
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This page walks you end-to-end through Multica Cloud — **sign up → install the [CLI](/cli) → start the [daemon](/daemon-runtimes) → create an [agent](/agents) → assign your first [task](/tasks)**. Takes about 5 minutes.
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One prerequisite: you already have at least one [AI coding tool](/providers) installed locally ([Claude Code](/providers#claude-code), [Codex](/providers#codex), [Cursor](/providers#cursor), [Copilot](/providers#copilot), [Gemini](/providers#gemini), [Hermes](/providers#hermes), [Kimi](/providers#kimi), [Kiro CLI](/providers#kiro-cli), [OpenCode](/providers#opencode), [OpenClaw](/providers#openclaw), or [Pi](/providers#pi)). The daemon auto-detects them on startup and refuses to start if none are present.
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## 1. Create an account
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Sign up at [multica.ai](https://multica.ai). You can log in with email (6-digit verification code) or Google.
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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.
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## 2. Install the Multica CLI
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**macOS / Linux (Homebrew recommended)**:
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```bash
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brew install multica-ai/tap/multica
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```
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**macOS / Linux (no Homebrew)**:
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```bash
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curl -fsSL https://raw.githubusercontent.com/multica-ai/multica/main/scripts/install.sh | bash
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```
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**Windows (PowerShell)**:
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```powershell
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irm https://raw.githubusercontent.com/multica-ai/multica/main/scripts/install.ps1 | iex
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```
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Verify the install:
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```bash
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multica version
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```
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## 3. Log in + start the daemon
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A single command handles login and starts the daemon:
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```bash
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multica setup
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```
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`multica setup` will:
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1. Configure the CLI to connect to Multica Cloud
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2. Open your browser for login (same email verification code / Google OAuth as the web)
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3. Store the generated PAT in `~/.multica/config.json`
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4. **Start the daemon automatically** — it begins polling for tasks every 3 seconds and sending heartbeats every 15 seconds
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<Callout type="info">
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**Using the desktop app?** The desktop app **starts the daemon automatically** on launch — no need to run `multica setup` by hand. See [Desktop app](/desktop-app).
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</Callout>
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Verify the daemon is running:
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```bash
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multica daemon status
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```
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`online` means it has registered with the server.
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## 4. Verify the runtime is online
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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.
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If it shows as offline, don't panic — see [Troubleshooting → Daemon can't reach the server](/troubleshooting#daemon-cant-reach-the-server).
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## 5. Create an agent
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In the web UI, go to **Settings → Agents** and click **New Agent**:
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- **Name** — the name shown for this agent on boards and in comments. Pick something you like
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- **Provider** — choose an AI coding tool you have installed locally (the dropdown only lists tools detected by your runtimes)
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- **Model** (optional) — the model selection inside that tool (a static list or dynamic discovery, depending on the provider)
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- **Instructions** (optional) — system prompt for this agent
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Once created, the agent shows up in your workspace member list and can be assigned work like a human member.
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## 6. Assign your first task
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Create an issue in the web UI, or from the CLI:
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```bash
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multica issue create --title "Add an ASCII architecture diagram to the README"
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```
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Assign the issue to the agent you just created — click its avatar in the web UI, or use the CLI:
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```bash
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multica issue assign MUL-1 --to my-agent-name
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```
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`--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.
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**What happens next from the daemon**:
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1. It picks up the task within 3 seconds (status goes from `queued` to `dispatched`)
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2. It invokes the matching AI coding tool to start work (status becomes `running`)
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3. The AI works locally — it may read your code directory, run commands, edit files
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4. When done, it reports the result back to Multica (status becomes `completed` or `failed`, depending on whether auto-retry kicks in)
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The web UI updates in **real time** (via WebSocket) — no refresh needed.
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## Next steps
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- [Daemon and runtimes](/daemon-runtimes) — how the daemon operates and what runtimes mean
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- [Tasks](/tasks) — task lifecycle and retry rules
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- [AI coding tools compared](/providers) — capability differences across the 11 tools
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- [Desktop app](/desktop-app) — if you'd rather not run the daemon yourself
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- [Self-host quickstart](/self-host-quickstart) — run your own backend
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