Daemon and runtimes

Multica records and coordinates the work; connected computers execute it. The daemon on a computer claims tasks and invokes the AI coding tools installed on that machine.

Daemon vs runtime

• The daemon is the Multica background process running on one computer. It connects to the server, discovers local tools, claims tasks, and reports results back.
• A runtime represents one concrete execution environment available to a workspace. It corresponds to one computer plus one AI coding tool — or one custom runtime profile — on that computer.

For example, a computer has both Claude Code and Codex installed and is connected to two workspaces. The daemon registers Claude Code and Codex runtimes for each workspace. Restarting the daemon updates the existing records; it does not keep creating new runtimes for the same combination.

Execution location and data boundaries

The AI coding tools a local runtime invokes, those tools' own login credentials, and your local code directories all stay on the connected computer. The Multica server does not execute commands on behalf of local tools, and it does not automatically upload your entire working directory.

So the team can collaborate, the server stores issues, comments, agent configuration, task context, run records, and the results agents write back. That content can include code snippets or other project context an agent chose to read and include in its replies.

Starting the daemon

With Multica Desktop, the app starts the daemon automatically — no extra command needed.

On the web, a remote computer, or a headless environment, install the Multica CLI first, then run:

multica daemon start

The daemon runs in the background by default. Common commands:

To place task workspaces on another disk, persist a root for the current profile with multica config set workspaces_root <path>, or pass --workspaces-root <path> to daemon start or daemon restart. The flag overrides MULTICA_WORKSPACES_ROOT, which overrides the profile config. Existing task directories are not moved when the root changes.

On startup, the daemon detects supported AI coding tools on PATH and registers runtimes for the workspaces you are allowed to connect. If a tool was just installed or signed in, restart the daemon to detect it again.

The daemon needs at least one built-in supported AI coding tool detected before it will start. Install methods and executable names are in Install AI coding tools.

Dispatch and online status

Once registered, a runtime keeps a persistent connection. When a new task enters the queue, the server notifies the matching daemon; the daemon also polls periodically as a backstop after connection interruptions. So when a runtime is online with spare capacity, tasks usually start immediately.

The daemon sends a heartbeat every 15 seconds. The server combines heartbeats and connection state to decide whether a runtime is online; after a daemon exits unexpectedly, the runtime usually shows as offline within about 3 minutes at the latest.

When a runtime is offline:

• Tasks already queued wait for the runtime to recover, for up to 2 hours.
• Tasks that were running fail; eligible issue or chat tasks can retry automatically.
• When the daemon starts again, it re-registers its runtimes and reclaims tasks that did not end cleanly last time.
• A runtime offline for more than 7 days with no agents bound to it (including archived ones) is cleaned up automatically.

Detailed states and retry rules are in Tasks.

Concurrency limits

One daemon executes at most 20 tasks at a time by default; each agent at most 6. Effective concurrency is the smaller of the two.

Once a limit is reached, new tasks keep queueing. You can adjust a single agent's concurrency in the agent settings, and the machine-wide cap through MULTICA_DAEMON_MAX_CONCURRENT_TASKS. Parallel runs compete for machine capacity, tool account quota, and the same working directory all at once.

Private and public runtimes

A local runtime is private by default: only the runtime owner can create agents on it. Workspace owners and admins are no exception — the runtime is someone else's computer, and running an agent there spends their machine and their tool credentials.

Only the runtime owner can make it public — workspace admins can rename or delete a runtime, but sharing one is the owner's decision. Other workspace members can then select this runtime as well; that does not share the underlying AI coding tool's login credentials — it only lets members route their agents' tasks to this computer.

Custom runtime profiles

If your team uses an internal wrapper, a version-pinned executable, or needs fixed extra arguments for a compatible tool, create a custom runtime profile.

A custom profile does not add a new communication protocol. You still pick one of the protocol families Multica already supports (the tool's integration protocol type; see AI coding tools comparison), and the command itself must be compatible with that family.

Creating a profile

Only workspace owners and admins can create, edit, or delete custom runtime profiles:

1. Open Runtimes and go to a computer where the command is installed.
2. Click Add custom runtime.
3. Choose the protocol family the command is actually compatible with.
4. Fill in the name, command, and fixed arguments, then save.

The profile is shared across the workspace. Each connected computer looks up the command on its own; only computers that can resolve it on PATH register the corresponding runtime. Creating a profile does not install the command, and it does not sign other members in to the tool.

The command field takes an executable and arguments, not a shell script. Plain arguments, quotes, and backslash escapes work; pipes, redirects, &&, ;, backticks, and environment variable expansion do not. When you need those behaviors, put them in a wrapper script and use that script as the command.

How your arguments are ordered

Everything you type in the command field stays directly after the executable, ahead of the arguments Multica adds:

<your command> <your fixed arguments> <Multica's protocol arguments> <the agent's custom arguments>

This is what makes a subcommand-style wrapper work. If your command is ccms start q36, the tool sees start q36 first and can select its subcommand before Multica's -p and the rest arrive — which is the only order such a wrapper accepts.

Your arguments used to be appended last instead. Most flag-style commands parse the same either way, but not all of them do — a command that distinguishes global flags from subcommand flags can care where a flag sits. If you already have a profile with fixed arguments, re-run one task on it after upgrading to confirm it still starts.

Two more consequences worth knowing:

• Multica's own values win a conflict. If your fixed arguments set a flag Multica also sets, Multica's value comes later and takes effect. Most importantly, a model chosen on the agent overrides a --model pinned in the profile. To pin a model for everyone, leave the agents' model field empty.
• Protocol-critical flags are ignored. -p, --output-format, --input-format, --permission-mode, and the equivalents for other families are dropped from your fixed arguments, because overriding them would break the daemon's connection to the tool. Subcommands and other positional arguments always pass through.

If a Desktop-launched daemon cannot find a command your terminal can run, set an absolute path for the current computer:

multica runtime profile set-path <profile-id> --path /absolute/path/to/command

Remove the path override:

multica runtime profile unset-path <profile-id>

Editing a profile only affects tasks claimed afterwards. Before deleting a profile, deal with the active agents still bound to its runtimes. Deleting only the runtime instance on one computer does not delete the profile — a running daemon will re-register it.

Troubleshooting an offline runtime

Check in this order:

1. Run multica daemon status to confirm the daemon is running.
2. Run multica daemon logs -f to look for login, network, or tool-detection errors.
3. Run command -v <tool-command> in the same environment to confirm the daemon can find it.
4. Open Multica's Runtimes page and check whether the target computer and the matching AI coding tool show as online.
5. After a tool install, a path change, or a profile update, run multica daemon restart.

If the problem persists, see Troubleshooting.

Next steps

• Install AI coding tools — install and verify a supported AI coding tool.
• AI coding tools comparison — compare models, session resumption, MCP, and skill support.
• Tasks — queueing, stopping, and retries.