graph TB
    subgraph Input
        A[User Query]
        B[Large Document<br/>10M+ tokens]
    end
    
    subgraph "Root Agent (Planner)"
        C[Analyze Metadata]
        D[Generate Strategy]
        E[Write Python Code]
    end
    
    subgraph "Execution Layer"
        F[Python REPL]
        G[Chunk Manager]
    end
    
    subgraph "Sub Agents (Workers)"
        H1[Worker 1]
        H2[Worker 2]
        H3[Worker N]
    end
    
    subgraph Output
        I[Aggregated Results]
        J[Final Answer]
    end
    
    A --> C
    B --> C
    C --> D
    D --> E
    E --> F
    F --> G
    G --> H1
    G --> H2
    G --> H3
    H1 --> I
    H2 --> I
    H3 --> I
    I --> J
    
    style A fill:#e3f2fd
    style B fill:#e3f2fd
    style C fill:#fff9c4
    style D fill:#fff9c4
    style E fill:#fff9c4
    style F fill:#f3e5f5
    style G fill:#f3e5f5
    style H1 fill:#e8f5e9
    style H2 fill:#e8f5e9
    style H3 fill:#e8f5e9
    style I fill:#fce4ec
    style J fill:#fce4ec

# Required
- Python 3.10 or higher
- pip package manager

# API Keys (choose at least one)
- OpenRouter API key (recommended for free tier)
- OpenAI API key
- Anthropic API key
- Ollama (for local deployment)

# Clone the repository
git clone https://github.com/MuhammadIndar/MCP-RLM.git
cd MCP-RLM

# Create virtual environment
python -m venv venv

# Activate virtual environment
# On Linux/macOS:
source venv/bin/activate
# On Windows:
venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# Configure environment
cp .env.EXAMPLE .env
# Edit .env with your API keys

# Start the server
python server.py

MCP RLM Server Started...
Listening on stdio...

cp .env.EXAMPLE .env

# OpenRouter (Recommended - includes free tier)
OPENROUTER_API_KEY=sk-or-v1-xxxxx

# OpenAI Official
OPENAI_API_KEY=sk-proj-xxxxx

# Anthropic Official  
ANTHROPIC_API_KEY=sk-ant-xxxxx

# Ollama Local (optional, set to 'dummy' if not used)
OLLAMA_API_KEY=dummy

providers:
  openrouter:
    type: "openai_compatible"
    base_url: "https://openrouter.ai/api/v1"
    api_key_env: "OPENROUTER_API_KEY"
  
  ollama_local:
    type: "openai_compatible"
    base_url: "http://localhost:11434/v1"
    api_key_env: "OLLAMA_API_KEY"
  
  openai_official:
    type: "openai_compatible"
    base_url: "https://api.openai.com/v1"
    api_key_env: "OPENAI_API_KEY"
  
  anthropic_official:
    type: "anthropic"
    api_key_env: "ANTHROPIC_API_KEY"

agents:
  root:
    provider_ref: "openrouter"
    model: "anthropic/claude-3.5-sonnet"
    temperature: 0.0
    max_tokens: 4096
  
  sub:
    provider_ref: "openrouter"
    model: "qwen/qwen-2.5-vl-7b-instruct:free"
    temperature: 0.0
    max_tokens: 1000

system:
  max_loops: 5  # Maximum refinement iterations

{
  "mcpServers": {
    "rlm-researcher": {
      "command": "/absolute/path/to/MCP-RLM/venv/bin/python",
      "args": ["/absolute/path/to/MCP-RLM/server.py"]
    }
  }
}

Use the analyze_massive_document tool to read /path/to/large_log.txt 
and find all ERROR entries from the last 24 hours

{
  "mcpServers": {
    "rlm-researcher": {
      "command": "/absolute/path/to/MCP-RLM/venv/bin/python",
      "args": ["/absolute/path/to/MCP-RLM/server.py"],
      "enabled": true
    }
  }
}

# Log Analysis
"Analyze /var/log/nginx/access.log and identify the top 10 IP addresses 
 making requests, along with their request patterns"

# Codebase Security Audit
"Scan the entire /src directory and find all instances where user input 
 is directly used in SQL queries without sanitization"

# Research Paper Synthesis
"Read all PDF files in /papers/machine-learning/ and summarize 
 the common evaluation metrics used across studies"

# Configuration Validation
"Parse /etc/config/*.yaml files and verify that all required 
 security parameters are present and properly configured"

MCP-RLM/
├── assets/
│   └── RLM.png              # Architecture diagram
│
├── .env.EXAMPLE             # Environment variables template
├── .gitignore               # Git ignore patterns
├── LICENSE                  # MIT License
├── README.md                # This documentation
├── config.yaml              # Provider & agent configuration
├── requirements.txt         # Python dependencies
│
├── server.py                # MCP server entry point
├── rlm_engine.py            # Core RLM recursive logic
├── llm_factory.py           # Multi-provider LLM abstraction
└── prompts.py               # System prompts for agents

# Install development dependencies
pip install -r requirements.txt

# Run linting
flake8 *.py

# Run type checking
mypy *.py

MIT License - Copyright (c) 2025 MCP-RLM Contributors