a2dd80d4f6
* feat(autopilot): skip dispatch when assignee runtime is offline (MUL-1899) Prevents scheduled autopilots from accumulating doomed tasks against offline / archived / unbound agents. Before this change, a paused laptop or crashed daemon would let a 5-minute-cron autopilot pile up thousands of queued agent_task_queue rows that no runtime would ever drain — this is the dominant source of the 89k stuck-task backlog flagged in MUL-1899. DispatchAutopilot now performs a pre-flight admission check on the assignee agent's runtime status. If the runtime is not 'online' (or the agent is archived / has no runtime bound / has no assignee), the run is recorded as 'skipped' with a failure_reason and no task is enqueued. Skipped runs still emit autopilot:run.done so the UI / activity feed reflect that the trigger fired and was evaluated. Skipped runs are deliberately NOT counted toward the failure-ratio auto-pause: a user who closes their laptop overnight should not have their autopilot paused. Sustained server-side failures keep their existing pause path via the failure monitor. Tests: added an integration test that creates an offline runtime and asserts DispatchAutopilot records a skipped run with no task enqueued. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> Co-authored-by: multica-agent <github@multica.ai> * feat(scheduler): expire stale queued tasks via TTL sweeper (MUL-1899) Companion to the dispatch-time admission gate added in this PR. The admission gate prevents *new* tasks from being enqueued against an offline runtime, but it does not drain the historical backlog (~89k stuck queued rows observed at MUL-1899 baseline) and does not help when a runtime goes offline *after* a task has already been queued. This adds a passive TTL sweeper: - New SQL query `ExpireStaleQueuedTasks` transitions queued tasks older than the TTL to status='failed' with failure_reason='queued_expired' and a clear error message. - Sweep is capped per tick (`queuedExpireBatchSize`, default 500) via a CTE+LIMIT so that draining a large backlog cannot monopolise the DB on a single tick. At 30s ticks the worst case is 60k rows/hour. - Wired into the existing 30s `runRuntimeSweeper` loop alongside `sweepStaleTasks` and reuses `taskSvc.HandleFailedTasks` so the expired tasks broadcast `task:failed` events, reconcile agent status, and roll back any in-progress issues — same lifecycle as any other failed task. - Default TTL = 2h. Conservatively above any reasonable "queued behind a long-running task" window (default agent timeout is 2h, sweeper runs every 30s) so legitimate work isn't expired. - Integration tests cover the happy path (stale → expired, fresh → left alone, correct status/reason/error) and the per-tick batch cap. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> Co-authored-by: multica-agent <github@multica.ai> * fix(autopilot): address review blockers from PR #2311 (MUL-1899) GPT-Boy review of the offline-runtime + queued-TTL PR flagged four blockers; this commit addresses them all. 1. Restore the 'skipped' autopilot_run status in the DB constraint. Migration 043 had removed 'skipped' along with the now-defunct concurrency_policy feature, so the new admission gate's INSERT of status='skipped' violated `autopilot_run_status_check` and broke `TestAutopilotDispatchSkipsWhenRuntimeOffline` in CI. New migration 079 re-adds 'skipped' to the CHECK list. The down migration migrates skipped → failed before re-tightening, mirror- ing what 043 did for the original removal. 2. Make `ExpireStaleQueuedTasks` race-safe. The CTE-then-UPDATE pattern could clobber a task that the daemon claimed between victim selection and the outer update. Two guards added: - `FOR UPDATE SKIP LOCKED` in the CTE so we never wait on a row that's currently being claimed (and never block the claim path either). - The outer UPDATE now re-checks `t.status = 'queued'` AND the TTL predicate so even if a row's lock is released after a successful claim, we cannot transition a now-dispatched/ running task to 'failed'. 3. Add a partial index for the queued-TTL sweeper. `idx_agent_task_queue_queued_created_at` on `created_at WHERE status = 'queued'` — keeps the 30s sweep query (status=queued AND created_at < ... ORDER BY created_at LIMIT 500) cheap even when historical terminal rows accumulate (~89k+ at MUL-1899 baseline). The partial predicate keeps the index tiny because only in-flight rows live in 'queued'. 4. Fix the failure-monitor denominator. `SelectAutopilotsExceedingFailureThreshold` had been counting 'skipped' toward total runs, which would have diluted the failure ratio: a 100%-failing autopilot could mask itself behind a wall of admission skips. With 'skipped' restored as a real status, the auto-pause monitor must explicitly exclude it from BOTH numerator and denominator — admission skips are neither a success nor a failure. Verified: `go test ./cmd/server/... ./internal/service/...` passes (including TestAutopilotDispatchSkipsWhenRuntimeOffline, TestExpireStaleQueuedTasks, TestExpireStaleQueuedTasksRespectsBatch Limit). `go build ./... && go vet ./...` clean. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> Co-authored-by: multica-agent <github@multica.ai> * fix(migrations): split queued-task TTL index into concurrent migration Per PR #2311 review: agent_task_queue is a hot table, so building the new partial index with plain CREATE INDEX inside migration 079 would hold ACCESS EXCLUSIVE on the queue and block dispatch during deploy. The migration runner does not allow CONCURRENTLY to share a file with other statements (documented in 068), so split the index into its own single-statement file 080 — matching the existing pattern in 035 / 067 / 074 / 075 / 078. Migration 079 keeps the autopilot_run constraint change. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> Co-authored-by: multica-agent <github@multica.ai> --------- Co-authored-by: Eve <eve@multica-ai.local> Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.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, GitHub Copilot CLI, OpenClaw, OpenCode, Hermes, Gemini, Pi, Cursor Agent, Kimi, and Kiro CLI.
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.
- Autonomous Execution — set it and forget it. Full task lifecycle management (enqueue, claim, start, complete/fail) with real-time progress streaming via WebSocket.
- 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, copilot, openclaw, opencode, hermes, gemini, pi, cursor-agent, kimi, kiro-cli) 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, GitHub Copilot CLI, OpenClaw, OpenCode, Hermes, Gemini, Pi, Cursor Agent, Kimi, or Kiro 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.
Multica vs Paperclip
| Multica | Paperclip | |
|---|---|---|
| Focus | Team AI agent collaboration platform | Solo AI agent company simulator |
| User model | Multi-user teams with roles & permissions | Single board operator |
| Agent interaction | Issues + Chat conversations | Issues + Heartbeat |
| Deployment | Cloud-first | Local-first |
| Management depth | Lightweight (Issues / Projects / Labels) | Heavy governance (Org chart / Approvals / Budgets) |
| Extensibility | Skills system | Skills + Plugin system |
TL;DR — Multica is built for teams that want to collaborate with AI agents on real projects together.
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 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, GitHub Copilot CLI,
OpenCode, OpenClaw, Hermes, Gemini,
Pi, Cursor Agent, Kimi, Kiro 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, GitHub Copilot CLI, OpenClaw, OpenCode, Hermes, Gemini, Pi, Cursor Agent, Kimi, or Kiro 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.