⚡ OmniHub (omnihub-cli)

A blazing fast, local-first, privacy-compliant personal memory hub and Model Context Protocol (MCP) server for developers.

OmniHub runs entirely on your local machine with a zero-network footprint. It eliminates external cloud dependencies, API keys, and network roundtrips by running specialized, highly optimized 4-bit quantized AI embedding pipelines directly inside an on-device WebAssembly environment.

🚀 Key Features

• 100% Offline AI Inference: Powered by an on-device WebAssembly ONNX runtime running all-MiniLM-L6-v2. Your data never leaves your computer.

• Blazing Fast Performance: Zero network latency. Sub-250ms semantic matching across your personal knowledge base.

• 70/30 Hybrid Search Engine: Combines 384-dimensional dense semantic vectors with localized high-frequency exact keyword extraction to ensure flawless recall accuracy.

• Local Heuristic Auto-Categorization: Automatically tags incoming logs into developer-centric groups (tech_stack, bug_fix, architecture, meeting_notes, idea) using ultra-fast local keyword mapping.

• Model Context Protocol (MCP) Support: Acts as a native MCP server out of the box. Connect it seamlessly to LLM clients like Claude Desktop, Cursor, or custom AI agents to give them long-term localized memory.

• Cryptographic Security: At-rest data storage is fully encrypted using robust local encryption standards, protecting your sensitive developer secrets.

📊 Performance & Scaling Benchmarks

OmniHub scales linearly on single-threaded CPU architectures. Tested extensively using hyperfine in an Apple Silicon environment, the local engine delivers massive efficiency upgrades compared to legacy cloud-dependent wrappers:

Why OmniHub 1.3.0 is 10x Faster:

1. Dimensionality Reduction: Slicing vector widths from 768 down to 384 dimensions reduces linear algebra operations by 50% per row scan.

2. Quantization Performance (q4): Utilizing 4-bit quantized integers reduces the model memory footprint to a tiny 14MB, enabling the CPU to load weights straight into L1/L2 caches with minimal memory bandwidth throttling.

🛠️ Installation

Ensure you have Bun or Node.js installed on your system, then install the package globally via npm:

npm install -g omnihub-cli

💻 CLI Usage

1. Log a Memory

Add a quick note or code snippet. If you omit the inline content, OmniHub automatically launches your system's default editor (e.g., nano or vim).

# Explicitly pass a category
omnihub log "Configured Docker Compose with multi-stage build caching flags." --category tech_stack

# Let the local heuristic auto-categorize your note
omnihub log "Fixed a runtime exception thrown when passing invalid JWT claims."
# Output: 🤖 Auto-categorizing locally... [bug_fix]

2. Search Memories

Perform a hybrid keyword and dense vector semantic query across your encrypted data history.

omnihub search "docker cache"

3. Database Migration / Reset

If you are upgrading from legacy pre-1.3.0 environments using old cloud structures, clear your environment to match the new 384-dimensional vector database:

omnihub reset

🤖 Model Context Protocol (MCP) Integration

OmniHub functions perfectly as a local standard input/output (stdio) MCP server. You can configure LLM agents to automatically store and extract engineering insights on your behalf.

Claude Desktop Configuration

Add the following snippet to your configuration file (located at ~/Library/Application Support/Claude/claude_desktop_config.json on macOS or %APPDATA%\Claude\claude_desktop_config.json on Windows):

{
  "mcpServers": {
    "omnihub": {
      "command": "omnihub",
      "args": ["run", "apps/mcp-server/index.ts"]
    }
  }
}

Exposed MCP Tools

• log_memory: Logs a structural memory, thought, or developer decision directly into your encrypted local cache (supports auto-categorization placeholders).

• search_context: Performs localized dense-vector semantic queries combined with structural parameters (limit, min_score, and category filtering).

• list_memories: Scans and outputs a clean, stripped sequence of recent logs (automatically omitting heavy raw vector dimensions for faster client-side parsing) with explicit category filters.

• edit_memory: Modifies the core content string or alters structural classification tags of an existing entry targeting a specific unique record ID.

• delete_memory: Safely drops an active unique tracking ID completely out of the local record array.

• export_memories: Compiles and marshals your entire structural knowledge base straight into a clean, portable standalone Markdown document stream.

🔒 Privacy & Architecture

OmniHub is uncompromised in its local-first approach.

• No Telemetry: Zero tracking analytics.

• No Cloud Syncing: No unexpected outbound payloads.

• Local Vectors: Embeddings are calculated strictly on your CPU using WebAssembly bindings.

• Security: Flat JSON store with cryptographically sound local read/write mechanisms.

📄 License

MIT © Malhar Sarang Bonde