@senoldogann/context-manager

🧠 The Neural Backbone for Autonomous AI Agents

Node.js wrapper for Cognitive Codebase Matrix (CCM) - Enables AI agents to understand and navigate your codebase with surgical precision.

🚀 Quick Start

Prerequisites

1. Node.js 16+ installed
2. Ollama installed and running (for local embeddings)
   • Download: https://ollama.com
   • Pull required model: ollama pull mxbai-embed-large

Installation

# 1. Install and configure for Claude Desktop, Antigravity, Cursor, etc.
npx @senoldogann/context-manager install

# 2. Index your project
npx @senoldogann/context-manager index --path .

That's it! Restart your AI editor and start asking questions about your code.

📖 What is CCM?

CCM transforms static source code into a dynamic, queryable Knowledge Graph:

• 🔍 Semantic Search - Find code by meaning ("where is auth logic?")
• 🧠 Graph Navigation - Understand relationships ("who calls this function?")
• 📍 Cursor Context - Get relevant code based on your position

🔧 Commands

The npm wrapper downloads pre-built binaries and passes commands through:

| Command | Description | |---------|-------------| | npx @senoldogann/context-manager install | Auto-configure MCP for editors | | npx @senoldogann/context-manager index --path <dir> | Index a project | | npx @senoldogann/context-manager query --text "..." | Search codebase | | npx @senoldogann/context-manager mcp | Run MCP server directly | | npx @senoldogann/context-manager eval --tasks <file> | Run evaluation tasks |

Options

# Watch mode - auto-reindex on file changes
npx @senoldogann/context-manager index --path . --watch

# Custom database path
npx @senoldogann/context-manager index --path . --db-path /custom/path

🔒 Privacy by Default

CCM uses a Local-First architecture:

• ✅ Your code never leaves your machine
• ✅ All embeddings run locally via Ollama
• ✅ No external API calls (unless you configure OpenAI)

⚙️ Configuration

Environment Variables

Create ~/.ccm/.env:

# Local (Recommended)
EMBEDDING_PROVIDER=ollama
EMBEDDING_HOST=http://127.0.0.1:11434
EMBEDDING_MODEL=mxbai-embed-large

# Cloud (Optional)
EMBEDDING_PROVIDER=openai
EMBEDDING_API_KEY=sk-your-key
EMBEDDING_MODEL=text-embedding-3-small

# Networking & Limits
EMBEDDING_TIMEOUT_SECS=30
CCM_MAX_FILE_BYTES=2097152

# MCP Security
CCM_ALLOWED_ROOTS=/Users/you/projects:/Users/you/sandbox
CCM_REQUIRE_ALLOWED_ROOTS=0

# MCP Runtime
CCM_MCP_ENGINE_CACHE_SIZE=8
CCM_MCP_DEBUG=0

# Optional: disable embeddings entirely (semantic search disabled)
CCM_DISABLE_EMBEDDER=0

# Optional: embed data files (md/json/yaml) into vector search
CCM_EMBED_DATA_FILES=0

# Binary checksum verification (0 = enforce, 1 = bypass)
CCM_ALLOW_UNVERIFIED_BINARIES=0

Production Tip: Set CCM_ALLOWED_ROOTS and enable CCM_REQUIRE_ALLOWED_ROOTS=1 to restrict MCP access.

🤖 Usage in AI

Once configured, ask your AI agent:

"Search for the authentication flow in this codebase."

"Read the graph for UserService and show me its callers."

"What functions call parse_config?"

📦 For Developers

This package handles:

1. OS/architecture detection
2. Binary download from GitHub Releases
3. Global persistence in ~/.ccm

✅ Binary Integrity

Downloads are verified against checksums.txt from the GitHub Release.
If the manifest is missing or a mismatch occurs, you can set CCM_ALLOW_UNVERIFIED_BINARIES=1 to bypass verification (not recommended).

📄 Data Files in Search

By default, data files (.md, .json, .yaml) are indexed but not embedded.
Enable CCM_EMBED_DATA_FILES=1 to include them in semantic search.

Source: https://github.com/senoldogann/LLM-Context-Manager

📝 Changelog

v0.1.21

• ✅ Release checksums (checksums.txt) for binary integrity
• ✅ MCP allowlist with optional strict enforcement
• ✅ Data file embedding is optional (CCM_EMBED_DATA_FILES)
• ✅ CLI/MCP integration tests

v0.1.20

• ✅ 100% evaluation pass rate
• ✅ Hybrid scoring (graph + semantic)
• ✅ Lazy indexing support
• ✅ Watch mode for auto-reindexing

Built with ❤️ in Rust