omni-cortex 1.9.0

Give Claude Code a perfect memory - auto-logs everything, searches smartly, and gets smarter over time

Omni Cortex MCP

A universal memory system for Claude Code that combines activity logging with intelligent knowledge storage.

What Is This?

For AI/ML experts: A dual-layer context system with activity provenance, hybrid semantic search (FTS5 + embeddings), and temporal importance decay. Think of it as Git + Elasticsearch + a knowledge graph for AI context.

For developers: It gives Claude Code a persistent, searchable memory that auto-logs everything and gets smarter over time. Like a senior developer's institutional knowledge—searchable, organized, and always available.

For everyone: It makes your AI assistant actually remember things. No more re-explaining your project every session.

Why Not Just Use CLAUDE.md or Basic Memory?

Feature	Claude Code	CLAUDE.md	Basic MCP	Omni-Cortex
Persists between sessions	❌	✅	✅	✅
Auto-logs all activity	❌	❌	❌	✅
Hybrid search (keyword + semantic)	❌	❌	❌	✅
Auto-categorizes memories	❌	❌	❌	✅
Importance decay + access boosting	❌	❌	❌	✅
Session history & context	❌	❌	❌	✅
Memory relationships	❌	❌	❌	✅
Cross-project search	❌	❌	❌	✅
Visual dashboard	❌	❌	❌	✅

The difference: Basic solutions are like sticky notes. Omni-Cortex is like having a trusted long-term employee who remembers everything, files it automatically, and hands you exactly what you need.

Features

• Zero Configuration: Works out of the box - just install and run setup
• Dual-Layer Storage: Activity logging (audit trail) + Knowledge store (memories)
• 18 MCP Tools: Full-featured API for memory management, activity tracking, session continuity, and cross-project search
• Semantic Search: AI-powered search using sentence-transformers (optional)
• Hybrid Search: Combines keyword (FTS5) + semantic search for best results
• Full-Text Search: SQLite FTS5-powered keyword search with smart ranking
• Auto-Categorization: Automatic memory type detection and tag suggestions
• Session Continuity: "Last time you were working on..." context
• Importance Decay: Frequently accessed memories naturally surface
• Auto Activity Logging: Automatically logs all tool calls via hooks

Getting Started (5 Minutes)

A step-by-step guide to get Omni Cortex running on your machine.

Prerequisites

• Python 3.10+ - Check with python --version
• Claude Code CLI - The Anthropic CLI tool
• pip - Python package manager (comes with Python)

Step 1: Install the Package

Option A: From PyPI (Recommended for most users)

pip install omni-cortex

Option B: From Source (For development/contributions)

git clone https://github.com/AllCytes/Omni-Cortex.git
cd Omni-Cortex
pip install -e ".[semantic]"

Expected output:

Successfully installed omni-cortex-1.7.1

Step 2: Run the Setup

omni-cortex-setup

This automatically:

• Adds Omni Cortex as an MCP server in ~/.claude.json
• Configures hooks in ~/.claude/settings.json for activity logging

Expected output:

✓ MCP server configured
✓ Hooks configured
Setup complete! Restart Claude Code to activate.

Step 3: Restart Claude Code

Close and reopen your Claude Code terminal. This loads the new MCP configuration.

Step 4: Verify It's Working

In Claude Code, try storing a memory:

Ask Claude: "Remember that the database uses SQLite for storage"

Claude should use the cortex_remember tool. Then verify:

Ask Claude: "What do you remember about the database?"

Claude should use cortex_recall and find your memory.

Step 5: Start the Dashboard (Optional)

The web dashboard lets you browse and search memories visually.

# Start the dashboard (opens http://localhost:5173)
omni-cortex-dashboard

Or manually:

# Terminal 1: Backend (uses dashboard's own venv)
cd dashboard/backend
.venv/Scripts/python -m uvicorn main:app --host 127.0.0.1 --port 8765 --reload

# Terminal 2: Frontend
cd dashboard/frontend
npm install
npm run dev

Open http://localhost:5173 in your browser.

Note: The dashboard has its own virtual environment at dashboard/backend/.venv with FastAPI and other web dependencies. This is separate from the project root .venv which contains the MCP server package.

Troubleshooting

❌ "omni-cortex-setup" command not found

Cause: pip installed to a location not in your PATH.

Solution:

# Find where pip installed it
python -m omni_cortex.setup

# Or add Python scripts to PATH (Windows)
# Add %APPDATA%\Python\Python3x\Scripts to your PATH

# On macOS/Linux, ensure ~/.local/bin is in PATH
export PATH="$HOME/.local/bin:$PATH"

❌ Claude doesn't see cortex_* tools

Cause: MCP server not configured or Claude Code not restarted.

Solution:

1. Check ~/.claude.json contains the omni-cortex MCP server entry
2. Fully close and reopen Claude Code (not just the terminal)
3. Run omni-cortex-setup again if needed

❌ "ModuleNotFoundError: No module named 'omni_cortex'"

Cause: Python environment mismatch.

Solution:

# Ensure you're using the same Python that pip used
which python  # or `where python` on Windows
pip show omni-cortex  # Check if installed

# Reinstall if needed
pip install --force-reinstall omni-cortex

❌ Dashboard won't start

Cause: Missing dependencies or port conflict.

Solution:

# Install backend dependencies
cd dashboard/backend
pip install -e .

# Check if port 8765 is in use
# Windows: netstat -ano | findstr :8765
# macOS/Linux: lsof -i :8765

# Use a different port if needed
uvicorn main:app --port 8766

❌ Semantic search not working

Cause: Semantic extras not installed.

Solution:

pip install omni-cortex[semantic]

First search will download the embedding model (~100MB).

---

Installation (Detailed)

Quick Install (Recommended)

# Install the package
pip install omni-cortex

# Run automatic setup (configures MCP server + hooks)
omni-cortex-setup

That's it! Omni Cortex will now:

• Automatically log all Claude Code tool calls
• Provide memory tools (cortex_remember, cortex_recall, etc.)
• Create a per-project database at .omni-cortex/cortex.db

With Semantic Search

For AI-powered semantic search capabilities:

pip install omni-cortex[semantic]
omni-cortex-setup

From Source

git clone https://github.com/AllCytes/Omni-Cortex.git
cd omni-cortex
pip install -e ".[semantic]"
python -m omni_cortex.setup

Manual Configuration

If you prefer manual setup, add to ~/.claude.json:

{
  "mcpServers": {
    "omni-cortex": {
      "command": "python",
      "args": ["-m", "omni_cortex.server"]
    }
  }
}

And optionally configure hooks in ~/.claude/settings.json for activity logging:

{
  "hooks": {
    "PreToolUse": [{
      "type": "command",
      "command": "python -m omni_cortex.hooks.pre_tool_use"
    }],
    "PostToolUse": [{
      "type": "command",
      "command": "python -m omni_cortex.hooks.post_tool_use"
    }]
  }
}

Uninstall

omni-cortex-setup --uninstall
pip uninstall omni-cortex

Tools

Memory Tools (6)

Tool	Description
cortex_remember	Store information with auto-categorization
cortex_recall	Search memories (modes: keyword, semantic, hybrid)
cortex_list_memories	List memories with filters and pagination
cortex_update_memory	Update memory content, tags, or status
cortex_forget	Delete a memory
cortex_link_memories	Create relationships between memories

Activity Tools (3)

Tool	Description
cortex_log_activity	Manually log an activity
cortex_get_activities	Query the activity log
cortex_get_timeline	Get a chronological timeline

Session Tools (3)

Tool	Description
cortex_start_session	Start a new session with context
cortex_end_session	End session and generate summary
cortex_get_session_context	Get context from previous sessions

Utility Tools (3)

Tool	Description
cortex_list_tags	List all tags with usage counts
cortex_review_memories	Review and update memory freshness
cortex_export	Export data to markdown or JSON

Global Tools (3)

Tool	Description
cortex_global_search	Search memories across all projects
cortex_global_stats	Get global index statistics
cortex_sync_to_global	Manually sync to global index

Memory Types

Memories are automatically categorized into:

• general - General notes and information
• warning - Cautions, things to avoid
• tip - Tips, tricks, best practices
• config - Configuration and settings
• troubleshooting - Debugging and problem-solving
• code - Code snippets and algorithms
• error - Error messages and failures
• solution - Solutions to problems
• command - CLI commands
• concept - Definitions and explanations
• decision - Architectural decisions

Storage

• Per-project: .omni-cortex/cortex.db in your project directory
• Global: ~/.omni-cortex/global.db for cross-project search

Configuration

Create .omni-cortex/config.yaml in your project:

schema_version: "1.0"
embedding_enabled: true
decay_rate_per_day: 0.5
freshness_review_days: 30
auto_provide_context: true
context_depth: 3

Web Dashboard

A visual interface for browsing, searching, and managing your memories.

Features

• Memory Browser: View, search, filter, and edit memories
• Ask AI: Chat with your memories using Gemini
• Real-time Updates: WebSocket-based live sync
• Statistics: Memory counts, types, tags distribution
• Project Switcher: Switch between project databases

Quick Start

# Backend (requires Python 3.10+)
cd dashboard/backend
pip install -e .
uvicorn main:app --host 0.0.0.0 --port 8765 --reload

# Frontend (requires Node.js 18+)
cd dashboard/frontend
npm install
npm run dev

Open http://localhost:5173 in your browser.

Ask AI Setup (Optional)

To enable the "Ask AI" chat feature, set your Gemini API key:

export GEMINI_API_KEY=your_api_key_here

See dashboard/README.md for full documentation.

Documentation

• Tool Reference - Complete documentation for all 18 tools with examples
• Configuration Guide - Configuration options and troubleshooting
• Teaching Materials (PDF):
  • docs/OmniCortex_QuickStart.pdf - 3-page quick start guide
  • docs/OmniCortex_FeatureComparison.pdf - Comparison with basic memory MCPs
  • docs/OmniCortex_Philosophy.pdf - Design principles and inspiration
  • docs/OmniCortex_CommandReference.pdf - All tools with parameters

Regenerating PDFs

To regenerate the teaching material PDFs:

# Requires reportlab
pip install reportlab

# Generate all 4 PDFs
python docs/create_pdfs.py

The PDFs use a light theme with blue/purple/green accents. Edit docs/create_pdfs.py to customize colors or content.

Development

Quick Setup (with Claude Code)

If you're using Claude Code, just run:

/dev-setup

This will guide you through setting up the development environment.

Manual Setup

# Clone and install in editable mode
git clone https://github.com/AllCytes/Omni-Cortex.git
cd Omni-Cortex
pip install -e .

# Dashboard backend has its own venv (already included in repo)
# If missing, set it up:
cd dashboard/backend
python -m venv .venv
.venv/Scripts/pip install -r requirements.txt  # Windows
# .venv/bin/pip install -r requirements.txt    # macOS/Linux
cd ../frontend && npm install
cd ../..

# Verify installation
omni-cortex --help
omni-cortex-dashboard --help

Important: Always use pip install -e . (editable mode) so changes are immediately reflected without reinstalling.

Project Structure

omni-cortex/
├── .venv/                       # Project root venv (omni-cortex MCP package)
├── src/omni_cortex/             # MCP server source code
├── dashboard/
│   ├── backend/
│   │   ├── .venv/               # Dashboard backend venv (FastAPI, uvicorn)
│   │   ├── main.py              # FastAPI application
│   │   └── database.py          # Database queries
│   └── frontend/                # Vue 3 + Vite frontend
├── adws/                        # Agentic Development Workflows
├── specs/                       # Implementation plans
│   ├── todo/                    # Plans waiting to be built
│   └── done/                    # Completed plans
└── tests/                       # Unit tests

Why two venvs? The dashboard is a standalone web application that can be packaged/deployed separately from the MCP server. They have different dependencies (MCP server needs mcp, dashboard needs fastapi).

Running Tests

pytest

# With coverage
pytest --cov=src/omni_cortex

See CONTRIBUTING.md for full development guidelines.

Security

Omni Cortex v1.0.3 has been security reviewed:

• All SQL queries use parameterized statements
• Input validation via Pydantic models
• Model name validation prevents code injection
• YAML loading uses safe_load()

License

MIT