07034f4455
* feat(server): configurable pgxpool size with sane defaults pgxpool.New(ctx, url) silently sets MaxConns = max(4, NumCPU). On the prod pods that resolved to 4, which got fully saturated by daemon claim/heartbeat traffic (~3800 acquires/s) and showed up as ~900ms acquire waits on every query — the actual root cause of the 3s+ /tasks/claim tail latency. The db pool stats logging from #1378 confirmed this with empty_acquire_delta == acquire_count_delta. Switch to pgxpool.ParseConfig + NewWithConfig and apply per-pod defaults of MaxConns=25 / MinConns=5, both overridable via env vars (DATABASE_MAX_CONNS / DATABASE_MIN_CONNS) so the size can be tuned in prod without a redeploy. The defaults follow the standard 'small pool, lots of waiters' guidance for Postgres (PG community / HikariCP formula `(core_count * 2) + effective_spindle_count`); 25 leaves headroom for bursts and occasional long queries while staying safely under typical managed-Postgres max_connections ceilings when multiplied across pods. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * fix(server): respect DATABASE_URL pool_* params; add precedence tests Address review feedback on #1381: - Configuration precedence is now explicit: DATABASE_MAX_CONNS env > pool_max_conns query param on DATABASE_URL > built-in default. Same for min_conns. Previously the env-empty path unconditionally overwrote whatever ParseConfig had read from the URL — a silent regression for deployments that already tuned pool size via the connection string. - Add unit tests in dbstats_test.go covering each precedence branch (defaults, URL-only, env-over-URL, partial URL, invalid env, min>max clamp). - Move pool tuning vars out of 'Required Variables' into a new 'Database Pool Tuning (Optional)' section in SELF_HOSTING_ADVANCED.md so self-hosters don't think they need to set them. - Add commented entries in .env.example. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * fix(server): invalid pool env falls back to URL/code default, never pgx 4 Address second round of review on #1381: Previous code passed cfg.MaxConns / cfg.MinConns as the envInt32 fallback, which meant an invalid DATABASE_MAX_CONNS value silently fell back to ParseConfig's value — i.e. pgx's built-in default of 4/0 when the URL had no pool_* params. That's exactly the bad value this PR exists to remove, and the previous test (TestPoolSizing_InvalidEnvFallsBack) accidentally locked it in. Compute the non-env fallback first (URL pool_* if present, else code default 25/5) and pass that to envInt32. Misconfigured env now lands on the same value as if the env were unset — never on the pgx default. Replace the loose 'max > 0' assertion with two precise tests: - invalid env + no URL param → code default (25/5) - invalid env + URL param → URL value Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> --------- Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.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 · X · 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, Codex, OpenClaw, OpenCode, Hermes, Gemini, Pi, and Cursor Agent.
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-hostRequires 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, openclaw, opencode, hermes, gemini, pi, cursor-agent) 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, OpenClaw, OpenCode, Hermes, Gemini, Pi, or Cursor Agent). 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, OpenCode,
OpenClaw, Hermes, Gemini,
Pi, Cursor Agent)
| 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, OpenClaw, OpenCode, Hermes, Gemini, Pi, or Cursor Agent |
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.