Cortex - Project Memory for AI Coding Assistants
Your AI forgets everything between sessions. Cortex gives it a brain.
Every time you start a new AI coding session, you waste 15-30 minutes re-explaining your project. Architecture, past decisions, known bugs, conventions — all gone.
Cortex captures everything automatically and injects it into your next session before you type a single character.
Works with Claude Code | Cursor | Cline | Copilot | Any MCP client
Why Cortex?
| Without Cortex |
With Cortex |
| "Here's my project structure again..." |
AI already knows your architecture |
| "We decided to use Redis because..." |
Decision auto-captured with full context |
| "The bug was in the auth middleware..." |
Bug pattern recorded, never repeated |
| "Don't touch that file, it's..." |
Convention remembered across sessions |
| 15-30 min context loading per session |
0 min. Full context injected automatically |
How It Works
You code with AI --> Cortex watches silently --> Memory builds automatically
|
Next session starts <-- Context injected into CLAUDE.md <-- Best context selected
Install. Code. That's it. Zero configuration needed.
Features
Real-Time Memory Capture
Cortex monitors your AI sessions live — not just at the end:
- Every 1 second — Watches for new messages
- Every 15 seconds — Fast local extraction (no API call)
- Every 20 messages — Deep LLM extraction in background
- On decisions/bugs detected — Immediate capture
- Status bar shows
Cortex: Live during active sessions
3-Layer Memory Architecture
Inspired by how human memory works:
Layer 1: Working Memory (hot) — Always injected (~800 tokens)
- Last session summary, recent decisions, open problems
- Auto-injected into
CLAUDE.md before every session
- Your AI reads this automatically
Layer 2: Episodic Memory (warm) — Session histories
- One file per session with full context
- Auto-generated Architectural Decision Records (ADRs)
- Searchable via CLI and MCP
Layer 3: Semantic Memory (cold) — Knowledge graph
- Full-text search across all layers
- Vector embeddings (coming in v0.2)
Auto-Generated Decision Logs
Every architectural decision captured with:
- What was decided and why
- Alternatives considered
- Files affected
- Full session context
- Memory Layers tree view (Working, Episodes, Decisions)
- Memory Health dashboard (0-100 score)
- Token budget tracking
- Live updates during sessions
CLAUDE.md Auto-Injection
<!-- CORTEX:START -->
## Project Memory (auto-managed by Cortex)
### Last Session
Fixed authentication bug in session middleware...
### Recent Decisions
- **Use Redis for sessions**: Latency requirements...
### Open Problems
- Rate limiting not implemented yet
_Last updated: 2026-03-26T10:30:00Z | Tokens: 227/800_
<!-- CORTEX:END -->
Claude Code, Cursor, and Cline read CLAUDE.md natively.
Quick Start
1. Install
Search "Cortex Memory" in VS Code Extensions, or:
ext install cortex-dev.cortex-memory
3. Code
Start coding with your AI assistant. Cortex runs silently in the background.
Works without an API key too — basic pattern-matching extraction runs locally.
What Gets Captured
| Signal |
Example |
Where It's Stored |
| Decisions |
"Let's go with Redis for sessions" |
decisions.md (ADR format) |
| Bug patterns |
"Root cause was a race condition" |
Episode + working memory |
| Architecture |
"Refactor auth into its own module" |
Episode + decision log |
| File changes |
Every file read, edited, created |
Tracked per episode |
| Session context |
What you worked on, what's next |
Working memory |
| Open problems |
Unresolved bugs, TODOs |
Working memory |
LLM Providers
| Provider |
Cost |
Setup |
| Gemini (default) |
Free (500 req/day) |
Get key at aistudio.google.com/apikey |
| Anthropic |
~$0.01/session |
Set cortex.apiKey in settings |
| Ollama |
Free (local) |
Install Ollama, set provider to ollama |
| No API key |
Free |
Works with basic pattern matching |
npm install -g cortex-memory
cortex status # Memory health score
cortex query "auth flow" # Search across all layers
cortex export # Export as single markdown
MCP Server (Cursor, Cline, Zed)
{
"cortex": {
"command": "node",
"args": ["path/to/cortex/dist/mcp/index.js"]
}
}
Tools: cortex_get_context | cortex_search | cortex_save_memory | cortex_get_decisions | cortex_status
Settings
| Setting |
Default |
Description |
cortex.llmProvider |
gemini |
Provider: gemini, anthropic, ollama |
cortex.apiKey |
— |
API key for Gemini or Anthropic |
cortex.maxWorkingMemoryTokens |
800 |
Token budget for working memory |
cortex.autoInjectClaudeMd |
true |
Auto-inject into CLAUDE.md |
Commands
| Command |
Description |
| Cortex: Set API Key |
Configure your LLM API key |
| Cortex: Show Memory Status |
Health score, token usage, stats |
| Cortex: Search Memories |
Full-text search |
| Cortex: Refresh Memory View |
Force refresh sidebar |
| Cortex: Initialize Project Memory |
Manual init (usually automatic) |
Privacy
- 100% local — All data in
.cortex/ on your machine
- No telemetry — Zero data collection, zero tracking
- No cloud — Only external call is to your chosen LLM
- Your data — Delete
.cortex/ to erase everything
- Git-safe — Auto-added to
.gitignore
Supported AI Assistants
| Assistant |
Integration |
How |
| Claude Code |
Native |
CLAUDE.md injection + session watching |
| Cursor |
MCP |
Via MCP server |
| Cline |
MCP |
Via MCP server |
| Copilot |
Passive |
Reads CLAUDE.md if present |
| Zed |
MCP |
Via MCP server |
| Continue |
MCP |
Via MCP server |
FAQ
Does this slow down my editor?
No. <200KB bundle. All processing in background.
Does it work without an API key?
Yes. Basic extraction works out of the box. API key enables deeper LLM-powered extraction.
How much does Gemini cost?
$0. Free tier = 500 requests/day. More than enough.
Can my team share memories?
Team sync via git planned for v0.2. You can commit .cortex/ to share now.
Contributing
See CONTRIBUTING.md. PRs welcome.
License
MIT
Stop explaining your codebase to AI. Let Cortex remember it for you.
