GZOO Cortex

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Local-first knowledge graph for developers. Watches your project files, extracts entities and relationships using LLMs, and lets you query across all your projects in natural language.

“What architecture decisions have I made across projects?”

Cortex finds decisions from your READMEs, TypeScript files, config files, and conversation exports — then synthesizes an answer with source citations.

Why

You work on multiple projects. Decisions, patterns, and context are scattered across hundreds of files. You forget what you decided three months ago. You re-solve problems you already solved in another repo.

Cortex watches your project directories, extracts knowledge automatically, and gives it back to you when you need it.

What It Does

• Watches your project files (md, ts, js, json, yaml) for changes
• Extracts entities: decisions, patterns, components, dependencies, constraints, action items
• Infers relationships between entities across projects
• Detects contradictions when decisions conflict
• Queries in natural language with source citations
• Routes intelligently between cloud and local LLMs
• Respects privacy — restricted projects never leave your machine
• Web dashboard with knowledge graph visualization, live feed, and query explorer
• MCP server for direct integration with Claude Code

Quick Start

1. Install

npm install -g @gzoo/cortex

Or install from source:

git clone https://github.com/gzoonet/cortex.git
cd cortex
npm install && npm run build && npm link

2. Setup

Run the interactive wizard:

cortex init

This walks you through:

• LLM provider — Anthropic, Google Gemini, Groq, OpenRouter, or Ollama (local)
• API key — saved securely to ~/.cortex/.env
• Routing mode — cloud-first, hybrid, local-first, or local-only
• Watch directories — which directories Cortex should monitor
• Budget limit — monthly LLM spend cap

Config is stored at ~/.cortex/cortex.config.json. API keys go in ~/.cortex/.env.

3. Register Projects

cortex projects add my-app ~/projects/app
cortex projects add api ~/projects/api
cortex projects list                       # verify

4. Watch & Query

cortex watch                               # start watching for changes
cortex query "what caching strategies am I using?"
cortex query "what decisions have I made about authentication?"
cortex find "PostgreSQL" --expand 2
cortex contradictions

5. Web Dashboard

cortex serve                               # open http://localhost:3710

Excluding Files & Directories

Cortex ignores node_modules, dist, .git, and other common directories by default. To add more:

cortex config exclude add docs             # exclude a directory
cortex config exclude add "*.log"          # exclude by pattern
cortex config exclude list                 # see all excludes
cortex config exclude remove docs          # remove an exclude

How It Works

Cortex runs a pipeline on every file change:

1. Parse — file content is chunked by a language-aware parser (tree-sitter for code, remark for markdown)
2. Extract — LLM identifies entities (decisions, components, patterns, etc.)
3. Relate — LLM infers relationships between new and existing entities
4. Detect — contradictions and duplicates are flagged automatically
5. Store — entities, relationships, and vectors go into SQLite + LanceDB
6. Query — natural language queries search the graph and synthesize answers

All data stays local in ~/.cortex/. Only LLM API calls leave your machine (and never for restricted projects).

LLM Providers

Cortex is provider-agnostic. It supports:

• Anthropic Claude (Sonnet, Haiku) — via native Anthropic API
• Google Gemini — via OpenAI-compatible API
• Any OpenAI-compatible API — OpenRouter, local proxies, etc.
• Ollama (Mistral, Llama, etc.) — fully local, no cloud required

Routing Modes

Mode	Cloud Cost	Quality	GPU Required
cloud-first	Varies by provider	Highest	No
hybrid	Reduced	High	Yes (Ollama)
local-first	Minimal	Good	Yes (Ollama)
local-only	$0	Good	Yes (Ollama)

Hybrid mode routes high-volume tasks (entity extraction, ranking) to Ollama and reasoning-heavy tasks (relationship inference, queries) to your cloud provider.

Requirements

• Node.js 20+
• LLM API key for cloud modes — Anthropic, Google Gemini, or any OpenAI-compatible provider
• Ollama (for hybrid/local modes) — install

Configuration

All config lives in ~/.cortex/cortex.config.json. API keys are in ~/.cortex/.env.

cortex config list                       # see all non-default settings
cortex config set llm.mode hybrid        # switch routing mode
cortex config set llm.budget.monthlyLimitUsd 10  # set budget
cortex config exclude add vendor         # exclude a directory from watching
cortex privacy set ~/clients restricted  # mark directory as restricted

Full configuration reference: docs/configuration.md

Commands

Command	Description
cortex init	Interactive setup wizard
cortex projects add <name> [path]	Register a project directory
cortex projects list	List registered projects
cortex watch [project]	Start watching for file changes
cortex query <question>	Natural language query with citations
cortex find <term>	Find entities by name
cortex ingest <file-or-glob>	One-shot file ingestion
cortex status	Graph stats, costs, provider status
cortex costs	Detailed cost breakdown
cortex contradictions	List active contradictions
cortex resolve <id>	Resolve a contradiction
cortex models list/pull/test/info	Manage Ollama models
cortex serve	Start web dashboard (localhost:3710)
cortex mcp	Start MCP server for Claude Code
cortex report	Post-ingestion summary
cortex privacy set <dir> <level>	Set directory privacy
cortex config list/get/set	Read/write configuration
cortex config exclude add/remove/list	Manage file/directory exclusions
cortex db	Database operations

Full CLI reference: docs/cli-reference.md

Web Dashboard

Run cortex serve to open a full web dashboard at http://localhost:3710 with:

• Dashboard Home — graph stats, recent activity, entity type breakdown
• Knowledge Graph — interactive D3-force graph with clustering, click to explore
• Live Feed — real-time file change and entity extraction events via WebSocket
• Query Explorer — natural language queries with streaming responses
• Contradiction Resolver — review and resolve conflicting decisions

MCP Server (Claude Code Integration)

Cortex includes an MCP server so Claude Code can query your knowledge graph directly:

claude mcp add cortex --scope user -- node /path/to/packages/mcp/dist/index.js

This gives Claude Code 4 tools: get_status, list_projects, find_entity, query_cortex.

Architecture

Monorepo with eight packages:

• @cortex/core — types, EventBus, config loader, error classes
• @cortex/ingest — file parsers (tree-sitter + remark), chunker, watcher, pipeline
• @cortex/graph — SQLite store, LanceDB vectors, query engine
• @cortex/llm — Anthropic/Gemini/OpenAI-compatible/Ollama providers, router, prompts, cache
• @cortex/cli — Commander.js CLI with 17 commands
• @cortex/mcp — Model Context Protocol server (stdio transport)
• @cortex/server — Express REST API + WebSocket relay
• @cortex/web — React + Vite + D3 web dashboard

Architecture docs: docs/

Privacy & Security

• Files classified as restricted are never sent to cloud LLMs
• Sensitive files (.env, .pem, .key) are auto-detected and blocked
• API key secrets are scanned and redacted before any cloud transmission
• All data stored locally in ~/.cortex/ — nothing phones home

Full security architecture: docs/security.md

Built With

• SQLite via better-sqlite3 — entity and relationship storage
• LanceDB — vector embeddings for semantic search
• Anthropic Claude — cloud LLM provider
• Google Gemini — cloud LLM provider (via OpenAI-compatible API)
• Ollama — local LLM inference
• tree-sitter — language-aware file parsing
• Chokidar — cross-platform file watching
• Commander.js — CLI framework
• React + Vite — web dashboard
• D3 — knowledge graph visualization

Contributing

See CONTRIBUTING.md for guidelines.

License

MIT — see LICENSE

About

Built by GZOO — an AI-powered business automation platform.

Cortex started as an internal tool to maintain context across multiple client projects. We open-sourced it because every developer who works on more than one thing loses context, and we think this approach — automatic file watching + knowledge graph + natural language queries — is the right way to solve it.