645e8a2983
* feat(issues): confirm assignment without a pre-flight run preview (MUL-5010) RunConfirmModal called POST /api/issues/preview-trigger on open and blocked the entire dialog behind a "检查中…" spinner until it landed. That query is keyed per issue id with staleTime 0, so every new issue was a guaranteed cache miss — the wait was structural, not incidental. The dialog also promised "会立即开始工作" while still checking whether anything would start. Redefine it as "dialog = confirm the assignment", not "confirm N runs": - Open fires no request. Note box and both buttons are usable on frame one. - Title/primary action become 确认指派; copy states the assignment as certain and the run as conditional, with no predicted count. - The write reports what actually happened: UpdateIssue returns runs_started (0/1) and batch-update returns the batch total, surfaced as one short toast — "已指派给 X，已启动 N 个 run", or just "已指派给 X". runs_started counts successful enqueues rather than predicate hits, since an insert can still no-op on the pending unique index. It is pointer+omitempty like Labels, so only the PUT reply carries it, and it is stripped in useUpdateIssue's onSuccess so a write-scoped fact never lands in the cache. The handoff-note version gate stays local and synchronous, now covering squads too by resolving the leader's runtime from the warm squad list — removing the last reason the dialog needed the server round-trip. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Co-authored-by: multica-agent <github@multica.ai> * test(issues): assert runs_started on the write responses (MUL-5010) The assign-confirm dialog now reports the enqueue outcome from the write reply instead of predicting it, so the number itself is the contract. Pin it directly on the responses: - single assign that enqueues → runs_started 1 - single assign that starts nothing → 0, via both routes (suppress_run, and assigning into backlog), so a client can tell "no run" apart from "old backend omitting the field" - batch → the real aggregate, asserted against a mixed selection where updated=3 but runs_started=2; the divergence from `updated` is the point - absent on GET, guarding the pointer+omitempty contract that keeps a write-scoped fact off every read path Each count is cross-checked against agent_task_queue so the assertions track real enqueues rather than the predicate. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Co-authored-by: multica-agent <github@multica.ai> * refactor(issues): drop runs_started + result toast, keep silent completion (MUL-5010) Final scope: the dialog only removes the preview wait; it does not add a post-submit result toast. Per the confirmed decision, revert everything that existed solely to report "N runs started" and keep completion silent, exactly as it was before this PR — the assignee and any run already surface through the issue's normal updates. Removed: - the success toast on submit and its two i18n keys (all four locales) - the `runs_started` response field on IssueResponse, the single/batch write counting, and dispatchIssueRun's bool return (back to void) - the `runs_started` type/schema/api-client additions and the onSuccess strip - server/internal/handler/issue_runs_started_test.go (only served the field) Kept: preview-trigger removal and the instant-usable dialog — note box and buttons live on the first frame, agent/squad handoff-note version gate resolved locally, and "确认指派" copy. Net diff vs base is now frontend-only; the backend and packages/core files are byte-identical to base again. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Co-authored-by: multica-agent <github@multica.ai> * docs(issues): fix stale headline comment referencing removed result toast (MUL-5010) The result toast was removed with runs_started, but the headline comment still said "the real one arrives in the result toast". Reword to state only what remains true: the copy names no run count because the run is conditional. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Co-authored-by: multica-agent <github@multica.ai> --------- Co-authored-by: Claude Opus 4.8 <noreply@anthropic.com> Co-authored-by: multica-agent <github@multica.ai>
Multica
Your next 10 hires won't be human.
The open-source managed agents platform.
Turn coding agents into real teammates — assign tasks, track progress, compound skills.
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What is Multica?
Multica turns coding agents into real teammates. Assign issues to an agent like you'd assign to a colleague — they'll pick up the work, write code, report blockers, and update statuses autonomously.
No more copy-pasting prompts. No more babysitting runs. Your agents show up on the board, participate in conversations, and compound reusable skills over time. Think of it as open-source infrastructure for managed agents — vendor-neutral, self-hosted, and designed for human + AI teams. Works with Claude Code, Codex, CodeBuddy, GitHub Copilot CLI, OpenCode, OpenClaw, Hermes, Pi, Cursor Agent, Kimi, Kiro CLI, Antigravity, Qoder CLI, and Trae CLI.
For larger teams, Squads add a stable routing layer: assign work to a group led by an agent, and the leader delegates to the right member.
Why "Multica"?
Multica — Multiplexed Information and Computing Agent.
The name is a nod to Multics, the pioneering operating system of the 1960s that introduced time-sharing — letting multiple users share a single machine as if each had it to themselves. Unix was born as a deliberate simplification of Multics: one user, one task, one elegant philosophy.
We think the same inflection is happening again. For decades, software teams have been single-threaded — one engineer, one task, one context switch at a time. AI agents change that equation. Multica brings time-sharing back, but for an era where the "users" multiplexing the system are both humans and autonomous agents.
In Multica, agents are first-class teammates. They get assigned issues, report progress, raise blockers, and ship code — just like their human colleagues. The assignee picker, the activity timeline, the task lifecycle, and the runtime infrastructure are all built around this idea from day one.
Like Multics before it, the bet is on multiplexing: a small team shouldn't feel small. With the right system, two engineers and a fleet of agents can move like twenty.
Features
Multica manages the full agent lifecycle: from task assignment to execution monitoring to skill reuse.
- Agents as Teammates — assign to an agent like you'd assign to a colleague. They have profiles, show up on the board, post comments, create issues, and report blockers proactively.
- Squads — group agents (and humans) under a leader agent and assign work to the squad. The leader decides who should pick it up, so routing stays stable as the team grows.
@FrontendTeaminstead of@alice-or-bob-or-carol. - Autonomous Execution — set it and forget it. Full task lifecycle management (enqueue, claim, start, complete/fail) with real-time progress streaming via WebSocket.
- Autopilots — schedule recurring work for agents. Cron triggers, webhooks, or manual runs — each autopilot creates the issue and routes it to an agent automatically, so daily standups, weekly reports, and periodic audits run themselves.
- Reusable Skills — every solution becomes a reusable skill for the whole team. Deployments, migrations, code reviews — skills compound your team's capabilities over time.
- Unified Runtimes — one dashboard for all your compute. Local daemons and cloud runtimes, auto-detection of available CLIs, real-time monitoring.
- Multi-Workspace — organize work across teams with workspace-level isolation. Each workspace has its own agents, issues, and settings.
Quick Install
macOS / Linux (Homebrew - recommended)
brew install multica-ai/tap/multica
Use brew upgrade multica-ai/tap/multica to keep the CLI current.
macOS / Linux (install script)
curl -fsSL https://raw.githubusercontent.com/multica-ai/multica/main/scripts/install.sh | bash
Use this if Homebrew is not available. The script installs the Multica CLI on macOS and Linux by using Homebrew when it is on PATH, otherwise it downloads the binary directly.
Windows (PowerShell)
irm https://raw.githubusercontent.com/multica-ai/multica/main/scripts/install.ps1 | iex
Then configure, authenticate, and start the daemon in one command:
multica setup # Connect to Multica Cloud, log in, start daemon
Self-hosting? Add
--with-serverto deploy a full Multica server on your machine:curl -fsSL https://raw.githubusercontent.com/multica-ai/multica/main/scripts/install.sh | bash -s -- --with-server multica setup self-hostThis pulls the official Multica images from GHCR (latest stable by default). Requires Docker. See the Self-Hosting Guide for details. If the selected GHCR tag has not been published yet, fall back to
make selfhost-buildfrom a checkout.
Getting Started
1. Set up and start the daemon
multica setup # Configure, authenticate, and start the daemon
The daemon runs in the background and auto-detects agent CLIs (claude, codex, codebuddy, copilot, opencode, openclaw, hermes, pi, cursor-agent, kimi, kiro-cli, agy, qodercli, traecli) on your PATH.
2. Verify your runtime
Open your workspace in the Multica web app. Navigate to Settings → Runtimes — you should see your machine listed as an active Runtime.
What is a Runtime? A Runtime is a compute environment that can execute agent tasks. It can be your local machine (via the daemon) or a cloud instance. Each runtime reports which agent CLIs are available, so Multica knows where to route work.
3. Create an agent
Go to Settings → Agents and click New Agent. Pick the runtime you just connected and choose a provider (Claude Code, Codex, CodeBuddy, GitHub Copilot CLI, OpenCode, OpenClaw, Hermes, Pi, Cursor Agent, Kimi, Kiro CLI, Antigravity, Qoder CLI, or Trae CLI). Give your agent a name — this is how it will appear on the board, in comments, and in assignments.
4. Assign your first task
Create an issue from the board (or via multica issue create), then assign it to your new agent. The agent will automatically pick up the task, execute it on your runtime, and report progress — just like a human teammate.
CLI
The multica CLI connects your local machine to Multica — authenticate, manage workspaces, and run the agent daemon.
| Command | Description |
|---|---|
multica login |
Authenticate (opens browser) |
multica daemon start |
Start the local agent runtime |
multica daemon status |
Check daemon status |
multica setup |
One-command setup for Multica Cloud (configure + login + start daemon) |
multica setup self-host |
Same, but for self-hosted deployments |
multica workspace list |
List your workspaces (current is marked with *) |
multica workspace switch <id|slug> |
Switch the default workspace for this profile |
multica issue list |
List issues in your workspace |
multica issue create |
Create a new issue |
multica update |
Update to the latest version |
See the CLI and Daemon Guide for the full command reference.
Architecture
┌──────────────┐ ┌──────────────┐ ┌──────────────────┐
│ Next.js │────>│ Go Backend │────>│ PostgreSQL │
│ Frontend │<────│ (Chi + WS) │<────│ (pgvector) │
└──────────────┘ └──────┬───────┘ └──────────────────┘
│
┌──────┴───────┐
│ Agent Daemon │ runs on your machine
└──────────────┘ (Claude Code, Codex, CodeBuddy, GitHub Copilot CLI,
OpenCode, OpenClaw, Hermes, Pi, Cursor Agent,
Kimi, Kiro CLI, Antigravity, Qoder CLI, Trae CLI)
| Layer | Stack |
|---|---|
| Frontend | Next.js 16 (App Router) |
| Backend | Go (Chi router, sqlc, gorilla/websocket) |
| Database | PostgreSQL 17 with pgvector |
| Agent Runtime | Local daemon executing Claude Code, Codex, CodeBuddy, GitHub Copilot CLI, OpenCode, OpenClaw, Hermes, Pi, Cursor Agent, Kimi, Kiro CLI, Antigravity, Qoder CLI, or Trae CLI |
Development
For contributors working on the Multica codebase, see the Contributing Guide.
Prerequisites: Node.js v20+, pnpm v10.28+, Go v1.26+, Docker
make dev
make dev auto-detects your environment (main checkout or worktree), creates the env file, installs dependencies, sets up the database, runs migrations, and starts all services.
See CONTRIBUTING.md for the full development workflow, worktree support, testing, and troubleshooting.
An iOS mobile client lives in apps/mobile/ — see its README for how to build it onto your own iPhone.