577018649e
* feat(issues): support human-readable issue URLs using issue keys (MUL-5354) Closes #5987. `/{ws}/issues/MUL-123` now opens the issue, the copy-link action shares that form, and a UUID URL rewrites itself to it. Existing UUID links keep working. Backend already resolved identifiers on `GET /api/issues/{id}`, but it compared the number only — every prefix with the right number opened the same issue, so no identifier URL could be canonical. Resolution now validates the prefix against the workspace's own (case-insensitively, matching `lookupIssueByIdentifier`), and the number parser bails on int32 overflow instead of truncating a digits-only UUID group into a plausible issue number. On the client the identifier stays a presentation concern: the route resolves it to the UUID before rendering, because the realtime updaters patch `issueKeys.detail(wsId, issue.id)` with the UUID from the websocket payload. A view keyed on the identifier would sit on a cache entry no realtime event can reach and silently stop updating. Resolution reuses the request the detail view would have made anyway and seeds the UUID-keyed entry, so an identifier URL costs no extra round trip. The desktop tab title/status glyph hops through the same resolution for the same reason. The URL rewrite lives in the new route wrapper rather than IssueDetail: the inbox renders IssueDetail in a side panel, where replacing the URL would navigate the user out of the inbox. No migration — `issue (workspace_id, number)` is already unique/indexed. Co-authored-by: multica-agent <github@multica.ai> * refactor(issues): make the single-request guarantee for identifier URLs explicit Review flagged that opening `/{ws}/issues/MUL-123` fires two detail requests. It does not, under the app's own QueryClient — but the guarantee was resting on something implicit, so make it structural. The old shape seeded the UUID-keyed entry from a `useEffect` after resolution, while the route enabled the UUID query in the same render. That held only because the seed effect happened to be declared before the UUID query's own effect, and because `createQueryClient` sets `staleTime: Infinity` so a seeded entry is never refetched. Neither is obvious from the code, and a diagnostic run under a bare `new QueryClient()` (staleTime 0) does show two calls — the second being a staleness refetch of an already-seeded entry, i.e. a harness artifact. `useCanonicalIssueId` becomes `useCanonicalIssue`, which owns both the resolution query and the canonical detail query and hands the resolution response to the latter as `initialData`. That is applied while the observer is created, so the canonical query never observes an empty cache and never starts a fetch of its own — no dependency on effect ordering, and no cache write that could race a realtime patch (`initialData` is ignored once the entry holds data). Callers collapse to one hook each: the route no longer runs its own detail query, and the desktop page drops its duplicate. Tests now build the client with `createQueryClient()` rather than a bare `new QueryClient()`, so request-count assertions measure production behavior instead of the harness, plus a direct assertion that an identifier URL costs exactly one request. Co-authored-by: multica-agent <github@multica.ai> * fix(issues): stop the request loop when an identifier names no issue Opening `/{ws}/issues/ZZZ-134` never reached "not found". It spun an unbounded request loop and left the UI on the loading skeleton forever. The route treated a failed resolution as "nothing resolved" and handed the raw identifier down to IssueDetail. IssueDetail mounted a second observer on the query that had just failed; `retryOnMount` refetched it, which flipped the resolve hook back to pending, which unmounted IssueDetail, which remounted it when the refetch failed — and around again. Measured with retry disabled to isolate it: 8,192 requests at 300ms, 32,768 at 600ms. Under the app's `retry: 1` the backoff only paces the loop, it still never converges. `useCanonicalIssue` now reports a terminal `notFound` read from the resolution query's own error state, rather than leaving callers to infer failure from "not resolving and no id" — an inference that cannot distinguish failed from in-flight. `IssueDetailRoute` renders the not-found UI itself and never hands an unresolved segment to a view that would query it again, so no second observer exists to restart the cycle. Same measurement after the fix: 1 request, settled, "not found" on screen. The not-found UI moves out of IssueDetail into a shared `IssueNotFound` so both render the identical state. Regression tests at both levels, with retry off so any count above 1 can only be a remount refetch: the hook settles a failed resolution without looping, and the real IssueDetailRoute holds at one request across waits and rerenders. Both fail against the previous code. Co-authored-by: multica-agent <github@multica.ai> --------- Co-authored-by: Bohan-J <bohan@devv.ai> 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.
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.
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.
Windows (PowerShell)
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? Set the
MULTICA_MODEenvironment variable towith-serverbefore running the installer to deploy a full Multica server on your machine:$env:MULTICA_MODE="with-server"; irm https://raw.githubusercontent.com/multica-ai/multica/main/scripts/install.ps1 | iex multica setup self-hostThis pulls the official Multica images from GHCR (latest stable by default). Requires Docker. See the Self-Hosting Guide for details.
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.