loop-prompting 2.1.1

Universal Loop Engineering System — iterative AI execution for any LLM provider with tool support

🔄 LOOP ENGINE

Generate → Evaluate → Refine → Converge

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Perfect for • Claude • Kiro • Cursor • Codex • LangChain • n8n • Any AI Workflow

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🎬 See It In Action

from loop_engine import run_loop

result = run_loop(
    task="Create a FastAPI user auth endpoint",
    success_criteria="JWT tokens, password hashing, type hints",
    failure_criteria="Hardcoded secrets, no validation",
    max_iterations=5
)
# ✨ Automatically iterates until perfect!

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🔥 How It Works

⚡ Why Loop Engine Beats Single-Shot AI

🤖 Traditional AI	🔄 Loop Engine
One attempt only	✅ Iterates until perfect
Manual re-prompting	✅ Automatic refinement
No quality control	✅ Built-in scoring (1-10)
Often misses requirements	✅ Checks YOUR criteria
Generic output	✅ Converges to YOUR standards
Unsafe code changes	✅ Git isolation (optional sandbox)
No validation	✅ Test execution integration

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Install

pip install loop-engine[all]  # All providers
# or
pip install loop-engine       # Just core

Set API key (any provider):

export ANTHROPIC_API_KEY="your_key"
# or OPENAI_API_KEY, GOOGLE_API_KEY, etc.

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Quick Start

CLI

loop-engine run  # Interactive mode

Python

from loop_engine import run_loop

result = run_loop(
    task="Create a FastAPI user auth endpoint",
    success_criteria="JWT tokens, password hashing, type hints, error handling",
    failure_criteria="Hardcoded secrets, no validation, missing docstrings",
    max_iterations=5,
    provider="anthropic"  # auto-detects if not specified
)

print(result.final.output)
print(f"Quality: {result.final.quality_score}/10")

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Supported Providers

Provider	Models	Setup
Anthropic	Claude 3.5 Sonnet, Claude 3 Opus	ANTHROPIC_API_KEY
OpenAI	GPT-4, GPT-4o	OPENAI_API_KEY
Google	Gemini 2.0 Flash, Gemini 1.5 Pro	GOOGLE_API_KEY
OpenRouter	200+ models	OPENROUTER_API_KEY
Groq	Llama 3.3, Mixtral (ultra-fast)	GROQ_API_KEY
DeepSeek	DeepSeek Chat, DeepSeek Coder	DEEPSEEK_API_KEY
AWS Bedrock	Claude, Llama on AWS	AWS credentials
Azure OpenAI	GPT-4 on Azure	AZURE_OPENAI_API_KEY
Ollama	Local models (free)	Ollama running locally

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Built-in Tools

Enable with --tools or enable_tools=True:

• read_file - Read file contents
• write_file - Write to files
• list_directory - List directory contents
• execute_command - Run shell commands
• search_files - Find files by pattern
• create_directory - Create directories

Test Execution (NEW!)

Run tests automatically during iterations:

result = run_loop(
    task="Write a function to validate emails",
    success_criteria="Regex validation, type hints, passes tests",
    failure_criteria="No validation, tests fail",
    test_command="pytest tests/test_email.py",  # ✨ NEW!
    max_iterations=5
)
# Tests run each iteration - quality score boosted if they pass!

Git Isolation (NEW!)

Safe code modifications in isolated environment:

result = run_loop(
    task="Refactor auth module to use async/await",
    success_criteria="All async, passes tests",
    failure_criteria="Breaking changes",
    enable_tools=True,
    test_command="pytest tests/",
    use_isolation=True,  # 🔒 Safe isolation!
    isolation_path=".",  # Your git repo
    max_iterations=8
)
# Creates temporary worktree → AI edits → Tests → Merges if good!

How Git Isolation Works:

1. Creates temporary copy (worktree) of your code
2. AI modifies the COPY, not your real files
3. Runs tests in isolation
4. If successful → merges back to main
5. If fails → deletes copy, your code untouched

When to use:

• ✅ Automated refactoring of production code
• ✅ Large multi-file changes
• ✅ When you want safety guarantees
• ❌ Simple code generation (overkill)

Custom Tools

from loop_engine import register_custom_tool

def my_tool(arg: str) -> dict:
    return {"result": f"Processed: {arg}"}

register_custom_tool(
    name="my_tool",
    func=my_tool,
    definition={
        "name": "my_tool",
        "description": "My custom tool",
        "parameters": {
            "type": "object",
            "properties": {
                "arg": {"type": "string", "description": "Argument"}
            },
            "required": ["arg"]
        }
    }
)

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Examples

Check /examples directory:

• basic_example.py - Simple usage (2 seconds)
• tools_example.py - File operations with tools
• complete_system_test.py - Full system validation

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Best Practices

For Simple Tasks (Fast)

# Just basic loop - 2-5 seconds per iteration
run_loop(task, success, failure, max_iterations=3)

For Code with Tests

# Add test execution for validation
run_loop(
    task, success, failure,
    test_command="pytest tests/",
    max_iterations=5
)

For Production Code (Maximum Safety)

# Full safety: isolation + tests
run_loop(
    task, success, failure,
    enable_tools=True,
    test_command="pytest tests/",
    use_isolation=True,  # Safe sandbox
    isolation_path=".",
    max_iterations=10
)

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Tips for Best Results

1. Be specific: "TypeScript, uses async/await, <200 lines" > "good code"
2. Set hard failures: "No eval(), no hardcoded secrets"
3. Start with 3-5 iterations: Most tasks converge quickly
4. Use tools for file ops: Enable tools when working with existing code
5. Add context: Provide project structure, patterns, requirements

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Configuration

Create ~/.loop-engine/config.yaml:

max_iterations: 5
convergence_threshold: 8
provider: auto

tools:
  enabled: false
  allowed_tools: [all]

providers:
  anthropic:
    model: claude-sonnet-4-20250514
    max_tokens: 4000

Or use loop-engine init to generate automatically.

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License

MIT License - see LICENSE file

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Support

• Examples: /examples directory
• Issues: GitHub Issues
• Config: loop-engine status to see current setup