f13969b996
* refactor(chat): generate quick actions server-side via the LLM layer (MUL-5573)
Follow-up suggestions were produced by a second, full provider CLI invocation
per chat turn: the daemon resumed the just-finished session and ran a
suggestion-only pass. That pass inherited the main turn's exec options, so its
20s budget had to cover process spawn, every MCP handshake, session replay, and
model reasoning at the agent's own thinking level — typically 8-15s of visible
skeleton, and every turn paid two provider cold starts.
Generate them here instead, through the same pkg/llm layer that backs chat
auto-titling. Suggestions need no tools, workdir, or agent identity — only the
tail of the conversation — so a bounded 8s call on the deployment's small model
replaces the whole resumed turn.
Quality changes that came with the move:
- The prompt now states the frame explicitly ("you write FOR THE USER"). The
old pass ran inside the agent's session and inherited the runtime brief's
identity, which drifted suggestions toward agent-operations actions.
- Previously-offered labels are replayed as ALREADY SUGGESTED. The old
architecture had the opposite effect: on providers that append on resume,
each pass saw its predecessor's JSON and anchored on it.
- A failed generation broadcasts failed=true. Before, a timeout delivered an
empty array — indistinguishable from "nothing worth suggesting", so every
slow pass read as a quality problem.
- The in-band footer is still stripped from replies but its actions are now
discarded, so a pre-upgrade session is not pinned to the retired
suggestions with the replacing pass suppressed.
The refresh path no longer enqueues an agent task: it validates the target and
calls the same generator, which also drops the not-resumable refusal — a session
whose runtime was rebound can now be refreshed. Client contract is unchanged
(chat:done pending flag, chat:quick_actions supplement); the only frontend
change is the pending window, resized from 30s to 12s to match the new budget.
Also removes the daemon's TMPDIR-after-cleanup hazard by construction: the old
pass started after runTask's defers had already deleted the temp dir it was
still pointed at.
Co-authored-by: multica-agent <github@multica.ai>
* refactor(chat): drop the quick-actions opt-out setting (MUL-5573)
Suggestions are always on. The Settings → Chat toggle is removed along with
the whole per-turn opt-out path it fed: the persisted client preference, the
quick_actions_enabled send field, the quick_actions_disabled task stamp, and
the eligibility gate that read it.
The toggle predates server-side generation, when it could only hide chips a
provider pass had already paid for. Now that generation is a bounded call the
server decides on, an off switch buys nothing a user would miss, and it was
the last piece of UI implying the feature might be unavailable.
agent_task_queue.quick_actions_disabled is no longer written (dropped from
CreateChatTask's INSERT; the column keeps its false default). Left in place
alongside regenerate_quick_actions_for for a later drop migration — removing
columns an already-running binary still inserts would break mid-deploy.
Co-authored-by: multica-agent <github@multica.ai>
* fix(chat): address quick-actions review findings (MUL-5573)
Four defects from review of the server-side generation change.
1. Automatic failures were reported as refresh failures. The generator
broadcast failed=true on any LLM error, but the client turns every
failed=true into a "couldn't refresh" toast — so an automatic timeout
popped a toast for an action the user never took. This also contradicted
ChatQuickActionsPayload.Failed, which documents false for the automatic
pass. The caller now passes its origin; only an explicit refresh reports.
2. Generation context was not bound to the target turn. The pass re-read the
session's newest messages while always writing to the task it was handed,
so a turn landing between the completion callback and the detached read
supplied the context for a reply it did not belong to. Worse, a user
typing a follow-up in the second after a reply left the window ending on
a user row, which the old code treated as "nothing to build on" — that
turn silently never got pills. The window is now anchored on the target
assistant message and queried strictly before it.
3. No concurrency or idempotency bound on generation. Refresh stopped
creating a task, so the busy check could not see a pass already running:
two refreshes both returned 202, spent two upstream calls, and raced to
write one row. Nothing bounded generation process-wide either. Adds a
per-session in-flight guard (refresh now 409s on a duplicate) and a
process-wide ceiling; a shed pass still resolves the client placeholder
so no skeleton hangs on work that never started.
4. A new daemon could not safely talk to an older server. The refresh task
discriminator was deleted, so a regenerate task from such a server fell
through to the ordinary chat path: no user message, but the agent would
answer anyway and the server would persist it as a real reply. The field
is restored as a refusal marker only — the task completes empty, which is
the shape the retired pass produced and which that server writes no row
for. Not a restored execution path.
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.
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.
License
Multica License — the complete Apache License 2.0 text incorporated together with additional conditions — see NOTICE for attribution notices.
- Providing Multica as a hosted service to third parties, or embedding it in a commercially distributed product, requires a commercial license obtained from the producer (condition 1a).
- Unless the producer has granted a written branding waiver, the Multica LOGO, product name, and copyright information may not be removed or modified in a Multica user interface. The user interface is defined by derivation — including
apps/web/,apps/desktop/,apps/mobile/,packages/views/, andpackages/ui/— and covers raw source, the frontend container image, and compiled desktop and mobile binaries (condition 1b). - Non-interface use (running only the
server/backend, the daemon, or the CLI) is exempt from the branding condition, but must retain the source and NOTICE attribution and state that the product is built on Multica, with a link back to this repository (condition 1c). - A branding waiver and a commercial license are separate grants; neither implies the other (condition 1d).