# 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](recipe/ralph-loop.ts)
>
> ```bash
> npm install
> npx tsx recipe/ralph-loop.ts
> ```

## What is a Ralph Loop?

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.

```
┌─────────────────────────────────────────────────┐
│                   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 | copilot ; done`:

```typescript
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: "gpt-5.1-codex-mini" });
      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](https://github.com/ClaytonFarr/ralph-playbook) architecture:

```typescript
import { readFile } from "fs/promises";
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();

  console.log(`Mode: ${mode} | Prompt: ${promptFile}`);

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

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

      const session = await client.createSession({
        model: "gpt-5.1-codex-mini",
        // Pin the agent to the project directory
        workingDirectory: process.cwd(),
        // Auto-approve tool calls for unattended operation
        onPermissionRequest: async () => ({ allow: true }),
      });

      // Log tool usage for visibility
      session.on((event) => {
        if (event.type === "tool.execution_start") {
          console.log(`  ⚙ ${event.data.toolName}`);
        }
      });

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

      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`

```markdown
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`

```markdown
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.

5. When authoring documentation, capture the why.
6. Implement completely. No placeholders or stubs.
7. 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.

```markdown
## 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
9. **Set `workingDirectory`**: Pin the session to your project root so tool operations resolve paths correctly
10. **Auto-approve permissions**: Use `onPermissionRequest` to allow tool calls without interrupting the loop

## 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

## See Also

- [Error Handling](error-handling.md) — timeout patterns and graceful shutdown for long-running sessions
- [Persisting Sessions](persisting-sessions.md) — save and resume sessions across restarts