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Merge pull request #696 from tonybaloney/cookbook/ralph-loop-recipe

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Add RALPH-loop recipes to Copilot SDK cookbook
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Aaron Powell
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# 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.py](recipe/ralph_loop.py)
>
> From the repository root, install dependencies and run:
>
> ```bash
> pip install -r cookbook/copilot-sdk/python/recipe/requirements.txt
> python cookbook/copilot-sdk/python/recipe/ralph_loop.py
> ```
>
> Make sure `PROMPT_build.md` and `PROMPT_plan.md` exist in your current working directory before running the loop.
## 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`:
```python
import asyncio
from pathlib import Path
from copilot import CopilotClient, MessageOptions, SessionConfig
async def ralph_loop(prompt_file: str, max_iterations: int = 50):
client = CopilotClient()
await client.start()
try:
prompt = Path(prompt_file).read_text()
for i in range(1, max_iterations + 1):
print(f"\n=== Iteration {i}/{max_iterations} ===")
# Fresh session each iteration — context isolation is the point
session = await client.create_session(
SessionConfig(model="gpt-5.1-codex-mini")
)
try:
await session.send_and_wait(
MessageOptions(prompt=prompt), timeout=600
)
finally:
await session.destroy()
print(f"Iteration {i} complete.")
finally:
await client.stop()
# Usage: point at your PROMPT.md
asyncio.run(ralph_loop("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:
```python
import asyncio
import sys
from pathlib import Path
from copilot import CopilotClient, MessageOptions, SessionConfig
async def ralph_loop(mode: str = "build", max_iterations: int = 50):
prompt_file = "PROMPT_plan.md" if mode == "plan" else "PROMPT_build.md"
client = CopilotClient()
await client.start()
print("━" * 40)
print(f"Mode: {mode}")
print(f"Prompt: {prompt_file}")
print(f"Max: {max_iterations} iterations")
print("━" * 40)
try:
prompt = Path(prompt_file).read_text()
for i in range(1, max_iterations + 1):
print(f"\n=== Iteration {i}/{max_iterations} ===")
session = await client.create_session(SessionConfig(
model="gpt-5.1-codex-mini",
# Pin the agent to the project directory
working_directory=str(Path.cwd()),
# Auto-approve tool calls for unattended operation
on_permission_request=lambda _req, _ctx: {
"kind": "approved", "rules": []
},
))
# Log tool usage for visibility
def log_tool_event(event):
if event.type.value == "tool.execution_start":
print(f" ⚙ {event.data.tool_name}")
session.on(log_tool_event)
try:
await session.send_and_wait(
MessageOptions(prompt=prompt), timeout=600
)
finally:
await session.destroy()
print(f"\nIteration {i} complete.")
print(f"\nReached max iterations: {max_iterations}")
finally:
await client.stop()
if __name__ == "__main__":
args = sys.argv[1:]
mode = "plan" if "plan" in args else "build"
max_iter = next((int(a) for a in args if a.isdigit()), 50)
asyncio.run(ralph_loop(mode, max_iter))
```
### 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
python -m pytest
## Validation
- Tests: `pytest`
- Typecheck: `mypy src/`
- Lint: `ruff check src/`
```
## 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 `working_directory`**: Pin the session to your project root so tool operations resolve paths correctly
10. **Auto-approve permissions**: Use `on_permission_request` 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

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#!/usr/bin/env python3
"""
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:
python ralph_loop.py # build mode, 50 iterations
python ralph_loop.py plan # planning mode
python ralph_loop.py 20 # build mode, 20 iterations
python ralph_loop.py plan 5 # planning mode, 5 iterations
"""
import asyncio
import sys
from pathlib import Path
from copilot import CopilotClient, MessageOptions, SessionConfig
async def ralph_loop(mode: str = "build", max_iterations: int = 50):
prompt_file = "PROMPT_plan.md" if mode == "plan" else "PROMPT_build.md"
client = CopilotClient()
await client.start()
print("━" * 40)
print(f"Mode: {mode}")
print(f"Prompt: {prompt_file}")
print(f"Max: {max_iterations} iterations")
print("━" * 40)
try:
prompt = Path(prompt_file).read_text()
for i in range(1, max_iterations + 1):
print(f"\n=== Iteration {i}/{max_iterations} ===")
session = await client.create_session(SessionConfig(
model="gpt-5.1-codex-mini",
# Pin the agent to the project directory
working_directory=str(Path.cwd()),
# Auto-approve tool calls for unattended operation
on_permission_request=lambda _req, _ctx: {
"kind": "approved",
"rules": [],
},
))
# Log tool usage for visibility
def log_tool_event(event):
if event.type.value == "tool.execution_start":
print(f" ⚙ {event.data.tool_name}")
session.on(log_tool_event)
try:
await session.send_and_wait(
MessageOptions(prompt=prompt), timeout=600
)
finally:
await session.destroy()
print(f"\nIteration {i} complete.")
print(f"\nReached max iterations: {max_iterations}")
finally:
await client.stop()
if __name__ == "__main__":
args = sys.argv[1:]
mode = "plan" if "plan" in args else "build"
max_iter = next((int(a) for a in args if a.isdigit()), 50)
asyncio.run(ralph_loop(mode, max_iter))