v0.0.1 CLI · v1 Graph + Vector RAG · MCP-native

Product Knowledge Graph for AI Agents

Cortex Context

Give GitHub Copilot and any AI coding agent deep, structured understanding of your product — specs, services, decisions and workflows — in a queryable graph.

Install on npm ↗ See how it works

npx @cortex-context/cli@latest init

Terminal
$ npx @cortex-context/cli@latest init

◆ Phase 1 Environment
✓ .env CORTEX_URL=http://localhost:8082
✓ cortex.config.yaml ejected
✓ docker compose up Neo4j + Cortex API

◆ Phase 2 MCP + Rules
✓ copilot-instructions Cortex block injected
✓ .vscode/mcp.json configured

◆ Phase 3 Git hook
✓ .git/hooks/post-commit auto-sync installed

Done in 6.1s → cortex-context doctor
4
I.S.I.R pillars
145+
Nodes in the graph
6
MCP tools built-in
100%
Local & private

Four pillars. Complete context.

Every piece of product knowledge maps to one of four universal concepts, giving AI agents a structured map of your entire codebase.

🎯
Pillar 1
Intent
The "Why"
Specs, requirements and user stories that explain the purpose behind every feature.
:Spec:UserStory:Epic
🏗️
Pillar 2
System
The "How"
Services, ADRs and APIs that form the structural skeleton of your product.
:Service:ADR:API
⚙️
Pillar 3
Implementation
The "What"
Modules, workflows and components — the concrete building blocks that make it run.
:Workflow:Module:Component
📡
Pillar 4
Runtime
The "Real world"
Alerts, deployments and incidents that ground AI responses in what's actually happening.
:Alert:Deployment:Incident

One command. Full setup.

A TypeScript CLI that wires your workspace to the knowledge graph in three phases: local Docker stack, MCP server config, and a git hook for automatic sync on every commit.

$ npx @cortex-context/cli@latest init npm ↗
cortex-context init 3-phase setup: local Docker stack + MCP server (Copilot / Claude) + post-commit git hook. Run once per workspace.
cortex-context sync Reads git diff HEAD~1, auto-extracts spec_ref from the branch name, and ingests the diff into the graph. Called automatically by the hook.
cortex-context sync --dry-run Previews the diff and detected spec_ref without sending to the server. Useful for debugging the hook.
cortex-context update Updates Skills and the MCP Server bundle in your workspace to the latest published version.
cortex-context doctor Checks Cortex connectivity, validates the local install, and reports the hook and MCP server status.
Example output
$ cortex-context sync --repo . → spec_ref detected: spec-151 ← branch: feature/151-spec150-port ✓ Diff captured 32 insertions · 4 files ✓ Ingested in 134ms POST /api/v1/ingest (:Commit)-[:IMPLEMENTS]->(:Spec {id:"spec-151"}) $ cortex-context doctor Cortex URL http://10.11.12.200:8082
Neo4j bolt://localhost:7687
Nodes in graph 150 (122 specs · 8 svc · 15 wf)
Embedder local (sentence-transformers)
MCP server stdio — GitHub Copilot / Claude
Git hook post-commit installed

Built for the AI-native team

🕸️
Graph-first exploration
FTS hits trigger 1–2 hop expansions — your agent sees a spec plus everything it impacts: services, decisions, workflows.
🔬
Hybrid RAG
Combine full-text precision with semantic similarity. Built-in local embeddings or OpenAI for natural-language queries.
🔌
MCP-native
Ships with a Model Context Protocol server. GitHub Copilot, Claude Desktop and any MCP agent connects out of the box.
🧩
Plugin dimensions
Add GitHub Issues, Confluence or Jira as a single YAML + Python file. No core code changes required.
🔒
Fully local
Everything runs on your machine or private server. Your source code never leaves your infrastructure.
📈
Impact analysis
Ask "which services are affected by spec-070?" and get a precise answer via 1–2 hop graph traversal — no grep, no guessing.
🔗
Branch-aware spec tracking
sync reads the branch name, extracts the spec ID from the feature/NNN-slug convention, and writes a (:Commit)-[:IMPLEMENTS]->(:Spec) edge automatically.
⏱️
Bitemporal versioning
Every node carries ingested_at and valid_from — know when the data was added and when the spec was written.

Works with your stack

Product-agnostic and open. Plug it into the tools your team already uses.

🤖 GitHub Copilot
🧠 Claude Desktop
VS Code
🕸️ Neo4j
🐍 FastAPI
🐳 Docker
🐙 GitHub
📄 Markdown / YAML
🔷 OpenAI Embeddings
📦 npm
🔍 query_product_context
🔍 get_spec_context
🔍 get_service_context
📊 Any MCP client

Up in 5 minutes

Three commands to a working knowledge graph.

1Install the CLI
npx @cortex-context/cli@latest init # or: npm install -g @cortex-context/cli
2Start the graph
docker compose up -d # Starts Neo4j + Cortex API
3Init & sync
cortex-context init --url http://localhost:8082 # ✅ MCP configured + git hook installed cortex-context sync --repo . # ✅ Graph updated — Copilot has context

Your codebase has
a memory now.

Stop pasting context into chat. Build a live knowledge graph your AI agents can actually query.