agent-ralph-loop-vercel-labs

name	agent-ralph-loop-vercel-labs
description	Continuous autonomy framework for the Vercel AI SDK implementing the Ralph Wiggum technique — an iterative outer loop that keeps feeding an AI agent a task until verifyCompletion confirms success. Use when building autonomous coding agents, long-running code migrations, refactoring tasks, or any workflow requiring iterative verification and feedback loops beyond single-shot LLM tool calls.

Ralph Loop Agent (Vercel Labs) 0.0.3

Overview

Ralph Loop Agent is an experimental npm package from Vercel Labs that implements the "Ralph Wiggum technique" for continuous AI agent autonomy. It wraps the Vercel AI SDK's generateText in an outer iteration loop that keeps running until a verifyCompletion function confirms the task is actually done — or a safety limit is hit.

Named after the persistently repeating Ralph Wiggum from The Simpsons, this approach embraces iterative improvement over single-shot perfection. Where standard AI SDK tool loops stop when the model finishes its tool calls, Ralph keeps going: verifying completion, providing feedback, and running another iteration until the task actually succeeds.

Think of it as while (true) for AI autonomy: the agent works, an evaluator checks the result, and if it's not done, the agent tries again with context from previous attempts.

Architecture: Two Nested Loops

Outer Ralph Loop (ralph-loop-agent)
├── Iteration 1
│   └── Inner AI SDK Tool Loop (generateText)
│       └── LLM ↔ tools ↔ LLM ↔ tools ... until toolStopWhen
├── verifyCompletion: "Is the task actually complete?"
├── If not done → inject feedback → Iteration 2
│   └── Inner AI SDK Tool Loop (with accumulated context)
├── ...
└── If done → return RalphLoopAgentResult

When to Use

• Building autonomous coding agents that run until a task is verified complete
• Long-running code migrations (Jest → Vitest, CJS → ESM, etc.)
• Dependency upgrades across large codebases
• Multi-file refactoring tasks requiring verification between iterations
• Feature implementation from specifications with automated review
• Any workflow where single-shot LLM tool calls are insufficient and iterative verification is needed

Installation

npm install ralph-loop-agent ai zod

The package depends on the Vercel AI SDK (ai ≥ 6.0.0) and @ai-sdk/provider-utils. Zod 4.x is a peer dependency.

Core Concepts

The Ralph Loop Pattern

Standard AI SDK generateText runs an inner tool loop: LLM calls tools, gets results, calls more tools, until stopWhen (typically stepCountIs(20)). Then it returns. Ralph wraps this in an outer loop:

1. Run generateText (inner tool loop)
2. Check stop conditions (iterations, tokens, cost)
3. Call verifyCompletion to check if the task is actually done
4. If not complete, inject feedback into conversation and go to step 1
5. If complete or stop condition hit, return final result

Key Components

RalphLoopAgent — The main class. Accepts a model, instructions, tools, stop conditions, and a verification function. Provides loop() for full execution and stream() for streaming the final iteration.

Stop Conditions — Functions that determine when to halt the outer loop: iterationCountIs(n), tokenCountIs(n), costIs(maxDollars). Multiple conditions can be combined as an array (OR'd together).

verifyCompletion — An async function called after each iteration. Returns { complete: boolean, reason?: string }. When complete is false, the reason string is injected as feedback for the next iteration.

RalphContextManager — Built-in context management for long-running loops. Tracks file reads/writes, maintains a change log, auto-summarizes older iterations when approaching token limits, and handles large files with line-range chunking.

Usage Examples

Basic Agent Loop

import { RalphLoopAgent, iterationCountIs } from 'ralph-loop-agent';

const agent = new RalphLoopAgent({
  model: 'anthropic/claude-opus-4.5',
  instructions: 'You are a helpful coding assistant.',
  stopWhen: iterationCountIs(10),
  verifyCompletion: async ({ result }) => ({
    complete: result.text.includes('DONE'),
    reason: 'Task completed successfully',
  }),
});

const { text, iterations, completionReason } = await agent.loop({
  prompt: 'Create a function that calculates fibonacci numbers',
});

console.log(text);
console.log(`Completed in ${iterations} iterations`);
console.log(`Reason: ${completionReason}`);

Migration Task with Real Verification

import { RalphLoopAgent, iterationCountIs } from 'ralph-loop-agent';

const migrationAgent = new RalphLoopAgent({
  model: 'anthropic/claude-opus-4.5',
  instructions: `You are migrating a codebase from Jest to Vitest.

    Completion criteria:
    - All test files use vitest imports
    - vitest.config.ts exists
    - All tests pass when running 'pnpm test'`,

  tools: { readFile, writeFile, execute },

  stopWhen: iterationCountIs(50),

  verifyCompletion: async () => {
    const checks = await Promise.all([
      fileExists('vitest.config.ts'),
      !await fileExists('jest.config.js'),
      noFilesMatch('**/*.test.ts', /from ['"]@jest/),
      fileContains('package.json', '"vitest"'),
    ]);

    return {
      complete: checks.every(Boolean),
      reason: checks.every(Boolean) ? 'Migration complete' : 'Structural checks failed',
    };
  },

  onIterationStart: ({ iteration }) => console.log(),
  :  .(),
});

 result =  migrationAgent.({
  : ,
});

.(result.);
.(result.);
.(result.);

Streaming the Final Iteration

const stream = await agent.stream({
  prompt: 'Build a calculator',
});

for await (const chunk of stream.textStream) {
  process.stdout.write(chunk);
}

Note: Streaming runs non-streaming iterations until verification passes or the final iteration is reached, then streams that last iteration.

Advanced Topics

Stop Conditions & Cost Control: Built-in pricing for Anthropic, OpenAI, Google, xAI, and DeepSeek models → Stop Conditions

Context Management for Long Loops: File tracking, change logs, auto-summarization, token budgets → Context Management

API Reference: Complete type definitions for RalphLoopAgent, settings, results, and verification → API Reference

CLI Example Architecture: Coding agent + Vercel Sandbox + Judge Agent pattern from the reference implementation → CLI Example