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Rewrite Ralph loop recipes: split into simple vs ideal versions
Align all 4 language recipes (Node.js, Python, .NET, Go) with the Ralph Playbook architecture: - Simple version: minimal outer loop with fresh session per iteration - Ideal version: planning/building modes, backpressure, git integration - Fresh context isolation instead of in-session context accumulation - Disk-based shared state via IMPLEMENTATION_PLAN.md - Example prompt templates (PROMPT_plan.md, PROMPT_build.md, AGENTS.md) - Updated cookbook README descriptions
This commit is contained in:
Anthony Shaw
@@ -1,6 +1,6 @@
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# RALPH-loop: Iterative Self-Referential AI Loops
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# Ralph Loop: Autonomous AI Task Loops
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Implement self-referential feedback loops where an AI agent iteratively improves work by reading its own previous output.
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Build autonomous coding loops where an AI agent picks tasks, implements them, validates against backpressure (tests, builds), commits, and repeats — each iteration in a fresh context window.
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> **Runnable example:** [recipe/ralph-loop.ts](recipe/ralph-loop.ts)
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>
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@@ -9,200 +9,217 @@ Implement self-referential feedback loops where an AI agent iteratively improves
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> npx tsx ralph-loop.ts
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> ```
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## What is RALPH-loop?
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## What is a Ralph Loop?
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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:
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A [Ralph loop](https://ghuntley.com/ralph/) is an autonomous development workflow where an AI agent iterates through tasks in isolated context windows. The key insight: **state lives on disk, not in the model's context**. Each iteration starts fresh, reads the current state from files, does one task, writes results back to disk, and exits.
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- **One prompt, multiple iterations**: The same prompt is processed repeatedly
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- **Self-referential feedback**: The AI reads its own previous work (file changes, git history)
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- **Completion detection**: Loop exits when a completion promise is detected in output
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- **Safety limits**: Always include a maximum iteration count to prevent infinite loops
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## Example Scenario
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You need to iteratively improve code until all tests pass. Instead of asking Copilot to "write perfect code," you use RALPH-loop to:
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1. Send the initial prompt with clear success criteria
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2. Copilot writes code and tests
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3. Copilot runs tests and sees failures
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4. Loop automatically re-sends the prompt
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5. Copilot reads test output and previous code, fixes issues
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6. Repeat until all tests pass and completion promise is output
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## Basic Implementation
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```typescript
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import { CopilotClient } from "@github/copilot-sdk";
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class RalphLoop {
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private client: CopilotClient;
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private iteration: number = 0;
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private maxIterations: number;
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private completionPromise: string;
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private lastResponse: string | null = null;
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constructor(maxIterations: number = 10, completionPromise: string = "COMPLETE") {
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this.client = new CopilotClient();
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this.maxIterations = maxIterations;
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this.completionPromise = completionPromise;
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}
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async run(initialPrompt: string): Promise<string> {
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await this.client.start();
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const session = await this.client.createSession({ model: "gpt-5.1-codex-mini" });
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try {
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while (this.iteration < this.maxIterations) {
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this.iteration++;
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console.log(`\n--- Iteration ${this.iteration}/${this.maxIterations} ---`);
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// Build prompt including previous response as context
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const prompt = this.iteration === 1
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? initialPrompt
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: `${initialPrompt}\n\nPrevious attempt:\n${this.lastResponse}\n\nContinue improving...`;
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const response = await session.sendAndWait({ prompt });
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this.lastResponse = response?.data.content || "";
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console.log(`Response (${this.lastResponse.length} chars)`);
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// Check for completion promise
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if (this.lastResponse.includes(this.completionPromise)) {
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console.log(`✓ Completion promise detected: ${this.completionPromise}`);
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return this.lastResponse;
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}
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console.log(`Continuing to iteration ${this.iteration + 1}...`);
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}
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throw new Error(
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`Max iterations (${this.maxIterations}) reached without completion promise`
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);
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} finally {
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await session.destroy();
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await this.client.stop();
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}
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}
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}
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// Usage
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const loop = new RalphLoop(5, "COMPLETE");
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const result = await loop.run("Your task here");
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console.log(result);
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```
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┌─────────────────────────────────────────────────┐
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│ loop.sh │
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│ while true: │
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│ ┌─────────────────────────────────────────┐ │
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│ │ Fresh session (isolated context) │ │
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│ │ │ │
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│ │ 1. Read PROMPT.md + AGENTS.md │ │
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│ │ 2. Study specs/* and code │ │
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│ │ 3. Pick next task from plan │ │
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│ │ 4. Implement + run tests │ │
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│ │ 5. Update plan, commit, exit │ │
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│ └─────────────────────────────────────────┘ │
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│ ↻ next iteration (fresh context) │
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└─────────────────────────────────────────────────┘
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```
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## With File Persistence
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**Core principles:**
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For tasks involving code generation, persist state to files so the AI can see changes:
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- **Fresh context per iteration**: Each loop creates a new session — no context accumulation, always in the "smart zone"
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- **Disk as shared state**: `IMPLEMENTATION_PLAN.md` persists between iterations and acts as the coordination mechanism
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- **Backpressure steers quality**: Tests, builds, and lints reject bad work — the agent must fix issues before committing
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- **Two modes**: PLANNING (gap analysis → generate plan) and BUILDING (implement from plan)
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## Simple Version
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The minimal Ralph loop — the SDK equivalent of `while :; do cat PROMPT.md | claude ; done`:
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```typescript
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import fs from "fs/promises";
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import path from "path";
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import { readFile } from "fs/promises";
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import { CopilotClient } from "@github/copilot-sdk";
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class PersistentRalphLoop {
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private client: CopilotClient;
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private workDir: string;
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private iteration: number = 0;
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private maxIterations: number;
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async function ralphLoop(promptFile: string, maxIterations: number = 50) {
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const client = new CopilotClient();
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await client.start();
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constructor(workDir: string, maxIterations: number = 10) {
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this.client = new CopilotClient();
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this.workDir = workDir;
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this.maxIterations = maxIterations;
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}
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try {
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const prompt = await readFile(promptFile, "utf-8");
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async run(initialPrompt: string): Promise<string> {
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await fs.mkdir(this.workDir, { recursive: true });
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await this.client.start();
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const session = await this.client.createSession({ model: "gpt-5.1-codex-mini" });
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for (let i = 1; i <= maxIterations; i++) {
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console.log(`\n=== Iteration ${i}/${maxIterations} ===`);
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try {
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// Store initial prompt
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await fs.writeFile(path.join(this.workDir, "prompt.md"), initialPrompt);
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while (this.iteration < this.maxIterations) {
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this.iteration++;
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console.log(`\n--- Iteration ${this.iteration} ---`);
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// Build context from previous outputs
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let context = initialPrompt;
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const prevOutputFile = path.join(this.workDir, `output-${this.iteration - 1}.txt`);
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try {
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const prevOutput = await fs.readFile(prevOutputFile, "utf-8");
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context += `\n\nPrevious iteration:\n${prevOutput}`;
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} catch {
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// No previous output yet
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}
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const response = await session.sendAndWait({ prompt: context });
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const output = response?.data.content || "";
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// Persist output
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await fs.writeFile(
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path.join(this.workDir, `output-${this.iteration}.txt`),
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output
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);
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if (output.includes("COMPLETE")) {
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return output;
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}
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// Fresh session each iteration — context isolation is the point
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const session = await client.createSession({ model: "claude-sonnet-4.5" });
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try {
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await session.sendAndWait({ prompt }, 600_000);
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} finally {
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await session.destroy();
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}
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throw new Error("Max iterations reached");
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} finally {
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await session.destroy();
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await this.client.stop();
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console.log(`Iteration ${i} complete.`);
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}
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} finally {
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await client.stop();
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}
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}
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// Usage: point at your PROMPT.md
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ralphLoop("PROMPT.md", 20);
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```
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This is all you need to get started. The prompt file tells the agent what to do; the agent reads project files, does work, commits, and exits. The loop restarts with a clean slate.
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## Ideal Version
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The full Ralph pattern with planning and building modes, matching the [Ralph Playbook](https://github.com/ClaytonFarr/ralph-playbook) architecture:
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```typescript
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import { readFile } from "fs/promises";
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import { execSync } from "child_process";
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import { CopilotClient } from "@github/copilot-sdk";
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type Mode = "plan" | "build";
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async function ralphLoop(mode: Mode, maxIterations: number = 50) {
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const promptFile = mode === "plan" ? "PROMPT_plan.md" : "PROMPT_build.md";
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const client = new CopilotClient();
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await client.start();
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const branch = execSync("git branch --show-current", { encoding: "utf-8" }).trim();
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console.log(`Mode: ${mode} | Prompt: ${promptFile} | Branch: ${branch}`);
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try {
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const prompt = await readFile(promptFile, "utf-8");
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for (let i = 1; i <= maxIterations; i++) {
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console.log(`\n=== Iteration ${i}/${maxIterations} ===`);
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// Fresh session — each task gets full context budget
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const session = await client.createSession({ model: "claude-sonnet-4.5" });
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try {
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await session.sendAndWait({ prompt }, 600_000);
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} finally {
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await session.destroy();
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}
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// Push changes after each iteration
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try {
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execSync(`git push origin ${branch}`, { stdio: "inherit" });
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} catch {
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execSync(`git push -u origin ${branch}`, { stdio: "inherit" });
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}
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console.log(`Iteration ${i} complete.`);
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}
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} finally {
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await client.stop();
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}
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}
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// Parse CLI args: npx tsx ralph-loop.ts [plan] [max_iterations]
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const args = process.argv.slice(2);
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const mode: Mode = args.includes("plan") ? "plan" : "build";
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const maxArg = args.find(a => /^\d+$/.test(a));
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const maxIterations = maxArg ? parseInt(maxArg) : 50;
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ralphLoop(mode, maxIterations);
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```
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### Required Project Files
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The ideal version expects this file structure in your project:
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```
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project-root/
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├── PROMPT_plan.md # Planning mode instructions
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├── PROMPT_build.md # Building mode instructions
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├── AGENTS.md # Operational guide (build/test commands)
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├── IMPLEMENTATION_PLAN.md # Task list (generated by planning mode)
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├── specs/ # Requirement specs (one per topic)
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│ ├── auth.md
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│ └── data-pipeline.md
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└── src/ # Your source code
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```
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### Example `PROMPT_plan.md`
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```markdown
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0a. Study `specs/*` to learn the application specifications.
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0b. Study IMPLEMENTATION_PLAN.md (if present) to understand the plan so far.
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0c. Study `src/` to understand existing code and shared utilities.
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1. Compare specs against code (gap analysis). Create or update
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IMPLEMENTATION_PLAN.md as a prioritized bullet-point list of tasks
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yet to be implemented. Do NOT implement anything.
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IMPORTANT: Do NOT assume functionality is missing — search the
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codebase first to confirm. Prefer updating existing utilities over
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creating ad-hoc copies.
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```
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### Example `PROMPT_build.md`
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```markdown
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0a. Study `specs/*` to learn the application specifications.
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0b. Study IMPLEMENTATION_PLAN.md.
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0c. Study `src/` for reference.
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1. Choose the most important item from IMPLEMENTATION_PLAN.md. Before
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making changes, search the codebase (don't assume not implemented).
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2. After implementing, run the tests. If functionality is missing, add it.
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3. When you discover issues, update IMPLEMENTATION_PLAN.md immediately.
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4. When tests pass, update IMPLEMENTATION_PLAN.md, then `git add -A`
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then `git commit` with a descriptive message.
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99999. When authoring documentation, capture the why.
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999999. Implement completely. No placeholders or stubs.
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9999999. Keep IMPLEMENTATION_PLAN.md current — future iterations depend on it.
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```
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### Example `AGENTS.md`
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Keep this brief (~60 lines). It's loaded every iteration, so bloat wastes context.
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```markdown
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## Build & Run
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npm run build
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## Validation
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- Tests: `npm test`
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- Typecheck: `npx tsc --noEmit`
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- Lint: `npm run lint`
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```
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## Best Practices
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1. **Write clear completion criteria**: Include exactly what "done" looks like
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2. **Use output markers**: Include `<promise>COMPLETE</promise>` or similar in completion condition
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3. **Always set max iterations**: Prevents infinite loops on impossible tasks
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4. **Persist state**: Save files so AI can see what changed between iterations
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5. **Include context**: Feed previous iteration output back as context
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6. **Monitor progress**: Log each iteration to track what's happening
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1. **Fresh context per iteration**: Never accumulate context across iterations — that's the whole point
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2. **Disk is your database**: `IMPLEMENTATION_PLAN.md` is shared state between isolated sessions
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3. **Backpressure is essential**: Tests, builds, lints in `AGENTS.md` — the agent must pass them before committing
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4. **Start with PLANNING mode**: Generate the plan first, then switch to BUILDING
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5. **Observe and tune**: Watch early iterations, add guardrails to prompts when the agent fails in specific ways
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6. **The plan is disposable**: If the agent goes off track, delete `IMPLEMENTATION_PLAN.md` and re-plan
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7. **Keep `AGENTS.md` brief**: It's loaded every iteration — operational info only, no progress notes
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8. **Use a sandbox**: The agent runs autonomously with full tool access — isolate it
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## Example: Iterative Code Generation
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```typescript
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const prompt = `Write a function that:
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1. Parses CSV data
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2. Validates required fields
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3. Returns parsed records or error
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4. Has unit tests
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5. Output <promise>COMPLETE</promise> when done`;
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const loop = new RalphLoop(10, "COMPLETE");
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const result = await loop.run(prompt);
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```
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## Handling Failures
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```typescript
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try {
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const result = await loop.run(prompt);
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console.log("Task completed successfully!");
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} catch (error) {
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console.error("Task failed:", error.message);
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// Analyze what was attempted and suggest alternatives
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}
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```
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## When to Use RALPH-loop
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## When to Use a Ralph Loop
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**Good for:**
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- Code generation with automatic verification (tests, linters)
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- Tasks with clear success criteria
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- Iterative refinement where each attempt learns from previous failures
|
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- Unattended long-running improvements
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- Implementing features from specs with test-driven validation
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- Large refactors broken into many small tasks
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- Unattended, long-running development with clear requirements
|
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- Any work where backpressure (tests/builds) can verify correctness
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**Not good for:**
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- Tasks requiring human judgment or design input
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- One-shot operations
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- Tasks with vague success criteria
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- Real-time interactive debugging
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- Tasks requiring human judgment mid-loop
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- One-shot operations that don't benefit from iteration
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- Vague requirements without testable acceptance criteria
|
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- Exploratory prototyping where direction isn't clear
|
||||
|
||||
@@ -1,128 +1,79 @@
|
||||
import { readFile } from "fs/promises";
|
||||
import { execSync } from "child_process";
|
||||
import { CopilotClient } from "@github/copilot-sdk";
|
||||
|
||||
/**
|
||||
* RALPH-loop implementation: Iterative self-referential AI loops.
|
||||
* The same prompt is sent repeatedly, with AI reading its own previous output.
|
||||
* Loop continues until completion promise is detected in the response.
|
||||
* Ralph loop: autonomous AI task loop with fresh context per iteration.
|
||||
*
|
||||
* Two modes:
|
||||
* - "plan": reads PROMPT_plan.md, generates/updates IMPLEMENTATION_PLAN.md
|
||||
* - "build": reads PROMPT_build.md, implements tasks, runs tests, commits
|
||||
*
|
||||
* Each iteration creates a fresh session so the agent always operates in
|
||||
* the "smart zone" of its context window. State is shared between
|
||||
* iterations via files on disk (IMPLEMENTATION_PLAN.md, AGENTS.md, specs/*).
|
||||
*
|
||||
* Usage:
|
||||
* npx tsx ralph-loop.ts # build mode, 50 iterations
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||||
* npx tsx ralph-loop.ts plan # planning mode
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||||
* npx tsx ralph-loop.ts 20 # build mode, 20 iterations
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||||
* npx tsx ralph-loop.ts plan 5 # planning mode, 5 iterations
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||||
*/
|
||||
class RalphLoop {
|
||||
private client: CopilotClient;
|
||||
private iteration: number = 0;
|
||||
private readonly maxIterations: number;
|
||||
private readonly completionPromise: string;
|
||||
public lastResponse: string | null = null;
|
||||
|
||||
constructor(maxIterations: number = 10, completionPromise: string = "COMPLETE") {
|
||||
this.client = new CopilotClient();
|
||||
this.maxIterations = maxIterations;
|
||||
this.completionPromise = completionPromise;
|
||||
}
|
||||
type Mode = "plan" | "build";
|
||||
|
||||
/**
|
||||
* Run the RALPH-loop until completion promise is detected or max iterations reached.
|
||||
*/
|
||||
async run(initialPrompt: string): Promise<string> {
|
||||
let session: Awaited<ReturnType<CopilotClient["createSession"]>> | null = null;
|
||||
async function ralphLoop(mode: Mode, maxIterations: number) {
|
||||
const promptFile = mode === "plan" ? "PROMPT_plan.md" : "PROMPT_build.md";
|
||||
|
||||
await this.client.start();
|
||||
try {
|
||||
session = await this.client.createSession({
|
||||
model: "gpt-5.1-codex-mini"
|
||||
const client = new CopilotClient();
|
||||
await client.start();
|
||||
|
||||
const branch = execSync("git branch --show-current", { encoding: "utf-8" }).trim();
|
||||
|
||||
console.log("━".repeat(40));
|
||||
console.log(`Mode: ${mode}`);
|
||||
console.log(`Prompt: ${promptFile}`);
|
||||
console.log(`Branch: ${branch}`);
|
||||
console.log(`Max: ${maxIterations} iterations`);
|
||||
console.log("━".repeat(40));
|
||||
|
||||
try {
|
||||
const prompt = await readFile(promptFile, "utf-8");
|
||||
|
||||
for (let i = 1; i <= maxIterations; i++) {
|
||||
console.log(`\n=== Iteration ${i}/${maxIterations} ===`);
|
||||
|
||||
// Fresh session — each task gets full context budget
|
||||
const session = await client.createSession({
|
||||
model: "claude-sonnet-4.5",
|
||||
});
|
||||
|
||||
try {
|
||||
while (this.iteration < this.maxIterations) {
|
||||
this.iteration++;
|
||||
console.log(`\n=== Iteration ${this.iteration}/${this.maxIterations} ===`);
|
||||
|
||||
// Build the prompt for this iteration
|
||||
const currentPrompt = this.buildIterationPrompt(initialPrompt);
|
||||
console.log(`Sending prompt (length: ${currentPrompt.length})...`);
|
||||
|
||||
const response = await session.sendAndWait({ prompt: currentPrompt }, 300_000);
|
||||
this.lastResponse = response?.data.content || "";
|
||||
|
||||
// Display response summary
|
||||
const summary = this.lastResponse.length > 200
|
||||
? this.lastResponse.substring(0, 200) + "..."
|
||||
: this.lastResponse;
|
||||
console.log(`Response: ${summary}`);
|
||||
|
||||
// Check for completion promise
|
||||
if (this.lastResponse.includes(this.completionPromise)) {
|
||||
console.log(`\n✓ Success! Completion promise detected: '${this.completionPromise}'`);
|
||||
return this.lastResponse;
|
||||
}
|
||||
|
||||
console.log(`Iteration ${this.iteration} complete. Checking for next iteration...`);
|
||||
}
|
||||
|
||||
// Max iterations reached without completion
|
||||
throw new Error(
|
||||
`Maximum iterations (${this.maxIterations}) reached without detecting completion promise: '${this.completionPromise}'`
|
||||
);
|
||||
} catch (error) {
|
||||
console.error(`\nError during RALPH-loop: ${error instanceof Error ? error.message : String(error)}`);
|
||||
throw error;
|
||||
await session.sendAndWait({ prompt }, 600_000);
|
||||
} finally {
|
||||
if (session) {
|
||||
await session.destroy();
|
||||
}
|
||||
await session.destroy();
|
||||
}
|
||||
} finally {
|
||||
await this.client.stop();
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Build the prompt for the current iteration, including previous output as context.
|
||||
*/
|
||||
private buildIterationPrompt(initialPrompt: string): string {
|
||||
if (this.iteration === 1) {
|
||||
// First iteration: just the initial prompt
|
||||
return initialPrompt;
|
||||
// Push changes after each iteration
|
||||
try {
|
||||
execSync(`git push origin ${branch}`, { stdio: "inherit" });
|
||||
} catch {
|
||||
execSync(`git push -u origin ${branch}`, { stdio: "inherit" });
|
||||
}
|
||||
|
||||
console.log(`\nIteration ${i} complete.`);
|
||||
}
|
||||
|
||||
// Subsequent iterations: include previous output as context
|
||||
return `${initialPrompt}
|
||||
|
||||
=== CONTEXT FROM PREVIOUS ITERATION ===
|
||||
${this.lastResponse}
|
||||
=== END CONTEXT ===
|
||||
|
||||
Continue working on this task. Review the previous attempt and improve upon it.`;
|
||||
console.log(`\nReached max iterations: ${maxIterations}`);
|
||||
} finally {
|
||||
await client.stop();
|
||||
}
|
||||
}
|
||||
|
||||
// Example usage demonstrating RALPH-loop
|
||||
async function main() {
|
||||
const prompt = `You are iteratively building a small library. Follow these phases IN ORDER.
|
||||
Do NOT skip ahead — only do the current phase, then stop and wait for the next iteration.
|
||||
// Parse CLI args
|
||||
const args = process.argv.slice(2);
|
||||
const mode: Mode = args.includes("plan") ? "plan" : "build";
|
||||
const maxArg = args.find((a) => /^\d+$/.test(a));
|
||||
const maxIterations = maxArg ? parseInt(maxArg) : 50;
|
||||
|
||||
Phase 1: Design a DataValidator class that validates records against a schema.
|
||||
- Schema defines field names, types (str, int, float, bool), and whether required.
|
||||
- Return a list of validation errors per record.
|
||||
- Show the class code only. Do NOT output COMPLETE.
|
||||
|
||||
Phase 2: Write at least 4 unit tests covering: missing required field, wrong type,
|
||||
valid record, and empty input. Show test code only. Do NOT output COMPLETE.
|
||||
|
||||
Phase 3: Review the code from phases 1 and 2. Fix any bugs, add docstrings, and add
|
||||
an extra edge-case test. Show the final consolidated code with all fixes.
|
||||
When this phase is fully done, output the exact text: COMPLETE`;
|
||||
|
||||
const loop = new RalphLoop(5, "COMPLETE");
|
||||
|
||||
try {
|
||||
const result = await loop.run(prompt);
|
||||
console.log("\n=== FINAL RESULT ===");
|
||||
console.log(result);
|
||||
} catch (error) {
|
||||
console.error(`\nTask did not complete: ${error instanceof Error ? error.message : String(error)}`);
|
||||
if (loop.lastResponse) {
|
||||
console.log(`\nLast attempt:\n${loop.lastResponse}`);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
main().catch(console.error);
|
||||
ralphLoop(mode, maxIterations).catch(console.error);
|
||||
|
||||
Reference in New Issue
Block a user