33bd8aeaa9
* fix(lark): allow rebinding a revoked Feishu bot to a different agent When a Feishu/Lark Bot is disconnected from agent A (status → revoked), the row is preserved for audit but still holds the (channel_type, config->>app_id) unique index slot. Binding the same Bot to agent B would fail with: duplicate key value violates unique constraint "idx_channel_installation_type_appid" (SQLSTATE 23505) because UpsertChannelInstallation conflicts on (workspace_id, agent_id, channel_type) — a different agent_id means no conflict match, so it tries INSERT and hits the app_id unique index. Fix: before the upsert, inside the same transaction, hard-delete any revoked installation with the same app_id in the same workspace. The delete is fenced to status=revoked so an active installation can never be silently removed. If no revoked row exists the delete is a no-op (deletes zero rows, returns nil error) and the upsert proceeds normally. Co-Authored-By: Claude <noreply@anthropic.com> Co-authored-by: multica-agent <github@multica.ai> * fix(lark): preserve same-agent bindings when reconnecting a revoked Feishu bot The cleanup added in the previous commit hard-deletes every revoked channel_installation sharing the app_id in the workspace before the upsert — including the row belonging to the agent currently being (re)installed. That regresses the common "disconnect then reconnect the same bot to the same agent" flow: disconnect only flips status to 'revoked' (bindings are preserved), and UpsertChannelInstallation conflicts on (workspace_id, agent_id, channel_type), so before this the same agent's row was reactivated in place — installation_id and every channel_user_binding / channel_chat_session_binding kept. Deleting it first forces an INSERT with a fresh installation_id, orphaning every member's account link (they must re-link) and all chat-session continuity; only the installer is re-bound. Fence the delete with `agent_id <> $agent_id` so it only clears a DIFFERENT agent's revoked row (the genuine app_id-slot blocker). The same agent's revoked row is left for the upsert to reactivate losslessly. Since idx_channel_installation_type_appid is globally unique on (channel_type, app_id), at most one row ever holds a given app_id, so the excluded row is exactly the one the upsert will reuse. Adds DB-backed regression tests: same-agent revoked row preserved, different-agent revoked row deleted, active row never deleted, other workspace fenced, plus end-to-end reactivation semantics (same agent keeps installation_id + bindings; different agent gets a fresh id). Co-authored-by: multica-agent <github@multica.ai> * fix(lark): clean dependent rows when hard-deleting a rebound Feishu installation Addresses review on #4997 (MUL-4134). channel_* has no FK/cascade (MUL-3515 §4), so hard-deleting a different-agent revoked installation left application-owned rows dangling at a removed installation_id: - channel_chat_session_binding: the outbound patcher would resolve a binding, then fail loading the deleted installation — turning a clean no-op into error logs. - channel_binding_token: a still-unexpired bind link (15 min TTL) could be redeemed into the deleted installation, reporting "bound" against a bot that no longer reaches the user. - channel_inbound_audit: dangling installation_id, where migration 124 models the old ON DELETE SET NULL as an app-layer NULL. - channel_user_binding: dead member links (a different agent is a distinct connection; links do not follow and can never be reused). Rework RemoveRevokedInstallationByAppID to resolve the single row holding the app_id and act only when it is revoked, in this workspace, and owned by another agent; then, on the caller's transaction, clear chat-session bindings, pending binding tokens and member links, NULL the audit references, and finally delete the row via the fenced query (defense in depth). Same-agent reconnect and active/other-workspace rows are no-ops. Adds DeleteChannelUserBindingsByInstallation, DeleteChannelBindingTokensByInstallation, and NullChannelInboundAuditInstallationID queries, plus a DB-backed test (TestChannelStore_RebindCleansDependentRows) asserting every dependent is cleaned and the audit row survives detached. Verified the test fails when the cleanup is skipped. Co-authored-by: multica-agent <github@multica.ai> * fix(lark): make the rebind cleanup race-safe with a guarded delete gate Addresses the concurrency must-fix on #4997 (MUL-4134). The prior shape read the candidate installation, checked revoked/workspace/agent in Go, cleaned the dependent rows, then ran the fenced delete. That read-then- clean-then-delete order has a TOCTOU: while B is rebinding the bot to a different agent, A can reconnect to the SAME agent and reactivate the row to 'active' in between. B still wipes A's user/chat/token bindings and NULLs its audit based on the stale "it was revoked" read, then the fenced delete no-ops (status is no longer revoked) — so A's installation survives active but its bindings are gone. Concurrent same-agent data loss, reintroduced. Make the guarded DELETE the atomic gate. DeleteChannelInstallationByAppID becomes DeleteRevokedChannelInstallationByAppID `:one ... RETURNING id`, and RemoveRevokedInstallationByAppID keys all dependent cleanup off the id the delete actually claimed. No separate read. Under READ COMMITTED a concurrent reactivation makes the DELETE re-check status='revoked' against the live row (EvalPlanQual): it claims nothing, returns pgx.ErrNoRows, and no dependents are touched. With no FK the cleanup can follow the claiming delete in the same transaction; any failure rolls the whole thing back. Adds TestChannelStore_RebindGuardedDeleteRaceWithReactivation: two real transactions race on one revoked installation — one reactivates and holds the row lock, the other runs the rebind cleanup and blocks on the guarded delete — asserting the installation and every binding stay intact. Verified this test fails on the old read-then-clean-then-delete shape and passes (also under -race) on the gated version. Co-authored-by: multica-agent <github@multica.ai> --------- Co-authored-by: jiangliangyou <jiangliangyou@xiaomi.com> Co-authored-by: Claude <noreply@anthropic.com> Co-authored-by: multica-agent <github@multica.ai> Co-authored-by: J <j@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.