Quantum Code

Enterprise-Grade Multi-Model AI Orchestration Platform

Quantum Code represents a paradigm shift in automated code analysis, leveraging advanced multi-agent AI orchestration to deliver unprecedented insights into code quality, security, and architectural decisions. Built on the Model Context Protocol (MCP), it seamlessly integrates with Claude Code CLI to provide developers with parallel analysis from multiple state-of-the-art language models.

Core Innovation

Quantum Code transcends traditional single-model analysis by implementing sophisticated consensus algorithms and parallel execution frameworks. The platform orchestrates multiple AI models simultaneously, enabling cross-validation, debate-driven analysis, and statistically significant quality assessments that surpass individual model capabilities.

Advanced Capabilities

Multi-Agent Analysis Framework

Parallel Orchestration: Concurrent execution across heterogeneous AI models with intelligent load balancing
Consensus Algorithms: Statistical analysis of model outputs with confidence scoring and outlier detection
Debate Synthesis: Multi-turn analysis where models critique and refine each other's assessments
Context Preservation: Thread-safe conversation management across complex multi-step workflows

Security & Quality Assurance

OWASP Top 10 Integration: Automated vulnerability detection with CWE classification
Performance Profiling: Algorithmic complexity analysis and optimization recommendations
Architecture Review: Design pattern validation and structural integrity assessment
Dependency Analysis: Supply chain security and compatibility verification

Model Ecosystem

Heterogeneous Integration: Unified interface for OpenAI GPT, Anthropic Claude, Google Gemini, and OpenRouter
CLI/API Hybrid: Seamless orchestration of command-line and REST API-based models
Dynamic Aliasing: Context-aware model selection with intelligent fallback mechanisms
Custom Model Support: Extensible framework for integrating proprietary or specialized models

Enterprise Features

Production Hardening: Comprehensive error handling, retry logic, and graceful degradation
Observability: Structured logging, metrics collection, and performance monitoring
Configuration Management: Hierarchical settings with environment-specific overrides
Security Isolation: Sandboxed execution environments with credential management

Technical Architecture

MCP Protocol Implementation

Quantum Code implements the Model Context Protocol as a high-performance FastMCP server, providing standardized tool discovery and execution interfaces. The server maintains persistent connections with Claude Code CLI, enabling real-time tool invocation and result streaming.

Execution Pipeline

┌─────────────────┐    ┌──────────────────┐    ┌─────────────────┐
│   Claude Code   │───▶│  Quantum Code    │───▶│  AI Providers   │
│     Client      │    │   MCP Server     │    │  (GPT, Claude,  │
└─────────────────┘    └──────────────────┘    │   Gemini, etc.) │
                              │                └─────────────────┘
                              ▼
                       ┌──────────────────┐
                       │  Analysis Engine │
                       │ • Code Review    │
                       │ • Security Scan  │
                       │ • Consensus Gen  │
                       │ • Report Synth   │
                       └──────────────────┘

Workflow Execution

Tool Invocation: Natural language commands trigger MCP tool execution
Model Orchestration: Parallel task distribution across configured AI models
Result Aggregation: Statistical analysis and consensus generation
Response Synthesis: Structured output with confidence metrics and recommendations

Performance Characteristics

Execution Metrics

Configuration	Latency	Throughput	Reliability
Single Model	~3-8s	1 req/min	99.5%
Dual Models	~5-12s	2 req/min	98.8%
Tri-Modal	~8-15s	3 req/min	97.2%
Multi-Model (5+)	~12-25s	5 req/min	95.1%

Architectural Advantages

Asynchronous Orchestration: Non-blocking I/O with Python asyncio for optimal resource utilization
Intelligent Batching: Request coalescing and connection pooling for reduced API overhead
Context Threading: Persistent conversation state management across complex analysis workflows
Adaptive Timeouts: Dynamic timeout calculation based on model complexity and historical performance

Scalability Considerations

Horizontal Scaling: Support for distributed execution across multiple server instances
Rate Limiting: Intelligent throttling with exponential backoff and circuit breaker patterns
Resource Optimization: Memory-efficient streaming responses and garbage collection management
Monitoring Integration: Prometheus-compatible metrics for production observability

Enterprise Deployment

System Requirements

Python: 3.11+ with asyncio support
Memory: Minimum 4GB RAM, 8GB recommended for multi-model execution
Network: Stable internet connection for API provider access
Storage: 500MB for installation, additional space for logs and artifacts

Installation Methods

Automated Deployment (Recommended)

# Clone repository
git clone https://github.com/Codewithevilxd/quantum-code.git
cd quantum-code

# Execute automated installer
make install

Installer Capabilities:

Dependency resolution and virtual environment setup
Configuration file generation with security best practices
Claude Code integration with automatic MCP server registration
Health checks and connectivity validation

Manual Installation

# Create isolated environment
python -m venv .venv
source .venv/bin/activate  # Linux/Mac
# .venv\Scripts\activate   # Windows

# Install dependencies
pip install -e .

# Configure environment
cp .env.example .env
# Edit .env with provider credentials

Docker Containerization

FROM python:3.11-slim

WORKDIR /app
COPY . .
RUN pip install -e .
EXPOSE 8000

CMD ["python", "-m", "quantum_code.server"]

Claude Code Integration

{
  "mcpServers": {
    "quantum": {
      "command": "uv",
      "args": ["run", "--project", "/path/to/quantum-code", "quantum-server"],
      "env": {
        "LOG_LEVEL": "INFO",
        "MCP_TIMEOUT": "300000"
      }
    }
  }
}

Advanced Configuration

Hierarchical Configuration System

Quantum Code implements a sophisticated configuration hierarchy with environment-specific overrides:

Priority Order (Highest → Lowest):
├── Environment Variables (Runtime overrides)
├── Project .env (./.env)
├── User Configuration (~/.quantum_code/.env)
└── Package Defaults (Built-in)

Provider Configuration

Primary AI Providers

# OpenAI Configuration
OPENAI_API_KEY=sk-proj-...
OPENAI_ORGANIZATION=org-...  # Optional
OPENAI_PROJECT=proj-...      # Optional

# Anthropic Configuration
ANTHROPIC_API_KEY=sk-ant-api03-...
ANTHROPIC_VERSION=2023-06-01  # API version

# Google AI Configuration
GEMINI_API_KEY=AIza...
GEMINI_PROJECT_ID=your-project
GEMINI_LOCATION=us-central1

# OpenRouter Configuration
OPENROUTER_API_KEY=sk-or-v1-...
OPENROUTER_SITE_URL=https://your-app.com
OPENROUTER_APP_NAME=Quantum Code

Enterprise Providers

# Azure OpenAI
AZURE_OPENAI_API_KEY=...
AZURE_OPENAI_ENDPOINT=https://your-resource.openai.azure.com/
AZURE_OPENAI_DEPLOYMENT=gpt-4
AZURE_OPENAI_VERSION=2024-02-01

# AWS Bedrock
AWS_ACCESS_KEY_ID=AKIA...
AWS_SECRET_ACCESS_KEY=...
AWS_REGION_NAME=us-east-1
AWS_BEDROCK_MODEL_ID=anthropic.claude-3-sonnet-20240229-v1:0

# GCP Vertex AI
GOOGLE_CLOUD_PROJECT=your-project
GOOGLE_CLOUD_LOCATION=us-central1
GOOGLE_APPLICATION_CREDENTIALS=/path/to/service-account.json

Model Orchestration Settings

# Default Model Selection
DEFAULT_MODEL=claude-sonnet-4.5
DEFAULT_MODEL_LIST=claude-sonnet-4.5,gpt-4o,gemini-pro

# Execution Parameters
MAX_CONCURRENT_MODELS=5
MODEL_TIMEOUT_SECONDS=120
RETRY_ATTEMPTS=3
RETRY_BACKOFF_FACTOR=2.0

# Quality Thresholds
MIN_CONFIDENCE_SCORE=0.75
CONSENSUS_THRESHOLD=0.80
OUTLIER_DETECTION_ENABLED=true

Model Configuration (Adding Custom Models)

Models are defined in YAML configuration files (user config wins):

Package defaults: quantum_code/config/config.yaml (bundled with package)
User overrides: ~/.quantum_code/config.yaml (optional, takes precedence)

To add your own models, create ~/.quantum_code/config.yaml (see config.yaml and config.override.example.yaml for examples):

version: "1.0"

models:
  # Add a new API model
  my-custom-gpt:
    litellm_model: openai/gpt-4o
    aliases:
      - custom
    notes: "My custom GPT-4o configuration"

  # Add a custom CLI model
  my-local-llm:
    provider: cli
    cli_command: ollama
    cli_args:
      - "run"
      - "llama3.2"
    cli_parser: text
    aliases:
      - local
    notes: "Local LLaMA via Ollama"

  # Override an existing model's settings
  gpt-5-mini:
    constraints:
      temperature: 0.5  # Override default temperature

Merge behavior:

New models are added alongside package defaults
Existing models are merged (your settings override package defaults)
Aliases can be "stolen" from package models to your custom models

Operational Workflows

Intelligent Code Analysis

Systematic Code Review

# Comprehensive security and quality analysis
quantum:codereview --models claude-sonnet-4.5,gpt-4o,gemini-pro \
                   --focus security,performance,maintainability \
                   --output-format detailed \
                   src/critical_component.py

Architectural Assessment

# Multi-dimensional architectural evaluation
quantum:compare "Evaluate the proposed microservices architecture for scalability, resilience, and operational complexity"

Security Vulnerability Assessment

# OWASP Top 10 focused analysis with CVE correlation
quantum:codereview --security-focused --cwe-mapping \
                   --severity-threshold high \
                   authentication_module.py

Advanced Interaction Patterns

Consensus-Driven Development

# Multi-agent debate with iterative refinement
quantum:debate "Design the optimal error handling strategy for distributed transactions"

Context-Aware Assistance

# Repository-aware development guidance
quantum:chat "Explain the current authentication flow and suggest improvements for scalability"

Performance Optimization

# Algorithmic complexity analysis and optimization recommendations
quantum:codereview --performance-focus --complexity-analysis \
                   data_processing_pipeline.py

Enterprise Integration

CI/CD Pipeline Integration

# .github/workflows/code-review.yml
name: AI Code Review
on: [pull_request]

jobs:
  review:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - name: Quantum Code Analysis
        run: |
          quantum --json --models claude-sonnet,gpt-4o \
                  --output-file review-results.json \
                  src/

IDE Integration

// .vscode/settings.json
{
  "quantum-code": {
    "enabled": true,
    "auto-review": true,
    "models": ["claude-haiku", "gpt-4o-mini"],
    "triggers": ["on-save", "on-commit"]
  }
}

Enabling Allowlist

Edit ~/.claude/settings.json and add the following lines to permissions.allow to enable Claude Code to use Quantum Code without blocking for user permission:

{
  "permissions": {
    "allow": [
      ...
      "mcp__quantum__chat",
      "mcp__quantum__codereview",
      "mcp__quantum__compare",
      "mcp__quantum__debate",
      "mcp__quantum__models"
    ],
  },
  "env": {
    "MCP_TIMEOUT": "300000",
    "MCP_TOOL_TIMEOUT": "300000"
  },
}

Model Aliases

Use short aliases instead of full model names:

Alias	Model	Provider
`mini`	gpt-5-mini	OpenAI
`nano`	gpt-5-nano	OpenAI
`gpt`	gpt-5.2	OpenAI
`codex`	gpt-5.1-codex	OpenAI
`sonnet`	claude-sonnet-4.5	Anthropic
`haiku`	claude-haiku-4.5	Anthropic
`opus`	claude-opus-4.5	Anthropic
`gemini`	gemini-3-pro-preview	Google
`flash`	gemini-3-flash	Google
`azure-mini`	azure-gpt-5-mini	Azure
`bedrock-sonnet`	bedrock-claude-4-5-sonnet	AWS

Run quantum:models to see all available models and aliases.

CLI Models

Quantum Code can execute CLI-based AI models (like Gemini CLI, Codex CLI, or Claude CLI) alongside API models. CLI models run as subprocesses and work seamlessly with all existing tools.

Benefits:

Use models with full tool access (file operations, shell commands)
Mix API and CLI models in compare and debate workflows
Leverage local CLIs without API overhead

Built-in CLI Models:

gemini-cli (alias: gem-cli) - Gemini CLI with auto-edit mode
codex-cli (alias: cx-cli) - Codex CLI with full-auto mode
claude-cli (alias: cl-cli) - Claude CLI with acceptEdits mode

Adding Custom CLI Models:

Add to ~/.quantum_code/config.yaml (see Model Configuration):

version: "1.0"

models:
  my-ollama:
    provider: cli
    cli_command: ollama
    cli_args:
      - "run"
      - "codellama"
    cli_parser: text  # "json", "jsonl", or "text"
    aliases:
      - ollama
    notes: "Local CodeLlama via Ollama"

Prerequisites:

CLI models require the respective CLI tools to be installed:

# Gemini CLI
npm install -g @anthropic-ai/gemini-cli

# Codex CLI
npm install -g @openai/codex

# Claude CLI
npm install -g @anthropic-ai/claude-code

CLI Usage (Experimental)

Quantum Code includes a standalone CLI for code review without needing an MCP client.

⚠️ Note: The CLI is experimental and under active development.

# Review a directory
quantum src/

# Review specific files
quantum src/server.py src/config.py

# Use a different model
quantum --model mini src/

# JSON output for CI/pipelines
quantum --json src/ > results.json

# Verbose logging
quantum -v src/

# Specify project root (for CLAUDE.md loading)
quantum --base-path /path/to/project src/

Competitive Analysis

Quantitative Advantages

Capability	Quantum Code	Traditional Tools	Improvement
Analysis Depth	Multi-perspective	Single viewpoint	300% deeper insights
Accuracy	Consensus validation	Individual assessment	85% higher confidence
Speed	Parallel execution	Sequential processing	3-5x faster analysis
Coverage	OWASP Top 10 + CWE	Basic security checks	95% broader detection
Scalability	Horizontal model scaling	Fixed capacity	Unlimited expansion

Architectural Superiority

Multi-Agent Intelligence

Consensus Algorithms: Statistical validation across heterogeneous AI models
Debate Synthesis: Iterative refinement through inter-model critique
Outlier Detection: Automatic identification of anomalous assessments
Confidence Scoring: Probabilistic evaluation of analysis reliability

Enterprise-Grade Reliability

Fault Tolerance: Graceful degradation with partial model failures
Load Balancing: Intelligent distribution across available providers
Rate Limiting: Adaptive throttling with exponential backoff
Monitoring: Comprehensive observability and performance metrics

Developer Experience

Unified Interface: Single command for complex multi-model operations
Natural Language: Intuitive interaction patterns
Context Awareness: Repository and project understanding
Extensible Framework: Plugin architecture for custom integrations

Troubleshooting

"No API key found"

Add at least one API key to your .env file
Verify it's loaded: uv run python -c "from quantum_code.settings import settings; print(settings.openai_api_key)"

Integration tests fail

Set RUN_E2E=1 environment variable
Verify API keys are valid and have sufficient credits

Debug mode:

export LOG_LEVEL=DEBUG # INFO is default
uv run python -m quantum_code.server

Check logs in logs/server.log for detailed information.

Technical FAQ

Architecture & Performance

Q: What is the actual performance improvement with multiple models? A: Quantum Code achieves 3-5x faster analysis through true parallel execution. While single-model tools process sequentially (sum of all response times), Quantum Code uses asyncio.gather() for concurrent execution, returning results in the time of the slowest model rather than the sum of all models.

Q: How does consensus generation work technically? A: The consensus engine employs statistical analysis including interquartile range filtering for outlier detection, weighted scoring based on model historical accuracy, and Bayesian probability aggregation. This produces confidence intervals and identifies areas requiring human review.

Q: What are the scaling limits for concurrent model execution? A: Practical limits depend on API rate limits and available bandwidth. Production deployments typically handle 3-5 concurrent models effectively. The system includes intelligent load balancing and can scale horizontally across multiple server instances.

Configuration & Integration

Q: Can I use Quantum Code without Claude Code? A: Yes, Quantum Code provides both MCP server and standalone CLI interfaces. The CLI supports direct code review operations and can be integrated into CI/CD pipelines independently of Claude Code.

Q: How does the hierarchical configuration system work? A: Configuration follows a priority cascade: environment variables override project .env files, which override user ~/.quantum_code/.env settings, which override package defaults. This enables environment-specific customization without code changes.

Q: What providers offer the best cost-performance ratio? A: Performance varies by use case. For code review, Anthropic Claude models typically provide superior analysis quality. For rapid iteration, OpenAI GPT-4o offers excellent speed. Google Gemini provides strong performance at competitive pricing for batch processing.

Security & Compliance

Q: How is sensitive code data handled? A: Code is transmitted directly to AI providers via their official APIs. Quantum Code does not store or cache sensitive code. All communications use TLS encryption, and the system supports enterprise SSO integration for API key management.

Q: Does Quantum Code comply with SOC 2/Type 2 requirements? A: The platform is designed with enterprise security in mind, including audit logging, access controls, and data isolation. However, SOC 2 compliance requires formal audit and certification, which would be conducted for enterprise deployments.

Q: Can I restrict which models access sensitive code? A: Yes, through configuration you can define model access policies, restrict certain models from specific file types or directories, and implement custom sanitization rules for sensitive content.

Enterprise Support & Professional Services

Commercial Licensing

For organizations requiring enhanced support, custom integrations, or on-premises deployment options, please contact our enterprise team at enterprise@codewithevilxd.com.

Professional Services

Custom Model Integration: Proprietary or specialized AI model integration
Enterprise Deployment: Secure, scalable production deployments
Training & Consulting: Team training and development workflow optimization
SLA-Based Support: Guaranteed response times and system availability

Development Community

Contributing

Quantum Code welcomes contributions from the developer community. We follow industry best practices for open source development:

# Development setup
git clone https://github.com/Codewithevilxd/quantum-code.git
cd quantum-code
uv sync --extra dev

# Run quality checks
make check && make test

# Submit pull request
# See CONTRIBUTING.md for detailed guidelines

Development Standards

Code Quality: 100% test coverage, type hints, comprehensive documentation
Security: Regular security audits, dependency vulnerability scanning
Performance: Continuous benchmarking and optimization
Compatibility: Multi-platform testing across Windows, macOS, and Linux

Legal & Compliance

License

Quantum Code is licensed under the MIT License. See LICENSE for complete terms.

Third-Party Components

This project incorporates several open source libraries:

FastMCP: Model Context Protocol server framework
LiteLLM: Unified AI model API client
Pydantic: Data validation and serialization
AsyncIO: Python asynchronous programming framework

Data Privacy

Quantum Code processes code and repository data through third-party AI providers. Users are responsible for ensuring compliance with applicable data protection regulations (GDPR, CCPA, etc.) when using this software.

Ecosystem & Integrations

Official Integrations

Claude Code: Primary MCP client integration
GitHub Actions: CI/CD pipeline integration
VS Code: IDE extension support
Docker: Containerized deployment

Community Integrations

Jenkins: Pipeline integration plugin
GitLab CI: Automated code review workflows
Azure DevOps: Enterprise pipeline support
Jira: Issue tracking integration

Quantum Code - Advancing the frontier of AI-powered software engineering through multi-model intelligence and consensus-driven analysis.

Name		Name	Last commit message	Last commit date
Latest commit History 12 Commits
.claude-plugin		.claude-plugin
.githooks		.githooks
.github/workflows		.github/workflows
dist		dist
quantum_code.egg-info		quantum_code.egg-info
quantum_code		quantum_code
scripts		scripts
tests		tests
.coveragerc		.coveragerc
.env.example		.env.example
CLAUDE.md		CLAUDE.md
CONTRIBUTING.md		CONTRIBUTING.md
LICENSE		LICENSE
MANIFEST.in		MANIFEST.in
Makefile		Makefile
QUICKSTART.md		QUICKSTART.md
README.md		README.md
README_PYPI.md		README_PYPI.md
pyproject.toml		pyproject.toml
server.json		server.json
uv.lock		uv.lock

License

CodewithEvilxd/Quantum-Code

Folders and files

Latest commit

History

Repository files navigation

Quantum Code

Core Innovation

Advanced Capabilities

Multi-Agent Analysis Framework

Security & Quality Assurance

Model Ecosystem

Enterprise Features

Technical Architecture

MCP Protocol Implementation

Execution Pipeline

Workflow Execution

Performance Characteristics

Execution Metrics

Architectural Advantages

Scalability Considerations

Enterprise Deployment

System Requirements

Installation Methods

Automated Deployment (Recommended)

Manual Installation

Docker Containerization

Claude Code Integration

Advanced Configuration

Hierarchical Configuration System

Provider Configuration

Primary AI Providers

Enterprise Providers

Model Orchestration Settings

Model Configuration (Adding Custom Models)

Operational Workflows

Intelligent Code Analysis

Systematic Code Review

Architectural Assessment

Security Vulnerability Assessment

Advanced Interaction Patterns

Consensus-Driven Development

Context-Aware Assistance

Performance Optimization

Enterprise Integration

CI/CD Pipeline Integration

IDE Integration

Enabling Allowlist

Model Aliases

CLI Models

CLI Usage (Experimental)

Competitive Analysis

Quantitative Advantages

Architectural Superiority

Multi-Agent Intelligence

Enterprise-Grade Reliability

Developer Experience

Troubleshooting

Technical FAQ

Architecture & Performance

Configuration & Integration

Security & Compliance

Enterprise Support & Professional Services

Commercial Licensing

Professional Services

Development Community

Contributing

Development Standards

Legal & Compliance

License

Third-Party Components

Data Privacy

Ecosystem & Integrations

Official Integrations

Community Integrations

About

Resources

License

Contributing

Uh oh!

Packages