9c9afd4a66
* feat(metrics): scrape-time BusinessSamplerCollector for active users / queued / runtime gauges (MUL-2947)
Adds an opt-in prometheus.Collector that runs a fixed set of read-only
SQL queries on every /metrics scrape and exposes the results as gauges:
- multica_active_users{window=5m|1h|24h}
- multica_active_workspaces{window=...}
- multica_agent_task_queued{source}
- multica_agent_task_running{source,runtime_mode}
- multica_agent_task_stuck_total{source}
- multica_runtime_online{runtime_mode,provider}
- multica_runtime_heartbeat_age_seconds{runtime_mode} (histogram)
- multica_workspace_total
Plus a self-introspection histogram
multica_business_sampler_query_seconds{name=...} and a counter
multica_business_sampler_query_errors_total{name=...} so the sampler's
own behaviour is observable on /metrics.
Production-safety contract per the PR4 brief:
- every query runs in its own BEGIN READ ONLY tx with
SET LOCAL statement_timeout = '500ms' (configurable)
- the sampler takes a dedicated *pgxpool.Pool option so operators
can isolate it from business traffic
- successful results are cached for 5–10s (default 8s) to absorb
concurrent scrapes from multiple Prometheus replicas
- every SQL has a hard LIMIT 100 fallback
- all label values flow through the existing BusinessMetrics
NormalizeTaskSource / NormalizeRuntimeMode / NormalizeRuntimeProvider
whitelists, so a misbehaving runtime cannot inflate cardinality
- sampler is OPT-IN via RegistryOptions.BusinessSampler — existing
callers that only pass Pool keep their current behaviour and never
start hitting the DB on /metrics
Tests cover: emit shape, TTL cache (one DB call per N scrapes),
bounded cardinality under malicious labels, opt-out (no leakage), and
DB-hang isolation (unreachable host -> /metrics returns within 5s,
query_errors_total advances).
Refs MUL-2947 (depends on PR2 / MUL-2948, merged in #3695).
Co-authored-by: multica-agent <github@multica.ai>
* fix(metrics): address PR4 review — wire sampler in main.go, fix LIMIT bug, add live-DB statement_timeout test
Three fixes from 大彪's review on #3706:
1. main.go was building NewRegistry without the BusinessSampler option,
so the collector was effectively dead code in prod. Now constructs a
dedicated 2-conn pgxpool (newSamplerDBPool) from the same DATABASE_URL
when METRICS_ADDR is set, plumbs it into RegistryOptions.BusinessSampler,
and defers Close() at shutdown. A pool-build failure logs and disables
the sampler instead of taking down the server.
2. queryActiveUsers / queryActiveWorkspaces previously wrapped the
distinct-user/workspace subquery in a 'LIMIT 100', then COUNT(*)'d
the result — capping the active-user gauge at 100 regardless of
reality. Removed the inner LIMIT; the COUNT scalar is one row anyway,
and metric cardinality is bounded by the fixed samplerWindows
allow-list, not by the SQL shape.
3. The previous DB-hang test only exercised the acquire-fails path. Added
business_sampler_pgsleep_test.go which connects to a live Postgres
(skips cleanly when DATABASE_URL is not set), runs SELECT pg_sleep(2)
inside a sampler-style tx with SET LOCAL statement_timeout = '500ms',
and asserts:
- the call returns in well under 1.5 s (proving the server-side
cancellation, not just our caller-side context)
- query_errors_total{name=pg_sleep_canary} advances
- the duration histogram records the cancellation
Verified locally: 550 ms, SQLSTATE 57014 'canceling statement due to
statement timeout' — exactly the safety net the PR claims.
Refs MUL-2947 / PR #3706.
Co-authored-by: multica-agent <github@multica.ai>
* test(metrics): assert SQLSTATE 57014 on pg_sleep cancellation
The previous assertion only checked that the query was cut off in well
under the sleep duration, which a caller-side context cancellation
would also satisfy. Capturing the inner pgconn.PgError and asserting
Code == "57014" ("query_canceled") nails down that Postgres itself
cancelled the statement because of the SET LOCAL statement_timeout —
so a regression that drops the SET LOCAL line fails this test loudly
instead of silently passing on context cancellation.
Refs MUL-2947 / PR #3706 review nit.
Co-authored-by: multica-agent <github@multica.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.
Website · Cloud · X · Self-Hosting · Contributing
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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.
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) 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, or Antigravity). 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)
| 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.
An iOS mobile client lives in apps/mobile/ — see its README for how to build it onto your own iPhone.