https://github.com/kazkozdev/multi-expert-consensus
👥 MELC is a distributed multi-agent LLM system that achieves reliable outputs through expert consensus and confidence-weighted validation.
https://github.com/kazkozdev/multi-expert-consensus
Last synced: about 2 months ago
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👥 MELC is a distributed multi-agent LLM system that achieves reliable outputs through expert consensus and confidence-weighted validation.
- Host: GitHub
- URL: https://github.com/kazkozdev/multi-expert-consensus
- Owner: KazKozDev
- License: mit
- Created: 2024-11-10T12:31:50.000Z (6 months ago)
- Default Branch: main
- Last Pushed: 2024-11-16T16:44:29.000Z (6 months ago)
- Last Synced: 2025-02-04T20:41:57.792Z (3 months ago)
- Language: Python
- Homepage:
- Size: 1.83 MB
- Stars: 0
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# 🤖 Multi-Expert LLM Consensus Method (MELC)
[](https://www.python.org/downloads/)
[](https://opensource.org/licenses/MIT)
[](https://docs.python.org/3/library/asyncio.html)
[](https://github.com/KazKozDev/multi-expert-consensus)
[](http://makeapullrequest.com)
MELC is a cutting-edge approach to query processing that leverages multi-agent LLM interactions through an integrated system of critical analysis and consensus validation. The method implements distributed expert evaluation with confidence-weighted responses to achieve high-reliability outputs in complex decision-making scenarios.
## 🏗️ Core Architecture
The method employs a three-phase processing pipeline:
```
User Query → [Parallel Expert Evaluation] → [Cross-Validation] → [Consensus Synthesis] → Final Response
```
## 🎬 Demo Preview
In this example, the user submits the query "How to be happy?". The system successfully processes the input through MELC - a cutting-edge approach to query processing that leverages multi-agent LLM interactions through an integrated system of critical analysis and consensus validation. The method implements distributed expert evaluation with confidence-weighted responses to achieve high-reliability outputs in complex decision-making scenarios.
## ⚡ Key Features
### 🔄 Distributed Expert Processing
- Parallel query processing by multiple LLM agents
- Confidence-weighted response system
- Asynchronous execution architecture
### 🔍 Critical Analysis Layer
- Cross-validation through dedicated critique agent
- Multi-dimensional response evaluation
- Confidence level assessment
### 🎯 Consensus Mechanism
- Synthetic response generation
- Multi-agent validation
- Iterative refinement process
## 🛠️ Technical Implementation
- **Execution Model**: Asynchronous processing via `asyncio`
- **Communication**: RESTful API interaction
- **Scalability**: Horizontally scalable architecture
- **Reliability**: Built-in error handling and timeout management
## 💻 Installation
```bash
# Clone the repository
git clone https://github.com/KazKozDev/multi-expert-consensus.git
# Navigate to the project directory
cd multi-expert-consensus
# Install dependencies
pip install -r requirements.txt
# Run the application
python src/main.py
```
## ✨ Benefits
- Enhanced response accuracy through multi-expert validation
- Quantifiable confidence metrics
- Reduced single-point-of-failure risks
- Transparent decision-making process
- Scalable and maintainable architecture
## 📊 Performance Characteristics
- Parallel processing capabilities
- Real-time response generation
- Built-in reliability metrics
- Iterative improvement mechanism
## 📝 License
MIT License
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Developed and maintained with modern LLM technology