现在大多数人把编程 AI 当一次性工具，问完就关，其实它完全可以像团队同事一样长期养着

multica 是个开源平台，把编程 AI 变成能分配任务、能跟进度、能越用越熟练的数字同事

https://github.com/multica-ai/multica…

TypeScript 为主，支持自托管

核心特点：

1. AI 当队员 - 每个 agent 有自己的档案，能领任务独立干活，不用你全程盯着
2. 组小队 - 多个 agent 编成一队，由一个队长统一派活
3. 技能会累积 - 用过的本事能复用，团队整体越跑越强
4. 统一看板 - 本地和云端的算力跑的活，都在一个面板里盯进度
5. 实时更新 - 任务进展实时回传，不用刷新等结果

CLI 装起来，Homebrew、脚本、PowerShell 任选，想自己搭服务器加个参数配 Docker 就行

带团队又想认真用好编程 AI 的，可以拿它当协作中枢试试

multica-ai/multica

Source: https://github.com/multica-ai/multica

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

English | 简体中文

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. @FrontendTeam instead 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-server to 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-host

This 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-build from 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.

CLI

The multica CLI connects your local machine to Multica — authenticate, manage workspaces, and run the agent daemon.

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)

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.