Cortex Hub
Provides integration with OpenAI's API for LLM generation, supporting models like GPT-4.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Cortex Hubsearch my codebase for the function validateToken"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Why Cortex?
Every AI coding agent works in isolation. Switch IDE, switch machine, switch project — the agent starts from zero. Your team's hard-won decisions, bug fixes, and architectural patterns live and die inside individual chat sessions.
Cortex Hub is a self-hosted backend that gives every agent on your team persistent memory, shared knowledge, and cross-project intelligence via a single MCP endpoint:
Claude Code Cursor Antigravity Codex Gemini
│ │ │ │ │
└──────────┴────────────┴───────────┴─────────┘
│
┌───────▼────────┐
│ Cortex Hub │ ← one MCP endpoint for everything
│ │
│ Memory │ Agents remember across sessions
│ Knowledge │ Team shares bug fixes, patterns
│ Code Intel │ Search any project's code by name
│ Quality Gates │ Enforce build/lint before commit
└────────────────┘What this means in practice
New machine, instant context:
You: (fresh laptop, just cloned your project)
> /install ← one command, MCP configured
> /cs ← session starts
Cortex: "Resuming from last session. You were working on JWT rotation
for the auth service. The team decided RS256 with 90-day expiry
(stored by Dev B on March 15). Related: payment service uses
the same pattern — see knowledge doc kdoc-4a2b."
You didn't bring any notes. You didn't clone the other repos.
Cortex remembered everything.Cross-project code search without cloning:
You: "How does the backend handle user auth?"
Agent: cortex_code_search(query: "user auth", repo: "my-backend")
→ validateToken (Function) — src/middleware/auth.ts
→ refreshSession (Function) — src/services/session.ts
You never cloned my-backend. Cortex indexed it server-side
and searched the AST graph across 15 repos in 20ms.Team knowledge that compounds:
Session 1 (Dev A, Claude Code):
Fixed auth bug → stored knowledge: "JWT needs audience claim for mobile"
Session 2 (Dev B, Cursor, different project):
Hit same auth issue → cortex_knowledge_search("JWT mobile") → instant fix
Session 3 (New hire, day 1):
/cs → sees all team decisions, patterns, bug fixes
Productive from hour one, not week three.Retrieval quality: 96.0% R@5 on LongMemEval (500 questions, 6 categories) — matching MemPalace (96.6%) with $0 cost, no API key, fully offline. MemPalace requires paid OpenAI embeddings; Cortex runs the model in-process for free.
Zero data leaves your infrastructure. Self-hosted on a $5/month VPS behind Cloudflare Tunnel. Handles 5+ concurrent agents. Local embedding by default — no API keys, no network, no rate limits.
Features
🧠 Code Intelligence (GitNexus)
Capability | Tool | What It Does |
Multi-project search |
| Omit |
360° symbol context |
| Callers, callees, execution flows for any function/class |
Blast radius |
| See downstream impact before editing |
Pre-commit risk |
| Analyze uncommitted changes, find affected flows |
Direct graph queries |
| Cypher against the AST knowledge graph |
Multi-repo registry |
| All indexed repos discoverable by name or slug |
Auto-reindex |
| Trigger after pushes |
Read source |
| Fetch raw file content from any indexed repo |
Smart cross-project search (added Apr 2026): call cortex_code_search(query: "...") without specifying repo and Cortex fans out across every indexed repo, runs both flow + symbol search, and returns a ranked list with refine hints. No more list_repos → guess → retry loops.
💾 Hierarchical Memory + Knowledge
Memory (per-agent, semantic recall across sessions):
cortex_memory_store/cortex_memory_searchBranch-scoped, project-scoped, with semantic deduplication
Knowledge Base (team-wide, structured):
cortex_knowledge_store/cortex_knowledge_searchHall types (MemPalace-inspired):
fact,event,discovery,preference,advice,generalTemporal validity:
valid_from/invalidated_at— query "what was true on date X"Supersession chain: mark old facts as replaced by new ones
Timeline view:
GET /api/knowledge/timeline— chronological exploration
// Store a fact with validity window
cortex_knowledge_store({
title: "JWT secret rotation policy",
content: "Rotate every 90 days, ...",
hallType: "fact",
validFrom: "2026-01-01"
})
// Later, when policy changes:
POST /api/knowledge/{id}/invalidate
body: { supersededBy: "new-doc-id" }🍳 Recipe System (Auto-Learning)
Inspired by HKUDS/OpenSpace — Cortex captures patterns from completed work automatically:
Auto-capture on
task.completeandsession_end— if execution log shows a non-trivial workflow, an LLM extracts it as a reusable recipeQuality metrics:
selection_count,applied_count,completion_count,fallback_countper docHybrid search ranking:
vector_similarity * 0.6 + effective_rate * 0.3 + recency * 0.1(only whenselection_count >= 3)Evolution: docs with
fallback_rate > 0.4flagged for LLM rewrite via/health-checkLineage DAG: parent → derived → fixed relationships tracked
Dashboard /knowledge page shows the Recipe Health Panel — capture pipeline status, quality distribution, origin breakdown (manual/captured/derived/fixed), recent capture log.
🔀 LLM Gateway (CLIProxy)
Multi-provider: Gemini, OpenAI, Anthropic, any OpenAI-compatible
Ordered fallback chains with automatic retry (429/502/503/504)
Format translation (Gemini ↔ OpenAI) handled transparently
Budget enforcement — daily/monthly token limits from Dashboard
Complexity-based routing —
model: "auto"selects tier based on task
🛡️ Quality Gates
4-dimension scoring after every session:
Dimension | Weight | Measures |
Build | 25 | Code compiles |
Regression | 25 | No existing tests broken |
Standards | 25 | Follows conventions |
Traceability | 25 | Changes linked to requirements |
Plus plan quality (cortex_plan_quality) — 8-criterion plan assessment before execution.
🔒 Compliance Enforcement
Session compliance score — graded A/B/C/D at session end across 5 categories (Discovery, Safety, Learning, Contribution, Lifecycle)
Adaptive hints — every MCP response includes context-aware suggestions
Hook-enforced workflow —
/csblocks edits until knowledge + memory recall calledPre-commit gates — git commits blocked until quality gates pass
📊 Dashboard (13 pages)
Overview — hero stats + per-project cards + recipe health
Sessions — agent session list with API key tracking
Quality — A→F grades with trend charts
Knowledge — browse + Recipe Health Panel + capture log
Projects — repo management with branch-aware indexing
Providers / Usage / Keys / Organizations / Settings — full admin
Mobile-responsive, dark theme
Benchmarks
Reproducible retrieval benchmarks against industry-standard datasets.
LongMemEval-S full 500 questions
Cortex Hub | MemPalace | |
R@5 | 96.0% | 96.6% |
R@10 | 97.8% | 98.2% |
NDCG@10 | 1.44 | 0.889 |
Embedding | Local (in-process, free) | OpenAI API (paid) |
API key needed | No | Yes |
Embedding speed | ~10ms/text | ~600ms/text |
Search (500 queries) | 52.6s | ~5 min |
Cost per run | $0 | ~$2-5 |
Cortex matches MemPalace within 0.6 points on R@5 — while being free, offline, and 60x faster per embedding. NDCG@10 is 62% higher: when Cortex finds the answer, it places it at #1, not just somewhere in top 5.
MemPalace requires a paid OpenAI API key for embeddings. Cortex runs Xenova/all-MiniLM-L6-v2 in-process — zero network, zero cost, zero rate limits.
# Run benchmark (no API key needed)
pnpm --filter @cortex/benchmarks bench:longmemeval
# Cleanup test data
pnpm --filter @cortex/benchmarks bench:longmemeval --cleanupSee benchmarks/README.md for full methodology, per-category breakdown, and results log.
Embedding Provider
Cortex supports two interchangeable embedding backends:
Provider | Model | Dim | Speed | Cost | Quality |
|
| 384 | ~10-50ms in-process | Free | 96.7% R@5 |
|
| 768 | ~600ms/text via API | $$ | 96.7% R@5 |
Local mode (default) runs the model in-process via @huggingface/transformers — no network, no API key, no rate limits, fully offline. Switch to Gemini via EMBEDDING_PROVIDER=gemini env var if needed.
Architecture
graph TB
subgraph Agents["AI Agents (any MCP client)"]
AG["Antigravity"]
CC["Claude Code"]
CU["Cursor"]
WS["Windsurf"]
CX["Codex"]
end
subgraph Gateway["Hub MCP Server"]
AUTH["API Key Auth"]
ROUTER["Tool Router (25 tools)"]
TEL["Telemetry + Hints Engine"]
end
subgraph Services["Backend Services (Docker)"]
direction LR
GN["GitNexus<br/>AST Graph :4848"]
QD["Qdrant<br/>Vectors :6333"]
DB["SQLite<br/>WAL Mode"]
CLIP["CLIProxy<br/>LLM Gateway :8317"]
end
subgraph Frontend["Dashboard"]
DASH["Next.js 15<br/>(13 pages, static export)"]
end
Agents --> AUTH --> ROUTER --> TEL
TEL --> Services
DASH --> DBNetwork Topology
Internet
├── cortex-mcp.jackle.dev ──── Hub MCP Server
└── hub.jackle.dev ─────────── Dashboard UI
│
Cloudflare Tunnel
│
┌─────────┼─────────┐
│ Docker Compose │
│ ├─ dashboard-web │ Nginx (UI + API proxy)
│ ├─ cortex-api │ Internal API + mem9
│ ├─ cortex-mcp │ 18+ MCP tools
│ ├─ qdrant │ vectors + knowledge
│ ├─ gitnexus │ AST code graph
│ ├─ llm-proxy │ CLIProxy
│ └─ watchtower │ auto-update
└────────────────────┘
Zero open ports on host.MCP Tools
Cortex exposes 25 tools via a single MCP endpoint:
# | Tool | Purpose |
1 |
| Start session, get project context + relevant knowledge |
2 |
| Close session with compliance grade |
3 |
| Check unseen changes from other agents |
4 |
| Multi-project AST/symbol search with smart fan-out |
5 |
| 360° symbol view |
6 |
| Blast radius analysis |
7 |
| Read raw source from any indexed repo |
8 |
| Trigger re-indexing |
9 |
| List indexed repos with names + slugs |
10 |
| Direct graph queries |
11 |
| Pre-commit risk analysis |
12 |
| Recall agent memories |
13 |
| Store findings |
14 |
| Search knowledge base (with hall_type + asOf filters) |
15 |
| Store knowledge with hall type + validity |
16 |
| Report build/test/lint results |
17 |
| 8-criterion plan assessment |
18 |
| Token savings + tool usage analytics |
19 |
| Backend service health check |
Cross-project search just works — no repo lookup needed:
cortex_code_search(query: "auth middleware jwt") // scans ALL projects
cortex_code_search(query: "auth middleware jwt", repo: "cortex-hub") // narrow to oneFull API reference:
docs/api/hub-mcp-reference.md
Quick Start
Run Agent (No Clone Needed)
# macOS / Linux — interactive wizard
curl -fsSL https://raw.githubusercontent.com/lktiep/cortex-hub/master/scripts/run-agent.sh | bash -s -- launch
# Headless daemon with preset
curl -fsSL https://raw.githubusercontent.com/lktiep/cortex-hub/master/scripts/run-agent.sh | bash -s -- start --daemon --preset fullstack# Windows
iwr -useb "https://raw.githubusercontent.com/lktiep/cortex-hub/master/scripts/run-agent.ps1" -OutFile $env:TEMP\run-agent.ps1
& $env:TEMP\run-agent.ps1 startOne-Command Project Setup
# macOS / Linux
curl -fsSL "https://raw.githubusercontent.com/lktiep/cortex-hub/master/scripts/install.sh" | bash
# Windows
iwr -useb "https://raw.githubusercontent.com/lktiep/cortex-hub/master/scripts/install.ps1" -OutFile $env:TEMP\install.ps1; & $env:TEMP\install.ps1Or inside Claude Code: type /install
The installer:
Auto-detects IDEs (Claude, Gemini, Cursor, Windsurf, VS Code, Codex)
Configures MCP for each
Installs enforcement hooks (
.claude/hooks/*)Creates project profile with stack detection
Auto-adds
.gitignoreentries for generated filesIdempotent — safe to re-run
Server Setup (Admin)
git clone https://github.com/lktiep/cortex-hub.git
cd cortex-hub
corepack enable && pnpm install
cp .env.example .env # add API keys
cd infra && docker compose up -dMulti-Agent Conductor
Cortex includes an experimental multi-agent orchestration layer for cross-IDE task delegation. It is not feature-complete — agents can already create/pickup tasks, but autonomous strategy execution and smart agent matching are still WIP.
📖 See docs/conductor.md for current capabilities, limitations, and the rough edges to expect.
Tech Stack
Layer | Technology | Role |
MCP Server | Hono on Node.js | Streamable HTTP + JSON-RPC, 25 tools |
Code Intel | GitNexus | AST parsing, execution flow, Cypher graph |
Embeddings | mem9 + Qdrant | Vector search with semantic recall |
LLM Proxy | CLIProxy | Multi-provider with fallback chains |
App DB | SQLite (WAL) | Sessions, quality, usage, knowledge metadata |
API | Hono | Dashboard backend + mem9 in-process |
Frontend | Next.js 15 + React 19 | Static export, served by nginx |
Infra | Docker Compose | Service orchestration |
Tunnel | Cloudflare Tunnel | Zero open ports |
Hooks | Lefthook | Stack-aware git hooks |
Monorepo | pnpm + Turborepo | Build orchestration |
Project Structure
cortex-hub/
├── apps/
│ ├── hub-mcp/ # MCP Server — 25 tools
│ ├── dashboard-api/ # Hono API + mem9 + recipe pipeline
│ └── dashboard-web/ # Next.js dashboard (13 pages)
├── packages/
│ ├── shared-types/ # TS type definitions
│ ├── shared-utils/ # Logger, common utilities
│ └── shared-mem9/ # Embedding pipeline + vector store
├── benchmarks/ # Reproducible benchmarks (LongMemEval, etc.)
├── infra/
│ ├── docker-compose.yml # Full stack
│ └── Dockerfile.* # Per-service builds
├── scripts/
│ ├── install.sh / .ps1 # Unified installer
│ ├── run-agent.sh / .ps1 # Agent daemon launcher
│ └── bootstrap.sh # Admin setup
├── docs/
│ ├── architecture/ # Design docs (recipe, conductor, gateway)
│ ├── conductor.md # ⚠️ Multi-agent orchestration (experimental)
│ └── guides/ # Onboarding, installation, use cases
├── templates/
│ ├── skills/install/ # /install slash command
│ └── workflows/ # Workflow templates (/code, /continue)
└── .cortex/ # Project profile + agent identityDocs
Document | Description |
System architecture with diagrams | |
Recipe System (auto-learning from execution) | |
LLM Gateway design | |
Quality gates + scoring | |
Multi-agent conductor (experimental) | |
Full MCP tool API reference | |
Benchmark methodology + results | |
Full installation guide | |
Use cases + system requirements |
Real-World Scenarios
Solo dev, multiple projects
You maintain 5 repos across 3 languages. You fix a deployment bug in project A. Next week, project B has the same issue. Without Cortex, you debug from scratch. With Cortex:
cortex_knowledge_search("docker nginx 502 after restart")
→ "Nginx caches DNS at startup. Fix: resolver 127.0.0.11 valid=5s"
(stored 6 days ago by you, in project A)Time saved: 30 min per known bug. Across 5 projects, that's hours/week.
Team of 3, shared codebase
Dev A refactors the auth middleware on Monday. Dev B starts a feature on Wednesday using the old auth pattern. Without Cortex, B's code breaks and nobody knows why. With Cortex:
/cs → "Dev A refactored auth middleware on Monday. New pattern uses
middleware.authenticate() instead of req.checkAuth(). See
knowledge doc kdoc-8f2a for migration steps."Zero "who changed this?" conversations. Zero broken PRs from stale patterns.
Onboarding a new team member
Day 1. New hire clones the repo. Runs /install. Opens Claude Code.
/cs → Cortex loads:
- 47 team knowledge docs (deployment patterns, API conventions, known gotchas)
- Recent session summaries (what's being worked on NOW)
- Code intelligence across all indexed repos
New hire: "How does the payment flow work?"
cortex_code_search(query: "payment flow checkout")
→ 3 projects with relevant code, ranked by relevance, with file pathsProductive on day 1, not week 3. No "ask Dave, he knows how it works."
Switching machines mid-task
Working on your Mac at the office. Continue on Windows VPS at home.
Same API key → same memory → same knowledge → same session context.
/cs resumes exactly where you left off.
No git stash, no notes, no "what was I doing?"Multi-IDE workflow
Debug in Claude Code (deep reasoning). UI work in Cursor (fast iteration). Code review in Antigravity (visual). All three share the same Cortex backend:
Claude Code: stores finding → "Race condition in WebSocket reconnect"
Cursor: picks up finding → applies fix in the UI component
Antigravity: reviews the fix → stores quality feedbackEvery agent builds on what the others learned. No repeated explanations.
System Requirements
Resource | Minimum | Recommended | Notes |
CPU | 2 vCPU | 4 vCPU | Qdrant vector search is CPU-bound |
RAM | 4 GB | 8 GB | Qdrant + GitNexus + Node services |
Disk | 20 GB | 50 GB | Vector indices grow with knowledge |
OS | Ubuntu 22.04+ | Ubuntu 24.04 LTS | Any Linux with Docker 24+ |
Best value: Hetzner CX22 (~$4.50/mo) handles 3-5 agents.
Cost
Component | Cost | Notes |
Linux server | $4.50/mo+ | Hetzner CX22 minimum |
Cloudflare Tunnel | Free | No open ports |
All services | Free | Self-hosted in Docker |
LLM API calls | Pay-per-use | Your own keys, budget-controlled |
Total | ~$5/mo + LLM usage |
Contributing
See Contributing Guide for development setup, commit conventions, and code standards.
License
MIT © Cortex Hub Contributors
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