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

Ralph Loop: Autonomous AI Task Loops

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.

Runnable example: recipe/ralph-loop.ts

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

What is a Ralph Loop?

A Ralph loop 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.

┌─────────────────────────────────────────────────┐
│                   loop.sh                       │
│  while true:                                    │
│    ┌─────────────────────────────────────────┐  │
│    │  Fresh session (isolated context)       │  │
│    │                                         │  │
│    │  1. Read PROMPT.md + AGENTS.md          │  │
│    │  2. Study specs/* and code              │  │
│    │  3. Pick next task from plan            │  │
│    │  4. Implement + run tests               │  │
│    │  5. Update plan, commit, exit           │  │
│    └─────────────────────────────────────────┘  │
│    ↻ next iteration (fresh context)             │
└─────────────────────────────────────────────────┘

Core principles:

• Fresh context per iteration: Each loop creates a new session — no context accumulation, always in the "smart zone"
• Disk as shared state: IMPLEMENTATION_PLAN.md persists between iterations and acts as the coordination mechanism
• Backpressure steers quality: Tests, builds, and lints reject bad work — the agent must fix issues before committing
• Two modes: PLANNING (gap analysis → generate plan) and BUILDING (implement from plan)

Simple Version

The minimal Ralph loop — the SDK equivalent of while :; do cat PROMPT.md | claude ; done:

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

async function ralphLoop(promptFile: string, maxIterations: number = 50) {
    const client = new CopilotClient();
    await client.start();

    try {
        const prompt = await readFile(promptFile, "utf-8");

        for (let i = 1; i <= maxIterations; i++) {
            console.log(`\n=== Iteration ${i}/${maxIterations} ===`);

            // Fresh session each iteration — context isolation is the point
            const session = await client.createSession({ model: "claude-sonnet-4.5" });
            try {
                await session.sendAndWait({ prompt }, 600_000);
            } finally {
                await session.destroy();
            }

            console.log(`Iteration ${i} complete.`);
        }
    } finally {
        await client.stop();
    }
}

// Usage: point at your PROMPT.md
ralphLoop("PROMPT.md", 20);

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.

Ideal Version

The full Ralph pattern with planning and building modes, matching the Ralph Playbook architecture:

import { readFile } from "fs/promises";
import { execSync } from "child_process";
import { CopilotClient } from "@github/copilot-sdk";

type Mode = "plan" | "build";

async function ralphLoop(mode: Mode, maxIterations: number = 50) {
    const promptFile = mode === "plan" ? "PROMPT_plan.md" : "PROMPT_build.md";
    const client = new CopilotClient();
    await client.start();

    const branch = execSync("git branch --show-current", { encoding: "utf-8" }).trim();
    console.log(`Mode: ${mode} | Prompt: ${promptFile} | Branch: ${branch}`);

    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 {
                await session.sendAndWait({ prompt }, 600_000);
            } finally {
                await session.destroy();
            }

            // Push changes after each iteration
            try {
                execSync(`git push origin ${branch}`, { stdio: "inherit" });
            } catch {
                execSync(`git push -u origin ${branch}`, { stdio: "inherit" });
            }

            console.log(`Iteration ${i} complete.`);
        }
    } finally {
        await client.stop();
    }
}

// Parse CLI args: npx tsx ralph-loop.ts [plan] [max_iterations]
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;

ralphLoop(mode, maxIterations);

Required Project Files

The ideal version expects this file structure in your project:

project-root/
├── PROMPT_plan.md              # Planning mode instructions
├── PROMPT_build.md             # Building mode instructions
├── AGENTS.md                   # Operational guide (build/test commands)
├── IMPLEMENTATION_PLAN.md      # Task list (generated by planning mode)
├── specs/                      # Requirement specs (one per topic)
│   ├── auth.md
│   └── data-pipeline.md
└── src/                        # Your source code

Example PROMPT_plan.md

0a. Study `specs/*` to learn the application specifications.
0b. Study IMPLEMENTATION_PLAN.md (if present) to understand the plan so far.
0c. Study `src/` to understand existing code and shared utilities.

1. Compare specs against code (gap analysis). Create or update
   IMPLEMENTATION_PLAN.md as a prioritized bullet-point list of tasks
   yet to be implemented. Do NOT implement anything.

IMPORTANT: Do NOT assume functionality is missing — search the
codebase first to confirm. Prefer updating existing utilities over
creating ad-hoc copies.

Example PROMPT_build.md

0a. Study `specs/*` to learn the application specifications.
0b. Study IMPLEMENTATION_PLAN.md.
0c. Study `src/` for reference.

1. Choose the most important item from IMPLEMENTATION_PLAN.md. Before
   making changes, search the codebase (don't assume not implemented).
2. After implementing, run the tests. If functionality is missing, add it.
3. When you discover issues, update IMPLEMENTATION_PLAN.md immediately.
4. When tests pass, update IMPLEMENTATION_PLAN.md, then `git add -A`
   then `git commit` with a descriptive message.

99999. When authoring documentation, capture the why.
999999. Implement completely. No placeholders or stubs.
9999999. Keep IMPLEMENTATION_PLAN.md current — future iterations depend on it.

Example AGENTS.md

Keep this brief (~60 lines). It's loaded every iteration, so bloat wastes context.

## Build & Run

npm run build

## Validation

- Tests: `npm test`
- Typecheck: `npx tsc --noEmit`
- Lint: `npm run lint`

Best Practices

1. Fresh context per iteration: Never accumulate context across iterations — that's the whole point
2. Disk is your database: IMPLEMENTATION_PLAN.md is shared state between isolated sessions
3. Backpressure is essential: Tests, builds, lints in AGENTS.md — the agent must pass them before committing
4. Start with PLANNING mode: Generate the plan first, then switch to BUILDING
5. Observe and tune: Watch early iterations, add guardrails to prompts when the agent fails in specific ways
6. The plan is disposable: If the agent goes off track, delete IMPLEMENTATION_PLAN.md and re-plan
7. Keep AGENTS.md brief: It's loaded every iteration — operational info only, no progress notes
8. Use a sandbox: The agent runs autonomously with full tool access — isolate it

When to Use a Ralph Loop

Good for:

• Implementing features from specs with test-driven validation
• Large refactors broken into many small tasks
• Unattended, long-running development with clear requirements
• Any work where backpressure (tests/builds) can verify correctness

Not good for:

• Tasks requiring human judgment mid-loop
• One-shot operations that don't benefit from iteration
• Vague requirements without testable acceptance criteria
• Exploratory prototyping where direction isn't clear