Antfarm: Multi-Agent Coding Teams in One Command

Most multi-agent frameworks want you to learn their whole ecosystem. Install Docker. Set up Redis. Configure Kafka. Write Python graphs.

Antfarm takes a different approach: one command, zero infrastructure, and it runs inside your existing OpenClaw setup.

Here's what that actually means—and why it matters.

The Problem With Current Multi-Agent Frameworks

I've used CrewAI. I've used AutoGen. I've experimented with LangGraph. They're all impressive engineering.

But here's the thing: none of them are designed for shipping code.

They're general-purpose orchestration tools. They assume you want conversational agents chatting with each other. They assume you'll build your own workflows from scratch. They assume you have DevOps capacity to run their infrastructure.

For most indie developers and small teams? That's too much overhead.

Antfarm isn't competing with these frameworks directly. It's solving a narrower problem: I want AI agents to write code and ship PRs, without babysitting them.

Who Built This (And Why It Matters)

Ryan Carson isn't a random GitHub user. He founded Treehouse in 2011—one of the largest online tech education platforms. The company was acquired in 2021. He was EY Entrepreneur of the Year in 2015.

More importantly, he's been iterating on this problem for years:

That's 17,600+ GitHub stars from someone who's been grinding on this specific problem. Not a weekend project—a progression of thinking.

The Ralph Loop: Why Fresh Context Matters

Before understanding Antfarm, you need to understand the Ralph pattern.

Geoffrey Huntley—the guy behind the original concept—put it simply:

"Ralph can replace the majority of outsourcing at most companies for greenfield projects."

The core idea is dead simple:

while :; do cat PROMPT.md | claude-code ; done

Run the AI coding tool. Let it work. When it finishes (or hits context limits), kill it and start fresh. Memory persists through git commits and progress files—not through conversation history.

Huntley calls it "deterministically bad in an undeterministic world." Each failure is a tuning opportunity. You're not debugging the AI—you're tuning the prompts like a guitar.

What Antfarm Actually Does

Antfarm scales the Ralph loop to multiple agents working in sequence.

Install it with one command inside OpenClaw:

install github.com/snarktank/antfarm

That's not a typo. Tell your OpenClaw agent that command, and it provisions everything: agent workspaces, cron polling, subagent permissions.

Three Workflows Out of the Box

The Key Differentiators

Deterministic workflows. Same steps, same order, every time. Not "hopefully the agent remembers to test."

Agents verify each other. The developer doesn't mark their own homework. A separate verifier checks every story against acceptance criteria.

Fresh context per step. Each agent gets a clean session. No context window bloat. No hallucinated state.

Automatic retry and escalation. Failed steps retry. If retries exhaust, it escalates to you. Nothing fails silently.

The Architecture: Minimal by Design

Here's what's actually running under the hood:

That's it. No Redis. No Kafka. No Docker. No container orchestrator.

Antfarm is a TypeScript CLI with zero external dependencies. It runs wherever OpenClaw runs.

Quick Example

Here's what using Antfarm actually looks like:

$ antfarm workflow install feature-dev
✓ Installed workflow: feature-dev

$ antfarm workflow run feature-dev "Add user authentication with OAuth"
Run: a1fdf573
Workflow: feature-dev
Status: running

$ antfarm workflow status "OAuth"
Run: a1fdf573
Workflow: feature-dev
Steps:
  [done   ] plan (planner)
  [done   ] setup (setup)
  [running] implement (developer)  Stories: 3/7 done
  [pending] verify (verifier)
  [pending] test (tester)
  [pending] pr (developer)
  [pending] review (reviewer)

You can also start a dashboard:

$ antfarm dashboard

It shows run status, step progress, and agent output in real-time.

Building Your Own Workflows

The bundled workflows are starting points. Define your own in YAML:

id: my-workflow
name: My Custom Workflow
agents:
  - id: researcher
    name: Researcher
    workspace:
      files:
        AGENTS.md: agents/researcher/AGENTS.md

steps:
  - id: research
    agent: researcher
    input: |
      Research {{task}} and report findings.
      Reply with STATUS: done and FINDINGS: ...
    expects: "STATUS: done"

If you can write a prompt, you can build a workflow. No Python required. No graph theory.

When to Use What

Let me be direct about when Antfarm makes sense:

For general-purpose multi-agent orchestration, CrewAI and AutoGen are more flexible. For complex stateful workflows, LangGraph gives you more control. But for "I want AI to write code and ship PRs"? Antfarm is purpose-built.

What the Community Is Saying

It's been 4 days. Here's what early users are reporting:

"Batteries-included agent teams with deterministic Ralph loops is exactly what most people need. The wild west 'just prompt it' approach works until you need reliability. Then you need structure."

"Sitting on a couch and adding with Slack some PRDs to my Ralph CLI and let it run is the way."

The main question people are asking: "How does SQLite handle scale?" The answer is that it doesn't need to—this is for individual developers, not enterprise SaaS.

Try It

If you're already running OpenClaw, it's one command:

install github.com/snarktank/antfarm

Then run your first workflow:

antfarm workflow run feature-dev "Add dark mode toggle to settings page"

Watch what happens. Tune as needed. That's the Ralph philosophy: deterministically bad, but tunable.

Sources

• Antfarm GitHub Repository — 248 stars, MIT license
• Ralph GitHub Repository — 9,903 stars
• ai-dev-tasks GitHub Repository — 7,470 stars
• Ryan Carson's personal website
• Treehouse history
• Geoffrey Huntley's Ralph article
• Ryan Carson on Crunchbase
• AutoGen GitHub — 54,416 stars
• CrewAI GitHub — 43,865 stars
• LangGraph GitHub — 24,510 stars
• DataCamp: Multi-agent framework comparison
• Original announcement tweet

All GitHub star counts verified via GitHub API on February 10, 2026.