AI tool comparison
AMUX vs Metrics SQL by Rill
Which one should you ship with? Here is the side-by-side panel verdict, pricing read, reviewer split, and community vote comparison.
Developer Tools
AMUX
Run dozens of parallel AI coding agents unattended via tmux
75%
Panel ship
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Community
Paid
Entry
AMUX is an open-source agent multiplexer that lets you run dozens of Claude Code (or other terminal AI coding agents) simultaneously, all managed from a single web dashboard — no complicated setup required. Built by the team at Mixpeek, it requires only Python 3 and tmux, with the entire server delivered as a single ~23,000-line Python file with embedded HTML/CSS/JS. The standout features are a self-healing watchdog that auto-compacts context when it drops below 20% and restarts stuck sessions, a SQLite-backed kanban board where agents atomically claim tasks to prevent duplicate work, and a REST API injected at startup that allows agents to coordinate with each other via simple curl calls. There's even a mobile PWA with offline support via Background Sync so you can monitor your agent army from your phone. In the "agentmaxxing" era, AMUX is the most complete open-source solution for running parallel AI coding agents unattended. Rather than babysitting one agent, you dispatch 5–20 agents to isolated worktrees and check back in as a reviewer. The MIT + Commons Clause license means it's free to self-host.
Developer Tools
Metrics SQL by Rill
One SQL semantic layer so AI agents stop hallucinating your KPIs
75%
Panel ship
—
Community
Paid
Entry
Metrics SQL is a SQL-based semantic layer from Rill Data that solves a specific and painful problem: AI agents that query your data warehouse tend to hallucinate aggregation logic, producing metrics that look plausible but are mathematically wrong. Metrics SQL lets analysts define business metrics once — revenue, MAU, conversion rate, ROAS — in a governed definition layer, and then exposes those definitions as queryable SQL tables. Every dashboard, notebook, and AI agent resolves from the same source. The technical approach is elegant: rather than inventing a new DSL, Metrics SQL extends SQL itself. An agent that knows SQL can query `SELECT * FROM metrics.weekly_revenue` and get correctly computed numbers without needing to know how revenue is defined, which tables it joins, or how edge cases like refunds are handled. The semantic layer intercepts the query, applies the governed definition, and returns correct results. The implications for AI-native data stacks are significant. Currently, one of the biggest failure modes for AI analysts and BI agents is inconsistent metric computation — different agents or dashboards produce different numbers for 'revenue' because they implement aggregation logic differently. Metrics SQL addresses this at the infrastructure level, not by improving agent prompting.
Reviewer scorecard
“This is exactly what the agentmaxxing workflow needs. Single Python file, no external services, and the kanban board preventing duplicate agent work is genuinely clever engineering. The self-healing watchdog alone saves hours of babysitting stuck sessions.”
“We've been burned by data agents that invent their own GROUP BY logic and produce wrong numbers that look right. Metrics SQL solves this at the infrastructure level — define revenue once, have every agent query the same definition. The SQL-native interface means no new tools for agents to learn; they just use the tables.”
“MIT + Commons Clause isn't really open source in the traditional sense — you can't build a commercial product on top of it. Also, coordinating 20+ agents that all share Claude Code rate limits means you'll hit API throttling walls faster than you think.”
“The value here is only as good as how well-maintained your metric definitions are — if analysts don't keep them updated, agents query stale or wrong definitions and you've added a layer of false confidence. Adopting a semantic layer also creates vendor dependency; migrating away from Rill's cloud later is a real switching cost. For smaller teams without dedicated data engineering, maintaining a semantic layer is overhead.”
“We're moving from one developer + one agent to one developer + agent swarm. AMUX is early infrastructure for that paradigm shift. The agent-to-agent coordination REST API hints at genuine multi-agent systems emerging from terminal tooling.”
“Data governance and AI agents are on a collision course. As more business decisions are delegated to AI, the correctness of KPI computation becomes load-bearing — a hallucinated revenue figure that influences a product decision is a serious failure mode. Metrics SQL represents a class of infrastructure that will become mandatory as AI takes on more analytical work.”
“The web dashboard with live terminal peeking is surprisingly polished for a side project. Being able to monitor your agent army from a mobile PWA while away from the desk is a genuinely practical touch.”
“I rely on AI to pull weekly performance data, and the number of times it's given me different 'correct' answers for the same metric is maddening. Having a single governed source that every AI query resolves against means I can trust the numbers I'm making decisions on. That trust is worth a lot.”
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