ffa8e16369
* fix(agent): attribute Grok usage from the turn's own model id A resumed Grok session with no configured model recorded its entire spend under the model id "unknown", which matches no pricing row — so the task reported $0 cost instead of its real spend. grok.go only learned the model from the session handshake, and ACP's `session/load` carries no model id (only `session/new` does). When neither the agent nor MULTICA_GROK_MODEL pins a model, `daemon.go` legitimately passes an empty model, leaving nothing to attribute the usage to. Every Grok turn stamps `result._meta.modelId` with what it actually billed against. Parse it in the shared ACP result parser and use it as the fallback in grok.go. Other ACP backends are untouched — they keep whatever the handshake gave them. Co-authored-by: J <agent@multica.ai> Co-authored-by: multica-agent <github@multica.ai> * fix(metrics): price the Grok catalog in server-side cost metrics server/internal/metrics/pricing.go carried no Grok rows at all, so RecordLLMUsage took the unpriced branch for every Grok turn: llm_cost_usd reported zero Grok spend while the tokens accumulated in llm_unpriced_tokens. Internal cost monitoring simply could not see Grok. Add the six SKUs xAI publishes rates for, mirroring the frontend table in packages/views/runtimes/utils.ts. Aliases are anchored exact matches like the gpt-5.6 rows, so `grok-composer-*` (in the catalog, absent from the price sheet) stays unmapped instead of inheriting a guessed rate. Short-context tier on purpose: xAI bills a request at 2x once its prompt reaches 200K tokens, but a usage record aggregates every model call in a turn and cannot say which tier an individual request hit. A regression test re-derives the cost of a real grok 0.2.106 turn from the table and checks it against the costUsdTicks xAI returned for that turn. Co-authored-by: J <agent@multica.ai> Co-authored-by: multica-agent <github@multica.ai> * docs(changelog): scope the Grok cost claim to what was actually fixed The v0.4.9 entry promised "accurate cost" in all four languages, but the fix corrected catalog pricing and cached-input double-counting — it did not implement xAI's 2x long-context tier, so a turn whose requests reach 200K prompt tokens still under-reports by up to 50%. Say what was fixed instead. Also correct two stale claims in the pricing comment: the daemon tags usage rows with the runtime provider `grok`, not `xai` (the bare `grok-*` keys are what make them resolve), and record why thresholding the long-context tier on an aggregated row would be worse than not pricing it at all. Co-authored-by: J <agent@multica.ai> Co-authored-by: multica-agent <github@multica.ai> * feat(usage): carry the provider's own cost through to the usage record Cost has always been derived client-side as tokens x a static rate, which cannot express request-level pricing rules. xAI bills a Grok request at 2x once its prompt reaches 200K tokens, and a task_usage row aggregates every model call in a turn — so the stored token counts genuinely cannot say which tier any individual request hit. Thresholding on the aggregate would be worse than the status quo: it turns a bounded 50% under-estimate into an unbounded over-estimate for turns made of many short requests. Grok already reports what it charged, per turn, in `_meta.usage.costUsdTicks`. Parse it, carry it through agent -> daemon -> API, and store it on task_usage as a nullable BIGINT of 1e-10 USD ticks (integer, so sub-cent turns stay exact end to end). NULL means the provider reported no cost — every pre-existing row and every provider that doesn't return one. No backfill: there is no authoritative figure to recover for those, and inventing one is the guess this removes. A single hourly bucket can mix rows that carry a cost with rows that don't, so task_usage_hourly gains both halves: `cost_usd_ticks` sums the authoritative side, and `uncosted_*_tokens` carry exactly the tokens that still need a rate-table estimate. Consumers report authoritative + estimate(uncosted), which degrades to today's behaviour when nothing in the bucket is authoritative. The existing token columns keep covering every row, so token displays are untouched. The new columns are additive with defaults, so the unique key, the dirty-queue shape, and migration 102's triggers are unaffected. Co-authored-by: J <agent@multica.ai> Co-authored-by: multica-agent <github@multica.ai> * feat(usage): prefer the provider's own cost over the rate table With the authoritative figure now stored, both cost consumers use it: the usage dashboard (estimateCost / estimateCostBreakdown) and the server-side llm_cost_usd metric. Each reports `authoritative + estimate(uncosted tokens)`, so a row or bucket that mixes priced and unpriced sources stays whole. The static rate tables remain, but for Grok they are now a fallback — they still price usage recorded by a daemon too old to report cost, and every provider that reports none. Custom pricing overrides likewise apply only to the estimated half: they are a user's guess at a rate, and the authoritative half is not a guess. A model with no rate-table row but a provider-reported cost now also drops out of the "unmapped models" banner, since asking the user to supply a rate for it would invite overriding a real bill. llm_cost_usd is labelled by token_type and the provider reports one number per turn, so the charge is distributed across the buckets in the rate table's own proportions. Only the total is authoritative; the split stays an estimate, which is why this scales the existing buckets rather than inventing a label. estimateCostBreakdown does the same, keeping the stacked chart summing to the headline figure instead of silently under-drawing every Grok row. Co-authored-by: J <agent@multica.ai> Co-authored-by: multica-agent <github@multica.ai> * docs(changelog): say Grok cost now follows xAI's actual charge The earlier wording scoped the claim down to catalog pricing and cached input because the long-context tier was still unhandled. It is handled now — the cost comes from what xAI charged for the turn — so the entry can say so. Co-authored-by: J <agent@multica.ai> Co-authored-by: multica-agent <github@multica.ai> * fix(usage): keep the provider's cost when the model has no rate row Both cost consumers bailed out before reading the authoritative figure when the rate table had no row for the model. A `grok-composer-*` turn — in the Grok Build catalog, absent from xAI's price sheet — was therefore reported as $0 spend even though xAI told us exactly what it charged. Worse on the client: estimateCost returned the real cost while estimateCostBreakdown returned zeros, so the headline and the stacked chart disagreed on precisely the rows whose cost is exact — and the unmapped-models banner was (correctly) hidden, so nothing explained the discrepancy. Handle the charge before the rate lookup in both places. Without rates there is nothing to split a total by, so it lands whole in the `input` bucket, the same fallback distributeAuthoritativeCost already uses when it has no shape to scale. Tokens with no rate keep going to llm_unpriced_tokens: "unpriced" describes the rate table, not the money. Co-authored-by: J <agent@multica.ai> Co-authored-by: multica-agent <github@multica.ai> * perf(usage): drop the historical rewrite from the cost-split migration Migration 213 rewrote every existing task_usage_hourly row to seed the uncosted counters. That is a full-table UPDATE inside a schema migration — lock time, WAL and bloat all scaling with table size — for rows this issue explicitly does not care about. Deleting the UPDATE alone would have zeroed historical cost: with `NOT NULL DEFAULT 0`, an untouched row asserts "nothing here needs estimating", so every pre-split bucket would report $0 until the rollup happened to touch it. Make the uncosted columns nullable with no default instead. NULL means "never recomputed since the split existed", readers COALESCE it to the row's own token total ("estimate all of it"), and the pre-split behaviour is preserved exactly — with nothing to seed, so no rewrite. A bare ADD COLUMN is metadata-only, so this is now fast DDL. Rows heal into the split naturally as the rollup recomputes their buckets. Verified on a fresh database: a legacy-shaped row reads back as its full tokens to estimate, and a group mixing legacy and post-split buckets sums to the authoritative cost plus both rows' estimable tokens. Co-authored-by: J <agent@multica.ai> Co-authored-by: multica-agent <github@multica.ai> --------- Co-authored-by: Bohan-J <bohan@devv.ai> Co-authored-by: J <agent@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.
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.