🧠 Codebase RLM Navigator

Recursive Language Model Intelligence for RooCode

A Model Context Protocol (MCP) server that uses Recursive Language Models (RLM) to transform your codebase into an intelligent, hierarchical knowledge map. Built specifically for RooCode integration, it eliminates context window limitations and prevents context rot by analyzing code in layers—from individual files to modules to system architecture—creating a persistent intelligence map that can be queried instantly.

Now with Standalone Mode: Analyze codebases directly from the filesystem without requiring Repomix!

🔄 What is RLM (Recursive Language Models)?

RLM is an architectural pattern that breaks down large codebases into hierarchical layers of understanding:

1. Level 1 (File Layer): Each file is analyzed individually for functions, classes, and dependencies
2. Level 2 (Module Layer): Related files are grouped and analyzed for collaboration patterns
3. Level 3 (System Layer): The entire architecture is synthesized from module summaries

This recursive approach solves the context window problem by:

• ✅ Preventing Context Rot: Analysis is cached and reused, not regenerated each time
• ✅ Breaking the Context Limit: Only relevant layers are loaded on-demand
• ✅ Maintaining Coherence: Each layer builds on the previous, preserving relationships
• ✅ Enabling Instant Queries: Pre-computed summaries mean zero re-analysis time

🎯 What Problem Does This Solve?

When working with large codebases in RooCode (or any AI coding assistant), you face critical challenges:

• Context Window Limitations: AI assistants can't load entire codebases at once
• Context Rot: AI forgets architectural decisions between conversations
• Manual File Navigation: You waste time explaining project structure repeatedly
• Inefficient Re-Analysis: Same code analyzed multiple times, wasting tokens and time
• Lost Relationships: AI doesn't understand how modules interact

Codebase RLM Navigator solves this using Recursive Language Models to create a persistent, hierarchical "intelligence map" that RooCode can query instantly—no context rot, no re-analysis, no token waste.

✨ Key Features

🚀 Intelligent Caching

• Analyzes files once, caches results forever (until code changes)
• MD5 hash-based change detection
• Parallel processing with ThreadPoolExecutor (5 concurrent workers)

🏗️ Three-Level RLM Architecture

1. File Level: Function signatures, dependencies, core responsibilities
2. Module Level: How files collaborate within folders
3. System Level: Overall architecture patterns and data flow

⚡ Instant Retrieval

• No re-parsing on subsequent queries
• Direct file content access
• Fuzzy file path matching

🔒 100% Local & Private

• Uses Ollama for LLM analysis (no cloud APIs)
• All data stays on your machine
• Works offline after initial setup

🎓 Use Cases

1️⃣ Onboarding to a New Codebase (Junior Dev Friendly)

Scenario: You just cloned a 50-file Python project and need to understand it quickly.

# In RooCode, simply ask:
"Use the codebase-rlm MCP tool to analyze this codebase and explain the architecture"

Important: Always specify "use the codebase-rlm MCP tool" to ensure RooCode uses the correct MCP server.

What happens:

• RooCode calls mcp--codebase-rlm--analyze_codebase() → Scans the filesystem directly and analyzes all files in parallel using RLM.
• Calls mcp--codebase-rlm--get_architecture() → Gets high-level system overview.
• Calls mcp--codebase-rlm--list_modules() → Shows you all modules with summaries.

Result: In 30 seconds, you understand the entire project structure without reading a single file. The RLM hierarchy prevents context rot—this analysis is cached forever.

2️⃣ Finding Where to Add a Feature

Scenario: You need to add user authentication but don't know where the auth logic lives.

# In RooCode:
"Use the codebase-rlm MCP tool to show me all modules in this project"
# Then:
"Use the codebase-rlm tool to read the details of the 'auth' module"
# Then:
"Use the codebase-rlm tool to get the full content of auth/login.py"

Important: Specify the MCP tool name to avoid confusion with other tools.

What happens:

• mcp--codebase-rlm--list_modules() → Shows all folders with brief descriptions
• mcp--codebase-rlm--read_module_details("auth") → Shows all files in auth folder with summaries
• mcp--codebase-rlm--get_file_content("auth/login.py") → Retrieves exact file content

Result: You navigate directly to the right file without guessing. The RLM structure maintains context across queries.

3️⃣ Code Review Preparation

Scenario: You're reviewing a PR that touches 10 files across 3 modules.

# In RooCode:
"Use the codebase-rlm MCP tool to analyze the 'api', 'database', and 'utils' modules and explain how they interact"

What happens:

• RooCode queries module summaries from the RLM hierarchy
• Understands data flow between modules
• Provides architectural context for your review

Result: You review with full context of how changes affect the system. No context rot between review sessions.

4️⃣ Refactoring Large Projects

Scenario: You need to refactor a monolithic app into microservices.

# In RooCode:
"Use the codebase-rlm MCP tool to show me the system architecture and identify tightly coupled modules"

What happens:

• mcp--codebase-rlm--get_architecture() → Reveals architecture patterns from RLM analysis
• Module summaries show dependencies
• You identify refactoring boundaries

Result: Data-driven refactoring decisions instead of guesswork. The recursive analysis reveals hidden coupling.

🛠️ Installation & Setup

Prerequisites

1. Python 3.12+

   python --version  # Must be 3.12 or higher

1. NVIDIA GPU with 6GB+ VRAM (Required for ministral-3 model)

• Minimum: RTX 3060, RTX 4060, GTX 1660 Ti, or any GPU with 6GB+ VRAM
• Recommended: RTX 4070 (12GB+) for faster analysis
• Check your GPU: nvidia-smi

1. Ollama (Local LLM runtime - this runs the AI model on your computer)

   # Install from https://ollama.ai/
   # After installation, verify it's working:
   ollama list

   # Download the ministral-3 model (requires 6GB VRAM minimum):
   ollama pull ministral-3:latest

1. Repomix (Optional - for manual snapshots)

   npm install -g repomix

Note: Without a compatible NVIDIA GPU, Ollama will run on CPU (10-50x slower). AMD/Intel GPUs are not currently supported by Ollama.

Step 1: Install Dependencies

pip install fastmcp requests

#### Git Ignore Setup

The project includes a .gitignore file that excludes the following:

• .repomix_rlm_cache/ - RLM analysis cache directory
• rlm-main/ - RLM main directory (external/submodule)
• RLM-Research.pdf - Research documentation
• guidelines.md - Project guidelines

These files are automatically excluded from git commits.

Step 2: Configure the MCP Server

Add this to your RooCode MCP settings file:

Windows: %APPDATA%\Code\User\globalStorage\rooveterinaryinc.roo-cline\settings\cline_mcp_settings.json

macOS/Linux: ~/.config/Code/User/globalStorage/rooveterinaryinc.roo-cline/settings/cline_mcp_settings.json

{
  "mcpServers": {
    "codebase-rlm": {
      "command": "python",
      "args": ["C:/Users/YourUsername/path/to/smart_context.py"],
      "disabled": false,
      "description": "Recursive Language Model codebase analyzer"
    }
  }
}

Important Notes:

• Replace the path with the actual location of smart_context.py
• The server name is codebase-rlm (this is what you reference in prompts)
• Always specify this MCP tool name when prompting RooCode to avoid confusion

Step 3: Restart RooCode

Close and reopen VS Code to load the MCP server.

📖 How to Use

Basic Workflow (Standalone - Recommended)

# 1. Navigate to your project directory in VS Code
# 2. In RooCode, ALWAYS specify the MCP tool:
"Use the codebase-rlm MCP tool to analyze this codebase and show me the architecture"

Advanced Workflow (Using Repomix)

If you prefer to use a pre-generated Repomix XML file:

# 1. Generate Repomix snapshot manually
repomix .

# 2. In RooCode:
"Use the codebase-rlm MCP tool to analyze this codebase using the repomix XML"

🚨 Important: How to Prompt RooCode

To avoid confusion with other MCP tools, always specify the tool name:

✅ Correct Prompts:

"Use the codebase-rlm MCP tool to analyze this codebase"
"Call the analyze_codebase tool from codebase-rlm"
"Use codebase-rlm to show me all modules"

❌ Incorrect Prompts (will confuse RooCode):

"Analyze this codebase"  # Too vague, might use wrong tool
"Show me the architecture"  # Doesn't specify which MCP server

Available MCP Tools

RooCode calls these tools when you specify the codebase-rlm MCP server:

⚙️ Configuration

Edit the Config class in smart_context.py:

class Config:
    OLLAMA_BASE_URL = "http://localhost:11434"  # Ollama API endpoint
    LLM_MODEL = "ministral-3:latest"            # Model for analysis
    CACHE_DIR = "./.repomix_rlm_cache"          # Cache location

Recommended Models

Note: VRAM = Video RAM on your graphics card. Check yours with nvidia-smi.

🚀 Advantages Over Traditional Methods

vs. Manual Code Reading

vs. Grep/Search Tools

vs. Loading Entire Codebase into AI

🐛 Troubleshooting

Issue: "Ollama connection refused"

Solution:

# Check if Ollama is running
ollama list

# Start Ollama service
ollama serve

Issue: "repomix-output.xml not found" (When using XML mode)

Solution:

# Either run without XML mode (recommended):
"Use codebase-rlm to analyze this codebase"

# Or generate the XML file manually:
repomix .

Issue: "Analysis is slow"

Causes & Fixes:

• No GPU: Requires NVIDIA GPU with 6GB+ VRAM for ministral-3. Check with nvidia-smi
• Large files: RLM only analyzes first 500,000 chars per file (configurable)
• Slow model: The default ministral-3:latest is the fastest option
• CPU fallback: Ollama on CPU is 10-50x slower than GPU

Issue: "Cache not updating after code changes"

Solution:

# Delete cache and re-analyze
rm -rf .repomix_rlm_cache
# Then in RooCode:
"Use codebase-rlm to analyze this codebase"

Issue: "RooCode uses wrong MCP tool"

Solution: Always specify the tool name in your prompt:

"Use the codebase-rlm MCP tool to [your request]"

🔧 Advanced Usage

Custom Prompts

Edit the prompts in RLMIndexer to customize analysis style:

# File-level analysis (line 139)
prompt = f"""You are a Senior Code Reviewer. Summarize this code file.
...
"""

# Module-level analysis (line 182)
prompt = f"""You are a Software Architect. Summarize this MODULE.
...
"""

# System-level analysis (line 256)
prompt = f"""You are the CTO. Provide a System Architecture Overview...
...
"""

Multi-Project Support

The cache is project-specific (based on XML path hash):

# Project A
cd /project-a
repomix .
# RooCode analyzes → cached in .repomix_rlm_cache/abc12345/

# Project B
cd /project-b
repomix .
# RooCode analyzes → cached in .repomix_rlm_cache/def67890/

🤝 Integration with RooCode Workflows

Workflow 1: Feature Development

1. Ask RooCode: "Use the codebase-rlm MCP tool to analyze this codebase"
   → RooCode calls mcp--codebase-rlm--analyze_codebase()
   → Builds RLM hierarchy (file → module → system)

2. Ask RooCode: "Use codebase-rlm to find where I should add user profile editing"
   → RooCode calls mcp--codebase-rlm--list_modules() + read_module_details()
   → Navigates the RLM structure

3. Ask RooCode: "Use codebase-rlm to show me the user model file"
   → RooCode calls mcp--codebase-rlm--get_file_content()
   → Retrieves from cached RLM map

4. Ask RooCode: "Add a profile_picture field to the User model"
   → RooCode edits the file with full architectural context
   → No context rot—RLM maintains relationships

Workflow 2: Bug Investigation

1. Ask RooCode: "Use codebase-rlm to explain the authentication flow"
   → RooCode queries auth module from RLM hierarchy

2. Ask RooCode: "Use codebase-rlm to show me where JWT tokens are validated"
   → RooCode retrieves specific file content

3. Ask RooCode: "Fix the token expiration bug"
   → RooCode makes targeted changes with full context

📊 Performance Metrics

Test Project: 50 Python files, ~10,000 lines of code

Cache Size: ~500KB for 50 files (JSON summaries)

🛡️ Security & Privacy

• ✅ No cloud APIs: Everything runs locally via Ollama
• ✅ No telemetry: Zero data collection
• ✅ Gitignore respected: Repomix excludes sensitive files
• ✅ Cache is local: Stored in .repomix_rlm_cache/ (add to .gitignore)

🗺️ Roadmap

• [ ] Support for non-XML Repomix formats (Markdown, Plain Text)
• [ ] Interactive CLI for standalone usage
• [ ] VS Code extension for direct integration
• [ ] Support for incremental updates (only re-analyze changed files)
• [ ] Multi-language syntax highlighting in summaries
• [ ] Export architecture diagrams (Mermaid/PlantUML)
• [ ] AMD/Intel GPU support (when Ollama adds support)

🤔 FAQ

Q: Does this work with non-Python projects? A: Yes! Repomix supports all languages. The LLM analyzes any code.

Q: Can I use a different LLM provider (OpenAI, Anthropic)? A: Currently Ollama-only, but you can modify OllamaClient to support other APIs.

Q: Do I really need an NVIDIA GPU? A: Technically no, but CPU analysis is 10-50x slower. For a 50-file project: GPU = 30s, CPU = 15min.

Q: How much disk space does the cache use? A: ~10KB per file (JSON summaries). A 100-file project = ~1MB cache.

Q: Does this replace reading code? A: No, it's a navigation tool. Use it to find what to read, then read the actual code.

Q: Can I share the cache with my team? A: Not recommended. Cache paths are machine-specific. Each dev should generate their own.

Q: What's the difference between RLM and RAG? A: RAG retrieves chunks on-demand. RLM pre-analyzes and caches hierarchical summaries, preventing context rot.

📄 License

MIT License - Feel free to use, modify, and distribute.

🙏 Acknowledgments

• [Repomix](https://github.com/yamadashy/repomix) - Codebase packaging tool
• [FastMCP](https://github.com/jlowin/fastmcp) - MCP server framework
• [Ollama](https://ollama.ai/) - Local LLM runtime
• [RooCode](https://roo.dev/) - AI coding assistant (formerly Roo Cline)

📞 Support

Issues? Open an issue on GitHub or ask RooCode:

"Use the codebase-rlm MCP tool to help me debug the Repomix RLM server"

Built with ❤️ for developers who want to understand code faster

_Stop reading files one by one. Start navigating with Recursive Language Model intelligence._