002ea0d879
* feat(issues): add configurable table view Co-authored-by: multica-agent <github@multica.ai> * test(issues): cover table columns in page fixture Co-authored-by: multica-agent <github@multica.ai> * fix(issues): make table column picker interactive Co-authored-by: multica-agent <github@multica.ai> * fix(issues): repair quick create and virtualize table rows Co-authored-by: multica-agent <github@multica.ai> * fix(issues): keep pinned table cells opaque Co-authored-by: multica-agent <github@multica.ai> * fix(issues): anchor full-width table rows Co-authored-by: multica-agent <github@multica.ai> * fix(issues): consolidate table controls Co-authored-by: multica-agent <github@multica.ai> * fix(issues): harden table pagination and export Co-authored-by: multica-agent <github@multica.ai> * feat(issues): add table quick search Co-authored-by: multica-agent <github@multica.ai> * fix(issues): make table window filters, selection, and export authoritative Round-2 review fixes for the issues table (MUL-4797): - Send the agents-working filter as a server ids facet so matches on unfetched pages surface and total/pagination/export agree; a present- but-empty id list yields an empty window instead of an unfiltered one. - Reset surface selection when the membership window changes and act on selection ∩ visible rows in the batch toolbar, so batch actions, Export selected, and the count all share one authoritative set. - Materialize the full flat window while table grouping is active, and suspend hierarchy nesting / parent-based grouping until the window is complete so structure cannot reshuffle as pages arrive; suppress header facet-count badges while the table window is partial. - Resolve actor directories and the property catalog at export time and fail the export instead of writing Unknown* actors or dropping configured property columns on cold/errored lookups. - Append a unique id tie-break to the list/grouped ORDER BY and mirror it in compareIssuesForSort so offset pages are stable across same-timestamp ties. Co-authored-by: multica-agent <github@multica.ai> * fix(issues): bound table structure window and align chip/transport/selection Round-3 review fixes for the issues table (MUL-4797): - Cap whole-window materialization at TABLE_STRUCTURE_MAX_WINDOW (1000): below it the remaining pages load automatically — hierarchy applies without scrolling to the last page — and above it grouping/hierarchy suspend with an explicit toolbar notice instead of triggering an unbounded workspace download from a persisted view option. - Give the agents-working chip the authoritative in-window running set (the ids-facet window query, shared key with the filter-on state) so its badge can no longer say 0 while the filter would find matches on unfetched pages; falls back to loaded-row scoping elsewhere. - Route ids-facet windows through a new POST /api/issues/query twin — hundreds of running-issue UUIDs overflow the ~8 KB GET request-line budget of common proxies. The body carries the same key/value pairs; the handler rebuilds the query string and delegates to ListIssues. - Reset surface selection during render (key-change pattern) instead of a post-commit effect, so no frame ever pairs new membership with the old selection. Co-authored-by: multica-agent <github@multica.ai> * fix(issues): harden table auto-pagination against errors and stale totals Round-4 review fixes for the issues table (MUL-4797): - Stop the structure materialization loop (and the scroll sentinel) when the window query is in error state — a persistently failing page left hasNextPage true and isFetchingNextPage false after every attempt, so the ungated effect refired forever. Resuming is an explicit toolbar Retry. The advancement decision now lives in a pure, tested shouldAutoLoadNextStructurePage helper. - Make the structure ceiling a hard stop: the ceiling check reads the LATEST page's total (pagination already advances on it, so a stale small page-1 total could re-open unbounded materialization), and the loop additionally halts on loaded count >= ceiling regardless of any reported total. - Drive the working (ids-facet) window to completion — it is inherently bounded by the running set — and treat it as the chip's authoritative scope only when complete, so >100 running issues no longer under-count as a single page. Co-authored-by: multica-agent <github@multica.ai> * fix(issues): make working-window pagination capped and unknown-aware Round-5 (final) review fixes for the issues table (MUL-4797): - The working (ids-facet) window now advances through the same shouldAutoLoadNextWindowPage gates as the structure loop — it shares the main table's cache key while the agents-working filter is on, so an uncapped chip-driven loop re-opened the very ceiling the table just enforced. An over-ceiling window stops after page one. - A cold-load failure of the flat window is an ERROR state, not an empty workspace: isEmpty only claims empty on a successful zero-result fetch, and the surface renders a dedicated failed-to-load state with a reachable Retry (the in-table Retry never mounted without data). - The chip scope is now tri-state honest: a COMPLETE window (or an empty running set) yields a precise count, keepPreviousData carries the last-known-complete set across re-keys, and everything else — cold resolving, failed, over the ceiling — presents as an explicit unknown ('Agents working: —') instead of a number derived from whichever incomplete window happened to be loaded. Co-authored-by: multica-agent <github@multica.ai> * fix(issues): single pagination owner and placeholder-honest chip scope Round-6 review fixes for the issues table (MUL-4797): - Exclude placeholder data from the working-window completeness gate: on a re-key (running set or facet change) keepPreviousData leaves the OLD key's rows visible, and pairing them with the new task snapshot published a precise-looking number for a scope nobody fetched. The scope now reads unknown until the new key resolves. - Make the shared table query single-owner while the agents-working filter is on: the chip's background loop no longer answers the same render snapshot as TableView's structure loop, and every auto caller (structure loop, working loop, scroll sentinel, retry) now uses fetchNextPage({cancelRefetch: false}) so a concurrent responder no-ops instead of cancel/restarting a fetch whose HTTP request is not abortable — which had been duplicating every offset. Co-authored-by: multica-agent <github@multica.ai> --------- Co-authored-by: Lambda <lambda@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.
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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.