eca6102365
* refactor(scheduler): add PlansForScope hook for non-cadence jobs
The current Manager.plansForTick assumes a uniform Cadence grid:
plan_times are derived via FloorPlan(db_now, Cadence). That works for
rollup_task_usage_hourly but not for the upcoming Autopilot schedule
dispatch job, where each trigger has its own cron expression and the
plan_times do not snap to a single global grid.
This change adds an optional JobSpec.PlansForScope hook. When set:
* Manager loads the latest stored plan for (job, scope) and passes
a new LatestPlanInfo to the hook (exported from the previously
private latestPlanInfo). The hook returns the plan_times to attempt
this tick.
* Cadence, CatchUpMode and CatchUpWindow are bypassed; the hook is
in full control of plan_time selection.
* MaxPlansPerTick still acts as a safety cap on the hook's output.
* All other timing fields (RunTimeout / StaleTimeout /
HeartbeatInterval / MaxAttempts / RetryBackoff / AllowStaleReentry)
and the lease/heartbeat/terminal-write SQL primitives are reused
unchanged.
JobSpec.validate now allows Cadence=0 when PlansForScope is set, and
makes the every_plan MaxPlansPerTick > 0 invariant fire only on
Cadence-driven every_plan jobs. Existing rollup_task_usage_hourly
behaviour is unchanged — that JobSpec leaves PlansForScope nil.
Tests:
* TestJobSpecValidatePlansForScopeRelaxesCadence — validate() rules.
* TestManagerPlansForScopeHookDrivesPlans — end-to-end hook delegation
through the manager (DB-backed), proving that hook-returned
plan_times go through the same tryClaim path, MaxPlansPerTick
truncates without erroring, and LatestPlanInfo is populated on the
second tick.
* TestManagerPlansForScopeHookEmptyIsNoOp — empty hook output is a
valid no-op.
No behaviour change for callers that don't set PlansForScope.
Refs MUL-3551
Co-authored-by: multica-agent <github@multica.ai>
* refactor(autopilot): add planned_at + DispatchAutopilotForPlan for occurrence idempotency
PR 2 of 3 for the scheduled-Autopilot refactor on MUL-3551.
Adds dispatch-layer idempotency for scheduled triggers. This is the
second line of defence behind the primary uq_sys_cron_execution
guarantee in sys_cron_executions: if a runner crashes between
"create autopilot_run" and "write SUCCESS in sys_cron_executions",
the next stale-steal retry re-enters dispatch with the SAME
(trigger_id, planned_at). Without a row-level guard, that retry
would create a duplicate autopilot_run, issue, and task.
Changes:
* Migration 124: ALTER TABLE autopilot_run ADD COLUMN planned_at
TIMESTAMPTZ + partial unique index on (trigger_id, planned_at)
WHERE both are NOT NULL. Manual / webhook / api dispatch leaves
planned_at NULL so they keep the existing semantics unchanged.
* autopilot.sql: CreateAutopilotRun now takes planned_at;
GetAutopilotRunByTriggerAndPlanned is the fast-path lookup used
by DispatchAutopilotForPlan to detect a prior attempt's row
without burning an INSERT.
* service.DispatchAutopilotForPlan: new entry point for scheduled
triggers that already know the canonical UTC plan_time of the
occurrence they are firing. Looks up an existing run for
(trigger_id, planned_at) and reuses it on a stale-steal retry;
otherwise dispatches normally with planned_at stamped on the
new run.
* service.DispatchAutopilot keeps its current signature for
manual / webhook / api callers (planned_at stays NULL).
* recordSkippedRun also threads planned_at so the skip path
participates in the same partial-unique guarantee.
* sqlc v1.31.1 regenerated autopilot.sql.go + models.go.
Unrelated workspace.sql.go drift restored.
Tests (against local Postgres):
* TestDispatchAutopilotForPlanIsIdempotent — first call creates a
run; second call with same (trigger, planned_at) reuses it
(autopilot_run row count stays at 1); third call with a different
planned_at on the same trigger creates a second run (proves we
are not collapsing legitimate occurrences).
* TestDispatchAutopilotForPlanRejectsZeroArgs — invalid trigger_id
and zero planned_at both fail loudly so callers cannot silently
disable the idempotency guard.
* Existing autopilot listener / squad / dispatch tests all still
pass.
This PR has no scheduler / handler / UI behaviour change on its own:
the new entry point exists but is not yet wired into the schedule
goroutine. PR 3 will register the autopilot_schedule_dispatch
JobSpec that consumes it and remove the legacy
cmd/server/autopilot_scheduler.go path.
Refs MUL-3551
Co-authored-by: multica-agent <github@multica.ai>
* fix(autopilot): DispatchAutopilotForPlan recovers partial-state runs
Review fix for #4443 (MUL-3551).
Before this change, DispatchAutopilotForPlan returned ANY existing
autopilot_run for (trigger_id, planned_at), including the
half-written rows produced when a runner crashed between
"CreateAutopilotRun" and "create downstream issue/task". The
scheduler handler would then write SUCCESS in sys_cron_executions
even though no issue or agent task was ever created, silently
losing the scheduled occurrence.
Fix:
* New isAutopilotRunComplete helper classifies an existing run:
- terminal status (completed / failed / skipped) → reuse.
- issue_created with valid issue_id → reuse (the issue
listener owns task creation from here).
- running with valid task_id → reuse (the task is queued).
- anything else → partial; must NOT short-circuit.
* New SQL RecoverPartialAutopilotRun marks a partial row FAILED
with a recovery reason AND clears its planned_at. The cleared
planned_at releases the partial-unique slot in
uq_autopilot_run_trigger_planned, letting the fresh dispatch
INSERT a new row at the same (trigger_id, planned_at) without
conflict.
* DispatchAutopilotForPlan now branches on the lookup:
complete run → return; partial run → recover-then-fresh-
dispatch; not-found → fresh dispatch. The fresh dispatch path
still goes through dispatchAutopilot, so the new row carries
the real issue_id / task_id by the time the handler returns.
* Tests: TestDispatchAutopilotForPlanRecoversPartialRun seeds a
partial run (status='running', task_id=NULL for run_only;
status='issue_created', issue_id=NULL for create_issue) and
asserts the retry:
- returns a DIFFERENT run row (no false reuse);
- leaves the partial row in status='failed', planned_at=NULL,
with a non-empty failure_reason for ops;
- produces a fresh row with planned_at preserved AND the
appropriate downstream linkage (task_id for run_only,
issue_id for create_issue);
- exactly one live row at (trigger_id, planned_at) after
recovery, so the partial-unique constraint is honoured.
Existing TestDispatchAutopilotForPlanIsIdempotent and
TestDispatchAutopilotForPlanRejectsZeroArgs still pass — the
complete-reuse path is unchanged for the realistic SUCCESS-state
case.
Refs MUL-3551
Co-authored-by: multica-agent <github@multica.ai>
---------
Co-authored-by: Eve <eve@multica-ai.local>
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, Kiro CLI, and Qoder 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, copilot, openclaw, opencode, hermes, gemini, pi, cursor-agent, kimi, kiro-cli, agy, qodercli) 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, Kiro CLI, Antigravity, or Qoder 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, GitHub Copilot CLI,
OpenCode, OpenClaw, Hermes, Gemini,
Pi, Cursor Agent, Kimi, Kiro CLI, Qoder 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, Kiro CLI, or Qoder 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.