* feat(search): implement full-text search for issues Add pg_bigm-based full-text search across issue titles and descriptions, with API endpoint, CLI subcommand, and web Cmd+K search dialog. - Migration 032: pg_bigm extension + GIN indexes on title/description - Server: GET /api/issues/search?q=... with pagination and total count - CLI: `multica issue search <query>` with table/json output - Web: Cmd+K command palette using cmdk, with debounced search Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * fix(search): address review feedback on search implementation 1. Escape LIKE special characters (%, _, \) in handler to prevent matching anomalies from user input. 2. Wire AbortController signal into searchIssues fetch so in-flight requests are actually cancelled on new input. 3. Fix offset=0 falsy check — use !== undefined instead of truthiness. 4. Merge results + count into single query using COUNT(*) OVER() window function, eliminating the duplicate DB round-trip. 5. Exclude done/cancelled issues by default; add include_closed parameter to API, CLI (--include-closed), and web client. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * fix(search): default web search to include all statuses Pass include_closed: true in the web Cmd+K search so results include done and cancelled issues by default, matching the reviewer's request. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * feat(search): add comment search with snippet extraction Extend search to cover issue comments in addition to title/description. Results are deduplicated at the issue level, with match_source and matched_snippet fields indicating where and what matched. - Migration 033: pg_bigm GIN index on comment.content - SQL: EXISTS subquery for comment matching, correlated subquery for snippet extraction, 3-tier ranking (title > description > comment) - Server: SearchIssueResponse with match_source and matched_snippet - Web: show comment icon + snippet below issue title when matched - CLI: MATCH column shows source and truncated snippet Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * feat(search): redesign search dialog to match Linear's spacious style - Widen dialog from sm (384px) to xl (576px) with top-20% positioning - Larger search input with icon, generous padding, and ESC hint - Use cmdk primitives directly for full style control - Taller result list (400px / 50vh), spacious result items (py-2.5) - Rounded-lg items with accent highlight on selection - Cleaner border separator between input and results --------- Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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 · Self-Hosting · Contributing
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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 and Codex.
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.
Getting Started
Multica Cloud
The fastest way to get started — no setup required: multica.ai
Self-Host with Docker
git clone https://github.com/multica-ai/multica.git
cd multica
cp .env.example .env
# Edit .env — at minimum, change JWT_SECRET
docker compose up -d # Start PostgreSQL
cd server && go run ./cmd/migrate up && cd .. # Run migrations
make start # Start the app
See the Self-Hosting Guide for full instructions.
CLI
The multica CLI connects your local machine to Multica — authenticate, manage workspaces, and run the agent daemon.
Option A — paste this to your coding agent (Claude Code, Codex, etc.):
Fetch https://github.com/multica-ai/multica/blob/main/CLI_INSTALL.md and follow the instructions to install Multica CLI, log in, and start the daemon on this machine.
Option B — install manually:
# Install
brew tap multica-ai/tap
brew install multica
# Authenticate and start
multica login
multica daemon start
The daemon auto-detects available agent CLIs (claude, codex) on your PATH. When an agent is assigned a task, the daemon creates an isolated environment, runs the agent, and reports results back.
See the CLI and Daemon Guide for the full command reference, daemon configuration, and advanced usage.
Quickstart
Once you have the CLI installed (or signed up for Multica Cloud), follow these steps to assign your first task to an agent:
1. Log in and start the daemon
multica login # Authenticate with your Multica account
multica daemon start # Start the local agent runtime
The daemon runs in the background and keeps your machine connected to Multica. It auto-detects agent CLIs (claude, codex) available 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 or Codex). 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.
That's it! Your agent is now part of the team. 🎉
Architecture
┌──────────────┐ ┌──────────────┐ ┌──────────────────┐
│ Next.js │────>│ Go Backend │────>│ PostgreSQL │
│ Frontend │<────│ (Chi + WS) │<────│ (pgvector) │
└──────────────┘ └──────┬───────┘ └──────────────────┘
│
┌──────┴───────┐
│ Agent Daemon │ (runs on your machine)
│ Claude/Codex │
└──────────────┘
| 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 or Codex |
Development
For contributors working on the Multica codebase, see the Contributing Guide.
Prerequisites: Node.js v20+, pnpm v10.28+, Go v1.26+, Docker
pnpm install
cp .env.example .env
make setup
make start
See CONTRIBUTING.md for the full development workflow, worktree support, testing, and troubleshooting.

