Naiyuan QingandClaude Opus 4.7
32740d0ee3
* fix(landing): align ZH copy with conventions and update tool list to 11 - Replace "Agent" with "智能体" in ZH marketing copy (lines 1-275) per conventions.zh.mdx — landing was the only surface still using "Agent" while UI, docs, and locales already use "智能体". Changelog-section technical names (Agent SDK / Agent runtime / Cursor Agent) preserved. - Replace the 4-tool list (Claude Code / Codex / OpenClaw / OpenCode) with the actual 11 supported tools across hero card, how-it-works step, and FAQ — this matches daemon-runtimes.mdx and the file's own changelog entries that already record the rollout of Cursor, Copilot, Gemini, Hermes, Kimi, Kiro CLI, and Pi. - Drop the "plug in and go" line; replace with an honest sentence about multica setup walking through OAuth + daemon start. Co-Authored-By: Claude Opus 4.7 (1M context) <[email protected]> * fix(i18n): correct daemon/runtime drift across modals, onboarding, docs - modals/zh-Hans: 4 places used "daemon" untranslated; conventions.zh.mdx rules Daemon -> 守护进程. Aligned. - onboarding/zh-Hans: line "把任务交给它们" was the only spot using "任务" for the task entity; rest of the file already uses lowercase "task" per conventions. Aligned. - onboarding (en + zh-Hans) runtime_aside.what_suffix: said runtime IS a background process. daemon-runtimes.mdx defines runtime = daemon × one AI coding tool (one machine + N tools = N runtimes). Replaced with the correct definition so new users form the right mental model on first contact. - onboarding (en + zh-Hans) step_platform headline+lede: said "Connect a runtime" but the next options are "install desktop / CLI / cloud waitlist" — those install a runtime source, not connect to one. Reworded. - onboarding/zh-Hans: 4 places used "AI 编码工具"; docs use "AI 编程工具" consistently. Unified on the docs term. - daemon-runtimes (en + zh): added cross-link to /desktop-app for users deciding between desktop daemon and CLI daemon. Co-Authored-By: Claude Opus 4.7 (1M context) <[email protected]> * feat(onboarding): localize starter-content (Getting Started project) The Getting Started project + welcome issue + 10 sub-issues that land in the workspace at the end of onboarding were hardcoded English. Chinese users finished a Chinese onboarding flow and arrived to an all-English workspace; the welcome issue's prompt to the agent was also English, so the agent's first reply tended to be English regardless of what templates the user picked. This commit adds Chinese parity, fixes the runtime definition error that was the source of similar drift in onboarding.json, and removes a few hardcoded UI specifics that would silently rot. Architecture: - Long-form markdown (~600 lines per language) lives in TS sibling files: starter-content-content-en.ts and starter-content-content-zh.ts. JSON locales were considered, but multi-paragraph markdown becomes unreadable single-line escape soup in JSON; keeping it in TS lets reviewers see the rendered shape and catch markdown regressions in code review. - starter-content-templates.ts is now a thin orchestrator: imports both content files, exports buildImportPayload({ ..., locale }), picks the right one at runtime. - StarterContentPrompt resolves locale from i18n.language (with a small startsWith("zh") helper so "zh-Hans-CN" or future variants still hit the ZH content). Content fixes (apply to both EN and ZH): - "A runtime is a small background process" was wrong (runtime = daemon × one AI coding tool, per docs). Replaced with the correct definition so the welcome agent doesn't seed an incorrect mental model. - Removed hardcoded "tabs at the top: 6 tabs" / "(third row)" / "6 templates" lists — those rot the moment product UI changes. Replaced with descriptions that don't depend on exact counts/positions. Conventions adherence (ZH): - agent → 智能体, daemon → 守护进程, runtime → 运行时, workspace → 工作区 - task / issue / skill stay lowercase English (per conventions.zh.mdx) - Product UI labels (Properties, Assignee, Status, Activity, Live card, Inbox, Members, Settings, Runtimes, Configure, Repositories, Instructions, Tasks, Skills, Autopilot, etc.) stay English so the doc text matches what the user sees on screen. Co-Authored-By: Claude Opus 4.7 (1M context) <[email protected]> * docs(conventions): formalize mixed-rule for task / issue / skill in CN The prior rule said issue/skill/task always render as lowercase English in Chinese text. That worked for UI strings but never matched what the sister docs actually do — tasks.zh.mdx is built around "执行任务", issues.zh.mdx titles "Issue 与 project", skills.zh.mdx titles "Skills". Three docs, three patterns, all sensible in their own context, none matching the old rule. Conventions also explicitly cited the docs as the voice standard, so the rule was internally inconsistent. This commit promotes the de facto pattern to a written rule: - UI strings, state names, code references → lowercase English ("排队中的 task", "创建子 issue", "为智能体注入 skill") - Doc titles / section headings → Title-case English OR Chinese term ("Issue 与 project", "Skills", "执行任务") - Doc prose where the entity is the running subject → Chinese term, with English in parentheses on first mention ("**执行任务**（task）是智能体每一次工作的单位") - API / DB fields → always task / issue / skill (`task_id`, etc.) Provides the term mapping (task ↔ 执行任务) explicitly so future translation PRs don't have to rediscover it. No code or other doc changes — tasks.zh.mdx already follows this pattern; this commit just formalizes it. Other ZH locale strings remain lowercase per the UI rule (which the locale audit + PR #2139 verified). Co-Authored-By: Claude Opus 4.7 (1M context) <[email protected]> * docs: add Projects page (en + zh) and Autopilot failure visibility note The audit found that 'projects' was the most prominently missing docs page — it appears as a sidebar nav item in onboarding's workspace preview, but users clicking through to docs found nothing on the topic. The other locale-but-no-doc pages (my-issues, labels, settings) are listed as follow-ups; this PR ships the highest-impact one. Also adds a missing piece in tasks.{mdx,zh.mdx}: the Autopilot no-auto-retry callout explained the *why* but never the *how do I notice* — added a sentence pointing users at Inbox + the issue status revert + the Autopilot page's run history. projects.mdx covers: - What a project is (container for related issues) - Fields: name, icon, description, lead, status, priority, progress - Project-issue many-to-one relationship + how progress is computed - Pinning to sidebar (personal preference) - Resources section (GitHub repos passed to daemon) - Delete behavior (issues unlinked, not deleted) - Lead can be a member or an agent Both pages registered in meta.json / meta.zh.json under "Workspace & team" group, between issues and comments. Co-Authored-By: Claude Opus 4.7 (1M context) <[email protected]> * chore(pr-template): add drift-prevention checkboxes for runtime/CN copy Two failure modes the docs+onboarding audit found, both caused by adding-a-thing without remembering all the places that thing surfaces: 1. New runtime / coding tool / UI tab gets recorded in changelog but not in landing FAQ ("Multica supports 4 tools" while changelog shows the 11th was added) or starter-content tutorial ("6 tabs at the top: Instructions / Skills / Tasks / Environment / Custom Args / Settings" stays frozen the moment a tab is added or renamed). 2. Chinese copy added without checking the canonical glossary — "Agent" survived in landing/zh.ts long after product UI standardized on "智能体" because nobody routed landing through the conventions review. Adding two checklist items to the PR template so authors see the specific paths to update at PR-creation time, before the drift ships. This is the final batch (5 / 5) from the audit. Co-Authored-By: Claude Opus 4.7 (1M context) <[email protected]> --------- Co-authored-by: Claude Opus 4.7 (1M context) <[email protected]>
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.
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.