LLMOrchestrator

A powerful framework for orchestrating and enhancing Large Language Model (LLM) outputs through multi-model orchestration, chain-of-thought reasoning, and diverse perspectives.

Overview

LLMOrchestrator is designed to enhance LLM outputs by: - Orchestrating multiple LLMs in various configurations - Implementing chain-of-thought reasoning with multiple perspectives - Leveraging different models for their specific strengths - Introducing controlled randomness and diversity in outputs - Providing robust verification and validation mechanisms

Features

• Multi-Model Orchestration: Chain different LLMs in various orders
• Chain-of-Thought Reasoning: Break down complex problems into steps
• Diverse Perspectives: Combine outputs from different models
• Adaptive Learning: Optimize prompts based on performance
• Parallel Processing: Handle multiple requests efficiently
• Caching: Improve response times for repeated queries
• Monitoring: Track performance and quality metrics
• Custom Verification: Implement domain-specific validation

Installation

bash pip install llm-orchestrator

Quick Start

```python from LLMOrchestrator.models import OpenAIModel, LocalModel from LLMOrchestrator.controller import Controller, PromptTemplate

Initialize models

generator = OpenAIModel( modelname="gpt-3.5-turbo", apikey="your-api-key" ) verifier = LocalModel( model_name="facebook/opt-125m", device="cpu" )

Create a prompt template

template = PromptTemplate( "Please provide a detailed analysis of: {prompt}" )

Initialize controller

controller = Controller( generator=generator, verifier=verifier, maxiterations=3, maxverifications=2, parallelprocessing=True, cacheenabled=True, adaptivelearning=True, monitoringenabled=True, prompt_template=template )

Execute with a prompt

result = controller.execute( prompt="Analyze the impact of artificial intelligence on healthcare.", stop_early=False )

Get performance metrics

metrics = controller.getvalidationmetrics() print(f"Quality Score: {metrics.qualityscore}") print(f"Confidence: {metrics.confidencescore}") print(f"Processing Time: {metrics.processing_time}s") ```

Advanced Usage

Chain-of-Thought Reasoning

```python

Configure for complex reasoning tasks

controller = Controller( generator=generator, verifier=verifier, maxiterations=5, # Allow more iterations for complex reasoning prompttemplate=PromptTemplate( "Let's solve this step by step:\n1. First, let's analyze...\n2. Then, we can consider...\n3. Finally, we can conclude...\n\nProblem: {prompt}" ) )

result = controller.execute( prompt="Explain the relationship between quantum computing and cryptography", stop_early=False ) ```

Parallel Processing with Multiple Models

```python

Process multiple prompts with different models

prompts = [ "Generate a technical specification for a new API", "Create a user interface design document", "Write a security assessment report" ]

results = controller.executeparallel( prompts=prompts, maxworkers=2, stop_early=False )

Get performance report

report = controller.getperformancereport() print(f"Total processing time: {report['totaltime']}s") print(f"Average quality score: {report['avgquality_score']}") ```

Custom Controller Implementation

```python from LLMOrchestrator.controller import CustomController

def custom_processing(output: str) -> str: # Add custom processing logic return output.upper()

controller = CustomController( customfunc=customprocessing, generator=generator, verifier=verifier, parallel_processing=True )

result = controller.execute( prompt="Generate a response", n=3 # Generate 3 variations ) ```

Use Cases

LLMOrchestrator is particularly effective for:

• Complex Reasoning Tasks: Break down multi-step problems
• Content Generation: Combine different models for writing and editing
• Technical Documentation: Generate and validate technical content
• Research Analysis: Synthesize information from multiple perspectives
• Quality Assurance: Implement multiple verification layers
• Performance Optimization: Cache and adapt to improve response times
• Prompt Engineering: Optimize prompts through performance tracking

Configuration

Create a .env file with your API keys: OPENAI_API_KEY=your-openai-key

Configure advanced settings in orchestration_config.json: json { "max_iterations": 3, "max_verifications": 2, "parallel_processing": true, "cache_enabled": true, "adaptive_learning": true, "monitoring_enabled": true }

Testing

Run the test suite: bash pytest tests/ -v

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Run tests
5. Submit a pull request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Citation

If you use this software in your research, please cite:

bibtex @software{llmorchestrator2024, author = {Srinivasan, Trisanth and Patapati, Santosh}, title = {LLMOrchestrator: A Multi-Model LLM Orchestration Framework for Reducing Bias and Iterative Reasoning}, year = {2025}, publisher = {GitHub}, url = {https://github.com/builtbypyro/LLMOrchestrator} }

Documentation

Full documentation is available in the docs directory.