c271f80999
* fix(daemon): label stalled skill-bundle downloads and make them retryable A skill bundle that could not be downloaded during task preparation surfaced as the bare string "resolve skill bundles: context deadline exceeded". taskfailure.Classify has no rule for a Go context deadline, so it landed in agent_error.unknown — a bucket that is NOT on the server's retry allowlist. A transient stall therefore became a terminal chat failure carrying a label nobody could act on, and the failure was invisible on the Usage page's Errors breakdown. (MUL-5370) - Add the platform-side reason skill_bundle_unavailable and put it on retryableReasons. Retrying is cheap and safe: the agent process never started, and bundles that did arrive are already cached on disk, so successive attempts converge. - Carry a sentinel error from the resolve loop so the reason is derived structurally rather than by matching the wrapped transport error's text, and name the skill, its declared size and the elapsed wait in the wrap — enough to tell "this bundle is too big for the link" from "the link is dead" without reading daemon logs. - Normalise the wire shape an OLD daemon produces (a non-empty catchall plus the previous "resolve skill bundles:" wrapper) on the server side. Installed daemons upgrade on their own cadence, and FailTask only classifies when the caller supplied nothing, so without this the fix would reach only hosts that happened to update — while the un-upgraded hosts most likely to be hitting the bug kept failing terminally. - Teach Classify about "deadline exceeded" and net/http's "Client.Timeout exceeded while awaiting" so any other Go-side deadline that reaches it as text stops falling into the unknown bucket too. - Backfill historical rows in both agent_task_queue and chat_message. Scoped to agent_error.unknown alone — the old wrapper string postdates the in-flight classifier by three weeks, so no row carrying it can hold the legacy coarse value — which keeps the down migration an exact inverse. Co-authored-by: multica-agent <github@multica.ai> * fix(chat): give chat its own failure copy for the refined reasons #5991 rebuilt the operator-facing failure labels around an open wire string with a raw-value fallback, but the chat bubble kept its own exact-key lookup against the six coarse values from migration 055. So all 14 agent_error.* values still missed and rendered the generic "Something went wrong and the agent couldn't finish replying" — the classification the backend had already computed was discarded at the last step, and that is the message the MUL-5370 reporter saw. - Add resolveFailureReasonKey in packages/core: exact match, else degrade an `agent_error.*` value to its family, else undefined. A reason newer than the shipped client now lands on the family line instead of the fallback. - Rekey the chat copy map by wire value and route it through the helper. Chat deliberately degrades to friendly copy rather than adopting the operator surfaces' raw-value fallback: it is read by the person who just sent a message, and the raw error is one click away under the collapsible. - Add refined chat copy (en / zh-Hans / ja / ko) only where it can say something the family line can't — a different next step: network, auth, quota, rate limit, context overflow, missing/outdated CLI, skill download. - Give skill_bundle_unavailable a label on the web and mobile surfaces and a class on the Usage page's Errors breakdown (runtime — the operator response is "check the daemon's link to Multica", the provider is not involved). - Mobile's two label maps were still coarse-only for the same reason; rekey them by wire value and fill in the refined taxonomy. Co-authored-by: multica-agent <github@multica.ai> --------- Co-authored-by: Bohan-J <bohan@devv.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.
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.
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.
Windows (PowerShell)
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? Set the
MULTICA_MODEenvironment variable towith-serverbefore running the installer to deploy a full Multica server on your machine:$env:MULTICA_MODE="with-server"; irm https://raw.githubusercontent.com/multica-ai/multica/main/scripts/install.ps1 | iex multica setup self-hostThis pulls the official Multica images from GHCR (latest stable by default). Requires Docker. See the Self-Hosting Guide for details.
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.