awesome-copilot/cookbook/copilot-sdk/nodejs/ralph-loop.md

RALPH-loop: Iterative Self-Referential AI Loops

Implement self-referential feedback loops where an AI agent iteratively improves work by reading its own previous output.

Runnable example: recipe/ralph-loop.ts

cd recipe && npm install
npx tsx ralph-loop.ts

What is RALPH-loop?

RALPH-loop is a development methodology for iterative AI-powered task completion. Named after the Ralph Wiggum technique, it embodies the philosophy of persistent iteration:

• One prompt, multiple iterations: The same prompt is processed repeatedly
• Self-referential feedback: The AI reads its own previous work (file changes, git history)
• Completion detection: Loop exits when a completion promise is detected in output
• Safety limits: Always include a maximum iteration count to prevent infinite loops

Example Scenario

You need to iteratively improve code until all tests pass. Instead of asking Copilot to "write perfect code," you use RALPH-loop to:

1. Send the initial prompt with clear success criteria
2. Copilot writes code and tests
3. Copilot runs tests and sees failures
4. Loop automatically re-sends the prompt
5. Copilot reads test output and previous code, fixes issues
6. Repeat until all tests pass and completion promise is output

Basic Implementation

import { CopilotClient } from "@github/copilot-sdk";

class RalphLoop {
    private client: CopilotClient;
    private iteration: number = 0;
    private maxIterations: number;
    private completionPromise: string;
    private lastResponse: string | null = null;

    constructor(maxIterations: number = 10, completionPromise: string = "COMPLETE") {
        this.client = new CopilotClient();
        this.maxIterations = maxIterations;
        this.completionPromise = completionPromise;
    }

    async run(initialPrompt: string): Promise<string> {
        await this.client.start();
        const session = await this.client.createSession({ model: "gpt-5" });

        try {
            while (this.iteration < this.maxIterations) {
                this.iteration++;
                console.log(`\n--- Iteration ${this.iteration}/${this.maxIterations} ---`);

                // Build prompt including previous response as context
                const prompt = this.iteration === 1
                    ? initialPrompt
                    : `${initialPrompt}\n\nPrevious attempt:\n${this.lastResponse}\n\nContinue improving...`;

                const response = await session.sendAndWait({ prompt });
                this.lastResponse = response?.data.content || "";

                console.log(`Response (${this.lastResponse.length} chars)`);

                // Check for completion promise
                if (this.lastResponse.includes(this.completionPromise)) {
                    console.log(`✓ Completion promise detected: ${this.completionPromise}`);
                    return this.lastResponse;
                }

                console.log(`Continuing to iteration ${this.iteration + 1}...`);
            }

            throw new Error(
                `Max iterations (${this.maxIterations}) reached without completion promise`
            );
        } finally {
            await session.destroy();
            await this.client.stop();
        }
    }
}

// Usage
const loop = new RalphLoop(5, "COMPLETE");
const result = await loop.run("Your task here");
console.log(result);

With File Persistence

For tasks involving code generation, persist state to files so the AI can see changes:

import fs from "fs/promises";
import path from "path";
import { CopilotClient } from "@github/copilot-sdk";

class PersistentRalphLoop {
    private client: CopilotClient;
    private workDir: string;
    private iteration: number = 0;
    private maxIterations: number;

    constructor(workDir: string, maxIterations: number = 10) {
        this.client = new CopilotClient();
        this.workDir = workDir;
        this.maxIterations = maxIterations;
    }

    async run(initialPrompt: string): Promise<string> {
        await fs.mkdir(this.workDir, { recursive: true });
        await this.client.start();
        const session = await this.client.createSession({ model: "gpt-5" });

        try {
            // Store initial prompt
            await fs.writeFile(path.join(this.workDir, "prompt.md"), initialPrompt);

            while (this.iteration < this.maxIterations) {
                this.iteration++;
                console.log(`\n--- Iteration ${this.iteration} ---`);

                // Build context from previous outputs
                let context = initialPrompt;
                const prevOutputFile = path.join(this.workDir, `output-${this.iteration - 1}.txt`);
                try {
                    const prevOutput = await fs.readFile(prevOutputFile, "utf-8");
                    context += `\n\nPrevious iteration:\n${prevOutput}`;
                } catch {
                    // No previous output yet
                }

                const response = await session.sendAndWait({ prompt: context });
                const output = response?.data.content || "";

                // Persist output
                await fs.writeFile(
                    path.join(this.workDir, `output-${this.iteration}.txt`),
                    output
                );

                if (output.includes("COMPLETE")) {
                    return output;
                }
            }

            throw new Error("Max iterations reached");
        } finally {
            await session.destroy();
            await this.client.stop();
        }
    }
}

Best Practices

1. Write clear completion criteria: Include exactly what "done" looks like
2. Use output markers: Include <promise>COMPLETE</promise> or similar in completion condition
3. Always set max iterations: Prevents infinite loops on impossible tasks
4. Persist state: Save files so AI can see what changed between iterations
5. Include context: Feed previous iteration output back as context
6. Monitor progress: Log each iteration to track what's happening

Example: Iterative Code Generation

const prompt = `Write a function that:
1. Parses CSV data
2. Validates required fields
3. Returns parsed records or error
4. Has unit tests
5. Output <promise>COMPLETE</promise> when done`;

const loop = new RalphLoop(10, "COMPLETE");
const result = await loop.run(prompt);

Handling Failures

try {
    const result = await loop.run(prompt);
    console.log("Task completed successfully!");
} catch (error) {
    console.error("Task failed:", error.message);
    // Analyze what was attempted and suggest alternatives
}

When to Use RALPH-loop

Good for:

• Code generation with automatic verification (tests, linters)
• Tasks with clear success criteria
• Iterative refinement where each attempt learns from previous failures
• Unattended long-running improvements

Not good for:

• Tasks requiring human judgment or design input
• One-shot operations
• Tasks with vague success criteria
• Real-time interactive debugging