mirror of
https://github.com/github/awesome-copilot.git
synced 2026-02-20 02:15:12 +00:00
Add RALPH-loop recipe to Copilot SDK cookbook
Add iterative RALPH-loop (Read, Act, Log, Persist, Halt) pattern implementations for all four supported languages: - C#/.NET: ralph-loop.cs with documentation - Node.js/TypeScript: ralph-loop.ts with documentation - Python: ralph_loop.py with documentation (async API) - Go: ralph-loop.go with documentation Each recipe demonstrates: - Self-referential iteration where AI reviews its own output - Completion promise detection to halt the loop - Max iteration safety limits - File persistence between iterations Verified against real Copilot SDK APIs: - Python: fully verified end-to-end with github-copilot-sdk - Node.js: fully verified end-to-end with @github/copilot-sdk - C#: compiles and runs successfully with GitHub.Copilot.SDK - Go: compiles against github.com/github/copilot-sdk/go v0.1.23
This commit is contained in:
Anthony Shaw
205
cookbook/copilot-sdk/python/ralph-loop.md
Normal file
205
cookbook/copilot-sdk/python/ralph-loop.md
Normal file
@@ -0,0 +1,205 @@
|
||||
# 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.py](recipe/ralph_loop.py)
|
||||
>
|
||||
> ```bash
|
||||
> cd python/recipe
|
||||
> pip install -r requirements.txt
|
||||
> python ralph_loop.py
|
||||
> ```
|
||||
|
||||
## 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 Claude to "write perfect code," you use RALPH-loop to:
|
||||
|
||||
1. Send the initial prompt with clear success criteria
|
||||
2. Claude writes code and tests
|
||||
3. Claude runs tests and sees failures
|
||||
4. Loop automatically re-sends the prompt
|
||||
5. Claude reads test output and previous code, fixes issues
|
||||
6. Repeat until all tests pass and completion promise is output
|
||||
|
||||
## Basic Implementation
|
||||
|
||||
```python
|
||||
import asyncio
|
||||
from copilot import CopilotClient, MessageOptions, SessionConfig
|
||||
|
||||
class RalphLoop:
|
||||
"""Iterative self-referential feedback loop using Copilot."""
|
||||
|
||||
def __init__(self, max_iterations=10, completion_promise="COMPLETE"):
|
||||
self.client = CopilotClient()
|
||||
self.iteration = 0
|
||||
self.max_iterations = max_iterations
|
||||
self.completion_promise = completion_promise
|
||||
self.last_response = None
|
||||
|
||||
async def run(self, initial_prompt):
|
||||
"""Run the RALPH-loop until completion promise detected or max iterations reached."""
|
||||
await self.client.start()
|
||||
session = await self.client.create_session(
|
||||
SessionConfig(model="gpt-5.1-codex-mini")
|
||||
)
|
||||
|
||||
try:
|
||||
while self.iteration < self.max_iterations:
|
||||
self.iteration += 1
|
||||
print(f"\n--- Iteration {self.iteration}/{self.max_iterations} ---")
|
||||
|
||||
# Build prompt including previous response as context
|
||||
if self.iteration == 1:
|
||||
prompt = initial_prompt
|
||||
else:
|
||||
prompt = f"{initial_prompt}\n\nPrevious attempt:\n{self.last_response}\n\nContinue improving..."
|
||||
|
||||
result = await session.send_and_wait(
|
||||
MessageOptions(prompt=prompt), timeout=300
|
||||
)
|
||||
|
||||
self.last_response = result.data.content if result else ""
|
||||
print(f"Response ({len(self.last_response)} chars)")
|
||||
|
||||
# Check for completion promise
|
||||
if self.completion_promise in self.last_response:
|
||||
print(f"✓ Completion promise detected: {self.completion_promise}")
|
||||
return self.last_response
|
||||
|
||||
print(f"Continuing to iteration {self.iteration + 1}...")
|
||||
|
||||
raise RuntimeError(
|
||||
f"Max iterations ({self.max_iterations}) reached without completion promise"
|
||||
)
|
||||
finally:
|
||||
await session.destroy()
|
||||
await self.client.stop()
|
||||
|
||||
# Usage
|
||||
async def main():
|
||||
loop = RalphLoop(5, "COMPLETE")
|
||||
result = await loop.run("Your task here")
|
||||
print(result)
|
||||
|
||||
asyncio.run(main())
|
||||
```
|
||||
|
||||
## With File Persistence
|
||||
|
||||
For tasks involving code generation, persist state to files so the AI can see changes:
|
||||
|
||||
```python
|
||||
import asyncio
|
||||
from pathlib import Path
|
||||
from copilot import CopilotClient, MessageOptions, SessionConfig
|
||||
|
||||
class PersistentRalphLoop:
|
||||
"""RALPH-loop with file-based state persistence."""
|
||||
|
||||
def __init__(self, work_dir, max_iterations=10):
|
||||
self.client = CopilotClient()
|
||||
self.work_dir = Path(work_dir)
|
||||
self.work_dir.mkdir(parents=True, exist_ok=True)
|
||||
self.iteration = 0
|
||||
self.max_iterations = max_iterations
|
||||
|
||||
async def run(self, initial_prompt):
|
||||
"""Run the loop with persistent state."""
|
||||
await self.client.start()
|
||||
session = await self.client.create_session(
|
||||
SessionConfig(model="gpt-5.1-codex-mini")
|
||||
)
|
||||
|
||||
try:
|
||||
# Store initial prompt
|
||||
(self.work_dir / "prompt.md").write_text(initial_prompt)
|
||||
|
||||
while self.iteration < self.max_iterations:
|
||||
self.iteration += 1
|
||||
print(f"\n--- Iteration {self.iteration} ---")
|
||||
|
||||
# Build context from previous outputs
|
||||
context = initial_prompt
|
||||
prev_output = self.work_dir / f"output-{self.iteration - 1}.txt"
|
||||
if prev_output.exists():
|
||||
context += f"\n\nPrevious iteration:\n{prev_output.read_text()}"
|
||||
|
||||
result = await session.send_and_wait(
|
||||
MessageOptions(prompt=context), timeout=300
|
||||
)
|
||||
response = result.data.content if result else ""
|
||||
|
||||
# Persist output
|
||||
output_file = self.work_dir / f"output-{self.iteration}.txt"
|
||||
output_file.write_text(response)
|
||||
|
||||
if "COMPLETE" in response:
|
||||
return response
|
||||
|
||||
raise RuntimeError("Max iterations reached")
|
||||
finally:
|
||||
await session.destroy()
|
||||
await self.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
|
||||
|
||||
```python
|
||||
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"""
|
||||
|
||||
async def main():
|
||||
loop = RalphLoop(10, "COMPLETE")
|
||||
result = await loop.run(prompt)
|
||||
|
||||
asyncio.run(main())
|
||||
```
|
||||
|
||||
## Handling Failures
|
||||
|
||||
```python
|
||||
try:
|
||||
result = await loop.run(prompt)
|
||||
print("Task completed successfully!")
|
||||
except RuntimeError as e:
|
||||
print(f"Task failed: {e}")
|
||||
if loop.last_response:
|
||||
print(f"\nLast attempt:\n{loop.last_response}")
|
||||
```
|
||||
|
||||
## 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
|
||||
Reference in New Issue
Block a user